alt Hacker NewsI'm building a Java HFT engine and the amount of things AI gets wrong is eye opening. If I didn't benchmark everything I'd end up with much less optimized solution.
Examples: AI really wants to use Project Panama (FFM) and while that can be significantly faster than traditional OO approaches it is almost never the best. And I'm not taking about using deprecated Unsafe calls, I'm talking about using primative arrays being better for Vector/SIMD operations on large sets of data. NIO being better than FFM + mmap for file reading.
You can use AI to build something that is sometimes better than what someone without domain specific knowledge would develop but the gap between that and the industry expected solution is much more than 100 hours.
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I think the main issue is treating LLM as a unrestrained black box, there's a reason nobody outside tech trust so blindly on LLMs.
The only way to make LLMs useful for now is to restrain their hallucinations as much as possible with evals, and these evals need to be very clear about what are the goal you're optimizing for.
See karpathy's work on the autoresearch agent and how it carry experiments, it might be useful for what you're doing.
I would say that if AI has to make decisions about picking between framework or constructs irrelevant to the domain at hand, it feels to me like you are not using the AI correctly.
> AI really wants to use Project Panama
It would help if you briefly specified the AI you are using here. There are wildly different results between using, say, an 8B open-weights LLM and Claude Opus 4.6.
Wouldn't Java always lose in terms of latency against a similarly optimized native code in, let's say, C(++)?
I am curious about what causes some to choose Java for HFT. From what I remember the amount of virgin sacrifices and dances with the wolves one must do to approach native speed in this particular area is just way too much of development time overhead.
Then you list all of the things you want it not to do and construct a prompt to audit the codebase for the presence of those things. LLMs are much better at reviewing code than writing it so getting what you want requires focusing more on feedback than creation instructions.
I've seen SQL injection and leaked API tokens to all visitors of a website :)
AI is extremely good at the things that it has many examples for. If what you are doing is novel then it is much less of a help, and it is far more likely to start hallucinating because 'I don't know' is not in the vocabulary of any AI.