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I read AI coding negativity on Hacker News and Reddit with more and more astonishment every day. It's like we live in different worlds. I expect the breadth of

I read AI coding negativity on Hacker News and Reddit with more and more astonishment every day. It's like we live in different worlds. I expect the breadth of tooling is partly responsible. What it means to you to "use the LLM code" could be very different from what it means to me. What LLM are we talking about? What context does it have? What IDE are you using? Personally, I wrote 200K lines of my B2B SaaS before agentic coding came around. With Sonnet 4 in Agent mode, I'd say I now write maybe 20% of the ongoing code from day to day, perhaps less. Interactive Sonnet in VS Code and GitHub Copilot Agents (autonomous agents running on GitHub's servers) do the other 80%. The more I document in Markdown, the higher that percentage becomes. I then carefully review and test.

skydhash 1y ago
> Personally, I wrote 200K lines of my B2B SaaS That would probably be 1000 line of Common Lisp.
- throwmeaway222 1y ago
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  - skydhash 1y ago
    I think that is the 200 lines of the perl version.
    - abeyer 1y ago
      you put linefeeds in your perl?
oblio 1y ago
What tech stack do you use? Betting in advance that it's JavaScript or Python, probably with very mainstream libraries or frameworks.
- va1a 1y ago
  Is this meant to detract from their situation? These tech stacks are mainstream because so many use them... it's only natural that AI would be the best at writing code in contexts where it has the most available training data.
  - feoren 1y ago
    > These tech stacks are mainstream because so many use them That's a tautology. No, those tech stacks are mainstream because it is easy to get something that looks OK up and running quickly. That's it. That's what makes a framework go mainstream: can you download it and get something pretty on the screen quickly? Long-term maintenance and clarity is absolutely not a strong selection force for what goes mainstream, and in fact can be an opposing force, since achieving long-term clarity comes with tradeoffs that hinder the feeling of "going fast and breaking things" within the first hour of hearing about the framework. A framework being popular means it has optimized for inexperienced developers feeling fast early, which is literally a slightly negative signal for its quality.
  - aDyslecticCrow 1y ago
    No, it's a clarification. There is massive difference between domains, and the parent post did not specify. If the AI can only decently do JS and Python, then it can fully explain the observed disparity in opinion of its usefulness.
- JustExAWS 1y ago
  You are exactly right in my case - JavaScript and Python dealing with the AWS CDK and SDK. Where there is plenty of documentation and code samples. Even when it occasionally gets it wrong, it’s just a matter of telling ChatGPT - “verify your code using the official documentation”. But honestly, even before LLMs when deciding on which technology, service, or frameworks to use I would always go with the most popular ones because they are the easiest to hire for, easiest to find documentation and answers for and when I myself was looking for a job, easiest to be the perfect match for the most jobs.
  - 1y ago
lcnPylGDnU4H9OF 1y ago
> It's like we live in different worlds. There is the huge variance in prompt specificity as well as the subtle differences inherent to the models. People often don't give examples when they talk about their experiences with AI so it's hard to get a read on what a good prompt looks like for a given model or even what a good workflow is for getting useful code out of it.
- ruszki 1y ago
  Some gave. Some even recorded it, and showed it, because they thought that they are good with it. But they weren’t good at all. They were slower than coding by hand, if you wanted to keep quality. Some were almost as quick as copy-pasting from the code just above the generated one, but their quality was worse. They even kept some bugs in the code during their reviews. So the different world is probably what the acceptable level of quality means. I know a lot of coders who don’t give a shit whether it makes sense what they’re doing. What their bad solution will cause in the long run. They ignore everything else, just the “done” state next to their tasks in Jira. They will never solve complex bugs, they simply don’t care enough. At a lot of places, they are the majority. For them, LLM can be an improvement. Claude Code the other day made a test for me, which mocked everything out from the live code. Everything was green, everything was good. On paper. A lot of people simply wouldn’t care to even review properly. That thing can generate a few thousands of lines of semi usable code per hour. It’s not built to review it properly. Serena MCP for example specifically built to not review what it does. It’s stated by their creators.
  - typpilol 1y ago
    Honestly I think LLMs really shine best when your first getting into a language. I just recently got into JavaScript and typescript and being able to ask the llm how to do something and get some sources and link examples is really nice. However using it in a language I'm much more familiar with really decreases the usefulness. Even more so when your code base is mid to large sized
    - ponector 1y ago
feoren 1y ago
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- brushfoot 1y ago
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  - feoren 1y ago
    > What makes you think I'm not "a developer who strongly values brevity and clarity" Some pieces of evidence that make me think that: 1. The base rate of developers who write massively overly verbose code is about 99%, and there's not a ton of signal to deviate from that base rate other than the fact that you post on HN (probably a mild positive signal). 2. An LLM writes 80% of your code now, and my prior on LLM code output is that it's on par with a forgetful junior dev who writes very verbose code. 3. 200K lines of code is a lot. It just is. Again, without more signal, it's hard to deviate from the base rate of what 200K-line codebases look like in the wild. 99.5% of them are spaghettified messes with tons of copy-pasting and redundancy and code-by-numbers scaffolded code (and now, LLM output). This is the state of software today. Keep in mind the bad programmers who make verbose spaghettified messes are completely convinced they're code-ninja geniuses; perhaps even more so than those who write clean and elegant code. You're allowed to write me off as an internet rando who doesn't know you, of course. To me, you're not you, you're every programmer who writes a 200k LOC B2B SaaS application and uses an LLM for 80% of their code, and the vast, vast majority of those people are -- well, not people who share my values. Not people who can code cleanly, concisely, and elegantly. You're a unicorn; cool beans. Before you used LLMs, how often were you copy/pasting blocks of code (more than 1 line)? How often were you using "scaffolds" to create baseline codefiles that you then modified? How often were you copy/pasting code from Stack Overflow and other sources?
  - 3form 1y ago
    At least to me what you said sounded like 200k is just with LLMs but before agents. But it's a very reasonable amount of code for 9 years of work.
  - dang 1y ago
codingdave 1y ago
And also ask: "How much money do you spend on LLMs?" In the long run, that is going to be what drives their quality. At some point the conversation is going to evolve from whether or not AI-assisted coding works to what the price point is to get the quality you need, and whether or not that price matches its value.
aDyslecticCrow 1y ago
I think its down to language and domain more than tools. No model ive tried can write, usefully debug or even explain cmake. (It invents new syntax if it gets stuck, i often have to prompt multiple AI to know if even the first response in the context was made-up) My luck with embedded c has been atrocious for existing codebase (burning millions of tolkens), but passable for small scripts. (Arduino projects) My experience with python is much better. Suggesting relevant libraries and functions, debugging odd errors, or even making small script on its own. Even the original github copilot which i got access to early was excellent on python. Alot of people that seem to have fully embraced agentic vibe-coding seem to be in the web or node.js domain. Which I've not done myself since pre-AI. I've tried most (free or trial) major models or schemes in hope that i find any of them useful, but not found much use yet.
systemf_omega 1y ago
> B2B SaaS Perhaps that's part of it. People here work on all kinds of industries. Some of us are implementing JIT compilers, mission-critical embedded systems or distributed databases. In code bases like this you can't just wing it without breaking a million things, so LLM agents tend to perform really poorly.
- sunrunner 1y ago
  > People here work on all kinds of industries. Yes, it would be nice to have a lot more context (pun intended) when people post how many LoC they introduced. B2B SaaS? Then can I assume that a browser is involved and that a big part of that 200k LoC is the verbose styling DSL we all use? On the other hand, Nginx, a production-grade web server, is 250k LoC (251,232 to be exact [1]). These two things are not comparable. The point being that, as I'm sure we all agree, LoC is not a helpful metric for comparison without more context, and different projects have vastly different amounts of information/feature density per LoC. [1] https://openhub.net/p/nginx https://openhub.net/p/nginx
  - godelski 1y ago
    > when people post how many LoC they introduced. Pretty ironic you and the GP talk about lines of code. From the article: Garman is also not keen on another idea about AI – measuring its value by what percentage of code it contributes at an organization. “It’s a silly metric,” he said, because while organizations can use AI to write “infinitely more lines of code” it could be bad code. “Often times fewer lines of code is way better than more lines of code,” he observed. “So I'm never really sure why that's the exciting metric that people like to brag about.” I'm with Garman here. There's no clean metric for how productive someone is when writing code. At best, this metric is naive, but usually it is just idiotic. Bureaucrats love LoC, commits, and/or Jira tickets because they are easy to measure but here's the truth: to measure the quality of code you have to be capable of producing said code at (approximately) said quality or better. Data isn't just "data" that you can treat as a black box and throw in algorithms. Data requires interpretation and there's no "one size fits all" solution. Data is nothing without its context. It is always biased and if you avoid nuance you'll quickly convince yourself of falsehoods. Even with expertise it is easy to convince yourself of falsehoods. Without expertise it is hopeless. Just go look at Reddit or any corner of the internet where there's armchair experts confidently talking about things they know nothing about. It is always void of nuance and vastly oversimplified. But humans love simplicity. You need to recognize our own biases.
hollowturtle 1y ago
B2B SaaS in most cases are sophisticated masks over some structured data, perhaps with great ux, automation and convenience, so I can see LLMs be more successful there, even so because there is more training data and many processes are streamlined. Not all domains are equal, go try develop a serious game, not the yet another simple and broken arcade, with llms and you'll have a different take
tetha 1y ago
I deal with a few code bases at work and the quality differs a lot between projects and frameworks. We have 1-2 small python services based on Flask and Pydantic, very structured and a well-written development and extension guide. The newer Copilot models perform very well with this, and improving the dev guidelines keep making it better. Very nice. We also have a central configuration of applications in the infrastructure and what systems they need. A lot of similarly shaped JSON files, now with a well-documented JSON schema (which is nice to have anyway). Again, very high quality. Someone recently joked we should throw these service requests at a model and let it create PRs to review. But currently I'm working in Vector and it's Vector remap language... it's enough of a mess that I'm faster working without any copilot "assistance". I think the main issue is that there is very little VRL code out in the open, and the remaps depend on a lot of unseen context, which one would have to work on giving to the LLM. Had similar experiences with OPA and a few more of these DSLs.
mxhwll 1y ago
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malfist 1y ago
Perhaps the issue is you were used to writing 200k lines of code. Most engineers would be agast at that. Lines of code is a debit not a credit
- rootnod3 1y ago
  In that case, LLMs are full on debt-machines.
  - threecheese 1y ago
    Ones that can remediate it though. If I am capable of safely refactoring 1,000 copies of a method, in a codebase that humans don’t look at, did it really matter if the workload functions as designed?
    - JustExAWS 1y ago
      In a type safe language like C# or Java, why could you need an LLM for that? it’s a standard guaranteed safe (as long as you aren’t using reflection) refactor with ReSharper.
    - sdenton4 1y ago
      Jeebus, 'safely' is carrying a hell of a lot of water there...
    - uoaei 1y ago
      Features present in all IDEs over the last 5 years or so are better and more verifiably correct for this task than probabilistic text generators.
- Deestan 1y ago
  I am now making an emotional reaction based on zero knowledge of the B2B codebase's environment, but to be honest I think it is relevant to the discussion on why people are "worlds apart". 200k lines of code is a failure state. At this point you have lost control and can only make changes to the codebase through immense effort, and not at a tolerable pace. Agentic code writers are good at giving you this size of mess and at helping to shovel stuff around to make changes that are hard for humans due to the unusable state of the codebase. If overgrown barely manageble codebases are all a person's ever known and they think it's normal that changes are hard and time-consuming and needing reams of code, I understand that they believe AI agents are useful as code writers. I think they do not have the foundation to tell mediocre from good code. I am extremely aware of the judgemental hubris of this comment. I'd not normally huff my own farts in public this obnoxiously, but I honestly feel it is useful for the "AI hater vs AI sucker" discussion to be honest about this type of emotion.
moi2388 1y ago
GitHub copilot, Microsoft copilot, Gemini, loveable, gpt, cursor with Claude models, you name it.
haburka 1y ago
It’s not just you, I think some engineers benefit a lot from AI and some don’t. It’s probably a combination of factors including: AI skepticism, mental rigidity, how popular the tech stack is, and type of engineering. Some problems are going to be very straightforward. I also think it’s that people don’t know how to use the tool very well. In my experience I don’t guide it to do any kind of software pattern or ideology. I think that just confuses the tool. I give it very little detail and have it do tasks that are evident from the code base. Sometimes I ask it to do rather large tasks and occasionally the output is like 80% of the way there and I can fix it up until it’s useful.
- mlyle 1y ago
  Yah. Latest thing I wrote was * Code using sympy to generate math problems testing different skills for students, with difficulty values affecting what kinds of things are selected, and various transforms to problems possible (e.g. having to solve for z+4 of 4a+b instead of x) to test different subskills (On this part, the LLM did pretty well. The code was correct after a couple of quick iterations, and the base classes and end-use interfaces are correct. There's a few things in the middle that are unnecessarily "superstitious" and check for conditions that can't happen, and so I need to work with the LLM to clean it up. * Code to use IRT to estimate the probability that students have each skill and to request problems with appropriate combinations of skills and difficulties for each student. (This was somewhat garbage. Good database & backend, but the interface to use it was not nice and it kind of contaminated things). * Code to recognize QR codes in the corners of worksheet, find answer boxes, and feed the image to ChatGPT to determine whether the scribble in the box is the answer in the correct form. (This was 100%, first time. I adjusted the prompt it chose to better clarify my intent in borderline cases). The output was, overall, pretty similar to what I'd get from a junior engineer under my supervision-- a bit wacky in places that aren't quite worth fixing, a little bit of technical debt, a couple of things more clever that I didn't expect myself, etc. But I did all of this in three hours and $12 expended. The total time supervising it was probably similar to the amount of time spent supervising the junior engineer... but the LLM turns things around quick enough that I don't need to context switch.
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albrewer 1y ago
My AI experience has varied wildly depending on the problem I'm working on. For web apps in Python, they're fantastic. For hacking on old engineering calculation code written in C/C++, it's an unmitigated disaster and an active hindrance.
- f1shy 1y ago
  Just last week I asked copilot to make a FastCGI client in C. It gave me 5 times code that did not compile. Afer some massaging I got it to compile, didn’t work. After some changes, works. No I say “i do not want to use libfcgi, just want a simple implementation”. After already one hour wrestling, I realize the whole thing blocks, I want no blocking calls… still half an hour later fighting, I’m slowly getting there. I see the code: a total mess. I deleted all, wrote from scratch a 350 lines file which wotks.
  - paool 1y ago
    Context engineering > vibe coding. Front load with instructions, examples, and be specific. How well you write the prompt greatly determines the output. Also, use Claude code not copilot.
    - kentm 1y ago
      At some point it becomes easier to just write the code. If the solution was 350 lines, then I'm guessing it was far easier for them to just write that rather then tweak instructions, find examples, etc to cajole the AI to writing workable code (that would then need to be reviewed and tweaked if doing it properly).
      - f1shy 1y ago
        Exactly, if I have to write a 340 lines prompt, I could very well start just writing code.
    - kortilla 1y ago
      “Just tell it how to write the code and then it will write the code.” No wonder the vast majority of AI adoption is failing to produce results.
rozgo 1y ago
It could be the language. Almost 100% of my code is written by AI, I do supervise as it creates and steer in the right direction. I configure the code agents with examples of all frameworks Im using. My choice of Rust might be disproportionately providing better results, because cargo, the expected code structure, examples, docs, and error messages, are so well thought out in Rust, that the coding agents can really get very far. I work on 2-3 projects at once, cycling through them supervising their work. Most of my work is simulation, physics and complex robotics frameworks. It works for me.
typpilol 1y ago
Honestly the best way to get good code at least with typescript and JavaScript is to have like 50 eslint plugins That way it constantly yells at sonnet 4 to get the code at least in a better state. If anyone is curious I have a massive eslint config for typescript that really gets good code out of sonnet. But before I started doing this the code it wrote was so buggy and it was constantly trying to duplicate functions into separate files etc
s1mplicissimus 1y ago
It's interesting how LLM enthusiasts will point to problems like IDE, context, model etc. but not the one thing that really matters: Which problem are you trying to solve? At this point my assumption is they learned that talking about this question will very quickly reveal that "the great things I use LLMs for" are actually personal throwaway pieces, not to be extended above triviality or maintained over longer than a year. Which, I guess, doesn't make for a great sales pitch.
- maigret 1y ago
  At work I need often smaller, short lived scripts to find this or that insight, or to use visualization to render some data and I find LLMs very useful at that. A non coding topic, but recently I had difficulty articulating a summarized state of a complex project, so I spoke 2 min in the microphone and it gave me a pretty good list of accomplishments, todos and open points. Some colleagues have found them useful for modernizing dependencies of micro services or to help getting a head start on unit test coverage for web apps. All kinds of grunt work that’s not really complex but just really moves quite some text. I agree it’s not life changing, but a nice help when needed.
- phito 1y ago
  It's amazing to make small custom apps and scripts, and they're such high quality (compared to what I would half-ass write and never finish/polish them) that they don't end up as "throwaway", I keep using them all the time. The LLM is saving me time to write these small programs, and the small programs boost my productivity. Often, I will solve a problem in a crappy single-file script, then feed it to Claude and ask to turn it into a proper GUI/TUI/CLI, add CI/CD workflows, a README, etc... I was very skeptical and reluctant of LLM assisted coding (you can look at my history) until I actually tried it last month. Now I am sold.
- wan23 1y ago
  I use it to do all the things that I couldn't be bothered to do before. Generate documentation, dump and transform data for one off analyses, write comprehensive tests, create reports. I don't use it for writing real production code unless the task is very constrained with good test coverage, and when I do it's usually to fix small but tedious bugs that were never going to get prioritized otherwise.
johnnyanmac 1y ago
> It's like we live in different worlds. We probably do, yes. the Web domain compared to a cybersecurity firm compared to embedded will have very different experiences. Because clearly there's a lot more code to train on for one domain than the other (for obvious reasons). You can have colleagues at the same company or even same team have drastically different experiences because they might be in the weeds on a different part of tech. > I then carefully review and test. If most people did this, I would have 90% less issues with AI. But as we expect, people see shortcuts and use them to cut corners, not give more times to polish the edges.
Ballas 1y ago
There is definitely a divide in users - those for which it works and those for which it doesn't. I suspect it comes down to what language and what tooling you use. People doing web-related or python work seem to be doing much better than people doing embedded C or C++. Similarly doing C++ in a popular framework like QT also yields better results. When the system design is not pre-defined or rigid like in QT, then you get completely unmaintainable code as a result. If you are writing code that is/can be "heavily borrowed" - things that have complete examples on Github, then an LLM is perfect.
- PUSH_AX 1y ago
  I think there are still lots of code “artisans” who are completely dogmatic about what code should look like, once the tunnel vision goes and you realise the code just enables the business it all of a sudden becomes a velocity God send.
  - gtsop 1y ago
    Two years in and we are waiting to see all you people (who are free of our tunnel vision) fly high with your velocity. I don't see anyone, am I doing something wrong? Your words predict an explosion of unimaginary magnitude for new code and for new buisnesses. Where is it? Nowhere. Edit: And dont start about how you vibed a SaaS service, show income numbers from paying customers (not buyouts)
    - hn_throwaway_99 1y ago
      There was this recent post about a Cloudflare OAuth client where the author checked in all the AI prompts, https://news.ycombinator.com/item?id=44159166 https://news.ycombinator.com/item?id=44159166. The author of the library (kentonv) comments in the HN thread that he said it took him a few days to write the library with AI help, while he thinks it would have taken weeks or months to write manually. Also, while it may be technically true we're "two years in", I don't think this is a fair assessment. I've been trying AI tools for a while, and the first time I felt "OK, now this is really starting to enhance my velocity" was with the release of Claude 4 in May of this year.
kortilla 1y ago
It could be because your job is boilerplate derivatives of well solved problems. Enjoy the next 1 to 2 years because yours is the job Claude is coming to replace. Stuff Wordpress templates should have solved 5 years ago.
physicsguy 1y ago
Do you not think part of it is just whether employers permit it or not? My conglomerate employer took a long time to get started and has only just rolled out agent mode in GH Copilot, but even that is in some reduced/restricted mode vs the public one. At the same time we have access to lots of models via an internal portal.
- randomNumber7 1y ago
  Companies that don't allow their devs to use LLMs will go bankrupt and in the meantime their employees will try to use their private LLM accounts.
LauraMedia 1y ago
As a practical example, I've recently tried out v0's new updated systems to scaffold a very simple UI where I can upload screenshots from videogames I took and tag them. The resulting code included an API call to run arbitrary SQL queries against the DB. Even after pointing this out, this API call was not removed or at least secured with authentication rules but instead /just/hidden/through/obscur/paths...
cobbzilla 1y ago
It really depends, and can be variable, and this can be frustrating. Yes, I’ve produced thousands of lines of good code with an LLM. And also yes, yesterday I wasted over an hour trying to define a single docker service block for my docker-compose setup. Constant hallucination, eventually had to cross check everything and discover it had no idea what it was doing. I’ve been doing this long enough to be a decent prompt engineer. Continuous vigilance is required, which can sometimes be tiring.
bubblyworld 1y ago
I think people react to AI with strong emotions, which can come from many places, anxiety/uncertainty about the future being a common one, strong dislike of change being another (especially amongst autists, whom I would guess based on me and my friend circle are quite common around here). Maybe it explains a lot of the spicy hot-takes you see here and on lobsters? People are unwilling to think clearly or argue in good faith when they are emotionally charged (see any political discussion). You basically need to ignore any extremist takes entirely, both positive and negative, to get a pulse on what's going on. If you look, there are people out there approaching this stuff with more objectivity than most (mitsuhiko and simonw come to mind, have a look through their blogs, it's a goldmine of information about LLM-based systems).
abm53 1y ago
I am also constantly astonished. That said, observing attempts by skeptics to “unsuccessfully” prompt an LLM have been illuminating. My reaction is usually either: - I would never have asked that kind of question in the first place. - The output you claim is useless looks very useful to me.
Fergusonb 1y ago
I agree, it's like they looked at GPT 3.5 one time and said "this isn't for me" The big 3 - Opus 4.1 GPT5 High, Gemini 2.5 Pro Are astonishing in their capabilities, it's just a matter of providing the right context and instructions. Basically, "you're holding it wrong"
deterministic 1y ago
Lines of code is not a useful metric for anything. Especially not productivity. The less code I write to solve a problem the happier I am.

skydhash 1y ago

> Personally, I wrote 200K lines of my B2B SaaS That would probably be 1000 line of Common Lisp.

throwmeaway222 1y ago
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- skydhash 1y ago
  I think that is the 200 lines of the perl version.
  - abeyer 1y ago
    you put linefeeds in your perl?

oblio 1y ago

What tech stack do you use? Betting in advance that it's JavaScript or Python, probably with very mainstream libraries or frameworks.

va1a 1y ago
Is this meant to detract from their situation? These tech stacks are mainstream because so many use them... it's only natural that AI would be the best at writing code in contexts where it has the most available training data.
- feoren 1y ago
  > These tech stacks are mainstream because so many use them That's a tautology. No, those tech stacks are mainstream because it is easy to get something that looks OK up and running quickly. That's it. That's what makes a framework go mainstream: can you download it and get something pretty on the screen quickly? Long-term maintenance and clarity is absolutely not a strong selection force for what goes mainstream, and in fact can be an opposing force, since achieving long-term clarity comes with tradeoffs that hinder the feeling of "going fast and breaking things" within the first hour of hearing about the framework. A framework being popular means it has optimized for inexperienced developers feeling fast early, which is literally a slightly negative signal for its quality.
- aDyslecticCrow 1y ago
  No, it's a clarification. There is massive difference between domains, and the parent post did not specify. If the AI can only decently do JS and Python, then it can fully explain the observed disparity in opinion of its usefulness.
JustExAWS 1y ago
You are exactly right in my case - JavaScript and Python dealing with the AWS CDK and SDK. Where there is plenty of documentation and code samples. Even when it occasionally gets it wrong, it’s just a matter of telling ChatGPT - “verify your code using the official documentation”. But honestly, even before LLMs when deciding on which technology, service, or frameworks to use I would always go with the most popular ones because they are the easiest to hire for, easiest to find documentation and answers for and when I myself was looking for a job, easiest to be the perfect match for the most jobs.
- 1y ago

lcnPylGDnU4H9OF 1y ago

> It's like we live in different worlds. There is the huge variance in prompt specificity as well as the subtle differences inherent to the models. People often don't give examples when they talk about their experiences with AI so it's hard to get a read on what a good prompt looks like for a given model or even what a good workflow is for getting useful code out of it.

ruszki 1y ago
Some gave. Some even recorded it, and showed it, because they thought that they are good with it. But they weren’t good at all. They were slower than coding by hand, if you wanted to keep quality. Some were almost as quick as copy-pasting from the code just above the generated one, but their quality was worse. They even kept some bugs in the code during their reviews. So the different world is probably what the acceptable level of quality means. I know a lot of coders who don’t give a shit whether it makes sense what they’re doing. What their bad solution will cause in the long run. They ignore everything else, just the “done” state next to their tasks in Jira. They will never solve complex bugs, they simply don’t care enough. At a lot of places, they are the majority. For them, LLM can be an improvement. Claude Code the other day made a test for me, which mocked everything out from the live code. Everything was green, everything was good. On paper. A lot of people simply wouldn’t care to even review properly. That thing can generate a few thousands of lines of semi usable code per hour. It’s not built to review it properly. Serena MCP for example specifically built to not review what it does. It’s stated by their creators.
- typpilol 1y ago
  Honestly I think LLMs really shine best when your first getting into a language. I just recently got into JavaScript and typescript and being able to ask the llm how to do something and get some sources and link examples is really nice. However using it in a language I'm much more familiar with really decreases the usefulness. Even more so when your code base is mid to large sized
  - ponector 1y ago

feoren 1y ago

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brushfoot 1y ago
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- feoren 1y ago
  > What makes you think I'm not "a developer who strongly values brevity and clarity" Some pieces of evidence that make me think that: 1. The base rate of developers who write massively overly verbose code is about 99%, and there's not a ton of signal to deviate from that base rate other than the fact that you post on HN (probably a mild positive signal). 2. An LLM writes 80% of your code now, and my prior on LLM code output is that it's on par with a forgetful junior dev who writes very verbose code. 3. 200K lines of code is a lot. It just is. Again, without more signal, it's hard to deviate from the base rate of what 200K-line codebases look like in the wild. 99.5% of them are spaghettified messes with tons of copy-pasting and redundancy and code-by-numbers scaffolded code (and now, LLM output). This is the state of software today. Keep in mind the bad programmers who make verbose spaghettified messes are completely convinced they're code-ninja geniuses; perhaps even more so than those who write clean and elegant code. You're allowed to write me off as an internet rando who doesn't know you, of course. To me, you're not you, you're every programmer who writes a 200k LOC B2B SaaS application and uses an LLM for 80% of their code, and the vast, vast majority of those people are -- well, not people who share my values. Not people who can code cleanly, concisely, and elegantly. You're a unicorn; cool beans. Before you used LLMs, how often were you copy/pasting blocks of code (more than 1 line)? How often were you using "scaffolds" to create baseline codefiles that you then modified? How often were you copy/pasting code from Stack Overflow and other sources?
- 3form 1y ago
  At least to me what you said sounded like 200k is just with LLMs but before agents. But it's a very reasonable amount of code for 9 years of work.
- dang 1y ago

codingdave 1y ago

And also ask: "How much money do you spend on LLMs?" In the long run, that is going to be what drives their quality. At some point the conversation is going to evolve from whether or not AI-assisted coding works to what the price point is to get the quality you need, and whether or not that price matches its value.

aDyslecticCrow 1y ago

I think its down to language and domain more than tools. No model ive tried can write, usefully debug or even explain cmake. (It invents new syntax if it gets stuck, i often have to prompt multiple AI to know if even the first response in the context was made-up) My luck with embedded c has been atrocious for existing codebase (burning millions of tolkens), but passable for small scripts. (Arduino projects) My experience with python is much better. Suggesting relevant libraries and functions, debugging odd errors, or even making small script on its own. Even the original github copilot which i got access to early was excellent on python. Alot of people that seem to have fully embraced agentic vibe-coding seem to be in the web or node.js domain. Which I've not done myself since pre-AI. I've tried most (free or trial) major models or schemes in hope that i find any of them useful, but not found much use yet.

systemf_omega 1y ago

> B2B SaaS Perhaps that's part of it. People here work on all kinds of industries. Some of us are implementing JIT compilers, mission-critical embedded systems or distributed databases. In code bases like this you can't just wing it without breaking a million things, so LLM agents tend to perform really poorly.

sunrunner 1y ago
> People here work on all kinds of industries. Yes, it would be nice to have a lot more context (pun intended) when people post how many LoC they introduced. B2B SaaS? Then can I assume that a browser is involved and that a big part of that 200k LoC is the verbose styling DSL we all use? On the other hand, Nginx, a production-grade web server, is 250k LoC (251,232 to be exact [1]). These two things are not comparable. The point being that, as I'm sure we all agree, LoC is not a helpful metric for comparison without more context, and different projects have vastly different amounts of information/feature density per LoC. [1] https://openhub.net/p/nginx https://openhub.net/p/nginx
- godelski 1y ago
  > when people post how many LoC they introduced. Pretty ironic you and the GP talk about lines of code. From the article: Garman is also not keen on another idea about AI – measuring its value by what percentage of code it contributes at an organization. “It’s a silly metric,” he said, because while organizations can use AI to write “infinitely more lines of code” it could be bad code. “Often times fewer lines of code is way better than more lines of code,” he observed. “So I'm never really sure why that's the exciting metric that people like to brag about.” I'm with Garman here. There's no clean metric for how productive someone is when writing code. At best, this metric is naive, but usually it is just idiotic. Bureaucrats love LoC, commits, and/or Jira tickets because they are easy to measure but here's the truth: to measure the quality of code you have to be capable of producing said code at (approximately) said quality or better. Data isn't just "data" that you can treat as a black box and throw in algorithms. Data requires interpretation and there's no "one size fits all" solution. Data is nothing without its context. It is always biased and if you avoid nuance you'll quickly convince yourself of falsehoods. Even with expertise it is easy to convince yourself of falsehoods. Without expertise it is hopeless. Just go look at Reddit or any corner of the internet where there's armchair experts confidently talking about things they know nothing about. It is always void of nuance and vastly oversimplified. But humans love simplicity. You need to recognize our own biases.

hollowturtle 1y ago

B2B SaaS in most cases are sophisticated masks over some structured data, perhaps with great ux, automation and convenience, so I can see LLMs be more successful there, even so because there is more training data and many processes are streamlined. Not all domains are equal, go try develop a serious game, not the yet another simple and broken arcade, with llms and you'll have a different take

tetha 1y ago

I deal with a few code bases at work and the quality differs a lot between projects and frameworks. We have 1-2 small python services based on Flask and Pydantic, very structured and a well-written development and extension guide. The newer Copilot models perform very well with this, and improving the dev guidelines keep making it better. Very nice. We also have a central configuration of applications in the infrastructure and what systems they need. A lot of similarly shaped JSON files, now with a well-documented JSON schema (which is nice to have anyway). Again, very high quality. Someone recently joked we should throw these service requests at a model and let it create PRs to review. But currently I'm working in Vector and it's Vector remap language... it's enough of a mess that I'm faster working without any copilot "assistance". I think the main issue is that there is very little VRL code out in the open, and the remaps depend on a lot of unseen context, which one would have to work on giving to the LLM. Had similar experiences with OPA and a few more of these DSLs.

mxhwll 1y ago

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malfist 1y ago

Perhaps the issue is you were used to writing 200k lines of code. Most engineers would be agast at that. Lines of code is a debit not a credit

rootnod3 1y ago
In that case, LLMs are full on debt-machines.
- threecheese 1y ago
  Ones that can remediate it though. If I am capable of safely refactoring 1,000 copies of a method, in a codebase that humans don’t look at, did it really matter if the workload functions as designed?
  - JustExAWS 1y ago
    In a type safe language like C# or Java, why could you need an LLM for that? it’s a standard guaranteed safe (as long as you aren’t using reflection) refactor with ReSharper.
  - sdenton4 1y ago
    Jeebus, 'safely' is carrying a hell of a lot of water there...
  - uoaei 1y ago
    Features present in all IDEs over the last 5 years or so are better and more verifiably correct for this task than probabilistic text generators.
Deestan 1y ago
I am now making an emotional reaction based on zero knowledge of the B2B codebase's environment, but to be honest I think it is relevant to the discussion on why people are "worlds apart". 200k lines of code is a failure state. At this point you have lost control and can only make changes to the codebase through immense effort, and not at a tolerable pace. Agentic code writers are good at giving you this size of mess and at helping to shovel stuff around to make changes that are hard for humans due to the unusable state of the codebase. If overgrown barely manageble codebases are all a person's ever known and they think it's normal that changes are hard and time-consuming and needing reams of code, I understand that they believe AI agents are useful as code writers. I think they do not have the foundation to tell mediocre from good code. I am extremely aware of the judgemental hubris of this comment. I'd not normally huff my own farts in public this obnoxiously, but I honestly feel it is useful for the "AI hater vs AI sucker" discussion to be honest about this type of emotion.

moi2388 1y ago

GitHub copilot, Microsoft copilot, Gemini, loveable, gpt, cursor with Claude models, you name it.

haburka 1y ago

It’s not just you, I think some engineers benefit a lot from AI and some don’t. It’s probably a combination of factors including: AI skepticism, mental rigidity, how popular the tech stack is, and type of engineering. Some problems are going to be very straightforward. I also think it’s that people don’t know how to use the tool very well. In my experience I don’t guide it to do any kind of software pattern or ideology. I think that just confuses the tool. I give it very little detail and have it do tasks that are evident from the code base. Sometimes I ask it to do rather large tasks and occasionally the output is like 80% of the way there and I can fix it up until it’s useful.

mlyle 1y ago
Yah. Latest thing I wrote was * Code using sympy to generate math problems testing different skills for students, with difficulty values affecting what kinds of things are selected, and various transforms to problems possible (e.g. having to solve for z+4 of 4a+b instead of x) to test different subskills (On this part, the LLM did pretty well. The code was correct after a couple of quick iterations, and the base classes and end-use interfaces are correct. There's a few things in the middle that are unnecessarily "superstitious" and check for conditions that can't happen, and so I need to work with the LLM to clean it up. * Code to use IRT to estimate the probability that students have each skill and to request problems with appropriate combinations of skills and difficulties for each student. (This was somewhat garbage. Good database & backend, but the interface to use it was not nice and it kind of contaminated things). * Code to recognize QR codes in the corners of worksheet, find answer boxes, and feed the image to ChatGPT to determine whether the scribble in the box is the answer in the correct form. (This was 100%, first time. I adjusted the prompt it chose to better clarify my intent in borderline cases). The output was, overall, pretty similar to what I'd get from a junior engineer under my supervision-- a bit wacky in places that aren't quite worth fixing, a little bit of technical debt, a couple of things more clever that I didn't expect myself, etc. But I did all of this in three hours and $12 expended. The total time supervising it was probably similar to the amount of time spent supervising the junior engineer... but the LLM turns things around quick enough that I don't need to context switch.
- 1y ago

albrewer 1y ago

My AI experience has varied wildly depending on the problem I'm working on. For web apps in Python, they're fantastic. For hacking on old engineering calculation code written in C/C++, it's an unmitigated disaster and an active hindrance.

f1shy 1y ago
Just last week I asked copilot to make a FastCGI client in C. It gave me 5 times code that did not compile. Afer some massaging I got it to compile, didn’t work. After some changes, works. No I say “i do not want to use libfcgi, just want a simple implementation”. After already one hour wrestling, I realize the whole thing blocks, I want no blocking calls… still half an hour later fighting, I’m slowly getting there. I see the code: a total mess. I deleted all, wrote from scratch a 350 lines file which wotks.
- paool 1y ago
  Context engineering > vibe coding. Front load with instructions, examples, and be specific. How well you write the prompt greatly determines the output. Also, use Claude code not copilot.
  - kentm 1y ago
    At some point it becomes easier to just write the code. If the solution was 350 lines, then I'm guessing it was far easier for them to just write that rather then tweak instructions, find examples, etc to cajole the AI to writing workable code (that would then need to be reviewed and tweaked if doing it properly).
    - f1shy 1y ago
      Exactly, if I have to write a 340 lines prompt, I could very well start just writing code.
  - kortilla 1y ago
    “Just tell it how to write the code and then it will write the code.” No wonder the vast majority of AI adoption is failing to produce results.

rozgo 1y ago

It could be the language. Almost 100% of my code is written by AI, I do supervise as it creates and steer in the right direction. I configure the code agents with examples of all frameworks Im using. My choice of Rust might be disproportionately providing better results, because cargo, the expected code structure, examples, docs, and error messages, are so well thought out in Rust, that the coding agents can really get very far. I work on 2-3 projects at once, cycling through them supervising their work. Most of my work is simulation, physics and complex robotics frameworks. It works for me.

typpilol 1y ago

Honestly the best way to get good code at least with typescript and JavaScript is to have like 50 eslint plugins That way it constantly yells at sonnet 4 to get the code at least in a better state. If anyone is curious I have a massive eslint config for typescript that really gets good code out of sonnet. But before I started doing this the code it wrote was so buggy and it was constantly trying to duplicate functions into separate files etc

s1mplicissimus 1y ago

It's interesting how LLM enthusiasts will point to problems like IDE, context, model etc. but not the one thing that really matters: Which problem are you trying to solve? At this point my assumption is they learned that talking about this question will very quickly reveal that "the great things I use LLMs for" are actually personal throwaway pieces, not to be extended above triviality or maintained over longer than a year. Which, I guess, doesn't make for a great sales pitch.

maigret 1y ago
At work I need often smaller, short lived scripts to find this or that insight, or to use visualization to render some data and I find LLMs very useful at that. A non coding topic, but recently I had difficulty articulating a summarized state of a complex project, so I spoke 2 min in the microphone and it gave me a pretty good list of accomplishments, todos and open points. Some colleagues have found them useful for modernizing dependencies of micro services or to help getting a head start on unit test coverage for web apps. All kinds of grunt work that’s not really complex but just really moves quite some text. I agree it’s not life changing, but a nice help when needed.
phito 1y ago
It's amazing to make small custom apps and scripts, and they're such high quality (compared to what I would half-ass write and never finish/polish them) that they don't end up as "throwaway", I keep using them all the time. The LLM is saving me time to write these small programs, and the small programs boost my productivity. Often, I will solve a problem in a crappy single-file script, then feed it to Claude and ask to turn it into a proper GUI/TUI/CLI, add CI/CD workflows, a README, etc... I was very skeptical and reluctant of LLM assisted coding (you can look at my history) until I actually tried it last month. Now I am sold.
wan23 1y ago
I use it to do all the things that I couldn't be bothered to do before. Generate documentation, dump and transform data for one off analyses, write comprehensive tests, create reports. I don't use it for writing real production code unless the task is very constrained with good test coverage, and when I do it's usually to fix small but tedious bugs that were never going to get prioritized otherwise.

johnnyanmac 1y ago

> It's like we live in different worlds. We probably do, yes. the Web domain compared to a cybersecurity firm compared to embedded will have very different experiences. Because clearly there's a lot more code to train on for one domain than the other (for obvious reasons). You can have colleagues at the same company or even same team have drastically different experiences because they might be in the weeds on a different part of tech. > I then carefully review and test. If most people did this, I would have 90% less issues with AI. But as we expect, people see shortcuts and use them to cut corners, not give more times to polish the edges.

Ballas 1y ago

There is definitely a divide in users - those for which it works and those for which it doesn't. I suspect it comes down to what language and what tooling you use. People doing web-related or python work seem to be doing much better than people doing embedded C or C++. Similarly doing C++ in a popular framework like QT also yields better results. When the system design is not pre-defined or rigid like in QT, then you get completely unmaintainable code as a result. If you are writing code that is/can be "heavily borrowed" - things that have complete examples on Github, then an LLM is perfect.

PUSH_AX 1y ago
I think there are still lots of code “artisans” who are completely dogmatic about what code should look like, once the tunnel vision goes and you realise the code just enables the business it all of a sudden becomes a velocity God send.
- gtsop 1y ago
  Two years in and we are waiting to see all you people (who are free of our tunnel vision) fly high with your velocity. I don't see anyone, am I doing something wrong? Your words predict an explosion of unimaginary magnitude for new code and for new buisnesses. Where is it? Nowhere. Edit: And dont start about how you vibed a SaaS service, show income numbers from paying customers (not buyouts)
  - hn_throwaway_99 1y ago
    There was this recent post about a Cloudflare OAuth client where the author checked in all the AI prompts, https://news.ycombinator.com/item?id=44159166 https://news.ycombinator.com/item?id=44159166. The author of the library (kentonv) comments in the HN thread that he said it took him a few days to write the library with AI help, while he thinks it would have taken weeks or months to write manually. Also, while it may be technically true we're "two years in", I don't think this is a fair assessment. I've been trying AI tools for a while, and the first time I felt "OK, now this is really starting to enhance my velocity" was with the release of Claude 4 in May of this year.

kortilla 1y ago

It could be because your job is boilerplate derivatives of well solved problems. Enjoy the next 1 to 2 years because yours is the job Claude is coming to replace. Stuff Wordpress templates should have solved 5 years ago.

physicsguy 1y ago

Do you not think part of it is just whether employers permit it or not? My conglomerate employer took a long time to get started and has only just rolled out agent mode in GH Copilot, but even that is in some reduced/restricted mode vs the public one. At the same time we have access to lots of models via an internal portal.

randomNumber7 1y ago
Companies that don't allow their devs to use LLMs will go bankrupt and in the meantime their employees will try to use their private LLM accounts.

LauraMedia 1y ago

As a practical example, I've recently tried out v0's new updated systems to scaffold a very simple UI where I can upload screenshots from videogames I took and tag them. The resulting code included an API call to run arbitrary SQL queries against the DB. Even after pointing this out, this API call was not removed or at least secured with authentication rules but instead /just/hidden/through/obscur/paths...

cobbzilla 1y ago

It really depends, and can be variable, and this can be frustrating. Yes, I’ve produced thousands of lines of good code with an LLM. And also yes, yesterday I wasted over an hour trying to define a single docker service block for my docker-compose setup. Constant hallucination, eventually had to cross check everything and discover it had no idea what it was doing. I’ve been doing this long enough to be a decent prompt engineer. Continuous vigilance is required, which can sometimes be tiring.

bubblyworld 1y ago

I think people react to AI with strong emotions, which can come from many places, anxiety/uncertainty about the future being a common one, strong dislike of change being another (especially amongst autists, whom I would guess based on me and my friend circle are quite common around here). Maybe it explains a lot of the spicy hot-takes you see here and on lobsters? People are unwilling to think clearly or argue in good faith when they are emotionally charged (see any political discussion). You basically need to ignore any extremist takes entirely, both positive and negative, to get a pulse on what's going on. If you look, there are people out there approaching this stuff with more objectivity than most (mitsuhiko and simonw come to mind, have a look through their blogs, it's a goldmine of information about LLM-based systems).

abm53 1y ago

I am also constantly astonished. That said, observing attempts by skeptics to “unsuccessfully” prompt an LLM have been illuminating. My reaction is usually either: - I would never have asked that kind of question in the first place. - The output you claim is useless looks very useful to me.

Fergusonb 1y ago

I agree, it's like they looked at GPT 3.5 one time and said "this isn't for me" The big 3 - Opus 4.1 GPT5 High, Gemini 2.5 Pro Are astonishing in their capabilities, it's just a matter of providing the right context and instructions. Basically, "you're holding it wrong"

deterministic 1y ago

Lines of code is not a useful metric for anything. Especially not productivity. The less code I write to solve a problem the happier I am.