By the same token, a software developer could perhaps also be likened to a compiler, taking language and/or visual information as input and turning it into code.

On a sidetrack, I was looking at the email address in the footer and wondered how cool it would have looked if geohot actually had a Hotmail address.

“Some people go as far as to believe that perception is reality and that truth is a construction.” To the author: ask them what they mean.

This post is such a cold take, and is going to age horribly.

Self driving cars fail because of regulatory requirements for five nines reliability, and they're doing inference over a dynamic noisy domain.

Autonomous engineering does not have these issues. Code doesn't need to be five nines correct, and the domain of inference is logical and basically static.

If the AI agent/coding companies didn't have their heads up their collective asses we could have fully spec driven autonomous coding within ~3 years, 100%.

> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense.

Obviously... in what way? I feel the anti-ai pattern is clear.

Self-driving cars don't work in my city so the whole concept is a hoax. LLMs don't code my proprietary language so it's a bubble.

> From this study (https://arxiv.org/abs/2507.09089)

I can tell this is going to be the most misquoted study in blogs and pop-sci books after the 10,000-hour mastery study. And it's just a preprint!

I use LLMs like autocomplete on steroids - fast but need reviews

> Or we could, you know, do the hard work and build better programming languages, compilers, and libraries. But that can’t be hyped up for billions.

100% this. I fear that AI will cause us to be stuck in a local optimum for the next decades where most of the code will be Python or JS because these are the languages best supported by LLMs. Don't get me wrong, Python and JS and mature and productive languages. That's fine. But we could have it so much better if there was more effort put into a next generation of tools that take all the harsh lessons learnt from the tools before and "just" do it better. I acknowledge that we get incremental improvements here and there but some things are just unfixable without breaking existing ecosystems.

I really hate the comparison of AI to a compiler. It's not like I'm writing a codebase in English. I'm still writing codebases in programming languages. AI just helps manage all the text.

I stopped reading at this point:

> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t.

This reeks of bias-dismissing massive investments as ‘obvious’ nonsense while hyping its own tinygrad as the ‘truth’ in AI coding.

Author is allowed to claim ‘most people do not care to find the truth’ but it’s hypocritical when the post ignores counterpoints, like PyTorch’s dominance in efficient coding benchmarks.

Author doesn’t seem to care about finding the full truth either, just the version that pumps its bag.