I spent 6 months thinking about this problem. Then built the solution in a day.

The Problem We're drowning in information. News, reports, expert opinions, policy documents — all of it arriving faster than we can process. Every time I asked an LLM to help me reason through something complex, I'd get one of two responses: A confident, well-structured answer that was subtly (or not so subtly) wrong A meandering response that contradicted itself halfway through I tried multi-agent debate setups. Same problem, just more expensive. Then I realized: the core issue isn't information overload. It's that cause-and-effect relationships are invisible. LLMs are great at pattern matching. They're terrible at causal reasoning — and they don't know it. What I Built After sitting on this idea for six months, I vibe coded CurioCat in a day using Claude Code. CurioCat is an open-source causal reasoning engine. Drop in any text — policy analysis, investment thesis, competitive intelligence, industry trends — and it maps out the causal structure, sources evidence for every claim, and