diff --git a/README.md b/README.md
index 4393c79..585a8a9 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # monoprop
 > because your operators deserve to propagate at escape velocity
 
-[![Documentation](https://github.com/Algorithmiq/monoprop/actions/workflows/docpages.yml/badge.svg)](https://docs.algorithmiq.fi/monoprop)
+[![Documentation](https://github.com/Algorithmiq/monoprop/actions/workflows/docpages.yml/badge.svg)](https://docs.monoprop.algorithmiq.tech/)
 [![Test monoprop](https://github.com/Algorithmiq/monoprop/actions/workflows/test.yml/badge.svg)](https://github.com/Algorithmiq/monoprop/actions/workflows/test.yml)
 [![codecov](https://codecov.io/gh/Algorithmiq/monoprop/graph/badge.svg?token=1GgmPnNUxk)](https://codecov.io/gh/Algorithmiq/monoprop)
 
@@ -18,9 +18,20 @@ the operator across cores and across nodes with MPI.
 
 ## Benchmarks
 
-Check out the comparison of `monoprop` against other open-source Pauli propagation engines in [benches/third_party]!
-![Runtime Benchmark](benches/third_party/pauli_prop/runtime.png)
-![Memory Benchmark](benches/third_party/pauli_prop/memory.png)
+Check out the comparison of `monoprop` against other open-source Pauli propagation engines:
+
+<p align="center">
+  <img src="docs/public/benchmarks/pauli_scaling_runtime.png" alt="PP Runtime Benchmark" width="49%" />
+  <img src="docs/public/benchmarks/pauli_scaling_memory.png" alt="PP Memory Benchmark" width="49%" />
+</p>
+
+and against [`MajoranaPropagation.jl`](https://github.com/SparqleSim/MajoranaPropagation.jl):
+
+<p align="center">
+  <img src="docs/public/benchmarks/majorana_results.png" alt="MP Benchmark" width="50%" />
+</p
+
+Head to our [benchmarks page](https://docs.monoprop.algorithmiq.tech/benchmarks) for more details.
 
 📖 **Full documentation:** <https://docs.algorithmiq.fi/monoprop>
 
@@ -43,7 +54,9 @@ from monoprop import MajoranaPropagator, ExpGate, Circuit, MajoranaOperator
 # Observable m_0 m_1 m_2 m_4, evolved under one Majorana rotation exp(-i θ/2 · M_γ),
 # generated by M_γ = i*m_4 m_5.
 observable = MajoranaOperator({(0, 1, 2, 4): 1.0}, num_modes=8)
-gate = ExpGate(MajoranaOperator({(4, 5): 1j}, num_modes=8))  # Hermitian generator: weight-2 => imaginary coeff
+gate = ExpGate(
+    MajoranaOperator({(4, 5): 1j}, num_modes=8)
+)  # Hermitian generator: weight-2 => imaginary coeff
 circuit = Circuit(gates=[gate], parameters=[0.5])  # one angle value per gate
 
 mp = MajoranaPropagator.from_circuit(circuit, observable, cutoff=16)
@@ -56,7 +69,9 @@ Qubit (Pauli) operators are simulated with `PauliPropagator`. Here we back-propa
 ```python
 from monoprop import PauliPropagator, ExpGate, Circuit, PauliOperator, Pauli
 
-observable = PauliOperator({"ZZ": 1.0}, num_qubits=2)  # num_qubits lives on the observable
+observable = PauliOperator(
+    {"ZZ": 1.0}, num_qubits=2
+)  # num_qubits lives on the observable
 gate = ExpGate(PauliOperator({Pauli("X", 0): 1.0}, num_qubits=2))  # exp(-i θ/2 · X_0)
 circuit = Circuit(gates=[gate], parameters=[0.5])  # one angle value per gate