the beautiful code
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Code that does not work is just text.
I’ve never thought of it that way. I’m going to add copy writer to my resume.
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Try to get one of these LLMs to update a package.json.
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I asked ChatGPT with help about bare metal 32-bit ARM (For the Pi Zero W) C/ASM, emulated in QEMU for testing, and after the third iteration of "use printf for output" -> "there's no printf with bare metal as target" -> "use solution X" -> "doesn't work" -> "ude printf for output" ... I had enough.
I used ChatGPT to help me make a package with SUSE's Open Build Service. It was actually quite good. Was pulling my hair out for a while until I noticed that the project I wanted to build had changes URLs and I was using an outdated one.
In the end I just had to get one last detail right. And then my ChatGPT 4 allowance dried up and they dropped me back down to 3 and it couldn't do anything. So I had to use my own brain, ugh.
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It's pretty random in terms of what is or isn't doable.
For me it's a big performance booster because I genuinely suck at coding and don't do too much complex stuff. As a "clean up my syntax" and a "what am I missing here" tool it helps, or at least helps in figuring out what I'm doing wrong so I can look in the right place for the correct answer on something that seemed inscrutable at a glance. I certainly can do some things with a local LLM I couldn't do without one (or at least without getting berated by some online dick who doesn't think he has time to give you an answer but sure has time to set you on a path towards self-discovery).
How much of a benefit it is for a professional I couldn't tell. I mean, definitely not a replacement. Maybe helping read something old or poorly commented fast? Redundant tasks on very commonplace mainstream languages and tasks?
I don't think it's useless, but if you ask it to do something by itself you can't trust that it'll work without singificant additional effort.
A lot of words to just say vibe coding
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My uncle. Very smart very neuronal. He knows the entire Internet, can you imagine? the entire internet. Like the mails of Crooked Hillary Clinton, that crook. You know what stands in that Mails? my uncle knows. He makes the best code. The most beautiful code. No one has ever seen code like it, but for him, he's a genius, like i am, i have inherited all his genius genes. It is very easy. He makes the best code. Sometimes he calls me and asks me: you are even smarter than i am. Can you look at my code?
All people say it. Tremendous code. All the experts said "No, generating formatted random text is not working code" but we did it.
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This has beeny experience as well. It keeps emphasizing "beauty" and keeps missing "correctness"
It generates an answer that looks correct. Actual correctness is accidental. That's how you wind up with documents with references that don't exist, it just knows what references look like.
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I asked ChatGPT with help about bare metal 32-bit ARM (For the Pi Zero W) C/ASM, emulated in QEMU for testing, and after the third iteration of "use printf for output" -> "there's no printf with bare metal as target" -> "use solution X" -> "doesn't work" -> "ude printf for output" ... I had enough.
Sounds like it's perfectly replicated the help forums it was trained on.
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Code that does not work is just text.
No the spell just fizzled. In my experience it happens far less often if you start with an Abra kabara and end it with an Alakazam!
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Try to get one of these LLMs to update a package.json.
Define "update"
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It generates an answer that looks correct. Actual correctness is accidental. That's how you wind up with documents with references that don't exist, it just knows what references look like.
wrote on last edited by [email protected]It doesn't 'know' anything. It is glorified text autocomplete.
The current AI is intelligent like how Hoverboards hover.
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The image is taken from Zhihu, a Chinese Quora-like site.
The prompt is talking about give a design of a certain app, and the response seems to talk about some suggested pages. So it doesn't seem to reflect the text.
But this in general aligns with my experience coding with llm. I was trying to upgrade my eslint from 8 to 9, and ask chatgpt to convert my eslint file, and it proceed to spit out complete garbage.
I thought this would be a good task for llm because eslint config is very common and well-documented, and the transformation is very mechanical, but it just cannot do it. So I proceed to read the documents and finished the migration in a couple hour...
I wouldn't say it's accurate that this was a "mechanical" upgrade, having done it a few times. They even have a migration tool which you'd think could fully do the upgrade but out of the probably 4-5 projects I've upgraded, the migration tool always produced a config that errored and needed several obscure manual changes to get working. All that to say it seems like a particularly bad candidate for llms
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It's not much use with a professional codebase as of now, and I say this as a big proponent of learning FOSS AI to stay ahead of the corpocunts
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No the spell just fizzled. In my experience it happens far less often if you start with an Abra kabara and end it with an Alakazam!
wrote on last edited by [email protected]Yeah, the Abra kabara init and Alakazam cleanup are an important part, specially until you have become good enough to configure your own init.
There is an alternative init, Abra Kadabra, which automatically adds a cleanup and some general fixes when it detects the end of the spell.
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I asked ChatGPT with help about bare metal 32-bit ARM (For the Pi Zero W) C/ASM, emulated in QEMU for testing, and after the third iteration of "use printf for output" -> "there's no printf with bare metal as target" -> "use solution X" -> "doesn't work" -> "ude printf for output" ... I had enough.
Did it at least try puts?
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Welp. Its actually very in line with the late stage capitalist system. All polish, no innovation.
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It catches things like spelling errors in variable names, does good autocomplete, and it’s useful to have it look through a file before committing it and creating a pull request.
It’s very useful for throwaway work like writing scripts and automations.
It’s useful not but a 10x multiplier like all the CEOs claim it is.
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It doesn't 'know' anything. It is glorified text autocomplete.
The current AI is intelligent like how Hoverboards hover.
wrote on last edited by [email protected]Llms are the smartest thing ever on subjects you have no fucking clue on.
On subjects you have at least 1 year experience with it suddenly becomes the dumbest shit youve ever seen. -
It catches things like spelling errors in variable names, does good autocomplete, and it’s useful to have it look through a file before committing it and creating a pull request.
It’s very useful for throwaway work like writing scripts and automations.
It’s useful not but a 10x multiplier like all the CEOs claim it is.
Fully agreed. Everybody is betting it'll get there eventually and trying to jockey for position being ahead of the pack, but at the moment there isn't any guarantee that it'll get to where the corpos are assuming it already is.
Which is not the same as not having better autocomplete/spellcheck/"hey, how do I format this specific thing" tools.
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This weekend I successfully used Claude to add three features in a Rust utility I had wanted for a couple years. I had opened issue requests, but no else volunteered. I had tried learning Rust, Wayland and GTK to do it myself, but the docs at the time weren’t great and the learning curve was steep. But Claude figured it all out pretty quick.
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llms are systems that output human-readable natural language answers, not true answers
And a good part of the time, the answers can often have a… subtly loose relationship with truth
