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docs: Add DOD/Immediate Mode heuristics and backlog future tracks

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Edward R. Gonzalez
2026-03-02 13:02:59 -05:00
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13
TASKS.md
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@@ -87,4 +87,17 @@ To ensure smooth execution, execute the tracks in the following order:
5. `testing_consolidation_20260302` (Refactors testing methodology; depends on tech debt cleanup)
6. `conductor_workflow_improvements_20260302` (Meta-level updates to skills/workflow docs; can be run anytime)
---
## Future Backlog (Post-Cleanup)
*To be evaluated in a future Tier 1 session after the immediate tech debt queue is cleared.*
### `gui_decoupling_controller`
**Context:** `gui_2.py` is over 3,500 lines and operates as a Monolithic God Object. It violates the "Data-Oriented & Immediate Mode" heuristics by owning complex business logic, orchestrator hooks (`_bg_create_track`), and markdown file building instead of acting as a pure view.
**Goal:** Create a headless `orchestrator_pm.py` or `app_controller.py` that handles the core lifecycle, allowing `gui_2.py` to be a lagless, immediate-mode projection of the state.
### `robust_json_parsing_tech_lead`
**Context:** In `conductor_tech_lead.py`, the `generate_tickets` function relies on a generic `try...except` block to parse the LLM's JSON ticket array. If the model hallucinates or outputs invalid JSON, it silently returns an empty array `[]`, causing the GUI to fail the track creation process without giving the model a chance to self-correct.
**Goal:** Implement a programmatic retry loop that catches `JSONDecodeError` and feeds the error back to the Tier 2 model for self-correction before failing the UI operation.

5
conductor/product-guidelines.md
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@@ -13,6 +13,11 @@
## Code Standards & Architecture
- **Data-Oriented & Immediate Mode Heuristics:** Align with the architectural values of engineers like Casey Muratori and Mike Acton.
- The GUI (`gui_2.py`) must remain a pure visualization of application state. It should not *own* complex business logic or orchestrator hooks (strive to decouple the 'Application' controller from the 'View').
- Treat the UI as an immediate mode frame-by-frame projection of underlying data structures.
- Optimize for zero lag and never block the main render loop with heavy Python JIT work.
- Utilize proper asynchronous batching and queue-based pipelines for background AI work, ensuring a data-oriented flow rather than tangled object-oriented state graphs.
- **Strict State Management:** There must be a rigorous separation between the Main GUI rendering thread and daemon execution threads. The UI should *never* hang during AI communication or script execution. Use lock-protected queues and events for synchronization.
- **Comprehensive Logging:** Aggressively log all actions, API payloads, tool calls, and executed scripts. Maintain timestamped JSON-L and markdown logs to ensure total transparency and debuggability.
- **Dependency Minimalism:** Limit external dependencies where possible. For instance, prefer standard library modules (like `urllib` and `html.parser` for web tools) over heavy third-party packages.