14 ms·

AI doesn't generate working products, that's still your job

• smckk 2mo ago
  "AI has dramatically accelerated the path to a first working version. It has not shortened the distance between a first working version and something production-grade." - Anuradha Weeraman
  • emoII 2mo ago
    I actually agree but what even is ”production grade”? More complexity and excessive fault handling? Nah, happy path coding ftw. Production grade software = collective understanding of the system imo
    • 9rx 2mo ago
      "Production-grade" typically means something that has been battle tested by users and has gone through all the trials and tribulations of dealing with their complains, suggestions, and other feedback to see an initial vision (the prototype) become what users actually want and need. The earlier quote might be slightly overblown as some of those complaints, suggestions, and feedback can be iterated on more quickly thanks to AI. However, I think you will find that the overall premise is sound: The feedback loop is where you will spend the vast majority of your time and no coding agent can speed that up. Code was never the real bottleneck. A full-day coding session now being a 15 minute coding session helps, every so slightly, but when you still need to spend weeks talking to the users to figure out what needs to be done in that day/15 minutes, shaving off a handful of hours relative to weeks remains but a drop in the bucket. The marginal improvement is barely worth recognizing.
  • xyzsparetimexyz 2mo ago
    Uhh yes it has
    • rtdq 2mo ago
      Why?
    • tovej 2mo ago
      Nuh-uh (please use your words to construct a full argument)
      • xyzsparetimexyz 2mo ago
        The parent comment didn't. Why should I? Surely, if AI can reduce the time from 0 to N by 10x, the time from N to full product is also significantly reduced? Its up to the parent comment to properly disprove that, not for me to prove it.
        • Planktonne 2mo ago
          > if AI can reduce the time from 0 to N by 10x, the time from N to full product is also significantly reduced Only if you assume that progress is entirely one-dimensional and there are never any trade-offs. We know both those assumptions are incorrect. An extreme example: leaping out of my office window gets me down to the ground much faster than taking the stairs. Because of recovery time, it does not at all speed my journey home from work.
          • skeledrew 2mo ago
            But if there's a slide outside that window then you have a fun, swift and safe decent.
  • deleted 2mo ago
    [deleted]
  • Jyaif 2mo ago
    Of course it did, what the heck is this guy smoking?
• madikz 2mo ago
  [flagged]
• chii 2mo ago
  The fear in most people is not of losing the job, but of losing their value in the market as ai raises the floor of capabilities of other people competing for the same job.
  • freehorse 2mo ago
    I think the main fear is that the (assumed) increase of productivity means that employers may require less employees, resulting to less positions in the market. But I don't think that expertise is no longer relevant or we see getting more people hired with less expertise because AI. If anything, there is this talk about companies not hiring junior engineers as much. Moreover, as getting the expertise could become harder, it could become more of a moat.
    • chii 2mo ago
      > require less employees, resulting to less positions in the market which i counter with the idea that more efficiency means more output for less - ala, more surplus. This extra surplus drives higher demand for more products/services. It's the same idea as economic resource becoming cheaper leading to a higher usage of it: namely, Jevons paradox.
      • freehorse 2mo ago
        This is an argument that circulates a lot, but there are more bottlenecks than programming in businesses etc, so it does not mean that demand for this resource can increase necessarily to a degree of covering out the productivity increase. For now, I don't think we see companies in general hiring more in order to increase their productivity, if anything we see more and more layoffs and restructures on AI grounds. This could change once it becomes more clear how AI will exactly be used and what the brave new world will look like, but for the medium term future it makes more sense to be concerned than hopeful.
  • ekidd 2mo ago
    What I actually fear is more subtle: I already do a fair bit of project management and technical leadership. I could do more. Sure, I'd miss the coding, but I also enjoy a lot of the stuff around it. But the goal is to expand what the AI can do in each generation. At this point, Fable 5 can ace almost any greenfield project a skilled developer might have written in a few days. But it's bad at refactoring, bad at keeping the code clean as it goes, and bad at discovering new insights as it codes. So Anthropic will train Fable 6, using benchmarks like SlopCodeBench that test maintenance over time. Now what about project management? Train Fable 7. What about product management and talking to stakeholders? Train Fable 8. What about market research and sales? Train Fable 9. By this point, Anthropic doesn't need to actually release these newest models to the public. Why, that might be dangerous! Instead, they write, "deisgn [sic] a successful software product and sell it plz." And they spin up a million dollars worth of compute and let it crank out SaaSes, iPhone apps, etc., driving entire software companies out of business. Then they spin up some more instances, and say, "make robot plz" and "try a thousand ways to make yrself smrater." I mean, Qwen and DeepSeek keep finding ways to pack more smarts into a given number of weights. Fable 9 will likely be able to do the same. Hell, Fable 5 can probably run 1,000 machine learning experiments now, just grinding through ideas the way ChatGPT's internal models grind through proofs. And this is my problem. If it were just programmers losing their jobs, well, sometimes professions die. But what makes you think it will stop with us? How far will this go in the next 4 years? The next 20?
• jdw64 2mo ago
  I've seen too many similar posts on Hacker News. From 2025 to 2026, I've seen countless articles with titles like 'The Prototype Isn't the Product.' I think these are defensive mechanisms, a kind of lullaby for the Gen AI era. Why is this discourse endlessly reproduced? In my view, it's because the industry is still searching for a new methodology to control the waterfall of Gen AI code. The cognitive dissonance that results is being resolved by relying on vague personal virtues like 'craftsmanship,' 'fundamentals of computer science,' and 'human judgment.' If the goal is to review Gen AI code in its entirety, the way an engineer would review a PR, then honestly, I don't see the point of using Gen AI in the first place. Yes, models lack judgment and only do pattern matching. But lately, I've noticed that in closed systems, Gen AI often produces more logically coherent code than humans do. If that's the case, maybe programmers should shift toward designing closed systems where algebraic data types ensure the program works correctly. Because using Gen AI means you're committing to codebases that go beyond individual cognitive limits. Once you start using Gen AI code, there's a subtle mismatch with human written code, a fundamental impedance mismatch, like the one between ORM and SQL. In that sense, I honestly don't know. The arguments that have been repeated for nearly a year all sound basically the same. But when I look closer, this isn't Gen AI era coding. It's just old era methodology with 'human' swapped out for 'AI.' If the subject changes, the methodology should change too. Looking at the countless repetitive posts on HN, it shows what HN programmers are afraid of. They're afraid of the destruction of their overall meta-methodology. All the arguments being made now are about how to become a good senior engineer in the old days. But is that analogy really appropriate for the volume of code AI is generating? The amount of code being generated is exploding. The amount of complexity is exploding. Responsibility is becoming unclear. These aren't issues of individual skill. Saying that drivers just need to be more careful when traffic increases is bad road policy. The core is that the roads and signaling systems need to change. A new subject requires a new methodology. In that sense, I think the recent post from Jane Street is more like a new solution. Of course, ADT doesn't guarantee that modeling always holds either. So honestly, I don't know. When I look at HN, it seems like all I see is what social signals people are most anxious about.
  • movedx01 2mo ago
    The lullaby for the Gen AI era exists, but its for CEOs and it is being played in management meetings, it's main theme is about maximizing EBITDA. The defensive mechanisms kick in because even though more code is generated than ever, we are not observing an equivalent rise in software quality or usefulness, some would perhaps argue it's even opposite. If gen ai for code was really what it is being sold as it would all be obvious to everyone, we would be seeing better software all around us everywhere and posts such as the one here would just be laughed off, delusional, but they are not. The code explosion did happen, the value of software this code makes - not yet. Not to say it won't, its just not here right now, and it never happening is still a possible outcome.
• tim-projects 2mo ago
  When you have built your working product try this prompt: - Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard. Then cry as the ai reveals that it didn't actually do anything close to what it said it did. I call this my million dollar prompt, as in it teaches you just how much you are being fooled.
  • KronisLV 2mo ago
    > Then cry as the ai reveals that it didn't actually do anything close to what it said it did. If using AI to generate code, you told it generate some code, so it did. No amount of "You are an expert developer" or "Make no mistakes" will change the fact that it just generates tokens and has a limited thinking budget. Adversarial review loops of N parallel agents looking at whatever characteristics you care about will make it better, even if it will Nx the tokens you need to achieve something, though in general it will be cheaper than N human reviewers (which you might not have). Obviously you shouldn't forget about traditional tooling for formatting and linting, as well as static code analysis and having test coverage that approaches 100%. It might be annoying to do manually, but AI has no issues with refactoring code to make it more testable and eventually will catch some issues that way. It's never going to be perfect in the 1st attempt. > Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard. This is far too vague though and will never be good, even sans AI. When it comes to AI, it will nitpick the fuck out of the codebase if you ask it to do and sometimes jump around between different approaches because neither is actually a good fit for the problem space (there might not be a good fit at all, just various tradeoffs). If you still ask it to find issues and there's nothing obvious, it will just make shit up in pursuit of being useful (RLHF). When it comes to people, you will get various standards, from "It looks like Java, ship it" to "You should rework a quarter of your codebase because I read about this one approach in an authoritatively written book that you should also follow because I view it as dogma and will hold back your merge until it all works exactly like I want it to." (you get all sorts of people and personalities). In my experience other people are no panacea either, nor is writing code all by myself. Fuck it, I'll take anything and everything to help me ship stuff that's good enough and on time (even if some/most? deadlines within the industry are made up). I'd argue that producing something that would pass most critique and could be considered "good code" (not "good enough") or even more broadly a "good product" might take about an order of magnitude more effort than most people and organizations actually can, or can budget for.
• sdevonoes 2mo ago
  The litmus test is this: do you enjoy consuming AI-generated stuff? I don’t. Whether it’s written text, or video, audio, restaurant menus, clothing pictures, documentation, airport control, ads… I do think there’s value in LLMs but as a sort of better search engines and q/a machines.
  • kuboble 2mo ago
    To be fair. I hate slop as much as any other person. But for me the issue is poor quality rather than just the fact that it's ai. I love good ai stuff even if it's obvious it's ai
    • lotsofpulp 2mo ago
      The problem becomes how to sift through it all to see what is and isn’t good. It was already hard enough with human generated crap to avoid wasting time on the pseudoscience, fabricated data, clickbait/ragebait, and product placement.
  • cyanregiment 2mo ago
    You enjoy good AI all the time you just don’t know it. Nobody enjoys bad 1-shot AI.
    • Forgeties79 2mo ago
      Nobody likes having their time routinely wasted by lazy copy/pasting of ChatGPT outputs, which is what too many people do.
    • darkerside 2mo ago
      Reminds me of CGI in movies. People who hate CGI really only hate bad CGI.
      • Planktonne 2mo ago
        This has been a thought-terminating cliché for years, but actually the conversation around this has been incredibly consistent despite attempts to dismiss it. People still call back to the days of practical effects as more visceral and more convincing because they were. CGI can be great, but it's so often an excuse to cut corners, and people recognise that.
• xyproto 2mo ago
  I can't tell if this is insightful or copium.
• imilev 2mo ago
  I think we have played this game long time ago. If products were a question of a single request then outsourcing companies would dominate over product ones. I think a lot of product development happens in the itearations after the intial prototype/MVP and so on. It is not only the technical aspect to it, you need to spend time on a problem deeply understand what are the root causes of pains and address them in your product, both from UX and also from technical perspective. People were able to "prompt" a product even before to an outsourcing company, but they'd rather pay the fee to a product company because of the expertese they have gained through out the years and all the users they've spoken to.
• jaccola 2mo ago
  The test is simple: have we seen great new products or improvements in the products we use over the past 12,24,36 months? The only great new product I’ve used is my LLM of choice, and those labs seem to be hiring more humans than ever. Maybe it’s true that Claude only just got good enough and that 12 months from now our day to day lives will be way better thanks to LLM-driven product improvements/breakthroughs. My bet is that 12 months from now we will still have no great improvements and the claim will be “LLMs only got good enough in Feb 2027 so you can’t judge anything yet!”
  • budsniffer952 2mo ago
    It's the opposite: people like you will be telling people using these tools successfully, "but where are all great new products???", which, of course, is almost immeasurable. The Internet is a better fax machine and all that.
    • tovej 2mo ago
      Who is using them successfully and what _are_ the great new products? Examples, please. It's easy to prove your point if it's true.
      • darkerside 2mo ago
        This question is a trap. Like programming languages. There's the products everybody hates and the ones nobody uses.
      • budsniffer952 2mo ago
        Nobody has to prove anything to you. I don't care one wink if you like or use AI.
        • suddenlybananas 2mo ago
          Why not prove it though?
    • 2mo ago
• jillesvangurp 2mo ago
  There's going to be plenty of work helping other companies make some sense of their vibe coded efforts. The value of individual projects might decrease, but there will be a lot more of them. And without help they won't actually work out all that well. I talked to a company that does not employ software engineers that were doing some things with Claude Code a few weeks ago. Insightful comment: I want that person to do what I hired them to do, not mess around with code. What they were trying to do was a bit out of their comfort zone and they were smart enough to realize it. There is going to be a lot more of this. What's very real is that companies selling one size fits all products to others are going to have a much harder time selling because everybody is going to expect a thing tailored to them because they now can. Delivering those things is still going to be work that needs to be done. A lot of work actually. People with experience building things with their own hands have an advantage. And if those people also understand the domain in which they are trying to do stuff, that's a double advantage. Like always, most people haven't got a clue about what they actually need. Figuring out what people need (consulting) and then delivering it has always been the job. But you might be able to take on a few more customers now. There won't be a shortage of those once people figure out software just got cheaper.
  • watwut 2mo ago
    > Insightful comment: I want that person to do what I hired them to do, not mess around with code. What does that mean? Who is that person who should not mess with the code?
    • embedding-shape 2mo ago
      I understood it as "code is a means to an end", lots of developers get stuck on just writing and maintaining code, often missing that the business that pay them don't give a damn (for better or worse) about the code or design itself, just that whatever goal they set are being met in the timeline they set.
    • deleted 2mo ago
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    • Philip-J-Fry 2mo ago
• geoelkh 2mo ago
  This is a greatly written article. Thanks
• colesantiago 2mo ago
  I have always said with AI there will always be new jobs. This is becoming more and more true every day.
• sqemo 2mo ago
  AI only does what it is instructed to do. Without deep domain knowledge, results produced from simple prompts alone cannot be turned into production-ready products. In reality, creating detailed prompts, conducting continuous reviews, and providing iterative feedback after the prototype stage often takes even more time than building the prototype itself.
• bonoboTP 2mo ago
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• deleted 2mo ago
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• ThePhysicist 2mo ago
  I'm about to throw away multiple months of LLM generated code for one of my side projects. I was really careful writing design specs and it wasn't even a new code base the LLM worked on, but still after several months of AI changes I feel my code degraded more and more into a subtle mess. Hard to explain, each individual change looked good and logical and on the surface the codebase looks fine, but looking at the whole picture everything is subtly wrong in multiple ways. The same goes for where I used AI for existing commercial code bases. I would love to have AI write production ready software for me, but it's just not there yet, there simply are things that good programmers and architects do that cannot be captured by the training loop of current generation LLMs. I notice the same pattern when using LLMs to write longer text like reports or scientific papers, individually each section they write makes sense but overall the whole document feels off in a hard to describe way. I think it's where you can see the difference between human intelligence and whatever it is LLMs have, it's not the same thing. We are much slower and less able on the small scale but seems we can do some higher level reasoning that is still impossible for LLMs. That always becomes clear when you point an LLM at an obvious flaw it produced and it goes "You are absolutely right!" as if it's obvious in hindsight but when running multiple "Please look for issues" iterations it would never have spotted the issue by itself. That said I think it will be absolutely fine writing a simple CRUD app for you e.g. using some popular JS framework, Tailwind for styling and a regular ORM, there's more than enough training data available for these things. But then again such software could be purchased before already e.g. as a SaaS template, I don't think LLMs are so revolutionary here, they just replace the template (but to be honest a good hand-written SaaS boilerplate is probably still better than a vibe coded one).
  • deleted 2mo ago
    [deleted]
  • jdthedisciple 2mo ago
    [flagged]
    • ThePhysicist 2mo ago
      Yeah I have a lot of experience writing software. My code is written mainly in Golang, but I had models write different code in Javascript, Rust, Python, Shell and other languages already. I used a variety of frontier models over the last years, always the best available model at the time. I prompt LLMs by writing design specs and iterate on them first, then let it implement them step by step, checking the results after each step. That works fine for simpler changes where I use the LLM to write code that I have mostly worked out in my head, it always goes wrong once I try to do that with larger features. I have tried a lot of different things like writing extensive RFCs and design docs for the whole codebase, building harnesses and evaluation loops to ensure we stick to specific paradigms in the codebases but the LLMs still deviate from that in sublte ways and spuriously introduce duplication, wrong abstractions or simple hacks. That said my codebases are quite complex, it's not run of the mill CRUD software, I suspect these LLMs would do much better on these. That's probably why other people report large success using AI based development, 90 % of apps out there are just plain RoR or Django backends, React or Next.js frontend or Android apps, and they are already built following strict cookie cutter recipes, LLMs have no trouble following these. My work is e.g. on novel parser generators, graph data persistence layers, format-preserving pseudonymization and personal information detection in unstructured data so there's really nothing that you can base the software design on apart from general principles, I suppose that is why the models struggle so much. There was a discussion here explaining the attention mechanism of the larger models and why they are not good at using their full context length, that was quite enlightening to me as it explained a lot of the behavior I saw on more complex changes, so I think one mistake I made was to have too long conversations with too much context (even though "on paper" the context length was fine and well within limits of the given model), I guess I need more careful conversation management and in general reduce the level of abstraction I'm working at with an LLM. For me at least they're not yet good enough to work at the business or concept level of abstraction, but they are capable of speeding up delivery of finished architectural designs. Maybe it's also a perception problem. A lot of people will just look at their AI generated software and check that it does what it's supposed to do on the happy path and they will be fine with that, calling it a day (and to be honest I did that too for projects with tight deadlines, though it feels irresponsible). Especially juniors or people without programming background don't care about how the code looks that the AI wrote, I only see these issues because I have 10+ years of experience working by hand in large codebases and I have developed a "taste" for what good code is supposed to look like for me. That might explain why people are feeling so radically different about LLMs, if you don't have all of that intrinsic baggage that senior level developers have amassed over their careers then AI generated code will always look good to you. And maybe they are right, could be that in 10 years no one looks at any code anymore and we just care about tests and making sure the behaviour is correct. To be honest I never looked at Assembly code in the last 10 years and I don't care how my compiler unrolls my loops (mostly) as it's a solved problem for me, maybe it will be similar with the higher level code, we just move the abstraction that we work at to a higher level. But I still feel that we don't have the right tools for working at this higher level yet.
• cynicalsecurity 2mo ago
  > Why this matters AI doesn't write articles, that's still your job.
• yieldcrv 2mo ago
  I’ve been a Lead Engineer in a prior life, and also led many offshore teams that executives and product managers thought were “bad” I have great results with AI assisted code bases and development I am the harness
• deleted 2mo ago
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• melezhik 2mo ago
  I like that one “ They understand what they're asking the model to produce. They review generated code with the same critical eye they'd apply to a junior engineer's pull request. They bring architectural thinking to the conversation, not just feature descriptions. They know when to push back on what the model suggests.” I use AI randomly recently and more and more see it as fantastic poc tool , also it’s really good at comparison existing tools and making some raw material for analysis
• Philip-J-Fry 2mo ago
  I think of it this way. If AI could build the product alone, then why would it ever be worth more than the tokens you spent on it? I think this would hold true 99% of the time. It's literally like asking someone "Can you pay me $1m for new gadget? I hired this other dude for $20p/h and he made it in a week". It simply doesn't add up. The 80/20 rule still stands.
  • fragmede 2mo ago
    Airlines capture almost none of the value they enable. The business man taking a business class plane trip to land a business deal doesn't pay more or less for the flight based on how big the deal is. If the tokens enable software worth a million dollars or zero dollars, it's all just tokens to the seller.
  • _heimdall 2mo ago
    The is the crux of why I don't get the vision that with AI everyone will be entrepreneurs. If everyone can start a successful business simply because of AI, why wouldn't their potential customers just do it themselves?
• melezhik 2mo ago
  Another take . If a (my) product is easy for AI to work with - I call it LLMable - that’s good metric or sign I move in the right direction . AI just guidelines me in that sense …
  • npn 2mo ago
    More like it is a generic idea that has been implemented 1000 times before so LLM already have the perfect solution. Well, not like I'm saying the product is doomed though, because like always implementation details matter.
    • melezhik 2mo ago
      Yep. Say for my dsci framework , I ask AI to convert gh actions pipelines to dsci , and the ratio of successful code generations vs hallucination is very important , need to have it high
• 0wis 2mo ago
  I’ll add that you still need the will and the care to drive it towards an useful product. AI still not magically generate useful products if you don’t ask it to do it and prune the results until it approaches what you desired to create.
• bravetraveler 2mo ago
  I wasn't strong-armed into shitting out products before LLMs, but sure, continue. Convince me this toil is a good thing. edit: Bonus points if you do it without the word 'growth', my pockets/rental are, effectively, the same size. Can't have one without the other, I'm afraid.
• kranner 2mo ago
  Most of this piece reads like the Claude output I have to read after every prompt, cleaned up with an /elegant-writing skill or something. I'm so used to skimming text in this voice, it's hard to pay attention to this. I honestly do not understand why people do this for writing that carries their signature, essentially. Do you want people to associate your name with skimmable fluff? We can talk to each other directly, please. The machine is helpful but it doesn't have to mediate every human interaction ever.
• jbdamask 2mo ago
  Agentic Engineer: "How'd you solve the icing problem?" Vibe Coder: "Icing problem?"
  • jpeeler 2mo ago
    Heh, reference was not immediately obvious to me (it's from Iron Man): https://www.youtube.com/watch?v=hHgkVOOTl_Y https://www.youtube.com/watch?v=hHgkVOOTl_Y
    • jbdamask 2mo ago
      Classic case of a CEO finally understanding why engineers are needed
• dtj1123 2mo ago
  This begs the question of what the fundamentals really are. There are a bajillion bootcamps and learn-x-in-a-weekend books out there, most of which seem to focus on getting you familiar with basic syntax rather than teaching the how and why. If memorising syntax and a standard library are no longer differentiators, then what exactly are you supposed to learn?
• deleted 2mo ago
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• dakolli 2mo ago
  Someone please tell this to my clients.
• smolder 2mo ago
  Blah blah. Software engineering is now prompt engineering. I'm not sure what the point of this article is.
  • goatlover 2mo ago
    Software engineering isn't the actual writing of the code, it's coming up with the appropriate solution. That doesn't go away or fundamentally change with prompting, and if you're in a team the code generation is only part of the project.
• artichokeheart 2mo ago
  AI doesn’t do shit, be honest.
• hollowturtle 2mo ago
  > An experienced engineer using modern AI tools can move at a pace that would have been unimaginable five years ago. I dunno sometimes I think I should have sticked with a simpler solution coded by hand with more tradeoffs, than trying doing more and spend days back and forth in an endless feedback loop. Or fighting back the Agent that is more than happy to over produce. IMO the problem is not the single developer using more agentic tooling, it's the whole work chain, who reviews your changes use agentic tooling assistance and over produce a lot of feedback and you're back with the clanker making many decisions that just drains you at the end of the day. I miss the times where we released many more simple things sith many more tradeoffs and business decisions
• waqasai123 2mo ago
  [flagged]
• FinnLobsien 2mo ago
  I think this is intuitively true, and I've noticed this even as someone who doesn’t have an engineering background. In my LLM coding experience, I’ve had this happen numerous times, for instance when building a card game. The LLM does a great job for a while, but adding special event cards becomes impossible because it imported a standard 52 card deck library instead of having a flexible data model with card objects. I suppose this is knowable and you could tell an LLM to do this from the beginning. It's just that when you view making a game (or any piece of software) as just a throwaway thing and don't start with deep thought about the implementation as an experienced engineer might, you don't think about this. I think part of the issue with AI-generated writing, code, or anything else, is that it robs us of the thinking that used to be baked into the process of making stuff. For my game, if I had to build it from scratch, I surely would've sketched out the kind of data model it requires to not waste my time. I do think some of this stuff is overblown though. It's true that LLMs don't create production-grade stuff, but not all software needs to be scalable, fast, and easily maintainable. This might be required for infrastructure or apps designed to (hopefully eventually) be used by millions. But it's not true for a minor utility, like if I want to build a meal-planning app for my family. I don't care if the app is the fastest it can be or if the data model could eventually support a feature to configure dietary preferences and allergies or to plan the meals for Google's tens of thousands of employees. There was a viral article a few years ago around how software could be like a home-cooked meal. And AI enables exactly that. A home-cooked meal doesn't require culinary perfection, but to feed the family and, maybe more importantly, be a gift of labor to the other person.
• me2too 2mo ago
  I wrote something very related to this: https://pgaleone.eu/ai/2026/07/26/use-your-brain/ https://pgaleone.eu/ai/2026/07/26/use-your-brain/
• batuhandumani 2mo ago
  A half-hearted and timid post. It’s very obvious it was written to avoid criticism. I think the author started writing this on a whim and couldnt bring it to a proper conclusion. What i cant understand is how it managed to get such a high points on hn. Because the piece is completely stale—neither praise nor criticism. The author’s own thoughts are so generic that we hear them every single day in our daily lives, and especially on Hacker News, in the back-and-forth between the “Coddites” and the “Vibers” crowd. My indirect take is that, since AI coding is already shrouded in uncertainty, there’s no need for it to remain in this kind of fog—this article will lose all relevance within five years and end up proving the author wrong.
  • zulban 2mo ago
    It has points because people agree with it.
    • batuhandumani 2mo ago
      Throughout history, there have been many examples of things that most people believed to be true but were actually false. :)
• sajithdilshan 2mo ago
  I have two different experiences with LLMs. First one is that I have vibe coded two different projects for work, one is a slack plugin which basically pushes alerts to a channel based on a people roaster and another one is a gmeet plugin to add a talking timer for each participant in the meeting. I used Opuse 4.6 and both were written with a node backend and I actually don't have a good understanding on how it works, but both works without any issue and are deployed in gcp. However, it was not a one shot prompt, but a very detailed step by step plan created for both apps and then executed including adding tests. Both of these apps are used for as internal team tools and we used to pay 20$ per person (back then the team was 5 people, but now it has grown) for the slack plugin before and after we built our own version of it, now it's just 0.07$ per month and that's the gcp infra cost. Second one in using LLMs more like a coding monkey for work. I design the architecture, discuss it with my teammates, we nitpick and refine the approach and agree on how it should be implemented and then we create detailed JIRA tickets and feed these tickets to LLM (in this case again Opus). However, again first it must create a detailed implementation plan and only after it was reviewed and approved by an engineer it is being implemented. If someone wants a quick working MVP, then I think the first option is the best to even test if it's possible to have a proof of concept. However, if someone wants to build a long lasting product, then throwaway that MVP and do a proper plan thinking about scalability and clean architecture from the start and use LLM as a code monkey. In my experience LLMs are still pretty bad at making better architecture and clean code decisions that is maintainable by humans in the long run.
  • rideontime 2mo ago
    This mirrors my experience. Vibe-coding one-off, low-risk apps, it's been fantastic. Trying to take the same approach when implementing features in massive legacy codebases has been an utter nightmare.
    • sajithdilshan 2mo ago
      Exactly, and in most companies engineers have to implement feature in an existing codebase and hardly get to work on a completely greenfield projects. In my experience most of the time LLM take a quick dirty shortcuts to implement thing in a legacy codebase. It work, but not efficient and would collapse under heavy load, especially DB query implementations
• reaithrowaway 2mo ago
  I build reai.no, an accounting system in Norway. I can say with 100% certainty that if you don't know what you're doing. Then it will still be a mess. I throw away 90% of the code that other devs come with and also from my own prompts. I also tend to close PRs if I'm not sure it is the best solution and just ask them to start from scratch since the second time you implement something (unless you do it completely blindly) probably made you realise how you could make it simpler :) I also focus a lot on teaching devs domain knowledge. Actually learning accounting so they can more independently understand how to build something "correctly".
• hoppp 2mo ago
  I agree that the people who vibe code driven by the fear of being left behind are in a sort of self fulfilling loop. The more code is generated without understanding, the less likely it will be maintained and it will be left behind the audited code in quality and in usefulness. It's so easy to get into AI that the only thing we can be left behind is personal experience or learning. But jumping in and spending on claude to vibe up a SaaS...pretty much anyone can do that now.
• f2hex 2mo ago
  Anyone who thinks this way has completely lost sight of what it means to use generative AI for software projects, namely, the concept of a tool, however sophisticated it may be, but still just a tool. The goal of a project can be anything from a prototype to a finished product, and using AI tools does not change what you want to achieve as the final result.
• sicher 2mo ago
  I am very product focused with my projects. I have Claude write 100% of the code but I challenge its suggestions and make damn sure there are ways for it to verify functionality and correctness. I don't read much of the generated code but am adamant that there are tests: unit, integration and (if possible) against other implementations. And I care a LOT about performance. So far (last 6-7 months) I've built tons of stuff in my spare time: A pdf generator lib, a scheme implementation (R5RS and R7RS) with AOT compilation, a screenplay editor, a code editor, a ripgrep-like lib/engine/cli tool for fast search in a workspace, a markdown parser, a Fountain (screenplay) parser, a lib for dealing with updating apps, tooling for finding duplicate code and generating codemaps - and much more. It's been an absolute ride.
• kouunji 2mo ago
  I find the base logic of this post flawed - it can make a prototype, but what about everything after the prototype? Well, um, then you work on those things too? It seems like the premise of articles like this assume that using AI means whatever you can one-shot from a four line prompt. There are obviously a lot of issues with it, but this just sounds like uncritical self-justification rather than any fundamental insight.
  • mortalapeman 2mo ago
    That's funny, I found the article to align exactly with my own personal experience of working with LLMs in cobdebases with poor test coverage, uncertain requirements and non standard production deployments. As soon as you are off the happy path, they start making really bad assumptions that break in production. Even the smartest LLMs haven't come close to what I'd consider good architecture for those environments.
• jere 2mo ago
  I'm tired of this "X was never the hard part" meme when in fact X (this time: prototyping) was in fact difficult.
• hermitwriter 2mo ago
  It's funny this post was AI generated.
• hnhvkm0w0p 2mo ago
  [dead]
• andai 2mo ago
  Human doesn't write blog post, that's still Claude's job...
• ianvarley 2mo ago
  Can we please not share things that are 100% AI written here, at least without attribution? This one struck me as such, and Pangram (which I believe has a pretty low false positive rate) calls it at literally 100%. If you can’t write your own words, it’s no wonder you can’t get the software past prototype phase.
• api 2mo ago
  Before: most code programmers write is thrown away. Most products fail. Now: most code LLMs write is thrown away. Most products fail. Knowing what to build and how at the high level (architecture and algorithms) has always been the most important part of programming, not banging out code. A 2X to 10X programmer isn’t that way because they bang code faster. They’re that way because they throw away less. Now that programmer wastes less LLM tokens. A big part of LLM appeal is that most code is not artisan craft designed to work beautifully and last forever to be part of something that will also last like a compiler for a popular language or a kernel. Most code is speculative and/or ephemeral. Much of it is also very boring.
• surfingdino 2mo ago
  I have 3+ years of experience working on projects that integrate LLMs into the "old" approach to building software. Overall, whenever clients used LLM to analyse inputs without expecting them to be accurate, we were getting decent results. Not so much on the generative side, where LLMs are still producing crap output. This is on projects where humans wrote most of the code. Things look way worse when working with vibed code. Over the last twelve months or so I encountered a number of issues with code and tests given to me by people with little or no coding experience claiming that this is a finished product and expecting me to "productionise it" (their words, not mine). Because they believe in LLMs like religious zealots and have no idea what programmers with actual experience of writing code are talking about there is a breakdown of communication and a lot of misunderstanding. On my latest project I was given a zip archive and asked to "make it work" by someone who calls themselves a "software architect" but has no idea how software development works. That person was surprised when we asked for a git repository, requirements, and design. None were given and we were told to use Claude to explain what the code is supposed to do. When we pressed for requirements the "architect" sent us a 4-page list of requirements based on the LLMs analysis of the contents of the zip file. It was so vague that it could apply to any project. Similarly, the architect vibed tickets in Jira and we had to clean them up as well, because of their vaugness and repetition or generic requirements. The three of us spent two months turning this turd into a piece of software with working tests and working implementation before we were told a PM will be assigned to our team and we will have to explain to him how the software works and what the requirements are. I am no longer on that project. I have had enough. Another client forced addition on an AI PR reviewer. After initial excitement every dev on a 200+ team started ignoring it, because the fixes it suggests actually break code. The marketing around LLMs convinced idiots that they are capable of producing software and that the greatest enemy of delivery are actual software developers with experience of delivering working software. The lowest point must be the recent series of ads I keep seeing on YouTube in which one character gets overly excited because he vibed a to do app... Every OS comes with one and if you don't like them, there are plenty of free and paid alternatives that cost less and work better than "your" vibed app. I can't wait for the VC money to run out and the LLM madness to go to hell.
  • bigstrat2003 2mo ago
• tonyhart7 2mo ago
  Yeah but back then we need 10 engineer, now we need only maybe 2 - 4
• il-b 2mo ago
  For some strange reason, most of the software I use is getting worse, not better.
• HarHarVeryFunny 2mo ago
  I prefer the article's own "The Prototype Isn't the Product" title, since that seems to better capture what's being discussed. With AI, it can be ridiculously quick to vibe code throwaway prototypes and personal use apps where "seems to mostly work" is the quality criteria, but what remains harder and slower is software engineering - building things where quality and maintainability are critical. There is much less discussion of this - we mostly see people wanting to show off some flashy demo they just vibe coded, not discussion of how useful AI was, or wasn't, in working on some large legacy codebase or whatever non-sexy non-trivial task their day-to-day professional work consists of. You are not going to get a 6am sunday call at home because your vibe-coded 3-D game prototype stopped working, but you surely will if there's a bug in the 24x7 production system that was just updated.
• maxnevermind 2mo ago
  > AI doesn't generate working products A little off the actual topic discussed in the article but I think this is why current AI cycle is over-hyped in terms of its broad impact on the economy. One of the most valuable thing, if not the most valuable, for any business is its ability to come up with new products/services/markets/niches, improvement in that could lead to a huge economic boost but LLMs are atrocious at that or any other truly novel things, that bottleneck still stands.
• niteshbhogta 2mo ago
  [flagged]
• deleted 2mo ago
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