# BuddAI Development Changelog

## Version 3.2 - Self-Learning & Optimization System

### 📊 Phase 1: Data Collection

Implemented comprehensive data logging to track user interactions and code quality.

- **Correction Logging**: Added `save_correction()` to store original vs. corrected code with user reasoning.
- **Compilation Logs**: Added `log_compilation_result()` to track hardware-specific compilation success rates.
- **Feedback System**: Enhanced `record_feedback()` to support comments and trigger failure analysis on negative feedback.
- **Database Updates**: Added tables for `corrections`, `compilation_log`, `feedback` (enhanced), and `code_rules`.

### 🔬 Phase 2: Pattern Extraction

Added intelligence to learn from the collected data.

- **Smart Learner**: Created `SmartLearner` class to diff code and extract patterns (e.g., `analogWrite` -> `ledcWrite`).
- **Hardware Profiles**: Created `HardwareProfile` class to manage hardware-specific syntax (ESP32 vs Arduino).
- **Rule Storage**: Learned patterns are stored in `code_rules` with confidence scores.
- **Prompt Injection**: `build_enhanced_prompt()` now dynamically injects high-confidence rules into the system prompt.

### ✅ Phase 3: Validation

Implemented pre-flight checks to ensure code quality before display.

- **Code Validator**: Created `CodeValidator` class to check for:
  - ESP32 PWM usage (ledcWrite enforcement).
  - Blocking delays in motor code.
  - Missing safety timeouts.
- **Auto-Fix**: The system can now automatically patch critical errors (like incorrect PWM calls) before showing code to the user.
- **Hardware Detection**: Automatically detects target hardware (ESP32, Arduino, Pico) from user prompts.

### 🔄 Phase 4: Feedback Loop

Established continuous improvement cycles.

- **Adaptive Learner**: Created `AdaptiveLearner` to analyze session history for implicit corrections ("actually, use X") and preferences.
- **Session Analysis**: Added `/analyze` command to scan the current session for learned lessons.
- **Explicit Teaching**: Added `/teach <rule>` command for manual rule insertion.

### 📈 Metrics & Fine-Tuning

Added tools to measure and cement progress.

- **Learning Metrics**: Created `LearningMetrics` to calculate accuracy trends and correction rates over 30 days.
- **Fine-Tuning Prep**: Created `ModelFineTuner` to export corrections into JSONL format for training local LLMs (Qwen).

### 🛠 New CLI Commands

- `/learn`: Extract patterns from stored corrections.
- `/analyze`: Analyze current session for implicit feedback.
- `/correct <reason>`: Mark previous response as wrong and save correction.
- `/good`: Mark previous response as correct.
- `/teach <rule>`: Explicitly teach a coding rule.
- `/validate`: Run validation checks on the last response.
- `/rules`: Display currently learned rules.
- `/metrics`: Show accuracy and improvement stats.
- `/train`: Export training data for fine-tuning.