If you’ve spent any time shipping AI-generated code this year, you know the drill. Copilot or Claude writes something that looks perfectly reasonable, the PR gets merged, and three days later you find out that “null-safe” function was anything but. The AI *said* it handled edge cases. It lied. Or rather, it hallucinated — and nobody checked.

That’s the exact problem [LUCID](https://github.com/gtsbahamas/hallucination-reversing-system) goes after, and its approach is kind of wild. Instead of trying to stop hallucinations from happening (which, by the way, three separate papers have now shown is mathematically impossible), LUCID leans into them. The name stands for “Leveraging Unverified Claims Into Deliverables,” and the core idea is to treat every hallucinated claim as a testable requirement.

Here’s how the 4-layer pipeline actually works. You feed it AI-generated code, and LUCID extracts all the implicit claims baked in — stuff like “this query is injection-safe” or “this handles concurrent access.” Then a second, adversarial AI pass checks each claim against the actual implementation. What you get back is a report showing exactly which assumptions were never verified. It’s basically a lie detector for your AI pair programmer.

The benchmarks are worth mentioning. On HumanEval, LUCID hit 100% pass@5 (all 164 problems), up from an 86.6% baseline. On SWE-bench, it jumped from 18.3% to 30.3%. And these were validated by running real test suites, not by asking another LLM if the code “looks right.” The whole thing runs on the Claude API, and the creator Ty Wells reported about $17 in API costs for six full iterations on a 30,000-line production TypeScript codebase. That’s absurdly cheap for that level of verification.

LUCID [popped up on Hacker News](https://news.ycombinator.com/item?id=47011695) around February 14-15, 2026, and the timing couldn’t be better. Everyone’s talking about AI coding tools, but almost nobody’s talking about what happens when those tools confidently produce garbage. You can grab it as a CLI tool, an MCP server, or even a [GitHub Action](https://github.com/gtsbahamas/hallucination-reversing-system) — there’s a free tier with 100 verifications a month if you want to kick the tires.

I’m genuinely curious where this goes. The philosophical flip — treating hallucination not as a bug but as a specification engine — is the kind of reframe that either ages brilliantly or terribly. But right now, with AI-generated code flooding production codebases everywhere, having *something* that systematically checks whether the code does what the AI claimed it does feels less like a nice-to-have and more like table stakes.