4 ms·

There are several rubs with that operating protocol extending beyond the "you're holding it wrong" claim. 1) There exists a threshold, only identifiable in ret

There are several rubs with that operating protocol extending beyond the "you're holding it wrong" claim. 1) There exists a threshold, only identifiable in retrospect, past which it would have been faster to locate or write the code yourself than to navigate the LLM's correction loop or otherwise ensure one-shot success. 2) The intuition and motivations of LLMs derive from a latent space that the LLM cannot actually access. I cannot get a reliable answer on why the LLM chose the approaches it did; it can only retroactively confabulate. Unlike human developers who can recall off-hand, or at least review associated tickets and meeting notes to jog their memory. The LLM prompter always documenting sufficiently to bridge this LLM provenance gap hits rub #1. 3) Gradually building prompt dependency where one's ability to take over from the LLM declines and one can no longer answer questions or develop at the same velocity themselves. 4) My development costs increasingly being determined by the AI labs and hardware vendors they partner with. Particularly when the former will need to increase prices dramatically over the coming years to break even with even 2025 economics.

simonw 9mo ago
The value I'm getting from this stuff is so large that I'll take those risks, personally.
- th0ma5 9mo ago
  [flagged]
  - scubbo 9mo ago
    Many people - simonw is the most visible of them, but there are countless others - have given up trying to convinced folks who are determined to not be convinced, and are simply enjoying their increased productivity. This is not a competition or an argument.
    - llmslave2 9mo ago
      Maybe they are struggling to convince others because they are unable to produce evidence that is able to convince people? My experience scrolling X and HN is a bunch of people going "omg opus omg Claude Code I'm 10x more productive" and that's it. Just hand wavy anecdotes based on their own perceived productivity. I'm open to being convinced but just saying stuff is not convincing. It's the opposite, it feels like people have been put under a spell. I'm following The Primeagen, he's doing a series where he is trying these tools on stream and following peoples advice on how to use them the best. He's actually quite a good programmer so I'm eager to see how it goes. So far he isn't impressed and thus neither am I. If he cracks it and unlocks significant productivity then I will be convinced.
      - enraged_camel 9mo ago
        >> Maybe they are struggling to convince others because they are unable to produce evidence that is able to convince people? Simon has produced plenty of evidence over the past year. You can check their submission history and their blog: https://simonwillison.net/ https://simonwillison.net/ The problem with people asking for evidence is that there's no level of evidence that will convince them. They will say things like "that's great but this is not a novel problem so obviously the AI did well" or "the AI worked only because this is a greenfield project, it fails miserably in large codebases".
        llmslave2 9mo ago
        It's true that some people will just continually move the goalposts because they are invested in their beliefs. But that doesn't mean that the skepticism around certain claims aren't relevant. Nobody serious is disputing that LLM's can generate working code. They dispute claims like "Agentic workflows will replace software developers in the short to medium term", or "Agentic workflows lead to 2-100x improvements in productivity across the board". This is what people are looking for in terms of evidence and there just isn't any. Thus far, we do have evidence that AI (at least in OSS) produces a 19% decrease in productivity [0]. We also have evidence that it harms our cognitive abilities [1]. Anecdotally, I have found myself lazily reaching for LLM assistance when encountering a difficult problem instead of thinking deeply about the problem. Anecdotally I also struggle to be more productive using AI-centric agents workflows in areas of expertise. We want evidence that "vibe engineering" is actually more productive across the entire lifespan of a software project. We want evidence that it produces better outcomes. Nobody has yet shown that. It's just people claiming that because they vibe coded some trivial project, all of software development can benefit from this approach. Recently a principle engineer at Google claimed that Claude Code wrote their team's entire year's worth of work in a single afternoon. They later walked that claim back, but most do not. I'm more than happy to be convinced but it's becoming extremely tiring to hear the same claims being parroted without evidence and then you get called a luddite when you question it. It's also tiring when you push them on it and they blame it on the model you use, and then the agent, and then the way you handle context, and then the prompts, and then "skill issue". Meanwhile all they have to show is some slop that could be hand coded in a couple hours by someone familiar with the domain. I use AI, I was pretty bullish on it for the last two years, and the combination of it simply not living up to expectations + the constant barrage of what feels like a stealth marketing campaign parroting the same thing over and over (the new model is way better, unlike the other times we said that) + the amount of absolute slop code that seems to continue to increase + companies like Microsoft producing worse and worse software as they shoehorn AI into every single product (Office was renamed to Copilot 365). I've become very sensitive to it, much in the same way I was very sensitive to the claims being made by certain VC backed webdev companies regarding their product + framework in the last few years. I'm not even going to bring up the economic, social, and environmental issues because I don't think they're relevant, but they do contribute to my annoyance with this stuff. [0] https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o... [1] https://news.harvard.edu/gazette/story/2025/11/is-ai-dulling-our-minds https://news.harvard.edu/gazette/story/2025/11/is-ai-dulling...
  - dang 9mo ago
    Since we asked you to stop hounding another user in this manner and you've continued to do it repeatedly, I've banned the account. This is not what Hacker News is for, and you've done it almost 50 times (!), almost 30 of which have been after we first asked you to stop. That is extreme, and totally unacceptable. https://news.ycombinator.com/item?id=46456850 https://news.ycombinator.com/item?id=46456850 https://news.ycombinator.com/item?id=44726957 https://news.ycombinator.com/item?id=44726957 https://news.ycombinator.com/item?id=44110805 https://news.ycombinator.com/item?id=44110805 (You've also been breaking the site guidelines in plenty of other places - e.g. https://news.ycombinator.com/item?id=46521516 https://news.ycombinator.com/item?id=46521516, https://news.ycombinator.com/item?id=46395646 https://news.ycombinator.com/item?id=46395646. This is not what this site is for, and destroys what it is for.)
theshrike79 9mo ago
I've said this multiple times: This is why you use this AI bubble (it IS a bubble) to use the VC-funded AI models for dirt cheap prices and CREATE tools for yourself. Need a very specific linter? AI can do it. Need a complex Roslyn analyser? AI. Any kind of scripting or automation that you run on your own machine. AI. None of that will go away or suddenly stop working when the bubble bursts. Within just the last 6 months I've built so many little utilities to speed up my work (and personal life) it's completely bonkers. Most went from "hmm, might be cool to..." to a good-enough script/program in an evening while doing chores. Even better, start getting the feel for local models. Current gen home hardware is getting good enough and the local models smart enough so you can, with the correct tooling, use them for suprisingly many things.
- lunar_mycroft 9mo ago
  A risk I see with this approach is that when the bubble pops, you'll be left dependent on a bunch of tools which you don't know how to maintain or replace on your own, and won't have/be able to afford access to LLMs to do it for you.
  - AstroBen 9mo ago
    I thought that initially, but I don't think the skills AI weakens in me are particularly valuable Let's say AI becomes too expensive - I more or less only have to sharpen up being able to write the language. My active recall of the syntax, common methods and libraries. That's not hard or much of a setback Maybe this would be a problem if you're purely vibe coding, but I haven't seen that work long term
  - baq 9mo ago
    Open source models hosted by independent providers (or even yourself, which if the bubble pops will be affordable if you manage to pick up hardware on fire sales) are already good enough to explain most code.
  - theshrike79 9mo ago
    The "tools" in this context are literally a few hundred lines of Python or Github CI build pipeline, we're not talking about 500kLOC massive applications. I'm building tools, not complete factories :) The AI builds me a better hammer specifically for the nails I'm nailing 90% of the time. Even if the AI goes away, I still know how the custom hammer works.
kaydub 9mo ago
> 1) There exists a threshold, only identifiable in retrospect, past which it would have been faster to locate or write the code yourself than to navigate the LLM's correction loop or otherwise ensure one-shot success. I can run multiple agents at once, across multiple code bases (or the same codebase but multiple different branches), doing the same or different things. You absolutely can't keep up with that. Maybe the one singular task you were working on, sure, but the fact that I can work on multiple different things without the same cognitive load will blow you out of the water. > 2) The intuition and motivations of LLMs derive from a latent space that the LLM cannot actually access. I cannot get a reliable answer on why the LLM chose the approaches it did; it can only retroactively confabulate. Unlike human developers who can recall off-hand, or at least review associated tickets and meeting notes to jog their memory. The LLM prompter always documenting sufficiently to bridge this LLM provenance gap hits rub #1. Tell the LLM to document in comments why it did things. Human developers often leave and then people with no knowledge of their codebase or their "whys" are even around to give details. Devs are notoriously terrible about documentation. > 3) Gradually building prompt dependency where one's ability to take over from the LLM declines and one can no longer answer questions or develop at the same velocity themselves. You can't develop at the same velocity, so drop that assumption now. There's all kinds of lower abstractions that you build on top of that you probably can't explain currently. > 4) My development costs increasingly being determined by the AI labs and hardware vendors they partner with. Particularly when the former will need to increase prices dramatically over the coming years to break even with even 2025 economics. You aren't keeping up with the actual economics. This shit is technically profitable, the unprofitable part is the ongoing battle between LLM providers to have the best model. They know software in the past has often been winner takes all so they're all trying to win.

simonw 9mo ago

The value I'm getting from this stuff is so large that I'll take those risks, personally.

th0ma5 9mo ago
[flagged]
- scubbo 9mo ago
  Many people - simonw is the most visible of them, but there are countless others - have given up trying to convinced folks who are determined to not be convinced, and are simply enjoying their increased productivity. This is not a competition or an argument.
  - llmslave2 9mo ago
    Maybe they are struggling to convince others because they are unable to produce evidence that is able to convince people? My experience scrolling X and HN is a bunch of people going "omg opus omg Claude Code I'm 10x more productive" and that's it. Just hand wavy anecdotes based on their own perceived productivity. I'm open to being convinced but just saying stuff is not convincing. It's the opposite, it feels like people have been put under a spell. I'm following The Primeagen, he's doing a series where he is trying these tools on stream and following peoples advice on how to use them the best. He's actually quite a good programmer so I'm eager to see how it goes. So far he isn't impressed and thus neither am I. If he cracks it and unlocks significant productivity then I will be convinced.
    - enraged_camel 9mo ago
      >> Maybe they are struggling to convince others because they are unable to produce evidence that is able to convince people? Simon has produced plenty of evidence over the past year. You can check their submission history and their blog: https://simonwillison.net/ https://simonwillison.net/ The problem with people asking for evidence is that there's no level of evidence that will convince them. They will say things like "that's great but this is not a novel problem so obviously the AI did well" or "the AI worked only because this is a greenfield project, it fails miserably in large codebases".
      - llmslave2 9mo ago
        It's true that some people will just continually move the goalposts because they are invested in their beliefs. But that doesn't mean that the skepticism around certain claims aren't relevant. Nobody serious is disputing that LLM's can generate working code. They dispute claims like "Agentic workflows will replace software developers in the short to medium term", or "Agentic workflows lead to 2-100x improvements in productivity across the board". This is what people are looking for in terms of evidence and there just isn't any. Thus far, we do have evidence that AI (at least in OSS) produces a 19% decrease in productivity [0]. We also have evidence that it harms our cognitive abilities [1]. Anecdotally, I have found myself lazily reaching for LLM assistance when encountering a difficult problem instead of thinking deeply about the problem. Anecdotally I also struggle to be more productive using AI-centric agents workflows in areas of expertise. We want evidence that "vibe engineering" is actually more productive across the entire lifespan of a software project. We want evidence that it produces better outcomes. Nobody has yet shown that. It's just people claiming that because they vibe coded some trivial project, all of software development can benefit from this approach. Recently a principle engineer at Google claimed that Claude Code wrote their team's entire year's worth of work in a single afternoon. They later walked that claim back, but most do not. I'm more than happy to be convinced but it's becoming extremely tiring to hear the same claims being parroted without evidence and then you get called a luddite when you question it. It's also tiring when you push them on it and they blame it on the model you use, and then the agent, and then the way you handle context, and then the prompts, and then "skill issue". Meanwhile all they have to show is some slop that could be hand coded in a couple hours by someone familiar with the domain. I use AI, I was pretty bullish on it for the last two years, and the combination of it simply not living up to expectations + the constant barrage of what feels like a stealth marketing campaign parroting the same thing over and over (the new model is way better, unlike the other times we said that) + the amount of absolute slop code that seems to continue to increase + companies like Microsoft producing worse and worse software as they shoehorn AI into every single product (Office was renamed to Copilot 365). I've become very sensitive to it, much in the same way I was very sensitive to the claims being made by certain VC backed webdev companies regarding their product + framework in the last few years. I'm not even going to bring up the economic, social, and environmental issues because I don't think they're relevant, but they do contribute to my annoyance with this stuff. [0] https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o... [1] https://news.harvard.edu/gazette/story/2025/11/is-ai-dulling-our-minds https://news.harvard.edu/gazette/story/2025/11/is-ai-dulling...
- dang 9mo ago
  Since we asked you to stop hounding another user in this manner and you've continued to do it repeatedly, I've banned the account. This is not what Hacker News is for, and you've done it almost 50 times (!), almost 30 of which have been after we first asked you to stop. That is extreme, and totally unacceptable. https://news.ycombinator.com/item?id=46456850 https://news.ycombinator.com/item?id=46456850 https://news.ycombinator.com/item?id=44726957 https://news.ycombinator.com/item?id=44726957 https://news.ycombinator.com/item?id=44110805 https://news.ycombinator.com/item?id=44110805 (You've also been breaking the site guidelines in plenty of other places - e.g. https://news.ycombinator.com/item?id=46521516 https://news.ycombinator.com/item?id=46521516, https://news.ycombinator.com/item?id=46395646 https://news.ycombinator.com/item?id=46395646. This is not what this site is for, and destroys what it is for.)

theshrike79 9mo ago

I've said this multiple times: This is why you use this AI bubble (it IS a bubble) to use the VC-funded AI models for dirt cheap prices and CREATE tools for yourself. Need a very specific linter? AI can do it. Need a complex Roslyn analyser? AI. Any kind of scripting or automation that you run on your own machine. AI. None of that will go away or suddenly stop working when the bubble bursts. Within just the last 6 months I've built so many little utilities to speed up my work (and personal life) it's completely bonkers. Most went from "hmm, might be cool to..." to a good-enough script/program in an evening while doing chores. Even better, start getting the feel for local models. Current gen home hardware is getting good enough and the local models smart enough so you can, with the correct tooling, use them for suprisingly many things.

lunar_mycroft 9mo ago
A risk I see with this approach is that when the bubble pops, you'll be left dependent on a bunch of tools which you don't know how to maintain or replace on your own, and won't have/be able to afford access to LLMs to do it for you.
- AstroBen 9mo ago
  I thought that initially, but I don't think the skills AI weakens in me are particularly valuable Let's say AI becomes too expensive - I more or less only have to sharpen up being able to write the language. My active recall of the syntax, common methods and libraries. That's not hard or much of a setback Maybe this would be a problem if you're purely vibe coding, but I haven't seen that work long term
- baq 9mo ago
  Open source models hosted by independent providers (or even yourself, which if the bubble pops will be affordable if you manage to pick up hardware on fire sales) are already good enough to explain most code.
- theshrike79 9mo ago
  The "tools" in this context are literally a few hundred lines of Python or Github CI build pipeline, we're not talking about 500kLOC massive applications. I'm building tools, not complete factories :) The AI builds me a better hammer specifically for the nails I'm nailing 90% of the time. Even if the AI goes away, I still know how the custom hammer works.

kaydub 9mo ago

> 1) There exists a threshold, only identifiable in retrospect, past which it would have been faster to locate or write the code yourself than to navigate the LLM's correction loop or otherwise ensure one-shot success. I can run multiple agents at once, across multiple code bases (or the same codebase but multiple different branches), doing the same or different things. You absolutely can't keep up with that. Maybe the one singular task you were working on, sure, but the fact that I can work on multiple different things without the same cognitive load will blow you out of the water. > 2) The intuition and motivations of LLMs derive from a latent space that the LLM cannot actually access. I cannot get a reliable answer on why the LLM chose the approaches it did; it can only retroactively confabulate. Unlike human developers who can recall off-hand, or at least review associated tickets and meeting notes to jog their memory. The LLM prompter always documenting sufficiently to bridge this LLM provenance gap hits rub #1. Tell the LLM to document in comments why it did things. Human developers often leave and then people with no knowledge of their codebase or their "whys" are even around to give details. Devs are notoriously terrible about documentation. > 3) Gradually building prompt dependency where one's ability to take over from the LLM declines and one can no longer answer questions or develop at the same velocity themselves. You can't develop at the same velocity, so drop that assumption now. There's all kinds of lower abstractions that you build on top of that you probably can't explain currently. > 4) My development costs increasingly being determined by the AI labs and hardware vendors they partner with. Particularly when the former will need to increase prices dramatically over the coming years to break even with even 2025 economics. You aren't keeping up with the actual economics. This shit is technically profitable, the unprofitable part is the ongoing battle between LLM providers to have the best model. They know software in the past has often been winner takes all so they're all trying to win.