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>
- Replace fixed time.sleep() calls with intelligent polling or WebDriverWait
- Add pytest-timeout dependency and --timeout=30 to prevent hangs
- Fixes test flakiness and improves test suite speed
Co-authored-by: Alexander Payne <apayne@MM.local>
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
