Comment on: ContextPool

Hey Product Hunt 👋We built ContextPool because we kept hitting the same wall: every time started a new Claude Code or Cursor session, my agent had zero memory of what we'd already figured out together. Same bugs re-discovered. Same architectural decisions re-explained. Same gotchas re-learned.It felt like working with a brilliant colleague who gets amnesia every morning.So we built a persistent memory layer specifically for AI coding agents. Here's how it works:1. Install with one curl command (30 seconds, single binary, no dependencies)2. Run `cxp init` - it scans your past sessions and extracts engineering insights using an LLM3. Your agent automatically loads relevant context via MCP at session startWhat it remembers isn't conversation summaries - it's actionable engineering knowledge:→ Bugs & root causes ("tokio panics on block_on in async context")→ Fixes & solutions ("Use #[tokio::main] instead of manual Runtime::new()")→ Design decisions ("Chose libsql over rusqlite for Turso compatibility")→ Gotchas ("macOS keychain blocks in MCP subprocess context")It works with Claude Code (zero config), Cursor, Windsurf, and Kiro. Local-first and privacy-first - raw transcripts never leave your machine, only extracted insights sync when you opt in.The team memory feature is what we are most excited about: push insights to a shared pool, and everyone on the team pulls the collective knowledge. Your teammate debugged something last week? Your agent already knows.Free and open source for local use. $7.99/mo for team sync.We'd love to hear: what's the most frustrating thing you keep re-explaining to your AI coding agent? And if you try it - what insights does it extract from your sessions?GitHub: https://github.com/syv-labs/cxp