Comment by munksbeer | Hacker News Reader

Comment by munksbeer | Hacker News Reader

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> That may be true for bloom filters or what have you. But the author states the obvious: all recent games are closed-source. So any algorithms or techniques for real-time 3D that an LLM was trained on are going to be a long way behind the state of the art.

Neither you, nor the author of the article has to use LLMs for coding. But if you want to, there are some practices you should follow to get best results, and that includes setting up your environment to give the agent the best chance for success. If you have various rules that are non-obvious, you add them to your AGENTS.md file (as we have at my corp and in my little niche). The agent will then follow those rules, and learn from the surrounding code.

I'll just be blunt here - I don't believe the author would have done anything more than the bare minimum to test his pre-existing bias that coding agents are bad. I don't believe he would have put the effort into getting it writing good quality code to suit the project he was working in.

We write high performance, low latency java for trading systems. Our codebase is highly structured around this and the READMEs and AGENTs file contain the information for how to successfully write code like this, originally for human consumption, and now agents.

And it works. So, I don't trust the article.

It can do A, ergo it can do B.

By the same logic, you have experience in low-latency numerical decision-making, ergo you could optimise a 3D rendering pipeline.

Yeah, no.

There is no magic to coding, any sort of coding. Humans learn how to do it by learning the rules. A coding agent can learn the rules, easily.

Great, what are you going to train it on?

I am trying to not sound offensive here, but given the question you're asking, I don't think we're going to join up here. I think one of us is missing something fairly fundamental, and my hunch is it is you.

The agent will already have seen enough super optimised code and read enough material about it. It will read the entire repo you're in, and understand how the code should be structured and written to work efficiently. And anything it doesn't get at first, you write about in the AGENTS.md file and tell it.

Then it works. If you can teach a human to write specialised 3d rendering code, you can distill the same teachings to an agent in text form, and it'll work.

I am supremely confident of this, and I bet that neither you nor the author of the article has bothered to try properly.

I'm not offended - great diplomatic putdown, well played

I suppose you wouldn't train it using a traditional LLM type of training and you might not have an LLM type of model either.

American Fuzzy Lop usually manages to generate valid files of any format you want, using a genetic algorithm that the author didn't even call ML. AlphaGo trained against itself.

Such things aren't impossible when you can automate the reward function, though you might have to come up with novel techniques.

Yeah, but we already have rules-based coding agents, they are called compilers.

I have a suspicion that a lot of the stuff people use AI coding agents for would be served just as well by, say, the WYSIWYG editor from Visual Basic 6. But we threw away that kind of technology long ago and settled for Reactslop, and now you need an LLM to write the Reactslop.

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