From fcd7252087f5709bd9a5c754dd97d62fd64572f8 Mon Sep 17 00:00:00 2001 From: Ed_ Date: Wed, 6 May 2026 15:50:38 -0400 Subject: [PATCH] docs(conductor): Synchronize docs for track 'Optimization pass for Data-Oriented Python heuristics' --- conductor/product.md | 2 +- conductor/tech-stack.md | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/conductor/product.md b/conductor/product.md index 503dd72..3cb68ef 100644 --- a/conductor/product.md +++ b/conductor/product.md @@ -86,7 +86,7 @@ For deep implementation details when planning or implementing tracks, consult `d - **Session Analysis:** Ability to load and visualize historical session logs with a dedicated tinted "Prior Session" viewing mode. - **Structured Log Taxonomy:** Automated session-based log organization into configurable directories (defaulting to `logs/sessions/`). Includes a dedicated GUI panel for monitoring and manual whitelisting. Features an intelligent heuristic-based pruner that automatically cleans up insignificant logs older than 24 hours while preserving valuable sessions. - **Clean Project Root:** Enforces a "Cruft-Free Root" policy by organizing core implementation into a `src/` directory and redirecting all temporary test data, configurations, and AI-generated artifacts to `tests/artifacts/`. -- **Performance Diagnostics:** Comprehensive, conditional per-component profiling across the entire application. Features a dedicated **Diagnostics Panel** providing real-time telemetry for FPS, Frame Time, CPU usage, and **Detailed Component Timings** for all GUI panels and background threads, including automated threshold-based latency alerts. +- **Performance Diagnostics:** High-precision, microsecond-accurate per-component profiling. Features a dedicated **Diagnostics Panel** providing real-time telemetry for FPS, Frame Time, CPU usage, and **Extended Metrics** (Hit Counts, Peak Latency, Minimum Latency) for all GUI panels and background logic (DAG Engine, Aggregation), utilizing the **"Less Python Does, the Better"** heuristic to minimize monitoring overhead. - **Automated UX Verification:** A robust IPC mechanism via API hooks and a modular simulation suite allows for human-like simulation walkthroughs and automated regression testing of the full GUI lifecycle across multiple specialized scenarios. - **Professional UI Theme & Typography:** Implements a high-fidelity visual system featuring **Inter** and **Maple Mono** fonts for optimal readability. Employs a cohesive "Subtle Rounding" aesthetic across all standard widgets, supported by custom **soft shadow shaders** for modals and popups to provide depth and professional polish. Includes a selectable **NERV UI theme** featuring a "Black Void" palette, zero-rounding geometry, and CRT-style visual effects (scanlines, status flickering). - **Rich Text & Syntax Highlighting:** Provides advanced rendering for messages, logs, and tool outputs using a hybrid Markdown system. Supports GitHub-Flavored Markdown (GFM) via `imgui_markdown` and integrates `ImGuiColorTextEdit` for high-performance syntax highlighting of code blocks (Python, JSON, C++, etc.). Includes automated language detection and clickable URL support. diff --git a/conductor/tech-stack.md b/conductor/tech-stack.md index c403fa9..7e7b7e5 100644 --- a/conductor/tech-stack.md +++ b/conductor/tech-stack.md @@ -70,7 +70,7 @@ - **Taxonomy & Artifacts:** Enforces a clean root by organizing core implementation into a `src/` directory, and redirecting session logs and artifacts to configurable directories (defaulting to `logs/sessions/` and `scripts/generated/`). Temporary test data and test logs are siloed in `tests/artifacts/` and `tests/logs/`. - **ApiHookClient:** A dedicated IPC client for automated GUI interaction and state inspection. - **mma-exec / mma.ps1:** Python-based execution engine and PowerShell wrapper for managing the 4-Tier MMA hierarchy and automated documentation mapping. -- **dag_engine.py:** A native Python utility implementing `TrackDAG` and `ExecutionEngine` for dependency resolution, cycle detection, transitive blocking propagation, and programmable task execution loops. Refined to decouple status management from dependency resolution to support external pool control. +- **dag_engine.py:** A native Python utility implementing `TrackDAG` and `ExecutionEngine` for dependency resolution, cycle detection, transitive blocking propagation, and programmable task execution loops. Optimized using **Kahn's Algorithm** and **iterative DFS** to eliminate recursion overhead and provide $O(V+E)$ performance. - **multi_agent_conductor.py:** Orchestrates the concurrent execution of implementation tracks using a non-blocking `ConductorEngine` and a thread-safe `WorkerPool`. Employs configurable concurrency limits and thread-local context isolation to manage multi-agent state. - **Thread-Local Context Isolation:** Utilizes `threading.local()` for managing per-thread AI client context (e.g., source tier tagging), ensuring thread safety during concurrent multi-agent execution. - **Asynchronous Tool Execution Engine:** Refactored MCP tool dispatch and AI client loops to use `asyncio.gather` and `asyncio.to_thread`, enabling parallel execution of independent tool calls within a single AI turn to reduce latency. @@ -87,3 +87,5 @@ " pattern where cross-module dependencies are resolved by generating signatures/contracts before implementation. +ncies are resolved by generating signatures/contracts before implementation. +