I'm launching FAF CLI v3.0.0 Championship Edition – a foundational AI context format standard that automatically generates and maintains project context for any AI tool in <50ms.
What it does: Scans any codebase and generates structured .faf files (YAML) that work with Claude Code, Cursor, OpenAI Codex CLI, Gemini CLI, Warp, Windsurf, and any AI that reads project context.
Key features:
- TURBO-CAT format discovery: Detects 153+ frameworks/configs automatically
- Bi-directional sync: Keeps .faf ↔ CLAUDE/tool.md in perfect sync (8ms average)
- DNA tracking: Audit trail for every context change
- Zero config: `faf init` and you're done
- TypeScript strict mode: 100% type-safe, zero errors
Technical highlights:
- C-Mirror engine: Context-mirroring with atomic writes (zero-slippage design)
- 1,000+ comprehensive tests (WJTTC GOLD certified)
- Sub-50ms operations target (championship performance) 18ms avg.
- Works offline, no API keys, no telemetry beyond basic analytics
Why it matters: Right now, every AI tool has its own context format. Claude wants CLAUDE.md, Cursor wants .cursorrules, others want different formats. FAF provides a single source of truth that connects with AI and works everywhere.
Open source: MIT license, free forever. No paid tiers, no vendor lock-in.
Built this after frustration with managing context across multiple AI tools. Tested with Google Gemini, Anthropic Claude, and OpenAI teams. Now approaching 10,000 downloads across ecosystem (CLI + MCP combined).
wolfejam•4mo ago
I'm launching FAF CLI v3.0.0 Championship Edition – a foundational AI context format standard that automatically generates and maintains project context for any AI tool in <50ms.
What it does: Scans any codebase and generates structured .faf files (YAML) that work with Claude Code, Cursor, OpenAI Codex CLI, Gemini CLI, Warp, Windsurf, and any AI that reads project context.
Key features: - TURBO-CAT format discovery: Detects 153+ frameworks/configs automatically - Bi-directional sync: Keeps .faf ↔ CLAUDE/tool.md in perfect sync (8ms average) - DNA tracking: Audit trail for every context change - Zero config: `faf init` and you're done - TypeScript strict mode: 100% type-safe, zero errors
Technical highlights: - C-Mirror engine: Context-mirroring with atomic writes (zero-slippage design) - 1,000+ comprehensive tests (WJTTC GOLD certified) - Sub-50ms operations target (championship performance) 18ms avg. - Works offline, no API keys, no telemetry beyond basic analytics
Why it matters: Right now, every AI tool has its own context format. Claude wants CLAUDE.md, Cursor wants .cursorrules, others want different formats. FAF provides a single source of truth that connects with AI and works everywhere.
Open source: MIT license, free forever. No paid tiers, no vendor lock-in.
Built this after frustration with managing context across multiple AI tools. Tested with Google Gemini, Anthropic Claude, and OpenAI teams. Now approaching 10,000 downloads across ecosystem (CLI + MCP combined).
GitHub: https://github.com/Wolfe-Jam/faf-cli npm: https://www.npmjs.com/package/faf-cli Full announcement: https://www.faf.one/blog/v3-launch
npm install -g faf-cli faf init
Happy to answer questions about the architecture, format discovery engine, or the context-mirroring approach!