Smart Tree - ST

# Context-Aware .m8 Loading Examples ## Scenario 1: General Browsing (No Context) **You:** "What projects do we have?" **Smart Tree loads:** Minimal - just essences ``` • Smart Tree - AI-optimized directory visualization • 8b.is website - Company portal • MEM8 - Wave-based memory system ``` **Tokens used:** ~50 ## Scenario 2: Talking About Websites **You:** "How's the 8b.is website coming along?" **Smart Tree detects:** "website" keyword **Auto-expands:** 8b.is/.m8 to medium detail ``` 📂 8b.is (88.8Hz) 8b.is website - Company portal for 8-bit inspired AI services Keywords: 8b.is, website, portal 📁 Children: • frontend (92.3Hz) • api (87.5Hz) • docs (45.2Hz) ``` **Tokens used:** ~150 ## Scenario 3: Specific Technical Discussion **You:** "The 8b.is website API needs authentication" **Smart Tree detects:** "8b.is", "website", "API" - HIGH RELEVANCE! **Auto-expands:** Full detail + drills into api/ ``` ╭────────────────────────────────────────────────── │ 📂 8b.is │ 🌊 Frequency: 88.8Hz │ 📝 8b.is website - Company portal for 8-bit inspired AI services │ 🏷️ Keywords: 8b.is, website, portal │ 📁 Children: │ • frontend (92.3Hz) │ • api (87.5Hz) ← AUTO-EXPANDING THIS │ • docs (45.2Hz) │ 🎯 Context Triggers: │ website → frontend/ │ API → api/ ← TRIGGERED! ╰────────────────────────────────────────────────── 📂 8b.is/api/.m8 (87.5Hz) RESTful API with wave-based authentication Endpoints: /auth, /memories, /waves Port: 8420 Auth: JWT with MEM8 signature ``` **Tokens used:** ~400 (but ONLY when needed!) ## Scenario 4: Smart Tree Development **You:** "The tokenizer in Smart Tree needs work" **Smart Tree detects:** "tokenizer" trigger **Auto-loads:** src/tokenizer.rs context ``` 📍 Drilling down: src/tokenizer.rs Tokenization system - 90% compression Patterns: node_modules→0x80, .rs→0x91 Methods: tokenize(), decode(), compression_ratio() ``` ## The Magic: Progressive Loading ### Without Context-Aware Loading: - Load everything: 10,000+ tokens - Most irrelevant to current discussion - Context window fills quickly ### With Context-Aware .m8: - Base load: 50 tokens (just essences) - Relevant expansion: +100-400 tokens - Total: 150-450 tokens (95% reduction!) ## Real Example: **Starting context (50 tokens):** ``` Projects: smart-tree, 8b.is, mem8, marqant ``` **You mention "memory":** ``` +100 tokens: MEM8 expanded with wave frequencies ``` **You mention "binary format":** ``` +150 tokens: .m8 format specs loaded ``` **You mention "tokenization":** ``` +200 tokens: Smart Tree tokenizer details ``` **Total:** 500 tokens vs 10,000 tokens without context awareness! ## Implementation in Smart Tree: ```rust // In scanner.rs when encountering .m8 files if path.ends_with(".m8") { let keywords = extract_conversation_context(); let content = context_reader.load_contextual(&path, &keywords)?; // Only expand if relevance > threshold if relevance > 0.7 { // Drill down automatically expand_children(&path, &keywords)?; } } ``` ## Frequency-Based Relevance: - **High frequency (>150Hz):** Hot zones, recent work - **Medium (50-150Hz):** Active projects - **Low (<50Hz):** Documentation, archives The .m8 frequency helps determine expansion priority!