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Comment by logicchains | Hacker News Reader

It depends on how the AI is used; there's a huge difference in productivity between a structured workflow with repeated automated feedback to the AI, and just ad-hoc prompting. For instance, Gemini 2.5 coding up a spec-compliant HTTP2.0 server in two weeks: https://outervationai.substack.com/p/building-a-100-llm-writ... . 15k lines of code and 30k lines of tests; no human coder could produce something like that so fast.

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Would that application not exist in the training data already?

The are around three Golang HTTP2.0 servers on Github, but it wasn't just regurgitating the application from memory, as if it was then it would have been mostly correct first try, and it wouldn't have needed to spend 80%+ of the development time in a code-compile-test cycle of fixing bugs identified by integration tests and spec conformance tests.

So you are saying that LLMs have no capacity to re-use existing work, and will just burn hours and money re-inventing wheels?

Welcome to The Future™!

If you tell it to reinvent the wheel, it will.

If you tell it to use existing libraries (and they are in its training data) it will do that instead.

If you tell it about libraries it hasn't encountered before it can use those instead.

It's cool that it made something that works, but that code is pretty bad. Trial and error is not the way to develop something like that.

I would not say that is ”how the AI is used”. That problem space is one where humans have spent unusually much time defining a spec and writing a test suite.

A million monkeys randomly typing could actually complete that task as well.

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