I find where AI models are confidently wrong, and I publish the proof.

How the operation actually runs

One person runs this, but almost none of it is done by hand. I designed and wired together a workshop of around thirty specialised agents, each kept to one narrow job it can't wander off from, because I trust thirty careful specialists more than one clever generalist having a good day. Most people using AI have one chatbot and a lot of faith; I built the checks instead.

• Nothing publishes on a hunch.

  Every piece runs a chain before it goes anywhere. One agent drafts, another checks every claim and every sum against a live source, an editor does a full pass, then a plain mechanical gate runs its pass-or-fail checks. Fail one and it doesn’t ship, however much I liked it.

• A second opinion that doesn’t share my blind spots.

  The agents are built to argue, not to nod along, because a model left to check its own work will read back the fact it just invented and tick it off as correct. On the judgement calls I also put the work past a model from a rival lab, trained differently and wrong in different places, and I weight its disagreement heavily.

• It notices, and it quietly learns.

  Small watchers I built keep an eye on things while I’m not looking, so a broken page or a real person’s reply never sits in a corner gathering dust for a week. Every draft is also held against a living list of banned words and AI tells so it doesn’t read like a machine wrote it, and the agents log their own mistakes, which I fold back in so the whole thing gets a little sharper each time.