BuddAI/CHANGELOG.md

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.