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Irani’s lab is now working to extend these “mind-reading” methods to the decoding of auditory information as well. Video could be another frontier. “What remains especially challenging is decoding video – for example, during dreaming,” she explains. “Dozens of images change every second, while an fMRI scan takes about two seconds. If we overcome all these obstacles, it’s possible that in the future we may even be able to read dreams.”

Can LLMs just read the code for you? No, for the same reason: even if they make no mistakes, their technical values will not match yours or those of your company. You can still use LLMs to help you read code, but you have to carefully read and review that LLM output, and you should also be carefully reading the code itself.

If you are not familiar with Codemode, it’s basically just a way to issue tool calls from within some language, in our case JavaScript. That allows you to compose those calls without necessarily going through the LLM’s context. Credit for naming goes to our friends at Cloudflare who coined it.

The Bitter Lesson, the hard truth, learned over and over again, that things that we thought required elaborate human rules and thinking can be solved with the brute force of better machine learning systems and more AI. The Bitter Lesson is everywhere among AI startups and companies adopting AI. A huge amount of effort went into building elaborate computer systems to feed AIs the right information at the right time, but AI systems have learned to seek out information themselves.

That brings me back to the momentum Pai describes. First principles thinking makes working that way feel natural. When you truly understand what you’re trying to accomplish, it’s easier to take a small step, learn from it, and keep going. If you’re doing it right, working with agents lets that back-and-forth happen much faster. You get faster learning loops and more momentum, oriented around deep understanding. To me, that’s the new flow state.

There will probably come a day when I always get sensible answers to these questions that convince me the model knows what it’s doing. But today is not that day. About half the questions I ask get answers that convince me3 the model has made a mistake

The personal website is coming back because people are rediscovering something the early Web made obvious: the Internet is more interesting when individuals have places of their own. We spent roughly twenty years moving into a handful of very large apartment buildings. Some people are starting to build houses again.

If every lab stopped training new models tomorrow, that wouldn’t change the fact that GPT-6 Astra and Fable 5.1 are already enough to change how large parts of the economy work. The capability overhang between what those models can do today and what most folks are using them for is massive.

Whatever else you say about the AI doomers, they have more than two decades’ history of explicitly spelling out exactly what they believe and why, even when it was complete science fiction to talk about AI at all. They’ve earned the right to be treated as sincere.

In fact, with Astra and Fable I feel like not only are the costs astronomical, but the models are also just not for me as a software engineer. And presumably that’s because these models increasingly are for other people. For lawyers, 3D artists, mathematicians, whoever uses computer use, etc.