29 ms·

AI makes the easy part easier and the hard part harder

• Trufa 8mo ago
  [flagged]
  • piskov 8mo ago
    > helping you understand what is happening If only there were things called comments, clean-code, and what have you
  • Forgeties79 8mo ago
    > It's so intriguing, I wonder if the people who are against it haven't even used it properly. I feel like this is a common refrain that sets an impossible bar for detractors to clear. You can simply hand wave away any critique with “you’re just not using it right.” If countless people are “using it wrong” then maybe there’s something wrong with the tool.
    • seanmcdirmid 8mo ago
      There are people who know how to code and people who don’t. AI is the same way, it isn’t a mystery.
      • Forgeties79 8mo ago
        And yet every LLM company pushes it as a simple chat bot that everyone should use for everything right now with no explanation or training. It can’t be a precision tool that requires expertise as well as a universally accessible, simple answer to all our problems. That doesn’t strike me as user error.
    • airstrike 8mo ago
      Illogical. I had Claude read a 2k LOC module on my codebase for a bug that was annoying me for a while. It found it in seconds, a one line fix. I had forgotten to account for translation in one single line. That's objectively valuable. People who argue it has no value or that it only helps normies who can't code or that sooner or later it will backfire are burying their heads in the sand.
      • wtetzner 8mo ago
        This feels like a strawman. Most criticisms of AI for coding are about how overblown the claimed benefits are, not that there are no benefits.
        • airstrike 8mo ago
          While that may very well be true, it's a valid reply to the GP who made this claim, not to my comment explaining to the parent why their argument was logically flawed.
          • deleted 8mo ago
            [deleted]
          • wtetzner 8mo ago
            Except that the GP didn't claim that AI had no value?
          • Forgeties79 8mo ago
            Just because you disagree with me doesn’t mean my argument is “logically flawed.” And as the other commenter said, I never said AI had no value. I have used various AI tools for probably 4 years now. If you’re going to talk to and about people in such a condescending way then you at least ask clarifying questions before jumping to the starkest, least charitable interpretation of their point.
      • Forgeties79 8mo ago
        >Illogical. Dismissive. Also kind of rude. > People who argue it has no value or that it only helps normies who can't code or that sooner or later it will backfire are burying their heads in the sand. I don’t think this describes most people and it’s certainly not what I think.
    • hippo22 8mo ago
      > If countless people are “using it wrong” then maybe there’s something wrong with the tool. Not really. Every tool in existence has people that use it incorrectly. The fact that countless people find value in the tool means it probably is valuable.
      • Forgeties79 8mo ago
        Not saying the tool doesn’t have value. I’m saying the tool has a problem.
    • potsandpans 8mo ago
      A bunch of people with no construction experience could collectively get together and start complaining that their ball pein hammers aren't working. Doesn't mean the hammers are bad, no matter how many people join the community. You need to learn how to use the tools.
      • rileymichael 8mo ago
        A bunch of people with poor programming experience could get together and start claiming their new tool is the future. Doesn’t mean the tool is actually useful, no matter how many people join the community.
        • potsandpans 8mo ago
          Except my analogy is correct and yours is clearly biased. Continue to not use the tools and become irrelevant.
          • Forgeties79 8mo ago
            I don’t think yours is correct or that theirs is biased.
    • dwallin 8mo ago
      When it comes to new emerging technologies everyone is searching the space of possibilities, exploring new ways to use said technologies, and seeing where it applies and creates value. In situations such as this, a positive sign is worth way more than a negative. The chances of many people not using it the right way are much much higher when no one really knows what the “right” way is. It then shows hubris and a lack of imagination for someone in such a situation to think they can apply their negative results to extrapolate to the situation at large. Especially when so many are claiming to be seeing positive utility.
  • zythyx 8mo ago
    > I wonder if the people who are against it haven't even used it properly. I swear this is the reason people are against AI output (there are genuine reasons to be against AI without using it: environmental impact, hardware prices, social/copyright issues, CSAM (like X/Grok)) It feels like a lot of people hear the negatives, and try it and are cynical of the result. Things like 2 r's in Strawberry and the 6-10 fingers on one hand led to multiple misinterpretations of the actual AI benefit: "Oh, if AI can't even count the number of letters in a word, then all its answers are incorrect" is simply not true.
  • seanmcdirmid 8mo ago
    HN has a huge anti AI crowd that is just as vocal and active as its pro AI crowd. My guess that this is true of the industry today and won’t be true of the industry 5 years from now: one of the crowds will have won the argument and the other will be out of the tech industry. Vibe coding and slop strawmen are still strawmen. The quality of the debate is obviously a problem
    • DrewADesign 8mo ago
      I don’t understand why people are so resistant to the idea that use cases actually matter here. If someone says “you’re an idiot because you aren’t writing good, structured prompts,” or “you’re too big of an idiot to realize that your AI-generated code sucks” before knowing anything about what the other person was trying to do, they’re either speaking entirely from an ideological bias, or don’t realize that other people’s coding jobs might look a whole lot more different than theirs do.
      • seanmcdirmid 8mo ago
        We don’t know anything about the commenters other than that they aren’t getting the same results with AI as we are. It’s like if someone complains that since they can’t write fast code and so you shouldn’t be able to either?
        • DrewADesign 8mo ago
          > We don’t know anything about the commenters other than that they aren’t getting the same results with AI as we are. Right. You don’t know what model they’re using, on what service, in what IDE, on what OS, if they’re making a SAP program, a Perl 5 CGI application, a Delphi application, something written in R, a c-based image processing plugin, a node website, HTML for a static site, Excel VBA, etc. etc. etc. > It’s like if someone complains that since they can’t write fast code and so you shouldn’t be able to either? If someone is saying that nobody can get good results from using AI then they’re obviously wrong. If someone says that they get good results with AI and someone else, knowing nothing about their task, says they’re too incompetent to determine that, then they’re wrong. If someone says AI is good for all use cases they’re wrong. If someone says they’re getting bad results using AI and someone else, knowing nothing about their task, says they’re too incompetent to determine that, then they’re wrong. If you make sweeping, declarative, black-and-white statements about AI coding either being good or bad, you’re wrong. If you make assumptions about the reason someone has deemed their experience with AI coding good or bad, not even knowing their use case, you’re wrong.
  • isodev 8mo ago
    What we call AI at the heart of coding agents, is the averaged “echo” of what people have published on the web that has (often illegitimately) ended up in training data. Yes it probably can spit out some trivial snippets but nothing near what’s needed for genuine software engineering. Also, now that StackOverflow is no longer a thing, good luck meaningfully improving those code agents.
    • blackcatsec 8mo ago
      Exactly this. Everything I've seen online is generally "I had a problem that could be solved in a few dozen lines of code and I asked the AI do it for me and it worked great!" But what they asked the AI to do is something people have done a hundred times over, on existing platform tech, and will likely have little to no capability to solve problems that come up 5-10 years from now. The reason AI is so good at coding right now is due to the 2nd Dot Com tech bubble that occurred between the simultaneous release of mobile platforms and the massive expansion of cloud technology. But now that the platforms that existed during that time will no longer exist, because it's no longer profitable to put something out there--the AI platforms will be less and less relevant. Sure, sites like reddit will probably still exist where people will begin to ask more and more information that the AI can't help with, and subsequently the AI will train off of that information; but the rate of that information is going to go down dramatically. In short, at some point the AI models will be worthless and I suspect that'll be whenever the next big "tech revolution" happens.
    • logicprog 8mo ago
      Coding agents are getting most meaningful improvements in coding ability from RLVR now, with priors formed by ingesting open source code and manuals directly, not SO, as the basis. The former doesn't rely on resources external to the AI companies at all, and can be scaled up as much as they like, while the latter will likely continue to expand, and they don't really need more of it if it doesn't. Not to mention that curated synthetic data has been shown to be very effective at training models, so they could generate their own textbooks based on open codebases or new languages or whatever and use that. Model collapse only happens when it's exclusively, and fully un-curated, model output that's being trained on.
  • franciscop 8mo ago
    I've seen some discussions and I'd say there's lots of people who are really against the hyped expectations from the AI marketing materials, not necessarily against the AI itself. Things that people are against that would seem to be against AI, but are not directly against AI itself: - Being forced to use AI at work - Being told you need to be 2x, 5x or 10x more efficient now - Seeing your coworkers fired - Seeing hiring freeze because business think no more devs are needed - Seeing business people make a mock UI with AI and boasting how programming is easy - Seeing those people ask you to deliver in impossible timelines - Frontend people hearing from backend how their job is useless now - Backend people hearing from ML Engineers how their job is useless now - etc When I dig a bit about this "anti-AI" trend I find it's one of those and not actually against the AI itself.
    • zozbot234 8mo ago
      The most credible argument against AI is really the expense involved in querying frontier models. If you want to strengthen the case for AI-assisted coding, try to come up with ways of doing that effectively with a cheap "mini"-class model, or even something that runs locally. "You can spend $20k in tokens and have AI write a full C compiler in a week!" is not a very sensible argument for anything.
      • seanmcdirmid 8mo ago
        Because hardware costs never goes down and energy efficiency never go up overtime? Whatever the value/$ is now, do you really think it is going to be constant?
        • ThrowawayR2 8mo ago
          If hardware industry news is any indication, hardware costs aren't going to be going down for GPUs, RAM, or much of anything over the next 3-5 years.
          • seanmcdirmid 8mo ago
  • existencebox 8mo ago
    I'm similarly bemused by those who don't understand where the anti-AI sentiment could come from, and "they must be doing it wrong" should usually be a bit of a "code smell". (Not to mention that I don't believe this post addresses any of the concrete concerns the article calls out, and makes it sound like much more of a strawman than it was to my reading.) To preempt that on my end, and emphasize I'm not saying "it's useless" so much as "I think there's some truth to what the OP says", as I'm typing this I'm finishing up a 90% LLM coded tool to automate a regular process I have to do for work, and it's been a very successful experience. From my perspective, a tool (LLMs) has more impact than how you yourself directly use it. We talk a lot about pits of success and pits of failure from a code and product architecture standpoint, and right now, as you acknowledge yourself in the last sentence, there's a big footgun waiting for any dev who turns their head off too hard. In my mind, _this is the hard part_ of engineering; keeping a codebase structured, guardrailed, well constrained, even with many contributors over a long period of time. I do think LLMs make this harder, since they make writing code "cheaper" but not necessarily "safer", which flies in the face of mantras such as "the best line of code is the one you don't need to write." (I do feel the article brushes against this where it nods to trust, growth, and ownership) This is not a hypothetical as well, but something I've already seen in practice in a professional context, and I don't think we've figured out silver bullets for yet. While I could also gesture at some patterns I've seen where there's a level of semantic complexity these models simply can't handle at the moment, and no matter how well architected you make a codebase after N million lines you WILL be above that threshold, even that is less of a concern in my mind than the former pattern. (And again the article touches on this re: vibe coding having a ceiling, but I think if anything they weaken their argument by limiting it to vibe coding.) To take a bit of a tangent with this comment though: I have come to agree with a post I saw a few months back, that at this point LLMs have become this cycle's tech-religious-war, and it's very hard to have evenhanded debate in that context, and as a sister post calls out, I also suspect this is where some of the distaste comes from as well.
  • tomhow 8mo ago
    Please don't use uppercase for emphasis. If you want to emphasize a word or phrase, put *asterisks* around it and it will get italicized. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
    • dredmorbius 8mo ago
      And you can escape asterisks using backslashes: Italic *Escaped asterisks* \*Double-Escaped asterisks\* *Italic* \*Escaped asterisks\* \\*Double-Escaped asterisks\\* (tomhow seems to have goofed his escapes above. As I've done many times myself...)
      • dredmorbius 8mo ago
        GP comment since corrected, for the curious. And/or future me.
• r2ob 8mo ago
  404
  • simonw 8mo ago
    I got that too, but then I tried the link a second time and it worked.
  • x3n0ph3n3 8mo ago
    That happened the first time I clicked, but it is back.
  • hsuduebc2 8mo ago
    Just refresh it
    • mattgreenrocks 8mo ago
      Which makes me wonder: how is serving static content at all nondeterministic?
  • MassiveQuasar 8mo ago
    Probably vibe codes his website..
• le-mark 8mo ago
  I vibe coded a retro emulator and assembler with tests. Prompts were minimal and I got really great results (Gemini 3). I tried vibe coding the tricky proprietary part of an app I worked on a few years ago; highly technical domain (yes vague don’t care to dox myself). Lots of prompting and didn’t get close. There are literally thousands of retro emulators on github. What I was trying to do had zero examples on GitHub. My take away is obvious as of now. Some stuff is easy some not at all.
  • zjp 8mo ago
    I call these "embarrassingly solved problems". There are plenty of examples of emulators on GitHub, therefore emulators exist in the latent spaces of LLMs. You can have them spit one out whenever you want. It's embarrassingly solved. There are no examples of what you tried to do.
    • AuthAuth 8mo ago
      Its license washing. The code is great because its already a problem solved by someone else. The AI can spit out the solution with no license and no attribution and somehow its legal. I hope American tech legislation holds that same energy once others start taking American IP and spitting it back out with no license or attribution.
      • irishcoffee 8mo ago
        The models need to get burned down and retrained with these considerations baked in.
        • blackqueeriroh 8mo ago
          No. We need to light all IP law on fire. You shouldn’t able to license or patent software.
          • reverius42 8mo ago
• zozbot234 8mo ago
  If the "hard part" is writing a detailed spec for the code you're about to commit to the project, AI can actually help you with that if you tell it to. You just can't skip that part of the work altogether and cede all control to a runaway slop generator.
• Zigurd 8mo ago
  It's pretty difficult to say what it's going to be three months from now. A few months ago Gemini 2.x in IDEA and related IDEs had to be dragged through coding tasks and would create dumb build time errors on its way to making buggy code. Gemini in Antigravity today is pretty interesting, to the point where it's worth experimenting with vague prompts just to see what it comes up with. Coding agents are not going to just change coding. They make a lot of detailed product management work obsolete and smaller team sizes will make it imperative to reread the agile manifesto and and discard scrum dogma.
• fHr 8mo ago
  as usual the last 20% need 80% and the other 80% need 20% but my god did Ai make my bs corpo easy repeatable shit work like skimming docs writing summaries, skimming jira confluence and so on actually easier and for 90% of bs crud app changes the first draft is also already pretty good tbh I don't write hard/difficult code more then once a week/month.
• kfarr 8mo ago
  I think it makes the annoying part less annoying? Also re: "I spent longer arguing with the agent and recovering the file than I would have spent writing the test myself." In my humble experience arguing with an LLM is a waste of time, and no-one should be spending time recovering files. Just do small changes one at a time, commit when you get something working, and discard your changes and try again if it doesn't. I don't think AI is a panacea, it's just knowing when it's the right tool for the job and when it isn't.
  • arwhatever 8mo ago
    But he started it …
  • swordsith 8mo ago
    Anyone not using version control or a IDE that will keep previous versions for a easy jump back is just being silly. If you're going to play with a kid who has a gun, wear your plates.
    • laserlight 8mo ago
      Once, I told a friend that it was stupid that Claude Code didn't have native IDE integration. His answer: “You don't need an IDE with Claude Code.” I've begun to suspect response that this technology triggers a kind of religion in some people. The technology is obviously perfect, so that any problems you might have are because of you.
      • surajrmal 8mo ago
        I find that I vastly prefer Gemini CLI to antigravity, despite the latter being an ide. Others feel the opposite. I believe it comes down to how you are using AI. It's great they both options exist for both types of people.
  • hyperadvanced 8mo ago
    I don’t think it’s “just” that easy. AI can be great at generating unit tests but it can and will also frequently silently hack said tests to make them pass rather than using them as good indicators of what the program is supposed to be doing.
• peteforde 8mo ago
  Daily agentic user here, and to me the problem here is the very notion of "vibe coding". If you're even thinking in those terms - this idea that never looking at the code has become a goal unto itself - then IMO you're doing LLM-assisted development wrong. This is very much a hot take, but I believe that Claude Code and its yolo peers are an expensive party trick that gives people who aren't deep into this stuff an artificially negative impression of tools that can absolutely be used in a responsible, hugely productive way. Seriously, every time I hear anecdotes about CC doing the sorts of things the author describes, I wonder why the hell anyone is expecting more than quick prototypes from an LLM running in a loop with no intervention from an experienced human developer. Vibe coding is riding your bike really fast with your hands off the handles. It's sort of fun and feels a bit rebellious. But nobody who is really good at cycling is talking about how they've fully transitioned to riding without touching the handles, because that would be completely stupid. We should feel the same way about vibe coding. Meanwhile, if you load up Cursor and break your application development into bite sized chunks, and then work through those chunks in a sane order using as many Plan -> Agent -> Debug conversations with Opus 4.5 (Thinking) as needed, you too will obtain the mythical productivity multipliers you keep accusing us of hallucinating.
  • swordsith 8mo ago
    good take, I wish opus 4.6 wasn't so pricy its great for planning.
    • peteforde 8mo ago
      I've been using 4.6 to do planning, and then switching to 4.5 for agent/debug. 4.5 sticks to a 200k context window, which is how you keep costs sane.
  • lanstin 8mo ago
    Things that claude code/vibe coding is great at: 1. Allowing non-developers to provide very detailed specs for the tools they want or experiences they are imagining 2. Allowing developers to write code using frameworks/languages they only know a bit of and don't like; e.g. I use it to write D3 visualizations or PNG extracts from datastores all the time, without having to learn PNG API or modern javascript frameworks. I just have to know enough to look at the console.log / backtrace and figure out where the fix can be. 3. Analysing large code bases for specific questions (not as accurate on "give me an overall summary" type questions - that one weird thing next to 19 normal things doesn't stick in its craw as much as for a cranky human programmer. It does seem to benefit cranking thru a list of smallish features/fixes rapidly, but even 4.5 or 4.6 seem to get stuck in weird dead ends rarely enough that I'm not expecting it, but often enough to be super annoying. I've been playing around with Gas Town swarming a large scale Java migration project, and its been N declarations of victory and still mvn test isn't even compiling. (mvn build is ok, and the pom is updated to the new stack, so it's not nothing). (These are like 50/50 app code/test code repos).
• arnonejoe 8mo ago
  Totally agree on ai assisted coding resulting in randomly changed code. Sometimes it’s subtle and other times entire methods are removed. I have moved back to just using a JetBrains IDE and coping files in to Gemini so that I can limit context. Then I use the IDE to inspect changes in a git diff, regression test everything, and after all that, commit.
• ernsheong 8mo ago
  404
• socketcluster 8mo ago
  I think AI is just a massive force multiplier. If your codebase has bad foundation and going in the wrong direction with lots of hacks, it will just write code which mirrors the existing style... And you get exactly was OP is suggesting. If however, your code foundations are good and highly consistent and never allow hacks, then the AI will maintain that clean style and it becomes shockingly good; in this case, the prompting barely even matters. The code foundation is everything. But I understand why a lot of people are still having a poor experience. Most codebases are bad. They work (within very rigid constraints, in very specific environments) but they're unmaintainable and very difficult to extend; require hacks on top of hacks. Each new feature essentially requires a minor or major refactoring; requiring more and more scattered code changes as everything is interdependent (tight coupling, low cohesion). Productivity just grinds to a slow crawl and you need 100 engineers to do what previously could have been done with just 1. This is not a new effect. It's just much more obvious now with AI. I've been saying this for years but I think too few engineers had actually built complex projects on their own to understand this effect. There's a parallel with building architecture; you are constrained by the foundation of the building. If you designed the foundation for a regular single storey house, you can't change your mind half-way through the construction process to build a 20-storey skyscraper. That said, if your foundation is good enough to support a 100 storey skyscraper, then you can build almost anything you want on top. My perspective is if you want to empower people to vibe code, you need to give them really strong foundations to work on top of. There will still be limitations but they'll be able to go much further. My experience is; the more planning and intelligence goes into the foundation, the less intelligence and planning is required for the actual construction.
  • isodev 8mo ago
    And what if the foundation was made by the AI itself? What’s the excuse then?
    • Qworg 8mo ago
      Your responsibility as a developer in this new world is design and validation. A poor foundation is a design problem. Throw it away and start again.
• uoaei 8mo ago
  Training is the process of regressing to the mean with respect to the given data. It's no surprise that it wears away sharp corners and inappropriately fills recesses of collective knowledge in the act of its reproduction.
  • esafak 8mo ago
    There is no reason that must be; it could be better than the sum of its parts by taking the best part of each. Humans can do that.
• piskov 8mo ago
  The pattern matching and absence or real thinking is still strong. Tried to move some excel generation logic from epplus to closedxml library. ClosedXml has basically the same API so the conversion was successful. Not a one-shot but relatively easy with a few manual edits. But closedxml has no batch operations (like apply style to the entire column): the api is there but internal implementation is on cell after cell basis. So if you have 10k rows and 50 columns every style update is a slow operaton. Naturally, told all about this to codex 5.3 max thinking level. The fucker still succumbed to range updates here and there. Told it explicitly to make a style cache and reuse styles on cells on same y axis. 5-6 attempts — fucker still tried ranges here and there. Because that is what is usually done. Not here yet. Maybe in a year. Maybe never.
• gamblor956 8mo ago
  It seems like a big part of the divide is that people who learned software engineering find vibe coding to be unsuitable for any project intended to be in use for more than a few while those who learned coding think vibe coding is the next big thing because they never have to deal with the consequences of the bad code.
  • habinero 8mo ago
    Yes. If you have some experience, you know that writing code is a small part of the job, and a much bigger chunk is anticipating and/or dealing with problems. People seem to think engineers like "clean code" because we like to be fancy and show off. Nah, it's clean like a construction site. I need to be able to get the cranes and the heavy machinery in and know where all the buried utilities are. I can't do that if people just build random sheds everywhere and dump their equipment and materials where they are.
• Sparkyte 8mo ago
  Yep it is why the work getting over the threshold is just as long as it was without AI. Someone mentioned it is a force multiplier I don't disagree with this, it is a force multiplier in the mundane and ordinary execution of tasks. Complex ones get harder and hard for it where humans visualize the final result where AI can't. It is predicting from input but it can't know the destination output if the destination isn't part of the input.
• 0xbadcafebee 8mo ago
  I don't think it makes any part harder. What it does do is expose what people have ignored their whole career: the hard part. The last 15 years of software development has been 'human vibe coding'; copy+pasting snippets from SO without understanding them, no planning, constant rearchitecting, shipping code to prod as long as it runs on your laptop. Now that the AI is doing it, suddenly people want to plan their work and enforce tests? Seems like a win-win to me. Even if it slows down development, that would be a win, because the result is enforcement of better quality.
  • LtWorf 8mo ago
    The people who were doing like that are completely happy with LLMs, it's the others who aren't.
  • jama211 8mo ago
    Well said. Much like the self driving debate we don’t need them to be perfect, just better than us to be useful, and clearly they already are for the most part.
• ctoth 8mo ago
  I'm working on a paper connecting articulatory phonology to soliton physics. Speech gestures survive coarticulatory overlap the same way solitons survive collision. The nonlinear dynamics already in the phonetics literature are structurally identical to soliton equations. Nobody noticed because these fields don't share conferences. The article's easy/hard distinction is right but the ceiling for "hard" is too low. The actually hard thing AI enables isn't better timezone bug investigation LOL! It's working across disciplinary boundaries no single human can straddle.
  • deleted 8mo ago
    [deleted]
• iugtmkbdfil834 8mo ago
  Some time back, my manager at the time, who shall remain nameless told the group that having AI is like having 10 people work for you ( he actually had a slightly smaller number, but it was said almost word for word like in the article ) with the expectation being set as: 'you should now be able to do 10x as much'. Needless to say, he was wrong and gently corrected over the course of time. In his defense, his use cases for LLMs at the time were summarizing emails in his email client.. so..eh.. not exactly much to draw realistic experience from. I hate to say it, but maybe nvidia CEO is actually right for once. We have a 'new smart' coming to our world. The type of a person that can move between worlds of coding, management, projects and CEOing with relative ease and translate between those worlds.
  • rootusrootus 8mo ago
    > his use cases for LLMs at the time were summarizing emails in his email client Sounds just like my manager. Though he never has made a proclamation that this meant developers should be 10x as productive or anything along those lines. On the contrary, when I made a joke about LLMs being able to replace managers before they get anywhere near replacing developers, he nearly hyperventilated. Not because he didn't believe me, but because he did, and already been thinking that exact thought. My conclusion so far is that if we get LLMs capable of replacing developers, then by extension we will have replaced a lot of other people first. And when people make jokes like "should have gone into a trade, can't replace that with AI" I think they should be a little more introspective; all the people who aspired to be developers but got kicked out by LLMs will be perfectly able to pivot to trades, and the barrier to entry is low. AI is going to be disruptive across the board.
    • iugtmkbdfil834 8mo ago
      I have half-jokingly talked about getting management, CEOs and board members replaced by LLMs. After all, at the very least, they are actually tested to ensure they do have guardrails to not do anything illegal and to shy away from unethical activities.
    • sdf2erf 8mo ago
• otterley 8mo ago
  If coding was always the “easy part,” what was the point of leetcode grinding for interview preparation?
  • jascha_eng 8mo ago
    The hard part of leet code is not the coding but learning to think about problems the correct way. You can solve leet code problems on the white board with some sketches it has nothing to do with the code itself.
  • SoftTalker 8mo ago
    Filtering for people willing to jump through unreasonable hoops.
    • sdf2erf 8mo ago
      Yeah this basically. They are trying to find a particular kind of person. The people who are truly exceptional at what they do wouldnt waste their time on leetcode crap. Theyd find/create a much better alternative opportunity to allocate their precious resources toward.
      • thedevilslawyer 8mo ago
        they're under 1 in 1000, so the rest are that "kind" of person.
• bsenftner 8mo ago
  People need to consider / realize that the vast majority of source code training data is Github, Gitlab, and essentially the huge sea of started, maybe completed, student and open source project. That large body of source code is for the most part unused, untested, and unsuccessful software of unknown quality. That source code is AI's majority training data, and an AI model in training has no idea what is quality software and what is "bad" software. That means the average source code generated by AI not necessarily good software. Considering it is an average of algorithms, it's surprising generated code runs at all. But then again, generating compiling code is actually trainable, so what is generated can receive extra training support. However, that does not improve the quality of the source code training data, just the fact that it will compile.
  • nayroclade 8mo ago
    This isn't really true though. Pre-training for coding models is just a mass of scraped source-code, but post-training is more than simply generating compiling code. It includes extensive reinforcement learning of curated software-engineering tasks that are designed to teach what high quality code looks like, and to improve abilities like debugging, refactoring, tool use, etc.
    • softwaredoug 8mo ago
      Well and also a lot of Claude Code users data as well. That telemetry is invaluable.
      • sarchertech 8mo ago
        Yeah but how is that any different. The vast majority of prompts are going to be either for failed experiments or one off scripts where no one cares about code quality or by below average developers who don’t understand code quality. Anthropic doesn’t know how to filter telemtry for code we want AI to emulate.
    • sarchertech 8mo ago
      There’s no objective measurement for high quality code, so I don’t think model creators are going to be particularly good at screening for it.
• esafak 8mo ago
  > On a personal project, I asked an AI agent to add a test to a specific file. The file was 500 lines before the request and 100 lines after. I asked why it deleted all the other content. It said it didn't. Then it said the file didn't exist before. I showed it the git history and it apologised, said it should have checked whether the file existed first. Ha! Yesterday an agent deleted the plan file after I told it to "forget about it" (as in, leave it alone).
  • cadamsdotcom 8mo ago
    These types of failures are par for the course, until the tools get better. I accept having to undo the odd unruly edit as part of the cost of getting the value. Much smaller issue when you have version control.
  • pixl97 8mo ago
    > I told it to "forget about it" (as in, leave it alone). I mean in a 'tistic kind of way that makes perfect sense.
• storus 8mo ago
  The "marathon of sprints" paradigm is now everywhere and AI is turning it to 120%. I am not sure how many devs can keep sprinting all the time without any rest. AI maybe can help but it tends to go off-rails quickly when not supervised and reading code one did not author is more exhausting than just fixing one's own code.
• deleted 8mo ago
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• api 8mo ago
  AI is at its best when it makes the boring verbose parts easier.
• marcus_holmes 8mo ago
  I think the author answers their own question at the end. The first 3/4 of the article is "we must be responsible for every line of code in the application, so having the LLM write it is not helping". The last 1/4 is "we had an urgent problem so we got the LLM to look at the code base and find the solution". The situation we're moving to is that the LLM owns the code. We don't look at the code. We tell the LLM what is needed, and it writes the code. If there's a bug, we tell the LLM what the bug is, and the LLM fixes it. We're not responsible for every line of code in the application. It's exactly the same as with a compiler. We don't look at the machine code that the compiler produces. We tell the compiler what we want, using a higher-level abstraction, and the compiler turns that into machine code. We trust compilers to do this error-free, because 50+ years of practice has proven to us that they do this error-free. We're maybe ~1 year into coding agents. It's not surprising that we don't trust LLMs yet. But we will. And it's going to be fascinating how this changes the Computer Science. We have interpreted languages because compilers got so good. Presumably we'll get to non-human-readable languages that only LLMs can use. And methods of defining systems to an LLM that are better than plain English.
  • johnbender 8mo ago
    Compilers don’t do this error free of course BUT if we want them too we can say what it means for a compiler to be correct very directly _one time_ and have it be done for all programs (see the definition for simulation in the CompCert compiler). This is a major and meaningful difference from AI which would need such a specification for each individual application you ask it to build because there is no general specification for correct translation from English to Code.
    • marcus_holmes 8mo ago
      > there is no general specification for correct translation from English to Code. that's an interesting point. Could there be? COBOL was originally an attempt to do this, but it ended up being more Code than English. I think this is the area we need to get better at if we're to trust LLMs like we trust compilers. I'm aware that there's a meme around "we have a method of completely specifying what a computer system should do, it's the code for that system". But again, there are levels of abstraction here. I don't think our current high-level languages are the highest possible level of abstraction.
• kittbuilds 8mo ago
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• crazygringo 8mo ago
  > Reading and understanding other people's code is much harder than writing code. I keep seeing this sentiment repeated in discussions around LLM coding, and I'm baffled by it. For the kind of function that takes me a morning to research and write, it takes me probably 10 or 15 minutes to read and review. It's obviously easier to verify something is correct than come up with the correct thing in the first place. And obviously, if it took longer to read code than to write it, teams would be spending the majority of their time in code review, but they don't. So where is this idea coming from?
  • wredcoll 8mo ago
    Because to verify something is correct you have to understand the what makes it correct which is 99% of writing the code in the first place.
    • crazygringo 8mo ago
      That doesn't make any sense to me. When the code is written, it's all laid out nicely for the reader to understand quickly and verify. Everything is pre-organized, just for you the reader. But in order to write the code, you might have to try 4 different top-level approaches until you figure out the one that works, try integrating with a function from 3 different packages until you find the one that works properly, hunt down documentation on another function you have to integrate with, and make a bunch of mistakes that you need to debug until it produces the correct result across unit test coverage. There's so much time spent on false starts and plumbing and dead ends and looking up documentation and debugging when you code. In contrast, when you read code that already has passing tests... you skip all that stuff. You just ensure it does what it claims and is well-written and look for logic or engineering errors or missing tests or questionable judgment. Which is just so, so much faster.
      • layer8 8mo ago
        > But in order to write the code, you might have to try 4 different top-level approaches until you figure out the one that works , try integrating with a function from 3 different packages until you find the one that works properly If you haven’t spent the time to try the different approaches yourself, tried the different packages etc., you can’t really judge if the code you’re reading is really the appropriate thing. It may look superficially plausible and pass some existing tests, but you haven’t deeply thought through it, and you can’t judge how much of the relevant surface area the tests are actually covering. The devil tends to be in the details, and you have to work with the code and with the libraries for a while to gain familiarity and get a feeling for them. The false starts and dead ends, the reading of documentation, those teach you what is important; without them you can only guess. Wihout having explored the territory, it’s difficult to tell if the place you’ve been teleported to is really the one you want to be in.
• djx22 8mo ago
  Don't let AI write code for you unless it's something trivial. Instead use it to plan things, high level stuff, discuss architecture, ask it to explain concepts. Use it as a research tool. It's great at that. It's bad at writing code when it needs to be performant or needs to span over multiple files. Especially when it spans over multiple files because that's where it starts hallucinating and introducing abstractions and boilerplate that's not necessary and it just makes your life harder when it comes to debugging. Imagine if every function you see starts checking for null params. You ask yourself: "when can this be null", right ? So it complicates your mental model about data flow to the point that you lose track of what's actually real in your system. And once you lose track of that it is impossible to reason about your system. For me AI has replaced searching on stack overflow, google and the 50+ github tabs in my browser. And it's able to answer questions about why some things don't work in the context of my code. Massive win! I am moving much faster because I no longer have to switch context between a browser and my code. My personal belief is that the people who can harness the power of AI to synthesize loads of information and keep polishing their engineering skills will be the ones who are going to land on their feet after this storm is over. At the end of the day AI is just another tool for us engineers to improve our productivity and if you think about what being an engineer looked like before AI even existed, more than 50% of our time was sifting through google search results, stack overflow, github issues and other people's code. That's now gone and in your IDE, in natural language with code snippets adapted to your specific needs.
  • whaleidk 8mo ago
    IME it’s actually really terrible at discussing architecture. It’s incredibly unimaginative and will just confirmation-bias whichever way you are leaning slightly more towards
• skybrian 8mo ago
  Diagnosing difficult bugs has often been considered the "hard part" and coding agents seem quite good at it? So I'm not sure this is a good rule of thumb. AI is better at doing some things than others, but the boundary is not that simple.
• blackqueeriroh 8mo ago
  I swear most of the comments on posts like these are no more original than an LLM, and often less so.
  • Capricorn2481 8mo ago
    Almost like it's been the exact same debate for two years and it's not worth spamming tech forums about on either side.
• rukuu001 8mo ago
  > The hard part is investigation, understanding context, validating assumptions, and knowing why a particular approach is the right one for this situation Yes. Another way to describe it is the valuable part. AI tools are great at delineating high and low value work.
• blacksqr 8mo ago
  > makes the easy part easier and the hard part harder That is to say, just like every headline-grabbing programming "innovation" of the last thirty years.
• threethirtytwo 8mo ago
  The truth is that it’s lowering the difficulty of work people used to consider hard. Which parts get easier depends on the role, but the change is already here. A lot of people are lying to themselves. Programming is in the middle of a structural shift, and anyone whose job is to write software is exposed to it. If your self-worth is tied to being good at this, the instinct to minimize what’s happening is understandable. It’s still denial. The systems improve month to month. That’s observable. Most of the skepticism I see comes from shallow exposure, old models, or secondhand opinions. If your mental model is based on where things were a year ago, you’re arguing with a version that no longer exists. This isn’t a hype wave. I’m a software engineer. I care about rigor, about taste, about the things engineers like to believe distinguish serious work. I don’t gain from this shift. If anything, it erodes the value of skills I spent years building. That doesn’t change the outcome. The evidence isn’t online chatter. It’s sitting down and doing the work. Entire applications can be produced this way now. The role changes whether people are ready to admit it or not. Debating the reality of it at this point mostly signals distance from the practice itself.
• tabs_or_spaces 8mo ago
  > My friend's panel raised a point I keep coming back to: if we sprint to deliver something, the expectation becomes to keep sprinting. Always. Tired engineers miss edge cases, skip tests, ship bugs. More incidents, more pressure, more sprinting. It feeds itself. Sorry but this is the whole point of software engineering in a company. The aim is to deliver value to customers at a consistent pace. If a team cannot manage their own burnout or expectations with their stakeholders then this is a weak team. It has nothing to do with using ai to make you go faster. Ai does not cause this at all.
• tern 8mo ago
  I think this is the wrong mental model. The correct one is: 'AI makes everything easier, but it's a skill in itself, and learning that skill is just as hard as learning any other skill.' For a more complete understand, you also have to add: 'we're in the ENIAC era of AI. The equivalents of high-level languages and operating systems haven't yet been invented.' I have no doubt the next few years will birth a "context engineering" academic field, and everything we're doing currently will seem hopelessly primitive. My mind changed on this after attempting complex projects—with the right structure, the capabilities appear unbounded in practice. But, of course, there is baked-in mean reversion. Doing the most popular and uncomplicated things is obviously easier. That's just the nature of these models.
  • xeyownt 8mo ago
    Funny how people only looks at the easy part, but not the cost part. "I did it with AI" = "I did it with an army of CPU burning considerable resources and owned by a foreign company." Give me an AI agent that I own and operate 100%, and the comparison will be fair. Otherwise it's not progress, but rather a theft at planetary scale.
    • tern 8mo ago
      If you want to play that game, you need to offer a fair comparison against the cost of "operating" the equivalent human being(s), measured in caloric input, waste output, etc.
      • strogonoff 8mo ago
        Minimising the cost of “operating” humans means getting rid of humans. When human beings are not slaves and operate in a fair system, humans doing things is part humans living. Believe it or not, they may actually enjoy doing things; taking away the ability to do things we enjoy, and especially for compensation, is in fact harmful.
        • tern 8mo ago
• TrackerFF 8mo ago
  My experience has been that if you fully embrace vibe coding...you can get some neat stuff accomplished, but the technical debt you accumulate is of such magnitude that you're basically a slave to the machine. Once the project crosses a couple of thousands of line of code, none of which you've written yourself, it becomes difficult to actually keep up what's happening. Even reviewing can become challenging since you get it all at once, and the LLM-esque coding style can at times be bloated and obnoxious. I think in the end, with how things are right now, we're going to see the rise of disposable code and software. The models can churn out apps / software which will solve your specific problem, but that's about it. Probably a big risk to all the one-trick pony SaaS companies out there.
• neoden 8mo ago
  Coding with AI assistants is just a completely different skill that one should not measure from the perspective of comparing it to the way human programmers write code. Mostly everything that we have: programming languages, frameworks, principles of software development in teams, agile/clean code/TDD/DRY and other debatable or well accepted practices — all this exists to overcome limitations of human mind. AI does not have them and have others. What I found to be useful for complex tasks is to use it as a tool to explore that highly-dimensional space that lies behind the task being solved. It rarely can be described as giving a prompt and coming back for a result. For me it's usually about having winding conversations, writing lists of invariants and partial designs and feeding them back in a loop. Hallucinations and mistakes become a signal that shows whether my understanding of the problem does or does not fit.
• nirui 8mo ago
  I'm feeling people are using AI in the wrong way. Current LLM is best used to generate a string of text that's most statically likely to form a sentence together, so from user's perspective, it's most useful as an alternative to manual search engine to allow user to find quick answers to a simple question, such as "how much soda is needed for baking X unit of Y bread", or "how to print 'Hello World' in a 10 times in a loop in X programming language". Beyond this use case, the result can be unreliable, and this is something to be expected. Sure, it can also generate long code and even an entire fine-looking project, but it generates it by following a statistical template, that's it. That's why "the easy part" is easy because the easy problem you try to solve is likely already been solved by someone else on GitHub, so the template is already there. But the hard, domain-specific problem, is less likely to have a publicly-available solution.
  • lijok 8mo ago
    I think you severely overestimate your understanding of how these systems work. We’ve been beating the dead horse of “next character approximation” for the last 5 years in these comments. Global maxima would have been reached long ago if that’s all there was to it. Play around with some frontier models, you’ll be pleasantly surprised.
    • Draiken 8mo ago
      Did I miss a fundamental shift in how LLMs work? Until they change that fundamental piece, they are literally that: programs that use math to determine the most likely next token.
      • gizmondo 8mo ago
        This point is irrelevant when discussing capabilities. It's like saying that your brain is literally just a bunch of atoms following a set of physics laws. Absolutely true but not particularly helpful. Complex systems have emergent properties.
        • nirui 8mo ago
• Eliezer 8mo ago
  Every time somebody writes an article like this without any dates and without saying which model they used, my guess is that they've simply failed to internalize the idea that "AI" is a moving target; nor understood that they saw a capability level from a fleeting moment of time, rather than an Eternal Verity about the Forever Limits of AI.
  • josefrichter 8mo ago
    Exactly. Basically their thought are invalidated often quicker than they hit the publish button.
  • iLoveOncall 8mo ago
    Funnily enough we have had those comments with every single model release saying "Oh yeah I agree Claude 3 was not good but now with Claude 3.5 I can vibe-code anything". Rinse and repeat with every model since. There also ARE intrinsic limits to LLMs, I'm not sure why you deny them?
    • Eliezer 8mo ago
      There's intrinsic limits to vanilla transformer stacks. Nobody knows where they are. We don't know how unvanilla Opus 4.6 or GPT 5.3 are. We don't know what's in development or which new ideas will pan out. But it will still probably be called an "LLM".
• josefrichter 8mo ago
  The OP’s example of AI writing 500 LOC, then deleting 400, and saying it didn’t… Last time I saw something like that was at least a year ago, or maybe form some weaker models. It seems to me the problem with articles like this is while they sometimes are true at the moment, they’re usually invalidated within weeks.
• jbrooks84 8mo ago
  Vibe coding does not have a ceiling, you just need to break things up and the human needs to use their brain to orchestrate
• ndr 8mo ago
  https://archive.ph/tUUMd https://archive.ph/tUUMd as the site randomly 404s
• jama211 8mo ago
  This article has some serious usage of either bad prompting, or terrible models, or they’re referencing the past with their stories. I have experience AI’s deleting things they shouldn’t but not since like, the gpt4 days. But that put aside, I don’t agree with the premise. It doesn’t make the hard parts harder, if you ACTUALLY spend half the time you’d have ORIGINALLY spent on the hard problem carefully building context and using smart prompting strategies. If you try and vibe code a hard problem in a one shot, you’re either gonna have a bad time straight away or you’re gonna have a bad time after you try and do subsequent prompting on the first codebase it spits out. People are terrible observers of time. If you would’ve taken a week to build something, they try with AI for 2 hours and end up with a mess and claim either it’s not saving them any time or it’s making them code so bad it loses them time in the long run. If instead they spent 8 hours slowly prompting bit by bit with loads of very specific requirements, technical specifications on exactly the code architecture it should follow with examples, build very slowly feature by feature, make it write tests and carefully add your own tests, observe it from the ground up and build a SOLID foundation, and spend day 2 slowly refining details and building features ONE BY ONE, you’d have the whole thing done in 2 days, and it’d be excellent quality. But barely anyone does it this way. They vibe code it and complain that after 3 non specific prompts the ai wasn’t magically perfect. After all these years of engineers complaining that their product manager or their boss is an idiot because they gave vague instructions and demanded it wasn’t perfect when they didn’t provide enough info, you’d think they’d be better at it given the chance. But no, in my experience coaching prompting, engineers are TERRIBLE at this. Even simple questions like “if I sent this prompt to you as an engineer, would you be able to do it based on the info here?” are things they don’t ask themselves. Next time you use ai, imagine being the ai. Imagine trying to deliver the work based on the info you’ve been given. Imagine a boss that stamped their foot if it wasn’t perfect first try. Then, stop writing bad prompts. Hard problems are easier with ai, if you treat hard problems with the respect they deserve. Almost no one does. /rant
  • moebrowne 8mo ago
    garbage in, garbage out
    • 8mo ago
• RataNova 8mo ago
  Speedups without changes in expectations just reset the baseline, and then you're sprinting forever
• ta20240528 8mo ago
  I'll vibe code when vibe-coding can make a frontier LLM from scratch. Meta-circularity is the real test. After all, I can make new humans :)
• theredbeard 8mo ago
  Skipping the investigation phase to jump straight to solutions has killed projects for decades. Requirements docs nobody reads, analysis nobody does, straight to coding because that feels like progress. AI makes this pattern incredibly attractive: you get something that looks like a solution in seconds. Why spend hours understanding the problem when you can have code right now? The article's point about AI code being "someone else's code" hits different when you realize neither of you built the context. I've been measuring what actually happens inside AI coding sessions; over 60% of what the model sees is file contents and command output, stuff you never look at. Nobody did the work of understanding by building / designing it. You're reviewing code that nobody understood while writing it, and the model is doing the same. This is why the evaluation problem is so problematic. You skipped building context to save time, but now you need that context to know if the output is any good. The investigation you didn't do upfront is exactly what you need to review the AI's work.
• koliber 8mo ago
  The article is gone, but going off the title here... If the easy stuff takes up 90% of the time, and the hard stuff 10%, then AI can be helpful. Personally, I can do "the easy stuff" with AI about 3-5x faster. So now I have a lot more free time for the hard stuff. I don't let the AI near the hard stuff as it often gets confused and I don't save much time. I might still use it as a thought partner, but don't give it access to make changes. Example: this morning I combined two codebases into one. I wrote both of them and had a good understanding of how everything worked. I had an opinion about some things I wanted to change while combining the two projects. I also had a strong opinion about how I wanted the two projects to interact with each other. I think it would have taken me about 2 workdays to get this done. Instead, with AI tooling, I got it done in 3 or so hours. I fired up another LLM to do the code review, and it found some stuff both I and the other LLM missed. This was valuable as a person developing things solo. It freed up time for me to post on HN. :)
  • causal 8mo ago
    Helpful, absolutely, but only if you're solving the right problem. Solving the wrong problem with AI is doubly harmful because it will almost always give you something that runs, but now you are on a path that takes a lot of willpower to give up.
• adamtaylor_13 8mo ago
  At this point, I don’t even know what to make of blog posts like this. The very first example of deleting 400+ lines from a test file. Sure, I've seen those types of mistakes from time-to-time but the vast majority of my experience is so far different from that, I don’t even know what to make of it. I’m sure some people have that experience some of the time, but… that’s just not been my experience at all. Source: Use AI across 7+ unrelated codebases daily for both personal and professional work. No, it’s not a panacea, but we’re at the stage that when I find myself arguing with AI about whether a file existed; I’m usually wrong.
• kittbuilds 8mo ago
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• kittbuilds 8mo ago
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• h4kunamata 8mo ago
  I know Ansible, homelab, Proxmox is my hobby, Debian is my gem. I asked ChatGPT to guide how to install qBittorrent, Radarr (movies), Sonarr(TV Series), Jackett(credentials/login) without exposing my home IP and have a solid home cinema using private tracker only. Everything had to be automated via Ansible using Proxmox "pct" CLI command, no copy and paste. Everything had to run from a single Proxmox Debian container aka LXC Everything network related had to use WireGuard via Proton VPN, if the VPN goes down, the container has zero network access, everything must be kill. Everything had to be automated, download is finished, format the files structure for Jellyfin accordingly, Jellyfin add the new movies, TV shows. It took me 3 nights to get everything up and running. Many Ansible examples were either wrong or didn't follow what I asked to the letter, I had to fix it. I am not a network expert and hate Iptables haha, you need to know the basic of firewall to understand what the ACLs are doing to understand when it does not work. Then Proxmox folder mapping and you name it. It would have taken me ages reading docs after docs to get things working, the "Arr services" is a black hole. For this example, it made the harder part easier, I was not just copy/paste, it was providing the information I didn't know instead of me having to "Google for it". I know the core of where things are running on, and here is where we have Engineers A and Engineers Z Engineers A: I know what I am doing, I am using AI to make the boring part easier so I can have fun elsewhere Engineers Z: I have no idea of what I am doing, I will just ask ChatGPT and we are done: 90-95% of engineers worldwide.
• CHB0403085482 8mo ago
  AI is an intern that thinks he's hot stuff: https://www.youtube.com/watch?v=TiwADS600Jc https://www.youtube.com/watch?v=TiwADS600Jc
• blirio 8mo ago
  The article describes the problems of using a AI chat app without setting up context, skills, MCP, etc Like yea the AI won’t know what you discussed in last weeks meeting by default. But if you do auto transcribe to your meetings (even in person just open zoom on one persons laptop), save them to a shared place and have everyone make this accessible in their LLM’s context then it will know.
• kajolshah_bt 8mo ago
  That description matches a lot of what we’ve seen in real products. AI does make some parts of development and workflows easier like summarizing data, generating initial drafts, or auto-completing repetitive patterns. Those wins are real. The hard part that becomes harder is not the technology. It’s the decision-making around it. When teams rush to integrate a model into core workflows without measuring outcomes or understanding user behavior, they end up with unpredictable results. For instance, we built an AI feature that looked great in demo, but in real usage it created confusion because users didn’t trust the auto-generated responses. The easy part (building it) was straightforward, but the hard part (framing it in a way people trusted and adopted) was surprisingly tough. In real systems, success with AI comes not from the model itself, but from clear boundaries, human checkpoints, and real measurements of value over time.