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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).
I also think they might never become reliable.
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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).
I also think they might never become reliable.
"Write a Python script that adds three numbers together".
Is that bar going up? I think it probably is, although not as fast/far as some believe. I also think that "unreliable" can still be "useful".
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I also think they might never become reliable.
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?
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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It is kind of a human problem too, although that the full testing suite takes X hours to run is also not fun, but it makes the human problem larger. Say you're Human A, working on a feature. Running the full testing suite takes 2 hours from start to finish. Every change you do to existing code needs to be confirmed to not break existing stuff with the full testing suite, so some changes it takes 2 hours before you ha…
This is the workflow that ChatGPT Codex demonstrates nicely. Launch any number of «robotic» tasks in parallel, then go on your own. Come back later to review the results and pick good ones.
His dismissal of smaller and local models suggests he underestimates their improvement potential. Give phi4 a run and see what I mean.
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…
Same way programs was way more efficient before and now they are "bloated" with packages, abstractions, slow implementations of algos and scaffolding.
The concept of what is good software development might be changing as well.
LLMs might not write the best code, but they sure can write a lot of it.
Tight feedback loops are the key in working productively with software. I see that in codebases up to 700k lines of code (legacy 30yo 4GL ERP systems). The best part is that AI-driven systems are fine with running even more tight loops than what a sane human would tolerate. Eg. running full linting, testing and E2E/simulation suite after any minor change. Or generating 4 versions of PR for the same task so that the h…
The problem is that every time you run your full automation with linting and tests, you’re filling up the context window more and more. I don’t know how people using Claude do it with its <300k context window. I get the “your message will exceed the length of this chat” message so many times.
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…
There's also those instances where Microsoft unleashed Copilot on the .NET repo, and it resulted in the most hilariously terrible PRs that required the maintainers to basically tell Copilot every single step it should take to fix the issue. They were basically writing the PRs themselves at that point, except doing it through an intermediary that was much dumber, slower and less practical than them. And don't get me s…
If you're fine with things that kinda working okay and you're not the best developer yourself then you probably think coding agents work really really well because the slop they produce isn't that much worse than yourself. In fact I know a mid-level dev who believes agent AIs write better code than himself.
If you're very critical of code quality then it's much tougher... This is even more true in complex codebases where simply following some existing pattern to add a new feature isn't going to cut it.
The degree to which it helps any individual developer will vary, and perhaps it's not that useful for yourself. For me over the last few months the tech has got to the point where I use it and trust it to write a fair percentage of my code. Unit tests are an example where I find it does a really good job.
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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?
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.