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Andrej Karpathy: Software in the era of AI [video]

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1480 points sandslash | 4 comments | 19 Jun 25 00:33 UTC | HN request time: 0.933s | source
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mentalgear ◴[19 Jun 25 09:33 UTC] No.44316934[source]▶
Meanwhile, I asked this morning Claude 4 to write a simple EXIF normalizer. After two rounds of prompting it to double-check its code, I still had to point out that it makes no sense to load the entire image for re-orientating if the EXIF orientation is fine in the first place.

Vibe vs reality, and anyone actually working in the space daily can attest how brittle these systems are.

Maybe this changes in SWE with more automated tests in verifiable simulators, but the real world is far to complex to simulate in its vastness.

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ramon156 ◴[19 Jun 25 10:13 UTC] No.44317136[source]▶
The real question is how long it'll take until they're not brittle
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kubb ◴[19 Jun 25 10:17 UTC] No.44317160[source]▶
Or will they ever be reliable. Your question is already making an assumption.
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diggan ◴[19 Jun 25 10:43 UTC] No.44317316[source]▶
They're reliable already if you change the way you approach them. These probabilistic token generators probably never will be "reliable" if you expect them to 100% always output exactly what you had in mind, without iterating in user-space (the prompts).
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kubb ◴[19 Jun 25 11:18 UTC] No.44317546[source]▶
I also think they might never become reliable.
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diggan ◴[19 Jun 25 11:24 UTC] No.44317591[source]▶
But what does that mean? If you tell the LLM "Say just 'hi' without any extra words or explanations", do you not get "hi" back from it?
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1. TeMPOraL ◴[19 Jun 25 11:28 UTC] No.44317612[source]▶
That's literally the wrong way to use LLMs though.

LLMs think in tokens, the less they emit the dumber they are, so asking them to be concise, or to give the answer before explanation, is extremely counterproductive.

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2. diggan ◴[19 Jun 25 11:32 UTC] No.44317636[source]▶
I was trying to make a point regarding "reliability", not a point about how to prompt or how to use them for work.
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3. TeMPOraL ◴[19 Jun 25 11:48 UTC] No.44317746[source]▶
This is relevant. Your example may be simple enough, but for anything more complex, letting the model have its space to think/compute is critical to reliability - if you starve it for compute, you'll get more errors/hallucinations.
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4. diggan ◴[19 Jun 25 12:04 UTC] No.44317850{3}[source]▶
Yeah I mean I agree with you, but I'm still not sure how it's relevant. I'd also urge people to have unit tests they treat as production code, and proper system prompts, and X and Y, but it's really beyond the original point of "LLMs aren't reliable" which is the context in this sub-tree.