⚡ Ultra-fast O(1) Active Inference & Bayesian Cognitive Governor for AI Agents (Antigravity, Claude, Cursor) & Embedded Systems. Zero-alloc C11 core (<36KB) + FastMCP server.
🧠 Micro-ActInf: Ultra-Fast $O(1)$ Active Inference & Bayesian Cognitive Governor
Eliminate context drift, hallucination loops, and conversational token bloat in AI agents (Antigravity, Claude Desktop, Cursor) with Karl Friston's Free Energy Principle and an ultra-lightweight 20KB C11 POMDP engine.
📑 Table of Contents - Why Micro-ActInf? (The Problem & Solution) - Feature Comparison vs Existing Frameworks - Dual-Target Architecture - How It Works: The Active Inference Loop - The 6 Cognitive Regimes & 4 Action Policies - Quickstart: Plug into AI Agents in 60 Seconds - 1. Google Antigravity - 2. Anthropic Claude Desktop - 3. Cursor / VS Code - 4. Pure Offline Local LLMs (Ollama / vLLM / llama.cpp) - Embedded Systems & Game Engines (C11 Core) - Mathematical Rigor - Empirical Benchmarks - 🇮🇷 راهنمای جامع فارسی (Persian Technical Guide)
💡 Why Micro-ActInf? (The Problem & Solution)
🚨 The AI Agent Crisis: 1. Context Drift & Goal Degradation: In multi-turn coding sessions, LLM agents forget high-level constraints, get distracted by minor side-tracks, and drift away from the original engineering objective. 2. Infinite Debugging Loops: When an error occurs, LLMs often attempt the same failed fix repeatedly or oscillate between conflicting implementations. 3. Conversational Token Waste: Up to 40% of generated tokens are squandered on polite filler ("Certainly!", "I'd be happy to help!"), repetitive apologies, and explanatory essays when you only needed production code. 4. Soft Constraints Fail: Prompt instructions ("be concise", "focus on tests") are probabilistic recommendations t
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