4 ms·
TFA's author's experience is the opposite of your claims. Your claims may be right in _your_ circumstances, but not theirs. > High performance algorithms are q
by psd1 2mo ago
TFA's author's experience is the opposite of your claims. Your claims may be right in _your_ circumstances, but not theirs.
> High performance algorithms are quite well documented
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. The author makes that point extremely clearly, and they have credibility.
> this reads a lot like someone who decided how they feel about LLM-driven engineering ~5 months ago
TFA? No. Your comments here? Yep. Try to imagine a world where different kinds of work have different applicability of tools.
- munksbeer 2mo ago> 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.
- psd1 2mo agoIt 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.
- munksbeer 2mo agoThere 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.
- psd1 2mo agoGreat, what are you going to train it on?
- munksbeer 2mo agoI 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.
- psd1 2mo agoI'm not offended - great diplomatic putdown, well played
- inigyou 2mo agoI 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.
- 2mo ago