compute-stack.md
1 # Sovereign OS Compute Stack 2 3 ## The Hierarchy (Bottom to Top) 4 5 ``` 6 ┌─────────────────────────────────────────────────────────────────┐ 7 │ LAYER 7: MULTI-AGENT ORCHESTRATION │ 8 │ Multiple Claude instances, consensus, parallel exploration │ 9 │ Cost: $$$$$ Latency: High Flexibility: Maximum │ 10 ├─────────────────────────────────────────────────────────────────┤ 11 │ LAYER 6: LARGE MODELS (Cloud) │ 12 │ Claude, GPT-4 - Complex reasoning, novel problems │ 13 │ Cost: $$$$ Latency: 1-10s Flexibility: Very High │ 14 ├─────────────────────────────────────────────────────────────────┤ 15 │ LAYER 5: MEDIUM MODELS (Local GPU) │ 16 │ 7-30B params - Reasoning, code generation, analysis │ 17 │ Cost: $$ Latency: 1-5s Flexibility: High │ 18 ├─────────────────────────────────────────────────────────────────┤ 19 │ LAYER 4: SMALL MODELS (Edge) │ 20 │ 1-7B params - Summarization, Q&A, formatting │ 21 │ Cost: $ Latency: 100ms-1s Flexibility: Medium │ 22 ├─────────────────────────────────────────────────────────────────┤ 23 │ LAYER 3: TINY MODELS (Embedded) │ 24 │ <1B params - Transcription, classification, EEG, embeddings │ 25 │ Cost: ¢ Latency: 10-100ms Flexibility: Low-Medium │ 26 ├─────────────────────────────────────────────────────────────────┤ 27 │ LAYER 2: DETERMINISTIC CODE │ ← INVERSION 28 │ Traditional software - Always same output for same input │ POINT 29 │ Cost: ~0 Latency: <1ms Flexibility: None │ 30 ├─────────────────────────────────────────────────────────────────┤ 31 │ LAYER 1: HARDWARE │ 32 │ CPU, GPU, NPU, FPGA, custom silicon │ 33 │ Cost: Fixed Latency: ns-μs Flexibility: None │ 34 ├─────────────────────────────────────────────────────────────────┤ 35 │ LAYER 0: ENERGY │ 36 │ Electricity, thermal management, physical substrate │ 37 │ Cost: Base Latency: N/A Flexibility: N/A │ 38 └─────────────────────────────────────────────────────────────────┘ 39 ``` 40 41 ## The Inversion Principle 42 43 > "At some point, AI becomes cheaper than writing code." 44 45 ### Traditional View (Code > AI) 46 ``` 47 For task T: 48 if (can_write_code(T)): 49 use_code() # Cheaper, faster 50 else: 51 use_ai() # Fallback for novel tasks 52 ``` 53 54 ### Emerging View (AI > Code) 55 ``` 56 For task T: 57 if (is_hot_path(T) AND is_stable(T)): 58 compile_to_code() # Optimization for known patterns 59 else: 60 use_ai() # Default for everything else 61 ``` 62 63 ### The Crossover Point 64 65 | Task Type | 2020 | 2023 | 2025 | Future | 66 |-----------|------|------|------|--------| 67 | Simple transform | Code | Code | Code | Code (always) | 68 | Text classification | Code | Edge AI | Edge AI | Embedded AI | 69 | Summarization | Code* | Cloud AI | Edge AI | Edge AI | 70 | Code generation | Human | Cloud AI | Cloud AI | Edge AI? | 71 | Novel reasoning | Human | Cloud AI | Cloud AI | Cloud AI | 72 73 *Required complex rules, brittle 74 75 ## Task Routing by Layer 76 77 ### Layer 3: Tiny Models (Embedded) 78 **Characteristics:** 79 - Always on 80 - <10 watts 81 - Millisecond latency 82 - Runs on phone, watch, IoT 83 84 **Tasks:** 85 | Task | Model | Why This Layer | 86 |------|-------|----------------| 87 | Voice transcription | Whisper-tiny | Real-time, always listening | 88 | EEG interpretation | Custom CNN | Low-latency biofeedback | 89 | Wake word detection | TinyML | Battery-efficient | 90 | Gaze classification | MobileNet | Real-time attention | 91 | Text embeddings | all-MiniLM | Local semantic search | 92 | Sentiment/intent | DistilBERT | Quick classification | 93 94 ### Layer 4: Small Models (Edge) 95 **Characteristics:** 96 - On-demand 97 - 10-50 watts 98 - Sub-second latency 99 - Runs on laptop, desktop 100 101 **Tasks:** 102 | Task | Model | Why This Layer | 103 |------|-------|----------------| 104 | Document summary | Qwen-3B | Privacy, speed | 105 | Simple Q&A | Phi-3 | No cloud needed | 106 | Format conversion | Qwen-3B | Deterministic enough | 107 | Draft writing | Mistral-7B | Interactive speed | 108 | Code completion | CodeQwen | IDE integration | 109 110 ### Layer 5: Medium Models (Local GPU) 111 **Characteristics:** 112 - Batch or interactive 113 - 100-300 watts 114 - 1-5 second latency 115 - Needs GPU 116 117 **Tasks:** 118 | Task | Model | Why This Layer | 119 |------|-------|----------------| 120 | Code review | CodeLlama-34B | Needs context | 121 | Research summary | Mixtral | Long context | 122 | Translation | NLLB | Quality matters | 123 | Image analysis | LLaVA | Multimodal | 124 125 ### Layer 6: Large Models (Cloud) 126 **Characteristics:** 127 - On-demand 128 - Pay per token 129 - Seconds latency 130 - Unlimited scale 131 132 **Tasks:** 133 | Task | Model | Why This Layer | 134 |------|-------|----------------| 135 | Complex reasoning | Claude | Novel problems | 136 | Architecture design | Claude | Needs judgment | 137 | Code implementation | Claude | Context + quality | 138 | This conversation | Claude | Meta-cognition | 139 140 ### Layer 7: Multi-Agent 141 **Characteristics:** 142 - Parallel exploration 143 - High cost 144 - Minutes latency 145 - Consensus building 146 147 **Tasks:** 148 | Task | Pattern | Why This Layer | 149 |------|---------|----------------| 150 | System design | Debate | Multiple perspectives | 151 | Bug hunting | Parallel search | Coverage | 152 | Research | Divide & conquer | Breadth | 153 154 ## Energy-Aware Routing 155 156 ```python 157 class EnergyAwareRouter: 158 """ 159 Route tasks considering energy budget. 160 161 On battery: Prefer tiny/small, cache aggressively 162 On power: Can use medium/large freely 163 Solar peak: Batch heavy tasks 164 """ 165 166 def route(self, task, energy_state): 167 if energy_state.on_battery: 168 if energy_state.battery_percent < 20: 169 return TINY_ONLY 170 elif energy_state.battery_percent < 50: 171 return PREFER_SMALL 172 else: 173 return NORMAL 174 175 if energy_state.is_solar_peak: 176 # Great time for batch processing 177 return ALLOW_MEDIUM 178 179 if energy_state.grid_carbon_intensity < 100: 180 # Clean grid, can use cloud 181 return ALLOW_LARGE 182 183 return PREFER_LOCAL 184 ``` 185 186 ## The Dictation Example 187 188 Your current dictation flow demonstrates the stack: 189 190 ``` 191 Voice (Physical) 192 → Microphone (Hardware) 193 → Audio buffer (Code) 194 → Whisper-tiny (Tiny Model) - Transcription 195 → Text stream 196 → Claude (Large Model) - Interpretation 197 → Structured actions 198 ``` 199 200 **Optimization opportunities:** 201 202 1. **Local intent detection** (Tiny) 203 - "Schedule meeting" → direct action, skip Claude 204 - "What's the weather" → edge API call 205 206 2. **Context caching** (Code) 207 - Recent transcripts in memory 208 - Phoenix state pre-loaded 209 210 3. **Batch interpretation** (Medium) 211 - Collect 30s of speech 212 - Summarize locally 213 - Only escalate decisions to Claude 214 215 4. **Async background** (Large) 216 - Non-urgent analysis queued 217 - Processed when convenient/cheap 218 219 ## Implementation Priority 220 221 ### Phase 1: Edge Foundation 222 - [ ] Install Ollama on all devices 223 - [ ] Deploy Whisper-tiny for always-on transcription 224 - [ ] Build local embedding index (Obsidian, Git) 225 - [ ] Simple intent classifier 226 227 ### Phase 2: Smart Routing 228 - [ ] Complexity estimator (done ✓) 229 - [ ] Energy state monitoring 230 - [ ] Cost tracking per layer 231 - [ ] Automatic escalation/de-escalation 232 233 ### Phase 3: Inversion 234 - [ ] Identify hot-path AI tasks 235 - [ ] Compile to deterministic code where stable 236 - [ ] AI generates its own optimizations 237 - [ ] Self-modifying routing rules 238 239 ### Phase 4: Multi-Agent 240 - [ ] Parallel Claude instances for exploration 241 - [ ] Consensus protocols 242 - [ ] Debate framework for decisions 243 - [ ] Distributed inference across devices 244 245 ## Cost Model 246 247 ``` 248 Annual cost at current usage patterns: 249 250 Layer 7 (Multi-Agent): $500/year (10 sessions × $50) 251 Layer 6 (Claude): $200/year (heavy daily use) 252 Layer 5 (Medium Local): $50/year (electricity) 253 Layer 4 (Small Local): $20/year (electricity) 254 Layer 3 (Tiny): $5/year (negligible) 255 Layer 2 (Code): ~$0 (already running) 256 Layer 1 (Hardware): Amortized (already own) 257 Layer 0 (Energy): Base cost (unavoidable) 258 ───────── 259 ~$800/year total AI compute 260 261 With optimization (shift 50% from L6 to L4/L5): 262 ~$400/year (50% reduction) 263 ``` 264 265 ## Philosophy 266 267 > The goal is not to minimize AI usage, but to maximize AI effectiveness per joule and per dollar. 268 269 1. **Use the right layer** - Don't send "what time is it" to Claude 270 2. **Cache aggressively** - Yesterday's insight is today's prior 271 3. **Compile patterns** - Repeated AI tasks become code 272 4. **Escalate gracefully** - Local failure → cloud success 273 5. **Learn from routing** - Track what works at each layer