6-node pipeline: Input -> Thinker -> Output (voice) + UI (screen) in parallel
- Output: text only (markdown, emoji). Never emits HTML or controls.
- UI: dedicated node for labels, buttons, tables. Tracks workspace state.
Replaces entire workspace on each update. Runs parallel with Output.
- Input: strict one-sentence perception. No more hallucinating responses.
- Thinker: controls removed from prompt, focuses on reasoning + tools.
- Frontend: markdown rendered in chat (bold, italic, code blocks, lists).
Label control type added. UI node meter in top bar.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- ThinkerNode: reasons about perception, decides tool use vs direct answer
- Python tool: subprocess execution with 10s timeout
- Auto-detects python code blocks in LLM output and executes them
- Tool call/result visible in trace + HUD
- Thinker meter in frontend (token budget: 4K)
- Flow: Input (perceive) -> Thinker (reason + tools) -> Output (speak)
- Tested: math (42*137=5754), SQLite (create+query), time, greetings
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Per-node context fill bars (input/output/memorizer/sensor)
- Color-coded: green <50%, amber 50-80%, red >80%
- Sensor meter shows tick count + latest deltas
- Token info in trace context events
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- SensorNode: 5s tick loop with delta-only emissions (clock, idle, memo changes)
- Input reframed as perceiver (describes what it heard, not commands)
- Output reframed as voice (acts on perception, never echoes it)
- Per-node token budgets: Input 2K, Output 4K, Memorizer 3K
- fit_context() trims oldest messages to stay within budget
- History sliding window: 40 messages max
- Facts capped at 20, trace file rotates at 500KB
- /api/send + /api/clear endpoints for programmatic testing
- test_cog.py test suite
- Listener context: physical/social/security awareness
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
