每天进步一点点,探索人与 AI 共同进化。
Improving a little every day, exploring human-AI co-evolution.
I build auditable workflows for AI agents: task packets, work receipts, handoffs, and human review boundaries.
My current focus is SACP, a lightweight protocol for making long-running AI agent work traceable, reviewable, and improvable.
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SACP
State-Aware Collaboration Protocol for auditable AI agent work. -
Solo AI Company OS
A Markdown-first operating system for human-AI collaboration: decisions, roles, worklogs, handoffs, and review records. -
token-prompt-compiler
Turns messy human intent into bounded, token-efficient task packets for LLM agents. -
Agent Flight Recorder
Turns agent runs into snapshots, evidence trails, improvement cards, and field notes. -
read-research-papers
A Codex skill implementing S. Keshav's three-pass method for progressively deeper paper reading. -
Portfolio
My bilingual personal portfolio for AI systems, learning, and selected work.
- AI agent accountability
- human-in-the-loop workflows
- task packets and work receipts
- prompt and context engineering
- evaluation evidence and dirty-run cases
- AI-assisted learning systems
- solo AI company workflows
I use GitHub as a public lab for turning AI workflow experiments into clearer protocols, tools, demos, and case studies.
The long-term question I care about:
How can humans and AI systems work together in a way that is traceable,
reviewable, improvable, and aligned with human judgment?
- GitHub: @aDragon0707
- Personal site: longju.alantern.com
- X: @Adragon021

