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Mohamed Boudra
6215e08efe
* feat(app): reveal streaming text as one continuous fade front Streaming assistant text is released by word and every character fades in over the same 150 ms envelope from a start time owned by the message. Start times never decrease in reading order, so a line paints as a single front moving left to right, however the text is chunked. Per-word envelopes with an internal stagger let the next word's first letter overtake the previous word's last letter, which read as holes and flashing words at the real frame rate; 100 ms frame strips hid it. Native renderers receive the fade ranges as a prop on a host view wrapping the root Text, so ranges and text land in the same Fabric mount transaction. The earlier binding sent ranges through a separate async module call keyed by view tag and had to reconcile them against the view's text; on a fast phone the UI thread lags JS by several commits, mounts and calls interleave, and every reconciliation heuristic guessed wrong. Native code no longer infers newness or creates start times. The mock load-test provider now separates cycles with a blank line so a cycle heading is not appended to the previous paragraph. * fix(ci): refresh streaming dependencies and isolate E2E setup * test(app): distinguish fade cleanup from Markdown replacement * test(app): detect restarted fades during Markdown updates * docs(app): keep streaming guidance focused on constraints and QA * test(app): control animation time in Android fade assertions Fixed wall-clock sleeps let emulator scheduling select the sampled animation frame. Inject the animator time source and drive paint at exact timestamps within one UI-thread operation; production keeps the system clock. Verify the mid-fade sample and the exact cleanup boundary without sleeps, polling, or retries. * fix(test): compile the desktop renderer before lifecycle checks The lifecycle fixture treated Metro /status as app readiness. Its first Settings click therefore spent its 90-second UI budget compiling the cold bundle. An isolated cold-cache run restricted to one CPU reproduced the click timeout before the app runtime had loaded. Reuse the browser suite's existing bundle warmup before launching Electron, and preserve Metro output in the lifecycle artifacts. The UI timeout, build-request timeout, lifecycle assertions and product behavior are unchanged. CI did not preserve Metro output, so its precise startup timing is not recoverable from the failed run. The retained log makes subsequent startup failures diagnosable without relying only on an empty renderer text dump. Validation: three complete lifecycle E2E runs under private Xvfb displays; existing Metro readiness tests; app typecheck; targeted lint; root format. * test(file-observer): await burst delivery through native recovery The deletion phase required every event within 10 seconds, but native coalescing recovery uses a 30-second safety audit followed by the existing reconciliation debounce. An idle diagnostic excludes that delayed audit and therefore cannot act as a completion barrier. Await the required delivered events instead, preserving every deletion assertion and the existing overall test watchdog. A real-filesystem test with controlled native notifications reproduces 95 outstanding paths at the old deadline and verifies completion when the safety audit delivers them. No production observer behavior changes. * test(file-observer): verify burst recovery through public events The controlled-clock regression constructed a private native backend, bypassing the interface used by consumers. Consolidate recovery coverage into the public observer integration test instead: drive real writes, deletes and directory renames, then await every required public event, including the 450 surviving files at their renamed paths. No public capability was added: subscribe callbacks already express completion. No production behavior or test timeout changes. * test(app): drive Android fade tests through the native host The instrumentation suite constructed TailFadeInAnimator directly, advanced its private clock, and counted FadeInSpan instances. It now mounts the WordFadeHost that React mounts, feeds it the ranges prop the bridge sends, and asserts rendered pixels: hidden before a start time, a monotone front inside and across words, settled frames equal to the baseline, and no frame callbacks left after settling. Span counts and internal timing state are no longer asserted. Choreographer cannot be advanced deterministically from a test and the fade epoch is the message's wall-clock start time, so the host takes a WordFadeFrameClock (time plus frame callbacks) as its smallest public seam; production keeps the Choreographer-backed default. Fixture text is sized in pixels so every sample stays on one line at any density; at 420 dpi the 32sp fixture wrapped and the column helper summed glyphs from the first line.
Voice Assistant
A voice-controlled terminal assistant that runs as a single local service.
Quick Start
# Install dependencies
npm install
# Copy environment variables
cp .env.example .env
# Edit .env and add your API keys (OpenAI, Deepgram)
# Run development servers
npm run dev
# Open browser to http://localhost:5173
Architecture
- Express Server (port 3000) - Serves API and built UI in production
- Vite Dev Server (port 5173) - Hot-reload React UI in development
- WebSocket (
/ws) - Real-time bidirectional communication - Agent - STT → LLM → TTS pipeline with terminal control
- Daemon - tmux-based terminal management (in-process)
Development
# Run both servers (recommended)
npm run dev
# Or run separately:
npm run dev:server # Express on port 3000
npm run dev:ui # Vite on port 5173
# Type checking
npm run typecheck
# Build for production
npm run build
# Start production server
npm start
Project Status
✅ Completed (Phases 1-2):
- Package setup and configuration
- Express server with WebSocket
- React UI with Vite
- WebSocket client with ping/pong testing
⏳ In Progress (Phase 3):
- Terminal control (tmux integration)
📋 Planned (Phases 4-9):
- LLM integration (OpenAI GPT-4)
- Agent orchestrator
- Speech-to-Text (Deepgram)
- Text-to-Speech (OpenAI)
- Audio streaming
- UI polish
See IMPLEMENTATION_PLAN.md for complete details.
Environment Variables
OPENAI_API_KEY=your-openai-key-here # GPT-4 and TTS
DEEPGRAM_API_KEY=your-deepgram-key-here # Streaming STT
STT_MODEL=whisper-1 # Optional: override to gpt-4o-transcribe, etc.
STT_CONFIDENCE_THRESHOLD=-3.0 # Optional: reject low-confidence clips
STT_DEBUG_AUDIO_DIR=.stt-debug # Optional: persist raw dictation audio for debugging
PASEO_HOME=~/.paseo # Runtime state directory (agents/, etc.)
PASEO_LISTEN=127.0.0.1:6767 # Listen address (host:port or /path/to/socket)
PASEO_HOME defaults to ~/.paseo and isolates runtime artifacts like agents/. PASEO_LISTEN controls the daemon listen address. For blue/green testing you can run a parallel server without touching production state:
PASEO_HOME=~/.paseo-blue PASEO_LISTEN=127.0.0.1:7777 npm run dev
Tech Stack
- Server: Express, TypeScript, ws (WebSocket)
- Client: React 18, Vite, TypeScript
- Terminal: tmux (via child_process)
- AI: OpenAI (LLM + TTS), Deepgram (STT)
Testing
Currently manual testing via:
- Start servers:
npm run dev - Open http://localhost:5173
- Test WebSocket connection (green status indicator)
- Click "Send Ping" button to test communication
More testing guidance as features are implemented.
License
MIT