jebarker

I don’t think the good programmer bad programmer framing is helpful. I think AI fits the working style of some developers better than others. Where it fits I’ve seen some developers become so productive that they suck all the air out of the room and drown everyone else in review - not clear if this is good or bad. Where it doesn’t fit I’ve seen developers shut down because they find the workflow so depressing and their former productivity craters. Similarly I’ve seen perfectly good developers turn into slop generation machines and go largely unchecked because everyone else gets alarm fatigue from reviewing their PRs.

The first time I heard this recording I basically got stuck in a loop of listening to it on repeat for a couple of weeks. I just couldn’t comprehend that this was improvised.

I don’t see why this weakens the example. He has repeatedly built and released games from his own engines and now language/compiler. Money was increasingly required but he had made that money through the same process.

He was able to do it because he had millions of dollars from selling previous games he had written in his own game engines (one of them 3D). If other devs want to try this approach and don’t succeed then so be it, but they’re free to use whatever approach they want. I was simply pointing out that the stereotype presented in the essay has exceptions.

But he literally made a real game and released it. It was a long process for sure, but you can’t say it’s infeasible.

> Ten years ago, the kind of person who implemented a new compiler because their compiler was slow was the kind of person who started programming a new 3d game engine when they wanted to make an indie game, i.e., the kind of person who never ships anything.

Jonathan Blow is a glaring exception to this idea. In order to make his latest game, which has shipped and is great, he wrote not only his own engine but his own programming language and compiler to build it in. Zero use of AI.

Somewhere this analogy breaks down is that the only pair of shoes I own that are completely repairable are the traditionally made leather boots I got for my wedding. But with AI generated code it seems endlessly possible to ask the agents to keep fixing the code as long as you don’t care about code quality or fundamental design changes happening frequently.

EDIT: I guess maybe agentic code maintenance is more akin to just making a new pair of shoes every time the current one shows any deficiency?

You are taking too narrow of a view. I agree that if you want to say something about the behavior of models in use today then you need to use those models. But that’s not the only reason to study questions like this.

It might be the case that the models internally predict the nationality of the user given other indicators, e.g. tells in the way they write.

Just because AI development moves fast and breaks things continually it doesn’t mean there’s no value in studying older models. Unless you think we understand all the behaviors of those older models.

As others have said it's mostly just consistency of practice. There's a wealth of resources available freely online for these topics and you learn by doing. I would caution setting goals/expectations based on what others have achieved in youtube videos though - this guy was/is a computer science undergrad at Cambridge, so he certainly has a high capacity for learning hard stuff and has been exposed to some of the best schooling on offer.

Good job that only honest people post things on the internet otherwise we’d have a problem

I think it’s plausible that attention could have been invented earlier and GPT demonstrated at smaller scale on earlier hardware. Then hardware timelines might have accelerated (or alternative approaches explored) and we’d have gotten to where we are earlier.