You can boost generative AIs like Claude with Lean — not just during the spec phase, but also for security hardening and bug hunting after implementation. Here’s a cool result from recently. There was an attack vector in Plonky2-whir that even the strongest AI models kept missing no matter how hard they searched. We found it with Lean + Claude, so I turned it into a reusable skill.This skill is pretty beginner-friendly. You can just paste it into Claude or Codex and try it out even if you don’t know anything about Lean. I think the difference between using Lean to decide on specs or database design and then verify them, versus regular vibe coding, is kind of like building with stones or bricks versus using reinforced concrete. At first the speed feels similar, but eventually you reach a height where stacking stones just won’t cut it anymore, and the gap in scale and safety becomes really obvious. Lean is basically the reinforced concrete of software development.Until about 10 years ago, I honestly saw formal verification and logic-based stuff as something that got pushed to the sidelines by machine learning — like “not practical,” “doesn’t make money,” or “another failed expert systems thing.” Fuzzy logic was used in rice cookers and trains, and even in fighter jets later on, but pure logic systems like Prolog felt like they were no longer the main character after deep learning took over. So seeing Lean now working really well with machine learning and delivering these surprisingly strong results feels pretty special to me, since I’ve always liked the logic side of things.