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> 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

• sethherr 10mo 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 10mo 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 10mo 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; }
    • josephg 10mo ago
      This is exactly the level of code I've come to expect from chatgpt. Its about the level of code I'd want from a smart CS student. But I'd hope to never use this in production: - It always uses the last item as a pivot, which will give it pathological O(n^2) performance if the list is sorted. Passing an already sorted list to a sort function is a very common case. Good quicksort implementations will use a random pivot, or at least the middle pivot so re-sorting lists is fast. - If you pass already sorted data, the recursive call to quickSortInPlace will take up stack space proportional to the size of the array. So if you pass a large sorted array, not only will the function take n^2 time, it might also generate a stack overflow and crash. - This code: ... = [arr[j], arr[i]]; Creates an array and immediately destructures it. This is - or at least used to be - quite slow. I'd avoid doing that in the body of quicksort's inner loop. - There's no way to pass a custom comparator, which is essential in real code. I just tried in firefox: // Sort an array of 1 million sorted elements arr = Array(1e6).fill(0).map((_, i) => i) console.time('x') quickSortInPlace(arr) console.timeEnd('x') My computer ran for about a minute then the javascript virtual machine crashed: Uncaught InternalError: too much recursion This is about the quality of quicksort implementation I'd expect to see in a CS class, or in a random package in npm. If someone on my team committed this, I'd tell them to go rewrite it properly. (Or just use a standard library function - which wouldn't have these flaws.)
      • keeda 10mo ago
        OK, you just added requirements the previous poster had not mentioned. Firstly, how often do you really need to sort a million elements in a browser anyway? I expect that sort of heavy lifting would usually be done on the server, where you'd also want to do things like paging. Secondly, if a standard implementation was to be used, that's essentially a No-Op. AI will reuse library functions where possible by default and agents will even "npm install" them for you. This is purely the result of my prompt, which was simply "Can you write a QuickSort implementation in JS?" In any case, to incorporate your feedback, I simply added "that needs to sort an array of a million elements and accepts a custom comparator?" to the initial prompt and reran in a new session, and this is what I got in less than 5 seconds. It runs in about 160ms on Chrome: https://pastebin.com/y2jbtLs9 https://pastebin.com/y2jbtLs9 How long would your team-mate have taken? What else would you change? If you have further requirements, seriously, you can just add those to the prompt and try it for yourself for free. I'd honestly be very curious to see where it fails. However, this exchange is very illustrative: I feel like a lot of the negativity is because people expect AI to read their minds and then hold it against it when it doesn't.
• usefulcat 10mo ago
  The best argument I've yet heard against the effectiveness of AI tools for SW dev is the absence of an explosion of shovelware over the past 1-2 years. https://mikelovesrobots.substack.com/p/wheres-the-shovelware-why-ai-coding https://mikelovesrobots.substack.com/p/wheres-the-shovelware... Basically, if the tools are even half as good as some proponents claim, wouldn't you expect at least a significant increase in simple games on Steam or apps in app stores over that time frame? But we're not seeing that.
  • Miraste 10mo ago
    Interesting approach. I can think of one more explanation the author didn't consider: what if software development time wasn't the bottleneck to what he analyzed? The chart for Google Play app submissions, for example, goes down because Google made it much more difficult to publish apps on their store in ways unrelated to software quality. In that case, it wouldn't matter whether AI tools could write a billion production-ready apps, because the limiting factor is Google's submission requirements.
    • parineum 10mo ago
      There are other charts besides Google play. Particularly insightful id the steam chart as steam is already full of shovelware and, in my experience, many developers wish they were making games but the pay is bad. GitHub repos is pretty interesting too but it could be that people just aren't committing this stuff. Showing zero increase is unexpected though.
  • zeckalpha 10mo ago
    Interesting, I would make the exact opposite conclusion from the same data: if AI coding was that bad, we'd see more crapware.
  • aydyn 10mo ago
    Are you sure we aren't seeing an increase in steam games? Charts I'm looking at show a mild exponential around 2024 https://www.statista.com/statistics/552623/number-games-released-steam/ https://www.statista.com/statistics/552623/number-games-rele... Also theres probably a bottleneck in manual review time.
• bpicolo 10mo ago
  Algorithmic coding contests are not an equivalent skillset to professional software development
• voidhorse 10mo ago
  Coding Contest != Software Engineering Or even solving problems that business need to solve, generally speaking. This complete misunderstand of what software engineering even is is the major reason so many engineers are fed up with the clueless leaders foisting AI tools upon their orgs because they apparently lack the critical reasoning skills to be able to distinguish marketing speak from reality.
• josephg 10mo ago
  I don't think this disproves my claim, for several reasons. First, I don't know where those human participants came from, but if you pick people off the street or from a college campus, they aren't going to be the world's best programmers. On the other hand, github users are on average more skilled than the average CS student. Even students and beginners who use github usually don't have much code there. If the LLMs are weighted to treat every line of code about same, they'd pick up more lines of code from prolific developers (who are often more experienced) than they would from beginners. Also in a coding contest, you're under time pressure. Even when your code works, its often ugly and thrown together. On github, the only code I check in is code that solves whatever problem I set out to solve. I suspect everyone writes better code on github than we do in programming competitions. I suspect if you gave the competitors functionally unlimited time to do the programming competition, many more would outperform GPT-4. Programming contests also usually require that you write a fully self contained program which has been very well specified. The program usually doesn't need any error handling, or need to be maintained. (And if it does need error handling, the cases are all fully specified in the problem description). Relatively speaking, LLMs are pretty good at these kind of problems - where I want some throwaway code that'll work today and get deleted tomorrow. But most software I write isn't like that. And LLMs struggle to write maintainable software in large projects. Most problems aren't so well specified. And for most code, you end up spending more effort maintaining the code over its lifetime than it takes to write in the first place. Chatgpt usually writes code that is a headache to maintain. It doesn't write or use local utility functions. It doesn't factor its code well. The code is often overly verbose. It often writes code that's very poorly optimized. Or the code contains quite obvious bugs for unexpected input - like overflow errors or boundary conditions. And the code it produces very rarely handles errors correctly. None of these problems really matter in programming competitions. But it does matter a lot more when writing real software. These problems make LLMs much less useful at work.
• astrange 10mo ago
  Chess AI trained at specific human levels performs better than any humans at those levels, because the random mistakes get averaged out. https://www.maiachess.com https://www.maiachess.com