alt Hacker NewsIt's not that we're assuming the AI has made no mistakes. It's that we're confident in the general capability of the models such that we're willing to accept it'll make some mistakes, but working together with it, we'll still achieve our end goals.
I presume you also know your own code has mistakes, too? You wouldn't refuse to even look at the code of a colleague who has ever made a coding mistake, would you?
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That's not really my point... my point is that it's an unsettled debate between speed and caution, and devs are not unanimous on it, unlike the claim of the original post that seemed to think everyone was reporting the same gains.
The divide with developers at the moment seems to be about people who believe in review & are being cautious (who are not seeing major time gains according to all reports I have seen), and those who trust it to be able to fix things forever and will no longer have to worry about understanding the code, so are happy to atrophy those skills.
Yes, my code will have mistakes, and I'll use LLMs for some tasks. I also review and have a mental model, which means I can easily fix them, no matter what happens in future with AI. Reviewing also means I'm seeing the limitations and common mistakes of LLMs.
A no-review approach will be far faster than this, obviously, but if scbench.ai is still true on the frontier models, it may come back to bite people when they have no idea how it works and the context becomes too big to maintain.
This attitude is exactly why AI bros don't make successful software lol. "Where's the software?" This. This is why there is no software.
Modern software development practice is built around that humans do make mistakes. Frequently. You don't cross your fingers and hope for the best, you assume it will happen and you put processes in place to prevent errors, catch them early, and make them easy to recover from.
It is a for-real science.
This is a different argument from Karpathy's prompt-and-forget one.
I personally have had good success with using agents as virtual pair programmers following some simple rules for how to make that process productive, but that is a very, very different approach. One that recognizes that both humans and agents are prone to errors and attempts to mitigate the problem rather than expecting it will magically work out through the power of AI.