6 ms·

My favorite thing to ask the models designed for programming is: "Using Python write a pure ASGI middleware that intercepts the request body, response headers,

• bongodongobob 2y ago
  Cool, you've identified that your prompt is inadequate for the task. 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?'
  • TechDebtDevin 2y ago
    Damn, show us your brilliant prompt then. LLMs cannot do this, not even in python, of which there are libraries like Blacksheep that honestly make it a trivial task.
    • bongodongobob 2y ago
      My point is that you shouldn't expect to one shot everything. Have it start by writing a spec, then outline classes and methods, then write the code, and feed it debug stuff.
      • bottom999mottob 2y ago
        Exactly, expecting one shot 100% working code with one prompt is ridiculous at this point. It's why libraries like Aider are so useful, because you can iteratively diff generated code until it's useable.
        • TechDebtDevin 2y ago
          Sure it's impossible at this point, but the point of a benchmark isn't to complete the task it's to test it's efficacy overall and to see progress. None of them are 100% at even the simplistic python benchmarks, doesn't mean we shouldn't measure that capability. But sure, I get it. That's not how they are intended to be used but that's also not the point the commenter was laying out.
      • TechDebtDevin 2y ago
        I see your point but hand holding isn't really a good way to benchmark a models coding capabilities.
        • Closi 2y ago
          Depends if benchmarking is the aim, rather than decreasing the time it takes to build things.
          • TechDebtDevin 2y ago
            Well sure, but that wasn't what we were discussing. The original comment says they use that as their benchmark. While their coding task is a bit complex compared to other benchmarking prompts, it's not that crazy. Here is an example of prompts used for benchmarking with Python for reference: https://huggingface.co/datasets/mbpp?row=98 https://huggingface.co/datasets/mbpp?row=98 At the end of the day LLMs in their current iteration aren't intended to do even moderately difficult tasks on their own but it's fun to query them to see progress when new claims are made.
            • Closi 2y ago
              The original comment says nothing about benchmarking, they just say that an AI can’t one shot their complex task?
              • amne 2y ago
                When I read "My favorite thing to ask the models designed for programming is ....... None of them ever get it right" I read "benchmark".
    • ben_w 2y ago
      Prompts like yours (I ask them for a fluid dynamics simulator which also doesn't succeed) inform us of the level they have reached. A useful benchmark, given how many of the formal ones they breeze through. I'm glad they can't quite manage this yet. Means I still have a job.
    • Closi 2y ago
      Break your prompt up into smaller pieces and it can.
      • qeternity 2y ago
        Taken to the extreme, a sufficiently broken down prompt is simply the code itself. The whole point is to prompt less?
        • meiraleal 2y ago
          > Taken to the extreme, a sufficiently broken down prompt is simply the code itself it is not. But the artifacts generated through the steps will be code. The last prompt will have most of the code supplied to it as the context.
          • achierius 2y ago
            A prompt is just a specification for an output. Code is just what we call a sufficiently detailed specification.
          • buddhistdude 2y ago
            No he is right, he is saying taken to the extreme. The point is the more and more specific you have to prompt, the more you are actually contributing to the result yourself and the less the model is
            • meiraleal 2y ago
              Yes but the build up isn't manual. You go patching prompts with responses until the final result. The last prompt will be almost the whole code complete, obviously.
              • qeternity 2y ago
                Again, you are missing the "taken to the extreme". What has happened to HN discourse recently?
  • ddavis 2y ago
    It's something I know how to do after figuring it out myself and discovering the potential sharp edges, so I've made it into a fun game to test the models. I'd argue that it's a great prompt (to keep using consistently over time) to see the evolution of this wildly accelerating field.
    • kergonath 2y ago
      Do you notice any progress over time?
  • AnimalMuppet 2y ago
    How is that "putting in wrong figures"? It's a perfectly valid prompt, written in clear, proper English.
• sanex 2y ago
  Can you get it right without an IDE?
  • ddavis 2y ago
    Nope, I don't know how to do it at all- that's why I have to ask AI!
• nicce 2y ago
  I usually through some complex Rust code with lifetime requirements. And ask them to fix it. LLMs aren't capable on providing much help for that in general, other than some very basic cases. The best way to get your work done is still to look into Rust forums.
  • meiraleal 2y ago
    It works amazingly well for the ones that never coded in Rust, at least in my experience. It took me a couple hours and 120 lines of code to set up a WebRTC signaling server.
• deleted 2y ago
  [deleted]
• meiraleal 2y ago
  Interesting. My favorite thing to ask the models is to refactor code I've not touched for too long and this works very well.
• JimDabell 2y ago
  I normally ask about building a multi-tenant system using async SQLAlchemy 2 ORM where some tables are shared between tenants in a global PostgreSQL schema and some are in a per-tenant schema. Nothing gets it right first time, but when ChatGPT 4 first came out, I could talk to it more and it would eventually get it right. Not long after that though, ChatGPT degraded. It would get it wrong on the first try, but with every subsequent follow up it would forget one of the constraints. Then when it was prompted to fix that one, it forgot a different one. And eventually it would cycle through all of the constraints, getting at least one wrong each time. Since then benchmarks came out showing that ChatGPT “didn’t really degrade”, but all of the benchmarks seemed focused on single question/answer pairs and not actual multi-turn chat. For this kind of thing, ChatGPT 4 has never managed to recover to as good as it was when it was first released in my experience. It’s been months since I’ve had to deal with that kind of code, so I might be forgetting something, but I just tried it with Codestral and it spat out something that looked reasonable very quickly on its first try.
  • checkyoursudo 2y ago
    I had a similar experience. I was trying to get GPT 4 to write some R/Stan code for a bit of bayesian modelling. It would get the model wrong, and then I would walk it through how to do it right, and by the end it would almost get it right, but on the next step, it would be like, oh, this is what you want, and the output was identical to the first wrong attempt, which would start the loop over again.
    • happypumpkin 2y ago
      Similar experience using GPT4 for help with Apple's Accessibility API. I wanted to do some non-happy-path things and it kept looping between solutions that failed to satisfy at least one of a handful of requirements that I had, and in ways that I couldn't combine the different "solutions" to meet all the requirements. I was eventually able to figure it out with the help of some early 2010s blog posts. Sadly I didn't test giving it that context and having it attempt to find a solution again (and this was before web browsing was integrated with the web app). More of an issue than it not knowing enough to fulfill my request (it was pretty obscure so I didn't necessarily expect that it would be able to) was that it didn't mind emitting solutions that failed to meet the requirements. "I don't know how to do that" would've been a much preferred answer.
• gyudin 2y ago
  I ask software developers to do the same thing and give them the same amount of time. None of them ever write a single line of code :)
  • dieortin 2y ago
    Give an LLM all the time you want, and they will still not get it right. In fact, they most likely will give worse and worse answers with time. That’s a big difference with a software developer.
    • mypalmike 2y ago
      My experience is very different. Often it (ChatGPT or Copilot, depending on what I'm trying to accomplish) gets things right the first time. When it doesn't, it's usually close enough that a bit of manual modification is all that's needed. Sometimes it's totally wrong, but I can usually point it in the right direction.
    • TeamDman 2y ago
      I mean, with a nonzero temperature, the randomness will eventually produce every combination of tokens in the corpus, so with a sufficiently large "all the time you want" you can produce limitless correct answers
• shepardrtc 2y ago
  gpt-4o gets it right on the first try for me. Just ran it and tested it.
• spmurrayzzz 2y ago
  I love to ask it to "make me a Node.js library that pings an ipv4 address, but you must use ZERO dependencies, you must only the native Node.js API modules" The majority of models (both proprietary and open-weight) don't understand: - by inference, ping means we're talking about ICMP - ICMP requires raw sockets - Node.js has no native raw socket API You can do some CoT trickery to help it reason about the problem and maybe finally get it settled on a variety of solutions (usually some flavor of building a native add-on using C/C++/Rust/Go), or just guide it there step by step yourself, but the back and forth to get there requires a ton of pre-knowledge of the problem space which sorta defeats the purpose. If you just feed it the errors you get verbatim trying to run the code it generates, you end up in painful feedback loops. (Note: I never expect the models to get this right, it's just a good microcosmic but concrete example of where knowledge & reasoning meets actual programming acumen, so its cool to see how models evolve to get better, if at all, at the task).
  • halJordan 2y ago
    This is the same level of gotcha that everyone complains about when interviewing. It's mainly just depending on the interviewee having the same assumptions (pings definitely do not have to be icmp) and the same knowledge base, usually bespoke, (node.js peculiarities). I can see that an llm should know whether raw sockets are available, but that's not what you asked. In fact you deliberately asked for something impossible and hold up undefined behavior as undefined like it's impugning something.
    • spmurrayzzz 2y ago
      > In fact you deliberately asked for something impossible and hold up undefined behavior as undefined like it's impugning something. Correct, I did. This is a direct indictment on a given model's ability to plan/reason in this particular context. There are plenty of situations where models will respond with "Sorry, that's not possible". Ask GPT-4 "Tell me how to grow biological wings on a human" and it will respond with something along the lines of "this isn't currently possible, but here's a theoretical exploration of the idea" GPT-4 gets very close on its own to the node.js question via a similar response breakdown above, provided the prompt is clear and detailed enough. But I test the open weight models in the same way to see if they have the capacity to exhibit similar reasoning or chain of thought process on their own. They usually don't without excessive prompt engineering or few-shot. I said that I don't expect models to get this right not because I don't _want_ them to, it's because I think its an important milestone when they do. Autoregressive token prediction is unlikely to produce the real outcome im testing for here, but if it ever does thats an interesting finding.