8 ms·

Yes, because they're more likely to understand that the computer isn't this magical black box, and that just because we've made ELIZA marginally better, doesn't

Yes, because they're more likely to understand that the computer isn't this magical black box, and that just because we've made ELIZA marginally better, doesn't mean it's actually good. Anecdata, but the people I've seen be dazzled by AI the most are people with little to no programming experience. They're also the ones most likely to look on computer experts with disdain.

dingnuts 11mo ago
[dead]
josephg 11mo ago
Well yeah. And because when an expert looks at the code chatgpt produces, the flaws are more obvious. It programs with the skill of the median programmer on GitHub. For beginners and people who do cookie cutter work, this can be incredible because it writes the same or better code they could write, fast and for free. But for experts, the code it produces is consistently worse than what we can do. At best my pride demands I fix all its flaws before shipping. More commonly, it’s a waste of time to ask it to help, and I need to code the solution from scratch myself anyway. I use it for throwaway prototypes and demos. And whenever I’m thrust into a language I don’t know that well, or to help me debug weird issues outside my area of expertise. But when I go deep on a problem, it’s often worse than useless.
- ethbr1 11mo ago
  This is why AI is the perfect management Rorschach test. To management (out of IC roles for long enough to lose their technical expertise), it looks perfect! To ICs, the flaws are apparent! So inevitably management greenlights new AI projects* and behaviors, and then everyone is in the 'This was my idea, so it can't fail' CYA scenario. * Add in a dash of management consulting advice here, and note that management consultants' core product was already literally 'something that looks plausible enough to make execs spend money on it'
- panarky 11mo ago
  > It programs with the skill of the median programmer on GitHub This is a common intuition but it's provably false. The fact that LLMs are trained on a corpus does not mean their output represents the median skill level of the corpus. Eighteen months ago GPT-4 was outperforming 85% of human participants in coding contests. And people who participate in coding contests are already well above the median skill level on Github. And capability has gone way up in the last 18 months.
  - sethherr 11mo ago
    Trying to figure out how to align this with my experiences (which match the parents’ comment), and I have an idea: Coding contests are not like my job at all. My job is taking fuzzy human things and making code that solves it. Frankly AI isn’t good at closing open issues on open source projects either.
  - incrudible 11mo ago
    > The fact that LLMs are trained on a corpus does not mean their output represents the median skill level of the corpus. It does, by default. Try asking ChatGPT to implement quicksort in JavaScript, the result will be dogshit. Of course it can do better if you guide it, but that implies you recognize dogshit, or at least that you use some sort of prompting technique that will veer it off the beaten path.
    - keeda 11mo ago
      I asked the free version of ChatGPT to implement quicksort in JS. I can't really see much wrong with it, but maybe I'm missing something? (Ugh, I just can't get HN to format code right... pastebin here: https://pastebin.com/tjaibW1x https://pastebin.com/tjaibW1x) ---- function quickSortInPlace(arr, left = 0, right = arr.length - 1) { if (left < right) { const pivotIndex = partition(arr, left, right); quickSortInPlace(arr, left, pivotIndex - 1); quickSortInPlace(arr, pivotIndex + 1, right); } return arr; } function partition(arr, left, right) { const pivot = arr[right]; let i = left; for (let j = left; j < right; j++) { if (arr[j] < pivot) { [arr[i], arr[j]] = [arr[j], arr[i]]; i++; } } [arr[i], arr[right]] = [arr[right], arr[i]]; // Move pivot into place return i; }
- torginus 11mo ago
  In my experience (with ChatGPT 5.1 as of late) is that the AI follows a problem->solution internal logic and doesn't think and try to structure its code. If you ask for an endpoint to a CRUD API, it'll make one. If you ask for 5, it'll repeat the same code 5 times and modify it for the use case. A dev wouldn't do this, they would try to figure out the common parts of code, pull them out into helpers, and try to make as little duplicated code as possible. I feel like the AI has a strong bias towards adding things, and not removing them. The most obviously wrong thing is with CSS - when I try to do some styling, it gets 90% of the way there, but there's almost always something that's not quite right. Then I tell the AI to fix a style, since that div is getting clipped or not correctly centered etc. It almost always keeps adding properties, and after 2-3 tries and an incredibly bloated style, I delete the thing and take a step back and think logically about how to properly lay this out with flexbox.
  - BurningFrog 11mo ago
    Bad code is only really bad if it needs to be maintained. If your AI reliably generates working code from a detailed prompt, the prompt is now the source that needs to be maintained. There is no important reason to even look at the generated code
    - AlexandrB 11mo ago
      I think this might be plausible in the future, but it needs a lot more tooling. For starters you need to be able to run the prompt through the exact same model so you can reproduce a "build".
      - josephg 11mo ago
        Even the exact same model isn't enough. There are several sources of nondeterminism in LLMs. These would all need to be squashed or seeded - which as far as I know isn't a feature that openai / anthropic / etc provide.
      - BurningFrog 11mo ago
        OK, then the current models aren't as good as I thought/hoped. I guess one thing it means is that we still need extensive test suites. I suppose an LLM can write those too.
- malfist 11mo ago
  By volume the vast majority of code on github is students. Think about that when you average github for ai
  - pseudalopex 11mo ago
    > By volume the vast majority of code on github is students. Who determined this? How?
rhetocj23 11mo ago
Yes. The people who are amazed with AI were never that good at a particular subject area in the first place - I dont care who you are. You were not good enough - how do I know this? Well I know economics, corporate finance, accounting et al very deeply. Ive engaged with LLMS for years now and still they cannot get below the surface level and are not improving further than this. Its easy to recall information, but something entirely different to do something with that information. Which is what those subject ares are all about - taking something (like a theory) and applying it in a disciplined manner given the context. Thats not to diminish what LLMs can do. But lets get real.
teaearlgraycold 11mo ago
I saw an ad for Lovable. The very first thing I noticed was an exchange where the promoter asked the AI to fix a horizontal scroll bar that was present on his product listing page. This is a common issue with web development, especially so for beginners. The AI’s solution? Hide overflow on the X axis. Probably the most common incorrect solution used by new programmers. But to the untrained eye the AI did everything correctly.

dingnuts 11mo ago

[dead]

josephg 11mo ago

Well yeah. And because when an expert looks at the code chatgpt produces, the flaws are more obvious. It programs with the skill of the median programmer on GitHub. For beginners and people who do cookie cutter work, this can be incredible because it writes the same or better code they could write, fast and for free. But for experts, the code it produces is consistently worse than what we can do. At best my pride demands I fix all its flaws before shipping. More commonly, it’s a waste of time to ask it to help, and I need to code the solution from scratch myself anyway. I use it for throwaway prototypes and demos. And whenever I’m thrust into a language I don’t know that well, or to help me debug weird issues outside my area of expertise. But when I go deep on a problem, it’s often worse than useless.

ethbr1 11mo ago
This is why AI is the perfect management Rorschach test. To management (out of IC roles for long enough to lose their technical expertise), it looks perfect! To ICs, the flaws are apparent! So inevitably management greenlights new AI projects* and behaviors, and then everyone is in the 'This was my idea, so it can't fail' CYA scenario. * Add in a dash of management consulting advice here, and note that management consultants' core product was already literally 'something that looks plausible enough to make execs spend money on it'
panarky 11mo ago
> It programs with the skill of the median programmer on GitHub This is a common intuition but it's provably false. The fact that LLMs are trained on a corpus does not mean their output represents the median skill level of the corpus. Eighteen months ago GPT-4 was outperforming 85% of human participants in coding contests. And people who participate in coding contests are already well above the median skill level on Github. And capability has gone way up in the last 18 months.
- sethherr 11mo ago
  Trying to figure out how to align this with my experiences (which match the parents’ comment), and I have an idea: Coding contests are not like my job at all. My job is taking fuzzy human things and making code that solves it. Frankly AI isn’t good at closing open issues on open source projects either.
- incrudible 11mo ago
  > The fact that LLMs are trained on a corpus does not mean their output represents the median skill level of the corpus. It does, by default. Try asking ChatGPT to implement quicksort in JavaScript, the result will be dogshit. Of course it can do better if you guide it, but that implies you recognize dogshit, or at least that you use some sort of prompting technique that will veer it off the beaten path.
  - keeda 11mo ago
    I asked the free version of ChatGPT to implement quicksort in JS. I can't really see much wrong with it, but maybe I'm missing something? (Ugh, I just can't get HN to format code right... pastebin here: https://pastebin.com/tjaibW1x https://pastebin.com/tjaibW1x) ---- function quickSortInPlace(arr, left = 0, right = arr.length - 1) { if (left < right) { const pivotIndex = partition(arr, left, right); quickSortInPlace(arr, left, pivotIndex - 1); quickSortInPlace(arr, pivotIndex + 1, right); } return arr; } function partition(arr, left, right) { const pivot = arr[right]; let i = left; for (let j = left; j < right; j++) { if (arr[j] < pivot) { [arr[i], arr[j]] = [arr[j], arr[i]]; i++; } } [arr[i], arr[right]] = [arr[right], arr[i]]; // Move pivot into place return i; }
torginus 11mo ago
In my experience (with ChatGPT 5.1 as of late) is that the AI follows a problem->solution internal logic and doesn't think and try to structure its code. If you ask for an endpoint to a CRUD API, it'll make one. If you ask for 5, it'll repeat the same code 5 times and modify it for the use case. A dev wouldn't do this, they would try to figure out the common parts of code, pull them out into helpers, and try to make as little duplicated code as possible. I feel like the AI has a strong bias towards adding things, and not removing them. The most obviously wrong thing is with CSS - when I try to do some styling, it gets 90% of the way there, but there's almost always something that's not quite right. Then I tell the AI to fix a style, since that div is getting clipped or not correctly centered etc. It almost always keeps adding properties, and after 2-3 tries and an incredibly bloated style, I delete the thing and take a step back and think logically about how to properly lay this out with flexbox.
- BurningFrog 11mo ago
  Bad code is only really bad if it needs to be maintained. If your AI reliably generates working code from a detailed prompt, the prompt is now the source that needs to be maintained. There is no important reason to even look at the generated code
  - AlexandrB 11mo ago
    I think this might be plausible in the future, but it needs a lot more tooling. For starters you need to be able to run the prompt through the exact same model so you can reproduce a "build".
    - josephg 11mo ago
      Even the exact same model isn't enough. There are several sources of nondeterminism in LLMs. These would all need to be squashed or seeded - which as far as I know isn't a feature that openai / anthropic / etc provide.
    - BurningFrog 11mo ago
      OK, then the current models aren't as good as I thought/hoped. I guess one thing it means is that we still need extensive test suites. I suppose an LLM can write those too.
malfist 11mo ago
By volume the vast majority of code on github is students. Think about that when you average github for ai
- pseudalopex 11mo ago
  > By volume the vast majority of code on github is students. Who determined this? How?

rhetocj23 11mo ago

Yes. The people who are amazed with AI were never that good at a particular subject area in the first place - I dont care who you are. You were not good enough - how do I know this? Well I know economics, corporate finance, accounting et al very deeply. Ive engaged with LLMS for years now and still they cannot get below the surface level and are not improving further than this. Its easy to recall information, but something entirely different to do something with that information. Which is what those subject ares are all about - taking something (like a theory) and applying it in a disciplined manner given the context. Thats not to diminish what LLMs can do. But lets get real.

teaearlgraycold 11mo ago

I saw an ad for Lovable. The very first thing I noticed was an exchange where the promoter asked the AI to fix a horizontal scroll bar that was present on his product listing page. This is a common issue with web development, especially so for beginners. The AI’s solution? Hide overflow on the X axis. Probably the most common incorrect solution used by new programmers. But to the untrained eye the AI did everything correctly.