Errors and uncaught exceptions are now automatically captured, deduplicated,
persisted to a rotating log file, and filed as bug report tasks in the
existing task queue — giving Timmy a sovereign, local issue tracker with
zero new dependencies.
- Add RotatingFileHandler writing errors to logs/errors.log (5MB rotate, 5 backups)
- Add error capture module with stack-trace hashing and 5-min dedup window
- Add FastAPI exception middleware + global exception handler
- Instrument all background loops (briefing, thinking, task processor) with capture_error()
- Extend task queue with bug_report task type and auto-approve rule
- Fix auto-approve type matching (was ignoring task_type field entirely)
- Add /bugs dashboard page and /api/bugs JSON endpoints
- Add ERROR_CAPTURED and BUG_REPORT_CREATED event types for real-time feed
- Add BUGS nav link to desktop and mobile navigation
- Add 16 tests covering error capture, deduplication, and bug report routes
Co-authored-by: Alexander Payne <apayne@MM.local>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
- Add MultiModalManager with capability detection for vision/audio/tools
- Define fallback chains: vision (llama3.2:3b -> llava:7b -> moondream)
tools (llama3.1:8b-instruct -> qwen2.5:7b)
- Update CascadeRouter to detect content type and select appropriate models
- Add model pulling with automatic fallback in agent creation
- Update providers.yaml with multi-modal model configurations
- Update OllamaAdapter to use model resolution with vision support
Tests: All 96 infrastructure tests pass
Inspired by OpenClaw-RL's multi-model orchestration, this adds four
features for custom model management:
1. Custom model registry (infrastructure/models/registry.py) — SQLite-backed
registry for GGUF, safetensors, HF checkpoint, and Ollama models with
role-based lookups (general, reward, teacher, judge).
2. Per-agent model assignment — each swarm persona can use a different model
instead of sharing the global default. Resolved via registry assignment >
persona default > global default.
3. Runtime model management API (/api/v1/models) — REST endpoints to register,
list, assign, enable/disable, and remove custom models without restart.
Includes a dashboard page at /models.
4. Reward model scoring (PRM-style) — majority-vote quality evaluation of
agent outputs using a configurable reward model. Scores persist in SQLite
and feed into the swarm learner.
New config settings: custom_weights_dir, reward_model_enabled,
reward_model_name, reward_model_votes.
54 new tests covering registry CRUD, API endpoints, agent assignments,
role lookups, and reward scoring.
https://claude.ai/code/session_01V4iTozMwcE2gjfnCJdCugC
- Add GrokBackend class in src/timmy/backends.py with full sync/async
support, health checks, usage stats, and cost estimation in sats
- Add consult_grok tool to Timmy's toolkit for proactive Grok queries
- Extend cascade router with Grok provider type for failover chain
- Add Grok Mode toggle card to Mission Control dashboard (HTMX live)
- Add "Ask Grok" button on chat input for direct Grok queries
- Add /grok/* routes: status, toggle, chat, stats endpoints
- Integrate Lightning invoice generation for Grok usage monetization
- Add GROK_ENABLED, XAI_API_KEY, GROK_DEFAULT_MODEL, GROK_MAX_SATS_PER_QUERY,
GROK_FREE config settings via pydantic-settings
- Update .env.example and docker-compose.yml with Grok env vars
- Add 21 tests covering backend, tools, and route endpoints (all green)
Local-first ethos preserved: Grok is premium augmentation only,
disabled by default, and Lightning-payable when enabled.
https://claude.ai/code/session_01FygwN8wS8J6WGZ8FPb7XGV
