https://www.youtube.com/watch?v=bLZJpTHXG9M Does this resonate with you?

Apple has announced the release of its newest next generation AI models using the M5 and M6 chips. I’m hoping Christmas comes early this year. Are you planning on buying any of Apple‘s new products?

I went to an AI business event yesterday in Seattle. Founders, engineers, VCs, quite the mix of people. Really great vibes, friendly crowd. I did get to talk about ICM, but engineers, founders, and investors did not quite get it. Files and folders are just not sexy enough to register. The only engineers who actually got it were old-school ML (machine learning) engineers. Carry on with ICM. We have a secret weapon. I've started calling it my ICBM. The majority of the tech world is still ignoring it.

A model reviewing its own work is not a review. It is the same judgement run twice, and it agrees with itself both times. - Maker and checker are separate passes - whatever produced the work does not get to grade it. - Fresh context, not just a fresh prompt - a different chat, ideally a different model, carrying none of the reasoning that built the thing. - A number it cannot argue with - a test, a diff, a count. Prose review on its own will find reasons to approve. The failure mode is not that a model lies to you. It is that it is still holding every assumption that produced the work, so asking whether the work is good just reasons forward from those assumptions and lands where it landed the first time. I have had this go wrong with real stakes on it. Folder bloat that survived an adversarial review and two further passes on the strongest model I had. A multi-repo restructure proposed off one of three files the model had actually read. A Git migration recommended on the grounds that we had a backup, which we had made ourselves an hour earlier. Every one of those cleared a self-check first. What changed it was making the check structural rather than something I remember to do. In my YouTube workspace no finding enters the register until it has been surfaced to me at the close of the query that produced it, so the system proposes a belief and a human commits it. In phronesis the distillation pass runs behind an eval gate allowed to reject the commit outright, so the pass that writes the profile is never the pass that decides the profile is good. Metrics carry the other half. A verdict written in prose can always be argued into place, a count or a failing test cannot - if the only evidence something worked is a paragraph the model wrote about its own output, there is no evidence. Where has a self-review waved something through that it should have caught, and what do you actually use as the outside check - a second model, a fresh chat, a test, someone else in here? More interested in the practical setups than the principle.

If you run a startup Rob Walling is a person you need to follow. He is the G.O.A.T. when it comes to bootstrapped SaaS but even if you are thinking about Venture Capital I think Rob is a person you need to listen to. If you are a SaaS founder let me know if this video resonated with you. https://www.youtube.com/watch?v=YoMi1rLjVrY