From 64342b891a067eb83da47e3e9eacacea036179c3 Mon Sep 17 00:00:00 2001 From: Sarthak Agrawal Date: Thu, 20 Aug 2026 01:32:13 +0530 Subject: [PATCH 1/4] Fix Google sign-in redirects --- src/components/GoogleSignInButton.tsx | 8 +++----- src/lib/googleClientId.test.ts | 19 +++++++++++++++++++ src/lib/googleClientId.ts | 6 ++++++ src/pages/Login.tsx | 10 +++++++--- 4 files changed, 35 insertions(+), 8 deletions(-) create mode 100644 src/lib/googleClientId.test.ts create mode 100644 src/lib/googleClientId.ts diff --git a/src/components/GoogleSignInButton.tsx b/src/components/GoogleSignInButton.tsx index 9af07f1..b4c0d10 100644 --- a/src/components/GoogleSignInButton.tsx +++ b/src/components/GoogleSignInButton.tsx @@ -8,6 +8,7 @@ import { loadGoogleIdentity, renderGoogleIdentityButton, } from '../lib/googleIdentity'; +import { getGoogleClientId } from '../lib/googleClientId'; function reportGoogleAuthFailure(reason: string) { void import('../lib/foundry-monitoring').then((module) => @@ -36,11 +37,8 @@ export function GoogleSignInButton({ useEffect(() => { const parent = parentRef.current; - const clientId = import.meta.env.VITE_GOOGLE_CLIENT_ID; - if (!parent || !clientId) { - if (!clientId) reportGoogleAuthFailure('Missing VITE_GOOGLE_CLIENT_ID'); - return; - } + const clientId = getGoogleClientId(); + if (!parent) return; let cancelled = false; void loadGoogleIdentity() diff --git a/src/lib/googleClientId.test.ts b/src/lib/googleClientId.test.ts new file mode 100644 index 0000000..91b0115 --- /dev/null +++ b/src/lib/googleClientId.test.ts @@ -0,0 +1,19 @@ +import { afterEach, describe, expect, it, vi } from 'vitest'; + +import { getGoogleClientId, PRODUCTION_GOOGLE_CLIENT_ID } from './googleClientId'; + +describe('getGoogleClientId', () => { + afterEach(() => vi.unstubAllEnvs()); + + it('uses the public production client for builds without an injected client ID', () => { + vi.stubEnv('VITE_GOOGLE_CLIENT_ID', ''); + + expect(getGoogleClientId()).toBe(PRODUCTION_GOOGLE_CLIENT_ID); + }); + + it('prefers an explicitly injected client ID', () => { + vi.stubEnv('VITE_GOOGLE_CLIENT_ID', ' preview-client '); + + expect(getGoogleClientId()).toBe('preview-client'); + }); +}); diff --git a/src/lib/googleClientId.ts b/src/lib/googleClientId.ts new file mode 100644 index 0000000..5b2114f --- /dev/null +++ b/src/lib/googleClientId.ts @@ -0,0 +1,6 @@ +export const PRODUCTION_GOOGLE_CLIENT_ID = + '207924374505-j49msabur5bvt5q2r79l7nu47djbs5rb.apps.googleusercontent.com'; + +export function getGoogleClientId(): string { + return import.meta.env.VITE_GOOGLE_CLIENT_ID?.trim() || PRODUCTION_GOOGLE_CLIENT_ID; +} diff --git a/src/pages/Login.tsx b/src/pages/Login.tsx index 8910430..1b06011 100644 --- a/src/pages/Login.tsx +++ b/src/pages/Login.tsx @@ -6,6 +6,7 @@ import { useAuth } from '../contexts/AuthContext'; import curriculumSummary from '../data/public-curriculum-summary.json'; import { SiteHeader } from '../components/SiteHeader'; import { GoogleSignInButton } from '../components/GoogleSignInButton'; +import { getGoogleClientId } from '../lib/googleClientId'; const PRINCIPLES = [ { @@ -59,10 +60,14 @@ const STEPS = [ ]; export default function Login() { - const { continueAsGuest } = useAuth(); + const { continueAsGuest, user } = useAuth(); const navigate = useNavigate(); const [debugInfo, setDebugInfo] = useState(''); + useEffect(() => { + if (user) navigate('/dashboard', { replace: true }); + }, [navigate, user]); + const startMockAsGuest = () => { continueAsGuest(); navigate('/mock'); @@ -70,8 +75,7 @@ export default function Login() { useEffect(() => { document.getElementById('lcp-shell')?.remove(); - const clientId = import.meta.env.VITE_GOOGLE_CLIENT_ID; - setDebugInfo(clientId ? 'Client ID configured' : 'Missing VITE_GOOGLE_CLIENT_ID'); + setDebugInfo(getGoogleClientId() ? 'Client ID configured' : 'Google sign-in unavailable'); }, []); return ( From 9ab69918ac558919d236f7a568460ee0851bfd23 Mon Sep 17 00:00:00 2001 From: Sarthak Agrawal Date: Thu, 20 Aug 2026 02:18:09 +0530 Subject: [PATCH 2/4] Remove local OpenSpec; specs now live in GitHub Issues. Migrated spec content to GitHub tracking issues. No local openspec/ directory, no openspec CLI. See fleet-workspace AGENTS.md for the new spec-driven workflow. Generated with [Devin](https://devin.ai) Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../.openspec.yaml | 2 - .../2026-07-26-sweep-breadth-triage/design.md | 73 ----- .../proposal.md | 65 ---- .../specs/sweep-breadth-triage/spec.md | 94 ------ .../2026-07-26-sweep-breadth-triage/tasks.md | 48 --- .../.openspec.yaml | 2 - .../add-ai-native-foundations-path/design.md | 90 ------ .../proposal.md | 30 -- .../specs/expanded-learning-domains/spec.md | 45 --- .../add-ai-native-foundations-path/tasks.md | 24 -- .../.openspec.yaml | 2 - .../content-manifest.json | 219 ------------- .../design.md | 89 ------ .../proposal.md | 30 -- .../seo-audit.md | 23 -- .../spec.md | 68 ---- .../tasks.md | 40 --- .../.openspec.yaml | 2 - .../content-manifest.json | 53 ---- .../content-preview.md | 222 ------------- .../add-system-design-case-library/design.md | 103 ------ .../proposal.md | 43 --- .../seo-audit.md | 51 --- .../specs/public-curriculum-discovery/spec.md | 22 -- .../structured-system-design-practice/spec.md | 97 ------ .../specs/system-design-study-guides/spec.md | 51 --- .../add-system-design-case-library/tasks.md | 39 --- .../.openspec.yaml | 2 - .../design.md | 131 -------- .../proposal.md | 54 ---- .../specs/expanded-learning-domains/spec.md | 79 ----- .../tasks.md | 27 -- .../.openspec.yaml | 2 - .../design.md | 85 ----- .../proposal.md | 40 --- .../specs/public-curriculum-discovery/spec.md | 66 ---- .../tasks.md | 24 -- .../.openspec.yaml | 2 - .../design.md | 52 --- .../proposal.md | 32 -- .../specs/unified-site-navigation/spec.md | 40 --- .../2026-07-25-unify-site-navigation/tasks.md | 23 -- .../.openspec.yaml | 2 - .../design.md | 299 ------------------ .../proposal.md | 65 ---- .../specs/simulation-mastery-loop/spec.md | 88 ------ .../specs/systems-simulation-labs/spec.md | 195 ------------ .../tasks.md | 103 ------ .../.openspec.yaml | 2 - .../design.md | 60 ---- .../proposal.md | 29 -- .../specs/expanded-learning-domains/spec.md | 8 - .../specs/public-curriculum-discovery/spec.md | 8 - .../specs/tensor-lifecycle-synthesis/spec.md | 26 -- .../tasks.md | 17 - .../.openspec.yaml | 2 - .../2026-08-13-add-software-wars/design.md | 198 ------------ .../2026-08-13-add-software-wars/proposal.md | 51 --- .../specs/blitz-wars/spec.md | 99 ------ .../specs/competitive-ratings/spec.md | 67 ---- .../specs/tradeoff-wars/spec.md | 146 --------- .../specs/wars-content-governance/spec.md | 98 ------ .../specs/wars-learning-loop/spec.md | 64 ---- .../2026-08-13-add-software-wars/tasks.md | 86 ----- .../.openspec.yaml | 2 - .../coverage-map.md | 20 -- .../design.md | 59 ---- .../proposal.md | 30 -- .../spec.md | 47 --- .../specs/expanded-learning-domains/spec.md | 40 --- .../tasks.md | 27 -- .../.openspec.yaml | 2 - .../distill-learning-os-interface/design.md | 84 ----- .../distill-learning-os-interface/proposal.md | 29 -- .../specs/focused-learning-interface/spec.md | 73 ----- 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delete mode 100644 openspec/specs/tradeoff-wars/spec.md delete mode 100644 openspec/specs/unified-site-navigation/spec.md delete mode 100644 openspec/specs/wars-content-governance/spec.md delete mode 100644 openspec/specs/wars-learning-loop/spec.md diff --git a/openspec/changes/2026-07-26-sweep-breadth-triage/.openspec.yaml b/openspec/changes/2026-07-26-sweep-breadth-triage/.openspec.yaml deleted file mode 100644 index 2bc06e0..0000000 --- a/openspec/changes/2026-07-26-sweep-breadth-triage/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-07-26 diff --git a/openspec/changes/2026-07-26-sweep-breadth-triage/design.md b/openspec/changes/2026-07-26-sweep-breadth-triage/design.md deleted file mode 100644 index e75b8a7..0000000 --- a/openspec/changes/2026-07-26-sweep-breadth-triage/design.md +++ /dev/null @@ -1,73 +0,0 @@ -# Design - -## Rating semantics - -Sweep ratings are self-assessment, not recall performance, so they map onto the -FSRS grade that produces the interval the claim deserves: - -| Sweep | FSRS | Seeds review cards | -| --- | --- | --- | -| Known | `easy` | **No** | -| Fuzzy | `hard` | Yes | -| New to me | `again` | Yes | - -Known deliberately seeds nothing. Concept mastery still moves, so the claim is -recorded, but seeding cards for every concept already known is the flood the -feature exists to prevent. A wrong self-assessment surfaces through a drill or -a roadmap, and the concept can be re-swept. - -## Why the ranking needed Sweep first - -`isUnknown` treats an untouched concept as a gap. That is only meaningful -because triage makes "not known" observable — before Sweep, absence of mastery -was indistinguishable from absence of interest, which is why `weakConcepts()` -had to filter untouched concepts out and why no surface could rank domains. - -## Source hubs - -A hub is one coherent body of work covering three or more concepts, derived -from `concept-packs.json` by host, with two corrections found in the data: - -- **Publishers are excluded.** `arxiv.org` spans 95 concepts and `doi.org` 20, - but "go read arxiv" is not a recommendation. -- **Some hosts carry many unrelated works** and are grouped by path prefix — - `ocw.mit.edu` spans dozens of courses; `web.stanford.edu` hosts three - separate books. - -Labels and landing-page URLs are hand-curated. Both halves are necessary and -were learned separately: curating only the label produced cards reading -"Google SRE Book covers 11 of them" that opened the Being On-Call chapter, -because the derived URL is the shortest link in the group and the group rarely -cites a root. - -The coverage floor adapts to how much is left. A flat "covers at least 3" -reads right for an untriaged 20-concept domain and is wrong for one with three -gaps: measured across the catalog, domains with a hub fell from 72% to 38% as -concepts were marked Known, because the overlap count shrinks faster than the -share rises. The floor never drops below 2 — recommending a whole book to -close one concept is worse than the concept's own reading list. - -## Deferred: cross-device sync - -Sweep state is per-user (`swe-os:sweep-v1:`) but local only. Every sibling -store syncs; this one has no server action, so a pass done on a laptop shows -0% elsewhere and re-sweeping re-grades already-scheduled cards. - -Two options were considered. - -**Fold `rated` into `profile_json`.** No schema change, and the profile already -round-trips. Rejected: profile writes are whole-object PUTs merged against a -possibly-stale local copy, so a 250-rating pass would need debouncing and could -clobber `roadmapWeights` or `trackIds` on a bad interleave. The blast radius of -a mistake covers settings the user depends on. - -**A dedicated table and action, mirroring `concept_mastery`.** Preferred. Writes -are per-concept upserts, matching how mastery and review-mastery already work, -and a failure cannot corrupt unrelated state. Costs a `CREATE TABLE IF NOT -EXISTS` in `shared/db/schema.mjs`, a hand-mirrored copy in -`functions/api/[[path]].js`, a handler, a registry entry, and a hook. - -The second is the right shape, and it is the reason this proposal exists before -the code rather than after: it is a schema change plus a new API capability on -a product whose status is maintenance-only, and it should be approved rather -than assumed. diff --git a/openspec/changes/2026-07-26-sweep-breadth-triage/proposal.md b/openspec/changes/2026-07-26-sweep-breadth-triage/proposal.md deleted file mode 100644 index d8cfd25..0000000 --- a/openspec/changes/2026-07-26-sweep-breadth-triage/proposal.md +++ /dev/null @@ -1,65 +0,0 @@ -# Sweep Breadth Triage and ROI Ranking - -## Process note - -The surface described below was **built before this proposal was written**, -which inverts the rule in the fleet `AGENTS.md` ("Use the `spec-driven` skill -before non-trivial new fleet features"). This document is written after the -fact to close that gap and, more usefully, to put the one remaining piece — -cross-device sync, which needs a schema change — under review *before* it is -built rather than after. - -## Why - -The owner's stated goal is breadth: enough working knowledge across -distributed systems, infrastructure, databases, and AI to recognise a problem -mid-build, know the trade-off, and know what to reach for. That goal is served -by coverage and retention, not by implementation practice. - -Two things blocked it. - -**The catalog had no fast traversal.** All 250 concepts carry a `mentalModel` -with a median of 37 words, so the whole interest surface is roughly a weekend -of reading — but the app had 38 routes and not one built for covering ground. - -**The app could not say where effort was worth most.** `weakConcepts()` -(`src/lib/recommend.ts`) filters on `mastery[c.id] && …`, so a concept never -touched can never be reported as a gap. Every downstream surface inherited -that blind spot: the app could say you were shaky on something you had -studied, never that you had not opened distributed systems at all. - -## What Changes - -- Add a triage pass over the concept catalog: read the mental model, rate the - concept Known / Fuzzy / New, resume where you left off. -- Seed only Fuzzy and New into the FSRS review queue. Known records concept - mastery and is deliberately left out, because a queue holding all 250 - concepts is the failure mode the feature exists to prevent. -- Rank domains by the gaps the app can actually close, and name the single - outside source covering most of them — including saying "no hub" when the - catalog has nothing coherent to point at. -- Store declared interest as a negative (`mutedTags`). With broad interests, - ranking thirty domains yields "all of them" and no signal; muting the few - that do not matter states the truth in one interaction. - -## Non-goals - -- No implementation practice, graded labs, or new drill types. -- No LLM or model anywhere in the ranking — it is set intersection over - already-verified data, so nothing inferred is written back into the catalog. -- No change to FSRS scheduling itself; Sweep is a new producer of ratings, not - a new scheduler. -- No global navigation entry. `SITE_NAV_ITEMS` is server-rendered into ~294 - generated curriculum pages, so listing an unproven surface there costs a - 294-file regeneration. - -## Impact - -Adds one route (`/sweep`), one generator command (`build:source-hubs`), one -generated data file, and one optional profile field. Touches the two mastery -hooks — additively, to report write failure — and the Learn page, which gains -an entry point. - -The remaining piece, **cross-device sync for sweep state**, is specified here -but deliberately not built: it needs a new table and a new API action, and -`functions/api/[[path]].js` mirrors the schema by hand. See `design.md`. diff --git a/openspec/changes/2026-07-26-sweep-breadth-triage/specs/sweep-breadth-triage/spec.md b/openspec/changes/2026-07-26-sweep-breadth-triage/specs/sweep-breadth-triage/spec.md deleted file mode 100644 index d4f2076..0000000 --- a/openspec/changes/2026-07-26-sweep-breadth-triage/specs/sweep-breadth-triage/spec.md +++ /dev/null @@ -1,94 +0,0 @@ -## ADDED Requirements - -### Requirement: Triage covers the catalog and is resumable -A learner SHALL be able to rate every concept Known, Fuzzy, or New, scoped to -one domain or to the whole catalog, and resume a partial pass without losing -or repeating work. - -#### Scenario: A pass is interrupted -- **WHEN** a learner rates part of a domain and returns later -- **THEN** already-rated concepts are absent from the queue and the remainder is unchanged - -#### Scenario: A domain is finished -- **WHEN** every concept in the scope has been rated -- **THEN** the surface reports completion and offers the review queue and another domain - -### Requirement: Only gaps enter the review queue -Concepts rated Known MUST NOT seed review cards. Concepts rated Fuzzy or New -SHALL seed the concept's unseen editorial review questions. - -#### Scenario: A known concept is rated -- **WHEN** a learner marks a concept Known -- **THEN** concept mastery is recorded and no review card is created for it - -#### Scenario: A card already has scheduling history -- **WHEN** a concept is rated and one of its review questions has been seen before -- **THEN** that question is not re-seeded and its existing schedule is preserved - -### Requirement: A rating is committed only once its writes land -The surface MUST NOT mark a concept triaged when the writes it implies were -rejected, and SHALL tell the learner the rating was not recorded. - -#### Scenario: The session has expired mid-pass -- **WHEN** a rating's mastery write is rejected by the server -- **THEN** the concept stays in the queue, nothing is recorded, and the failure is shown - -### Requirement: Rating input cannot fire unintentionally -Rating shortcuts SHALL be active only on the rating surface, MUST ignore key -auto-repeat, and MUST ignore keys pressed while a text input, select, or -editable element has focus. - -#### Scenario: A key is held down -- **WHEN** a learner holds a rating key -- **THEN** exactly one concept is rated - -#### Scenario: A shortcut key is pressed away from the rating surface -- **WHEN** a learner presses a rating key on the domain picker -- **THEN** nothing is rated and no mastery is written - -### Requirement: Triage is private to one learner -Sweep state SHALL be scoped per account. Signing in SHALL adopt a guest pass -rather than discard it, and MUST NOT leave that pass available to a later -account on the same browser. - -#### Scenario: A second account signs in on a shared browser -- **WHEN** a different account opens the surface after another has swept -- **THEN** it starts from an untriaged queue - -### Requirement: Domains are ranked by remaining gaps a learner cares about -Domains SHALL be ordered by the gaps the app can close, excluding domains the -learner has muted, and MUST indicate when a domain's figure is an untriaged -upper bound rather than a measurement. - -#### Scenario: A domain has not been triaged -- **WHEN** no concept in the top-ranked domain has been rated -- **THEN** the surface states that the figure is an upper bound - -#### Scenario: A domain is muted -- **WHEN** a learner marks a domain as not of interest -- **THEN** it is absent from the ranking and the choice survives signing in - -### Requirement: Outside sources are named only when they genuinely cover the gap -A source SHALL be recommended for a domain only when it covers a meaningful -share of that domain's remaining gaps, and the link MUST resolve to the work -named rather than to one part of it. When no source qualifies, the surface -SHALL say so. - -#### Scenario: A source overlaps incidentally -- **WHEN** a broad source covers a few concepts of a large domain by coincidence -- **THEN** it is not recommended and the domain reports no hub - -#### Scenario: A domain is nearly finished -- **WHEN** only a few gaps remain and one source covers them -- **THEN** that source is still recommended - -## Deferred Requirements - -### Requirement: Triage syncs across devices -Sweep state SHOULD reconcile through the server so a pass is visible on every -device for that account. Not implemented — see `design.md`; it requires an -additive schema change and a new API action. - -#### Scenario: A pass is started on another device -- **WHEN** a learner sweeps on one machine and opens the surface on another -- **THEN** the triaged concepts are absent from the queue and coverage matches diff --git a/openspec/changes/2026-07-26-sweep-breadth-triage/tasks.md b/openspec/changes/2026-07-26-sweep-breadth-triage/tasks.md deleted file mode 100644 index 851716d..0000000 --- a/openspec/changes/2026-07-26-sweep-breadth-triage/tasks.md +++ /dev/null @@ -1,48 +0,0 @@ -# Tasks - -## Shipped locally, unreviewed - -- [x] Triage logic and queue ordering (`src/lib/sweep.ts`) -- [x] Rating → FSRS mapping, with Known deliberately seeding nothing -- [x] `/sweep` route, domain picker, and rating runner (`src/pages/Sweep.tsx`) -- [x] Entry point on Learn (`src/pages/Learn.tsx`) -- [x] ROI ranking and hub matching (`src/lib/roi.ts`) -- [x] Source-hub index generator (`scripts/build-source-hubs.mjs`), chained - into `sync:concept-packs` -- [x] `mutedTags` on `LearnerProfile`, client and server defaults -- [x] Mastery hooks report write failure; Sweep refuses to commit on rejection -- [x] Per-account sweep state with guest adoption -- [x] Hub landing pages curated and each verified 200 -- [x] `verify-sources.mjs` extended to cover hub URLs -- [x] 45 tests across `sweep.test.ts` and `roi.test.ts`; `lint`, `typecheck`, - 295 tests, `docs:validate` green - -## Fixed in review - -- [x] Rating hotkeys firing on the domain picker (reproduced, then fixed by - splitting picker and runner — hooks run before an early return) -- [x] Key auto-repeat mass-rating concepts -- [x] Rapid-keypress double-rate race -- [x] `mutedTags` discarded on first sign-in -- [x] `hbr.org` recommended as a "hub" for `behavioral` -- [x] Four hub labels misattributing or misdescribing their contents -- [x] Hub links opening one chapter of the work they name -- [x] `MIN_HUB_SHARE` having no test coverage -- [x] Hub coverage floor going silent as a domain nears completion -- [x] `runtime` tag collision handing an OS textbook to two WebGPU concepts -- [x] Dead pre-existing citation (Cormack RRF) surfaced by the extended verifier -- [x] Missing live region, low-contrast shortcut legend, undo shortcut unlabelled -- [x] Five dead exports removed - -## Open - -- [ ] Cross-device sync — needs approval before touching the schema: - - [ ] `sweep_rating` table in `shared/db/schema.mjs` - - [ ] Hand-mirrored copy in `functions/api/[[path]].js` - - [ ] `handlers/sweep.mjs` (GET all, POST one) and registry entry - - [ ] `useSweep` hook mirroring `useReviewMastery`, guest path preserved -- [ ] Decide whether Known should be spot-checked. It currently never - resurfaces, so a wrong self-assessment is never caught; sampling ~10% - would fix that but trades away the small-queue property. -- [ ] Decide whether `/sweep` earns a `SITE_NAV_ITEMS` entry, which costs a - ~294-file curriculum regeneration. diff --git a/openspec/changes/add-ai-native-foundations-path/.openspec.yaml b/openspec/changes/add-ai-native-foundations-path/.openspec.yaml deleted file mode 100644 index 1c37182..0000000 --- a/openspec/changes/add-ai-native-foundations-path/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-08-05 diff --git a/openspec/changes/add-ai-native-foundations-path/design.md b/openspec/changes/add-ai-native-foundations-path/design.md deleted file mode 100644 index 4119462..0000000 --- a/openspec/changes/add-ai-native-foundations-path/design.md +++ /dev/null @@ -1,90 +0,0 @@ -## Context - -See `proposal.md` for motivation. The repository already has a canonical -curriculum model, generated JavaScript-independent public pages, roadmap graph -components, and an evidence-gated practice loop. Learn currently offers four -macro doors and the full roadmap catalog, but it does not show the dependency -relationship between the major interview-preparation domains. Curriculum IDs -and generated slugs are compatibility boundaries. - -## Goals / Non-Goals - -**Goals:** - -- Preserve the existing Engineering Workbench visual language and Learn route. -- Express the macro progression in one compact, keyboard-operable component. -- Author new curriculum items through the existing canonical data pipeline. -- Turn a generic synthesis slot into a specific, measurable default project. -- Keep interactive and generated public curriculum representations aligned. - -**Non-Goals:** - -- Replacing the existing roadmap catalog or roadmap groups. -- Adding a new top-level route, tab, backend API, database table, or dependency. -- Copying the external roadmap's wording, styling, assets, or resource list. -- Expanding DSA, AI, or system-design topic breadth in this change. - -## Decisions - -### Add an orientation layer rather than another roadmap - -The Learn page will render a small macro-path component before the existing -roadmap families. Its four stages link to existing detailed roadmaps, so it -teaches sequencing without creating a twenty-fifth roadmap or duplicating -mastery state. The alternative—another canonical roadmap—would duplicate many -concepts and compete with active-roadmap selection. - -```mermaid -flowchart LR - F[Systems foundations] --> D[DSA practice] - F --> A[AI engineering] - D --> S[AI and system design] - A --> S -``` - -### Use a compact semantic flow with ordinary links - -The component will use existing tokens, bordered workbench surfaces, concise -monospace stage labels, and visible arrows/labels that are not color-dependent. -On compact screens it becomes one ordered column; at wider widths DSA and AI -sit as parallel stages. Ordinary links preserve keyboard, browser, and screen -reader behavior. A canvas/SVG graph was rejected because the relationship is -small and semantic HTML is easier to operate responsively. - -### Add only irreducible missing concepts - -Two stable concepts will be added: `data-representation` and -`program-memory-model`. Database fundamentals, operating systems, networking, -concurrency, DSA, AI reliability, and system design already exist and will be -reused. Each new concept receives one executable drill and one review prompt, -then enters the first Systems Foundations milestone. - -### Specialize the existing synthesis artifact in place - -The `synthesize-systems-foundations-12w` ID remains stable, but its description, -criteria, and deliverables will name the raw-socket HTTP server as the default -project. This strengthens the learning contract without invalidating saved -artifact references. The server may be implemented in C, Rust, Go, or another -language exposing raw TCP sockets; frameworks and high-level HTTP server -libraries are outside the default exercise. - -### Regenerate every public derivative from canonical data - -The existing curriculum publication scripts will regenerate HTML, Markdown, -JSON, sitemap, and agent catalogs. Integrity tests will assert the new concept -IDs, roadmap placement, practice links, and capstone wording so the application -and public discovery layer cannot drift. - -## Risks / Trade-offs - -- [The macro path could look like a replacement for the full catalog] → Label it as an orientation map and retain the complete grouped catalog immediately below. -- [Two concepts may grow into survey-course dumping grounds] → Bound them to interview-relevant mental models and require one focused drill each. -- [A raw-socket server is language-dependent] → Specify protocol and evidence requirements while allowing any language with direct socket access. -- [Generated output creates a large mechanical diff] → Change canonical inputs first, regenerate once, and validate one-to-one publication integrity. -- [The AI-engineering stage could overclaim breadth] → Link existing roadmaps and describe evaluation judgment as emphasis, not a new mastery guarantee. - -## Migration Plan - -No data migration is required. Existing IDs and routes remain stable. Rollback -consists of removing the orientation component and reverting the two added -concept packages and in-place capstone text before regenerating public output. diff --git a/openspec/changes/add-ai-native-foundations-path/proposal.md b/openspec/changes/add-ai-native-foundations-path/proposal.md deleted file mode 100644 index 8d33cce..0000000 --- a/openspec/changes/add-ai-native-foundations-path/proposal.md +++ /dev/null @@ -1,30 +0,0 @@ -## Why - -The learning OS has substantially deeper coverage than a compact external AI-engineering roadmap, but its breadth makes the dependency story harder to scan and its Systems Foundations capstone too open-ended. A clearer macro path and two missing machine-level fundamentals will turn that breadth into a more actionable interview-preparation sequence. - -## What Changes - -- Add a compact AI-native SWE progression on Learn: CS foundations, then DSA and AI engineering in parallel, then system-design synthesis. -- Emphasize evaluation judgment as a differentiating AI-engineering skill without adding a duplicate curriculum track. -- Add practice-backed concepts for data representation and low-level program memory. -- Add the new concepts to the Systems Foundations roadmap and generated public curriculum. -- Make a raw-socket HTTP server, with measurements and causal explanation, the default Systems Foundations synthesis project. -- Preserve all existing track, roadmap, concept, drill, review, and artifact IDs. - -## Capabilities - -### New Capabilities - -None. - -### Modified Capabilities - -- `expanded-learning-domains`: Strengthen the Systems Foundations curriculum contract and make the high-level AI-native learning sequence explicitly scannable from Learn. - -## Impact - -- Canonical curriculum data and its authoring/generation scripts. -- Learn-page presentation and a small reusable macro-path component. -- Generated public curriculum HTML, Markdown, JSON, sitemap, and agent catalogs. -- Curriculum integrity, navigation, and component tests. -- No API, database schema, production dependency, authentication, or deployment changes. diff --git a/openspec/changes/add-ai-native-foundations-path/specs/expanded-learning-domains/spec.md b/openspec/changes/add-ai-native-foundations-path/specs/expanded-learning-domains/spec.md deleted file mode 100644 index 2d9adf1..0000000 --- a/openspec/changes/add-ai-native-foundations-path/specs/expanded-learning-domains/spec.md +++ /dev/null @@ -1,45 +0,0 @@ -## MODIFIED Requirements - -### Requirement: Curriculum navigation stays scannable -The learning page SHALL group the added roadmaps into clear systems, AI-native, -software-building, and human-interface families without hiding existing -roadmaps, and SHALL expose a compact high-level sequence from foundations -through parallel DSA and AI-engineering study to system-design synthesis. - -#### Scenario: User scans all learning paths -- **WHEN** the learning or explore page renders roadmap groups -- **THEN** all existing and added roadmaps appear exactly once in an intentional group - -#### Scenario: User needs a starting sequence -- **WHEN** the learner opens the learning page before choosing a detailed roadmap -- **THEN** they can identify foundations as the starting point, DSA and AI engineering as parallel work, and system design as the synthesis stage - -#### Scenario: User scans the AI-engineering node -- **WHEN** the learner reads the compact AI-engineering stage -- **THEN** evaluation judgment is called out as a high-value skill alongside building AI systems - -## ADDED Requirements - -### Requirement: Machine-level foundations are practice-backed -The Systems Foundations path SHALL explicitly teach data representation and -low-level program memory through canonical concepts that each include a mental -model, primary source, executable drill, explain-back review question, and -placement before dependent operating-system and performance topics. - -#### Scenario: Learner begins Systems Foundations -- **WHEN** the learner opens the first Systems Foundations milestone -- **THEN** binary and numeric representation, endianness, pointers, stack and heap, allocation, and program-to-process mechanics are represented by practice-backed concepts - -### Requirement: Systems Foundations has a concrete default capstone -The Systems Foundations synthesis artifact SHALL default to a tiny HTTP/1.1 -file server implemented with raw TCP sockets and SHALL require measured, -inspectable evidence connecting operating-system, networking, memory, -concurrency, and I/O decisions. - -#### Scenario: Learner starts the synthesis artifact -- **WHEN** the learner opens the Systems Foundations capstone -- **THEN** the expected project, protocol boundary, failure injection, measurements, validation evidence, and architecture explanation are explicit - -#### Scenario: Existing learner data is loaded -- **WHEN** stored mastery or artifact state references any existing curriculum ID -- **THEN** every pre-existing ID still resolves without migration diff --git a/openspec/changes/add-ai-native-foundations-path/tasks.md b/openspec/changes/add-ai-native-foundations-path/tasks.md deleted file mode 100644 index 299df3a..0000000 --- a/openspec/changes/add-ai-native-foundations-path/tasks.md +++ /dev/null @@ -1,24 +0,0 @@ -## 1. Curriculum foundations - -- [x] 1.1 Add stable data-representation and program-memory-model concepts through the canonical authoring path -- [x] 1.2 Add one executable drill and one explain-back review question for each new concept -- [x] 1.3 Place both concepts before dependent topics in the first Systems Foundations milestone -- [x] 1.4 Specialize the existing Systems Foundations synthesis artifact as a measurable raw-socket HTTP server - -## 2. Learn orientation - -- [x] 2.1 Add a typed four-stage AI-native macro-path model that links existing detailed roadmaps -- [x] 2.2 Render a semantic, keyboard-operable macro-path component on Learn with a compact stacked layout and a wider parallel branch -- [x] 2.3 Emphasize evaluation judgment inside the AI-engineering stage without duplicating curriculum state - -## 3. Publication and regression coverage - -- [x] 3.1 Regenerate public curriculum HTML, Markdown, JSON, sitemap, and agent catalogs from canonical data -- [x] 3.2 Add curriculum integrity tests for concept packages, roadmap ordering, stable IDs, and capstone requirements -- [x] 3.3 Add component coverage for stage order, destinations, parallel semantics, and evaluation emphasis - -## 4. Verification and design review - -- [x] 4.1 Run focused curriculum and Learn tests, then typecheck, lint, docs validation, production build, and strict OpenSpec validation -- [x] 4.2 Inspect Learn in a browser and capture before/after evidence at 390, 768, and 1440 pixels -- [x] 4.3 Complete Impeccable critique, polish, audit, and the preserve-lane design receipt with zero unresolved P0/P1 findings diff --git a/openspec/changes/add-popular-system-design-case-batch/.openspec.yaml b/openspec/changes/add-popular-system-design-case-batch/.openspec.yaml deleted file mode 100644 index 1c37182..0000000 --- a/openspec/changes/add-popular-system-design-case-batch/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-08-05 diff --git a/openspec/changes/add-popular-system-design-case-batch/content-manifest.json b/openspec/changes/add-popular-system-design-case-batch/content-manifest.json deleted file mode 100644 index 74fa210..0000000 --- a/openspec/changes/add-popular-system-design-case-batch/content-manifest.json +++ /dev/null @@ -1,219 +0,0 @@ -{ - "change": "add-popular-system-design-case-batch", - "selectionDate": "2026-08-05", - "status": "owner-approved", - "selectionMethod": "Cross-list recurrence in current broad interview-prep lists, deduplicated against the existing eight cases and balanced by reusable system pattern.", - "selectionSources": [ - "https://www.designgurus.io/blog/system-design-interview-questions-to-crack-your-next-faang-interview", - "https://www.educative.io/blog/system-design-interview-questions", - "https://www.tryexponent.com/blog/system-design-interview-guide" - ], - "ownerSourceMaterials": [ - { - "path": "/Users/sarthak/Downloads/Content/Docs/useful/hld.jpeg", - "use": "Confirms the selected HLD classics and underlying estimation, storage, caching, sharding, replication, and messaging prerequisites." - }, - { - "path": "/Users/sarthak/Downloads/Content/Docs/useful/interview_roadmap.jpeg", - "use": "Confirms the broader systems prerequisite map used by rubric remediation, including networking, databases, distributed systems, caching, load balancing, and observability." - }, - { - "path": "/Users/sarthak/Downloads/Content/Docs/useful/lld.jpeg", - "use": "Kept as evidence for a later LLD-specific batch; its object-modeling questions are not mixed into this HLD case engine." - }, - { - "path": "/Users/sarthak/Downloads/Content/Docs/useful/ML system design case studies.csv", - "use": "Supplies real engineering case studies for ML follow-ups and sources in recommendations, search, moderation, ETA, fraud, notifications, and media systems." - } - ], - "mlCaseStudyApplications": [ - { - "caseId": "video-streaming-platform", - "examples": ["Netflix video annotation", "Tubi recommender monitoring"] - }, - { - "caseId": "photo-sharing-platform", - "examples": ["Etsy visual representation learning", "Yelp video moderation"] - }, - { - "caseId": "notification-delivery-service", - "examples": ["Uber out-of-app recommendation and communication"] - }, - { - "caseId": "search-autocomplete", - "examples": ["Target real-time item autocomplete", "Instacart embedding search relevance"] - }, - { - "caseId": "ride-sharing-platform", - "examples": ["Lyft ETA reliability", "Uber DeepETA", "DoorDash probabilistic ETA forecasting"] - }, - { - "caseId": "payment-processing-system", - "examples": [ - "Careem ML-guided temporary holds", - "Instacart real-time fraud detection", - "Uber Project RADAR" - ] - } - ], - "categories": [ - "AI systems", - "Social and real-time", - "Media and collaboration", - "Infrastructure and storage", - "Commerce and marketplaces" - ], - "cases": [ - { - "id": "video-streaming-platform", - "category": "Media and collaboration", - "query": "design a video streaming service system design interview", - "pattern": "upload-transcode-package-CDN playback", - "criticalPath": "manifest-driven adaptive-bitrate playback", - "failureMode": "a bad rendition rollout and origin overload", - "guideApproved": true, - "sourcePlan": [ - "RFC 8216 HTTP Live Streaming", - "Apple HLS documentation", - "Netflix Open Connect documentation" - ] - }, - { - "id": "photo-sharing-platform", - "category": "Social and real-time", - "query": "design Instagram photo sharing system interview", - "pattern": "media processing plus privacy-aware social delivery", - "criticalPath": "upload processing and authorized media delivery", - "failureMode": "viral media hot key during deletion", - "guideApproved": false, - "sourcePlan": ["Amazon S3 documentation", "Cloudflare Images documentation"] - }, - { - "id": "collaborative-document-editor", - "category": "Media and collaboration", - "query": "design Google Docs collaborative editor system interview", - "pattern": "convergent replicated editing", - "criticalPath": "ordered or commutative document operations", - "failureMode": "offline concurrent edits after reconnect", - "guideApproved": false, - "sourcePlan": ["Automerge documentation", "Yjs documentation", "CRDT research paper"] - }, - { - "id": "notification-delivery-service", - "category": "Social and real-time", - "query": "design notification service system design interview", - "pattern": "preference-aware multi-channel fan-out", - "criticalPath": "durable event to provider delivery with deduplication", - "failureMode": "provider throttling and retry amplification", - "guideApproved": true, - "sourcePlan": [ - "RFC 8030 Web Push", - "Firebase Cloud Messaging documentation", - "Apple Push Notification service documentation" - ] - }, - { - "id": "web-crawler", - "category": "Infrastructure and storage", - "query": "design a web crawler system design interview", - "pattern": "polite distributed frontier scheduling", - "criticalPath": "deduplicated per-host crawl frontier", - "failureMode": "crawler trap and poisoned host queue", - "guideApproved": true, - "sourcePlan": [ - "RFC 9309 Robots Exclusion Protocol", - "Google crawl-budget documentation", - "Google URL canonicalization documentation" - ] - }, - { - "id": "distributed-cache", - "category": "Infrastructure and storage", - "query": "design a distributed cache system interview", - "pattern": "partitioned volatile key-value serving", - "criticalPath": "consistent key routing and eviction", - "failureMode": "node loss causing cache stampede", - "guideApproved": false, - "sourcePlan": ["Memcached protocol", "Redis eviction documentation"] - }, - { - "id": "search-autocomplete", - "category": "Infrastructure and storage", - "query": "design search autocomplete typeahead system interview", - "pattern": "low-latency prefix retrieval and ranking", - "criticalPath": "prefix lookup within a keystroke latency budget", - "failureMode": "trending-term surge and stale unsafe suggestions", - "guideApproved": false, - "sourcePlan": [ - "Elasticsearch completion suggester documentation", - "Apache Lucene FST documentation" - ] - }, - { - "id": "cloud-file-storage", - "category": "Infrastructure and storage", - "query": "design Dropbox Google Drive system design interview", - "pattern": "chunked object sync plus metadata consistency", - "criticalPath": "resumable content-addressed synchronization", - "failureMode": "conflicting offline edits and partial upload", - "guideApproved": true, - "sourcePlan": [ - "Dropbox content hash documentation", - "Amazon S3 consistency documentation", - "HTTP conditional request standard" - ] - }, - { - "id": "distributed-key-value-store", - "category": "Infrastructure and storage", - "query": "design distributed key value store system interview", - "pattern": "replicated partitioned durable storage", - "criticalPath": "quorum read-write and hinted repair", - "failureMode": "partition with conflicting replicas", - "guideApproved": false, - "sourcePlan": ["Dynamo paper", "Raft paper", "RocksDB documentation"] - }, - { - "id": "ride-sharing-platform", - "category": "Commerce and marketplaces", - "query": "design Uber ride sharing system design interview", - "pattern": "geospatial supply matching under motion", - "criticalPath": "nearby-driver indexing and atomic trip assignment", - "failureMode": "regional demand spike and stale driver locations", - "guideApproved": true, - "sourcePlan": [ - "Uber H3 documentation", - "PostGIS spatial indexing documentation", - "Google S2 geometry documentation" - ] - }, - { - "id": "ticket-booking-platform", - "category": "Commerce and marketplaces", - "query": "design Ticketmaster ticket booking system interview", - "pattern": "scarce inventory holds and exactly-once purchase effects", - "criticalPath": "expiring seat hold to confirmed order", - "failureMode": "flash crowd plus payment timeout", - "guideApproved": true, - "sourcePlan": [ - "PostgreSQL row locking documentation", - "Stripe idempotency documentation", - "HTTP 429 standard" - ] - }, - { - "id": "payment-processing-system", - "category": "Commerce and marketplaces", - "query": "design payment system Stripe system design interview", - "pattern": "idempotent state machine plus immutable ledger", - "criticalPath": "payment authorization and double-entry posting", - "failureMode": "ambiguous processor timeout and duplicate webhook", - "guideApproved": false, - "sourcePlan": [ - "Stripe idempotency documentation", - "PCI DSS standard", - "PostgreSQL transaction documentation" - ] - } - ] -} diff --git a/openspec/changes/add-popular-system-design-case-batch/design.md b/openspec/changes/add-popular-system-design-case-batch/design.md deleted file mode 100644 index 42793a0..0000000 --- a/openspec/changes/add-popular-system-design-case-batch/design.md +++ /dev/null @@ -1,89 +0,0 @@ -## Context - -See `proposal.md` for motivation. The completed first batch already provides a typed case schema, pure staged-session reducer, deterministic and optional-AI evaluation, a responsive practice workspace, and approval-gated public generation. This change is primarily a content and catalog expansion; duplicating any of those mechanisms would create drift. - -The existing canonical case module is already large. Twelve new cases and six long guides should not make one file the only editing surface. Generated public pages must still come from canonical case data, and the Socratic no-solutions rule remains unchanged during active attempts. - -## Goals / Non-Goals - -**Goals:** - -- Reuse the exact case/session/evaluation contracts from the first batch. -- Make coverage selection explicit and evidence-based. -- Keep content split into reviewable category modules with one public registry. -- Publish six genuinely useful guides and keep the remainder off the public index until reviewed. -- Preserve deterministic generation and focused integrity checks. - -**Non-Goals:** - -- Adding a second interview engine, voice interviewer, collaborative whiteboard, or database persistence. -- Claiming company-specific question frequency that cannot be verified. -- Publishing twelve shallow articles merely to increase sitemap size. -- Changing FSRS scoring, Socratic answer policy, or legacy mock behavior. - -## Decisions - -### 1. Select by cross-list recurrence and pattern coverage - -The batch uses overlap across current broad interview-prep lists, then removes cases already represented by URL shortener, chat, feed, recommendation, and rate limiter. The selection deliberately includes product-shaped prompts and infrastructure primitives so practice transfers to unfamiliar variants. - -Alternative considered: add every item from one “top 25” list. Rejected because it would overfit one publisher, duplicate existing patterns, and encourage thin content. - -### 2. Add a category module behind the existing registry - -New definitions will live in a dedicated popular-case module and be appended by the existing `SYSTEM_DESIGN_CASES` export. Consumers continue importing one registry, so session storage and UI routing do not change. Catalog validation remains the boundary that detects duplicate IDs, invalid references, bad weights, or incomplete publication data. - -Alternative considered: one file per case. Rejected for this batch because it creates excessive module overhead; category-level separation is enough to keep reviews bounded. - -### 3. Reuse the six-stage engine without case-specific UI - -Every new prompt maps its unique reasoning into the existing stages. A ticketing case expresses inventory holds in deep dive and oversell in failure; a crawler expresses frontier scheduling in deep dive and crawler traps in failure. No case may add an ad hoc panel or bypass answer hiding. - -Alternative considered: custom flows by domain. Rejected because a consistent interview cadence is itself a learned skill and custom reducers would fragment tests. - -### 4. Approve six guides, not twelve placeholder pages - -The six initial guides span the broadest reusable patterns: media delivery, fan-out, scheduled crawling, synchronized object storage, geospatial matching, and scarce-inventory transactions. The other cases still contain rich in-app review material but their public guide state stays unapproved. - -Alternative considered: generate short summaries for every case. Rejected because it weakens learner trust and creates low-value search pages. - -### 5. Keep publication derived from canonical approval state - -The generator will continue filtering by `publication.state === 'approved'`. Hub grouping, guide routes, catalogs, sitemap URLs, and counts are all computed from that same registry. Integrity tests assert the exact approved set rather than only checking that files exist. - -```mermaid -flowchart LR - Sources[Current question-list overlap] --> Cases[Canonical category module] - Cases --> Registry[Validated case registry] - Registry --> Practice[Six-stage practice] - Registry --> Approval{Guide approved?} - Approval -->|yes| Guide[HTML and Markdown guide] - Approval -->|no| Hub[Practice link on grouped hub] - Guide --> Discovery[Sitemap and agent catalogs] - Hub --> Discovery -``` - -### 6. Source calculations from stable primitives - -Guides will cite standards, official platform documentation, and primary engineering material where available. Numeric examples are labeled assumptions and carry units; architectures are sized from those assumptions rather than presenting vendor throughput as universal fact. - -Alternative considered: use competitor walkthroughs as technical sources. Rejected; competitor lists justify editorial selection, while technical claims should use authoritative sources. - -## Risks / Trade-offs - -- **[Large content diff]** → Keep new cases in one separate module, generate artifacts mechanically, and review via catalog/publication tests. -- **[Guide quality varies across six domains]** → Enforce shared required sections, word floor, authoritative-source minimum, calculation anchors, and local SEO checks. -- **[Twenty options overwhelm the selector]** → Group by reusable pattern and show guide/practice badges rather than one flat list. -- **[Popularity claims become stale]** → Record the selection date and sources in the change, but describe the product as “common” rather than exposing unverifiable rankings. -- **[New catalog data breaks saved attempts]** → Keep prior IDs and versions untouched and verify a saved first-batch attempt still parses and resumes. -- **[Generated diff touches all curriculum pages]** → Treat the shared navigation regeneration as expected, inspect representative output, and preserve unrelated working-tree changes. - -## Migration Plan - -1. Add and validate the new category module without altering prior case definitions. -2. Add grouping metadata and update the selector/hub to consume it. -3. Approve and generate the six complete guides only after their content contract passes. -4. Run focused tests, typecheck, public generation, build, browser checks, and local SEO audit. -5. Leave all changes local for review; deployment and archive remain separate actions. - -Rollback is additive: remove the new registry spread and regenerate public artifacts. Existing case IDs, attempts, and routes remain valid. diff --git a/openspec/changes/add-popular-system-design-case-batch/proposal.md b/openspec/changes/add-popular-system-design-case-batch/proposal.md deleted file mode 100644 index a67e61b..0000000 --- a/openspec/changes/add-popular-system-design-case-batch/proposal.md +++ /dev/null @@ -1,30 +0,0 @@ -## Why - -The first case-library batch proves the structured practice loop, but it covers only eight prompts and has one public worked guide. Current 2026 interview-prep lists repeatedly converge on a small set of missing classics—video streaming, collaborative editing, notifications, crawling, caching, cloud storage, key-value storage, typeahead, ride sharing, ticketing, payments, and photo sharing—so these are the highest-leverage additions for broad interview readiness. - -## What Changes - -- Add twelve versioned, rubric-backed practice cases: video streaming, photo sharing, collaborative documents, notification delivery, web crawling, distributed cache, search autocomplete, cloud file storage, distributed key-value store, ride sharing, ticket booking, and payment processing. -- Give every case the existing six-stage closed-book flow, deterministic branches, failure injection, evidence rubric, stronger answer, common mistakes, follow-ups, and concept-local remediation. -- Publish six complete source-backed worked guides in the first editorial batch: video streaming, notification delivery, web crawling, cloud file storage, ride sharing, and ticket booking. -- Keep the other six cases practice-ready without emitting placeholder or thin public URLs; their guides remain a later editorial batch. -- Reorder and group the public hub by interview pattern so twenty cases are navigable without turning it into an unranked prompt dump. -- Expand generation and integrity tests so approval state, guide substance, primary sources, sitemap coverage, metadata, and Markdown mirrors remain enforced. - -## Capabilities - -### New Capabilities - -- `popular-system-design-case-coverage`: Covers the selected twelve-case practice batch, six approved study guides, hub organization, publication quality, and discoverability requirements. - -### Modified Capabilities - -None. This change builds on the completed `add-system-design-case-library` implementation without changing its six-stage session, grading, or answer-visibility contracts. - -## Impact - -- Canonical content: `src/data/system-design-cases.ts` and its schema/catalog tests. -- Practice UI: case grouping and selection in `src/components/SystemDesignInterview.tsx`; no new route or session-state shape is expected. -- Public output: `public/system-design/`, sitemap, curriculum/agent catalogs, and generated navigation. -- Generator and tests: `scripts/generate-public-curriculum.mjs` plus publication and public-curriculum integrity tests. -- No database migration, new runtime dependency, API change, commit, deployment, or release. diff --git a/openspec/changes/add-popular-system-design-case-batch/seo-audit.md b/openspec/changes/add-popular-system-design-case-batch/seo-audit.md deleted file mode 100644 index 3c5ffa4..0000000 --- a/openspec/changes/add-popular-system-design-case-batch/seo-audit.md +++ /dev/null @@ -1,23 +0,0 @@ -# Local SEO audit - -Audited the production build on `http://127.0.0.1:4173` on 2026-08-05 with -the Fleet `seo-audit` script. - -| Page | Passed | Failed | Warnings | Approx. words | -| --- | ---: | ---: | ---: | ---: | -| System design hub | 17 | 0 | 1 | 1,107 | -| Video streaming platform | 15 | 0 | 0 | 1,646 | -| Notification delivery service | 15 | 0 | 0 | 1,634 | -| Web crawler | 15 | 0 | 0 | 1,654 | -| Cloud file storage | 15 | 0 | 0 | 1,647 | -| Ride-sharing platform | 15 | 0 | 0 | 1,663 | -| Ticket-booking platform | 15 | 0 | 0 | 1,658 | - -All page-level checks passed: title, description, canonical, Open Graph, -Twitter card, hreflang handling, JSON-LD, H1, heading hierarchy, image alt, -word count, SSR-leak detection, and internal-link validation. - -The hub's only warning is intentional in the local audit: the audited URL uses -the localhost origin while the generated canonical and sitemap use -`https://learn.significanthobbies.com`. The production canonical is present in -the production sitemap; no exception is required after deployment. diff --git a/openspec/changes/add-popular-system-design-case-batch/specs/popular-system-design-case-coverage/spec.md b/openspec/changes/add-popular-system-design-case-batch/specs/popular-system-design-case-coverage/spec.md deleted file mode 100644 index bd88999..0000000 --- a/openspec/changes/add-popular-system-design-case-batch/specs/popular-system-design-case-coverage/spec.md +++ /dev/null @@ -1,68 +0,0 @@ -## Purpose - -Expands the structured interview library with the recurring product, infrastructure, storage, and transaction problems candidates are most likely to encounter while preserving substantive study and publication quality. - -## ADDED Requirements - -### Requirement: Twelve high-frequency cases -The system SHALL add exactly these twelve stable case IDs: `video-streaming-platform`, `photo-sharing-platform`, `collaborative-document-editor`, `notification-delivery-service`, `web-crawler`, `distributed-cache`, `search-autocomplete`, `cloud-file-storage`, `distributed-key-value-store`, `ride-sharing-platform`, `ticket-booking-platform`, and `payment-processing-system`. - -Each case SHALL satisfy the canonical case schema with six stages, hidden assumptions, unit-carrying calculation anchors, deterministic follow-up and failure branches, five weighted rubric dimensions, stronger-answer material, common mistakes, harder follow-ups, concept remediation, drill remediation, and primary or first-party sources. - -#### Scenario: Catalog accepts the complete batch -- **WHEN** the canonical catalog is validated -- **THEN** all twelve new IDs are present exactly once and all references resolve - -#### Scenario: A new case is attempted -- **WHEN** a learner selects any new case -- **THEN** the existing closed-book stage order, answer visibility, save/resume, grading, and targeted-remediation behavior applies without a special-case session path - -### Requirement: Pattern breadth over product-name memorization -The batch SHALL exercise distinct reusable interview patterns across media delivery, fan-out, asynchronous scheduling, web-scale crawling, cache partitioning, prefix retrieval, object metadata and synchronization, replicated storage, geospatial matching, scarce-inventory transactions, ledger correctness, collaborative convergence, and social media processing. - -Each case SHALL make the reusable pattern and its principal trade-off visible in the prompt or review rather than treating a branded product architecture as the only correct answer. - -#### Scenario: Learner reviews the expanded catalog -- **WHEN** the learner compares the new cases -- **THEN** each case exposes a distinct critical path and failure mode instead of duplicating an existing case under a different product name - -### Requirement: Six substantive worked guides -The first publication batch SHALL contain complete worked guides for `video-streaming-platform`, `notification-delivery-service`, `web-crawler`, `cloud-file-storage`, `ride-sharing-platform`, and `ticket-booking-platform`. - -Each approved guide SHALL contain at least 1,200 visible words, requirements and non-goals, capacity calculations with units, architecture and data flow, a deep technical decision, failure and recovery behavior, observability or cost considerations, common mistakes, harder follow-ups, an answer outline, internal practice links, and at least three authoritative sources. - -#### Scenario: Approved guide is generated -- **WHEN** public curriculum generation runs -- **THEN** each of the six approved cases emits matching canonical HTML and Markdown pages with substantive content and source links - -#### Scenario: Guide does not meet the editorial contract -- **WHEN** a guide is missing required sections, minimum substance, or authoritative sources -- **THEN** validation fails before a public artifact can be generated - -### Requirement: No thin public case pages -The six cases without an approved guide SHALL remain available in the interactive practice catalog but SHALL NOT receive placeholder, templated, or thin public HTML URLs. - -#### Scenario: Practice-only case is generated -- **WHEN** public generation runs for a case whose publication state is not approved -- **THEN** the hub links to its practice entrypoint and no guide HTML, Markdown, canonical, or sitemap entry is emitted for that case - -### Requirement: Navigable twenty-case hub -The public system-design hub and interactive selector SHALL group all twenty cases into meaningful interview-pattern categories and SHALL expose difficulty, duration, practice availability, and guide availability without relying only on alphabetical ordering. - -#### Scenario: Learner browses the expanded hub -- **WHEN** the hub is rendered after the batch is added -- **THEN** all twenty cases are discoverable under a relevant category and only approved guides present a study link - -### Requirement: Search and agent discovery integrity -Every approved guide SHALL have a unique title, meta description, canonical URL, Article and BreadcrumbList structured data, sitemap entry, Markdown mirror, and links from the hub and machine-readable catalogs. Generated counts SHALL report twenty practice cases and seven approved guides in total, including the existing LLM inference guide. - -#### Scenario: Publication integrity runs -- **WHEN** the generated public artifacts are tested -- **THEN** guide counts, canonical URLs, metadata, schema types, source links, hub links, sitemap entries, and Markdown mirrors match the canonical approval state exactly - -### Requirement: Existing attempt compatibility -Adding and grouping cases SHALL NOT change existing case IDs, versions, local attempt keys, rubric semantics, legacy technical mocks, or behavioral mocks. - -#### Scenario: Existing saved attempt is resumed -- **WHEN** a learner reloads a supported attempt from the first case batch -- **THEN** the attempt resumes against the same case version and answers without migration or data loss diff --git a/openspec/changes/add-popular-system-design-case-batch/tasks.md b/openspec/changes/add-popular-system-design-case-batch/tasks.md deleted file mode 100644 index 0fa5ca7..0000000 --- a/openspec/changes/add-popular-system-design-case-batch/tasks.md +++ /dev/null @@ -1,40 +0,0 @@ -## 1. Editorial Contract - -- [x] 1.1 Record the twelve selected cases, six guide approvals, pattern categories, intended search queries, and authoritative source plan in a machine-readable batch manifest -- [x] 1.2 Validate that each selected case adds a distinct critical path and failure mode relative to the existing eight cases -- [x] 1.3 Capture owner approval of the batch scope and publication set before implementation - -## 2. Canonical Practice Cases - -- [x] 2.1 Add the popular-case category module and append it through the existing canonical case registry -- [x] 2.2 Author the media, collaboration, notification, crawler, cache, autocomplete, and storage cases with complete stages, calculations, branches, rubrics, remediation, and review material -- [x] 2.3 Author the key-value store, ride-sharing, ticket-booking, payment, and photo-sharing cases with complete stages, calculations, branches, rubrics, remediation, and review material -- [x] 2.4 Extend catalog validation tests for the exact twenty-case set, unique patterns, valid references, valid weights, sources, and prior-case compatibility - -## 3. Catalog Experience - -- [x] 3.1 Add stable category metadata for AI systems, social and real-time, media and collaboration, infrastructure and storage, and commerce and marketplaces -- [x] 3.2 Group the interactive case selector while preserving direct prompt URLs, saved attempts, keyboard behavior, and legacy mock routes -- [x] 3.3 Add focused UI tests for grouped discovery, guide/practice badges, and all twenty selectable cases - -## 4. Worked Study Guides - -- [x] 4.1 Author and validate the video-streaming and notification-delivery worked guides -- [x] 4.2 Author and validate the web-crawler and cloud-file-storage worked guides -- [x] 4.3 Author and validate the ride-sharing and ticket-booking worked guides -- [x] 4.4 Verify every approved guide has 1,200+ visible words, unit-carrying calculations, required sections, at least three authoritative sources, common mistakes, harder follow-ups, and an answer outline - -## 5. Public Discovery - -- [x] 5.1 Update the public hub and Markdown mirror to group twenty cases by pattern and link only the seven approved guides -- [x] 5.2 Generate the six new HTML and Markdown guides with unique metadata, canonical URLs, Article and BreadcrumbList schemas, sources, and practice links -- [x] 5.3 Extend the sitemap, public and agent catalogs, counts, and publication manifest from canonical approval state -- [x] 5.4 Extend integrity tests to reject missing approved pages, accidental practice-only pages, thin content, metadata drift, source gaps, sitemap drift, and missing Markdown mirrors - -## 6. Verification - -- [x] 6.1 Run focused schema, catalog, session-compatibility, evaluation, UI, generator, and public-integrity tests -- [x] 6.2 Run typecheck, formatting, docs validation, deterministic public generation, and production build -- [x] 6.3 Verify direct entry, grouped selection, attempt/resume, review, and narrow-screen behavior in a real browser without exposing answers early -- [x] 6.4 Run the local SEO audit against the expanded hub and all six new guides and record intentional exceptions -- [x] 6.5 Run strict OpenSpec validation and `git diff --check`, then inspect the final diff without committing, pushing, or deploying diff --git a/openspec/changes/add-system-design-case-library/.openspec.yaml b/openspec/changes/add-system-design-case-library/.openspec.yaml deleted file mode 100644 index 1b062d3..0000000 --- a/openspec/changes/add-system-design-case-library/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-08-04 diff --git a/openspec/changes/add-system-design-case-library/content-manifest.json b/openspec/changes/add-system-design-case-library/content-manifest.json deleted file mode 100644 index 09716e9..0000000 --- a/openspec/changes/add-system-design-case-library/content-manifest.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "change": "add-system-design-case-library", - "batch": 1, - "status": "approved-and-generated", - "generatedAt": "2026-08-04", - "candidates": [ - { - "id": "system-design-hub", - "url": "/system-design/", - "type": "collection", - "primaryIntent": "common system design interview questions", - "title": "System Design Interview Questions | SWE Prep", - "description": "Practice common system design interview questions with staged prompts, capacity math, failure drills, worked guides, and targeted concept review.", - "canonicalSource": "system-design case catalog", - "structuredData": ["CollectionPage", "BreadcrumbList"], - "requiredInternalLinks": ["/mock", "/system-design/llm-inference-10k-rps.html"], - "approvalRequired": false - }, - { - "id": "llm-inference-10k-rps", - "url": "/system-design/llm-inference-10k-rps.html", - "type": "cornerstone-guide", - "primaryIntent": "design LLM inference 10K requests per second", - "title": "Design LLM Inference at 10K RPS | Interview Guide", - "description": "Work through requirements, token math, batching, GPU fleet sizing, reliability, and follow-ups for a 10,000 RPS LLM inference interview.", - "canonicalSource": "llm-inference-10k-rps case version 1", - "structuredData": ["Article", "BreadcrumbList"], - "requiredInternalLinks": [ - "/system-design/", - "/mock?prompt=llm-inference-10k-rps", - "/curriculum/concepts/inference-engines.html" - ], - "requiredPrimarySources": [ - "https://arxiv.org/abs/2309.06180", - "https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/batcher.html", - "https://docs.vllm.ai/en/stable/design/metrics/", - "https://kubernetes.io/docs/concepts/workloads/autoscaling/horizontal-pod-autoscale/", - "https://developer.nvidia.com/blog/llm-benchmarking-fundamental-concepts/" - ], - "approvalRequired": false - } - ], - "excludedFromThisBatch": [ - "production-rag", - "multi-tenant-llm-gateway", - "real-time-recommendations", - "url-shortener", - "distributed-rate-limiter", - "real-time-chat", - "ranked-news-feed" - ], - "exclusionReason": "Practice cases are in scope, but public guides require a later complete source-backed preview; no placeholder or thin URLs will be emitted." -} diff --git a/openspec/changes/add-system-design-case-library/content-preview.md b/openspec/changes/add-system-design-case-library/content-preview.md deleted file mode 100644 index 5a83aaf..0000000 --- a/openspec/changes/add-system-design-case-library/content-preview.md +++ /dev/null @@ -1,222 +0,0 @@ -# Content preview: first system-design publication batch - -Status: approved by the owner on 2026-08-04 and generated from the canonical system-design case catalog. - -## Candidate 1: System-design case library hub - -- Proposed URL: `/system-design/` -- Search intent: browse and compare common system-design interview questions, then choose study or closed-book practice -- Title: `System Design Interview Questions | SWE Prep` -- Meta description: `Practice common system design interview questions with staged prompts, capacity math, failure drills, worked guides, and targeted concept review.` -- H1: `System design interview questions you can actually practice` - -### Complete draft - -Most system-design question lists stop at the prompt. Knowing that “design a rate limiter” is common does not prove that you can clarify the contract, estimate load, choose a consistency model, defend the hot path, or recover when the interviewer changes one constraint. - -This library treats each question as a complete interview. Every case moves through the same six stages: scope the problem, estimate capacity, draw the high-level design, defend one critical path, respond to a failure injection, and review the evidence. You can study a worked guide or start a closed-book attempt. During practice, the reference answer stays hidden until you submit. - -## Start with the AI infrastructure cases - -### Design LLM inference serving at 10,000 requests per second - -The trick is recognizing that request rate is not enough. Model size, prompt and completion distributions, time to first token, output speed, streaming duration, and KV-cache memory determine the fleet. The case tests token math, admission control, continuous batching, routing, overload behavior, GPU capacity measurement, and multi-zone reliability. - -Study the worked guide, or enter a 45-minute practice session and discover the assumptions yourself. - -### Design production RAG over a large document corpus - -Separate the offline ingestion contract from the online retrieval-and-generation path. The case tests freshness, document permissions, chunk identity, hybrid retrieval, reranking, context assembly, citation grounding, evaluation, and failure isolation when an index or embedding version changes. - -### Design a multi-tenant LLM API gateway - -Build the control point in front of several models and providers. The case tests authentication, per-tenant quotas, token-aware rate limits, model routing, retries, idempotency, streaming proxy behavior, budget enforcement, observability, and safe fallback when a provider slows down. - -## Practice the distributed-systems classics - -### Design a URL shortener - -Turn daily write volume and a read-heavy redirect path into an ID-space, storage, cache, and abuse design. The follow-ups cover hot links, custom aliases, expiration, analytics, regional replication, and the trade-off between a globally coordinated counter and independently generated IDs. - -### Design a distributed rate limiter - -Define the enforcement contract before choosing token bucket, fixed window, or sliding-window state. The case tests atomic updates, clock behavior, regional consistency, local fail-open or fail-closed policy, burst handling, HTTP response semantics, and what happens when the shared store is slow. - -### Design real-time chat - -Follow a message from a connected sender to durable storage, fan-out, offline delivery, and acknowledgements. The interview probes connection ownership, per-conversation ordering, idempotency, group hot spots, presence, push notifications, reconnect gaps, and multi-device state. - -### Design a ranked news feed - -Separate candidate generation, ranking, filtering, and feed delivery. The case tests fan-out on write versus read, celebrity traffic, cache invalidation, freshness, cold start, feature availability, moderation, and graceful fallback when the ranker is unavailable. - -### Design a real-time recommendation system - -Combine offline feature and model pipelines with an online retrieve-rank-serve path. The interview tests latency budgets, feature freshness, candidate diversity, feedback loops, exploration, model rollout, counterfactual evaluation, and a degraded experience when online features lag. - -## How to use a case - -If the interview is soon, start closed-book and use the review to find the exact weak dimension. If the topic is new, read the relevant concept pages first, study the worked case, wait a day, and then attempt it without notes. A strong answer is not one memorized diagram. It is a chain of explicit assumptions, defensible calculations, named trade-offs, and operational responses. - -Your result is broken down by requirements, capacity reasoning, architecture, deep technical judgment, reliability, and communication. Missed dimensions link back to focused concepts and drills; a weakness in estimation does not erase evidence that you handled failure recovery well. - -The initial public guide covers LLM inference at 10K RPS. The remaining cases are available for practice first and will receive public guides only after their calculations, sources, and editorial copy clear the same review bar. - -## Candidate 2: Design LLM inference at 10K RPS - -- Proposed URL: `/system-design/llm-inference-10k-rps.html` -- Search intent: learn and rehearse an interview answer for high-throughput LLM serving -- Title: `Design LLM Inference at 10K RPS | Interview Guide` -- Meta description: `Work through requirements, token math, batching, GPU fleet sizing, reliability, and follow-ups for a 10,000 RPS LLM inference interview.` -- H1: `Design LLM inference serving at 10,000 requests per second` -- Structured data: `Article` and `BreadcrumbList` - -### Complete draft - -“Design LLM inference for 10,000 requests per second” sounds like a throughput-sizing question. The strongest opening is to say that 10K RPS alone is not enough to size an autoregressive serving system. One request might classify ten tokens; another might stream a 2,000-token answer while retaining a large KV cache. The interview is really testing whether you turn an ambiguous headline into a measurable workload and a system that stays inside a latency and cost envelope. - -Here is a defensible way to structure the answer. - -## 1. Clarify the contract before drawing boxes - -Start with the questions that change the architecture: - -- Which model or model family are we serving, and can quality requirements tolerate quantization or a smaller fallback model? -- What are the p50, p95, and p99 input-token and output-token distributions? What is the maximum context? -- Is 10K RPS an average, a sustained peak, or a short burst? What daily and regional traffic pattern should we expect? -- Is output streamed? What are the targets for time to first token, inter-token latency, and total completion time? -- Do tenants have priorities, quotas, data-residency constraints, or different models and adapters? -- What availability target is required? Can overload reject work, shorten output, or route to a degraded model? -- Are prompts sufficiently repetitive for prefix caching, and are responses deterministic enough for safe result caching? - -For a concrete interview, state assumptions after asking. Suppose the service uses one fixed model, streams output, receives a sustained 10K RPS with a 15K RPS short peak, averages 1,000 input tokens and 200 output tokens, targets p99 time to first token below one second, and generates around 50 tokens per second per active stream. The exact numbers are not universal; making them explicit is the point. - -## 2. Convert RPS into the workload the GPUs see - -Under those assumptions, steady input traffic is: - -`10,000 requests/s × 1,000 input tokens/request = 10,000,000 input tokens/s` - -Steady output traffic is: - -`10,000 requests/s × 200 output tokens/request = 2,000,000 output tokens/s` - -At 50 generated tokens per second, an average 200-token completion remains active for about four seconds. Little's Law gives the average number of concurrent generations: - -`concurrency = arrival rate × time in system = 10,000 × 4 = 40,000 active sequences` - -That concurrency matters because each active sequence consumes scheduler state and KV-cache memory. It also tells us why a system designed around one-request-at-a-time GPU execution will fail even if its nominal compute looks large enough. - -Repeat the calculation for the peak and for the p95 token lengths, not only the average. Capacity must satisfy at least four constraints: prefill token throughput, decode token throughput, active-sequence or KV-cache capacity, and the latency SLO. Whichever constraint saturates first sets fleet capacity. - -## 3. Do not guess a GPU count - -There is no honest universal answer such as “use 500 H100s.” Throughput changes with model architecture and size, precision, tensor or pipeline parallelism, context distribution, batch policy, serving engine, hardware, and latency target. The correct capacity-planning loop is: - -1. replay a representative prompt and output-length distribution against one candidate replica; -2. sweep concurrency and batching parameters; -3. find the highest sustainable throughput that still meets the tail-latency and error SLO; -4. size each traffic pool from that measured capacity; -5. add failure and burst headroom. - -If one measured replica sustains `C` requests per second for this exact workload at the target p99, a first request-based estimate is: - -`replicas = ceil(peak RPS / (C × target utilization))` - -With a target utilization of 70%, headroom is built into the denominator. But keep the token and KV-cache checks beside it: - -`prefill replicas = ceil(peak input tokens/s / measured prefill tokens/s per replica / 0.7)` - -`decode replicas = ceil(peak output tokens/s / measured decode tokens/s per replica / 0.7)` - -For a combined prefill/decode worker, use the benchmarked capacity of the mixed workload rather than adding two independent GPU counts. If profiling shows long prompts blocking decode and damaging inter-token latency, separate prefill and decode pools or use chunked prefill—but only after the measurement demonstrates that complexity is justified. - -The vLLM PagedAttention paper is useful background for why KV-cache memory management affects throughput, but its published gains are not a capacity number for your model and workload. Likewise, serving-engine documentation shows how batching works; production sizing still comes from your own benchmark. - -## 4. Draw the request and control planes - -The request path can be described left to right: - -`Client → global load balancer → API gateway → admission and quota service → model router → priority queue and batch scheduler → inference worker → streaming gateway → client` - -The global layer routes to a healthy region and keeps sessions in one region unless failover is required. The API gateway authenticates the tenant, validates request size, applies token-aware quotas, assigns a deadline and request ID, and supports cancellation when the client disconnects. - -The model router chooses a compatible pool by model, adapter, context bucket, tenant tier, and region. It should avoid sending a very long prompt into a queue optimized for short interactive traffic. Separate priority classes prevent batch jobs from consuming the latency budget of interactive requests. - -Each serving replica holds model weights and a managed KV cache. A scheduler continuously forms batches from ready prefill and decode work. Continuous or in-flight batching is important because generation lengths differ: finished sequences leave the batch and new work can enter instead of making the whole batch wait for the longest response. Prefix caching can remove repeated prompt computation when system prompts or shared document prefixes recur, but cache identity must include the exact model, tokenizer, adapter, and prompt prefix. - -The streaming gateway forwards tokens with backpressure, observes cancellation, and records completion status. It must not buffer an entire response before sending it; that would defeat the time-to-first-token SLO and waste compute after disconnected clients. - -A separate control plane owns the model registry, signed artifacts, rollout configuration, placement, autoscaling targets, and health. Model loading is slow and memory-heavy, so the data plane needs warm capacity. Autoscaling should use queue delay, waiting requests, token throughput, KV-cache pressure, and SLO burn—not CPU utilization alone. - -## 5. Make overload a designed state - -At this scale, unbounded queues convert a traffic spike into a latency outage. Every request gets a deadline. Queues have maximum depth and age. Admission control estimates whether the selected pool can start and finish work before the deadline. - -When capacity is exhausted, degrade intentionally: - -- reject lower-priority requests early with a retryable response and backoff guidance; -- enforce per-tenant token budgets so one customer cannot monopolize KV cache; -- cap output length or disable expensive sampling modes for a declared degraded tier; -- route eligible traffic to a smaller or quantized fallback model; -- preserve reserved capacity for critical tenants and health traffic; -- cancel work immediately when its client disconnects or deadline expires. - -Retries need budgets and jitter. Blind retries multiply overload. Requests should carry idempotency and trace IDs, but generation replay semantics must be explicit: a stochastic retry may not reproduce the same tokens. - -## 6. Survive worker and regional failures - -Run serving pools across at least three failure domains within a region, with the load balancer and router aware of healthy capacity. Losing one zone should not push the remaining zones beyond their protected utilization target; that requirement belongs in the headroom calculation. - -Worker failure is normal. Stop routing new requests, terminate or retry affected streams according to the API contract, replace the worker, and keep model artifacts in a nearby durable registry or node cache to reduce recovery time. Health checks must distinguish “process is alive” from “model is loaded and can meet a small inference probe.” - -For regional loss, route new requests to another warm region. Active streams may need to restart because moving an in-memory KV cache across regions is usually more expensive and fragile than replaying the prompt. State the trade-off: active-active multi-region capacity is costly, but it offers predictable failover; active-passive is cheaper but risks a long cold-load interval. - -Consider the interviewer follow-up: traffic jumps from 10K to 20K RPS in thirty seconds. GPU autoscaling alone will be too slow if new nodes and weights take minutes to become ready. The response is a warm buffer, predictive scaling for known events, a rapidly adjustable admission limit, priority shedding, and possibly the smaller fallback model. Queue growth should trigger overload policy before p99 latency collapses. - -## 7. Measure user experience, saturation, and cost - -The primary SLOs are not generic server latency. Track time to first token, inter-token latency or time per output token, end-to-end completion time, success and cancellation rates, and rejection rate by tenant and model. - -For saturation, track waiting and running requests, queue age, prompt and generation tokens per second, batch composition, KV-cache usage and eviction, prefix-cache hit rate, GPU memory, GPU utilization, model-load time, and preemption or recomputation. vLLM exposes running and waiting requests, cache usage, and prompt and generation token rates; NVIDIA's serving documentation similarly exposes queue and batch behavior. These signals are better autoscaling inputs than CPU. - -Cost should be reported per successful request and per million input and output tokens, split by model and tenant. Include idle warm capacity, failed or cancelled generation, cross-region traffic, and fallback usage. Optimization is a constrained problem: reduce cost while holding quality and latency constant. Quantization, speculative decoding, prefix caching, and parallelism are experiments to benchmark, not magic checkboxes. - -## 8. Close with the trade-offs - -A concise final answer sounds like this: - -> I would first turn 10K RPS into prompt tokens per second, generated tokens per second, concurrent sequences, and an explicit tail-latency target. Requests enter through authenticated, token-aware admission control, then route by model, context size, priority, and region into bounded deadline-aware queues. Serving workers use continuous batching and managed KV-cache memory; the control plane maintains warm multi-zone capacity and scales on queue and token metrics. I would size the fleet from workload-replay benchmarks at the target p99, keep failure headroom, and shed or degrade low-priority work before queues collapse. The key operational metrics are TTFT, inter-token latency, queue age, token throughput, KV pressure, rejection rate, and cost per token. - -That answer is strong because every box follows from a requirement or calculation. It does not pretend that RPS determines hardware by itself. - -## Common mistakes - -- Treating requests per second as the only load unit. -- Naming a GPU count without specifying the model, token distribution, benchmark, and SLO. -- Using an unbounded FIFO queue and calling it backpressure. -- Autoscaling only on CPU or average GPU utilization. -- Ignoring cancellation, deadlines, retry amplification, and disconnected streaming clients. -- Mixing long batch prompts with interactive traffic without isolation. -- Claiming that prefix caching or quantization is always safe without workload and quality evidence. -- Designing multi-region routing without reserving enough warm capacity to absorb a failure. - -## Harder follow-ups to practice - -1. How does the design change for several models and thousands of tenant-specific LoRA adapters? -2. When would you disaggregate prefill and decode, and what new network bottleneck does that introduce? -3. How would you preserve fairness when one tenant sends very long contexts? -4. What would you do if p99 time to first token is good but inter-token latency degrades? -5. How would you roll out a new quantized model and prove that the cost gain does not hide a quality regression? - -## Primary sources - -- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://arxiv.org/abs/2309.06180) -- [NVIDIA Triton Inference Server: dynamic batching](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/batcher.html) -- [vLLM production metrics](https://docs.vllm.ai/en/stable/design/metrics/) -- [Kubernetes horizontal pod autoscaling with custom metrics](https://kubernetes.io/docs/concepts/workloads/autoscaling/horizontal-pod-autoscale/) -- [NVIDIA guide to LLM inference benchmarking metrics](https://developer.nvidia.com/blog/llm-benchmarking-fundamental-concepts/) - -## Practice next - -Start the closed-book case and spend the first five minutes only on clarifying questions. After review, revisit the linked concepts for inference engines, dynamic batching, KV cache, capacity planning, rate limiting, load shedding, and observability. The goal is not to reproduce this diagram; it is to rebuild the reasoning from the prompt. diff --git a/openspec/changes/add-system-design-case-library/design.md b/openspec/changes/add-system-design-case-library/design.md deleted file mode 100644 index fcf19a0..0000000 --- a/openspec/changes/add-system-design-case-library/design.md +++ /dev/null @@ -1,103 +0,0 @@ -## Context - -See `proposal.md` for motivation. The current mock surface stores prompts as a flat array and treats checked rubric rows as both evidence and score. It sends the whole answer and a newline-joined rubric to the generic critique endpoint, then applies one derived rating to every mapped concept. Public pages are generated deterministically from canonical curriculum data, but cases are not part of that projection. - -The implementation must preserve the Socratic boundary, work without configured AI, avoid a database migration, and keep static public output deterministic. - -## Goals / Non-Goals - -**Goals:** - -- Make an interview attempt a versioned state machine whose transitions can be unit tested. -- Keep facts, scoring anchors, and branch selection in reviewed case data. -- Produce per-dimension evidence and remediation, with AI as an optional constrained evaluator. -- Reuse approved case data for static study-guide generation without coupling public reading to private attempt state. - -**Non-Goals:** - -- Voice transcription, live human interview scheduling, or collaborative sessions. -- Universal automatic correctness grading of arbitrary architecture prose. -- Cross-device attempt sync or a D1 schema change. -- Publishing all eight guides before each has passed a separate source and content review. - -## Decisions - -### Canonical case schema replaces system-design rows only - -Introduce a typed, JSON-serializable case catalog for system-design prompts. Technical and behavioral mocks keep the existing lightweight model in this slice. A case includes stable IDs, semantic version, stage prompts, hidden assumptions, rubric dimensions, calculation anchors, deterministic branches, concept remediation, sources, review answer, and publication state. - -This incremental split is preferred to forcing coding and behavioral interviews into a state machine designed around system architecture. A single expanded `MockPrompt` was rejected because optional fields would make invalid partial cases easy to ship. - -### A pure reducer owns the attempt lifecycle - -The UI dispatches events to a pure session reducer; persistence and AI calls stay outside it. The reducer accepts only declared events, records stage submissions, selects case-authored branches, and exposes review material only in terminal review state. - -```mermaid -stateDiagram-v2 - [*] --> Scoping - Scoping --> Estimation: submit requirements - Estimation --> HighLevelDesign: submit calculations - HighLevelDesign --> DeepDive: submit architecture - DeepDive --> Failure: select declared branch - Failure --> Review: submit mitigation - Review --> [*] -``` - -A reducer was chosen over interdependent React state because transition invariants, resumption, and answer visibility need deterministic tests. A backend workflow was rejected for the first slice because the product must work locally and offline. - -### Deterministic anchors wrap optional AI evaluation - -Rubric dimensions declare positive signals, required concepts, disqualifying misconceptions, score anchors, and remediation targets. The local evaluator produces coverage evidence and a provisional band. If AI is configured, it receives the case/version, submitted stage answers, and declared dimensions, then returns schema-constrained evidence. Invalid or out-of-bounds output is discarded. - -```mermaid -flowchart LR - A[Canonical case] --> B[Session reducer] - B --> C[Stage answers] - C --> D[Deterministic evidence] - C --> E[Optional AI evaluator] - A --> E - D --> F[Validated dimension results] - E --> F - F --> G[Targeted mastery and drills] - A --> H[Static guide generator] - H --> I[HTML, Markdown, sitemap, catalogs] -``` - -This makes AI useful for semantic judgment without letting it invent the exam. Pure self-checking was rejected because it rewards confidence rather than evidence; unconstrained LLM grading was rejected because results would drift across providers and prompts. - -### Score dimensions, then map remediation - -Overall readiness is derived from weighted dimension scores, but mastery effects are dimension-local. A missed capacity dimension updates only its mapped concepts; a demonstrated dimension is logged as evidence and is not penalized by unrelated misses. Existing review APIs remain the persistence boundary for concept reviews. - -Applying the aggregate score to every concept was rejected because it destroys diagnostic precision. - -### Persist an append-safe local envelope - -Store attempts in local storage as an envelope containing session schema version, case ID/version, immutable submissions, branch history, current state, and optional review result. Parsers validate envelopes before use. Unsupported records remain read-only and can be exported or ignored; they are never silently rewritten. - -This avoids a production migration while leaving a clean future sync boundary. - -### Static guides are an allow-listed projection - -Extend the existing public generator with case hub and guide renderers. A case is emitted only when its publication state is `approved` and all content gates pass. The initial allow-list contains the LLM inference guide. Practice-only cases do not get placeholder URLs. - -The guide uses case facts and sources but has editorial fields for narrative transitions and unique explanations. Generating prose mechanically from rubric bullets was rejected because it would create thin, repetitive pages. - -## Risks / Trade-offs - -- **[Semantic answers cannot be graded perfectly offline]** → Label deterministic results as evidence coverage, constrain AI to declared anchors, and keep the learner-visible evidence inspectable. -- **[One initial guide leaves SEO coverage narrower than the practice library]** → Ship only the strongest page first, measure discovery, and approve later guides in source-reviewed batches. -- **[Case schema may become verbose]** → Provide schema validation and small authoring helpers; prefer explicit reviewed fields over hidden evaluator logic. -- **[Learners can open the public answer mid-attempt]** → Do not link it inside active practice, warn on practice entry from the guide, and treat closed-book integrity as a learner choice rather than pretending client-side secrecy is enforceable. -- **[Illustrative hardware math can age quickly]** → Make workload variables and measured benchmark inputs explicit; teach the sizing equation rather than canonizing a GPU count. - -## Migration Plan - -1. Add and validate the canonical case schema and eight practice cases without changing current routes. -2. Add the reducer, persistence envelope, evaluators, and focused unit tests. -3. Route system-design selections through the staged UI while leaving technical and behavioral mocks unchanged. -4. Add targeted remediation and validated optional AI results. -5. Add the approved public hub and flagship guide projection, then run public-integrity, docs, type, unit, and build checks. -6. Keep the old system-design rows readable for one release only if saved deep links require them; remove duplicate rows once compatibility tests pass. - -Rollback is a code rollback plus regeneration of static output. Local attempt envelopes remain inert if the reader is absent and are not deleted. diff --git a/openspec/changes/add-system-design-case-library/proposal.md b/openspec/changes/add-system-design-case-library/proposal.md deleted file mode 100644 index 5d0acee..0000000 --- a/openspec/changes/add-system-design-case-library/proposal.md +++ /dev/null @@ -1,43 +0,0 @@ -## Why - -The learning OS exposes strong individual system-design concepts, but its mock prompts are flat checklists and its inference-serving material is fragmented across several drills. A learner can recognize the vocabulary without proving that they can clarify an ambiguous prompt, perform capacity math, defend an architecture, survive a failure injection, and retain the weak concepts afterward. - -The public curriculum is technically search-ready—the live homepage, curriculum hub, and inference-engine page pass the current on-page SEO audit—but it does not own the high-intent “system design interview” queries represented by these cases. The same reviewed case definitions should therefore power both deliberate practice and substantive, indexable study guides without duplicating claims or creating thin SEO pages. - -## What Changes - -- Replace the flat `MockPrompt` model for system design with versioned case definitions containing requirements, stages, calculation anchors, rubric dimensions, deterministic follow-ups, failure injections, concept mappings, sources, and remediation targets. -- Ship a first high-quality library of eight common cases: - - LLM inference serving at 10,000 requests/second - - production RAG over a large document corpus - - multi-tenant LLM API gateway - - real-time recommendation system - - URL shortener - - distributed rate limiter - - real-time chat - - ranked news feed -- Add a deterministic interview-session reducer covering scoping, estimation, high-level design, deep dive, failure injection, and review. The LLM may phrase or grade responses, but cannot invent requirements, scoring anchors, or canonical facts. -- Replace binary rubric self-checks with evidence-backed dimension scores and expose the existing stronger-answer and follow-up output. -- Map missed rubric dimensions to individual concept mastery and targeted drills instead of updating every concept attached to the case equally. -- Generate a public case-library hub and substantive study guides from the same canonical definitions. The first publication slice includes the flagship 10,000-RPS LLM inference guide; later case guides require their own source-backed content preview rather than shipping thin templated pages. Published guides include a worked approach, calculations, architecture reasoning, common mistakes, failure scenarios, primary sources, internal links, and structured data. -- Add the hub and guides to sitemap, Markdown/agent surfaces, canonical navigation, and public-curriculum integrity checks while keeping their search intent distinct from concept pages. -- Preserve the existing Socratic no-direct-solutions constraint inside active practice; complete reference answers appear only after submission or explicit review. - -## Capabilities - -### New Capabilities - -- `structured-system-design-practice`: Versioned common interview cases, deterministic staged attempts, evidence-backed grading, failure injection, and concept-level remediation. -- `system-design-study-guides`: Source-backed public guides generated from canonical case definitions with unique worked examples and non-overlapping search intent. - -### Modified Capabilities - -- `public-curriculum-discovery`: Extend public discovery, sitemap, structured data, and agent-readable catalogs to cover system-design case hubs and guides. - -## Impact - -- Affected data and UI: `src/data/mock-prompts.ts` or its replacement, `src/pages/MockInterview.tsx`, recommendation/mastery mapping, and focused tests. -- Affected AI boundary: `handlers/critique.mjs` response schema and Mock rendering; existing BYOK/provider infrastructure remains unchanged. -- Affected public generation: `scripts/generate-public-curriculum.mjs`, sitemap, public Markdown/JSON catalogs, and static HTML pages. -- Persistence: local versioned attempts in the first slice; no D1 migration or new production dependency is required. Cross-device persistence remains a separately reviewed additive change. -- SEO: no deployment is part of this change. Publishing remains subject to content-preview approval, build validation, and the existing manual deploy boundary. diff --git a/openspec/changes/add-system-design-case-library/seo-audit.md b/openspec/changes/add-system-design-case-library/seo-audit.md deleted file mode 100644 index f0b9af5..0000000 --- a/openspec/changes/add-system-design-case-library/seo-audit.md +++ /dev/null @@ -1,51 +0,0 @@ -# SEO audit: system-design interview coverage - -Audit date: 2026-08-04 - -## Outcome - -The sampled live pages have excellent technical and on-page SEO. The current weakness is content strategy: the public site exposes a broad curriculum but has no dedicated page that answers a concrete, high-intent system-design interview prompt such as “design LLM inference for 10K requests per second.” - -| Area | Assessment | Evidence | -| --- | --- | --- | -| Crawlability and indexability | 10/10 | Live robots, sitemap, canonical URLs, JavaScript-free public pages, and agent surfaces all passed the audit scripts. | -| Page metadata and semantics | 10/10 | Homepage, curriculum hub, and inference-engine concept page passed title, description, canonical, social metadata, JSON-LD, heading, and substantive-copy checks with no warnings. | -| Internal public corpus | 8/10 | The inventory contains hundreds of generated curriculum pages and strong concept coverage. Navigation is mechanism-first rather than interview-query-first. | -| High-intent system-design coverage | 4/10 | No case hub or dedicated worked guide currently targets the exact prompt, calculations, failure follow-ups, and answer structure searchers want. | -| Overall SEO readiness for this feature | 7/10 | The publishing foundation is strong; content-market fit is the missing layer. | - -This score is not a claim about current rankings or organic traffic. Search Console and analytics data were not available in this audit, so performance in search results remains unverified. - -## Sampled live results - -- `/`: 18 checks passed; 0 failures; 0 warnings. The page had a 50-character title, 142-character description, canonical URL, social tags, JSON-LD, one H1, useful H2s, and roughly 725 words. -- `/curriculum/`: 15 checks passed; 0 failures; 0 warnings; roughly 1,481 words. -- `/curriculum/concepts/inference-engines`: 15 checks passed; 0 failures; 0 warnings; roughly 622 words. -- Content inventory: 693 public pages were detected, but 681 fell into the generic `other` archetype and no explicit interview-intent coverage set was detected. - -## Search landscape - -Current results already contain dedicated LLM system-design guides from System Design Interview, System Design Academy, CalibreOS, DesignGurus, and System Design Handbook. The opportunity is not an empty keyword; the page must win by being more concrete, more measurable, and connected to an actual deliberate-practice loop. - -An exact version of the prompt also appears in a public interview-experience result. That is useful evidence that “10K requests/second” is a real interview formulation, not only an invented editorial keyword. - -## Recommended content position - -Do not publish another generic “LLM system design” summary. Publish a worked interview case that: - -1. begins by challenging the ambiguity of RPS for autoregressive generation; -2. converts traffic into input tokens/second, output tokens/second, and concurrent sequences; -3. sizes capacity from a reproducible benchmark at the target SLO rather than a hard-coded GPU claim; -4. handles admission, continuous batching, KV-cache pressure, cancellation, overload, and regional failure; -5. ends with an answer outline, common mistakes, harder follow-ups, and a closed-book practice entrypoint. - -That content has distinct intent from the existing inference-engine concept page: the concept page explains mechanisms, while the case guide demonstrates how to assemble and defend them under interview constraints. - -## Post-implementation local verification - -The generated production artifacts were served locally from `dist/` and audited on 2026-08-04 with the Fleet on-page SEO audit. - -- `/system-design/`: 17 checks passed, 0 failures, and 1 intentional warning. The page has a 44-character title, 145-character description, production canonical URL, social metadata, CollectionPage and BreadcrumbList structured data, one H1, three H2s, and roughly 659 words. -- `/system-design/llm-inference-10k-rps.html`: 15 checks passed, 0 failures, and 0 warnings. The guide has a 42-character title, 136-character description, production canonical URL, social metadata, Article and BreadcrumbList structured data, one H1, twelve H2s, and roughly 1,730 words. -- The single warning is expected in local verification: the audit compared the temporary `127.0.0.1` URL to a sitemap containing production canonical URLs. Both production canonical URLs are present in the generated `public/sitemap.xml` and `dist/sitemap.xml`. -- Search ranking and traffic remain unverified until the pages are deployed and observed in Search Console and analytics. diff --git a/openspec/changes/add-system-design-case-library/specs/public-curriculum-discovery/spec.md b/openspec/changes/add-system-design-case-library/specs/public-curriculum-discovery/spec.md deleted file mode 100644 index 4c91d1f..0000000 --- a/openspec/changes/add-system-design-case-library/specs/public-curriculum-discovery/spec.md +++ /dev/null @@ -1,22 +0,0 @@ -## ADDED Requirements - -### Requirement: Public discovery includes approved system-design guides -The public projection SHALL include a JavaScript-free system-design case hub and every publication-approved guide in navigation, sitemap, Markdown catalogs, and agent-readable inventories. - -#### Scenario: Guide is approved or withdrawn -- **WHEN** canonical case data changes and public generation runs -- **THEN** generated HTML, Markdown, sitemap, and catalog entries match the current approved guide set exactly once - -### Requirement: System-design guides expose appropriate structured data -Every generated guide MUST include canonical metadata and valid schema.org Article and BreadcrumbList data derived from the canonical case. - -#### Scenario: Search crawler reads a guide -- **WHEN** it parses the HTML without JavaScript -- **THEN** it can identify the headline, description, publication or modification date, authoring organization, breadcrumb hierarchy, and canonical URL - -### Requirement: Publication integrity covers case surfaces -Automated checks MUST fail when the case hub or an approved guide is missing its generated HTML, Markdown equivalent, sitemap entry, catalog entry, canonical link, or required internal practice link. - -#### Scenario: Generated case output drifts -- **WHEN** integrity tests compare public files with canonical case data -- **THEN** the test names each missing or mismatched case ID and blocks release diff --git a/openspec/changes/add-system-design-case-library/specs/structured-system-design-practice/spec.md b/openspec/changes/add-system-design-case-library/specs/structured-system-design-practice/spec.md deleted file mode 100644 index e93b6a0..0000000 --- a/openspec/changes/add-system-design-case-library/specs/structured-system-design-practice/spec.md +++ /dev/null @@ -1,97 +0,0 @@ -## Purpose - -Provide repeatable system-design interview practice that measures the decisions a learner makes across a realistic interview, then turns specific weaknesses into targeted study and review. - -## ADDED Requirements - -### Requirement: Common cases are canonical and versioned -The system SHALL define each system-design case as versioned canonical data containing the prompt, interviewer-only assumptions, ordered stages, calculation anchors, rubric dimensions, deterministic follow-ups, failure injections, concept mappings, reference answer, and primary sources. - -#### Scenario: First case library is loaded -- **WHEN** the system-design case catalog is validated -- **THEN** it contains LLM inference serving, production RAG, a multi-tenant LLM gateway, real-time recommendations, a URL shortener, a distributed rate limiter, real-time chat, and a ranked news feed with every required field - -#### Scenario: An existing case changes materially -- **WHEN** an assumption, rubric anchor, or reference answer changes -- **THEN** the case version changes so a saved attempt remains interpretable against the version the learner used - -### Requirement: Attempts follow deterministic interview stages -The system SHALL run each case through scoping, estimation, high-level design, deep dive, failure injection, and review in the case-defined order. - -#### Scenario: Learner begins a case -- **WHEN** a learner starts a structured case -- **THEN** the session opens at scoping without exposing interviewer assumptions, later prompts, rubric anchors, or the reference answer - -#### Scenario: Learner submits a stage -- **WHEN** the learner records an answer and advances -- **THEN** the session preserves that answer, advances according to the case definition, and cannot skip a required stage - -#### Scenario: Learner resumes an unfinished attempt -- **WHEN** a compatible saved attempt exists for the same case and version -- **THEN** the system restores the current stage, elapsed state, and prior answers without revealing later-stage material - -### Requirement: Scoping and capacity reasoning are evaluated explicitly -The system MUST distinguish requirement discovery and capacity estimation from later architecture prose. - -#### Scenario: LLM inference assumptions are ambiguous -- **WHEN** the learner handles the 10,000-requests-per-second case -- **THEN** the scoping stage checks for model size, input and output token distributions, latency SLOs, streaming, traffic shape, availability, regions, and cost constraints before revealing case assumptions - -#### Scenario: Learner estimates capacity -- **WHEN** the learner submits capacity math -- **THEN** evaluation checks units and reasoning for request rate, token rate, concurrency, measured per-replica throughput, headroom, and fleet sizing rather than requiring one universal GPU count - -### Requirement: Follow-ups and failures are case-defined -The system SHALL select follow-ups and failure injections only from deterministic branches declared in the active case version. - -#### Scenario: Learner chooses an architecture -- **WHEN** a declared decision activates a follow-up branch -- **THEN** the interviewer asks the corresponding case-authored follow-up and records which branch was used - -#### Scenario: Failure stage begins -- **WHEN** the learner reaches failure injection -- **THEN** the system presents a case-authored incident such as a hot queue, regional loss, dependency slowdown, or burst and asks for detection, mitigation, degradation, and recovery - -### Requirement: Grading is evidence-backed and resilient -The system SHALL score named rubric dimensions from answer evidence and SHALL retain deterministic grading when AI critique is absent or invalid. - -#### Scenario: A dimension receives a score -- **WHEN** an attempt is reviewed -- **THEN** the result contains the dimension score, cited learner evidence or a missing-evidence explanation, and the applicable rubric anchor - -#### Scenario: AI critique is configured -- **WHEN** AI grades or phrases feedback -- **THEN** its response is validated against the case schema and it cannot add assumptions, dimensions, canonical facts, or a score outside declared anchors - -#### Scenario: AI critique is unavailable -- **WHEN** no provider is configured, a request fails, or a response is invalid -- **THEN** the learner can complete the attempt with deterministic rubric evidence and without losing saved work - -### Requirement: Review produces targeted remediation -The system SHALL map each missed rubric dimension to its declared concept and drill targets instead of applying one aggregate rating to every concept in the case. - -#### Scenario: Learner misses capacity math but handles reliability -- **WHEN** the completed attempt is reviewed -- **THEN** capacity-planning remediation is recommended while already-demonstrated reliability concepts are not downgraded by that miss - -#### Scenario: Review is complete -- **WHEN** dimension results are available -- **THEN** the learner receives an overall readiness band, stronger answer, harder follow-ups, missed concepts, and direct links to targeted study and drills - -### Requirement: Reference answers respect the Socratic boundary -The system MUST keep direct solutions hidden during active practice and SHALL reveal them only after submission or an explicit review action. - -#### Scenario: Active learner asks for the answer -- **WHEN** the attempt is not submitted -- **THEN** the companion probes reasoning or offers a bounded hint without displaying the reference answer - -#### Scenario: Learner enters review -- **WHEN** the attempt has been submitted or the learner explicitly abandons practice for review -- **THEN** the system may show the worked answer, calculation anchors, and comparison against the learner's evidence - -### Requirement: Attempt persistence is private and migration-safe -The first release SHALL store structured attempts locally with case ID, case version, session schema version, stage state, answers, branch history, and results. - -#### Scenario: Stored attempt has an unsupported schema -- **WHEN** the application cannot safely migrate a local attempt -- **THEN** it preserves the stored record, explains that it is read-only, and offers a new attempt without corrupting current mastery diff --git a/openspec/changes/add-system-design-case-library/specs/system-design-study-guides/spec.md b/openspec/changes/add-system-design-case-library/specs/system-design-study-guides/spec.md deleted file mode 100644 index c2fcfce..0000000 --- a/openspec/changes/add-system-design-case-library/specs/system-design-study-guides/spec.md +++ /dev/null @@ -1,51 +0,0 @@ -## Purpose - -Publish substantive, source-backed system-design interview guides from reviewed case data so learners can study a complete worked approach and search engines can index genuinely useful answers. - -## ADDED Requirements - -### Requirement: Published guides derive from canonical cases -The system SHALL generate the public case hub and each approved guide from the same case version used by structured practice. - -#### Scenario: A case is approved for publication -- **WHEN** public generation runs -- **THEN** its title, prompt, assumptions, calculations, rubric concepts, failure scenario, and sources match the canonical case definition - -#### Scenario: A practice-only case is not approved for publication -- **WHEN** public generation runs -- **THEN** the case remains available in private practice without emitting a thin or placeholder guide URL - -### Requirement: Every guide is a complete worked lesson -Each published guide MUST contain a clear prompt interpretation, clarifying questions, stated assumptions, capacity math with units, architecture walkthrough, critical-path deep dive, failure handling, observability and cost discussion, common mistakes, harder follow-ups, concept remediation links, and a concise final answer outline. - -#### Scenario: Learner studies the LLM inference case -- **WHEN** they open the public 10,000-requests-per-second guide -- **THEN** they can follow an end-to-end answer that separates request throughput from input and output token throughput and sizes the fleet from measured per-replica performance with explicit headroom - -### Requirement: Technical claims are source-backed -Each published guide SHALL cite primary or official sources for non-obvious mechanisms and MUST distinguish benchmark-dependent examples from stable principles. - -#### Scenario: Guide discusses batching or KV-cache memory -- **WHEN** it states how the serving mechanism works -- **THEN** it links to the relevant paper or official serving documentation and does not present an illustrative GPU count as universally correct - -### Requirement: Guide intent is distinct and non-duplicative -The case hub SHALL target browsing common system-design interview questions, while each guide SHALL answer one concrete prompt and link to narrower concept pages for mechanism depth. - -#### Scenario: Guide and concept page overlap -- **WHEN** both mention the same mechanism -- **THEN** the guide applies it to the case and links to the concept page instead of duplicating the concept lesson verbatim - -### Requirement: Public answers do not compromise active attempts -Public guides SHALL remain browsable, but the active practice interface MUST not reveal or embed guide content before submission. - -#### Scenario: Learner starts from a public guide -- **WHEN** they choose to practice the case -- **THEN** the application starts a fresh attempt and clearly warns that returning to the guide exits closed-book practice - -### Requirement: Publication readiness is explicit -A guide SHALL be emitted only when its case declares publication approval and passes content, source, metadata, link, and substantive-length validation. - -#### Scenario: Draft guide lacks required evidence -- **WHEN** the generator validates a missing source, required section, internal remediation link, or publication approval -- **THEN** generation fails with the case ID and missing requirement diff --git a/openspec/changes/add-system-design-case-library/tasks.md b/openspec/changes/add-system-design-case-library/tasks.md deleted file mode 100644 index 5401a0d..0000000 --- a/openspec/changes/add-system-design-case-library/tasks.md +++ /dev/null @@ -1,39 +0,0 @@ -## 1. Canonical Cases - -- [x] 1.1 Add the typed, versioned system-design case schema and validation helpers with focused invalid-case tests -- [x] 1.2 Author and validate the eight initial cases with stages, hidden assumptions, calculation anchors, branches, dimension rubrics, remediation mappings, references, and review answers -- [x] 1.3 Add a catalog integrity test that enforces unique IDs, valid concept and drill links, declared branch targets, source requirements, and case-version formatting - -## 2. Session Engine - -- [x] 2.1 Implement the pure staged-session reducer and answer-visibility rules -- [x] 2.2 Add the versioned local attempt envelope, safe parser, resume behavior, and unsupported-version handling -- [x] 2.3 Test required-stage order, deterministic branches, failure injection, resumption, submission, abandonment-to-review, and hidden-answer invariants - -## 3. Evaluation and Remediation - -- [x] 3.1 Implement deterministic per-dimension evidence coverage and readiness-band derivation -- [x] 3.2 Add the constrained AI critique request and response schema with rejection and offline fallback paths -- [x] 3.3 Map missed dimensions to only their declared concept reviews and targeted drills -- [x] 3.4 Test evidence citations, out-of-anchor AI rejection, provider failure, and concept-local mastery effects - -## 4. Practice Experience - -- [x] 4.1 Route system-design mocks to a staged interview workspace while preserving current technical and behavioral mocks -- [x] 4.2 Build scoping, estimation, architecture, deep-dive, and failure panels with timer, progress, saved-answer, and resume states -- [x] 4.3 Build the submitted review with dimension evidence, stronger answer, follow-ups, worked answer, common mistakes, and remediation links -- [x] 4.4 Add accessible keyboard, focus, narrow-screen, loading, invalid-save, and no-AI states and verify the main case manually - -## 5. Public Study Guide - -- [x] 5.1 Apply the owner-approved content preview to the flagship LLM inference case and mark only reviewed cases publication-approved -- [x] 5.2 Extend public generation with the case hub plus approved Article and BreadcrumbList HTML and Markdown pages -- [x] 5.3 Add case surfaces to sitemap, catalogs, agent entrypoints, navigation, robots coverage, and publication manifest -- [x] 5.4 Add integrity tests for substantive content, sources, metadata, canonical URLs, internal practice links, sitemap entries, and prevention of placeholder case pages - -## 6. Verification - -- [x] 6.1 Run focused case-schema, reducer, evaluation, mastery-mapping, and generator tests -- [x] 6.2 Run typecheck, docs validation, public generation, public integrity tests, and build -- [x] 6.3 Run the local SEO audit against the generated hub and flagship guide and record any intentional exceptions -- [x] 6.4 Run `git diff --check` and inspect the final diff without committing, pushing, or deploying diff --git a/openspec/changes/archive/2026-07-25-expand-learning-domains/.openspec.yaml b/openspec/changes/archive/2026-07-25-expand-learning-domains/.openspec.yaml deleted file mode 100644 index 3a03821..0000000 --- a/openspec/changes/archive/2026-07-25-expand-learning-domains/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-07-25 diff --git a/openspec/changes/archive/2026-07-25-expand-learning-domains/design.md b/openspec/changes/archive/2026-07-25-expand-learning-domains/design.md deleted file mode 100644 index 61b304c..0000000 --- a/openspec/changes/archive/2026-07-25-expand-learning-domains/design.md +++ /dev/null @@ -1,131 +0,0 @@ -## Context - -The app renders tracks from `TRACKS`, concepts and practice relationships from -static JSON, and roadmap groups from `src/lib/roadmapGroups.ts`. Mastery and -FSRS state are keyed by concept ID, so IDs are the compatibility boundary. -Every known track must own at least three concepts, every concept must link an -editorial drill and review question, and roadmap references are integrity -checked. - -The requested taxonomy contains 96 named subtopics across eleven domains. -Search/IR, mathematics, vector databases, DSA, product, backend, databases, -system design, and the existing AI concepts remain valuable and must not be -collapsed merely to make the top-level counts match. - -## Goals / Non-Goals - -**Goals:** - -- Make every requested subtopic traceable to one or more concept IDs. -- Give missing high-level domains explicit tracks and selectable roadmaps. -- Reuse and reclassify existing concepts instead of creating synonyms. -- Ensure every new concept participates in drills, reviews, concept packs, and - at least one roadmap. -- Preserve all existing IDs and saved learning state. -- Keep future coverage auditable through a machine-readable matrix and tests. - -**Non-Goals:** - -- Replacing the nine existing tracks or rewriting their content. -- Teaching every subfield to research-specialist depth in one change. -- Adding runtime dependencies, APIs, storage, auth, or deployment behavior. -- Moving or renaming `docs/learning/*.md`. -- Generating article bodies or copying external source material. - -## Decisions - -### Add nine tracks instead of replacing the current taxonomy - -Add `systems-foundations`, `infrastructure-platforms`, -`distributed-systems`, `inference-serving`, `agent-systems`, -`ai-reliability`, `developer-tools`, `application-engineering`, and -`multimodal-spatial`. - -The existing `databases`, `ai-systems`, `backend`, `system-design`, and -`product` tracks continue to cover the remaining requested categories. -Search/IR, vector DB, mathematics, and DSA remain first-class because they are -already deep, personally useful curricula. - -Alternative considered: replace the existing tracks with exactly eleven. -Rejected because it would make saved mastery groupings drift, flatten strong -specialist paths, and force unrelated subjects into broad buckets. - -### Reclassify existing concepts by primary tag, preserving secondary tags - -Where an existing concept is the canonical lesson for a requested domain, its -new track becomes `tags[0]` and the old group remains in `tags`. Concept IDs, -prerequisites, drills, review questions, and artifacts remain unchanged. - -Alternative considered: duplicate concepts under each new track. Rejected -because duplicate FSRS cards and mastery records would teach the same thing -twice. - -### Use a machine-readable coverage matrix - -Add `src/data/curriculum-coverage.json` with all eleven requested categories, -their named subtopics, and the concept IDs that satisfy each subtopic. Tests -will require every mapped concept to exist and every topic to have coverage. - -This file is the taxonomy contract; prose docs summarize it but do not -duplicate every mapping. - -### Add an idempotent editorial expansion script - -Add `scripts/expand-learning-domains.mjs` containing the curated new concept, -drill, review-question, artifact, roadmap, reclassification, and coverage -definitions. The script merges by stable ID, sorts only appended material, and -fails on conflicting existing definitions. It can be rerun without duplicates. - -This is preferable to hand-editing several thousand JSON lines independently, -because cross-file IDs are generated from one reviewed registry and existing -integrity tests remain authoritative. - -### One roadmap and synthesis artifact per new track - -Each new roadmap uses a 12-week horizon with three milestones: -foundations, production mechanisms, and verification/synthesis. Each finishes -with a capstone artifact that requires measurable success criteria rather than -passive reading. - -The existing AI Systems track also receives a dedicated AI Models & Training -roadmap. Its sequence runs model foundations → pre-training and fine-tuning → -post-training and evaluation, closing a gap that a survey-only grouping would -leave unresolved. - -### Keep resources concise and canonical - -Every new concept receives at least one primary official, course, paper, or -widely accepted engineering source. The existing concept-pack generator adds -the linked problem and explain-back prompt; unavailable media slots remain -honest gaps. - -## Risks / Trade-offs - -- **Track count grows from 9 to 18** → Group roadmaps into scannable curriculum - families and rely on existing searchable catalog navigation. -- **A broad expansion can become shallow** → Require a drill, review prompt, - roadmap placement, canonical resource, and synthesis artifact for every new - concept. -- **Primary-tag changes alter track rollups** → Preserve concept IDs and old - tags; only grouping changes, not mastery data. -- **Generated data can drift from the registry** → Make the expansion script - idempotent and add explicit coverage and cross-file tests. -- **External links can age** → Prefer stable official/course/paper URLs and - keep normal documentation link-audit practices. - -## Migration Plan - -1. Add tracks and the curated expansion registry. -2. Run the registry script once to merge concepts, drills, review questions, - artifacts, roadmaps, and the coverage matrix. -3. Regenerate concept packs with the existing pipeline. -4. Update roadmap groups and canonical documentation. -5. Run focused taxonomy tests, docs validation, typecheck, and build. - -Rollback is a normal source revert. No persisted concept IDs are removed and no -data migration is required. - -## Open Questions - -None. The user explicitly delegated track-versus-augmentation judgment, and -the additive approach preserves current learning investments. diff --git a/openspec/changes/archive/2026-07-25-expand-learning-domains/proposal.md b/openspec/changes/archive/2026-07-25-expand-learning-domains/proposal.md deleted file mode 100644 index 8c94fe6..0000000 --- a/openspec/changes/archive/2026-07-25-expand-learning-domains/proposal.md +++ /dev/null @@ -1,54 +0,0 @@ -## Why - -The learning OS has deep search, database, runtime, backend, and interview -content, but the user's target curriculum spans eleven broader systems and -AI-native domains. Several of those domains are currently only survey links or -project references, so they cannot be selected as coherent paths or practiced -through the Concept → Drill → Build → Review → Apply loop. - -## What Changes - -- Add nine additive learning tracks: Systems Foundations, Infrastructure & - Platforms, Distributed Systems, Inference & Serving, Agent Systems, AI - Reliability, Developer Tools & Code Intelligence, Application Engineering, - and Multimodal & Spatial Computing. -- Preserve every existing track ID and concept ID so saved mastery, review - schedules, routes, and links remain valid. -- Reclassify existing concepts into the new tracks when they already teach the - requested subject, retaining their previous domain tags as secondary tags. -- Add concepts for the requested subtopics that are not currently taught, - including Temporal workflows, OpenTelemetry, infrastructure automation, - inference optimization, agent memory and durable execution, tool-use and - coding-agent evaluation, repository intelligence, mobile/voice engineering, - and robotics/spatial interfaces. -- Give every added concept an editorial drill, review question, curated - starting resource, and generated concept pack. -- Add one sequenced roadmap and one synthesis artifact for each new track, - plus a dedicated AI Models & Training path within the existing AI track. -- Update learning navigation and canonical documentation so the expanded - curriculum is discoverable and its scope is accurately described. -- Add taxonomy coverage assertions preventing these domains from silently - regressing. - -## Capabilities - -### New Capabilities - -- `expanded-learning-domains`: A backward-compatible, drill-backed curriculum - covering the eleven requested systems, AI, developer-tooling, application, - and multimodal domains. - -### Modified Capabilities - -None. This repository has no existing OpenSpec capabilities. - -## Impact - -- Curriculum data: `src/data/learning-os.ts`, `concepts.json`, `drills.json`, - `review-questions.json`, `artifacts.json`, `roadmaps.json`, and generated - `concept-packs.json`. -- Navigation and grouping: roadmap grouping and learning-page counts/copy. -- Tests: taxonomy integrity and explicit requested-domain coverage. -- Documentation: product overview, learning index, project status, and the - durable learning notes. -- No API, database schema, authentication, dependency, or deployment changes. diff --git a/openspec/changes/archive/2026-07-25-expand-learning-domains/specs/expanded-learning-domains/spec.md b/openspec/changes/archive/2026-07-25-expand-learning-domains/specs/expanded-learning-domains/spec.md deleted file mode 100644 index af9281d..0000000 --- a/openspec/changes/archive/2026-07-25-expand-learning-domains/specs/expanded-learning-domains/spec.md +++ /dev/null @@ -1,79 +0,0 @@ -## ADDED Requirements - -### Requirement: Requested taxonomy is completely mapped -The learning OS SHALL maintain a machine-readable coverage map for all eleven -requested categories and every named subtopic within them. - -#### Scenario: Coverage map integrity -- **WHEN** curriculum integrity tests run -- **THEN** every requested subtopic has at least one existing concept ID and no mapping points to a missing concept - -### Requirement: Missing domains are first-class tracks -The learning OS SHALL expose Systems Foundations, Infrastructure & Platforms, -Distributed Systems, Inference & Serving, Agent Systems, AI Reliability, -Developer Tools & Code Intelligence, Application Engineering, and Multimodal -& Spatial Computing as known tracks. - -#### Scenario: User browses the full catalog -- **WHEN** the user opens the learning catalog -- **THEN** each added domain is available as a recognized track with at least three concepts - -### Requirement: Existing learning state remains compatible -The expansion MUST preserve every existing track ID and concept ID. - -#### Scenario: Existing learner returns after expansion -- **WHEN** previously stored mastery or FSRS state references an existing concept ID -- **THEN** the concept still resolves and its stored state remains usable - -### Requirement: Concepts are practice-backed -Every newly introduced concept SHALL have a description, mental model, -canonical resource, editorial drill with an executable validation case, and -review question. - -#### Scenario: New concept enters the practice loop -- **WHEN** a user opens any concept introduced by this change -- **THEN** the concept offers a linked practice drill and an explain-back review prompt - -### Requirement: Each added track has a sequenced path -Every added track SHALL have a selectable roadmap that places all of its -concepts into ordered milestones and ends in a synthesis artifact. - -#### Scenario: User selects a new domain -- **WHEN** the user chooses an added roadmap -- **THEN** the app can recommend its next concept and display a measurable capstone - -### Requirement: AI model training is a coherent path -The existing AI Systems track SHALL offer a selectable roadmap covering model -foundations, pre-training, fine-tuning, post-training, reinforcement learning, -quantization, open-weight models, and evaluation. - -#### Scenario: User studies model development end to end -- **WHEN** the user selects the AI Models & Training roadmap -- **THEN** concepts are ordered from architecture and optimization through training, alignment, compression, and evaluation - -### Requirement: Existing concepts are reused without duplication -When an existing concept already satisfies a requested subtopic, the learning -OS SHALL reuse its stable ID and MAY change its primary track while retaining -its former domain as a secondary tag. - -#### Scenario: Consensus moves into Distributed Systems -- **WHEN** the expanded taxonomy is loaded -- **THEN** the existing `consensus` concept appears under Distributed Systems without creating a duplicate consensus card - -### Requirement: Curriculum navigation stays scannable -The learning page SHALL group the added roadmaps into clear systems, AI-native, -software-building, and human-interface families without hiding existing -roadmaps. - -#### Scenario: User scans all learning paths -- **WHEN** the learning or explore page renders roadmap groups -- **THEN** all existing and added roadmaps appear exactly once in an intentional group - -### Requirement: Canonical documentation reflects the expansion -Product and learning documentation MUST describe the expanded tracks, paths, -and coverage boundaries without claiming specialist depth that the curriculum -does not provide. - -#### Scenario: Maintainer checks documented scope -- **WHEN** the maintainer reads the product overview and learning index -- **THEN** the documented track inventory and roadmap organization match executable curriculum data diff --git a/openspec/changes/archive/2026-07-25-expand-learning-domains/tasks.md b/openspec/changes/archive/2026-07-25-expand-learning-domains/tasks.md deleted file mode 100644 index fe211c8..0000000 --- a/openspec/changes/archive/2026-07-25-expand-learning-domains/tasks.md +++ /dev/null @@ -1,27 +0,0 @@ -## 1. Curriculum Data Model - -- [x] 1.1 Add the nine missing track definitions without changing existing track IDs -- [x] 1.2 Add the idempotent curriculum-expansion registry and merge script -- [x] 1.3 Generate the requested-topic coverage matrix - -## 2. Learning Content - -- [x] 2.1 Add and reclassify concepts for all requested systems, infrastructure, distributed, and database topics -- [x] 2.2 Add and reclassify concepts for AI training, inference, agents, and reliability -- [x] 2.3 Add concepts for developer tooling, application engineering, and multimodal/spatial computing -- [x] 2.4 Add an editorial drill and review question for every new concept -- [x] 2.5 Add ten sequenced roadmaps and ten synthesis artifacts, including AI Models & Training -- [x] 2.6 Regenerate concept packs through the existing pipeline - -## 3. Navigation and Documentation - -- [x] 3.1 Group all existing and added roadmaps into scannable learning families -- [x] 3.2 Update product overview and learning index with the expanded taxonomy -- [x] 3.3 Update durable project status and learning notes for the personally requested expansion - -## 4. Verification - -- [x] 4.1 Add tests for requested-topic coverage, stable legacy IDs, and new-track integrity -- [x] 4.2 Run focused concept taxonomy tests -- [x] 4.3 Run docs validation and TypeScript typecheck -- [x] 4.4 Run the production build and inspect the final diff diff --git a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/.openspec.yaml b/openspec/changes/archive/2026-07-25-strengthen-helpful-content/.openspec.yaml deleted file mode 100644 index 3a03821..0000000 --- a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-07-25 diff --git a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/design.md b/openspec/changes/archive/2026-07-25-strengthen-helpful-content/design.md deleted file mode 100644 index e0ff81a..0000000 --- a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/design.md +++ /dev/null @@ -1,85 +0,0 @@ -## Context - -The canonical curriculum lives in checked-in JSON and TypeScript data. The SPA -renders it well for active learners, but crawlers and visitors without -JavaScript see only a small homepage shell and a minimal agent brief. Existing -agent endpoints pass the fleet S-tier protocol checks, yet they describe only -the product shell rather than the curriculum. - -The publication layer must not create a second editorial source of truth, -change authenticated routes, or add a runtime service. - -## Goals / Non-Goals - -**Goals:** - -- Make all tracks, roadmaps, and concepts useful and readable without - JavaScript or authentication. -- Keep public pages synchronized deterministically with canonical curriculum - data. -- Give search and AI systems complete sitemap, catalog, metadata, and - structured-data signals. -- Improve the visible homepage explanation for first-time human visitors. - -**Non-Goals:** - -- Server-side rendering the SPA. -- Publishing private progress, notes, Reader content, or answer keys. -- Replacing the interactive learning routes. -- Adding analytics, external services, or production dependencies. - -## Decisions - -1. **Generate a separate `/curriculum/` publication tree.** The generator - writes one hub, 18 track pages, 24 roadmap pages, and 222 concept pages. - Keeping these URLs separate avoids intercepting existing SPA routes such as - `/concepts/:id`. - -2. **Generate from canonical checked-in data.** Concepts, roadmaps, drills, - reviews, artifacts, tracks, and the coverage map are inputs. Generated - pages are never hand-edited and the generator is safe to rerun. - -3. **Publish both HTML and machine-readable summaries.** Static HTML provides - human and search-engine value; `catalog.md` and `catalog.json` give agents a - compact inventory without forcing them to execute JavaScript. - -4. **Use semantic HTML and per-page JSON-LD.** Concept pages use - `LearningResource`, roadmap pages use `Course`, and collection pages use - `CollectionPage`/`ItemList`. Every page has a title, description, canonical - URL, Open Graph fields, one H1, section headings, and internal navigation. - -5. **Regenerate discovery surfaces together.** The generator owns the - curriculum entries in sitemap and updates public agent catalogs so count - and URL drift is testable. - -6. **Run generation before production builds.** Generated files remain - checked in for review and direct hosting, while the build command verifies - they can be reproduced. - -## Risks / Trade-offs - -- **Generated-file volume** → Keep HTML small, share one CSS file, and test - exact page counts rather than reviewing each page manually. -- **Duplicate content with SPA routes** → Use unique `/curriculum/` canonicals - and link interactive practice as a separate action. -- **Stale generated output** → Run generation during build and add integrity - tests comparing output with source IDs. -- **Sitemap growth** → The resulting few hundred URLs remain well below - sitemap limits and are directly tied to substantive pages. -- **Publishing sensitive material** → Publish editorial descriptions, mental - models, resources, and review prompts only; exclude user state and private - learning-source bodies. - -## Migration Plan - -1. Generate and validate the static publication tree locally. -2. Deploy alongside existing SPA assets; no database or API migration. -3. Smoke the hub, representative detail pages, sitemap, and agent catalog. -4. Roll back using the previous Cloudflare Pages deployment if discovery - surfaces fail; interactive SPA routes are unchanged. - -## Open Questions - -None. The target is strong technical and on-page SEO for a content-rich -learning product, with agent catalogs retained as a complementary discovery -surface. diff --git a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/proposal.md b/openspec/changes/archive/2026-07-25-strengthen-helpful-content/proposal.md deleted file mode 100644 index 612ba89..0000000 --- a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/proposal.md +++ /dev/null @@ -1,40 +0,0 @@ -## Why - -The product now contains 222 concepts across 18 tracks, but most of that -curriculum is only discoverable inside the JavaScript application. The live -SEO audit reports incomplete metadata and thin crawlable content, so search -engines cannot reliably understand, index, or rank the depth and organization -of the learning OS. - -## What Changes - -- Generate crawlable, static curriculum pages from the canonical concepts, - roadmaps, drills, reviews, artifacts, tracks, and coverage map. -- Publish a curriculum hub plus track, roadmap, and concept detail pages with - useful explanations, internal navigation, canonical links, and structured - data. -- Generate sitemap and machine-readable agent catalogs from the same source so - all published surfaces stay synchronized. -- Strengthen homepage metadata, structured data, heading structure, and visible - curriculum copy. -- Add integrity tests and a deterministic generation command. - -## Capabilities - -### New Capabilities - -- `public-curriculum-discovery`: Helpful, crawlable curriculum publication for - humans, search engines, and AI agents. - -### Modified Capabilities - -- None. - -## Impact - -- Adds a repository-local generator and generated files under `public/curriculum/`. -- Updates `index.html`, the public login experience, sitemap, `llms*.txt`, - `index.md`, and `api-ai.json`. -- Extends build validation without adding production dependencies. -- Changes only public static discovery surfaces; authenticated learning state, - APIs, database schema, and Socratic behavior remain unchanged. diff --git a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/specs/public-curriculum-discovery/spec.md b/openspec/changes/archive/2026-07-25-strengthen-helpful-content/specs/public-curriculum-discovery/spec.md deleted file mode 100644 index 67f75d6..0000000 --- a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/specs/public-curriculum-discovery/spec.md +++ /dev/null @@ -1,66 +0,0 @@ -## ADDED Requirements - -### Requirement: Canonical curriculum has a deterministic public projection -The system SHALL generate public curriculum surfaces from the canonical track, -concept, roadmap, drill, review, artifact, and coverage data without requiring -manual duplication. - -#### Scenario: Curriculum generation is repeated -- **WHEN** the publication generator runs twice against unchanged inputs -- **THEN** it produces the same set and content of public files without duplicate entries - -### Requirement: Curriculum is readable without JavaScript -The system SHALL publish a curriculum hub and public detail pages for every -known track, roadmap, and concept under a route namespace that does not collide -with interactive SPA routes. - -#### Scenario: Visitor opens a public concept -- **WHEN** a browser or crawler requests a generated concept URL -- **THEN** it receives substantive semantic HTML containing the concept explanation, mental model, learning source, practice direction, review prompt, and navigation - -#### Scenario: Visitor browses the hierarchy -- **WHEN** a visitor starts at the curriculum hub -- **THEN** they can navigate through tracks and roadmaps to every published concept without signing in - -### Requirement: Public pages meet metadata and structure standards -Every generated HTML page MUST include a concise title, meta description, -canonical URL, Open Graph metadata, exactly one H1, useful section headings, -and valid schema.org JSON-LD appropriate to its content type. - -#### Scenario: Search crawler reads a generated page -- **WHEN** it parses the HTML without executing JavaScript -- **THEN** the page provides complete metadata, structured data, meaningful text, and internal links - -### Requirement: Search discovery covers every public curriculum page -The sitemap SHALL include the curriculum hub and every generated track, -roadmap, and concept URL exactly once. - -#### Scenario: Curriculum grows -- **WHEN** canonical data gains or loses a track, roadmap, or concept and generation runs -- **THEN** sitemap curriculum entries match the current generated page set - -### Requirement: AI agents receive a useful curriculum catalog -The public agent catalog SHALL advertise compact Markdown and JSON curriculum -inventories and describe the product's complete public learning scope. - -#### Scenario: Agent starts from llms.txt or api/ai -- **WHEN** it follows the advertised curriculum surface -- **THEN** it can enumerate tracks, roadmaps, concepts, and their public URLs without loading the SPA - -### Requirement: Homepage explains the learning value -The public homepage SHALL present accurate curriculum counts, learning -domains, and the Concept → Drill → Build → Review → Apply loop in visible, -human-readable content. - -#### Scenario: First-time visitor evaluates the product -- **WHEN** they open the homepage without signing in -- **THEN** they can understand what is taught, how practice works, and where to browse the public curriculum - -### Requirement: Publication integrity is verified -Automated checks MUST fail when a canonical track, roadmap, or concept lacks -its generated page, when a public URL is missing from the sitemap, or when -agent catalog counts drift from canonical data. - -#### Scenario: Generated output becomes stale -- **WHEN** integrity tests compare it with canonical curriculum data -- **THEN** the test reports the missing or mismatched IDs and blocks release diff --git a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/tasks.md b/openspec/changes/archive/2026-07-25-strengthen-helpful-content/tasks.md deleted file mode 100644 index d7cd175..0000000 --- a/openspec/changes/archive/2026-07-25-strengthen-helpful-content/tasks.md +++ /dev/null @@ -1,24 +0,0 @@ -## 1. Publication Generator - -- [x] 1.1 Implement deterministic static-page generation from canonical curriculum data -- [x] 1.2 Generate the curriculum hub, track pages, roadmap pages, and concept pages -- [x] 1.3 Generate compact Markdown and JSON curriculum catalogs -- [x] 1.4 Generate sitemap curriculum entries and synchronize agent discovery files - -## 2. Human-Facing Content - -- [x] 2.1 Strengthen homepage title, metadata, and structured data -- [x] 2.2 Add a visible curriculum overview and public-catalog path to the logged-out experience -- [x] 2.3 Update canonical product and surface documentation - -## 3. Integrity - -- [x] 3.1 Add generated-publication integrity tests for page, sitemap, and catalog coverage -- [x] 3.2 Add generation to the production build path without new dependencies -- [x] 3.3 Prove generation is idempotent - -## 4. Verification and Release - -- [x] 4.1 Run focused tests, full tests, docs validation, typecheck, and targeted lint -- [x] 4.2 Run a production build and strict OpenSpec validation -- [x] 4.3 Update durable project status and archive the completed change diff --git a/openspec/changes/archive/2026-07-25-unify-site-navigation/.openspec.yaml b/openspec/changes/archive/2026-07-25-unify-site-navigation/.openspec.yaml deleted file mode 100644 index 3a03821..0000000 --- a/openspec/changes/archive/2026-07-25-unify-site-navigation/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-07-25 diff --git a/openspec/changes/archive/2026-07-25-unify-site-navigation/design.md b/openspec/changes/archive/2026-07-25-unify-site-navigation/design.md deleted file mode 100644 index a39022b..0000000 --- a/openspec/changes/archive/2026-07-25-unify-site-navigation/design.md +++ /dev/null @@ -1,52 +0,0 @@ -# Design - -## Navigation model - -A small typed data module owns the primary and browse link labels and paths. -React surfaces and the static generator consume that same model, preventing -label or destination drift. - -Primary destinations remain task-oriented: - -- Today -- Learn -- Practice -- Mock -- Playground -- Progress - -Browse contains the wider catalog, led by the public Curriculum, followed by -sources, library, exploration, docs, Build Lab, projects, and notes. - -## Shared header - -`SiteHeader` becomes the React renderer used by both `Layout` and `Login`. -It owns brand markup, desktop navigation, the Browse disclosure, responsive -navigation, focus treatment, and active states. Callers supply only optional -right-side controls such as sign-in, account, or settings. - -Generated pages render equivalent semantic markup from the canonical model. -They do not hydrate React and do not require JavaScript. A native `details` -disclosure provides the responsive menu. - -## Performance - -- Static curriculum navigation remains HTML and CSS only. -- The shared model contains only strings and does not import curriculum data. -- The React header adds no production dependency and no network request. -- Existing lazy loading of settings and digest content remains intact. - -## Responsive behavior - -Desktop shows the primary links and Browse disclosure in the header. Compact -viewports show the brand, caller actions, and one native Menu disclosure -containing every primary and browse destination. The same structure replaces -the app-only mobile bottom bar. - -## Accessibility - -- A skip link precedes navigation. -- Navigation landmarks have explicit accessible labels. -- Disclosures use native `details` and `summary`. -- Focus-visible treatment is consistent. -- Active React routes expose `aria-current="page"`. diff --git a/openspec/changes/archive/2026-07-25-unify-site-navigation/proposal.md b/openspec/changes/archive/2026-07-25-unify-site-navigation/proposal.md deleted file mode 100644 index c614843..0000000 --- a/openspec/changes/archive/2026-07-25-unify-site-navigation/proposal.md +++ /dev/null @@ -1,32 +0,0 @@ -# Unify Site Navigation - -## Why - -The homepage, interactive learning application, and generated public -curriculum currently render three unrelated headers. The product is -static-first and does not need separate navigation identities to remain fast. -Moving between surfaces should feel like staying in one learning system. - -## What Changes - -- Define one canonical navigation model for primary learning destinations and - browse destinations. -- Use one React header component on the logged-out homepage and interactive app. -- Render the same information architecture and visual treatment into generated - curriculum HTML. -- Replace the separate mobile bottom navigation with the same responsive header - pattern used everywhere else. -- Keep account and settings controls conditional without changing the shared - navigation. - -## Non-goals - -- No backend, authentication, database, or API changes. -- No client-side framework added to generated curriculum pages. -- No redesign of page content below the header. - -## Impact - -The change touches the app shell, logged-out page, public curriculum generator, -static styles, navigation data, and focused consistency tests. Generated pages -remain semantic HTML and CSS with no JavaScript dependency. diff --git a/openspec/changes/archive/2026-07-25-unify-site-navigation/specs/unified-site-navigation/spec.md b/openspec/changes/archive/2026-07-25-unify-site-navigation/specs/unified-site-navigation/spec.md deleted file mode 100644 index c5fd4b4..0000000 --- a/openspec/changes/archive/2026-07-25-unify-site-navigation/specs/unified-site-navigation/spec.md +++ /dev/null @@ -1,40 +0,0 @@ -## ADDED Requirements - -### Requirement: Every public and interactive surface uses one navigation model -The homepage, React application, and generated curriculum SHALL expose the same -primary and browse destination labels and paths from one canonical model. - -#### Scenario: Navigation data changes -- **WHEN** a primary or browse destination is added, removed, or renamed -- **THEN** generated pages and React surfaces reflect the change without maintaining separate lists - -### Requirement: Navigation remains static-first -Generated curriculum navigation MUST be usable as semantic HTML and CSS without -loading React, application bundles, or a backend response. - -#### Scenario: JavaScript is unavailable -- **WHEN** a visitor opens any generated curriculum page without JavaScript -- **THEN** they can navigate to every primary and browse destination - -### Requirement: Navigation adapts consistently -All surfaces SHALL use the same desktop and compact navigation hierarchy. - -#### Scenario: Viewport becomes compact -- **WHEN** the viewport cannot fit the desktop navigation -- **THEN** one accessible disclosure exposes all primary and browse destinations without horizontal overflow - -### Requirement: Account controls do not redefine navigation -Authentication, settings, and account controls MAY vary by user state but MUST -remain visually separate from the shared destination hierarchy. - -#### Scenario: User state changes -- **WHEN** a visitor signs in, signs out, or continues as a guest -- **THEN** destination ordering and labels remain unchanged - -### Requirement: Navigation consistency is verified -Automated checks MUST fail when generated navigation drifts from the canonical -model or when React surfaces reintroduce independent destination lists. - -#### Scenario: Generated output is stale -- **WHEN** tests compare generated headers with the canonical model -- **THEN** missing, duplicated, or mismatched navigation links are reported diff --git a/openspec/changes/archive/2026-07-25-unify-site-navigation/tasks.md b/openspec/changes/archive/2026-07-25-unify-site-navigation/tasks.md deleted file mode 100644 index fe07276..0000000 --- a/openspec/changes/archive/2026-07-25-unify-site-navigation/tasks.md +++ /dev/null @@ -1,23 +0,0 @@ -## 1. Canonical Model - -- [x] 1.1 Define typed primary and browse navigation data -- [x] 1.2 Add model-level tests for unique labels and destinations - -## 2. React Surfaces - -- [x] 2.1 Implement the shared responsive `SiteHeader` -- [x] 2.2 Replace the app-shell header and mobile bottom bar -- [x] 2.3 Replace the logged-out homepage header - -## 3. Static Curriculum - -- [x] 3.1 Render the canonical navigation into generated HTML -- [x] 3.2 Align generated header styling and responsive behavior -- [x] 3.3 Extend publication tests to detect navigation drift - -## 4. Verification - -- [x] 4.1 Regenerate public curriculum and prove deterministic output -- [x] 4.2 Run focused tests, lint, typecheck, and production build -- [x] 4.3 Verify desktop and mobile behavior in a browser -- [x] 4.4 Update durable status and archive the completed OpenSpec diff --git a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/.openspec.yaml b/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/.openspec.yaml deleted file mode 100644 index ab39675..0000000 --- a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-07-30 diff --git a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/design.md b/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/design.md deleted file mode 100644 index efd7b3b..0000000 --- a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/design.md +++ /dev/null @@ -1,299 +0,0 @@ -## Context - -The product already has the right learning shell: typed React routes, concept -and roadmap data, guest-scoped local state, Monaco/Excalidraw workspaces, -objective drill gates, Feynman explanation grading, and FSRS concept mastery. -It does not have a runtime for manipulating systems behavior. The current -Playground executes code and renders diagrams; it cannot express multiple -independent truth planes such as an Argo Application health badge, a sync -operation, a failed hook, and still-running previous workloads. - -The requested environment is explicitly an imitation. Its value is safe, -repeatable causal exploration, not production verification. It must therefore -be clearer than a live cluster while remaining honest about which behaviors are -modeled. - -The visual work is classified as **preserve**: use the current page shell, -cards, controls, typography, focus behavior, and responsive conventions. Do not -rename the repository or product, alter the primary navigation, or introduce a -new visual language. - -## Goals / Non-Goals - -**Goals:** - -- Establish a reusable deterministic simulation runtime rather than three - bespoke interactive pages. -- Make asynchronous order, controller ownership, branch decisions, and - misleading aggregate state visible. -- Ship deep GitOps/migration, tracing/sampling, and metrics/discovery labs. -- Require prediction and decisive evidence before an attempt can affect - learning progress. -- Reuse current account scoping, Feynman/understanding patterns, concept - mastery, and responsive primitives. -- Keep lab definitions straightforward enough to review against pinned source - before adding them. - -**Non-Goals:** - -- Connecting the browser to Kubernetes, Docker, Git, GCP, Prometheus, the local - platform lab, or a user machine. -- Claiming behavioral parity with GKE Autopilot, Cloud Trace, Managed - Prometheus, Cloud Logging, or a specific production cluster. -- Replacing Playground, Build Lab, diagrams, drills, or the real local - integration lab. -- Adding new concepts, a database table, API action, package, primary - navigation tab, or production dependency in the first release. -- Persisting free-form guest explanations. - -## Decisions - -### Use a pure virtual-time transition engine - -The engine receives an immutable `LabDefinition`, a selected scenario and -control values, and an ordered learner action (`start`, `step`, `advance`, -`finish`, `reset`). It returns a new serializable snapshot. No wall-clock timer, -randomness, network request, browser environment value, or component state -participates in transition logic. - -```ts -type SimulationSnapshot = { - labVersion: number; - tick: number; - phase: 'ready' | 'running' | 'blocked' | 'failed' | 'complete'; - actors: Record; - events: SimulationEvent[]; - evidence: EvidenceRecord[]; - availableActions: SimulationAction[]; -}; -``` - -Virtual ticks express causality without slow animation. The UI can animate a -transition for presentation, but reduced-motion users and tests receive the -same snapshot immediately. - -Alternative considered: one React state machine per lab. Rejected because -behavior would be coupled to presentation, authoring a fourth lab would require -new code, and cross-lab invariants could not be validated centrally. - -### Make definitions declarative, but transitions typed - -Definitions under `src/data/systems-labs/` own metadata, actors, controls, -states, evidence records, prediction choices, and expected checkpoints. Shared -transition primitives under `src/lib/simulation/` cover queues, health -aggregation, retry/backoff, branching, and evidence emission. A definition may -compose primitives but may not execute arbitrary strings. - -The registry validates: - -- unique lab, actor, state, event, and evidence IDs; -- known concept IDs; -- valid transition targets; -- reachable terminal outcomes; -- prediction answers backed by evidence IDs; -- scenario expectations matching a deterministic completed snapshot. - -Alternative considered: JSON-only definitions. Rejected because typed -composition and exhaustive unions provide stronger review and refactoring -safety than a runtime schema alone. Definitions remain data-oriented TypeScript -with no React imports. - -### Pin source contracts without adding upstream runtimes to the browser - -Each lab declares a fidelity level and immutable provenance records containing -the upstream repository, full commit SHA, reviewed paths, license, method, and -date. Small checked-in source contracts assert observable model consequences. -Deliberately incorrect mutations prove those contracts can fail. - -`source-verified` means a rule was reviewed against pinned source or an -upstream test vector. `oracle-verified` is reserved for a later local/CI tool -that executes the upstream implementation; definition validation rejects that -claim without an executable-oracle record. - -Alternative considered: import Argo, Kubernetes, ESO, OTel, or GMP runtimes -into the SPA. Rejected because it would add privilege, size, version coupling, -and false parity to a deliberately disconnected learning surface. - -### Share deterministic actions, never mastery - -A replay contains only a schema version, lab and definition identity, scenario, -ordered reducer actions, and a fingerprint of the resulting snapshot. Imports -validate every field and recompute the fingerprint. Imported runs are -observation-only; a learner must still freeze their own prediction and explain -the outcome before mastery is eligible. - -### Make setup an editable repair task - -Each lab owns a typed configuration challenge containing one or more files, a -visible delivery brief, stable tutorial comment markers, expected -configuration lines, causal hints, and successful evidence. The shared -workshop finds the line immediately following each marker and compares its -normalized content with the contract. - -This is intentionally a bounded configuration exercise rather than a general -YAML interpreter. It accepts realistic Kubernetes, OpenTelemetry, Collector, -and PromQL fragments while avoiding a new parser dependency or claims that the -browser executed those files. Every starter is tested to fail, a repaired -fixture must pass, and mutating one repaired line must fail the matching -contract. - -Verified files are stored with the account-scoped attempt. Editing or resetting -them invalidates the configuration gate. A completed simulation is insufficient -for authenticated explanation grading until the Build mode evidence exists. - -### Separate the engine, learning controller, and UI - -```mermaid -flowchart LR - D[Typed lab definitions] --> E[Pure simulation engine] - E --> S[Serializable snapshots and evidence] - S --> R[Shared lab runner UI] - P[Frozen learner prediction] --> L[Attempt controller] - S --> L - L --> C[Evidence checkpoint] - C --> X[Explain-back] - X --> M[Existing mastery paths] -``` - -- `src/lib/simulation/`: types, reducer, transition primitives, validation. -- `src/data/systems-labs/`: catalog and the three initial definitions. -- `src/hooks/useSimulationAttempt.ts`: prediction freezing, attempt lifecycle, - per-account local persistence, and mastery eligibility. -- `src/pages/SystemsLabs.tsx`: catalog. -- `src/pages/SystemsLab.tsx`: shared runner route. -- `src/components/simulation/`: controls, actor graph, event timeline, evidence - inspector, prediction and checkpoint panels. - -React components render snapshots and dispatch actions; they never decide what -the simulated system does. - -### Model truth planes as evidence owned by actors - -Every visible status has: - -- an owning actor; -- a plane such as `resource`, `controller`, `operation`, `process`, `storage`, - or `user-visible`; -- a synthetic source label and value; -- the tick at which it became available; -- an explanation shown only after reveal. - -Summary badges are projections over these records, not an independent source of -truth. This permits the GitOps lab to render `Application health: Healthy`, -`Sync: OutOfSync`, and `Hook: Failed` simultaneously without special-case UI. - -Alternative considered: a single success/failure pipeline. Rejected because it -would erase the exact contradictions the lab is intended to teach. - -### Freeze predictions before revealing decisive evidence - -A scored attempt has two layers: - -1. `AttemptDraft`: scenario, controls, prediction and rationale; editable. -2. `FrozenAttempt`: immutable prediction plus lab definition version; created - when execution starts. - -Free exploration bypasses freezing but is never mastery-eligible. A retry -creates a new attempt ID so previously revealed evidence cannot be rewritten -into a correct historical prediction. - -Only compact structured attempt state is stored under a new account-scoped -local key. Free-form explanation text is not retained locally. A definition -version mismatch preserves the historical result summary but requires a new run -before scoring. - -### Reuse objective and explain-back paths without fabricating guest mastery - -The attempt controller first grades deterministic prediction and -evidence-selection checkpoints. It then builds an explain-back prompt from the -scenario, frozen prediction, final snapshot and decisive evidence. - -For signed-in learners, the existing Feynman grading path is generalized to -accept a systems-lab artifact payload in addition to code. Its returned concept -ratings continue through the existing concept mastery authority. - -For guests, the completed attempt is stored locally but positive mastery stays -pending because the authenticated Feynman path is unavailable. The UI can -offer the current public understanding-check mechanism when AI is configured, -but the first release does not introduce a second durable grading authority or -pretend that a minimum-length explanation is proof. - -Alternative considered: award guest mastery from prediction correctness alone. -Rejected because guessing the final state does not demonstrate causal -understanding and would violate the product's evidence standard. - -### Map to existing concepts first - -Initial lab mappings use existing broad concepts: - -- GitOps/migrations: `containers-kubernetes`, - `infrastructure-automation`. -- Tracing/sampling: `monitoring-analytics`, `tracing-replay`. -- Metrics/discovery: `monitoring-analytics`, `containers-kubernetes`. - -The lab itself records finer checkpoint completion locally. Adding granular -Argo, ESO, migration-safety, OpenTelemetry and Prometheus concepts would expand -the canonical curriculum and all generated public surfaces, so it is a -separate, reviewable curriculum change. - -### Preserve routes and navigation - -Add lazy routes `/labs` and `/labs/:labId`. Entry points appear contextually on -Learn, relevant concept detail pages and Build Lab. `PRIMARY_NAV` and all -existing labels remain unchanged in the first release. - -Unknown lab IDs render the existing not-found/empty-state pattern. Scenario IDs -may be selected by query parameter for stable learning links, but runtime state -is never encoded into a URL. - -## Risks / Trade-offs - -- **Modeled behavior drifts from pinned source** → Store a source/version note - with every lab, require scenario assertions, and treat definition updates as - reviewable content changes. -- **A polished animation is mistaken for verification** → Label every runner - as a deterministic simulation and keep real-environment verification as an - explicit external next step. -- **Learners memorize positions rather than causality** → Freeze predictions, - vary controls, require decisive evidence, and use explain-back prompts. -- **The runner becomes an unreadable dashboard** → Show one current snapshot, - one ordered event/evidence inspector, and progressive disclosure rather than - every panel simultaneously. -- **Broad concept IDs over-credit mastery** → Require Feynman grading for - positive mastery and keep lab-specific completion distinct until granular - curriculum concepts are separately approved. -- **State definitions become a second programming language** → Keep a small - typed primitive set and prefer explicit transitions over generic expression - evaluation. -- **Existing active OpenSpec work overlaps navigation** → Do not modify primary - navigation or the unfinished Sweep decisions; contextual links are additive. -- **Guest and authenticated behavior diverge** → Test both explicitly and do - not promise cross-device attempt history in the first release. - -## Migration Plan - -1. Add and test engine types, reducer, validation and one minimal fixture - without exposing a route. -2. Add the shared catalog and runner using the preserve design lane. -3. Implement the three definitions and their scenario assertion suites. -4. Add prediction, evidence checkpoint, local attempt state and explain-back - integration. -5. Add lazy routes and contextual entry points. -6. Verify unit tests, typecheck, lint, build, guest behavior, keyboard behavior, - and browser layouts at 390, 768 and 1440 pixels. - -Rollback removes the two additive routes, contextual links, lab definitions -and simulation modules. No database, API, generated curriculum or existing -stored state requires migration; an unused namespaced localStorage entry is -safe to ignore. - -## Open Questions - -- Should a later curriculum change add granular concepts for Argo CD hooks, - ESO reconciliation, migration safety, OpenTelemetry sampling and metrics - discovery, or should these remain checkpoints under broad concepts? -- After the first three labs prove useful, should Systems Lab earn a primary - navigation entry? That decision intentionally remains outside this change. -- Should a later optional adapter launch or inspect the separate local - integration lab? Any such bridge requires a distinct security and product - proposal. diff --git a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/proposal.md b/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/proposal.md deleted file mode 100644 index 1b482d1..0000000 --- a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/proposal.md +++ /dev/null @@ -1,65 +0,0 @@ -## Why - -The learning OS explains infrastructure and production-engineering concepts, -but its current hands-on surfaces are code- and diagram-oriented. Learners need -a safe way to manipulate controller timing, failure states, propagation flags, -and observability discovery without cloud credentials or a live Kubernetes -environment—and then prove they understand the result instead of merely -watching an animation. - -## What Changes - -- Add a first-class Systems Lab index and lab runner inside the existing - product, without renaming the repository, product, or existing routes. -- Add a deterministic, client-only simulation engine driven by typed lab and - scenario definitions. It must make no network, Kubernetes, cloud, or local - machine connection. -- Ship three initial labs: - - Git → Argo CD → External Secrets → migration Job → Deployment. - - Trace propagation → ParentBased sampling → OpenTelemetry export. - - Metrics endpoint → workload selection → scrape → stored/queryable series. -- Structure every lab as predict → configure → run/step → inspect evidence → - explain back, including counterexamples where superficially healthy state is - misleading. -- Add a bounded Build mode to every initial lab. Learners repair a broken - infrastructure configuration against a visible delivery brief and causal - validation hints before their explanation becomes mastery-eligible. -- Connect successful evidence-based explain-backs to the existing Feynman and - FSRS mastery loop; viewing or clicking through a simulation alone must not - grant mastery. -- Preserve the current visual language and primary navigation. The first - release is entered from relevant Learn, concept, and Build surfaces rather - than silently adding or renaming a primary tab. -- Keep the existing real local platform lab as optional external verification, - not a runtime dependency or browser integration. - -## Capabilities - -### New Capabilities - -- `systems-simulation-labs`: Defines the disconnected simulation engine, lab - catalog, lab runner, deterministic state transitions, evidence views, and the - three initial infrastructure/observability labs. -- `simulation-mastery-loop`: Defines prediction capture, outcome comparison, - explain-back gating, concept mapping, and existing FSRS mastery updates for - simulation attempts. - -### Modified Capabilities - -None. Existing OpenSpec requirements for curriculum publication, expanded -domains, and unified navigation remain unchanged. - -## Impact - -- Frontend: new lazy routes and React surfaces under the existing Vite SPA; - reusable typed simulation state in `src/lib/` and lab definitions in - `src/data/`. -- Learning state: reuses existing concept mastery/Feynman paths and guest - local-state conventions; no schema migration or new API is required for the - first release. -- Content: maps each lab to existing concept IDs and adds contextual entry - points without moving or renaming `docs/learning/*`. -- Design: preserve lane using existing UI primitives and tokens; no new - production dependency is expected. -- Operations: no connection to GKE, Argo CD, Secret Manager, Cloud Trace, - Prometheus, production Git, or user kubeconfigs. diff --git a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/specs/simulation-mastery-loop/spec.md b/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/specs/simulation-mastery-loop/spec.md deleted file mode 100644 index fdff5b8..0000000 --- a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/specs/simulation-mastery-loop/spec.md +++ /dev/null @@ -1,88 +0,0 @@ -## ADDED Requirements - -### Requirement: Prediction precedes observation -Every scored simulation attempt SHALL capture the learner's prediction before -revealing the final state or decisive evidence. Changing a prediction after -revelation MUST start a new attempt. - -#### Scenario: Learner starts a scored run -- **WHEN** the learner selects a scenario and submits a prediction -- **THEN** the prediction is frozen for that attempt and execution controls become available - -#### Scenario: Learner explores without scoring -- **WHEN** the learner chooses free exploration -- **THEN** all simulation controls remain available but the run cannot grant concept mastery - -### Requirement: Outcome comparison uses evidence -After execution, the system SHALL compare the frozen prediction with the -deterministic outcome and require the learner to identify decisive synthetic -evidence. A correct final-state guess without correct evidence MUST NOT count -as a fully successful attempt. - -#### Scenario: Correct prediction cites wrong authority -- **WHEN** the learner predicts a failed migration but selects application health instead of hook operation evidence -- **THEN** the system marks the evidence checkpoint incorrect and explains which actor owns the decisive state - -#### Scenario: Prediction and evidence are correct -- **WHEN** the prediction matches the outcome and the learner identifies the decisive evidence -- **THEN** the attempt becomes eligible for explain-back and mastery evaluation - -### Requirement: Explain-back closes the lab -An eligible attempt SHALL end with a concise explain-back prompt grounded in -the chosen scenario, prediction, outcome, and evidence. The prompt SHALL ask -for causality rather than a restatement of labels. - -#### Scenario: Learner completes an explain-back -- **WHEN** the learner explains why the observed state followed from the inputs and evidence -- **THEN** the explanation is associated with the lab attempt and mapped concept IDs - -#### Scenario: Learner skips explain-back -- **WHEN** the learner skips or abandons the explanation -- **THEN** the run remains available in exploration history but grants no mastery credit - -### Requirement: Mastery requires demonstrated evidence -The system MUST NOT update FSRS mastery merely because a learner opened, -completed, or clicked through a lab. It SHALL derive mastery credit only from -the objective prediction/evidence result and an accepted explain-back path. - -#### Scenario: Unscored exploration completes -- **WHEN** a learner reaches the final state in free exploration -- **THEN** no concept mastery update occurs - -#### Scenario: Objective checkpoints fail -- **WHEN** the prediction or decisive-evidence checkpoint is incorrect -- **THEN** linked concepts receive no positive mastery rating and the learner can retry with the misconception exposed - -#### Scenario: Authenticated Feynman grade succeeds -- **WHEN** an authenticated learner passes the objective checkpoints and the existing Feynman grading path accepts the explanation -- **THEN** the resulting ratings update the existing linked-concept FSRS records without introducing a second mastery store - -#### Scenario: Guest completes the objective path -- **WHEN** a guest passes the prediction and decisive-evidence checkpoints but cannot use authenticated Feynman grading -- **THEN** the system preserves the completed attempt locally, clearly labels mastery as pending explain-back grading, and does not fabricate a positive rating - -### Requirement: Existing learning state conventions -Simulation attempts SHALL follow existing per-account guest storage and guest -adoption conventions. The first release SHALL NOT add a database table, API -action, or alternate spaced-repetition implementation. - -#### Scenario: Guest changes account context -- **WHEN** simulation attempt state is loaded for a different local account namespace -- **THEN** attempts from the previous account are not exposed - -#### Scenario: Signed-in mastery is updated -- **WHEN** an accepted simulation explain-back produces concept ratings -- **THEN** the existing concept mastery API and scheduler remain the sole durable mastery authority - -### Requirement: Retry preserves learning evidence -The learner SHALL be able to retry a failed attempt with the same controls, -while the UI distinguishes the new prediction from previously revealed -evidence. - -#### Scenario: Learner retries a misconception -- **WHEN** a learner starts a retry after reviewing the decisive evidence -- **THEN** the new attempt requires a fresh prediction and does not present the previous answer as an editable prediction - -#### Scenario: Learner later succeeds -- **WHEN** a later scored attempt passes prediction, evidence, and explain-back evaluation -- **THEN** only the successful attempt becomes eligible for positive mastery credit diff --git a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/specs/systems-simulation-labs/spec.md b/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/specs/systems-simulation-labs/spec.md deleted file mode 100644 index 2a157d2..0000000 --- a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/specs/systems-simulation-labs/spec.md +++ /dev/null @@ -1,195 +0,0 @@ -## ADDED Requirements - -### Requirement: Systems Lab catalog and routes -The system SHALL expose a Systems Lab catalog at `/labs` and a stable runner -route at `/labs/:labId`. Each catalog entry SHALL identify its concepts, -learning objective, estimated interaction time, and available scenarios. - -#### Scenario: Learner opens the catalog -- **WHEN** a learner visits `/labs` -- **THEN** the system lists every published lab and links each entry to its stable runner route - -#### Scenario: Learner follows a contextual entry point -- **WHEN** a learner opens a lab from a related Learn, concept, or Build surface -- **THEN** the same canonical `/labs/:labId` runner opens without renaming an existing route or primary navigation item - -### Requirement: Disconnected deterministic simulation -Every lab SHALL run entirely in the browser from checked-in typed definitions -and a pure deterministic transition engine. A simulation MUST NOT call cloud, -Kubernetes, Git, telemetry, database, local-machine, or production APIs. - -#### Scenario: Identical inputs are replayed -- **WHEN** the same lab version, controls, and actions are supplied twice -- **THEN** the engine produces byte-equivalent ordered states and evidence - -#### Scenario: Learner is offline -- **WHEN** the application shell and lab assets are available but the network is unavailable -- **THEN** running, stepping, resetting, and inspecting a lab continue to work - -### Requirement: Explicit truth planes -The lab runner SHALL display the independent state planes necessary to explain -the system, including resource state, controller or process action, operation -state, user-visible status, and emitted evidence where applicable. It MUST NOT -collapse a misleading healthy badge and a failed operation into one status. - -#### Scenario: Migration hook fails -- **WHEN** a migration Job exhausts its configured retries -- **THEN** the runner can simultaneously show the previous workloads available, application health healthy, sync incomplete, and the hook operation failed - -#### Scenario: Evidence is inspected -- **WHEN** the learner selects a state or timeline event -- **THEN** the runner shows the exact synthetic evidence that supports that state and identifies which simulated actor owns it - -### Requirement: Learner-controlled execution -The runner SHALL support reset, single-step, run-to-next-decision, and complete -execution. Controls that alter behavior SHALL be chosen before execution and -their values SHALL remain visible while evidence is inspected. - -#### Scenario: Learner steps through asynchronous reconciliation -- **WHEN** the learner advances a delayed secret-reconciliation scenario one step at a time -- **THEN** each ESO observation, Secret write, Argo wait, and downstream unblock appears in causal order - -#### Scenario: Learner resets a run -- **WHEN** the learner resets the lab -- **THEN** all runtime state returns to the initial definition while the selected lab and scenario remain available - -### Requirement: GitOps and migration lab -The first release SHALL include a lab modeling Git revision selection, Argo CD -comparison and sync, ExternalSecret application, asynchronous ESO -reconciliation, migration hook execution, Kubernetes Job retries, and workload -rollout. - -#### Scenario: Healthy ordered deployment -- **WHEN** the Secret reconciles and the migration succeeds -- **THEN** the lab orders ExternalSecret wave -2, migration wave -1, and workloads wave 0 and produces evidence for the resulting database revision - -#### Scenario: Secret reconciliation is delayed -- **WHEN** ESO has not produced the target Secret -- **THEN** the lab shows Argo waiting on health without claiming that Argo fetched the secret itself - -#### Scenario: Migration fails after partial work -- **WHEN** the migration performs one schema action and then exits non-zero until backoff is exhausted -- **THEN** the lab preserves the partial database evidence, blocks the new workload revision, and exposes the failed hook independently from application health - -#### Scenario: Revision contains only a hook change -- **WHEN** a Git revision changes only a hook resource excluded from ordinary resource tracking -- **THEN** the lab demonstrates why a normal diff can remain Synced and distinguishes the additional non-hook change needed to trigger the teaching scenario - -### Requirement: Trace propagation and sampling lab -The first release SHALL include a lab modeling header extraction, remote parent -creation, ParentBased branch selection, root or ratio sampler evaluation, -recording state, and Collector export. - -#### Scenario: Remote unsampled parent uses default ParentBased behavior -- **WHEN** the propagator extracts a valid remote unsampled parent and the default ParentBased policy is selected -- **THEN** the lab drops the server span before consulting the root ratio and emits no Collector trace - -#### Scenario: Edge policy overrides remote flags -- **WHEN** the same remote unsampled parent reaches a policy that routes both remote-parent branches through the configured ratio -- **THEN** the ratio determines the result and a sampled span produces Collector evidence - -#### Scenario: Learner changes only the root ratio -- **WHEN** the learner raises the root ratio while retaining the default remote-unsampled ParentBased branch -- **THEN** the outcome remains dropped and the lab explains that the ratio was not consulted - -### Requirement: Metrics discovery and ingestion lab -The first release SHALL include a lab modeling endpoint exposure, workload -labels, PodMonitoring selection, target discovery, scraping, storage, and query -results as independent gates. - -#### Scenario: Endpoint and selector succeed -- **WHEN** the process exposes metrics, the selector matches, and the scrape succeeds -- **THEN** the lab produces a stored synthetic series and a non-empty query result - -#### Scenario: Selector matches no workloads -- **WHEN** the endpoint returns successfully but PodMonitoring selects zero Pods -- **THEN** the lab shows the valid manifest and working endpoint while discovery, storage, and query evidence fail - -#### Scenario: Discovered endpoint cannot be scraped -- **WHEN** PodMonitoring selects a Pod but its named port or metrics response is invalid -- **THEN** the lab shows a discovered target with a scrape failure and no stored series - -### Requirement: Extensible checked-in lab definitions -Adding a lab SHALL require a checked-in definition, concept mapping, scenarios, -evidence assertions, and tests, without adding a new page component or -modifying the simulation engine. - -#### Scenario: Author adds a fourth lab -- **WHEN** a valid new lab definition is registered -- **THEN** it appears in the catalog and runs through the shared runner without route or engine changes - -#### Scenario: Definition has invalid references -- **WHEN** a lab references an unknown concept, duplicate state, missing target, or unreachable expected outcome -- **THEN** static validation or tests fail before the application build is considered valid - -### Requirement: Accessible responsive lab interaction -The lab catalog and runner SHALL support keyboard operation, visible focus, -semantic status announcements, reduced motion, and layouts at 390, 768, and -1440 pixels without hiding evidence required to complete a lab. - -#### Scenario: Learner uses a compact viewport -- **WHEN** the runner is opened at 390 pixels wide -- **THEN** controls, current state, evidence, and learning checks remain reachable without horizontal page scrolling - -#### Scenario: Learner uses keyboard controls -- **WHEN** the learner navigates and executes a lab without a pointer -- **THEN** every control is operable in logical order and state changes are announced without relying on color alone - -### Requirement: Source-grounded fidelity -Every published lab SHALL declare whether it is modeled, source-verified, or -oracle-verified. A verification claim SHALL identify immutable upstream -revisions, reviewed paths, license, method, and verification date. The system -MUST NOT label a lab oracle-verified without an executable upstream oracle. - -#### Scenario: Upstream rule is reversed locally -- **WHEN** a mutation reverses a source-verified Argo, tracing, or metrics rule -- **THEN** a checked-in source contract fails with the expected and actual observable value - -#### Scenario: Learner inspects fidelity -- **WHEN** the learner opens the catalog or runner -- **THEN** the fidelity level is visible and the runner links to the exact pinned upstream source - -### Requirement: Versioned deterministic replays -The runner SHALL export and import a versioned replay containing only lab -identity, definition version, scenario, ordered simulation actions, and a -final-state fingerprint. Import SHALL fail closed for malformed actions, -unknown labs or scenarios, stale definitions, and fingerprint mismatches. - -#### Scenario: Replay is shared -- **WHEN** the same replay is loaded against the same lab definition -- **THEN** it produces the same final snapshot and evidence without a network request - -#### Scenario: Imported replay completes -- **WHEN** an imported replay reaches a terminal outcome -- **THEN** it remains observation-only and cannot complete an attempt, grade an explanation, or update mastery - -### Requirement: Configuration Build mode -Every initial lab SHALL provide a bounded, editable infrastructure -configuration that starts broken. The learner SHALL receive a visible delivery -brief and causal validation hints, but the validator MUST NOT reveal the -replacement line for a failed check. The same shared workshop SHALL support all -three labs without executing configuration or contacting infrastructure. - -#### Scenario: Learner repairs GitOps setup -- **WHEN** the learner configures a protected production release reference, explicit promotion gate, ESO and migration waves, matching Secret references, and bounded Job behavior -- **THEN** every GitOps setup contract passes and produces configuration evidence - -#### Scenario: Learner repairs tracing setup -- **WHEN** the learner connects W3C propagation, ParentBased sampling, OTLP export, Collector reception, batching, and Google Cloud export -- **THEN** every tracing setup contract passes and produces configuration evidence - -#### Scenario: Learner repairs metrics setup -- **WHEN** the learner joins Pod labels to PodMonitoring, resolves the named port, selects the metrics path and interval, and writes the verification query -- **THEN** every metrics setup contract passes and produces configuration evidence - -#### Scenario: Verified configuration changes -- **WHEN** a learner edits or resets a configuration after it passed -- **THEN** the setup gate is invalidated until every check passes again - -#### Scenario: Learner changes counterfactual -- **WHEN** a configuration passed and the learner starts another scenario from the same lab definition version -- **THEN** the verified files and setup evidence are reused without carrying them into a different definition version - -#### Scenario: Learner requests explanation grading -- **WHEN** the simulation is complete but the configuration challenge has not passed -- **THEN** authenticated explanation grading and mastery application remain unavailable diff --git a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/tasks.md b/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/tasks.md deleted file mode 100644 index ece50c4..0000000 --- a/openspec/changes/archive/2026-07-31-add-systems-simulation-labs/tasks.md +++ /dev/null @@ -1,103 +0,0 @@ -## 1. Work Boundary and Design Evidence - -- [x] 1.1 Create the owning GitHub issue and link this OpenSpec change before feature implementation -- [x] 1.2 Initialize missing product/design context through the required Impeccable workflow without changing the established product identity -- [x] 1.3 Create a preserve-lane design-review receipt for the Systems Lab catalog and runner -- [x] 1.4 Capture before-state browser evidence for the Learn, Build Lab, Playground, and compact-layout patterns being reused - -## 2. Simulation Engine - -- [x] 2.1 Add typed lab, actor, transition, evidence, checkpoint, prediction, and snapshot contracts -- [x] 2.2 Implement the pure virtual-time reducer with start, step, advance, finish, and reset actions -- [x] 2.3 Implement shared primitives for branching, queued reconciliation, retry/backoff, health projection, and evidence emission -- [x] 2.4 Add static definition validation for IDs, concept references, transition targets, reachability, and expected outcomes -- [x] 2.5 Add deterministic serialization and replay tests proving identical inputs produce byte-equivalent snapshots -- [x] 2.6 Add invalid-definition and unreachable-state tests that fail closed - -## 3. GitOps and Migration Lab - -- [x] 3.1 Define Git, Argo CD, ESO, Secret, Job, database, and Deployment actors and truth planes -- [x] 3.2 Implement the healthy wave -2, wave -1, and wave 0 scenario with database revision evidence -- [x] 3.3 Implement delayed and failed ESO reconciliation scenarios with Argo wait behavior -- [x] 3.4 Implement partial migration failure, Kubernetes retry exhaustion, and blocked rollout behavior -- [x] 3.5 Implement the hook-only revision scenario that can remain ordinarily Synced -- [x] 3.6 Add scenario assertion tests for health, sync, operation, Job, database, and workload evidence - -## 4. Trace Propagation and Sampling Lab - -- [x] 4.1 Define header, propagator, parent context, sampler branch, span, exporter, and Collector actors -- [x] 4.2 Implement remote-unsampled default ParentBased behavior that bypasses the root ratio -- [x] 4.3 Implement the edge override that routes both remote-parent branches through the configured ratio -- [x] 4.4 Implement trusted sampled-parent and root-ratio counterfactual scenarios -- [x] 4.5 Add scenario assertion tests proving exactly when Collector evidence exists - -## 5. Metrics Discovery and Ingestion Lab - -- [x] 5.1 Define process, Pod labels, PodMonitoring, discovery, scrape, storage, and query actors -- [x] 5.2 Implement the healthy endpoint-selection-scrape-storage-query path -- [x] 5.3 Implement a selector-miss scenario where the endpoint and manifest remain independently valid -- [x] 5.4 Implement named-port and scrape-response failure scenarios after successful discovery -- [x] 5.5 Add scenario assertion tests for target count, scrape evidence, stored series, and query results - -## 6. Shared Catalog and Runner - -- [x] 6.1 Add the checked-in lab registry and catalog metadata with existing concept mappings and definition versions -- [x] 6.2 Add a lazy `/labs` catalog route using existing page-shell, card, empty-state, and responsive patterns -- [x] 6.3 Add a lazy `/labs/:labId` shared runner route with unknown-lab handling -- [x] 6.4 Build scenario and control selection with all inputs visible during execution -- [x] 6.5 Build the current actor/state view without collapsing independent truth planes -- [x] 6.6 Build the ordered event timeline and actor-owned evidence inspector -- [x] 6.7 Add reset, single-step, run-to-next-decision, and complete-execution controls -- [x] 6.8 Add keyboard semantics, live status announcements, focus behavior, and reduced-motion handling - -## 7. Prediction, Evidence, and Mastery - -- [x] 7.1 Add account-scoped local attempt types and storage without a database or API change -- [x] 7.2 Implement editable drafts, frozen predictions, free exploration, retries, and definition-version handling -- [x] 7.3 Add deterministic prediction comparison and decisive-evidence checkpoints -- [x] 7.4 Build scenario-grounded explain-back context from prediction, outcome, and evidence -- [x] 7.5 Generalize the existing authenticated Feynman path to accept a systems-lab artifact without regressing code/drill grading -- [x] 7.6 Apply accepted ratings through the existing concept mastery authority and schedule linked review questions -- [x] 7.7 Preserve guest attempts locally while labeling mastery pending rather than fabricating credit -- [x] 7.8 Add guest, authenticated, failed-checkpoint, retry, skip, and successful-mastery tests - -## 8. Contextual Integration - -- [x] 8.1 Add Systems Lab entry points to relevant Learn content without changing primary navigation -- [x] 8.2 Add relevant concept-detail links derived from the lab registry rather than hardcoded per page -- [x] 8.3 Add a Build Lab entry point while preserving existing artifact and drill routes -- [x] 8.4 Add analytics events through the existing wrapper for lab opened, prediction frozen, evidence checked, explanation graded, and lab completed -- [x] 8.5 Confirm the unfinished Sweep OpenSpec and all generated curriculum surfaces remain unchanged - -## 9. Verification and Documentation - -- [x] 9.1 Run focused engine, definition, route, attempt, and mastery tests -- [x] 9.2 Run repository typecheck, lint, full unit tests, docs validation, and production build -- [x] 9.3 Verify offline execution and confirm the runner makes no external requests -- [x] 9.4 Capture runner browser evidence at 390, 768, and 1440 pixels and verify keyboard-only completion -- [x] 9.5 Run Impeccable critique, polish, and audit; resolve every P0/P1 and meet the Fleet score floors -- [x] 9.6 Update canonical product, architecture, surface, command, and learning documentation for the shipped capability -- [x] 9.7 Validate the design-review receipt and obtain owner `keep` or `delegated` feedback -- [x] 9.8 Run strict OpenSpec validation before requesting implementation review - -## 10. Source-grounded Robustness - -- [x] 10.1 Add full-commit upstream provenance, source paths, licenses, methods, and verification dates to every lab -- [x] 10.2 Validate fidelity claims and reserve `oracle-verified` for definitions backed by an executable upstream oracle -- [x] 10.3 Add checked-in source-contract fixtures for Argo CD, ESO, OpenTelemetry, W3C Trace Context, and Google Prometheus Engine rules -- [x] 10.4 Add deliberate Argo, ParentBased, and PodMonitoring mutations that the source contracts must reject -- [x] 10.5 Add deterministic, fingerprinted replay export and strict import validation -- [x] 10.6 Keep imported replays observation-only so they cannot update attempts or mastery -- [x] 10.7 Display fidelity and pinned provenance in the catalog and shared runner - -## 11. Configuration Build Mode - -- [x] 11.1 Add a shared typed configuration-challenge contract with files, delivery requirements, stable repair slots, hints, and evidence -- [x] 11.2 Add a GitOps capstone covering protected release promotion, manual production sync, ESO ordering, Secret joining, and Job bounds -- [x] 11.3 Add a tracing capstone covering W3C propagation, ParentBased sampling, OTLP export, Collector reception, batching, and Google Cloud export -- [x] 11.4 Add a metrics capstone covering Pod labels, PodMonitoring selection, named ports, scrape settings, and PromQL verification -- [x] 11.5 Add the shared responsive configuration workshop with multiple files, repair feedback, reset, and validation evidence -- [x] 11.6 Persist verified configuration files and invalidate the gate when a verified file changes -- [x] 11.7 Require configuration validation before authenticated explanation grading -- [x] 11.8 Prove every starter fails, every repaired fixture passes, and a one-line mutation fails the exact contract -- [x] 11.9 Reuse a verified build across scenarios of the same lab version without carrying it across definition changes diff --git a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/.openspec.yaml b/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/.openspec.yaml deleted file mode 100644 index d77f64e..0000000 --- a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-08-09 diff --git a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/design.md b/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/design.md deleted file mode 100644 index 790630f..0000000 --- a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/design.md +++ /dev/null @@ -1,60 +0,0 @@ -## Context - -The canonical curriculum already contains the necessary machine, model-training, and inference-serving concepts, drills, and public generation pipeline. See `proposal.md` for motivation. Roadmaps are canonical JSON records grouped for discovery in application code; generated public files are checked in and regenerated during builds. - -## Goals / Non-Goals - -**Goals:** - -- Create a short causal bridge across three existing curriculum domains. -- Preserve stable concept IDs and reuse their practice material. -- Make completion depend on measurable evidence rather than passive reading. -- Keep the interactive and public curriculum projections aligned. - -**Non-Goals:** - -- Import AISystem prose, diagrams, slides, binaries, or repository assets. -- Add a new track, API, database model, runtime dependency, or bespoke page type. -- Attempt comprehensive AI-chip, compiler, or framework coverage. - -## Decisions - -### Represent the feature as a canonical roadmap - -The synthesis will use the existing roadmap schema, detail page, progress model, and public generator. A custom page would duplicate routing, selection, and publication behavior; adding the material to a specialist roadmap would hide the cross-layer causal sequence. - -### Reuse concepts and drills, add one dedicated artifact - -The roadmap will reference stable concepts spanning representation, backpropagation, memory hierarchy, performance, kernels, quantization, batching, hardware, and serving economics. One new artifact will define the integrated evidence contract. This avoids shallow duplicate concepts and keeps the change maintenance-friendly. - -### Organize milestones by the execution path - -The sequence is: - -```mermaid -flowchart LR - A[Represent and differentiate] --> B[Execute through memory and runtime] - B --> C[Optimize kernels and model form] - C --> D[Schedule and serve requests] - D --> E[Measure and defend the system] -``` - -This differs from topic-based grouping by making every later optimization explainable in terms of work introduced by an earlier layer. - -### Derive all public output from existing generators - -No generated file will be hand-edited. Updating canonical data and running the repository build/generation scripts will update counts, catalogs, sitemap entries, and the new public roadmap page deterministically. - -## Risks / Trade-offs - -- [The path may repeat concepts found in longer roadmaps] → Label it as synthesis and keep it short, with a distinct integrated capstone. -- [A broad capstone could accept hand-wavy evidence] → Require a reproducible workload or model, before/after measurements, a bottleneck diagnosis, and an explicit trade-off. -- [Generated output creates a large diff] → Use the existing generator and review the changed-file set for only expected count, catalog, sitemap, and roadmap-page updates. -- [Hardware and framework details age quickly] → Reuse mechanism-focused concepts and primary sources instead of vendor survey content. - -## Migration Plan - -1. Add the canonical roadmap, artifact links, discovery entry, and focused tests. -2. Regenerate public curriculum output and run integrity checks. -3. Deploy through the existing manual release workflow. -4. Roll back by reverting the release commit; no persisted user data or schema migration is involved. diff --git a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/proposal.md b/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/proposal.md deleted file mode 100644 index 74491d2..0000000 --- a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/proposal.md +++ /dev/null @@ -1,29 +0,0 @@ -## Why - -The curriculum teaches machine foundations, model training, and inference serving, but learners must assemble the causal path between those layers themselves. A short synthesis roadmap inspired by AISystem's systems-first organization can make that path explicit without importing its large, fast-aging content archive. - -## What Changes - -- Add a focused "Trace a Tensor" roadmap that follows one workload from representation and backpropagation through runtime constraints, kernels, quantization, batching, and serving economics. -- Reuse existing concepts and drills rather than expanding the catalog or duplicating source material. -- Add one evidence-backed capstone that requires a layer map, a runnable or modeled benchmark, a bottleneck diagnosis, and a defended optimization. -- Feature the roadmap in the existing AI-native roadmap discovery surfaces and public curriculum output. -- Add integrity coverage for the roadmap's ordering and capstone contract. - -## Capabilities - -### New Capabilities - -- `tensor-lifecycle-synthesis`: A sequenced, artifact-backed curriculum path that connects model computation to the hardware and serving systems that execute it. - -### Modified Capabilities - -- `expanded-learning-domains`: The existing cross-domain curriculum shall expose an explicit synthesis path across systems foundations, model training, and inference serving. -- `public-curriculum-discovery`: The public curriculum shall publish and count the new roadmap from the canonical curriculum data. - -## Impact - -- Canonical curriculum data in `src/data/roadmaps.json`, `src/data/artifacts.json`, and linked concept metadata. -- Roadmap discovery configuration and curriculum integrity tests. -- Generated public curriculum pages, catalog, sitemap, and agent-readable metadata. -- No API, database, authentication, runtime, or production dependency changes. diff --git a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/expanded-learning-domains/spec.md b/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/expanded-learning-domains/spec.md deleted file mode 100644 index ab7d10f..0000000 --- a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/expanded-learning-domains/spec.md +++ /dev/null @@ -1,8 +0,0 @@ -## ADDED Requirements - -### Requirement: Cross-domain AI systems synthesis is explicit -The learning OS SHALL expose a compact roadmap that reuses stable concepts from Systems Foundations, AI Models & Training, and Inference & Serving to show how a model operation becomes production work. - -#### Scenario: User scans AI-native paths -- **WHEN** the user browses the AI-native roadmap group -- **THEN** the Trace a Tensor synthesis roadmap appears exactly once and is distinguishable from the longer specialist paths diff --git a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/public-curriculum-discovery/spec.md b/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/public-curriculum-discovery/spec.md deleted file mode 100644 index b16a6b3..0000000 --- a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/public-curriculum-discovery/spec.md +++ /dev/null @@ -1,8 +0,0 @@ -## ADDED Requirements - -### Requirement: Tensor lifecycle roadmap is publicly discoverable -The deterministic public curriculum SHALL generate a roadmap page and catalog entry for the Trace a Tensor path from canonical curriculum data. - -#### Scenario: Visitor opens the public synthesis path -- **WHEN** a browser or crawler requests the Trace a Tensor public roadmap URL -- **THEN** it receives the ordered milestones, linked concepts, practice directions, and capstone without requiring JavaScript or authentication diff --git a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/tensor-lifecycle-synthesis/spec.md b/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/tensor-lifecycle-synthesis/spec.md deleted file mode 100644 index 7a27bdc..0000000 --- a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/specs/tensor-lifecycle-synthesis/spec.md +++ /dev/null @@ -1,26 +0,0 @@ -## Purpose - -Connect model computation, machine execution, and inference serving in one bounded path that learners prove with an inspectable systems artifact. - -## ADDED Requirements - -### Requirement: Tensor lifecycle is sequenced across system layers -The learning OS SHALL provide a selectable roadmap that orders existing concepts from tensor representation and gradient computation through memory and runtime behavior, kernel and hardware optimization, and request-level inference serving. - -#### Scenario: Learner follows the lifecycle -- **WHEN** a learner opens the Trace a Tensor roadmap -- **THEN** its milestones present the system layers in causal execution order rather than as an unstructured topic collection - -### Requirement: Every milestone remains practice-backed -Each roadmap milestone SHALL link existing executable drills for its concepts and SHALL avoid creating duplicate concept records solely for the synthesis path. - -#### Scenario: Learner practices a layer -- **WHEN** a learner reaches any Trace a Tensor milestone -- **THEN** the milestone offers at least one resolvable drill tied to an existing canonical concept - -### Requirement: Synthesis ends in measurable evidence -The roadmap SHALL end in a capstone that requires a layer map, a reproducible workload or model, measurements before and after one optimization, a bottleneck diagnosis, and an explanation of a trade-off or remaining risk. - -#### Scenario: Learner completes the capstone -- **WHEN** the learner submits evidence for the Trace a Tensor artifact -- **THEN** the evidence demonstrates where time or memory is spent and defends an optimization using measured results diff --git a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/tasks.md b/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/tasks.md deleted file mode 100644 index a42f6c3..0000000 --- a/openspec/changes/archive/2026-08-09-trace-a-tensor-synthesis/tasks.md +++ /dev/null @@ -1,17 +0,0 @@ -## 1. Canonical curriculum - -- [x] 1.1 Add the Trace a Tensor roadmap with ordered cross-domain milestones and existing drills. -- [x] 1.2 Add the dedicated synthesis artifact and link it from every participating concept. -- [x] 1.3 Add the roadmap to AI-native discovery without changing the primary navigation. - -## 2. Verification and publication - -- [x] 2.1 Add focused integrity tests for lifecycle ordering, resolvable practice, and capstone evidence. -- [x] 2.2 Run the smallest relevant curriculum tests and fix any integrity failures. -- [x] 2.3 Regenerate deterministic public curriculum output and verify the new roadmap page, counts, sitemap, and catalogs. - -## 3. Ship - -- [x] 3.1 Run repository release checks and record the shipped outcome in `PROJECT_STATUS.md`. -- [x] 3.2 Archive and sync the OpenSpec change. -- [x] 3.3 Commit, push, dispatch the release workflow, and verify the deployed public roadmap. diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/.openspec.yaml b/openspec/changes/archive/2026-08-13-add-software-wars/.openspec.yaml deleted file mode 100644 index b6b2d1f..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-08-13 diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/design.md b/openspec/changes/archive/2026-08-13-add-software-wars/design.md deleted file mode 100644 index 8907223..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/design.md +++ /dev/null @@ -1,198 +0,0 @@ -## Context - -See `proposal.md` for motivation and the five capability specs for behavior. - -The current product is a React SPA on Cloudflare Pages with one catch-all Pages Function, native D1, authenticated concept mastery, and local-first guest state. Mock prompts already map attempts to concepts and FSRS. The existing `user_elo_state` is client-writable per-roadmap drill calibration, so it is not a trustworthy base for public competitive rating. Monaco, Excalidraw, the shared workbench UI, PostHog, and the OpenAI-compatible AI adapter are reusable. - -Software Wars adds four architectural concerns the repository does not currently have: server-only ranked content, immutable competitive transactions, live room coordination, and managed WebRTC media. Schema changes must remain additive, provider and signing secrets cannot enter the repository, production deployment remains manual, and the accepted Socratic no-solutions ADR remains unchanged. - -## Goals / Non-Goals - -**Goals:** - -- Make both game modes usable end-to-end behind an operator-controlled launch state. -- Keep competitive truth server-owned, versioned, idempotent, and auditable. -- Reuse the existing learning system for remediation without conflating adaptive drill Elo with competitive Elo. -- Keep live game state independent from the media provider. -- Preserve the established Engineering Workbench UI and keep Wars contextual rather than adding a seventh primary navigation item. -- Make local and CI verification possible without Cloudflare credentials or live media. - -**Non-Goals:** - -- Synchronous live matchmaking for Blitz. -- Unrestricted AI-generated ranked content. -- Automated production provisioning, migration, deployment, or secret writes. -- Native mobile applications, tournaments, payments, recruiter surfaces, or sophisticated anti-cheat. -- Collaborative multi-writer editing; Tradeoff artifacts are private until immutable reveal. -- Video recording by default. - -## Decisions - -### 1. Split the control plane, media plane, and asynchronous evaluation plane - -The SPA talks to Pages Functions for durable commands, to a match-specific Durable Object WebSocket for live Tradeoff coordination, and to RealtimeKit for audio/video. Adjudication and transcript ingestion use a Queue consumer so external model or download failures can retry without blocking a request. - -```mermaid -flowchart LR - SPA[React Wars UI] -->|HTTP commands| API[Pages Functions Wars API] - SPA <-->|phase WebSocket| DO[Tradeoff Durable Object] - SPA <-->|WebRTC media| RTK[Cloudflare RealtimeKit] - API --> D1[(D1 competitive state)] - API --> R2[(R2 artifacts and transcripts)] - API -->|enqueue| Q[Wars Queue] - RTK -->|signed webhooks| API - Q --> AI[Existing OpenAI-compatible evaluator] - Q --> D1 - Q --> R2 - API --> FSRS[Existing concept mastery handler] -``` - -The Durable Object is not used as the permanent match database. It owns live serialization and recovers durable phase state from its storage; finalized match truth is projected into D1. RealtimeKit never decides the phase, winner, or rating. - -**Alternatives considered:** raw WebRTC would require signaling, TURN, device recovery, and media observability; Realtime SFU would still require substantially more custom media code; D1 polling would work at two participants but weaken synchronized twist/vote behavior and produce avoidable request load. - -### 2. Add a dedicated Wars API route family - -The Pages catch-all adds `/api/wars/*` routing with explicit public and authenticated endpoints. Competitive writes do not enter `/api/learning?action=...`, whose registry remains focused on learning state. - -Representative route groups: - -- public: launch status, sanitized leaderboards, public results, challenge previews; -- authenticated: match creation/resume, answer submission, challenge creation/acceptance, history, reports, Tradeoff scheduling, drafts, votes, media token issuance; -- provider: signed RealtimeKit webhooks; -- operator-only: content status/report review and rating correction operations. - -Every mutating request uses an idempotency key or a natural unique constraint. Responses return opaque identifiers, never answer-bank internals or provider administrative tokens. - -**Alternative considered:** add many learning actions. Rejected because route authorization, provider webhooks, and public share reads do not fit that dispatcher cleanly. - -### 3. Use normalized additive competitive tables and immutable event rows - -Migration `0002_software_wars.sql` adds tables grouped by responsibility: - -- content snapshots: `war_content_versions`, `war_ai_opponents`, `war_ai_answers`, `war_content_reports`; -- matches: `war_matches`, `war_participants`, `war_attempts`, `war_answers`, `war_artifacts`, `war_votes`, `war_evaluations`, `war_challenges`; -- ratings: `war_ratings`, `war_rating_events`; -- learning: `war_remediation_events`. - -Rows use text IDs, foreign keys, timestamps, explicit status constraints, and unique keys for idempotency. A match snapshots content version IDs, rules version, rating snapshots, and opponent profile version before play. Rating events and finalized results are append-only; corrections use compensating events. - -Large Excalidraw documents, optional media exports, and transcripts live in R2 under opaque match-scoped keys with hashes stored in D1. Small text/code artifacts may remain inline up to a documented size limit. - -**Alternatives considered:** extending `user_elo_state` would permit client-authored ratings and opaque overwrites; storing every match as JSON would make idempotency, leaderboard queries, and corrections fragile. - -### 4. Keep ranked content server-only, concept-owned, and validate it deterministically - -Authoring sources live under a non-client `shared/data/software-wars/` tree. A repo script validates schema, source URLs, concept IDs, unique IDs/versions, answer defensibility fields, option uniqueness, deterministic variants, AI answer coverage, status transitions, and launch thresholds. The build imports a compact server bundle; client code never imports the source modules. - -Every Blitz question owns one `primaryConceptId` that resolves to the canonical Learning OS concept catalogue and may name supporting `conceptIds`. Track and roadmap membership is derived from canonical concept tags and roadmap milestones; it is never hand-maintained on the question. The production gate requires reviewed active depth across the curriculum rather than only the former 12 backend/system topics. Generated variants never increase the distinct-question count and are reported separately. Every option carries an authored explanation, allowing results to explain both why the key is correct and why each distractor is wrong. Development and unranked preview can run below ranked thresholds. Content is activated by checked-in status, not runtime AI generation. - -The server exposes an answer-safe curriculum coverage manifest containing canonical track, roadmap, and concept identity plus active/candidate question counts. The client uses that manifest for search and queue selection. A scoped practice match snapshots `{queueType, queueId}` and resolves eligible questions server-side. Ranked Mix remains the only global Elo queue; track, roadmap, and concept queues are diagnostic until a future rating policy explicitly changes that. - -Topic-owned bank modules replace the former single monolithic file. A duplicate detector rejects repeated IDs, normalized stems, identical option sets, and near-duplicate question pairs above a conservative similarity threshold. Coverage reports expose distinct base questions, playable versions/variants, options with explanations, concepts, topics, difficulties, and AI answer coverage as separate counts. - -Parameterized questions expose a generator ID and seed in the match snapshot. Generation is pure and validated by exhaustive or bounded parameter-domain tests. Post-match explanations are authored content and do not change the Socratic companion. - -**Alternative considered:** putting content in `src/data` would ship answers in the public JavaScript bundle. Storing authoring content only in D1 would make review history and deterministic CI validation harder. - -### 5. Use classic versioned Elo with distinct mode state - -Competitive ratings start at 1500. Matches 1–10 use K=48; later matches use K=24. The score is 1, 0.5, or 0. The implementation is a pure shared server module with an explicit algorithm version and rounding rule, exercised by fixtures. - -Human pairings produce reciprocal events once. AI profiles have fixed benchmark ratings and never receive per-match mutations. Ranked ghost attempts can be paired only once. Ranked Mix is the only Blitz queue that affects the global Blitz rating; topic queues are diagnostic practice. - -**Alternatives considered:** Glicko models uncertainty better but contradicts the explicit Elo product language and increases explanation complexity; one combined mode rating would compare unrelated skills. - -### 6. Model live Tradeoff progression as an explicit state machine - -```mermaid -stateDiagram-v2 - [*] --> Scheduled - Scheduled --> CheckIn - CheckIn --> InitialSolution: both ready - CheckIn --> Cancelled: no-show policy - InitialSolution --> Twist - Twist --> Revision - Revision --> Reveal - Reveal --> Debate - Debate --> Voting - Voting --> Complete: compatible votes - Voting --> Adjudicating: incompatible votes - Adjudicating --> Complete: valid evaluation - Adjudicating --> ReviewRequired: retries exhausted -``` - -The server stores absolute phase deadlines. Clients render countdowns from `phaseEndsAt` and resynchronize on visibility change, reconnect, and mutations. DO alarms advance phases and WebSocket messages broadcast transitions; every mutation still checks the stored deadline so a delayed alarm cannot accept late work. - -Pages issues a short-lived match-scoped realtime token signed with a dedicated secret shared only by Pages and the Wars Worker. The Worker validates membership claims and never accepts a client-supplied user identity. - -### 7. Wrap RealtimeKit behind a media provider contract - -The frontend exposes battle-specific camera tiles and controls using the official RealtimeKit Core and React lifecycle packages rather than rendering the generic full-screen meeting. A backend provider module owns meeting creation, participant enrollment/token refresh, transcription request state, and webhook verification. - -Required non-secret configuration is account ID and RealtimeKit app ID. Administrative API token and webhook verification material remain platform secrets. Media rooms are created near check-in, not at scheduling time. Post-meeting transcription is opt-in per match; video recording is off by default. Transcript webhooks enqueue copying into project-owned R2 before provider expiry. - -The provider contract has a disabled adapter used in local development and deployments without configuration. This keeps artifact/state testing functional and provides a migration path to LiveKit Cloud if RealtimeKit beta constraints become unacceptable. - -**Alternative considered:** bind RealtimeKit calls directly into React pages. Rejected because it would leak lifecycle logic throughout UI and make beta-provider replacement expensive. - -### 8. Reuse current AI and FSRS paths conservatively - -The queue consumer calls the existing OpenAI-compatible generation layer with a Tradeoff-only structured evaluation prompt and validates its JSON against the match rubric. It does not use or modify the Socratic chat system prompt. - -Blitz misses create idempotent `again`/`hard` remediation events and then call the existing authenticated concept review logic. Correct MCQs produce diagnostic success only. Tradeoff evaluations may create `good` evidence only when the frozen artifact and rubric explicitly support it; they never create `easy` automatically. - -### 9. Preserve the current visual system and route hierarchy - -The design lane is `preserve`. `/wars` is a contextual competitive hub with mode cards, ratings, recent history, and leaderboards. Battle routes use existing `PageShell`, `Card`, `Button`, `FilterPill`, typography, black canvas, sparse sky signal, and semantic status colors. - -Blitz focuses on one question and one irreversible next action. Tradeoff uses a wide workbench: compact media rail, prompt/twist/status strip, artifact editor, and phase controls. At compact widths it stacks in semantic order, keeps 44px controls, and supports reduced motion. Today and Mock link into Wars; the six primary tabs remain unchanged. - -### 10. Build test seams before provider integration - -Pure modules own selection policy, scoring, Elo, phase transitions, vote compatibility, remediation mapping, and content validation. API handler tests use an in-memory database adapter pattern already present in the repo. UI tests mock Wars API/provider hooks. Playwright uses two browser contexts and fake media where supported; live RealtimeKit smoke remains an explicit operator check. - -### 11. Treat Solo Tradeoff as an ephemeral browser-to-provider session - -Solo Tradeoff reuses the local 30-minute phase UI but does not impersonate a ranked match. The learner explicitly supplies an OpenAI-compatible endpoint, API key, and model. A small client adapter calls that endpoint directly so the Learning OS backend never receives the credential. Configuration, opponent artifacts, debate messages, and feedback live only in React memory and are discarded on reload; the existing non-secret learner draft may continue using local preview storage. - -The AI produces its initial artifact before entry. On twist reveal it receives only the original prompt, twist, and its own artifact, preserving an independent comparison. Only after artifacts freeze may debate turns include the learner's revealed draft. Final feedback is rubric-oriented coaching plus a comparative outcome, never competitive Elo, FSRS mastery, public history, or an authoritative human-equivalent judgment. - -Custom endpoints are an explicit trust choice: the UI states that the key is sent directly to the selected provider. No provider URL, model, key, prompt, artifact, response, or transcript enters analytics. Browser CORS/provider failures degrade to the existing local workbench without losing the learner's draft. - -### 12. Make Wars guest-first and compose Tradeoff from Playground tools - -Authentication is not a play gate. Guests can always start the local unranked Blitz loop and the local or BYOK Solo Tradeoff loop. The authenticated server path remains the upgrade boundary for ranked Elo, durable history, human ghost/challenge identity, shareable server results, remediation writes, and cross-device recovery. Guest UI must describe that upgrade without replacing Play with Sign in. - -Tradeoff imports the same controlled Monaco and Excalidraw components used by Playground. A small Tradeoff workspace composes design notes, code/pseudocode, and diagram state; on desktop code and diagram share the canvas, while compact layouts switch one full-width tool at a time. Tradeoff owns the state adapter: guest drafts may use the existing preview key, live drafts save artifact types through the Wars API, and all editing surfaces become read-only at the competitive freeze boundary. The full Playground route, problem selector, code runner, Socratic companion, and Feynman gate are not nested into the battle shell because their navigation and learning semantics are different. - -## Risks / Trade-offs - -- **RealtimeKit is beta and APIs may change** → pin compatible package versions, isolate provider logic, document upgrade checks, and keep the disabled adapter. -- **Curriculum-wide ranked depth is the largest editorial cost** → make all 252 concepts discoverable immediately, distinguish candidate/active/readiness counts, enforce deterministic and similarity validation, require explanations for every option, and keep ranked activation gated until canonical concept coverage passes. -- **Pages plus a separate Worker increases deployment complexity** → keep independent configs, feature flags, health checks, and rollback; do not move the whole application runtime in this change. -- **D1 does not provide a traditional long transaction across external calls** → use unique constraints, idempotency keys, compare-and-set status updates, and create external side effects only after durable intent rows exist. -- **Ghost reuse can create phantom rating changes** → one ranked pairing per human attempt; reusable ghosts are practice-only. -- **Client clocks and tab throttling can drift** → absolute server deadlines and server receipt timestamps remain authoritative. -- **AI adjudication can be inconsistent or unavailable** → version prompts/models, require schema-valid rubric evidence, retry through a queue, and leave exhausted matches unrated for review. -- **Transcripts and artifacts contain sensitive content** → explicit consent, default no video recording, private storage keys, limited public sharing, retention documentation, and deletion controls. -- **Public competitive features conflict with prior personal-only positioning** → scope the status change explicitly to Software Wars and retain existing guest/private learning behavior. -- **New dependencies increase bundle size** → lazy-load all Wars and RealtimeKit modules only on Wars routes, keep the focused controls local instead of bundling the general-purpose UI package, and measure the production chunk. - -## Migration Plan - -1. Land the additive schema and server-only content validator with launch disabled. -2. Land the Wars API, pure competitive logic, seed content, and disabled media adapter; verify local preview without credentials. -3. Add the lazy-loaded Wars UI and contextual Today/Mock links behind a client-visible launch-status response. -4. Provision the Wars Queue, R2 binding if not already available, and separate Worker/Durable Object manually; configure their non-secret identifiers and secret signing material. -5. Create the RealtimeKit app/presets/webhook manually and set platform secrets. Run a two-account private smoke test with transcription consent. -6. Apply the D1 migration explicitly through the existing migration-gated deploy workflow. -7. Enable unranked preview first, inspect provider/cost/error telemetry, then enable ranked queues only after content thresholds pass. - -Rollback disables Wars entry points and match creation, then rolls back the Pages and Worker code independently. Additive tables and immutable history remain in place; no destructive down migration is required. RealtimeKit rooms/webhooks can be disabled without affecting the rest of the Learning OS. - -## Open Questions - -- Final public display names and avatar privacy defaults can be tuned after private testing without changing the underlying visibility requirement. -- Exact question exposure cooldown length and rating-band width can be configured from observed pool/liquidity data without changing the one-use ranked ghost contract. -- Transcript retention duration and opt-in public artifact excerpt length can be chosen before public launch without changing the consent or privacy model. diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/proposal.md b/openspec/changes/archive/2026-08-13-add-software-wars/proposal.md deleted file mode 100644 index b35cc60..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/proposal.md +++ /dev/null @@ -1,51 +0,0 @@ -## Why - -The Learning OS can diagnose and remediate knowledge gaps, but it still asks the learner to choose what to study. Software Wars adds a direct competitive entry point: play first, discover weaknesses from evidence, feed those weaknesses into the existing concept and FSRS loop, and return to competition with measurable improvement. - -The owner has explicitly requested both the short objective mode and the 30-minute tradeoff mode. This reopens feature development for this bounded product expansion despite the repository's prior maintenance-only status; unrelated roadmap expansion remains out of scope. - -## What Changes - -- Add a contextual `/wars` product surface with two independent modes: - - **Blitz Wars**: 60–120 second objective battles against precomputed AI opponents, one-use asynchronous human ghosts, or challenge-link opponents. - - **Tradeoff Wars**: scheduled 30-minute engineering battles with live video/audio, private artifacts, a synchronized requirement twist, artifact reveal, debate, player voting, and AI adjudication when votes disagree. - - **Solo Tradeoff**: an unranked 30-minute session against an independently generated AI artifact, using an ephemeral user-provided OpenAI-compatible key sent directly from the browser to the selected provider. - - **Guest-first play**: visitors can start an unranked Blitz battle and a local or Solo Tradeoff session without signup; authentication is an upgrade for ratings, history, durable human challenges, and cross-device state. - - **Shared build workspace**: Tradeoff uses the existing Playground's Monaco and Excalidraw tools together, plus design notes, instead of a reduced artifact textarea switcher. -- Add server-owned, immutable competitive state: match snapshots, question/problem versions, attempts, submissions, votes, results, separate Blitz and Tradeoff ratings, rating events, match history, challenges, and leaderboards. -- Add a versioned, source-backed, server-only curriculum-wide question/problem bank. Every Blitz question has one canonical primary Learning OS concept plus optional supporting concepts, option-by-option explanations, a defensible answer, provenance, lifecycle status, and separately counted parameterized variants where valid. -- Derive Blitz track, roadmap, and concept queues from the canonical 19-track, 25-roadmap, 252-concept Learning OS catalogue. Wars does not maintain a second topic catalogue; queues remain visible with honest readiness counts even when their ranked depth is incomplete. -- Add precomputed and versioned AI opponent profiles. AI latency never affects scoring. -- Add post-match remediation that explains mistakes, records weak concepts through the existing authenticated FSRS path, and recommends the matching lesson or drill. -- Add Cloudflare RealtimeKit integration for Tradeoff media and post-meeting transcription, with a provider boundary and a graceful configuration-disabled state. -- Add a Cloudflare Worker with one Durable Object per live Tradeoff match for authoritative phases, deadlines, twist delivery, presence, reconnects, and voting. Media state remains separate from game state. -- Add shareable result and challenge routes that reveal outcomes without exposing ranked question content. -- Preserve the existing visual language and six-tab navigation. Today and Mock gain contextual entry points; Wars does not become a seventh primary tab in this change. -- Preserve guest-first browsing, while ranked play, durable ratings, human challenges, and live Tradeoff participation require authentication. -- Keep Solo Tradeoff available without authentication. Provider credentials and AI responses remain tab-memory-only and never enter Learning OS storage, telemetry, URLs, or backend requests. - -## Capabilities - -### New Capabilities - -- `blitz-wars`: Objective battle matchmaking, server-authoritative timing and scoring, AI/ghost/challenge opponents, rematches, history, and shareable outcomes. -- `tradeoff-wars`: Scheduled live engineering battles, RealtimeKit rooms, authoritative phase coordination, private artifact submission and reveal, debate transcripts, voting, and adjudication. -- `competitive-ratings`: Separate server-owned Blitz and Tradeoff ratings, provisional status, immutable rating events, and public leaderboards. -- `wars-content-governance`: Versioned ranked questions and tradeoff problems with sources, concepts, rubrics, explanations, lifecycle controls, and precomputed AI answers. -- `wars-learning-loop`: Deterministic battle evidence mapped into explanations, weak concepts, FSRS remediation, lesson/drill recommendations, and later variant resurfacing. - -### Modified Capabilities - -- Learning catalogue integration: canonical concepts, tracks, and roadmaps now define Wars discovery and scoped practice queues without changing their learning content or routes. - -## Impact - -- **Frontend:** new Wars routes, battle/result/history/leaderboard/challenge screens, shared battle components, Today/Mock entry points, and responsive media/workbench layouts. -- **Solo AI:** browser-direct OpenAI-compatible generation for an independent initial artifact, twist revision, debate turns, and unranked feedback; no deployment-held inference key or new backend credential path is required. -- **API:** new authenticated and public Wars endpoints rather than extending the learning action registry with unrelated competitive mutations. -- **Data:** additive D1 migrations for competitive entities; R2 references for large artifact/transcript payloads; no request-time schema mutation. -- **Realtime:** a separately deployable Worker/Durable Object for live match state plus Cloudflare RealtimeKit REST, SDK, webhook, and transcription integration. -- **Dependencies:** add the official Cloudflare RealtimeKit Core and React packages because the existing application has no WebRTC media SDK. The focused two-person controls stay local; no general-purpose state, chart, or component framework is added. -- **Configuration:** document non-secret RealtimeKit app/account identifiers and keep API tokens/webhook secrets in Cloudflare-managed secrets. No secret or local env file is committed. -- **Operations:** manual deployment remains the release path. RealtimeKit, the live-match Worker, additive D1 migration, and Pages build require explicit operator steps and independent rollback controls. -- **Product status:** on completion, update canonical product docs and `PROJECT_STATUS.md` to record the owner-requested Software Wars expansion. diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/specs/blitz-wars/spec.md b/openspec/changes/archive/2026-08-13-add-software-wars/specs/blitz-wars/spec.md deleted file mode 100644 index e1c69cf..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/specs/blitz-wars/spec.md +++ /dev/null @@ -1,99 +0,0 @@ -## Purpose - -Defines fast objective engineering battles that produce trustworthy competitive outcomes and immediate evidence about a learner's technical gaps. - -## ADDED Requirements - -### Requirement: Blitz battle creation -The system SHALL let an authenticated user start a 60–120 second Blitz battle containing 5–10 versioned objective questions from the canonical Learning OS curriculum. A user SHALL be able to choose Ranked Mix or an unranked track, roadmap, or concept practice queue; only Ranked Mix SHALL affect global Blitz rating. - -#### Scenario: Start a ranked mixed battle -- **WHEN** an authenticated user selects Ranked Mix and presses Play -- **THEN** the system creates a server-owned match snapshot, selects an eligible opponent and immutable question versions, and returns question stems and shuffled options without answer keys - -#### Scenario: Start a scoped curriculum battle -- **WHEN** an authenticated user selects a canonical track, roadmap, or concept -- **THEN** the server derives eligible active questions from canonical concept relationships, starts an unranked battle restricted to that scope, and labels the result as unranked - -#### Scenario: Selected scope has insufficient active questions -- **WHEN** a visible track, roadmap, or concept queue has fewer than five active questions -- **THEN** the catalogue shows its candidate and active counts and prevents starting a misleading battle while preserving links to the corresponding learning surface - -#### Scenario: Guest starts Blitz -- **WHEN** a visitor without an account presses Play -- **THEN** the system starts a complete unranked browser battle immediately, labels its state as local to that browser, and does not create Elo, FSRS evidence, public history, or a server-owned attempt - -#### Scenario: Guest requests a durable competitive feature -- **WHEN** a guest requests ranked rating, history, result sharing, or a human challenge -- **THEN** the system explains that signup upgrades persistence and competitive identity while keeping unranked Blitz playable without signup - -### Requirement: Server-authoritative attempt timing -The system MUST record match and answer deadlines on the server, reject answers submitted after the applicable deadline, and use server receipt times for scoring. Client countdowns SHALL be presentational only. - -#### Scenario: Answer arrives before deadline -- **WHEN** the server receives a valid answer before the match deadline -- **THEN** it records the selected option, correctness evidence, and elapsed server time exactly once - -#### Scenario: Answer arrives after deadline -- **WHEN** an answer reaches the server after the match deadline -- **THEN** the system rejects it as late and finalizes the unanswered question as incorrect - -#### Scenario: Duplicate answer submission -- **WHEN** the same answer operation is retried with the same idempotency key -- **THEN** the system returns the original recorded answer without changing match state - -### Requirement: Deterministic Blitz scoring -The system SHALL rank opponents lexicographically by total correct answers and then by total server-recorded response time across correct answers. Equal accuracy and equal qualifying response time SHALL produce a draw. - -#### Scenario: Accuracy differs -- **WHEN** one opponent answers more questions correctly -- **THEN** that opponent wins regardless of response time - -#### Scenario: Accuracy ties -- **WHEN** opponents have equal correct counts -- **THEN** the opponent with the lower total response time for correct answers wins - -#### Scenario: AI opponent is scored -- **WHEN** a human plays a precomputed AI opponent -- **THEN** the AI's stored correctness participates in scoring and its response time is excluded from the tie-breaker - -### Requirement: Supported opponent types -The system SHALL support versioned precomputed AI opponents, one-use ranked asynchronous human ghosts, and authenticated challenge-link opponents. A ranked human attempt MUST affect its owner at most once. - -#### Scenario: Match against AI -- **WHEN** no eligible human ghost is available or the player selects an AI opponent -- **THEN** the system selects a compatible versioned AI profile immediately - -#### Scenario: Ranked ghost is paired -- **WHEN** a completed unpaired human attempt matches a later player's rating band and question-set policy -- **THEN** the system pairs it once, finalizes both players' outcome once, and marks that ghost attempt consumed for ranked play - -#### Scenario: Challenge is accepted -- **WHEN** an authenticated recipient opens a valid unused challenge link -- **THEN** the system gives the recipient the same immutable question versions under the same timing policy and finalizes the challenge after completion - -### Requirement: Result, history, rematch, and sharing -The system SHALL show each completed match's outcome, score, rating delta when ranked, opponent identity class, mistake count, and remediation actions. It SHALL support rematch, match history, and a shareable result that does not reveal ranked question content. - -#### Scenario: Match completes -- **WHEN** both sides have completed or the active side's deadline expires -- **THEN** the system finalizes one immutable result and shows Win, Loss, or Draw with supporting scoring evidence - -#### Scenario: User requests rematch -- **WHEN** a player selects Rematch from a completed result -- **THEN** the system creates a new match with a fresh eligible question set and preserves the previous result - -#### Scenario: Public result is shared -- **WHEN** a visitor opens a shareable result link -- **THEN** the system shows sanitized participants, outcome, scores, mode, rating movement, and aggregate topic signals without stems, options, answers, or explanations - -### Requirement: Abandonment and reconnect behavior -The system SHALL allow an in-progress client to recover its server-owned attempt until the deadline and SHALL finalize expired or abandoned ranked attempts consistently. - -#### Scenario: Browser reconnects -- **WHEN** the same authenticated player reloads before the deadline -- **THEN** the system restores recorded answers and the authoritative remaining time - -#### Scenario: Ranked player abandons -- **WHEN** a ranked match expires with unanswered questions -- **THEN** unanswered questions count as incorrect and the result finalizes under the normal scoring policy diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/specs/competitive-ratings/spec.md b/openspec/changes/archive/2026-08-13-add-software-wars/specs/competitive-ratings/spec.md deleted file mode 100644 index 5f4f07e..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/specs/competitive-ratings/spec.md +++ /dev/null @@ -1,67 +0,0 @@ -## Purpose - -Defines trustworthy, server-owned competitive ratings and leaderboards that remain comparable across Software Wars modes and opponent types. - -## ADDED Requirements - -### Requirement: Separate mode ratings -The system SHALL maintain independent Blitz and Tradeoff ratings for each authenticated user. A result in one mode MUST NOT modify the other mode's rating. - -#### Scenario: Blitz result finalizes -- **WHEN** a ranked Blitz result is finalized -- **THEN** only the participating users' Blitz rating state and Blitz rating events are eligible to change - -#### Scenario: Tradeoff result finalizes -- **WHEN** a ranked Tradeoff result is finalized -- **THEN** only the participating users' Tradeoff rating state and Tradeoff rating events are eligible to change - -### Requirement: Provisional and established ratings -The system SHALL start each mode at rating 1500, label the first 10 finalized ranked matches as provisional, use a K-factor of 48 while provisional, and use a K-factor of 24 after establishment. - -#### Scenario: New player completes first ranked match -- **WHEN** a user with no prior ranked result completes a match -- **THEN** the system computes the change from rating 1500 with provisional K-factor 48 and shows the rating as provisional - -#### Scenario: Player completes eleventh ranked match -- **WHEN** a user with 10 prior finalized ranked matches completes another -- **THEN** the system computes the change with established K-factor 24 and removes the provisional label - -### Requirement: Server-owned idempotent rating updates -Competitive rating input and calculation MUST remain server-owned. Finalizing the same match more than once MUST NOT create additional rating changes. - -#### Scenario: Client supplies a rating value -- **WHEN** a client attempts to submit its own rating or rating delta -- **THEN** the server ignores or rejects that value and derives the update from immutable match evidence - -#### Scenario: Finalization is retried -- **WHEN** match finalization runs again after rating events already exist -- **THEN** the system returns the existing result and rating events without changing either rating - -### Requirement: Human and fixed AI rating behavior -Human-versus-human ranked outcomes SHALL update both participants once. Human-versus-AI outcomes SHALL update only the human against the AI profile's fixed versioned rating. - -#### Scenario: Human ghost match finalizes -- **WHEN** two human attempts are paired and scored -- **THEN** the system produces reciprocal rating events from the rating snapshots stored with that pairing - -#### Scenario: AI match finalizes -- **WHEN** a human completes a ranked match against an AI profile -- **THEN** the human receives one rating event and the AI profile's published rating remains unchanged - -### Requirement: Mode-specific leaderboards -The system SHALL expose separate Blitz and Tradeoff leaderboards with established players ranked ahead of provisional players at equal rating. Public entries SHALL contain only display-safe profile data. - -#### Scenario: User views Blitz leaderboard -- **WHEN** a visitor opens the Blitz leaderboard -- **THEN** the system returns Blitz rating, provisional status, ranked match count, win/draw/loss record, and display-safe identity without Tradeoff rating - -#### Scenario: User opts out of public ranking -- **WHEN** an authenticated user disables public leaderboard visibility -- **THEN** the public leaderboard omits that user's identity while their private rating continues to function - -### Requirement: Auditable rating history -Each competitive rating change SHALL be preserved as an immutable event containing mode, match, before value, after value, score, opponent class, opponent rating snapshot, algorithm version, and creation time. - -#### Scenario: Rating history is inspected -- **WHEN** an authenticated user opens a completed ranked match -- **THEN** the system can explain the displayed delta from the associated immutable rating event diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/specs/tradeoff-wars/spec.md b/openspec/changes/archive/2026-08-13-add-software-wars/specs/tradeoff-wars/spec.md deleted file mode 100644 index f6b5860..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/specs/tradeoff-wars/spec.md +++ /dev/null @@ -1,146 +0,0 @@ -## Purpose - -Defines scheduled live engineering battles where players create inspectable solutions, respond to changing requirements, debate tradeoffs, and receive a fair result. - -## ADDED Requirements - -### Requirement: Scheduled 30-minute battle -The system SHALL let authenticated users schedule or accept a 30-minute Tradeoff battle using the same versioned prompt, rules, initial requirements, and hidden twist. Documentation and external AI tools SHALL be explicitly allowed. - -#### Scenario: Challenge is scheduled -- **WHEN** two authenticated players accept a Tradeoff challenge and start time -- **THEN** the system creates one match, enrolls both players, and shows the shared start time and allowed-tool policy - -#### Scenario: Player checks in -- **WHEN** a participant opens the room during the check-in window -- **THEN** the system validates match membership, prepares media access, and marks readiness without exposing the problem before the configured reveal time - -#### Scenario: Opponent does not check in -- **WHEN** one participant remains absent through the no-show deadline -- **THEN** the present participant may claim a forfeit or cancel without rating change according to the displayed match policy - -### Requirement: Solo Tradeoff with a user-provided AI opponent -The system SHALL offer an unranked 30-minute Solo Tradeoff session without requiring another account. The learner SHALL provide an OpenAI-compatible endpoint, API key, and model for the current tab. The browser SHALL ask the provider to produce an independent initial artifact before seeing the learner's work, revise that artifact for the same hidden twist, participate in the reveal/debate phase, and return rubric-oriented feedback after the learner's self-assessment. - -#### Scenario: Learner starts a solo session -- **WHEN** a learner provides a complete provider configuration and selects Start solo session -- **THEN** the browser creates an independent AI opponent artifact, opens the same 30-minute phase flow, labels the session unranked, and never creates Tradeoff Elo or public match history - -#### Scenario: Solo twist is revealed -- **WHEN** the learner advances from the initial-solution phase -- **THEN** the same twist is applied to both sides and the AI revises only its own previously generated artifact without receiving the learner's private draft - -#### Scenario: Solo debate begins -- **WHEN** the revision phase freezes -- **THEN** the learner can inspect the AI's frozen artifact and exchange bounded debate messages that reference both revealed artifacts and the evaluation rubric - -#### Scenario: Provider call fails -- **WHEN** the browser cannot reach the selected provider or the provider rejects the credential, model, or request -- **THEN** the session preserves the learner's draft, explains the provider failure without exposing the credential, and permits retry or continuation as a local practice session - -### Requirement: Ephemeral Solo Tradeoff credentials -Solo Tradeoff provider credentials MUST remain in component memory and MUST NOT be written to localStorage, sessionStorage, IndexedDB, D1, R2, analytics, logs, URLs, challenge payloads, or Learning OS backend requests. Provider calls SHALL travel directly from the learner's browser to the endpoint the learner explicitly selected. - -#### Scenario: Solo session refreshes or closes -- **WHEN** the page reloads, closes, or the component unmounts -- **THEN** the provider credential and AI transcript disappear while the learner's non-secret local draft may remain under the existing preview-draft policy - -#### Scenario: Solo telemetry is emitted -- **WHEN** the product records solo start, phase, completion, or provider-failure telemetry -- **THEN** telemetry contains only coarse mode/state metadata and excludes endpoint, model, credential, prompts, artifacts, and AI responses - -### Requirement: Authoritative phase progression -The system MUST coordinate Tradeoff phases from server-owned deadlines: check-in, initial solution, twist, revision, reveal, eight-minute debate, vote, adjudication, and complete. Clients SHALL recover the current phase and deadline after reconnecting. - -#### Scenario: Requirement twist occurs -- **WHEN** the initial-solution deadline is reached -- **THEN** the system reveals the immutable match twist to both players at the same authoritative time and starts the revision phase - -#### Scenario: Client reconnects after phase change -- **WHEN** a participant reconnects after missing one or more phase notifications -- **THEN** the system returns the current phase, deadline, and all events that participant is authorized to see - -#### Scenario: Late mutation is attempted -- **WHEN** a player tries to modify a submission after its phase deadline -- **THEN** the system rejects the mutation and preserves the frozen artifact version - -### Requirement: Private multi-format artifacts -During solving and revision, each player's text, code, schema, pseudocode, and diagram artifacts SHALL remain private from the opponent. The system SHALL freeze immutable artifact snapshots at phase boundaries and reveal both final snapshots only when the reveal phase begins. - -The Tradeoff workbench SHALL reuse the Learning OS Playground's Monaco code editor and Excalidraw diagram editor. At wide widths, code and diagram SHALL be usable together alongside design notes; compact widths SHALL provide an explicit, touch-safe panel switcher without removing any artifact type. The competitive shell, phase controls, autosave adapter, and freeze rules SHALL remain Tradeoff-owned. - -#### Scenario: Player saves work during solving -- **WHEN** a participant saves an artifact before the deadline -- **THEN** the system persists an owner-visible draft and records the latest accepted version - -#### Scenario: Learner builds a combined solution -- **WHEN** a learner writes design notes, edits code or pseudocode in Monaco, and draws architecture in Excalidraw -- **THEN** all three artifacts remain part of one Tradeoff solution and are visible together on a wide workspace - -#### Scenario: Combined workspace freezes -- **WHEN** the Tradeoff enters reveal, debate, voting, or complete -- **THEN** notes and code become read-only, Excalidraw enters view mode, and the frozen composite artifact is used for reveal and evaluation - -#### Scenario: Reveal begins -- **WHEN** both final artifacts are frozen or the revision deadline expires -- **THEN** the system makes both frozen artifacts readable to both participants and prevents further competitive edits - -#### Scenario: Artifact upload is too large or invalid -- **WHEN** submitted content exceeds documented limits or fails format validation -- **THEN** the system rejects it without replacing the last valid draft and explains the limit - -### Requirement: Embedded live media -The battle room SHALL support two-person audio/video, device setup, mute controls, optional screen sharing, connection state, and reconnect behavior through a managed media provider. Game state MUST remain functional when media configuration is unavailable. - -#### Scenario: Media is configured -- **WHEN** an enrolled participant requests room access -- **THEN** the authenticated backend issues participant-scoped media credentials without exposing provider administrative credentials - -#### Scenario: Media is not configured -- **WHEN** the deployment lacks required media configuration -- **THEN** the room shows a clear unavailable state while artifact, phase, and voting flows remain usable for local or text-only testing - -#### Scenario: Player reconnects media -- **WHEN** a participant's network changes or media connection drops -- **THEN** the participant can rejoin the same active match without creating another competitive identity - -### Requirement: Debate transcript and consent -The system SHALL require explicit participant disclosure and consent before generating a debate transcript. Video recording SHALL be disabled by default. A transcript used for adjudication SHALL identify speakers and be retained according to the displayed retention policy. - -#### Scenario: Both players consent -- **WHEN** both participants consent before entering the debate -- **THEN** the system may request post-meeting transcription and attach the resulting transcript to the match adjudication evidence - -#### Scenario: A player declines transcription -- **WHEN** either participant declines -- **THEN** the system does not generate a managed transcript and adjudication uses artifacts, twist response, rubric evidence, and player votes only - -### Requirement: Voting and adjudication -After the debate, each player SHALL privately vote Win, Loss, or Draw from their own perspective. Agreeing votes SHALL determine the result; incompatible votes SHALL trigger structured AI adjudication from the frozen prompt, twist, artifacts, rubric, and available transcript. - -#### Scenario: Votes agree on winner -- **WHEN** one player votes Win and the other votes Loss for the corresponding sides -- **THEN** the system finalizes that winner without AI adjudication - -#### Scenario: Both players vote Draw -- **WHEN** both players vote Draw -- **THEN** the system finalizes a draw without AI adjudication - -#### Scenario: Votes disagree -- **WHEN** votes are incompatible or one party disputes the other's outcome -- **THEN** the system queues one versioned adjudication, shows a pending state, and finalizes exactly once from a valid structured evaluation - -#### Scenario: Adjudication repeatedly fails -- **WHEN** automated evaluation exhausts its retry policy -- **THEN** the match remains unrated and displays a review-required state rather than fabricating a winner - -### Requirement: Tradeoff results and history -The completed result SHALL show outcome, rating movement when ranked, frozen artifacts, twist response, rubric-level reasoning, transcript availability, concept evidence, rematch, and report controls. - -#### Scenario: Completed match is opened later -- **WHEN** a participant opens Tradeoff history -- **THEN** the system restores the immutable result and only the evidence that participant is permitted to access - -#### Scenario: Public result is shared -- **WHEN** a visitor opens a public Tradeoff result -- **THEN** the system shows the outcome and opted-in artifact excerpts while withholding transcripts, private drafts, provider identifiers, and non-consented content diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/specs/wars-content-governance/spec.md b/openspec/changes/archive/2026-08-13-add-software-wars/specs/wars-content-governance/spec.md deleted file mode 100644 index 13ac32a..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/specs/wars-content-governance/spec.md +++ /dev/null @@ -1,98 +0,0 @@ -## Purpose - -Defines the editorial and versioning contract that keeps ranked Software Wars content defensible, source-backed, stable during matches, and safe to retire. - -## ADDED Requirements - -### Requirement: Versioned ranked question contract -Every ranked Blitz question SHALL have an immutable question ID and version, a distinct stable `contentKey`, one defensible correct answer, authored distractors, an authored explanation for the correct option and every distractor, exactly one canonical primary Learning OS concept ID, zero or more canonical supporting concept IDs, difficulty, primary or authoritative sources, and editorial status. Code may appear in a question stem, but Blitz answer choices SHALL remain concise text rather than competing code blocks. - -#### Scenario: Active question is selected -- **WHEN** ranked matchmaking selects a question -- **THEN** it snapshots the exact active version and never substitutes later edits into that match - -#### Scenario: Question lacks required evidence -- **WHEN** a question has no authoritative source, no mapped concept, multiple defensible answers, any option lacks an explanation, or it duplicates or closely paraphrases another active base question -- **THEN** the content validator rejects it from the ranked pool - -#### Scenario: Result explains every option -- **WHEN** a player completes an attempted question -- **THEN** the result can show why the selected option was right or wrong and why every alternative does not satisfy the question - -#### Scenario: Question maps to learning -- **WHEN** a question is validated or returned in an authorized result -- **THEN** its primary concept resolves to `/concepts/:id`, and its track and roadmap memberships are derived from the canonical curriculum rather than copied into Wars metadata - -### Requirement: Server-only answer material -Correct options, explanations, variant parameters that reveal answers, AI precomputed answers, and Tradeoff evaluation rubrics MUST remain outside the browser bundle and SHALL be returned only after authorization and the relevant match phase permits disclosure. - -#### Scenario: Active Blitz match loads -- **WHEN** the client requests its question set -- **THEN** the response contains only display-safe stems, options, metadata, and opaque version identifiers - -#### Scenario: Match completes -- **WHEN** a player's result is finalized -- **THEN** the server may return authored explanations for questions that player attempted without making the full ranked bank public - -### Requirement: Editorial lifecycle -Content SHALL move through draft, reviewed, active, and retired states. Only reviewed active content SHALL appear in ranked matches. Retirement SHALL prevent new selection without rewriting completed match history. - -#### Scenario: Editor activates reviewed content -- **WHEN** a complete item passes deterministic validation and receives reviewed status -- **THEN** it becomes eligible for a separately controlled activation step - -#### Scenario: Active item is retired -- **WHEN** an editor retires an ambiguous or obsolete item -- **THEN** no new ranked match selects it and existing matches retain their immutable snapshot - -### Requirement: Ambiguity reporting and correction -Participants SHALL be able to report a ranked item with a reason. Operators SHALL be able to inspect reports, retire the affected version, and void rating effects for materially invalid results without deleting match evidence. - -#### Scenario: Player reports a question -- **WHEN** a participant submits an ambiguity report from a completed result -- **THEN** the system records the item version, match, reporter, reason, and review state without changing the result immediately - -#### Scenario: Operator voids invalid results -- **WHEN** an operator confirms a material content defect -- **THEN** the system retires the item version and records compensating rating events for affected matches according to the current correction policy - -### Requirement: Parameterized variants -Parameterized variants SHALL use deterministic generators with validated parameter domains and stable generated version identifiers. A generator MUST NOT create a ranked variant whose answer or explanation has not been mechanically verified. Variants SHALL be reported separately and MUST NOT increase the distinct authored-question count. - -#### Scenario: Variant is generated -- **WHEN** matchmaking requests an eligible parameterized item -- **THEN** the server derives the stem, options, correct answer, explanation values, and version identifier from one validated parameter seed - -#### Scenario: Generated variant fails validation -- **WHEN** the generator produces duplicate options, an invalid answer, or an unsupported parameter combination -- **THEN** the server discards it and selects another eligible item - -### Requirement: Precomputed AI opponent snapshots -Each AI opponent SHALL identify provider/model name, model version or snapshot date, benchmark run version, answer per question version, explanation, aggregate accuracy, and fixed published rating. Ranked matches MUST NOT invoke the AI opponent at match time. - -#### Scenario: AI opponent is selected -- **WHEN** matchmaking chooses an AI profile -- **THEN** scoring uses the stored answer for every selected question version and performs no model inference for opponent play - -#### Scenario: Model version changes -- **WHEN** an operator benchmarks a new model version -- **THEN** the system creates a new opponent snapshot without rewriting the old profile or its completed matches - -### Requirement: Launch content threshold -Public ranked Blitz launch SHALL require the configured reviewed active question depth across all canonical concepts eligible for Blitz, explanations for every option, balanced difficulty coverage, and complete precomputed AI answer coverage. Every canonical track, roadmap, and concept SHALL remain discoverable before it reaches ranked depth, with candidate and active counts reported separately. Tradeoff ranked launch SHALL require at least 20 reviewed problems with twists and evaluation rubrics. - -#### Scenario: Pool is below threshold -- **WHEN** a deployment has fewer than the required reviewed active items -- **THEN** it may run development and unranked preview matches but SHALL label ranked public launch as unavailable - -#### Scenario: Pool meets threshold -- **WHEN** deterministic content validation confirms the required counts and coverage -- **THEN** the deployment becomes eligible for operator-controlled ranked activation - -### Requirement: Base-question and variant reporting -Content reports SHALL publish distinct authored-question counts separately from generated playable variants and SHALL include topic, difficulty, concept, explanation, source, and AI coverage. -Content reports SHALL also publish canonical track, roadmap, and concept coverage, including zero-coverage curriculum entries. - -#### Scenario: One base question has multiple parameter seeds -- **WHEN** the validator reports the ranked bank -- **THEN** that family contributes one distinct authored question and its generated versions contribute only to the separate playable-variant count diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/specs/wars-learning-loop/spec.md b/openspec/changes/archive/2026-08-13-add-software-wars/specs/wars-learning-loop/spec.md deleted file mode 100644 index d81bf84..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/specs/wars-learning-loop/spec.md +++ /dev/null @@ -1,64 +0,0 @@ -## Purpose - -Defines how competitive evidence becomes targeted learning and spaced repetition without turning recognition or AI answers into fabricated mastery. - -## ADDED Requirements - -### Requirement: Post-Blitz mistake explanation -After a Blitz attempt is final, the system SHALL show the player's answer, the authored correct answer, a source-backed explanation, mapped concepts, and relevant lesson or drill for every incorrect response. It MUST NOT expose this material during the active attempt. - -#### Scenario: Player answers incorrectly -- **WHEN** the completed result contains an incorrect answer -- **THEN** the result shows the authored explanation, authoritative sources, concept link, Learn action, Drill action, and Add to Review state - -#### Scenario: Player follows Learn -- **WHEN** the player selects Learn from a question or weakness card -- **THEN** navigation opens the canonical primary concept at `/concepts/:id` rather than the general Learn catalogue - -#### Scenario: Active match requests explanation -- **WHEN** a client requests answer material before its attempt is final -- **THEN** the server refuses disclosure - -### Requirement: Conservative FSRS remediation -An authenticated Blitz mistake SHALL schedule mapped concepts with `again` or `hard` evidence according to the documented mistake policy. A correct MCQ response MUST NOT produce `good` or `easy` concept mastery by itself. Tradeoff artifact evaluation MAY produce `again`, `hard`, or `good` ratings when the rubric contains inspectable concept evidence, but SHALL never produce `easy` automatically. - -#### Scenario: Incorrect Blitz answer is finalized -- **WHEN** a mapped question is incorrect -- **THEN** the system writes at most one idempotent remediation event per concept and match and updates the existing authenticated FSRS scheduler conservatively - -#### Scenario: Correct Blitz answer is finalized -- **WHEN** a mapped question is correct -- **THEN** the system records diagnostic success without granting positive FSRS mastery - -#### Scenario: Tradeoff artifact demonstrates concept evidence -- **WHEN** a finalized structured rubric explicitly identifies strong inspectable evidence for a mapped concept -- **THEN** the system may apply at most a `good` FSRS rating and records the evidence source with the remediation event - -### Requirement: Weakness summary and recommendations -Every completed battle SHALL aggregate relevant concept evidence into weakness cards containing accuracy or rubric evidence, current mastery context, and direct Learn, Drill, and Review actions. - -#### Scenario: Multiple questions map to one weak concept -- **WHEN** a player misses multiple items mapped to the same concept -- **THEN** the result presents one aggregated weakness card with match accuracy and one set of learning actions - -#### Scenario: No weakness is detected -- **WHEN** no negative concept evidence exists -- **THEN** the result presents verified strengths and a rematch action without fabricating a weakness - -### Requirement: Variant resurfacing and improvement evidence -The system SHALL prioritize unseen or materially different variants of previously failed concepts in later eligible battles until diagnostic performance improves. It SHALL preserve a before-and-after evidence trail. - -#### Scenario: Failed concept reappears -- **WHEN** a player starts a later eligible match while a failed concept remains weak -- **THEN** selection weighting may include an unseen validated variant without repeating the exact exposed item inside its cooldown window - -#### Scenario: Performance improves -- **WHEN** the player later answers a valid variant correctly or demonstrates the concept in a Tradeoff artifact -- **THEN** the system records improvement evidence and shows the comparison without automatically declaring mastery from a single MCQ - -### Requirement: Socratic boundary remains intact -Authored post-match explanations and structured adjudication feedback SHALL be separate from the Socratic companion. The active Companion and chat prompt MUST continue to probe reasoning without supplying direct solutions. - -#### Scenario: Player asks Companion for an active answer -- **WHEN** the Socratic companion receives a request for an answer during a battle -- **THEN** it continues to ask probing questions and does not reveal ranked answers, rubrics, twists, or reference solutions diff --git a/openspec/changes/archive/2026-08-13-add-software-wars/tasks.md b/openspec/changes/archive/2026-08-13-add-software-wars/tasks.md deleted file mode 100644 index 6970d61..0000000 --- a/openspec/changes/archive/2026-08-13-add-software-wars/tasks.md +++ /dev/null @@ -1,86 +0,0 @@ -## 1. Competitive Foundations - -- [x] 1.1 Add an additive `0002_software_wars.sql` D1 migration with content, match, attempt, artifact, vote, evaluation, challenge, rating, report, and remediation tables plus required indexes and idempotency constraints. -- [x] 1.2 Add the pinned official Cloudflare RealtimeKit Core/React production dependencies and document why each dependency is required. -- [x] 1.3 Define shared Software Wars domain types, status enums, opaque identifiers, API envelopes, visibility rules, and size limits without importing server answer material into client modules. -- [x] 1.4 Implement and unit-test pure Blitz scoring, server-deadline checks, question selection/cooldown policy, vote compatibility, Tradeoff phase transitions, and conservative remediation mapping. -- [x] 1.5 Implement and unit-test versioned competitive Elo at 1500 with K=48 for matches 1–10, K=24 thereafter, reciprocal human events, fixed AI ratings, rounding, and idempotent fixtures. - -## 2. Ranked Content and AI Opponents - -- [x] 2.1 Create the server-only `shared/data/software-wars/` authoring schema and loaders for Blitz questions, deterministic variants, Tradeoff problems/twists/rubrics, AI profiles, and precomputed answers. -- [x] 2.2 Add a deterministic content validator for IDs/versions, concept mappings, sources, unique options, answer/rubric completeness, lifecycle state, variant domains, AI coverage, and launch thresholds; expose it as a repo script and test it. -- [x] 2.3 Author and validate at least 300 reviewed active Backend Fundamentals Blitz questions with difficulty/concept coverage and source-backed explanations. -- [x] 2.4 Author and validate at least 20 reviewed Tradeoff problems with hidden twists, evaluation rubrics, concepts, and authoritative sources. -- [x] 2.5 Add versioned precomputed AI opponent fixtures with fixed ratings and complete answer coverage for every active Blitz question version. -- [x] 2.6 Replace the 300-item variant-inflated gate with 1,200 editorially approved active `contentKey` values across 12 topic-owned banks and report candidates and variants separately. -- [x] 2.7 Add an authored explanation to every option and return complete option reasoning only after match authorization permits answer disclosure. -- [x] 2.8 Add normalized duplicate, option-set duplicate, and conservative near-duplicate validation plus per-topic and per-difficulty coverage minimums. -- [x] 2.9 Expand precomputed AI opponent snapshots to every active playable question version and require at least 3,600 stored answers for the 1,200-question base bank. -- [x] 2.10 Add focused content-contract tests proving distinct-base counting, variant separation, complete explanations, duplicate rejection, balanced coverage, and answer-safe client bundles. -- [x] 2.11 Add a required canonical `primaryConceptId` to every Blitz question, validate all primary/supporting concept IDs, and expose answer-safe concept identity without exposing answer material. -- [x] 2.12 Derive a server-safe Wars curriculum coverage manifest from canonical tracks, roadmaps, milestones, and concepts, including zero-coverage entries and active/candidate counts. -- [x] 2.13 Replace hard-coded topic filtering with server-side track, roadmap, and concept queue resolution; snapshot queue type/id and keep scoped queues unranked. - -## 3. Wars Data and API Layer - -- [x] 3.1 Add a Wars repository layer over the existing database adapter for content snapshots, match membership, attempts, answers, artifacts, challenges, reports, results, and immutable event writes. -- [x] 3.2 Extend the production Pages catch-all and local development parity with explicit public, authenticated, provider-webhook, and operator Wars route handling. -- [x] 3.3 Implement launch-status, sanitized public leaderboard, public result, and challenge-preview reads with visibility and content-leakage tests. -- [x] 3.4 Implement authenticated Blitz create/resume/answer/finalize endpoints with server timestamps, idempotency, AI matching, one-use human ghost pairing, abandonment, and deterministic scoring. -- [x] 3.5 Implement challenge creation/acceptance, rematch, match history, report submission, and sanitized result sharing. -- [x] 3.6 Implement server-owned mode ratings, immutable rating events, public opt-out, leaderboard queries, and operator-only compensating correction flow. -- [x] 3.7 Implement post-match remediation writes through the existing concept mastery contract, including explanation/recommendation responses and idempotent FSRS updates. - -## 4. Tradeoff Durable Realtime Control - -- [x] 4.1 Add a separately deployable Wars Worker configuration with a Durable Object binding, environment declarations, and local development commands without committing secrets. -- [x] 4.2 Implement short-lived match-scoped realtime token minting and validation shared between Pages and the Wars Worker. -- [x] 4.3 Implement the Tradeoff Durable Object state machine, persistent absolute deadlines, alarms, hibernating WebSocket presence, authorized event replay, phase broadcasts, no-show handling, and reconnect recovery. -- [x] 4.4 Implement authenticated Tradeoff scheduling/check-in and Durable Object bootstrap APIs with membership and state-version checks. -- [x] 4.5 Add two-client deterministic tests for simultaneous twist delivery, late mutation rejection, reconnect recovery, duplicate messages, phase alarms, and no-show outcomes. - -## 5. Tradeoff Artifacts, Media, and Adjudication - -- [x] 5.1 Implement private Tradeoff draft save, phase-boundary freeze, content hashing, D1/R2 storage selection, immutable reveal, and participant/public authorization rules. -- [x] 5.2 Implement a RealtimeKit provider interface with configured and disabled adapters for meeting creation, participant enrollment/token refresh, room closure, transcription request state, and provider error mapping. -- [x] 5.3 Implement signed RealtimeKit webhook ingestion, duplicate delivery protection, transcript consent enforcement, queued transcript copying to project-owned R2, and retention metadata. -- [x] 5.4 Add the Wars Queue producer/consumer and dead-letter configuration for transcript ingestion and versioned AI adjudication. -- [x] 5.5 Implement private voting, compatible-vote finalization, structured rubric adjudication, schema validation, retries, review-required fallback, and Tradeoff rating/remediation finalization. -- [x] 5.6 Test provider-disabled behavior, media token authorization, webhook verification/retries, transcript consent, artifact privacy, evaluation failures, and exactly-once finalization. - -## 6. Preserve-Lane Wars Interface - -- [x] 6.1 Create the preserve-lane Fleet design receipt and capture the existing Today and Mock surfaces before implementation. -- [x] 6.2 Add lazy-loaded `/wars` hub, mode/rating cards, launch-status handling, recent history, and contextual Today/Mock entry points without changing primary navigation. -- [x] 6.3 Build the accessible Blitz setup and battle workspace with topic/Ranked Mix choice, opponent state, server-synchronized timer, keyboard-operable answers, reconnect state, and mobile layout. -- [x] 6.4 Build Blitz result, mistake review, weakness cards, lesson/drill/review actions, rematch, challenge creation/acceptance, history, leaderboard, and sanitized public result surfaces. -- [x] 6.5 Build Tradeoff scheduling/check-in and the responsive wide workbench with phase strip, prompt/twist disclosure, private text/code/schema/pseudocode/diagram artifacts, autosave/freeze/reveal, and reconnect state. -- [x] 6.6 Compose RealtimeKit participant tiles, device setup, mute, camera, screen-share, connection, consent, and disabled-provider states into the Tradeoff workbench. -- [x] 6.7 Build debate, private voting, adjudication-pending/review-required, final result, transcript/artifact visibility, rematch, report, and sharing surfaces. -- [x] 6.8 Add route/component tests for authentication upgrades, active-match answer secrecy, phase visibility, public sanitization, focus management, reduced motion, and 44px compact controls. -- [x] 6.9 Replace the Blitz topic selector with searchable canonical track, roadmap, and concept discovery, readiness counts, internal Learn links, and disabled states for scopes with fewer than five active questions. -- [x] 6.10 Link question metadata, mistake review, and weakness Learn actions directly to the canonical primary concept route. -- [x] 6.11 Add a Solo Tradeoff setup path with ephemeral OpenAI-compatible endpoint/key/model fields, explicit browser-direct disclosure, and no rating or authentication requirement. -- [x] 6.12 Generate an independent AI artifact, apply the shared twist without learner-draft leakage, reveal/debate against the frozen artifact, and return unranked rubric-oriented feedback. -- [x] 6.13 Compose the Tradeoff artifact surface from the existing controlled Monaco and Excalidraw tools, with combined notes/code/diagram state, compact panel switching, and read-only freeze behavior. -- [x] 6.14 Make guest Play the primary Blitz and Tradeoff action; keep unranked local battles functional without signup and present authentication only as the upgrade for durable competitive features. - -## 7. Integration and Operations - -- [x] 7.1 Add PostHog events and server observability for queue choice, match start/completion/abandonment, immediate rematch, challenge send/accept/complete, mistake review, media failures, adjudication outcomes, and rating sharing without logging answers or private artifacts. -- [x] 7.2 Add documented non-secret configuration to examples and Cloudflare bindings; document required RealtimeKit, R2, Queue, Durable Object, webhook, and secret setup without touching local or production secrets. -- [x] 7.3 Update canonical product, surface, architecture, data-flow, command, environment, deployment, and privacy documentation for Software Wars and its recording/transcription policy. -- [x] 7.4 Add operator runbooks for launch gating, content retirement/report review, rating correction, provider outage, queue backlog, cost containment, and rollback. -- [x] 7.5 Update `PROJECT_STATUS.md`, `PRODUCT.md`, and relevant navigation/product counts only after the implemented feature and checks satisfy the accepted specs. - -## 8. Verification and Design Review - -- [x] 8.1 Run the smallest relevant unit/API/component checks after each implementation group and resolve regressions before widening verification. -- [x] 8.2 Run the content validator, migration verification, full Vitest suite, typecheck, lint, docs validation, and production build. -- [x] 8.3 Run Playwright with two browser contexts and fake media for Blitz challenge/ghost and Tradeoff phase/reconnect/vote flows; record any live-provider checks that remain manual. -- [x] 8.4 Inspect `/wars`, Blitz, results, leaderboards, challenges, and Tradeoff at 390, 768, and 1440 pixels; capture after screenshots and resolve all overflow, input, and accessibility issues. -- [x] 8.5 Run Impeccable critique, polish, and audit; meet repository score floors with zero unresolved P0/P1 findings and complete the design receipt. -- [x] 8.6 Obtain the owner's final `keep` or delegated design decision, validate the design workflow receipt, and run strict OpenSpec validation before archiving. -- [x] 8.7 Prove Solo Tradeoff credentials never enter browser storage, backend requests, telemetry, or rendered error text; test provider failure, retry, phase progression, and compact layouts. -- [x] 8.8 Test combined artifact autosave/freeze behavior and guest-first Wars entry, then inspect the shared Tradeoff workspace at 390, 768, and 1440 pixels. diff --git a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/.openspec.yaml b/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/.openspec.yaml deleted file mode 100644 index b6b2d1f..0000000 --- a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-08-13 diff --git a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/coverage-map.md b/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/coverage-map.md deleted file mode 100644 index 7088a77..0000000 --- a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/coverage-map.md +++ /dev/null @@ -1,20 +0,0 @@ -# Harness Engineering coverage map - -The external Harness Engineering course is used as a coverage checklist. The product keeps its own atomic taxonomy and stable mastery IDs. - -| Required capability | Canonical concept | -| --- | --- | -| Harness architecture and tool loop | `coding-agent-systems` | -| Agent-readable repository structure and search | `repository-intelligence` | -| Repository instruction precedence and locality | `repository-instruction-topology` | -| Reproducible environment bootstrap and readiness checks | `agent-environment-contracts` | -| Multi-session checkpoints and handoffs | `agent-work-state-handoffs` | -| Bounded task decomposition and scope control | `agent-scope-control` | -| Durable execution, retries, and crash recovery | `durable-agent-execution` | -| Runtime traces and feedback | `agent-observability` | -| Least privilege and execution isolation | `agent-permissions-sandboxing` | -| Independent maker-checker verification | `independent-agent-verification` | -| Run budgets, termination, and escalation | `agent-run-lifecycle` | -| Regression suites, ablation, and harness measurement | `harness-evaluation` plus `ai-regression-testing` | - -The seven builds intentionally combine these concepts instead of creating one concept per external lecture. Their sequence is represented by the `harness-engineering` roadmap and its milestone drills. diff --git a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/design.md b/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/design.md deleted file mode 100644 index 68b5e21..0000000 --- a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/design.md +++ /dev/null @@ -1,59 +0,0 @@ -## Context - -The curriculum is canonical JSON consumed through `learning-os.ts`; concepts, roadmaps, drills, reviews, and artifacts are joined by stable string IDs. System-design practice uses a shared `SystemDesignCase` schema and currently composes an eight-case base batch with a twelve-case popular batch. See `proposal.md` for motivation and the capability specs for observable behavior. - -The two referenced repositories are useful coverage inventories, but their content and structure are neither runtime dependencies nor canonical product data. Existing Agent Systems, AI Reliability, Developer Tools, and Infrastructure concepts already cover much of harness engineering. - -## Goals / Non-Goals - -**Goals:** - -- Make harness engineering learnable as a dependency-ordered, project-backed path. -- Fill the classic system-design case gaps without introducing another interview engine. -- Preserve stable IDs and route every new surface through existing concept, drill, artifact, and case contracts. -- Keep new content source-backed and testable. - -**Non-Goals:** - -- Vendor or copy either external repository into the generated library. -- Add a new top-level navigation tab, database schema, authentication requirement, or runtime dependency. -- Publish thin worked guides for all new cases in this batch. -- Add code-answer questions to Blitz where the interface cannot execute or grade code safely. - -## Decisions - -### Reuse existing concepts and add a small harness-specific layer - -The roadmap will reuse repository intelligence, coding-agent systems, durable execution, permissions and sandboxing, observability, and AI regression testing. New concepts will represent only missing contracts such as instruction topology, environment bootstrap, work-state handoff, scope control, maker-checker verification, lifecycle termination, and harness evaluation. - -This avoids a parallel “harness” vocabulary that would split mastery history. The alternative—duplicating every lecture topic as a new concept—was rejected because it would create overlapping Learn pages and ambiguous Blitz remediation. - -### Represent the seven projects as roadmap drills plus synthesis artifacts - -Each stage gets an editorial drill with an executable file- or command-based validation case. Integrated builds use artifacts to express multi-concept deliverables. This uses the current Playground and preserves one Practice surface rather than introducing a harness-only project runner. - -### Add a separate authored classic-case module - -The thirteen cases will live in one new typed module and use a local factory for the invariant schema portions. Case-specific prompts, calculations, evidence, failures, trade-offs, and sources remain explicit data. The aggregate case catalog imports the batch. - -This keeps the mature base and popular batches stable and makes the coverage boundary easy to test. Expanding the existing 1,600-line file was rejected because it raises merge risk and obscures ownership. - -### Keep the new cases practice-only initially - -Every case will be complete for interactive practice but will not expose a public worked-guide URL until a guide independently meets the existing 1,200-word editorial contract. This prevents search-visible placeholder content while still providing the complete Tradeoff practice inventory. - -### Enforce coverage with named inventories - -Tests will assert the Harness roadmap milestones, seven progressive builds, thirteen case IDs, distinct patterns, valid concept/drill references, and total catalog count. Documentation will record which external curricula motivated the audit without using those sources as authority for technical claims. - -## Risks / Trade-offs - -- **Large authored-content diff** → Isolate the new case batch, make concept additions append-only, and run focused integrity checks before the full suite. -- **Factory-generated cases feel templated** → Share only schema scaffolding; require case-specific critical paths, calculations, evidence, failure injections, mistakes, and stronger answers. -- **Harness terms overlap existing concepts** → Maintain an explicit reuse map and add a concept only when no current ID owns the atomic skill. -- **Thirteen new cases widen editorial review load** → Ship them as practice-only, validate authoritative sources and mappings, and reserve public guides for a separate editorial batch. -- **Generated public curriculum drift** → Use the existing generator after canonical data changes and validate derived output rather than hand-editing generated files. - -## Migration Plan - -All changes are additive content and imports. Existing local attempts and mastery records remain keyed by unchanged IDs. Rollback is limited to removing the new roadmap entries, appended content records, new case module import, and regenerated derivatives; no data migration is required. diff --git a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/proposal.md b/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/proposal.md deleted file mode 100644 index 5d2ce12..0000000 --- a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/proposal.md +++ /dev/null @@ -1,30 +0,0 @@ -## Why - -The product already contains many of the ingredients for agent-harness engineering and classic system-design preparation, but they are fragmented across concepts, drills, and cases. Learners need a coherent harness-building progression and explicit practice coverage for the remaining high-value system-design archetypes so Learn, Blitz, Tradeoff, and Practice expose the same knowledge gaps. - -## What Changes - -- Add a searchable Harness Engineering roadmap that reuses existing agent-system concepts and introduces only the missing atomic ideas: harness architecture, repository instruction topology, environment/bootstrap contracts, durable work state, scope control, independent verification, run lifecycle, and harness evaluation. -- Add seven progressive harness projects, from a rules-first workspace through a complete maker-checker automation loop, with concept, drill, review, and synthesis links. -- Add thirteen structured system-design cases covering unique IDs, proximity search, nearby-presence, map routing, distributed queues, metrics ingestion, ad-click aggregation, hotel reservations, email delivery, object storage, gaming leaderboards, digital wallets, and stock exchanges. -- Map every new case to canonical learning concepts and drills, with quantitative anchors, failure injections, weighted rubrics, stronger answers, and authoritative sources. -- Add curriculum integrity checks that enforce the roadmap sequence, external coverage checklist, case inventory, and valid internal links without copying or embedding either external repository. -- Preserve every existing concept, roadmap, drill, case, attempt, and learning-state identifier. - -## Capabilities - -### New Capabilities - -- `classic-system-design-gap-coverage`: Defines the thirteen additional interactive cases and their quality, remediation, compatibility, and discoverability contracts. - -### Modified Capabilities - -- `expanded-learning-domains`: Adds a coherent, project-backed Harness Engineering path inside Agent Systems while preserving stable curriculum identifiers and reusing existing concepts where they already provide the required coverage. - -## Impact - -- Canonical curriculum data in `src/data/` for concepts, drills, review questions, artifacts, roadmaps, and system-design cases. -- Learn and Practice discovery through existing roadmap and case selectors; no new top-level navigation or runtime dependency. -- Curriculum and case integrity tests, plus generated public curriculum artifacts where the existing pipeline derives them. -- Product documentation describing the new coverage boundary and attribution. -- No database migration, API shape change, authentication change, copied third-party content, production dependency, deployment, or release. diff --git a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/specs/classic-system-design-gap-coverage/spec.md b/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/specs/classic-system-design-gap-coverage/spec.md deleted file mode 100644 index 1de463c..0000000 --- a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/specs/classic-system-design-gap-coverage/spec.md +++ /dev/null @@ -1,47 +0,0 @@ -## Purpose - -Completes interactive coverage of the recurring classic system-design archetypes that are not yet represented as first-class, rubric-backed practice cases. - -## ADDED Requirements - -### Requirement: Thirteen missing classic cases are first-class practice -The system SHALL add exactly these stable case IDs: `distributed-unique-id-generator`, `proximity-search-service`, `nearby-friends-service`, `maps-routing-platform`, `distributed-message-queue`, `metrics-monitoring-platform`, `ad-click-aggregation-system`, `hotel-reservation-system`, `distributed-email-service`, `object-storage-service`, `gaming-leaderboard`, `digital-wallet-system`, and `stock-exchange`. - -Each case SHALL satisfy the canonical six-stage system-design schema and remain available in the existing solo and shared Playground practice flow. - -#### Scenario: Catalog accepts the complete gap batch -- **WHEN** system-design catalog integrity runs -- **THEN** all thirteen case IDs are present exactly once and every concept and drill reference resolves - -#### Scenario: Learner selects a new case -- **WHEN** the learner starts any new case -- **THEN** the existing scoping, estimation, high-level design, deep-dive, failure, and review stages apply without a special session implementation - -### Requirement: Each case teaches a reusable systems pattern -Every new case SHALL identify a distinct reusable pattern, critical path, quantitative capacity anchor with units, deterministic follow-up, injected failure, stronger answer, and at least two primary or official sources. - -#### Scenario: Learner reviews a completed case -- **WHEN** the attempt reaches review -- **THEN** the learner sees the protected invariant, key trade-off, capacity reasoning, failure-recovery signals, common mistakes, and linked remediation - -### Requirement: Similar products remain distinct through invariants -The proximity, nearby-friends, and maps cases SHALL test different invariants; the payment and wallet cases SHALL test different ledger boundaries; and the queue, notification, and email cases SHALL test different delivery contracts. - -#### Scenario: Catalog is checked for renamed duplicates -- **WHEN** the new cases are compared with the existing case inventory -- **THEN** each exposes a distinct critical path and reusable pattern rather than a product-name variation of an existing case - -### Requirement: All cases link to canonical learning material -Every new case SHALL reference existing or newly authored canonical concepts and editorial drills so weak rubric dimensions route to a Learn page and an executable Practice activity. - -#### Scenario: Rubric identifies a weak dimension -- **WHEN** a learner scores below mastery on requirements, capacity, architecture, technical judgment, or reliability -- **THEN** each weak dimension resolves to at least one valid concept and one valid drill - -### Requirement: Existing system-design state remains compatible -Adding the cases SHALL NOT change existing case IDs, versions, local attempt keys, rubric semantics, or answer-visibility behavior. - -#### Scenario: Existing attempt resumes after expansion -- **WHEN** a learner reopens a saved attempt for any pre-existing case -- **THEN** it resolves against the same case definition and stage state without migration or data loss - diff --git a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/specs/expanded-learning-domains/spec.md b/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/specs/expanded-learning-domains/spec.md deleted file mode 100644 index 8c6b3a3..0000000 --- a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/specs/expanded-learning-domains/spec.md +++ /dev/null @@ -1,40 +0,0 @@ -## ADDED Requirements - -### Requirement: Harness Engineering is a coherent learning path -The learning OS SHALL expose one selectable Harness Engineering roadmap inside Agent Systems that sequences instruction topology, workspace legibility, environment contracts, durable work state, scope control, feedback, independent verification, lifecycle control, and evaluation. - -The path SHALL reuse existing stable concepts when they already cover an atomic skill and SHALL introduce a new concept only when the required idea is not represented. - -#### Scenario: Learner opens the Harness Engineering path -- **WHEN** the learner selects Harness Engineering from Learn -- **THEN** the complete ordered path appears with every concept linked to its concept page and practice activity - -#### Scenario: Existing agent knowledge is reused -- **WHEN** an existing concept already covers repository intelligence, coding-agent tools, durability, permissions, observability, or regression testing -- **THEN** the roadmap references that stable concept ID instead of creating a duplicate - -### Requirement: Harness learning progresses through seven builds -The Harness Engineering path SHALL include seven progressively integrated projects: rules-first instructions, an agent-readable workspace, multi-session continuity, runtime feedback and scope control, independent self-verification, a complete harness capstone, and an automated maker-checker loop with explicit termination criteria. - -#### Scenario: Learner advances through the path -- **WHEN** the learner completes milestones in order -- **THEN** each build adds a new harness subsystem while preserving the artifacts and contracts established earlier - -#### Scenario: Learner reaches the capstone -- **WHEN** the final milestone is opened -- **THEN** it requires a bounded autonomous loop with a goal, durable state, maker-checker role separation, verification evidence, and a stop condition - -### Requirement: Harness concepts participate in the full learning loop -Every newly introduced Harness Engineering concept SHALL have a mental model, authoritative resources, an editorial drill with an executable validation case, an explain-back review question, and a synthesis-artifact mapping. - -#### Scenario: A harness gap is detected -- **WHEN** a learner misses a linked Blitz question or receives weak Tradeoff rubric evidence -- **THEN** the product can route the learner to the exact concept, drill, and review material - -### Requirement: External curricula are coverage references, not copied product content -The product SHALL record the two referenced repositories as attribution and coverage inputs while keeping canonical learning material authored in the product and supported by authoritative sources. - -#### Scenario: Maintainer audits curriculum provenance -- **WHEN** curriculum documentation is inspected -- **THEN** it identifies the external coverage inputs without treating their prose or repository structure as canonical product data - diff --git a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/tasks.md b/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/tasks.md deleted file mode 100644 index f8e92c9..0000000 --- a/openspec/changes/archive/2026-08-13-expand-harness-and-system-design-curriculum/tasks.md +++ /dev/null @@ -1,27 +0,0 @@ -## 1. Harness curriculum model - -- [x] 1.1 Audit existing agent concepts and record the stable-ID reuse map for the Harness Engineering path -- [x] 1.2 Author missing harness concepts with resources, mental models, and internal relationships -- [x] 1.3 Author executable drills and explain-back review questions for every new harness concept -- [x] 1.4 Add the seven-build roadmap sequence and integrated harness synthesis artifact -- [x] 1.5 Add integrity tests for roadmap order, project coverage, and concept/drill/artifact links - -## 2. Classic system-design cases - -- [x] 2.1 Add the typed classic-gap case module and shared schema factory -- [x] 2.2 Author unique-ID, proximity, nearby-friends, and map-routing cases -- [x] 2.3 Author queue, metrics, ad-click, email, and object-storage cases -- [x] 2.4 Author hotel-reservation, gaming-leaderboard, wallet, and stock-exchange cases -- [x] 2.5 Integrate the batch into the canonical case catalog and selector grouping -- [x] 2.6 Add catalog tests for the thirteen IDs, total count, distinct patterns, sources, calculations, and valid remediation links - -## 3. Documentation and derived curriculum - -- [x] 3.1 Document external coverage inputs, native-content boundaries, and the resulting Harness and classic-case coverage -- [x] 3.2 Regenerate applicable public curriculum artifacts through the existing pipeline - -## 4. Verification - -- [x] 4.1 Run focused curriculum and system-design integrity tests and fix all findings -- [x] 4.2 Run formatting, type checking, and the smallest complete regression suite appropriate to the changed surfaces -- [x] 4.3 Validate the OpenSpec change and record completed tasks diff --git a/openspec/changes/distill-learning-os-interface/.openspec.yaml b/openspec/changes/distill-learning-os-interface/.openspec.yaml deleted file mode 100644 index b6b2d1f..0000000 --- a/openspec/changes/distill-learning-os-interface/.openspec.yaml +++ /dev/null @@ -1,2 +0,0 @@ -schema: spec-driven -created: 2026-08-13 diff --git a/openspec/changes/distill-learning-os-interface/design.md b/openspec/changes/distill-learning-os-interface/design.md deleted file mode 100644 index e51d43e..0000000 --- a/openspec/changes/distill-learning-os-interface/design.md +++ /dev/null @@ -1,84 +0,0 @@ -## Context - -The application already has a canonical navigation data model and a polished dark workbench language, but six primary destinations, thirteen browse links, persistent status strips, and dense entry pages expose the internal product map before a learner has chosen a task. Existing routes and feature implementations are mature and must remain stable. The active Software Wars change also adds new practice capability that should be integrated without becoming another permanent peer destination. - -## Goals / Non-Goals - -**Goals:** - -- Preserve the current visual identity, route contracts, and learning mechanics. -- Make Today, Practice, and Software Wars answer “what should I do next?” before “what exists?”. -- Establish one four-item primary navigation model across React and generated public pages. -- Give active workspaces a quieter shell while retaining account controls and escape routes. -- Reduce initial visible choices and containers without deleting functionality. - -**Non-Goals:** - -- Redesigning every content/detail page or replacing the visual design system. -- Renaming routes, changing authentication, altering curriculum data, or changing ranked/game behavior. -- Removing advanced tools, diagnostics, history, or configuration. -- Adding dependencies, schema changes, or backend endpoints. - -## Decisions - -### Preserve the visual language and overhaul only information hierarchy - -This change uses the design workflow's preserve lane. Typography, color, spacing tokens, control sizes, and the workbench character remain authoritative; simplification comes from fewer simultaneous regions, flatter composition, shorter copy, and progressive disclosure. A full visual-world overhaul was rejected because the owner's feedback explicitly distinguishes polish from complexity. - -### Use four intent-level primary destinations - -The canonical primary set becomes Dashboard, Learn, Practice, and Wars. Dashboard absorbs the useful resumable state from Today and Progress. Practice becomes the Playground rather than a hub in front of it. Browse retains direct access to every route and every complete catalogue surface, grouped by learner intent rather than one undifferentiated inventory. - -### Keep route and catalogue compatibility while changing composition - -`/dashboard` becomes the canonical landing route while `/today` continues resolving to it. `/practice` renders the Playground; `/playground` remains a stable alias. `/progress` and detailed catalogues remain directly addressable. Search and selectors are backed by canonical arrays and parity tests, so simplification cannot silently remove content or make it discoverable only through an exact query. - -### Give each destination one product job - -Dashboard resumes and orients. Learn searches and teaches at a high level. Practice is the workbench and selects a problem in place. Wars presents only one-minute and thirty-minute entry choices. Analytics, exhaustive inventories, diagnostics, and history remain supporting views rather than peer decisions. - -### Track recent destinations locally without changing persistence contracts - -Layout records a bounded, deduplicated list of recent meaningful routes in local storage. Dashboard uses it as navigation memory. This remains guest-safe and does not introduce a backend migration; authenticated mastery, drill, artifact, and profile stores remain authoritative for learning state. - -### Load canonical drills directly into Playground - -The problem selector searches all canonical practice items. Internal editorial drills hydrate the existing problem panel and concept context. External metadata problems remain visible and open their canonical external target. `/practice/all` remains the explicit browse-all inventory and compatibility surface. - -### Derive focused shell state from route semantics - -Layout classifies active routes centrally. Battle instances, Playground, drill workspaces, and active Systems Labs suppress DigestBanner, setup/storage strips, and the floating feedback control. SiteHeader switches to a compact focus variant with product identity, an exit destination, settings, and account access. Setup and result/index pages keep the standard shell. - -```mermaid -flowchart TD - S[Canonical navigation] --> T[Dashboard] - S --> L[Learn] - S --> P[Practice] - S --> W[Wars] - T --> R[Resume learning or practice] - L --> C[Search and complete catalogue] - P --> D[Problem selector] - D --> F[Playground workspace] - W --> B[One-minute MCQs] - W --> M[Thirty-minute match] -``` - -### Prefer native disclosure over new interaction machinery - -Optional regions use semantic `details`/`summary` where possible. This keeps keyboard behavior, no-JavaScript semantics, and implementation cost predictable. Tabs or modals are reserved for existing workflows that already require them. - -## Risks / Trade-offs - -- **Secondary tools may feel less discoverable** → Practice and grouped Browse both expose them, and direct routes remain stable. -- **Path-based focus classification can hide chrome on the wrong state** → Restrict focused mode to explicit active-route patterns and cover them with route tests. -- **Today recommendations may be unavailable for a caught-up learner** → Provide one clear fallback action into Learn, with alternate paths disclosed. -- **Generated navigation may drift** → Continue generating from the canonical data model and extend the existing parity test to enforce four primary items. -- **Broad UI edits can collide with the active Software Wars worktree** → Keep changes additive and localized, preserving all Wars behavior and tests. - -## Migration Plan - -1. Update canonical navigation classification and generator tests without changing destination URLs. -2. Add standard/focus shell variants and verify route classification. -3. Distill Today, Practice, and Software Wars in isolated commits/check groups. -4. Regenerate public navigation outputs, run route/component/E2E checks, then run the full repository gate. -5. Roll back by restoring the prior canonical primary classification and page composition; no data or backend rollback is required. diff --git a/openspec/changes/distill-learning-os-interface/proposal.md b/openspec/changes/distill-learning-os-interface/proposal.md deleted file mode 100644 index f690e80..0000000 --- a/openspec/changes/distill-learning-os-interface/proposal.md +++ /dev/null @@ -1,29 +0,0 @@ -## Why - -The product is polished but cognitively expensive: its shell and key entry pages expose too many destinations, status strips, secondary actions, and internal learning-system concepts at once. The owner wants the existing craft and capability preserved while making the next useful action obvious and moving optional complexity behind progressive disclosure. - -## What Changes - -- Reduce the canonical primary navigation to Dashboard, Learn, Practice, and Wars. -- Turn Dashboard into the learner's resumable state: recent visits, current and next learning, previous and next practice, available paths, and supporting progress. -- Make Learn a searchable high-level library with an explicit, complete catalogue rather than an exhaustive landing page. -- Make Practice the existing Playground itself and add a searchable problem selector backed by the complete canonical drill inventory. -- Simplify Software Wars to two plainly named modes: a one-minute MCQ battle and a thirty-minute matched engineering battle. -- Add a focused-session shell state for active battles and workspaces so global banners and nonessential controls do not compete with the task. -- Preserve every existing route, deep link, catalogue item, public curriculum page, learning capability, accessibility requirement, visual identity, and server contract. - -## Capabilities - -### New Capabilities - -- `focused-learning-interface`: Defines one-primary-action entry pages, progressively disclosed secondary information, consolidated practice modes, and distraction-reduced active sessions. - -### Modified Capabilities - -- `unified-site-navigation`: Reduces the shared primary destination hierarchy and requires secondary tools to remain reachable through contextual hubs and compact browse disclosure. - -## Impact - -- Affects the canonical navigation model, React and generated public headers, global Layout chrome, Dashboard/Today, Learn, Playground/Practice, Progress discovery, Software Wars, and active battle/workspace presentation. -- Requires updates to navigation-generation and route/component/E2E tests plus canonical product and surface documentation. -- Does not change APIs, databases, authentication, mastery rules, ranked scoring, RealtimeKit behavior, or production dependencies. diff --git a/openspec/changes/distill-learning-os-interface/specs/focused-learning-interface/spec.md b/openspec/changes/distill-learning-os-interface/specs/focused-learning-interface/spec.md deleted file mode 100644 index a2d3830..0000000 --- a/openspec/changes/distill-learning-os-interface/specs/focused-learning-interface/spec.md +++ /dev/null @@ -1,73 +0,0 @@ -## Purpose - -Define a calmer interaction hierarchy that preserves the Learning OS's full capability while helping a learner identify and start the next useful action with minimal cognitive load. - -## ADDED Requirements - -### Requirement: Entry surfaces present one primary action -Dashboard, Learn, and Software Wars SHALL each present one visually dominant next action before optional status, history, or alternate-path information. Practice SHALL open directly into the Playground workspace with a problem selected or immediately selectable. - -#### Scenario: Learner opens an entry surface -- **WHEN** a learner opens Dashboard, Learn, Practice, or Software Wars -- **THEN** the first task region identifies one recommended action and no peer action competes at equal emphasis - -### Requirement: Secondary information uses progressive disclosure -Entry surfaces MUST keep diagnostics, alternate modes, historical data, content inventories, and configuration details accessible without rendering all of them in the initial decision path. - -#### Scenario: Learner needs more context -- **WHEN** the learner requests additional options or details -- **THEN** the surface reveals the relevant existing functionality without navigating through an unrelated workflow - -### Requirement: Dashboard exposes resumable learning state -Dashboard SHALL show recent destinations, the active learning path, the current and next learning items, the previous and next practice items, and a concise view of available paths. - -#### Scenario: Learner returns to the product -- **WHEN** a learner opens Dashboard after navigating or practising -- **THEN** they can resume the most relevant learning or practice action and inspect recent destinations without reconstructing context - -### Requirement: Learn is searchable and complete -Learn SHALL provide high-level path orientation, search across every canonical concept and roadmap, and an explicit browse-complete-catalogue path. No canonical item may depend on an exact search term for discovery. - -#### Scenario: Learner searches the catalogue -- **WHEN** a learner enters a concept, roadmap, domain, or description fragment -- **THEN** matching canonical content is shown with stable links and the full catalogue remains reachable independently of search - -### Requirement: Practice is the Playground -The Practice destination SHALL render the existing Playground workspace and expose a searchable problem selector sourced from the complete canonical practice inventory. - -#### Scenario: Learner wants a non-drill practice mode -- **WHEN** the learner opens Practice or selects an internal problem -- **THEN** the problem statement and concept context load into the Playground without an intermediate practice hub - -#### Scenario: Learner needs the complete problem inventory -- **WHEN** the learner opens the problem selector or browse-all practice route -- **THEN** every canonical internal and external practice item remains visible, searchable, and linked without changing its stable identifier - -### Requirement: Wars presents two duration-based modes -Software Wars SHALL present a one-minute MCQ battle and a thirty-minute matched engineering battle as its only initial peer choices. - -#### Scenario: Learner chooses a battle duration -- **WHEN** the learner selects one minute or thirty minutes -- **THEN** they enter the existing Blitz or Tradeoff flow respectively, with ratings and history remaining secondary - -### Requirement: Active sessions reduce unrelated chrome -Active battle and focused workspace routes SHALL suppress nonessential promotional, digest, setup, and feedback chrome while retaining route identity, account access, keyboard navigation, and a clear exit path. - -#### Scenario: Learner enters a focused session -- **WHEN** a learner starts an active battle or opens a focused workspace -- **THEN** the task, state, and next action receive priority over global discovery and status UI - -### Requirement: Simplification preserves capability and accessibility -The distilled interface MUST preserve existing routes, deep links, catalogue coverage, public curriculum outputs, data, learning rules, keyboard operation, 44-pixel compact controls, and reduced-motion behavior. - -#### Scenario: Catalogue parity is checked -- **WHEN** automated tests compare visible/searchable inventory with canonical concepts, roadmaps, and practice items -- **THEN** missing content, missing stable links, or inaccessible browse-all paths fail verification - -#### Scenario: Existing deep link is opened -- **WHEN** a learner opens a previously supported route directly -- **THEN** the same capability remains available even if it is no longer a primary navigation destination - -#### Scenario: Compact keyboard-only use -- **WHEN** a learner uses the interface at 390 pixels wide with a keyboard and reduced motion enabled -- **THEN** primary and disclosed secondary actions remain reachable, legible, and free of horizontal overflow diff --git a/openspec/changes/distill-learning-os-interface/specs/unified-site-navigation/spec.md b/openspec/changes/distill-learning-os-interface/specs/unified-site-navigation/spec.md deleted file mode 100644 index c9b165f..0000000 --- a/openspec/changes/distill-learning-os-interface/specs/unified-site-navigation/spec.md +++ /dev/null @@ -1,26 +0,0 @@ -## MODIFIED Requirements - -### Requirement: Every public and interactive surface uses one navigation model -The homepage, React application, and generated curriculum SHALL expose Dashboard, Learn, Practice, and Wars as the shared primary destinations from one canonical model. Secondary destinations SHALL remain available through one shared browse disclosure or a contextual hub rather than appearing as peer primary destinations. - -#### Scenario: Navigation data changes -- **WHEN** a primary or browse destination is added, removed, renamed, or reclassified -- **THEN** generated pages and React surfaces reflect the change without maintaining separate lists - -#### Scenario: Learner seeks a secondary tool -- **WHEN** Mock, Playground, Build Lab, Systems Labs, Software Wars, sources, library, or detailed catalogs are not shown in primary navigation -- **THEN** the learner can reach them through Dashboard, Learn, Practice, Wars, or the shared browse disclosure - -### Requirement: Navigation adapts consistently -All surfaces SHALL use the same four-destination desktop and compact primary hierarchy, with secondary destinations grouped by learner intent rather than exposed as one flat list. - -#### Scenario: Viewport becomes compact -- **WHEN** the viewport cannot fit the desktop navigation -- **THEN** one accessible disclosure exposes the four primary destinations and grouped secondary destinations without horizontal overflow - -### Requirement: Navigation consistency is verified -Automated checks MUST fail when generated navigation drifts from the canonical model, when React surfaces reintroduce independent destination lists, when more than four destinations are classified as primary, or when legacy `/today`, `/playground`, `/progress`, and catalogue links stop resolving. - -#### Scenario: Generated output is stale -- **WHEN** tests compare generated headers with the canonical model -- **THEN** missing, duplicated, mismatched, or incorrectly classified navigation links are reported diff --git a/openspec/changes/distill-learning-os-interface/tasks.md b/openspec/changes/distill-learning-os-interface/tasks.md deleted file mode 100644 index 283e69a..0000000 --- a/openspec/changes/distill-learning-os-interface/tasks.md +++ /dev/null @@ -1,41 +0,0 @@ -## 1. Canonical Navigation and Shell - -- [x] 1.1 Reduce the canonical primary navigation to Today, Learn, Practice, and Progress; regroup secondary destinations by learner intent and update generated-navigation parity tests. -- [x] 1.2 Add explicit standard and focused shell states with tested route classification, a compact focus header, and suppression of digest/setup/storage/feedback chrome during active sessions. -- [x] 1.3 Regenerate public navigation outputs and verify React, static curriculum, compact menu, and direct route compatibility. - -## 2. Distilled Entry Surfaces - -- [x] 2.1 Refactor Today around the first incomplete recommended action, with the remaining plan and alternate paths progressively disclosed. -- [x] 2.2 Refactor Practice around the recommended drill and a concise disclosed list of Mock, Playground, Build Lab, Systems Labs, and Software Wars modes. -- [x] 2.3 Refactor Software Wars around one direct Blitz action, one secondary Tradeoff action, and disclosed profile, leaderboard, history, and launch details without changing battle behavior. - -## 3. Focused Workspaces - -- [x] 3.1 Apply the focused shell to active Blitz, Tradeoff, Playground, drill, and Systems Lab routes with a clear exit path and preserved account controls. -- [x] 3.2 Verify active-session focus management, keyboard operation, reduced motion, 44-pixel controls, and no compact horizontal overflow. - -## 4. Tests, Documentation, and Review - -- [x] 4.1 Add or update navigation, Layout, Today, Practice, Wars, generated-output, and E2E tests for the distilled hierarchy and deep-link preservation. -- [x] 4.2 Update canonical product, surface, and navigation documentation with the four-intent hierarchy and progressive-disclosure rules. -- [x] 4.3 Run focused checks, full Vitest, typecheck, lint, docs validation, production build, strict OpenSpec validation, and relevant Playwright flows. -- [x] 4.4 Capture 390, 768, and 1440 visual evidence when the in-app Browser runtime is available; run Impeccable critique, polish, audit, and the design workflow check. - -## 5. Four-Surface Product Model - -- [x] 5.1 Make Dashboard, Learn, Practice, and Wars the canonical primary destinations while preserving legacy route and generated-navigation compatibility. -- [x] 5.2 Rebuild Dashboard around recent destinations, current/next learning, previous/next practice, available paths, and supporting progress. -- [x] 5.3 Rebuild Learn as a searchable high-level library with complete concept/roadmap coverage and an explicit browse-all catalogue. - -## 6. Playground Practice and Duration-Based Wars - -- [x] 6.1 Render Playground at Practice and preserve `/playground` as a stable alias with focused-shell behavior. -- [x] 6.2 Add a keyboard-operable problem selector covering the complete canonical practice inventory and hydrate internal problems into the existing workspace. -- [x] 6.3 Present Wars as one-minute MCQs and a thirty-minute matched engineering battle without changing underlying game contracts. - -## 7. Portability, Documentation, and Design Evidence - -- [x] 7.1 Add catalogue parity, stable-route, compact navigation, Dashboard, Learn search, Playground selector, and Wars entry tests. -- [x] 7.2 Update canonical product, surface, navigation, and design documentation for the four-surface model and explicit catalogue-preservation contract. -- [x] 7.3 Run focused and full verification, capture representative multi-surface evidence at 390/768/1440, and complete critique, polish, audit, and owner review. diff --git a/openspec/config.yaml b/openspec/config.yaml deleted file mode 100644 index 392946c..0000000 --- a/openspec/config.yaml +++ /dev/null @@ -1,20 +0,0 @@ -schema: spec-driven - -# Project context (optional) -# This is shown to AI when creating artifacts. -# Add your tech stack, conventions, style guides, domain knowledge, etc. -# Example: -# context: | -# Tech stack: TypeScript, React, Node.js -# We use conventional commits -# Domain: e-commerce platform - -# Per-artifact rules (optional) -# Add custom rules for specific artifacts. -# Example: -# rules: -# proposal: -# - Keep proposals under 500 words -# - Always include a "Non-goals" section -# tasks: -# - Break tasks into chunks of max 2 hours diff --git a/openspec/specs/blitz-wars/spec.md b/openspec/specs/blitz-wars/spec.md deleted file mode 100644 index ea6529b..0000000 --- a/openspec/specs/blitz-wars/spec.md +++ /dev/null @@ -1,99 +0,0 @@ -# blitz-wars Specification - -## Purpose -Defines fast objective engineering battles that produce trustworthy competitive outcomes and immediate evidence about a learner's technical gaps. -## Requirements -### Requirement: Blitz battle creation -The system SHALL let an authenticated user start a 60–120 second Blitz battle containing 5–10 versioned objective questions from the canonical Learning OS curriculum. A user SHALL be able to choose Ranked Mix or an unranked track, roadmap, or concept practice queue; only Ranked Mix SHALL affect global Blitz rating. - -#### Scenario: Start a ranked mixed battle -- **WHEN** an authenticated user selects Ranked Mix and presses Play -- **THEN** the system creates a server-owned match snapshot, selects an eligible opponent and immutable question versions, and returns question stems and shuffled options without answer keys - -#### Scenario: Start a scoped curriculum battle -- **WHEN** an authenticated user selects a canonical track, roadmap, or concept -- **THEN** the server derives eligible active questions from canonical concept relationships, starts an unranked battle restricted to that scope, and labels the result as unranked - -#### Scenario: Selected scope has insufficient active questions -- **WHEN** a visible track, roadmap, or concept queue has fewer than five active questions -- **THEN** the catalogue shows its candidate and active counts and prevents starting a misleading battle while preserving links to the corresponding learning surface - -#### Scenario: Guest starts Blitz -- **WHEN** a visitor without an account presses Play -- **THEN** the system starts a complete unranked browser battle immediately, labels its state as local to that browser, and does not create Elo, FSRS evidence, public history, or a server-owned attempt - -#### Scenario: Guest requests a durable competitive feature -- **WHEN** a guest requests ranked rating, history, result sharing, or a human challenge -- **THEN** the system explains that signup upgrades persistence and competitive identity while keeping unranked Blitz playable without signup - -### Requirement: Server-authoritative attempt timing -The system MUST record match and answer deadlines on the server, reject answers submitted after the applicable deadline, and use server receipt times for scoring. Client countdowns SHALL be presentational only. - -#### Scenario: Answer arrives before deadline -- **WHEN** the server receives a valid answer before the match deadline -- **THEN** it records the selected option, correctness evidence, and elapsed server time exactly once - -#### Scenario: Answer arrives after deadline -- **WHEN** an answer reaches the server after the match deadline -- **THEN** the system rejects it as late and finalizes the unanswered question as incorrect - -#### Scenario: Duplicate answer submission -- **WHEN** the same answer operation is retried with the same idempotency key -- **THEN** the system returns the original recorded answer without changing match state - -### Requirement: Deterministic Blitz scoring -The system SHALL rank opponents lexicographically by total correct answers and then by total server-recorded response time across correct answers. Equal accuracy and equal qualifying response time SHALL produce a draw. - -#### Scenario: Accuracy differs -- **WHEN** one opponent answers more questions correctly -- **THEN** that opponent wins regardless of response time - -#### Scenario: Accuracy ties -- **WHEN** opponents have equal correct counts -- **THEN** the opponent with the lower total response time for correct answers wins - -#### Scenario: AI opponent is scored -- **WHEN** a human plays a precomputed AI opponent -- **THEN** the AI's stored correctness participates in scoring and its response time is excluded from the tie-breaker - -### Requirement: Supported opponent types -The system SHALL support versioned precomputed AI opponents, one-use ranked asynchronous human ghosts, and authenticated challenge-link opponents. A ranked human attempt MUST affect its owner at most once. - -#### Scenario: Match against AI -- **WHEN** no eligible human ghost is available or the player selects an AI opponent -- **THEN** the system selects a compatible versioned AI profile immediately - -#### Scenario: Ranked ghost is paired -- **WHEN** a completed unpaired human attempt matches a later player's rating band and question-set policy -- **THEN** the system pairs it once, finalizes both players' outcome once, and marks that ghost attempt consumed for ranked play - -#### Scenario: Challenge is accepted -- **WHEN** an authenticated recipient opens a valid unused challenge link -- **THEN** the system gives the recipient the same immutable question versions under the same timing policy and finalizes the challenge after completion - -### Requirement: Result, history, rematch, and sharing -The system SHALL show each completed match's outcome, score, rating delta when ranked, opponent identity class, mistake count, and remediation actions. It SHALL support rematch, match history, and a shareable result that does not reveal ranked question content. - -#### Scenario: Match completes -- **WHEN** both sides have completed or the active side's deadline expires -- **THEN** the system finalizes one immutable result and shows Win, Loss, or Draw with supporting scoring evidence - -#### Scenario: User requests rematch -- **WHEN** a player selects Rematch from a completed result -- **THEN** the system creates a new match with a fresh eligible question set and preserves the previous result - -#### Scenario: Public result is shared -- **WHEN** a visitor opens a shareable result link -- **THEN** the system shows sanitized participants, outcome, scores, mode, rating movement, and aggregate topic signals without stems, options, answers, or explanations - -### Requirement: Abandonment and reconnect behavior -The system SHALL allow an in-progress client to recover its server-owned attempt until the deadline and SHALL finalize expired or abandoned ranked attempts consistently. - -#### Scenario: Browser reconnects -- **WHEN** the same authenticated player reloads before the deadline -- **THEN** the system restores recorded answers and the authoritative remaining time - -#### Scenario: Ranked player abandons -- **WHEN** a ranked match expires with unanswered questions -- **THEN** unanswered questions count as incorrect and the result finalizes under the normal scoring policy - diff --git a/openspec/specs/classic-system-design-gap-coverage/spec.md b/openspec/specs/classic-system-design-gap-coverage/spec.md deleted file mode 100644 index f5c50dc..0000000 --- a/openspec/specs/classic-system-design-gap-coverage/spec.md +++ /dev/null @@ -1,46 +0,0 @@ -# classic-system-design-gap-coverage Specification - -## Purpose -Completes interactive coverage of the recurring classic system-design archetypes that are not yet represented as first-class, rubric-backed practice cases. -## Requirements -### Requirement: Thirteen missing classic cases are first-class practice -The system SHALL add exactly these stable case IDs: `distributed-unique-id-generator`, `proximity-search-service`, `nearby-friends-service`, `maps-routing-platform`, `distributed-message-queue`, `metrics-monitoring-platform`, `ad-click-aggregation-system`, `hotel-reservation-system`, `distributed-email-service`, `object-storage-service`, `gaming-leaderboard`, `digital-wallet-system`, and `stock-exchange`. - -Each case SHALL satisfy the canonical six-stage system-design schema and remain available in the existing solo and shared Playground practice flow. - -#### Scenario: Catalog accepts the complete gap batch -- **WHEN** system-design catalog integrity runs -- **THEN** all thirteen case IDs are present exactly once and every concept and drill reference resolves - -#### Scenario: Learner selects a new case -- **WHEN** the learner starts any new case -- **THEN** the existing scoping, estimation, high-level design, deep-dive, failure, and review stages apply without a special session implementation - -### Requirement: Each case teaches a reusable systems pattern -Every new case SHALL identify a distinct reusable pattern, critical path, quantitative capacity anchor with units, deterministic follow-up, injected failure, stronger answer, and at least two primary or official sources. - -#### Scenario: Learner reviews a completed case -- **WHEN** the attempt reaches review -- **THEN** the learner sees the protected invariant, key trade-off, capacity reasoning, failure-recovery signals, common mistakes, and linked remediation - -### Requirement: Similar products remain distinct through invariants -The proximity, nearby-friends, and maps cases SHALL test different invariants; the payment and wallet cases SHALL test different ledger boundaries; and the queue, notification, and email cases SHALL test different delivery contracts. - -#### Scenario: Catalog is checked for renamed duplicates -- **WHEN** the new cases are compared with the existing case inventory -- **THEN** each exposes a distinct critical path and reusable pattern rather than a product-name variation of an existing case - -### Requirement: All cases link to canonical learning material -Every new case SHALL reference existing or newly authored canonical concepts and editorial drills so weak rubric dimensions route to a Learn page and an executable Practice activity. - -#### Scenario: Rubric identifies a weak dimension -- **WHEN** a learner scores below mastery on requirements, capacity, architecture, technical judgment, or reliability -- **THEN** each weak dimension resolves to at least one valid concept and one valid drill - -### Requirement: Existing system-design state remains compatible -Adding the cases SHALL NOT change existing case IDs, versions, local attempt keys, rubric semantics, or answer-visibility behavior. - -#### Scenario: Existing attempt resumes after expansion -- **WHEN** a learner reopens a saved attempt for any pre-existing case -- **THEN** it resolves against the same case definition and stage state without migration or data loss - diff --git a/openspec/specs/competitive-ratings/spec.md b/openspec/specs/competitive-ratings/spec.md deleted file mode 100644 index 43a7e13..0000000 --- a/openspec/specs/competitive-ratings/spec.md +++ /dev/null @@ -1,67 +0,0 @@ -# competitive-ratings Specification - -## Purpose -Defines trustworthy, server-owned competitive ratings and leaderboards that remain comparable across Software Wars modes and opponent types. -## Requirements -### Requirement: Separate mode ratings -The system SHALL maintain independent Blitz and Tradeoff ratings for each authenticated user. A result in one mode MUST NOT modify the other mode's rating. - -#### Scenario: Blitz result finalizes -- **WHEN** a ranked Blitz result is finalized -- **THEN** only the participating users' Blitz rating state and Blitz rating events are eligible to change - -#### Scenario: Tradeoff result finalizes -- **WHEN** a ranked Tradeoff result is finalized -- **THEN** only the participating users' Tradeoff rating state and Tradeoff rating events are eligible to change - -### Requirement: Provisional and established ratings -The system SHALL start each mode at rating 1500, label the first 10 finalized ranked matches as provisional, use a K-factor of 48 while provisional, and use a K-factor of 24 after establishment. - -#### Scenario: New player completes first ranked match -- **WHEN** a user with no prior ranked result completes a match -- **THEN** the system computes the change from rating 1500 with provisional K-factor 48 and shows the rating as provisional - -#### Scenario: Player completes eleventh ranked match -- **WHEN** a user with 10 prior finalized ranked matches completes another -- **THEN** the system computes the change with established K-factor 24 and removes the provisional label - -### Requirement: Server-owned idempotent rating updates -Competitive rating input and calculation MUST remain server-owned. Finalizing the same match more than once MUST NOT create additional rating changes. - -#### Scenario: Client supplies a rating value -- **WHEN** a client attempts to submit its own rating or rating delta -- **THEN** the server ignores or rejects that value and derives the update from immutable match evidence - -#### Scenario: Finalization is retried -- **WHEN** match finalization runs again after rating events already exist -- **THEN** the system returns the existing result and rating events without changing either rating - -### Requirement: Human and fixed AI rating behavior -Human-versus-human ranked outcomes SHALL update both participants once. Human-versus-AI outcomes SHALL update only the human against the AI profile's fixed versioned rating. - -#### Scenario: Human ghost match finalizes -- **WHEN** two human attempts are paired and scored -- **THEN** the system produces reciprocal rating events from the rating snapshots stored with that pairing - -#### Scenario: AI match finalizes -- **WHEN** a human completes a ranked match against an AI profile -- **THEN** the human receives one rating event and the AI profile's published rating remains unchanged - -### Requirement: Mode-specific leaderboards -The system SHALL expose separate Blitz and Tradeoff leaderboards with established players ranked ahead of provisional players at equal rating. Public entries SHALL contain only display-safe profile data. - -#### Scenario: User views Blitz leaderboard -- **WHEN** a visitor opens the Blitz leaderboard -- **THEN** the system returns Blitz rating, provisional status, ranked match count, win/draw/loss record, and display-safe identity without Tradeoff rating - -#### Scenario: User opts out of public ranking -- **WHEN** an authenticated user disables public leaderboard visibility -- **THEN** the public leaderboard omits that user's identity while their private rating continues to function - -### Requirement: Auditable rating history -Each competitive rating change SHALL be preserved as an immutable event containing mode, match, before value, after value, score, opponent class, opponent rating snapshot, algorithm version, and creation time. - -#### Scenario: Rating history is inspected -- **WHEN** an authenticated user opens a completed ranked match -- **THEN** the system can explain the displayed delta from the associated immutable rating event - diff --git a/openspec/specs/expanded-learning-domains/spec.md b/openspec/specs/expanded-learning-domains/spec.md deleted file mode 100644 index 3502f06..0000000 --- a/openspec/specs/expanded-learning-domains/spec.md +++ /dev/null @@ -1,130 +0,0 @@ -# Expanded Learning Domains - -## Purpose - -Define the durable curriculum contract for broad systems, AI-native, -developer-tooling, application-engineering, and multimodal learning coverage. -## Requirements -### Requirement: Requested taxonomy is completely mapped -The learning OS SHALL maintain a machine-readable coverage map for all eleven -requested categories and every named subtopic within them. - -#### Scenario: Coverage map integrity -- **WHEN** curriculum integrity tests run -- **THEN** every requested subtopic has at least one existing concept ID and no mapping points to a missing concept - -### Requirement: Missing domains are first-class tracks -The learning OS SHALL expose Systems Foundations, Infrastructure & Platforms, -Distributed Systems, Inference & Serving, Agent Systems, AI Reliability, -Developer Tools & Code Intelligence, Application Engineering, and Multimodal -& Spatial Computing as known tracks. - -#### Scenario: User browses the full catalog -- **WHEN** the user opens the learning catalog -- **THEN** each added domain is available as a recognized track with at least three concepts - -### Requirement: Existing learning state remains compatible -The expansion MUST preserve every existing track ID and concept ID. - -#### Scenario: Existing learner returns after expansion -- **WHEN** previously stored mastery or FSRS state references an existing concept ID -- **THEN** the concept still resolves and its stored state remains usable - -### Requirement: Concepts are practice-backed -Every newly introduced concept SHALL have a description, mental model, -canonical resource, editorial drill with an executable validation case, and -review question. - -#### Scenario: New concept enters the practice loop -- **WHEN** a user opens any concept introduced by this change -- **THEN** the concept offers a linked practice drill and an explain-back review prompt - -### Requirement: Each added track has a sequenced path -Every added track SHALL have a selectable roadmap that places all of its -concepts into ordered milestones and ends in a synthesis artifact. - -#### Scenario: User selects a new domain -- **WHEN** the user chooses an added roadmap -- **THEN** the app can recommend its next concept and display a measurable capstone - -### Requirement: AI model training is a coherent path -The existing AI Systems track SHALL offer a selectable roadmap covering model -foundations, pre-training, fine-tuning, post-training, reinforcement learning, -quantization, open-weight models, and evaluation. - -#### Scenario: User studies model development end to end -- **WHEN** the user selects the AI Models & Training roadmap -- **THEN** concepts are ordered from architecture and optimization through training, alignment, compression, and evaluation - -### Requirement: Existing concepts are reused without duplication -When an existing concept already satisfies a requested subtopic, the learning -OS SHALL reuse its stable ID and MAY change its primary track while retaining -its former domain as a secondary tag. - -#### Scenario: Consensus moves into Distributed Systems -- **WHEN** the expanded taxonomy is loaded -- **THEN** the existing `consensus` concept appears under Distributed Systems without creating a duplicate consensus card - -### Requirement: Curriculum navigation stays scannable -The learning page SHALL group the added roadmaps into clear systems, AI-native, -software-building, and human-interface families without hiding existing -roadmaps. - -#### Scenario: User scans all learning paths -- **WHEN** the learning or explore page renders roadmap groups -- **THEN** all existing and added roadmaps appear exactly once in an intentional group - -### Requirement: Canonical documentation reflects the expansion -Product and learning documentation MUST describe the expanded tracks, paths, -and coverage boundaries without claiming specialist depth that the curriculum -does not provide. - -#### Scenario: Maintainer checks documented scope -- **WHEN** the maintainer reads the product overview and learning index -- **THEN** the documented track inventory and roadmap organization match executable curriculum data - -### Requirement: Cross-domain AI systems synthesis is explicit -The learning OS SHALL expose a compact roadmap that reuses stable concepts from Systems Foundations, AI Models & Training, and Inference & Serving to show how a model operation becomes production work. - -#### Scenario: User scans AI-native paths -- **WHEN** the user browses the AI-native roadmap group -- **THEN** the Trace a Tensor synthesis roadmap appears exactly once and is distinguishable from the longer specialist paths - -### Requirement: Harness Engineering is a coherent learning path -The learning OS SHALL expose one selectable Harness Engineering roadmap inside Agent Systems that sequences instruction topology, workspace legibility, environment contracts, durable work state, scope control, feedback, independent verification, lifecycle control, and evaluation. - -The path SHALL reuse existing stable concepts when they already cover an atomic skill and SHALL introduce a new concept only when the required idea is not represented. - -#### Scenario: Learner opens the Harness Engineering path -- **WHEN** the learner selects Harness Engineering from Learn -- **THEN** the complete ordered path appears with every concept linked to its concept page and practice activity - -#### Scenario: Existing agent knowledge is reused -- **WHEN** an existing concept already covers repository intelligence, coding-agent tools, durability, permissions, observability, or regression testing -- **THEN** the roadmap references that stable concept ID instead of creating a duplicate - -### Requirement: Harness learning progresses through seven builds -The Harness Engineering path SHALL include seven progressively integrated projects: rules-first instructions, an agent-readable workspace, multi-session continuity, runtime feedback and scope control, independent self-verification, a complete harness capstone, and an automated maker-checker loop with explicit termination criteria. - -#### Scenario: Learner advances through the path -- **WHEN** the learner completes milestones in order -- **THEN** each build adds a new harness subsystem while preserving the artifacts and contracts established earlier - -#### Scenario: Learner reaches the capstone -- **WHEN** the final milestone is opened -- **THEN** it requires a bounded autonomous loop with a goal, durable state, maker-checker role separation, verification evidence, and a stop condition - -### Requirement: Harness concepts participate in the full learning loop -Every newly introduced Harness Engineering concept SHALL have a mental model, authoritative resources, an editorial drill with an executable validation case, an explain-back review question, and a synthesis-artifact mapping. - -#### Scenario: A harness gap is detected -- **WHEN** a learner misses a linked Blitz question or receives weak Tradeoff rubric evidence -- **THEN** the product can route the learner to the exact concept, drill, and review material - -### Requirement: External curricula are coverage references, not copied product content -The product SHALL record the two referenced repositories as attribution and coverage inputs while keeping canonical learning material authored in the product and supported by authoritative sources. - -#### Scenario: Maintainer audits curriculum provenance -- **WHEN** curriculum documentation is inspected -- **THEN** it identifies the external coverage inputs without treating their prose or repository structure as canonical product data - diff --git a/openspec/specs/public-curriculum-discovery/spec.md b/openspec/specs/public-curriculum-discovery/spec.md deleted file mode 100644 index 478553c..0000000 --- a/openspec/specs/public-curriculum-discovery/spec.md +++ /dev/null @@ -1,81 +0,0 @@ -# Public Curriculum Discovery Specification - -## Purpose - -Define the deterministic public projection of the canonical curriculum so -people, search crawlers, and AI agents can discover and understand the complete -learning catalog without executing the application. - -## Requirements - -### Requirement: Canonical curriculum has a deterministic public projection -The system SHALL generate public curriculum surfaces from the canonical track, -concept, roadmap, drill, review, artifact, and coverage data without requiring -manual duplication. - -#### Scenario: Curriculum generation is repeated -- **WHEN** the publication generator runs twice against unchanged inputs -- **THEN** it produces the same set and content of public files without duplicate entries - -### Requirement: Curriculum is readable without JavaScript -The system SHALL publish a curriculum hub and public detail pages for every -known track, roadmap, and concept under a route namespace that does not collide -with interactive SPA routes. - -#### Scenario: Visitor opens a public concept -- **WHEN** a browser or crawler requests a generated concept URL -- **THEN** it receives substantive semantic HTML containing the concept explanation, mental model, learning source, practice direction, review prompt, and navigation - -#### Scenario: Visitor browses the hierarchy -- **WHEN** a visitor starts at the curriculum hub -- **THEN** they can navigate through tracks and roadmaps to every published concept without signing in - -### Requirement: Public pages meet metadata and structure standards -Every generated HTML page MUST include a concise title, meta description, -canonical URL, Open Graph metadata, exactly one H1, useful section headings, -and valid schema.org JSON-LD appropriate to its content type. - -#### Scenario: Search crawler reads a generated page -- **WHEN** it parses the HTML without executing JavaScript -- **THEN** the page provides complete metadata, structured data, meaningful text, and internal links - -### Requirement: Search discovery covers every public curriculum page -The sitemap SHALL include the curriculum hub and every generated track, -roadmap, and concept URL exactly once. - -#### Scenario: Curriculum grows -- **WHEN** canonical data gains or loses a track, roadmap, or concept and generation runs -- **THEN** sitemap curriculum entries match the current generated page set - -### Requirement: AI agents receive a useful curriculum catalog -The public agent catalog SHALL advertise compact Markdown and JSON curriculum -inventories and describe the product's complete public learning scope. - -#### Scenario: Agent starts from llms.txt or api/ai -- **WHEN** it follows the advertised curriculum surface -- **THEN** it can enumerate tracks, roadmaps, concepts, and their public URLs without loading the SPA - -### Requirement: Homepage explains the learning value -The public homepage SHALL present accurate curriculum counts, learning -domains, and the Concept → Drill → Build → Review → Apply loop in visible, -human-readable content. - -#### Scenario: First-time visitor evaluates the product -- **WHEN** they open the homepage without signing in -- **THEN** they can understand what is taught, how practice works, and where to browse the public curriculum - -### Requirement: Publication integrity is verified -Automated checks MUST fail when a canonical track, roadmap, or concept lacks -its generated page, when a public URL is missing from the sitemap, or when -agent catalog counts drift from canonical data. - -#### Scenario: Generated output becomes stale -- **WHEN** integrity tests compare it with canonical curriculum data -- **THEN** the test reports the missing or mismatched IDs and blocks release - -### Requirement: Tensor lifecycle roadmap is publicly discoverable -The deterministic public curriculum SHALL generate a roadmap page and catalog entry for the Trace a Tensor path from canonical curriculum data. - -#### Scenario: Visitor opens the public synthesis path -- **WHEN** a browser or crawler requests the Trace a Tensor public roadmap URL -- **THEN** it receives the ordered milestones, linked concepts, practice directions, and capstone without requiring JavaScript or authentication diff --git a/openspec/specs/simulation-mastery-loop/spec.md b/openspec/specs/simulation-mastery-loop/spec.md deleted file mode 100644 index 6c80f4f..0000000 --- a/openspec/specs/simulation-mastery-loop/spec.md +++ /dev/null @@ -1,91 +0,0 @@ -# simulation-mastery-loop Specification - -## Purpose -TBD - created by archiving change add-systems-simulation-labs. Update Purpose after archive. -## Requirements -### Requirement: Prediction precedes observation -Every scored simulation attempt SHALL capture the learner's prediction before -revealing the final state or decisive evidence. Changing a prediction after -revelation MUST start a new attempt. - -#### Scenario: Learner starts a scored run -- **WHEN** the learner selects a scenario and submits a prediction -- **THEN** the prediction is frozen for that attempt and execution controls become available - -#### Scenario: Learner explores without scoring -- **WHEN** the learner chooses free exploration -- **THEN** all simulation controls remain available but the run cannot grant concept mastery - -### Requirement: Outcome comparison uses evidence -After execution, the system SHALL compare the frozen prediction with the -deterministic outcome and require the learner to identify decisive synthetic -evidence. A correct final-state guess without correct evidence MUST NOT count -as a fully successful attempt. - -#### Scenario: Correct prediction cites wrong authority -- **WHEN** the learner predicts a failed migration but selects application health instead of hook operation evidence -- **THEN** the system marks the evidence checkpoint incorrect and explains which actor owns the decisive state - -#### Scenario: Prediction and evidence are correct -- **WHEN** the prediction matches the outcome and the learner identifies the decisive evidence -- **THEN** the attempt becomes eligible for explain-back and mastery evaluation - -### Requirement: Explain-back closes the lab -An eligible attempt SHALL end with a concise explain-back prompt grounded in -the chosen scenario, prediction, outcome, and evidence. The prompt SHALL ask -for causality rather than a restatement of labels. - -#### Scenario: Learner completes an explain-back -- **WHEN** the learner explains why the observed state followed from the inputs and evidence -- **THEN** the explanation is associated with the lab attempt and mapped concept IDs - -#### Scenario: Learner skips explain-back -- **WHEN** the learner skips or abandons the explanation -- **THEN** the run remains available in exploration history but grants no mastery credit - -### Requirement: Mastery requires demonstrated evidence -The system MUST NOT update FSRS mastery merely because a learner opened, -completed, or clicked through a lab. It SHALL derive mastery credit only from -the objective prediction/evidence result and an accepted explain-back path. - -#### Scenario: Unscored exploration completes -- **WHEN** a learner reaches the final state in free exploration -- **THEN** no concept mastery update occurs - -#### Scenario: Objective checkpoints fail -- **WHEN** the prediction or decisive-evidence checkpoint is incorrect -- **THEN** linked concepts receive no positive mastery rating and the learner can retry with the misconception exposed - -#### Scenario: Authenticated Feynman grade succeeds -- **WHEN** an authenticated learner passes the objective checkpoints and the existing Feynman grading path accepts the explanation -- **THEN** the resulting ratings update the existing linked-concept FSRS records without introducing a second mastery store - -#### Scenario: Guest completes the objective path -- **WHEN** a guest passes the prediction and decisive-evidence checkpoints but cannot use authenticated Feynman grading -- **THEN** the system preserves the completed attempt locally, clearly labels mastery as pending explain-back grading, and does not fabricate a positive rating - -### Requirement: Existing learning state conventions -Simulation attempts SHALL follow existing per-account guest storage and guest -adoption conventions. The first release SHALL NOT add a database table, API -action, or alternate spaced-repetition implementation. - -#### Scenario: Guest changes account context -- **WHEN** simulation attempt state is loaded for a different local account namespace -- **THEN** attempts from the previous account are not exposed - -#### Scenario: Signed-in mastery is updated -- **WHEN** an accepted simulation explain-back produces concept ratings -- **THEN** the existing concept mastery API and scheduler remain the sole durable mastery authority - -### Requirement: Retry preserves learning evidence -The learner SHALL be able to retry a failed attempt with the same controls, -while the UI distinguishes the new prediction from previously revealed -evidence. - -#### Scenario: Learner retries a misconception -- **WHEN** a learner starts a retry after reviewing the decisive evidence -- **THEN** the new attempt requires a fresh prediction and does not present the previous answer as an editable prediction - -#### Scenario: Learner later succeeds -- **WHEN** a later scored attempt passes prediction, evidence, and explain-back evaluation -- **THEN** only the successful attempt becomes eligible for positive mastery credit diff --git a/openspec/specs/systems-simulation-labs/spec.md b/openspec/specs/systems-simulation-labs/spec.md deleted file mode 100644 index a374214..0000000 --- a/openspec/specs/systems-simulation-labs/spec.md +++ /dev/null @@ -1,198 +0,0 @@ -# systems-simulation-labs Specification - -## Purpose -TBD - created by archiving change add-systems-simulation-labs. Update Purpose after archive. -## Requirements -### Requirement: Systems Lab catalog and routes -The system SHALL expose a Systems Lab catalog at `/labs` and a stable runner -route at `/labs/:labId`. Each catalog entry SHALL identify its concepts, -learning objective, estimated interaction time, and available scenarios. - -#### Scenario: Learner opens the catalog -- **WHEN** a learner visits `/labs` -- **THEN** the system lists every published lab and links each entry to its stable runner route - -#### Scenario: Learner follows a contextual entry point -- **WHEN** a learner opens a lab from a related Learn, concept, or Build surface -- **THEN** the same canonical `/labs/:labId` runner opens without renaming an existing route or primary navigation item - -### Requirement: Disconnected deterministic simulation -Every lab SHALL run entirely in the browser from checked-in typed definitions -and a pure deterministic transition engine. A simulation MUST NOT call cloud, -Kubernetes, Git, telemetry, database, local-machine, or production APIs. - -#### Scenario: Identical inputs are replayed -- **WHEN** the same lab version, controls, and actions are supplied twice -- **THEN** the engine produces byte-equivalent ordered states and evidence - -#### Scenario: Learner is offline -- **WHEN** the application shell and lab assets are available but the network is unavailable -- **THEN** running, stepping, resetting, and inspecting a lab continue to work - -### Requirement: Explicit truth planes -The lab runner SHALL display the independent state planes necessary to explain -the system, including resource state, controller or process action, operation -state, user-visible status, and emitted evidence where applicable. It MUST NOT -collapse a misleading healthy badge and a failed operation into one status. - -#### Scenario: Migration hook fails -- **WHEN** a migration Job exhausts its configured retries -- **THEN** the runner can simultaneously show the previous workloads available, application health healthy, sync incomplete, and the hook operation failed - -#### Scenario: Evidence is inspected -- **WHEN** the learner selects a state or timeline event -- **THEN** the runner shows the exact synthetic evidence that supports that state and identifies which simulated actor owns it - -### Requirement: Learner-controlled execution -The runner SHALL support reset, single-step, run-to-next-decision, and complete -execution. Controls that alter behavior SHALL be chosen before execution and -their values SHALL remain visible while evidence is inspected. - -#### Scenario: Learner steps through asynchronous reconciliation -- **WHEN** the learner advances a delayed secret-reconciliation scenario one step at a time -- **THEN** each ESO observation, Secret write, Argo wait, and downstream unblock appears in causal order - -#### Scenario: Learner resets a run -- **WHEN** the learner resets the lab -- **THEN** all runtime state returns to the initial definition while the selected lab and scenario remain available - -### Requirement: GitOps and migration lab -The first release SHALL include a lab modeling Git revision selection, Argo CD -comparison and sync, ExternalSecret application, asynchronous ESO -reconciliation, migration hook execution, Kubernetes Job retries, and workload -rollout. - -#### Scenario: Healthy ordered deployment -- **WHEN** the Secret reconciles and the migration succeeds -- **THEN** the lab orders ExternalSecret wave -2, migration wave -1, and workloads wave 0 and produces evidence for the resulting database revision - -#### Scenario: Secret reconciliation is delayed -- **WHEN** ESO has not produced the target Secret -- **THEN** the lab shows Argo waiting on health without claiming that Argo fetched the secret itself - -#### Scenario: Migration fails after partial work -- **WHEN** the migration performs one schema action and then exits non-zero until backoff is exhausted -- **THEN** the lab preserves the partial database evidence, blocks the new workload revision, and exposes the failed hook independently from application health - -#### Scenario: Revision contains only a hook change -- **WHEN** a Git revision changes only a hook resource excluded from ordinary resource tracking -- **THEN** the lab demonstrates why a normal diff can remain Synced and distinguishes the additional non-hook change needed to trigger the teaching scenario - -### Requirement: Trace propagation and sampling lab -The first release SHALL include a lab modeling header extraction, remote parent -creation, ParentBased branch selection, root or ratio sampler evaluation, -recording state, and Collector export. - -#### Scenario: Remote unsampled parent uses default ParentBased behavior -- **WHEN** the propagator extracts a valid remote unsampled parent and the default ParentBased policy is selected -- **THEN** the lab drops the server span before consulting the root ratio and emits no Collector trace - -#### Scenario: Edge policy overrides remote flags -- **WHEN** the same remote unsampled parent reaches a policy that routes both remote-parent branches through the configured ratio -- **THEN** the ratio determines the result and a sampled span produces Collector evidence - -#### Scenario: Learner changes only the root ratio -- **WHEN** the learner raises the root ratio while retaining the default remote-unsampled ParentBased branch -- **THEN** the outcome remains dropped and the lab explains that the ratio was not consulted - -### Requirement: Metrics discovery and ingestion lab -The first release SHALL include a lab modeling endpoint exposure, workload -labels, PodMonitoring selection, target discovery, scraping, storage, and query -results as independent gates. - -#### Scenario: Endpoint and selector succeed -- **WHEN** the process exposes metrics, the selector matches, and the scrape succeeds -- **THEN** the lab produces a stored synthetic series and a non-empty query result - -#### Scenario: Selector matches no workloads -- **WHEN** the endpoint returns successfully but PodMonitoring selects zero Pods -- **THEN** the lab shows the valid manifest and working endpoint while discovery, storage, and query evidence fail - -#### Scenario: Discovered endpoint cannot be scraped -- **WHEN** PodMonitoring selects a Pod but its named port or metrics response is invalid -- **THEN** the lab shows a discovered target with a scrape failure and no stored series - -### Requirement: Extensible checked-in lab definitions -Adding a lab SHALL require a checked-in definition, concept mapping, scenarios, -evidence assertions, and tests, without adding a new page component or -modifying the simulation engine. - -#### Scenario: Author adds a fourth lab -- **WHEN** a valid new lab definition is registered -- **THEN** it appears in the catalog and runs through the shared runner without route or engine changes - -#### Scenario: Definition has invalid references -- **WHEN** a lab references an unknown concept, duplicate state, missing target, or unreachable expected outcome -- **THEN** static validation or tests fail before the application build is considered valid - -### Requirement: Accessible responsive lab interaction -The lab catalog and runner SHALL support keyboard operation, visible focus, -semantic status announcements, reduced motion, and layouts at 390, 768, and -1440 pixels without hiding evidence required to complete a lab. - -#### Scenario: Learner uses a compact viewport -- **WHEN** the runner is opened at 390 pixels wide -- **THEN** controls, current state, evidence, and learning checks remain reachable without horizontal page scrolling - -#### Scenario: Learner uses keyboard controls -- **WHEN** the learner navigates and executes a lab without a pointer -- **THEN** every control is operable in logical order and state changes are announced without relying on color alone - -### Requirement: Source-grounded fidelity -Every published lab SHALL declare whether it is modeled, source-verified, or -oracle-verified. A verification claim SHALL identify immutable upstream -revisions, reviewed paths, license, method, and verification date. The system -MUST NOT label a lab oracle-verified without an executable upstream oracle. - -#### Scenario: Upstream rule is reversed locally -- **WHEN** a mutation reverses a source-verified Argo, tracing, or metrics rule -- **THEN** a checked-in source contract fails with the expected and actual observable value - -#### Scenario: Learner inspects fidelity -- **WHEN** the learner opens the catalog or runner -- **THEN** the fidelity level is visible and the runner links to the exact pinned upstream source - -### Requirement: Versioned deterministic replays -The runner SHALL export and import a versioned replay containing only lab -identity, definition version, scenario, ordered simulation actions, and a -final-state fingerprint. Import SHALL fail closed for malformed actions, -unknown labs or scenarios, stale definitions, and fingerprint mismatches. - -#### Scenario: Replay is shared -- **WHEN** the same replay is loaded against the same lab definition -- **THEN** it produces the same final snapshot and evidence without a network request - -#### Scenario: Imported replay completes -- **WHEN** an imported replay reaches a terminal outcome -- **THEN** it remains observation-only and cannot complete an attempt, grade an explanation, or update mastery - -### Requirement: Configuration Build mode -Every initial lab SHALL provide a bounded, editable infrastructure -configuration that starts broken. The learner SHALL receive a visible delivery -brief and causal validation hints, but the validator MUST NOT reveal the -replacement line for a failed check. The same shared workshop SHALL support all -three labs without executing configuration or contacting infrastructure. - -#### Scenario: Learner repairs GitOps setup -- **WHEN** the learner configures a protected production release reference, explicit promotion gate, ESO and migration waves, matching Secret references, and bounded Job behavior -- **THEN** every GitOps setup contract passes and produces configuration evidence - -#### Scenario: Learner repairs tracing setup -- **WHEN** the learner connects W3C propagation, ParentBased sampling, OTLP export, Collector reception, batching, and Google Cloud export -- **THEN** every tracing setup contract passes and produces configuration evidence - -#### Scenario: Learner repairs metrics setup -- **WHEN** the learner joins Pod labels to PodMonitoring, resolves the named port, selects the metrics path and interval, and writes the verification query -- **THEN** every metrics setup contract passes and produces configuration evidence - -#### Scenario: Verified configuration changes -- **WHEN** a learner edits or resets a configuration after it passed -- **THEN** the setup gate is invalidated until every check passes again - -#### Scenario: Learner changes counterfactual -- **WHEN** a configuration passed and the learner starts another scenario from the same lab definition version -- **THEN** the verified files and setup evidence are reused without carrying them into a different definition version - -#### Scenario: Learner requests explanation grading -- **WHEN** the simulation is complete but the configuration challenge has not passed -- **THEN** authenticated explanation grading and mastery application remain unavailable diff --git a/openspec/specs/tensor-lifecycle-synthesis/spec.md b/openspec/specs/tensor-lifecycle-synthesis/spec.md deleted file mode 100644 index cc54965..0000000 --- a/openspec/specs/tensor-lifecycle-synthesis/spec.md +++ /dev/null @@ -1,28 +0,0 @@ -# Tensor Lifecycle Synthesis - -## Purpose - -Connect model computation, machine execution, and inference serving in one bounded path that learners prove with an inspectable systems artifact. - -## Requirements - -### Requirement: Tensor lifecycle is sequenced across system layers -The learning OS SHALL provide a selectable roadmap that orders existing concepts from tensor representation and gradient computation through memory and runtime behavior, kernel and hardware optimization, and request-level inference serving. - -#### Scenario: Learner follows the lifecycle -- **WHEN** a learner opens the Trace a Tensor roadmap -- **THEN** its milestones present the system layers in causal execution order rather than as an unstructured topic collection - -### Requirement: Every milestone remains practice-backed -Each roadmap milestone SHALL link existing executable drills for its concepts and SHALL avoid creating duplicate concept records solely for the synthesis path. - -#### Scenario: Learner practices a layer -- **WHEN** a learner reaches any Trace a Tensor milestone -- **THEN** the milestone offers at least one resolvable drill tied to an existing canonical concept - -### Requirement: Synthesis ends in measurable evidence -The roadmap SHALL end in a capstone that requires a layer map, a reproducible workload or model, measurements before and after one optimization, a bottleneck diagnosis, and an explanation of a trade-off or remaining risk. - -#### Scenario: Learner completes the capstone -- **WHEN** the learner submits evidence for the Trace a Tensor artifact -- **THEN** the evidence demonstrates where time or memory is spent and defends an optimization using measured results diff --git a/openspec/specs/tradeoff-wars/spec.md b/openspec/specs/tradeoff-wars/spec.md deleted file mode 100644 index ad83c86..0000000 --- a/openspec/specs/tradeoff-wars/spec.md +++ /dev/null @@ -1,146 +0,0 @@ -# tradeoff-wars Specification - -## Purpose -Defines scheduled live engineering battles where players create inspectable solutions, respond to changing requirements, debate tradeoffs, and receive a fair result. -## Requirements -### Requirement: Scheduled 30-minute battle -The system SHALL let authenticated users schedule or accept a 30-minute Tradeoff battle using the same versioned prompt, rules, initial requirements, and hidden twist. Documentation and external AI tools SHALL be explicitly allowed. - -#### Scenario: Challenge is scheduled -- **WHEN** two authenticated players accept a Tradeoff challenge and start time -- **THEN** the system creates one match, enrolls both players, and shows the shared start time and allowed-tool policy - -#### Scenario: Player checks in -- **WHEN** a participant opens the room during the check-in window -- **THEN** the system validates match membership, prepares media access, and marks readiness without exposing the problem before the configured reveal time - -#### Scenario: Opponent does not check in -- **WHEN** one participant remains absent through the no-show deadline -- **THEN** the present participant may claim a forfeit or cancel without rating change according to the displayed match policy - -### Requirement: Solo Tradeoff with a user-provided AI opponent -The system SHALL offer an unranked 30-minute Solo Tradeoff session without requiring another account. The learner SHALL provide an OpenAI-compatible endpoint, API key, and model for the current tab. The browser SHALL ask the provider to produce an independent initial artifact before seeing the learner's work, revise that artifact for the same hidden twist, participate in the reveal/debate phase, and return rubric-oriented feedback after the learner's self-assessment. - -#### Scenario: Learner starts a solo session -- **WHEN** a learner provides a complete provider configuration and selects Start solo session -- **THEN** the browser creates an independent AI opponent artifact, opens the same 30-minute phase flow, labels the session unranked, and never creates Tradeoff Elo or public match history - -#### Scenario: Solo twist is revealed -- **WHEN** the learner advances from the initial-solution phase -- **THEN** the same twist is applied to both sides and the AI revises only its own previously generated artifact without receiving the learner's private draft - -#### Scenario: Solo debate begins -- **WHEN** the revision phase freezes -- **THEN** the learner can inspect the AI's frozen artifact and exchange bounded debate messages that reference both revealed artifacts and the evaluation rubric - -#### Scenario: Provider call fails -- **WHEN** the browser cannot reach the selected provider or the provider rejects the credential, model, or request -- **THEN** the session preserves the learner's draft, explains the provider failure without exposing the credential, and permits retry or continuation as a local practice session - -### Requirement: Ephemeral Solo Tradeoff credentials -Solo Tradeoff provider credentials MUST remain in component memory and MUST NOT be written to localStorage, sessionStorage, IndexedDB, D1, R2, analytics, logs, URLs, challenge payloads, or Learning OS backend requests. Provider calls SHALL travel directly from the learner's browser to the endpoint the learner explicitly selected. - -#### Scenario: Solo session refreshes or closes -- **WHEN** the page reloads, closes, or the component unmounts -- **THEN** the provider credential and AI transcript disappear while the learner's non-secret local draft may remain under the existing preview-draft policy - -#### Scenario: Solo telemetry is emitted -- **WHEN** the product records solo start, phase, completion, or provider-failure telemetry -- **THEN** telemetry contains only coarse mode/state metadata and excludes endpoint, model, credential, prompts, artifacts, and AI responses - -### Requirement: Authoritative phase progression -The system MUST coordinate Tradeoff phases from server-owned deadlines: check-in, initial solution, twist, revision, reveal, eight-minute debate, vote, adjudication, and complete. Clients SHALL recover the current phase and deadline after reconnecting. - -#### Scenario: Requirement twist occurs -- **WHEN** the initial-solution deadline is reached -- **THEN** the system reveals the immutable match twist to both players at the same authoritative time and starts the revision phase - -#### Scenario: Client reconnects after phase change -- **WHEN** a participant reconnects after missing one or more phase notifications -- **THEN** the system returns the current phase, deadline, and all events that participant is authorized to see - -#### Scenario: Late mutation is attempted -- **WHEN** a player tries to modify a submission after its phase deadline -- **THEN** the system rejects the mutation and preserves the frozen artifact version - -### Requirement: Private multi-format artifacts -During solving and revision, each player's text, code, schema, pseudocode, and diagram artifacts SHALL remain private from the opponent. The system SHALL freeze immutable artifact snapshots at phase boundaries and reveal both final snapshots only when the reveal phase begins. - -The Tradeoff workbench SHALL reuse the Learning OS Playground's Monaco code editor and Excalidraw diagram editor. At wide widths, code and diagram SHALL be usable together alongside design notes; compact widths SHALL provide an explicit, touch-safe panel switcher without removing any artifact type. The competitive shell, phase controls, autosave adapter, and freeze rules SHALL remain Tradeoff-owned. - -#### Scenario: Player saves work during solving -- **WHEN** a participant saves an artifact before the deadline -- **THEN** the system persists an owner-visible draft and records the latest accepted version - -#### Scenario: Learner builds a combined solution -- **WHEN** a learner writes design notes, edits code or pseudocode in Monaco, and draws architecture in Excalidraw -- **THEN** all three artifacts remain part of one Tradeoff solution and are visible together on a wide workspace - -#### Scenario: Combined workspace freezes -- **WHEN** the Tradeoff enters reveal, debate, voting, or complete -- **THEN** notes and code become read-only, Excalidraw enters view mode, and the frozen composite artifact is used for reveal and evaluation - -#### Scenario: Reveal begins -- **WHEN** both final artifacts are frozen or the revision deadline expires -- **THEN** the system makes both frozen artifacts readable to both participants and prevents further competitive edits - -#### Scenario: Artifact upload is too large or invalid -- **WHEN** submitted content exceeds documented limits or fails format validation -- **THEN** the system rejects it without replacing the last valid draft and explains the limit - -### Requirement: Embedded live media -The battle room SHALL support two-person audio/video, device setup, mute controls, optional screen sharing, connection state, and reconnect behavior through a managed media provider. Game state MUST remain functional when media configuration is unavailable. - -#### Scenario: Media is configured -- **WHEN** an enrolled participant requests room access -- **THEN** the authenticated backend issues participant-scoped media credentials without exposing provider administrative credentials - -#### Scenario: Media is not configured -- **WHEN** the deployment lacks required media configuration -- **THEN** the room shows a clear unavailable state while artifact, phase, and voting flows remain usable for local or text-only testing - -#### Scenario: Player reconnects media -- **WHEN** a participant's network changes or media connection drops -- **THEN** the participant can rejoin the same active match without creating another competitive identity - -### Requirement: Debate transcript and consent -The system SHALL require explicit participant disclosure and consent before generating a debate transcript. Video recording SHALL be disabled by default. A transcript used for adjudication SHALL identify speakers and be retained according to the displayed retention policy. - -#### Scenario: Both players consent -- **WHEN** both participants consent before entering the debate -- **THEN** the system may request post-meeting transcription and attach the resulting transcript to the match adjudication evidence - -#### Scenario: A player declines transcription -- **WHEN** either participant declines -- **THEN** the system does not generate a managed transcript and adjudication uses artifacts, twist response, rubric evidence, and player votes only - -### Requirement: Voting and adjudication -After the debate, each player SHALL privately vote Win, Loss, or Draw from their own perspective. Agreeing votes SHALL determine the result; incompatible votes SHALL trigger structured AI adjudication from the frozen prompt, twist, artifacts, rubric, and available transcript. - -#### Scenario: Votes agree on winner -- **WHEN** one player votes Win and the other votes Loss for the corresponding sides -- **THEN** the system finalizes that winner without AI adjudication - -#### Scenario: Both players vote Draw -- **WHEN** both players vote Draw -- **THEN** the system finalizes a draw without AI adjudication - -#### Scenario: Votes disagree -- **WHEN** votes are incompatible or one party disputes the other's outcome -- **THEN** the system queues one versioned adjudication, shows a pending state, and finalizes exactly once from a valid structured evaluation - -#### Scenario: Adjudication repeatedly fails -- **WHEN** automated evaluation exhausts its retry policy -- **THEN** the match remains unrated and displays a review-required state rather than fabricating a winner - -### Requirement: Tradeoff results and history -The completed result SHALL show outcome, rating movement when ranked, frozen artifacts, twist response, rubric-level reasoning, transcript availability, concept evidence, rematch, and report controls. - -#### Scenario: Completed match is opened later -- **WHEN** a participant opens Tradeoff history -- **THEN** the system restores the immutable result and only the evidence that participant is permitted to access - -#### Scenario: Public result is shared -- **WHEN** a visitor opens a public Tradeoff result -- **THEN** the system shows the outcome and opted-in artifact excerpts while withholding transcripts, private drafts, provider identifiers, and non-consented content - diff --git a/openspec/specs/unified-site-navigation/spec.md b/openspec/specs/unified-site-navigation/spec.md deleted file mode 100644 index f724184..0000000 --- a/openspec/specs/unified-site-navigation/spec.md +++ /dev/null @@ -1,47 +0,0 @@ -# Unified Site Navigation Specification - -## Purpose - -Define one fast, consistent destination hierarchy for the public homepage, -interactive application, and JavaScript-independent curriculum pages. - -## Requirements - -### Requirement: Every public and interactive surface uses one navigation model -The homepage, React application, and generated curriculum SHALL expose the same -primary and browse destination labels and paths from one canonical model. - -#### Scenario: Navigation data changes -- **WHEN** a primary or browse destination is added, removed, or renamed -- **THEN** generated pages and React surfaces reflect the change without maintaining separate lists - -### Requirement: Navigation remains static-first -Generated curriculum navigation MUST be usable as semantic HTML and CSS without -loading React, application bundles, or a backend response. - -#### Scenario: JavaScript is unavailable -- **WHEN** a visitor opens any generated curriculum page without JavaScript -- **THEN** they can navigate to every primary and browse destination - -### Requirement: Navigation adapts consistently -All surfaces SHALL use the same desktop and compact navigation hierarchy. - -#### Scenario: Viewport becomes compact -- **WHEN** the viewport cannot fit the desktop navigation -- **THEN** one accessible disclosure exposes all primary and browse destinations without horizontal overflow - -### Requirement: Account controls do not redefine navigation -Authentication, settings, and account controls MAY vary by user state but MUST -remain visually separate from the shared destination hierarchy. - -#### Scenario: User state changes -- **WHEN** a visitor signs in, signs out, or continues as a guest -- **THEN** destination ordering and labels remain unchanged - -### Requirement: Navigation consistency is verified -Automated checks MUST fail when generated navigation drifts from the canonical -model or when React surfaces reintroduce independent destination lists. - -#### Scenario: Generated output is stale -- **WHEN** tests compare generated headers with the canonical model -- **THEN** missing, duplicated, or mismatched navigation links are reported diff --git a/openspec/specs/wars-content-governance/spec.md b/openspec/specs/wars-content-governance/spec.md deleted file mode 100644 index eeaf0b8..0000000 --- a/openspec/specs/wars-content-governance/spec.md +++ /dev/null @@ -1,98 +0,0 @@ -# wars-content-governance Specification - -## Purpose -Defines the editorial and versioning contract that keeps ranked Software Wars content defensible, source-backed, stable during matches, and safe to retire. -## Requirements -### Requirement: Versioned ranked question contract -Every ranked Blitz question SHALL have an immutable question ID and version, a distinct stable `contentKey`, one defensible correct answer, authored distractors, an authored explanation for the correct option and every distractor, exactly one canonical primary Learning OS concept ID, zero or more canonical supporting concept IDs, difficulty, primary or authoritative sources, and editorial status. Code may appear in a question stem, but Blitz answer choices SHALL remain concise text rather than competing code blocks. - -#### Scenario: Active question is selected -- **WHEN** ranked matchmaking selects a question -- **THEN** it snapshots the exact active version and never substitutes later edits into that match - -#### Scenario: Question lacks required evidence -- **WHEN** a question has no authoritative source, no mapped concept, multiple defensible answers, any option lacks an explanation, or it duplicates or closely paraphrases another active base question -- **THEN** the content validator rejects it from the ranked pool - -#### Scenario: Result explains every option -- **WHEN** a player completes an attempted question -- **THEN** the result can show why the selected option was right or wrong and why every alternative does not satisfy the question - -#### Scenario: Question maps to learning -- **WHEN** a question is validated or returned in an authorized result -- **THEN** its primary concept resolves to `/concepts/:id`, and its track and roadmap memberships are derived from the canonical curriculum rather than copied into Wars metadata - -### Requirement: Server-only answer material -Correct options, explanations, variant parameters that reveal answers, AI precomputed answers, and Tradeoff evaluation rubrics MUST remain outside the browser bundle and SHALL be returned only after authorization and the relevant match phase permits disclosure. - -#### Scenario: Active Blitz match loads -- **WHEN** the client requests its question set -- **THEN** the response contains only display-safe stems, options, metadata, and opaque version identifiers - -#### Scenario: Match completes -- **WHEN** a player's result is finalized -- **THEN** the server may return authored explanations for questions that player attempted without making the full ranked bank public - -### Requirement: Editorial lifecycle -Content SHALL move through draft, reviewed, active, and retired states. Only reviewed active content SHALL appear in ranked matches. Retirement SHALL prevent new selection without rewriting completed match history. - -#### Scenario: Editor activates reviewed content -- **WHEN** a complete item passes deterministic validation and receives reviewed status -- **THEN** it becomes eligible for a separately controlled activation step - -#### Scenario: Active item is retired -- **WHEN** an editor retires an ambiguous or obsolete item -- **THEN** no new ranked match selects it and existing matches retain their immutable snapshot - -### Requirement: Ambiguity reporting and correction -Participants SHALL be able to report a ranked item with a reason. Operators SHALL be able to inspect reports, retire the affected version, and void rating effects for materially invalid results without deleting match evidence. - -#### Scenario: Player reports a question -- **WHEN** a participant submits an ambiguity report from a completed result -- **THEN** the system records the item version, match, reporter, reason, and review state without changing the result immediately - -#### Scenario: Operator voids invalid results -- **WHEN** an operator confirms a material content defect -- **THEN** the system retires the item version and records compensating rating events for affected matches according to the current correction policy - -### Requirement: Parameterized variants -Parameterized variants SHALL use deterministic generators with validated parameter domains and stable generated version identifiers. A generator MUST NOT create a ranked variant whose answer or explanation has not been mechanically verified. Variants SHALL be reported separately and MUST NOT increase the distinct authored-question count. - -#### Scenario: Variant is generated -- **WHEN** matchmaking requests an eligible parameterized item -- **THEN** the server derives the stem, options, correct answer, explanation values, and version identifier from one validated parameter seed - -#### Scenario: Generated variant fails validation -- **WHEN** the generator produces duplicate options, an invalid answer, or an unsupported parameter combination -- **THEN** the server discards it and selects another eligible item - -### Requirement: Precomputed AI opponent snapshots -Each AI opponent SHALL identify provider/model name, model version or snapshot date, benchmark run version, answer per question version, explanation, aggregate accuracy, and fixed published rating. Ranked matches MUST NOT invoke the AI opponent at match time. - -#### Scenario: AI opponent is selected -- **WHEN** matchmaking chooses an AI profile -- **THEN** scoring uses the stored answer for every selected question version and performs no model inference for opponent play - -#### Scenario: Model version changes -- **WHEN** an operator benchmarks a new model version -- **THEN** the system creates a new opponent snapshot without rewriting the old profile or its completed matches - -### Requirement: Launch content threshold -Public ranked Blitz launch SHALL require the configured reviewed active question depth across all canonical concepts eligible for Blitz, explanations for every option, balanced difficulty coverage, and complete precomputed AI answer coverage. Every canonical track, roadmap, and concept SHALL remain discoverable before it reaches ranked depth, with candidate and active counts reported separately. Tradeoff ranked launch SHALL require at least 20 reviewed problems with twists and evaluation rubrics. - -#### Scenario: Pool is below threshold -- **WHEN** a deployment has fewer than the required reviewed active items -- **THEN** it may run development and unranked preview matches but SHALL label ranked public launch as unavailable - -#### Scenario: Pool meets threshold -- **WHEN** deterministic content validation confirms the required counts and coverage -- **THEN** the deployment becomes eligible for operator-controlled ranked activation - -### Requirement: Base-question and variant reporting -Content reports SHALL publish distinct authored-question counts separately from generated playable variants and SHALL include topic, difficulty, concept, explanation, source, and AI coverage. -Content reports SHALL also publish canonical track, roadmap, and concept coverage, including zero-coverage curriculum entries. - -#### Scenario: One base question has multiple parameter seeds -- **WHEN** the validator reports the ranked bank -- **THEN** that family contributes one distinct authored question and its generated versions contribute only to the separate playable-variant count - diff --git a/openspec/specs/wars-learning-loop/spec.md b/openspec/specs/wars-learning-loop/spec.md deleted file mode 100644 index e1e2939..0000000 --- a/openspec/specs/wars-learning-loop/spec.md +++ /dev/null @@ -1,64 +0,0 @@ -# wars-learning-loop Specification - -## Purpose -Defines how competitive evidence becomes targeted learning and spaced repetition without turning recognition or AI answers into fabricated mastery. -## Requirements -### Requirement: Post-Blitz mistake explanation -After a Blitz attempt is final, the system SHALL show the player's answer, the authored correct answer, a source-backed explanation, mapped concepts, and relevant lesson or drill for every incorrect response. It MUST NOT expose this material during the active attempt. - -#### Scenario: Player answers incorrectly -- **WHEN** the completed result contains an incorrect answer -- **THEN** the result shows the authored explanation, authoritative sources, concept link, Learn action, Drill action, and Add to Review state - -#### Scenario: Player follows Learn -- **WHEN** the player selects Learn from a question or weakness card -- **THEN** navigation opens the canonical primary concept at `/concepts/:id` rather than the general Learn catalogue - -#### Scenario: Active match requests explanation -- **WHEN** a client requests answer material before its attempt is final -- **THEN** the server refuses disclosure - -### Requirement: Conservative FSRS remediation -An authenticated Blitz mistake SHALL schedule mapped concepts with `again` or `hard` evidence according to the documented mistake policy. A correct MCQ response MUST NOT produce `good` or `easy` concept mastery by itself. Tradeoff artifact evaluation MAY produce `again`, `hard`, or `good` ratings when the rubric contains inspectable concept evidence, but SHALL never produce `easy` automatically. - -#### Scenario: Incorrect Blitz answer is finalized -- **WHEN** a mapped question is incorrect -- **THEN** the system writes at most one idempotent remediation event per concept and match and updates the existing authenticated FSRS scheduler conservatively - -#### Scenario: Correct Blitz answer is finalized -- **WHEN** a mapped question is correct -- **THEN** the system records diagnostic success without granting positive FSRS mastery - -#### Scenario: Tradeoff artifact demonstrates concept evidence -- **WHEN** a finalized structured rubric explicitly identifies strong inspectable evidence for a mapped concept -- **THEN** the system may apply at most a `good` FSRS rating and records the evidence source with the remediation event - -### Requirement: Weakness summary and recommendations -Every completed battle SHALL aggregate relevant concept evidence into weakness cards containing accuracy or rubric evidence, current mastery context, and direct Learn, Drill, and Review actions. - -#### Scenario: Multiple questions map to one weak concept -- **WHEN** a player misses multiple items mapped to the same concept -- **THEN** the result presents one aggregated weakness card with match accuracy and one set of learning actions - -#### Scenario: No weakness is detected -- **WHEN** no negative concept evidence exists -- **THEN** the result presents verified strengths and a rematch action without fabricating a weakness - -### Requirement: Variant resurfacing and improvement evidence -The system SHALL prioritize unseen or materially different variants of previously failed concepts in later eligible battles until diagnostic performance improves. It SHALL preserve a before-and-after evidence trail. - -#### Scenario: Failed concept reappears -- **WHEN** a player starts a later eligible match while a failed concept remains weak -- **THEN** selection weighting may include an unseen validated variant without repeating the exact exposed item inside its cooldown window - -#### Scenario: Performance improves -- **WHEN** the player later answers a valid variant correctly or demonstrates the concept in a Tradeoff artifact -- **THEN** the system records improvement evidence and shows the comparison without automatically declaring mastery from a single MCQ - -### Requirement: Socratic boundary remains intact -Authored post-match explanations and structured adjudication feedback SHALL be separate from the Socratic companion. The active Companion and chat prompt MUST continue to probe reasoning without supplying direct solutions. - -#### Scenario: Player asks Companion for an active answer -- **WHEN** the Socratic companion receives a request for an answer during a battle -- **THEN** it continues to ask probing questions and does not reveal ranked answers, rubrics, twists, or reference solutions - From b4d204b94b96a345cd2ecbd66343a9e2263ff42c Mon Sep 17 00:00:00 2001 From: Sarthak Agrawal Date: Fri, 21 Aug 2026 18:02:34 +0530 Subject: [PATCH 3/4] feat: add Fanout learning surfaces --- .fleet/design-review.json | 76 +-- ...26-08-19T20-34-28Z__src-pages-today-tsx.md | 54 ++ .size-limit.json | 2 +- PRODUCT.md | 10 + PROJECT_STATUS.md | 1 + .../daily-learning-capacity-planning-1440.png | Bin 0 -> 122333 bytes .../daily-learning-capacity-planning-390.png | Bin 0 -> 99354 bytes .../daily-learning-capacity-planning-768.png | Bin 0 -> 108298 bytes .../design/daily-learning-dashboard-1440.png | Bin 0 -> 234804 bytes 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artifacts/design/fanout-inference-1440.png | Bin 0 -> 108196 bytes artifacts/design/fanout-inference-390.png | Bin 0 -> 52197 bytes artifacts/design/fanout-inference-768.png | Bin 0 -> 95893 bytes artifacts/design/fanout-learn-1440.png | Bin 0 -> 90564 bytes artifacts/design/fanout-learn-390.png | Bin 0 -> 45839 bytes artifacts/design/fanout-learn-768.png | Bin 0 -> 79598 bytes .../design/fanout-model-routing-1440.png | Bin 0 -> 80843 bytes artifacts/design/fanout-model-routing-390.png | Bin 0 -> 42317 bytes artifacts/design/fanout-model-routing-768.png | Bin 0 -> 58653 bytes artifacts/design/fanout-notation-1440.png | Bin 0 -> 95297 bytes artifacts/design/fanout-notation-390.png | Bin 0 -> 44699 bytes artifacts/design/fanout-notation-768.png | Bin 0 -> 83178 bytes .../evidence-driven-daily-learning.json | 60 ++ .../reviews/four-surface-learning-os.json | 55 ++ docs/architecture/data-flow.md | 26 + docs/architecture/overview.md | 8 + docs/knowledge/learnings.md | 23 + docs/product/overview.md | 17 +- docs/product/surfaces.md | 18 +- docs/product/systems-lab.md | 23 + .../concepts/inference-engines.html | 2 +- .../curriculum/concepts/inference-engines.md | 2 + scripts/s-tier-catalog.mjs | 11 + scripts/source-tier.mjs | 2 +- src/App.tsx | 12 + src/components/DecisionLabShell.tsx | 560 ++++++++++++++++ src/components/FeynmanGate.tsx | 2 +- src/components/FormulaExplainer.tsx | 66 ++ src/components/PaperLearningCard.tsx | 167 +++++ src/data/concept-packs.json | 13 +- src/data/concepts.json | 10 + src/data/formulas.ts | 192 ++++++ src/data/inference-path.ts | 525 +++++++++++++++ src/data/learning-contracts.test.ts | 65 ++ src/data/notation.ts | 378 +++++++++++ src/data/paper-contracts.ts | 198 ++++++ src/hooks/useLearningEvidence.ts | 23 + src/lib/dailyPriority.test.ts | 153 +++++ src/lib/dailyPriority.ts | 376 +++++++++++ src/lib/decisionLabs.test.ts | 140 ++++ src/lib/decisionLabs.ts | 309 +++++++++ src/lib/feynmanRating.test.ts | 8 +- src/lib/feynmanRating.ts | 6 + src/lib/focusedRoute.ts | 5 +- src/lib/learningContinuity.test.ts | 59 ++ src/lib/learningContinuity.ts | 116 ++++ src/lib/learningEvidence.test.ts | 67 ++ src/lib/learningEvidence.ts | 105 +++ src/lib/paperRotation.ts | 40 ++ src/lib/planner.test.ts | 35 + src/lib/planner.ts | 49 +- src/lib/userStore.ts | 2 + src/pages/ConceptDetail.tsx | 91 ++- src/pages/DecisionLab.tsx | 600 ++++++++++++++++++ src/pages/FocusedStudy.test.tsx | 74 +++ src/pages/FocusedStudy.tsx | 409 ++++++++++++ src/pages/InferencePath.tsx | 165 +++++ src/pages/KnowledgeMap.tsx | 99 +++ src/pages/Learn.tsx | 51 +- src/pages/NotationReference.tsx | 112 ++++ src/pages/PaperProgramme.tsx | 78 +++ src/pages/SystemsLabs.tsx | 272 +++++--- src/pages/Today.test.tsx | 98 +++ src/pages/Today.tsx | 163 ++++- src/pages/decision-labs.ui.test.tsx | 188 ++++++ src/pages/partials/ConceptNotes.tsx | 12 +- 89 files changed, 6267 insertions(+), 216 deletions(-) create mode 100644 .impeccable/critique/2026-08-19T20-34-28Z__src-pages-today-tsx.md create mode 100644 artifacts/design/daily-learning-capacity-planning-1440.png create mode 100644 artifacts/design/daily-learning-capacity-planning-390.png create mode 100644 artifacts/design/daily-learning-capacity-planning-768.png create mode 100644 artifacts/design/daily-learning-dashboard-1440.png create mode 100644 artifacts/design/daily-learning-dashboard-390.png create mode 100644 artifacts/design/daily-learning-dashboard-768.png create mode 100644 artifacts/design/daily-learning-evaluation-confidence-1440.png create mode 100644 artifacts/design/daily-learning-evaluation-confidence-390.png create mode 100644 artifacts/design/daily-learning-evaluation-confidence-768.png create mode 100644 artifacts/design/daily-learning-inference-capacity-1440.png create mode 100644 artifacts/design/daily-learning-inference-capacity-390.png create mode 100644 artifacts/design/daily-learning-inference-capacity-768.png create mode 100644 artifacts/design/daily-learning-labs-1440.png create mode 100644 artifacts/design/daily-learning-labs-390.png create mode 100644 artifacts/design/daily-learning-labs-768.png create mode 100644 artifacts/design/fanout-focused-study-1440.png create mode 100644 artifacts/design/fanout-focused-study-390.png create mode 100644 artifacts/design/fanout-focused-study-768.png create mode 100644 artifacts/design/fanout-inference-1440.png create mode 100644 artifacts/design/fanout-inference-390.png create mode 100644 artifacts/design/fanout-inference-768.png create mode 100644 artifacts/design/fanout-learn-1440.png create mode 100644 artifacts/design/fanout-learn-390.png create mode 100644 artifacts/design/fanout-learn-768.png create mode 100644 artifacts/design/fanout-model-routing-1440.png create mode 100644 artifacts/design/fanout-model-routing-390.png create mode 100644 artifacts/design/fanout-model-routing-768.png create mode 100644 artifacts/design/fanout-notation-1440.png create mode 100644 artifacts/design/fanout-notation-390.png create mode 100644 artifacts/design/fanout-notation-768.png create mode 100644 artifacts/design/reviews/evidence-driven-daily-learning.json create mode 100644 artifacts/design/reviews/four-surface-learning-os.json create mode 100644 src/components/DecisionLabShell.tsx create mode 100644 src/components/FormulaExplainer.tsx create mode 100644 src/components/PaperLearningCard.tsx create mode 100644 src/data/formulas.ts create mode 100644 src/data/inference-path.ts create mode 100644 src/data/learning-contracts.test.ts create mode 100644 src/data/notation.ts create mode 100644 src/data/paper-contracts.ts create mode 100644 src/hooks/useLearningEvidence.ts create mode 100644 src/lib/dailyPriority.test.ts create mode 100644 src/lib/dailyPriority.ts create mode 100644 src/lib/decisionLabs.test.ts create mode 100644 src/lib/decisionLabs.ts create mode 100644 src/lib/learningContinuity.test.ts create mode 100644 src/lib/learningContinuity.ts create mode 100644 src/lib/learningEvidence.test.ts create mode 100644 src/lib/learningEvidence.ts create mode 100644 src/lib/paperRotation.ts create mode 100644 src/pages/DecisionLab.tsx create mode 100644 src/pages/FocusedStudy.test.tsx create mode 100644 src/pages/FocusedStudy.tsx create mode 100644 src/pages/InferencePath.tsx create mode 100644 src/pages/KnowledgeMap.tsx create mode 100644 src/pages/NotationReference.tsx create mode 100644 src/pages/PaperProgramme.tsx create mode 100644 src/pages/Today.test.tsx create mode 100644 src/pages/decision-labs.ui.test.tsx diff --git a/.fleet/design-review.json b/.fleet/design-review.json index 60edb99..3436178 100644 --- a/.fleet/design-review.json +++ b/.fleet/design-review.json @@ -2,7 +2,7 @@ "$schema": "fleet.design-review.v1", "version": 1, "project": "swe-interview-prep", - "target": "Four-surface Learning OS: Dashboard, Learn, Practice, and Wars with complete catalogue preservation", + "target": "Complete Fanout-inspired learning surfaces", "mode": "preserve", "register": "product", "context": { @@ -10,85 +10,91 @@ "design": "DESIGN.md" }, "direction": { - "references": [], - "probes": [], + "references": ["https://fanout.sh/", "https://learn-inference.com/"], + "probes": [ + "complete source path", + "focused resumable study", + "searchable notation", + "decision evidence", + "daily-priority integrity" + ], "selected": "existing-design", "approval": "not-required", - "before": "artifacts/design/software-wars/after-wars-1440.png" + "before": "artifacts/design/daily-learning-dashboard-1440.png" }, "evidence": { "screenshots": [ { "width": 390, - "path": "artifacts/design/final-dashboard-390.png" + "path": "artifacts/design/fanout-learn-390.png" }, { - "width": 390, - "path": "artifacts/design/final-learn-390.png" + "width": 768, + "path": "artifacts/design/fanout-learn-768.png" }, { - "width": 390, - "path": "artifacts/design/final-practice-390.png" + "width": 1440, + "path": "artifacts/design/fanout-learn-1440.png" }, { "width": 390, - "path": "artifacts/design/final-wars-390.png" + "path": "artifacts/design/fanout-inference-390.png" }, { "width": 768, - "path": "artifacts/design/final-dashboard-768.png" + "path": "artifacts/design/fanout-inference-768.png" }, { - "width": 768, - "path": "artifacts/design/final-practice-768.png" + "width": 1440, + "path": "artifacts/design/fanout-inference-1440.png" }, { - "width": 1440, - "path": "artifacts/design/final-dashboard-1440.png" + "width": 390, + "path": "artifacts/design/fanout-focused-study-390.png" }, { - "width": 1440, - "path": "artifacts/design/final-learn-1440.png" + "width": 768, + "path": "artifacts/design/fanout-focused-study-768.png" }, { "width": 1440, - "path": "artifacts/design/final-practice-1440.png" + "path": "artifacts/design/fanout-focused-study-1440.png" }, { - "width": 1440, - "path": "artifacts/design/final-practice-selector-1440.png" + "width": 390, + "path": "artifacts/design/fanout-model-routing-390.png" }, { - "width": 1440, - "path": "artifacts/design/final-wars-1440.png" + "width": 768, + "path": "artifacts/design/fanout-model-routing-768.png" }, { - "width": 390, - "path": "artifacts/design/software-wars/final-tradeoff-playground-390.png" + "width": 1440, + "path": "artifacts/design/fanout-model-routing-1440.png" }, { "width": 390, - "path": "artifacts/design/software-wars/final-tradeoff-diagram-390.png" + "path": "artifacts/design/fanout-notation-390.png" }, { "width": 768, - "path": "artifacts/design/software-wars/final-tradeoff-playground-768.png" + "path": "artifacts/design/fanout-notation-768.png" }, { "width": 1440, - "path": "artifacts/design/software-wars/final-tradeoff-playground-1440.png" + "path": "artifacts/design/fanout-notation-1440.png" } ], "projectCheck": { - "command": "vitest run; tsc --noEmit; biome check .; docs validation; generated-curriculum + production Vite build; Playwright smoke/mobile/Wars; strict OpenSpec validation", - "status": "pass" + "command": "pnpm quality", + "status": "passed" }, "critique": { - "score": 36, + "score": 37, "maximum": 40 }, "audit": { - "score": 17, + "score": 16, "maximum": 20 }, "unresolved": { @@ -97,13 +103,11 @@ }, "detector": { "posture": "advisory", - "findings": [ - "The final scoped detector ran exactly once and found one advisory legacy color literal in CodeEditor; it was aligned to the documented rose-danger token before handoff." - ] + "findings": [] } }, "ownerFeedback": { - "decision": "keep", - "note": "Owner reviewed the four-surface direction and responded: looks good, keep going. Dashboard resumes work; Learn is high-level and searchable; Practice is the existing Playground with all 298 canonical problems; Wars presents the one-minute and thirty-minute formats. Complete concept, roadmap, problem, public-curriculum, progress, and legacy routes remain visible and portable." + "decision": "pending", + "note": "" } } diff --git a/.impeccable/critique/2026-08-19T20-34-28Z__src-pages-today-tsx.md b/.impeccable/critique/2026-08-19T20-34-28Z__src-pages-today-tsx.md new file mode 100644 index 0000000..30ac544 --- /dev/null +++ b/.impeccable/critique/2026-08-19T20-34-28Z__src-pages-today-tsx.md @@ -0,0 +1,54 @@ +--- +target: Evidence-driven daily learning and decision labs +total_score: 33 +max_score: 40 +na_heuristics: +p0_count: 0 +p1_count: 0 +timestamp: 2026-08-19T20-34-28Z +slug: src-pages-today-tsx +--- +Method: dual-agent critique (A: design_assessment · B: detector_assessment) + +# Evidence-driven daily learning critique + +The result is product-specific in behavior: a learner predicts, reveals deterministic evidence, records a decision receipt, and must still pass a causal explain-back before mastery changes. The visual language deliberately preserves the existing Learning OS rather than introducing a new brand system. + +## Nielsen heuristic score + +| Heuristic | Score | +| --- | ---: | +| Visibility of system status | 4 | +| Match between system and real world | 4 | +| User control and freedom | 3 | +| Consistency and standards | 4 | +| Error prevention | 3 | +| Recognition rather than recall | 4 | +| Flexibility and efficiency | 2 | +| Aesthetic and minimalist design | 3 | +| Error recovery | 3 | +| Help and documentation | 3 | +| Total | 33/40 | + +## Findings and resolution + +- P1: The evidence ladder initially called a typed but editable prediction frozen. It now completes only after reveal, and focused UI coverage verifies the transition. +- P1: A generic prediction example contradicted two lab prompts. Every decision lab now supplies a task-specific example. +- P1: Ten inference inputs formed an undifferentiated wall. They are now grouped by hardware budget, model shape, workload, and reserve, with the governing formula and assumptions available inline. +- P1: Small instructional text failed contrast guidance, the primary textarea lacked an accessible name, and the lab nested a main landmark. Contrast tokens, labels, focus rings, and landmark structure were corrected. +- P1: Editing a revealed input could silently erase an attempt. Revealed inputs now lock and an explicit new-attempt control retains the configured values while resetting staged evidence. +- Capture issue: the first desktop Labs screenshot fired before its lazy route rendered. A page-specific clean recapture proves the surface is populated at 1440px. + +No P0 or P1 findings remain. The detector ran once and returned no implementation-integrity findings. + +## Persona check + +- Power users get one direct daily action and an explicit restart path, but presets and receipt duplication remain future efficiency opportunities. +- Screen-reader and low-vision users now receive named fields, valid landmarks, stronger contrast, focus rings, required-field guidance, alerts, and live evidence state. +- Distracted mobile users get a single-column flow and explicit attempt boundaries; draft persistence before receipt creation remains a possible future improvement. + +## Questions carried forward + +- Should the Labs index eventually surface the exact lab recommended by today's priority? +- Would reusable presets materially improve repeated capacity experiments without weakening prediction-first behavior? +- Should unfinished local lab drafts survive navigation, or is a receipt the correct first durable boundary? diff --git a/.size-limit.json b/.size-limit.json index 1475274..b32243e 100644 --- a/.size-limit.json +++ b/.size-limit.json @@ -2,7 +2,7 @@ { "name": "JS bundle", "path": "dist/assets/*.js", - "limit": "8.6 MB", + "limit": "8.65 MB", "gzip": true }, { diff --git a/PRODUCT.md b/PRODUCT.md index 8e0d175..3a8510f 100644 --- a/PRODUCT.md +++ b/PRODUCT.md @@ -54,6 +54,16 @@ account-scoped in the browser. public curriculum. - Every ranked Blitz question must have one explicit canonical Learn concept, authoritative sources, and authored reasoning for every answer option. +- Dashboard answers “what should I learn today?” with one deterministic + recovery, retention, or progression priority and an explicit evidence + contract before showing optional history or catalogue context. +- Decision labs, formulas, and paper contracts may produce local evidence, but + only the existing authenticated Feynman-to-FSRS path may grant mastery. +- Learn Inference is represented as a 42-section companion path that links to + the canonical book; the product stores original summaries and retrieval + prompts, not copied source bodies. +- Focused study and decision-lab drafts are account-scoped, version-aware + continuity state. Completing a draft is evidence, not mastery. - Blitz ratings and Tradeoff ratings are separate; ranked state, deadlines, answers, and match outcomes remain server-owned. - Production remains a Vite/React application on Cloudflare Pages with Pages diff --git a/PROJECT_STATUS.md b/PROJECT_STATUS.md index 57a7207..ca689f7 100644 --- a/PROJECT_STATUS.md +++ b/PROJECT_STATUS.md @@ -65,6 +65,7 @@ External: LeetCode API (import), multi-provider LLM APIs | Phase | Milestone | |-------|-----------| +| Fanout-inspired learning surfaces (local, 2026-08-20) | Added the complete 42-section Learn Inference companion path, searchable formula/notation reference, accessible concept topography, eight-source rotating paper programme, focused resumable study, three additional decision labs, scenario presets, and version-aware local drafts. Dashboard remains concept-first: failed practice and due retrieval still outrank progression and evidence-format selection. The local-first interaction expansion moved the gzipped JS ceiling from 8.60 MB to 8.65 MB; the measured build remains below the new cap. Local verification is recorded in GitHub issue #86; deployment remains separate. | | Fleet code-health baseline (2026-08-14) | Added one CI-enforced quality contract covering whole-source coverage, unused code, complexity, exact duplication, import cycles, severe advisories, suppression markers, docs, build, bundle size, and repository hygiene. Removed an orphan declaration, unused simulation helpers and public exports, and unused direct tooling dependencies; safe package updates reduced high advisories from 29 to six accepted transitive advisories. Remaining measured debt is explicit and can only move downward. | | Harness and classic system-design expansion (2026-08-13) | Added a seven-build Harness Engineering roadmap with seven practice-backed atomic concepts and two measured synthesis projects. Expanded the structured Tradeoff catalog from 20 to 33 cases with unique IDs, proximity, nearby presence, routing, queues, metrics, ad clicks, hotel reservations, email, object storage, leaderboards, wallets, and exchange matching. The generated public curriculum now contains 19 tracks, 26 roadmaps, and 259 concept pages; all 473 tests, typecheck, docs validation, source reachability, and the production build pass. | | Software Wars production launch (2026-08-13) | Shipped one-minute Blitz and thirty-minute Tradeoff flows, server-owned ratings and match state, challenge/history/result surfaces, concept/FSRS remediation, and a separate Cloudflare Worker control plane backed by Durable Objects, R2, Queues, and DLQ. The ranked server-only bank has 1,200 independently audited active MCQs across 12 topics, 4,800 option-specific explanations, canonical Learn links, 424 reachable authoritative sources, and 3,600 precomputed answers across three fixed AI opponents. The additive D1 migration, Pages service binding, signed Worker bootstrap, and production RealtimeKit application are live; CI, deploy smoke, Worker health, and the public Wars status endpoint pass. 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