An interactive map of hidden AI dev agent action paths
Visualizes the exact four-step path where AI code assistance becomes action authority.

Hand-written scripts instead of live models make agent failures deterministic and teachable.
Developers learning about AI agents, students
Observable notebooks · AI playgrounds · Interactive tutorials
Visualizes the exact four-step path where AI code assistance becomes action authority.
Turning CLI agent logs into a visual graph with sound effects is a clever way to debug thinking.
Sub-cent CPU-only voice agent with vision-keyed proactivity beats cloud APIs on cost.
Browser automation agent when BrowserUse and MultiOn already exist.
WebGPU-powered Gemma 4 demo brings local inference to a standard agent architecture explainer.
They show a surprisingly large effect: putting models into an interleaved-thinking regime with a stateful IPython REPL yields massive score boosts (>4x on GPT-OSS-120B, double-digit gains up to frontier models). The repo isn't just a paper — it includes pragmatic engineering (a patched vLLM image, ipybox/daytona integration, solver configs) so you can reproduce the results, but expect nontrivial infra setup and API/key requirements.