6 ms·

2x, not 10x: coding with LLMs in 2026

• segmondy 2mo ago
  200x coding with LLM
  • cherrylemonsoda 2mo ago
    Link 1 impressive thing
• iLoveOncall 2mo ago
  Even this is overstating it IMO. I already convert from multipliers to percentage of increase, so when someone claims 10x they very likely mean +100% productivity, and here 2x means +20% productivity, which seems about right. Nobody that was normally productive before LLMs has suddenly 10x'ed their output now. The problem is that 20% productivity when it comes to generating code, really doesn't translate in 20% productivity increase overall, when you take into account the fact that the code quality is worse, the fact that writing code is actually not the majority of your time spent, and that people get burnt out from the usage.
  • jdlshore 2mo ago
    2x is 100% increase.
    • iLoveOncall 2mo ago
      I know, and I'm saying that 2x claims should be interpreted as an actual 20% increase, and 10x claims as an actual 100% increase. People are just bad at estimating their own productivity.
      • didibus 2mo ago
        Ya, I feel 10% to 50% increase seems more realistic, when I see 100% to 900% increase claims it seems a bit extrapolated, at least I don't feel I'm seeing it at my work. And then it depends if we imply a given feature/fix/enhancement takes half the time as before, or we imply that we can get twice as many features/fix/enhancements done in the same amount of time as before. Those are not equivalent. I think the latter might be more accurate as to how it helps.
      • kaffekaka 2mo ago
        Yes, people (myself included) have a strong tendency to use hyperbolic factors, "ten times", "100x" because true estimates are very hard to do.
• what_hn 2mo ago
  Does a 60x speedup count? Are you still copying and pasting from chatgpt, because if so your definitely doing it wrong.
  • iLoveOncall 2mo ago
    [flagged]
    • throwitaway222 2mo ago
      Skimmed, it's using the word llm a lot, no instances of the word Agent, codex, claude. The person writing this isn't using LLMs correctly.
  • forlorn_mammoth 2mo ago
    Wait, how about an infinite speedup? A friend of mine couldn't code, and now he can. So he is infinity-x better at coding thanks to LLMS! Take that, you mere 10x-ers! Your days are toast!
    • dgellow 2mo ago
      Left behind, as the infinite-x rockstar developer roars off on their motorbike, trailing a plume of dust behind
  • prmoustache 2mo ago
    If 60x was a thing, we would see many fantastic products appearing on the market, every product would have improved or goten major new interesting features. Yet it hasn't really happened.
    • wongarsu 2mo ago
      I believe I have seen 60x improvements on certain classes of bugs. Just not on development as a whole And I've also seen the other side, where vibe coding on projects that had bad code quality to start with leads to bug riddled messes. Projects with beautiful and elaborate test harnesses, that break seconds after contact with a real human user
• gashad 2mo ago
  This reminds me of themes I recently saw in [Harness Engineering is not Enough: Why Software Factories Fail](https://www.youtube.com/watch?v=Ib5GBkD555M https://www.youtube.com/watch?v=Ib5GBkD555M) (Warning: the last 3 slides seem like an advertisement). One thing I liked is how Dex has a little graphic he glossed over showing software development is - 25% planning & aligning with other teams - 25% coding - 25% testing/verifying - 25% code review/rework One argument was that agentic coding speeds up that coding part a bunch. So maybe there's 2x speedup in coding. But that's only a small speedup in the totality of everything software engineers do.
  • whateveracct 2mo ago
    > So maybe there's 2x speedup in coding. But that's only a small speedup in the totality of everything software engineers do. Amdahl's Law should be familiar to anyone with a 4y computer science/engineering degree. Why aren't they applying it to their own throughput?
    • t-writescode 2mo ago
      Well, there’s probably several reasons: 1) because they’re not doing those others spheres of work 2) because they don’t think about the work nor how tired they are afterwards 3) they’re proselytizing AI work as the future and that conflicts with that vision 4) the other shoe hasn’t dropped 5) they really don’t see it 6) some people genuinely hate programming and this helps them skip that. I’m sure others as well.
    • ChadNauseam 2mo ago
      Let's apply it: - 25% planning & aligning with other teams - 25% coding - 25% testing/verifying - 25% code review/rework I'd say that thanks to LLM assistance I'm 10x faster at coding, code review/rework, and testing/verifying. (LLMs can partially automate testing/verifying too, and the code is higher quality now as well so less testing/verifying is necessary). So that leaves us with: - 92.5% planning & aligning with other teams - 2.5% coding - 2.5% testing/verifying - 2.5% code review/rework Obviously, that makes zero sense as a split. If you saw any organization doing that, you'd suggest having fewer teams, more silos, etc. Maybe you have designers produce code, instead of showing the designs to coders and having the coders implement it. Maybe you force all your engineers to dogfood the product that way they can identify issues themselves rather than needing QA teams to do it. And so on. so the last category, "planning & aligning with other teams", falls too.
• deergomoo 2mo ago
  > As for documentation, I've found this simple instruction to vastly improve LLMs' output: > Never write READMEs, docstrings, or comments. I will write those myself later. And yes, I really mean this. This is quite validating as I came to the exact same conclusion myself. We’re required to use an LLM for every task at work that touches code†, and I was really struggling to get Claude to stop with the long waffly comments that reiterate the next few lines of code in 3x as many characters, making contextless references to subtasks in whatever harness du jour we’re using this week. No amount of examples or explanation of what I wanted would make it stop. And then I realised of course, I’m asking something which has no concept of meaning (or, indeed, anything) to only add meaningful comments. More fool me I guess. Of course, it’s ultimately pointless given all of my colleagues are regularly opening PRs with more comments than code anyway. 80% of my code review responses these days are just increasingly exasperated “pointless comment, please remove”. † This is just as infantilising as it sounds, by the way
  • sync 2mo ago
    You can create a PostToolUse hook [0] which will automatically chastise Claude to shrink comments to one line. It's not perfect but better than the sea of prose it tends to generate by default. Bonus is that it would apply to your colleagues as well! [0] https://github.com/chrisvariety/branch-fiction/blob/deb37f2ba780a29082a294e48b06f92c239e6b4c/.claude/settings.json#L6-L16 https://github.com/chrisvariety/branch-fiction/blob/deb37f2b...
    • exographicskip 2mo ago
      Good thinking! The multiline paragraph comments drive me up the wall. No amount of agents.md / commit to memory updates has stopped agents from littering the code base with useless comments. Think I'll add it to pre-commit hooks getting called by posttooluse
  • nevertoolate 2mo ago
    > We’re required to use an LLM for every task at work that touches code Why not just write the code in the prompt so LLM can paste it.
• dan_gee 2mo ago
  [dead]
• imoverclocked 2mo ago
  ... the official return of pair programming! The best part: LLM's save you from having to argue with a human. Added bonus: now you get to master two tools instead of one.
• troupo 2mo ago
  > Never write READMEs, docstrings, or comments. I found that latest codes don't write comments in code by default. And when they do, they write stupid shit like "This was code that did X, it was now removed". You have to explcitly prompt them to write comments in code. They are still useful for you, the user. But are arguably useful for the model, too, given how many of them (especially Claude) only reads small chunks of files. So I'd rather have code comments than it reproducing a picture from incomplete data.
  • wongarsu 2mo ago
    A small mention in your user-wide Claude.md (or Agents.md or whatever you use) about the desired types of comments goes a long way. The typical "why, not what", clarifying larger-picture stuff, and everything else you find in every book on code quality
• lazopm 2mo ago
  The way I see it you should calibrate the way you work with LLMs based on how confident you are on that specific area, and if it's your responsibility to own/understand it. Here's how it feels for me: * Learning stage: 0.5x - 1x. I change my system prompt to teacher mode, taking the productivity hit for actually learning the system/tool pays off dividends later. I change my system prompt to "teacher mode" and slowly loosen it as I get more confident. * Working-knowledge: 2x - 3x. Once I am ramped up enough I feel like I can get a decent productivity boost. Most of the time is spent at the planning stage. This is my mode for areas I don't really own or care about, just need to get work done. * Mastered: 10x+ I have been doing web front end for 12+ years, I can quickly review plan/implementations and for my initial prompt I already know most of what I want built. 1x == my speed before AI
  • dev_hugepages 2mo ago
    Hey, could you share the prompt you're using for "teacher mode"?
    • leleat 2mo ago
      Someone posted their AGENTS.md some time ago, that is used in practice to teach their students[1]. That might be useful to you in this context as well. [1]: https://news.ycombinator.com/item?id=48359858 https://news.ycombinator.com/item?id=48359858
    • SirYandi 2mo ago
      [flagged]
  • Zigurd 2mo ago
    Some domains don't have the established regularity of architecture that it takes to achieve 10X efficiency. Two I can think of off the top of my head are embedded systems, with a variety of sensors, outputs, processing power, and memory, and novel protocols like ATProto, for which training data is thin on the ground.
  • Aurornis 2mo ago
    > * Mastered: 10x+ This really needs to be calibrated to the type of work and complexity. I can actually believe that LLMs would speed up basic web dev work in small, simple codebases 10X for simple requests. These conversations usually turn into people talking past each other because they’re working on different things. For other less routine and more complex work, expecting a 10X productivity boost is not realistic at all. It doesn’t matter how good you get at writing prompts and reviewing plans. LLMs just don’t solve everything for you in a good way. Some times the true nature of the problem is revealed while implementing it and by deferring everything to an LLM you spend days throwing tokens at the wrong thing. There is a lot of work where the LLM speed up comes from helping you quickly search docs and codebases and double check your code, but handing the entire thing off to an LLM isn’t reasonable. These tasks aren’t going to reach this mythical 10X productivity boost that is genuinely achievable for much simpler work.
• alfiedotwtf 2mo ago
  > As such, I use LLMs mainly to produce a rough draft of the code which I then iterate on heavily, at least until I like the general structure. Weird… I would have said this was how it was about 2 years ago, but no way in 2026.
  • heaney-555 2mo ago
    [flagged]
    • badsectoracula 2mo ago
      People are still figuring out how to use LLMs/AI and considering how feelings based and hard is to reproduce many of the results people get, a lot of the improved approaches are basically at "trust me bro" reliability. TBH i expect it'll take several years before people largely settle on what works good and what works bad (or doesn't work) and that's assuming things wont chance in a massive way during that time. And this isn't really anything new, people rarely come up with some new way of programming and have everyone learn and understand best uses from the start - it takes years (and that is assuming everyone will agree that'd be something worth bothering about).
• deleted 2mo ago
  [deleted]
• esafak 2mo ago
  This is a dated take to me. I think the next stage in unlocking productivity is so called "loop engineering"; figuring out how to effectively not read all the code while ensuring quality. To me that means implementing statistical quality control and formal methods. Before I get there I have to figure out how to reliably audit plan adherence. The problem is that when the specs are in natural language, as they are, you need a fallible LLM to verify it.
  • pornel 2mo ago
    I agree that removing human from the loop is the next stage, but we're not there yet beyond small programs. Agents left alone tend to create so much tech debt, that once the program becomes so messy that they can't fix one bug without creating two new bugs, it's too late to even clean that up. The program will be super tidy in superficial aspects that linters catch (everything neatly formatted and verbosely commented), and roughly appear to do what it's supposed to do, but everything in between will be "I can't even". We need something else than formal methods, because the problem is usually in lack of simplicity - you get four versions of the problem solved in four times in four in different ways, each uniquely flawed and just incompatible enough with the others that unifying them is too big and hairy for the agent, and will result in eight different glue adapters written in the process.
  • petesergeant 2mo ago
    This will be a deeply unpopular opinion, but I have an 80k line Go project I've created where I haven't read the source, but would absolutely stake its quality against most hand-written projects of a similar size[0]. Recent LLMs struggle with the author's stated issues only inline: they're entirely capable of going back and evaluating codebases to find architectural issues and LLM slop signatures, especially when you use other models to check one model's output. This wasn't true until Fable-class models, but it's true now. 0: https://github.com/pjlsergeant/byre https://github.com/pjlsergeant/byre
  • fibuladev 2mo ago
    We built a conversational terraform platform at work, you describe what you need for your infra and it produces the terraform code following company standards and rules. What is important for us is the LLM never writes any code. It just extracts a structured spec from the conversation, and a deterministic engine (plain python, no LLM) renders the code from approved modules only. So there is no "code quality" discussion at all, and since the spec is structured and not natural language, you don't need another LLM to verify it, the risk moves to the extraction step which is small and easy to eval. But probably this only works in narrow domains where the output is composable from approved blocks (IaC, config etc), and its not a new idea, just old discipline behind a chat interface. But in that slice the speedup is way beyond 2x.
• tristor 2mo ago
  My observation is that AI tools generally make people /less/ productive. They are more productive in that they produce more stuff, but they are less productive in that the stuff they are producing has lower inherent value delivery. The primary place where I see this in software teams as a Product person is in document creation. If you are using an AI tool to produce internal documents, this is likely a net-negative value activity that actually reduces the overall productivity of the team. Why? Because writing is thinking. By handing off document creation to an LLM for a document that's supposed to communicate important details between teams (e.g. technical design docs, requirements docs, strategy docs) you are actually handing off thinking, or rather handing off not-thinking as AI tools cannot "think". The outcome of is poorly "thought out" slop that generates more work for everyone involved to resolve /or/ everyone simply ignores the document and the previous processes stop being useful. If anything, I'd say AI tools in /most/ enterprises as people are trying to use them now are at least a -30% of productivity. If used correctly for taking a human-written/thought requirements doc, converting it into an interactive prototype that can be critiqued and ultimately included as part of the solution alignment within the requirements doc, and then is handed to an engineering team that is effective at using humans and AI to produce code, then it's probably a benefit. But most businesses lack the internal rigor, quality culture, and data governance to support properly applying AI tools in a high context manner internal to their business.
  • wannabe44 2mo ago
    Have you worked with codebases where people produced slop even before LLMs? In such codebases LLMs are a godsend. They can churn around huge amounts of code and find needles in the haystack.
    • tristor 2mo ago
      Since I'm on the Product side these days, I don't spend much time delving into the codebase at this point in my career. I can believe it though, as I've done quite a few personal side projects using langchain and various harnesses. The problem I see is not necessarily that LLMs build things badly (although I know that can also be true), its that because people are using LLMs to generate documents, we are then building the wrong thing entirely (whether with an LLM or not, whether badly or not). Removing human thought from the process of development is generally a net negative, IMO.
• watso 2mo ago
  You still have to know what you’re doing. That came from years of doing it by hand. Where is that necessary experience going to come from for the current and future generations of juniors?
  • peheje 2mo ago
    Shipping. Breaking stuff. Fixing it. And discovering, repeatedly, that the tool sounded much more confident than it had any right to. Like always. This is why the average carpenter you hire in 2026 still won’t do a perfect job, despite carpentry having existed for thousands of years.
    • watso 2mo ago
      So you don't think fundamentals built by actually coding will be necessary to produce well structured and maintainable code with an LLM?
  • ipaddr 2mo ago
    In 10 years llms will handle it all from start to finish.
    • kazinator 2mo ago
      We have passed peak LLM already. In 10 years, something might be handling "it all" from start to finish, but it won't be LLMs.
      • swordsith 2mo ago
        Saying we're at the Peak of LLMS is like saying we never went further than everest, these are probabilistic state machines sure, but the real value is the effort put into tooling and training these models in RL situations.
• hungryhobbit 2mo ago
  >As such, I use LLMs mainly to produce a rough draft of the code which I then iterate on heavily, at least until I like the general structure Tell me you just started with agentic programming without telling me you just started using agentic programming. Look, don't get me wrong: new folks learning tech should absolutely write articles about it! But their claims might very well change once they learn more .. and I strongly suspect that will be the case here.
  • _se 2mo ago
    Tell me you're bad at software engineering without telling me you're bad at software engineering.
    • mckee_plus_plus 2mo ago
      Tell me you're bad at people skills without telling me you're bad at people skills
      • pdsOne 2mo ago
        Such is life.
        • cherrylemonsoda 2mo ago
          Tell me you're alive without telling me you're living
• msephton 2mo ago
  Does the number × depend on how big your thinking is? If you are only 2× then you need to think bigger, aim higher, etc. I think the limit on the 2× is not the ai? Where is the limit now? I think it's unknown.
  • trey-jones 2mo ago
    Definitely feels like a lot more than 2x to me. Any time it feels like the agent is taking a long time I have to look in the mirror and say to myself "What could you have done in 5 minutes?"
• cyanregiment 2mo ago
  0.25x because I scope bigger now and then I may get a few "free" React components but largely doing all that work of putting it together. Wouldn't trade it though. Feel like I can overall do more with less time and energy. At the end of the day, AI is making me work more (good thing). If you count that as productivity, then sure.
  • didibus 2mo ago
    > AI is making me work more (good thing) How is that good?
    • cyanregiment 2mo ago
      I wrote a whole thing and realized the answer is a lot more philosophical. I don't know why working more is good. Maybe its dormant Protestant work ethic still finding a use case in my secular mind
      • didibus 2mo ago
        You work more for your employer? At no additional cost to them?
    • sejje 2mo ago
      It makes me feel fulfilled. It gives me things I want. Building is in my nature. The work I'm doing is for myself (I'm not GP). But work isn't just "my job for the man." You can build things for your home, your shop, your family, your business if you have one.
      • didibus 2mo ago
        Then call it your hobby, right?
• rglover 2mo ago
  I mean the "x amount" is relative to your existing skill level, right? It's worth asking ourselves "why does the x amount matter?" I get the desire to define KPIs to estimate productivity gains. But this all seems to be—rather rapidly—leading to an increasingly dehumanized reality (both figuratively and literally) so we can...produce more software? I love building software, and I enjoy using LLMs to help me do it, but there's just this weird vibe I can't quite shake about how we're trying to quantify all of this.
• smashinlabs 2mo ago
  More than 2x for me. The models are accelerators, but the biggest gains came from building workflows around them. I spend most of my time documenting what I want, while implementation and much of the surrounding work become relatively cheap. I've found that AI amplifies engineering experience more than it replaces it. The better you are at framing problems and evaluating solutions, the more productive it becomes.
• ericol 2mo ago
  I'm not sure about the x, but the first thing that arises from that is, I feel like in my case it's way higher than 2. The 2nd thing is, how do I measure that. --- In my case, the details of my work (Kinda DevOps, kinda Senior Dev) makes it that having an LLM to do the heavy lifting allows me to do things not only faster, but better, and across domains I do not hold expertise on. An example of the effect of LLMs in my daily work is that I'm in the middle of a PHP upgrade for a rather large legacy application, and the "heavy lifting" is really out of the scale, letting me concentrate on what really matters, while at the same time if I do keep "the harness" tight I'm certain the results are the correct ones. Also correcting course is just as cheap. Not having to worry on the tooling on exchange has the incredible desirable result of my velocity being incomparable to what it was before. Then we have the side effect of how easy to do transfer knowledge: Rather than telling the QA guy how to do the work specific for this task, I defined a set of files (.md documents, skills, an off-the-shelf customised MCP server) that assist QA into doing the work in a way that helps me do my job better and faster. There's also a clear possibility that what I'm doing will expand to the rest of the team I am in, completely altering the way in which we approach development. If we take 'x' as 'mileage', yours might vary. Mine has, and I'm baffled at the positive net results I AM getting. Also, this what I do (coding?) is extremely fun again. Being able to work close to the speed of thought is the best high.
  • soperj 2mo ago
    > allows me to do things not only faster, but better, and across domains I do not hold expertise on. How would you know it's better when you have no expertise?
    • heaney-555 2mo ago
      Because it produces the desired output. The purpose of a program. There are many domains where an intelligent human can act as a discriminator for output without knowing exactly in precise detail how the process itself works.
      • fc417fc802 2mo ago
• pantelisk 2mo ago
  While I agree with the premise, I think this angle only applies on work one was going to do no matter what. The real power of these tools is that there are so many ideas people would like to try, but never have the time or motivation to pursue. So the comparison is not only "built with and without LLM" but "would you even build this if you didn't have the LLM?". The gap in productivity in this case is much more wide.
  • keeganpoppen 2mo ago
    that and 2x is "i'm 5'10" and i round up to 6'" low.
  • infecto 2mo ago
    Agree so much. So many small bugs, nits, tweaks I just send off to an LLM agent to figure it out.
  • SkepticalWhale 2mo ago
    Yes but I’ve seen some devs waste a lot of time using AI to build something that was a bad idea to begin with. Without AI they might have first spent more time validating the idea was worth it.
    • petesergeant 2mo ago
      > Without AI they might have first spent more time validating the idea was worth it. Seems optimistic
    • marssaxman 2mo ago
      For me it is exactly the opposite. AI makes it so easy to create test fixtures and run experiments that I now spend much more time validating ideas than I could ever afford to do before. When I write code for production, it's not "this ought to work", it's "here are the figures showing how well this works, on this dataset", where the dataset is also much larger than anything I would have used before, because I used AI to generate the tools which collected and organized it.
• localhoster 2mo ago
  When I approach a new area or task I first explore it, by hand, and implement, by hand, the task. This way, I get a deeper understanding and it gives me some momentum to llm the rest of the related tasks. Till I loose enough grip to "re-dive in" that area and to again, do stuff my hand. Some tasks, I only do by hand, and some tasks, I only use llm for. Really depends on the task, my the project and on, and honesty, my instrest and availability at the moment.
• jryan49 2mo ago
  All these hot takes that LLMs are -10x, 100x, 2x are kind of silly. It really depends on: What you are using it for? What language, what framework, how many LOC, what domain, how much docs can the LLM read, etc etc. What are you optimizing for? Cost? Human knowing how your code works? Getting something out the door? How Good You Are at Prompting the LLM for THAT specific set of parameters? I find AI can save me like 5x time on something, and in other cases it's wasted 5x time. Overtime I'm hopefully learning how to use it better.
• Rustwerks 2mo ago
  I've found a variant of the Gell-Mann Amnesia effect with regards to LLM coding. They're really magical if you're not familiar with the language, domain, or frameworks you are trying to use. But if you're familiar with those things then you'll often catch them lying to your face and producing a lot of plausible nonsense.
  • sejje 2mo ago
    It's dropping off hard, though. I think web-search fixed a lot of it. It used to be terrible for car repair advice, now it's mostly right.
• geraneum 2mo ago
  > Never write READMEs, docstrings, or comments. I will write those myself later. And yes, I really mean this. The precise and rigorous practice of “engineering” in 2026.
• robofanatic 2mo ago
  I think LLMS have reached to a point where now its just about money. you have to decide whether you really want to spend your tokens to do X or you can just do it yourself and use the tokens for something else.
• techpression 2mo ago
  I spent 30min with Opus 5 generating some CSS and components for my Phoenix LiveView template project (which I use for starting new projects). Then I spent around 5 hours parsing and cleaning up the output. I could of course not have done that, but since it's my spare time project I can care about the code and quality. I did get wins for sure (and I did save time), and I'm sure most people wouldn't even spend five minutes cleaning up, but this is definitely one place where people talk about different things regarding whatever x speedup they get.
  • kypro 2mo ago
    > but since it's my spare time project I can care about the code and quality. I suppose it's yet to be seen, but I'm seeing a lot of spaghetti code being dump into the codebase I'm working on at the moment by other devs. I don't think it's wrong per se, but human code would have thought more about the right abstractions and trade-offs for future maintainability. There is a gap right now between what an AI can and can't do, and I don't think it's just a time and cost limitation either. It seems like current AIs are very good at writing good code at a surface level, but very bad code when you zoom out a little. We have a lot of non-technical people committing code these days to constrained microservices and from time to time things break and I'll take a look and I'm always just like wtf am I reading? You see code so bad that you'd immediately fire a human engineer had the wrote it. Thing like explicit hacks to bypass errors that it should not be bypassing or mock data to fake some API that it wasn't able to access. Stuff which literally no human coder I've ever worked with would be contemplate doing... I think it remains to be seen if the 2-10x speed up some are claiming to have today will persist if the junk code continues to grow.
    • techpression 2mo ago
      Everyone can drive really fast if you give them a fast car, but very few can stay on the road when driving fast. LLMs are like free supercars for everyone, except people know nothing about how supercars work or how to drive them. Same thing with software, going fast is incredibly hard if you do it for any period of time, and I think this is something we will read more about in a couple of years, not that human written code never turned out to be complete disasters, but I believe with LLMs we will see at a completely different scale. And yes, car analogies suck.
• baron3dl 2mo ago
  I won't argue this title isn't the author's experience, but it's misleading to rest a quantitative claim on a qualitative argument, while also refusing to fully exploit the technology. From a firm that has fully embraced agentic engineering, they offered topline stats on their measurements of a 4x boost from November to February, and 8x on top from February to May. That's 32x since November 2025, coding with LLMs. And like whatever, this is second hand and I'm not going to disclose the source. Actual hard, published, and peer-reviewed research is needed here to backup quantitative claims. 2x, 10x, 32x, whatever.
• badsectoracula 2mo ago
  Personally i'm only using local LLMs that i can run on my 7 year old PC (that also has a GPU with 24GB VRAM because reasons :-P) so i'm not sure how much that experience matches what others are doing (though roughly speaking what i see people complain about Claude doing doesn't feel that different from what i see the local stuff doing, so i guess the drawbacks aren't scaled down as model sizes increase). I'm not sure about 1x, 2x or 10x increase as these metrics are about code written but that isn't a productivity metric (something pretty much every half-decent programmer would agree with before LLMs - remember stories about Bill Gates saying that more LoCs being good for software is like more weight is good for airplanes or Bill Atkinson's story about adding -2000 LoCs to improve QuickDraw?). But they can certainly help "get you going" faster in that if you're stuck on something (for whatever reason - including "that feels too much drudgery") or have issues starting something, you can have an LLM take a stab at it and it'll produce "something". Sometimes it is enough by itself, but more often than not it'll need tweaks (either directly or having the LLM do it). I got to make a bunch of things i couldn't convince myself to do - e.g. an image viewer that doesn't suck (based on my arbitrary judgement), a game database, a script to convert a git repository into static html pages that kinda look like GitHub, etc. They can also help find (and sometimes fix) bugs or other "code smell" issues. They're not that great for exact results (without tool calling -and knowledge on how to use them effectively- at least) but when it comes to fuzzy / vague stuff like "check out this code <code dump here> can you spot any issues?" they always tend to find some stuff (even if it is hallucinations :-P but sometimes they find actual issues too or whatever hallucination they come up with reveals some actual issues with the code that you didn't spot by yourself). I've been dabbling with Rust recently and asked Qwen 3.6 35B-A3B to judge my code and it wrote "6.5/10, will compile but looks like C in Rust" :-P. The article says: > Never write READMEs, docstrings, or comments. I will write those myself later. And yes, I really mean this. And sure, LLMs aren't that great about those (i do let them leave whatever comments they want though and remove them later myself - i think those comments help during the generation/prediction - basically how they "think", kinda like the reasoning phase), but they can be very good at things like "here is the code, here is the documentation for it, spot discrepancies" (i had Devstral Small 2 do this and it hallucinated a few discrepancies but also found real stuff i missed in the docs). I've tried to use Devstral Small 2 for some API docs but found it'd sometimes make assumptions about what function do or how. One approach that might work, but i haven't tried yet, is to write the "guide" myself, then have the LLM write the function docs using both the guide and the code as reference. The reason i think this will work is because it got things 95% correct just having access to the function code alone (and without the rest of the codebase), so the "guide" would help it reach 99%. I do not expect it to get to 100% so a manual edit pass will need to be done anyway (and i have an idea for a tool to assist in the manual edit pass for that - a tool that i'll probably get an LLM to write - BTW good luck coming up if all that stuff would increase or decrease productivity for an actual product and not some random stuff i'm toying with :-P). One other thing i've also found LLMs useful recently is to have them use the stuff you make and see how they try to use it. I have an old project, a GUI toolkit i've been hacking on every now and then since 2011 or so, though it was never a priority. Yesterday i decided to dump all the header files to Qwen 35B-A3B (i use 4bit quantization that gives me a 256k context - it isn't particularly smart but it is neat to not have to micromanage context size like i have to do with Devstral Small 2 or Qwen 27B where both of them aren't very usable speedwise at anything above 32k context sizes). Then i asked it to just make a few small programs and it did[0] (the shot shows a paint app, a calendar, a todo list and a unit converter). Pretty much every program found bugs in the library :-P and gave me ideas on how to improve things. In general i get the impression that LLMs aren't great at architecting things but if you do the architecture yourself and write them a framework to use, they should do a fine job at it. [0] http://runtimeterror.com/pages/iv/images/d50c436990db203a07f7b7a8e4957611.png http://runtimeterror.com/pages/iv/images/d50c436990db203a07f...
• throwaway0123_5 2mo ago
  I agree with the general premise with respect to current SOTA, but for making nice plots that I would've never otherwise made or learned how to make (to the same standard), easily 100x.
• taf2 2mo ago
  yeah i can't say if it's 2x or 10x or 100x i don't know. i just know i barely open vim anymore. I still find my rate of development is limited by how much my single mind can hold at any given time. I am however accomplishing with a much higher rate of confidence many many more daily tasks usually by 10am... the limit is usually my willingness to imagine and think critically about an idea with enough clarity to result in something that make sense. It's a different energy then normal development but similar enough.
• p0w3n3d 2mo ago
  My measurements are Claude is 6x - 10x on prototyping and MVC then it starts to be 2x at most. You can also witness 0.5x (asking simple tasks might require more time than performing them by oneself)
• fg137 2mo ago
  I recently removed about 50% of code in a feature submitted by a colleague, because it's a pile of over-engineered mess that either never gets used or caused trouble for us. We probably would not have added the code in the first place if we actually discussed the design. With the code removed, the feature is now much simpler and more maintenable. And that's the second time this happened over the past few months.
  • sejje 2mo ago
    One time before LLMs we outsourced this project. It was something to sign file headers, written in ruby by an overseas company. It was something like 4500 lines long, and I wound up reducing it to something like 550 lines. My story, like yours, has nothing to do with the topic at hand. It happened many times to me, as well. Humans write terrible code. You didn't even mention if the code you're talking about was generated or not.
    • fg137 2mo ago
      I think my last paragraph should have provided enough context.
• 5555watch 2mo ago
  I'm probably a weird outlier. Coming from academia, it's ranging from 1x to infinity-x (as in, certain tasks wouldn't even be touched if not for AI). For stuff that I'm used to (R) I can write nice and compact spaghetti (long %>% pipes). I'm not comfortable when a working script doesn't fit the screen (plus a few scrolls max). My style is probably easy only to me. When I teach, I don't teach it in particular. AI gives me thousands of lines of codes for those. 10k once. It's cool if it suffices to source it all, but working with that is not pleasant. But if I can reproduce a paper in a one-shot (it used to be an hour, but recently it got so much better), that's a task that would not have been even attempted years ago. And I'm talking about a methods paper with no available Github (or, as often happens, when the existing Github is useless)
  • hellohello2 2mo ago
    Interesting. Framing it as 1x to infinity-x matches my experience too. I've have good success with it reproducing papers with existing code, but not such much with one-shotting new code. Do you have any particular setup for this i.e. special validation prompts or multi-agent stuff or do you just ask something like reproduce paper X until it gets results Y?
    • 5555watch 2mo ago
      I used to chat with "the paper" until both me and the AI were roughly on the same page at what's happening. It's important, as a "full read" of the "full paper" is not always used efficiently if not discussed, I've noticed. I then ask it for a detailed step by step realization (of the algorithm, or the paper/section/whatever), plus some context. Then I pass this as a prompt to a fresh new instance, and task it to implement in full based on the description (Gemini Deep Think was quite good with that). Now I feel like 5.6 Sol Ultra is capable of doing roughly the same with its Agents, so it's getting easier in my experience. With the Codex, it can adjust or correct until the output is suitable. I'm sure it depends on the field and the method.
      • hellohello2 2mo ago
        Thanks a lot for sharing! I'll try the discussion to unpack context, that sounds interesting. I end up having such discussions in separate chats anyways to understand what's happening so it makes sense to do it upfront.
• champagnepapi 2mo ago
  Here’s another one claiming 10% https://news.ycombinator.com/item?id=49113774 https://news.ycombinator.com/item?id=49113774
• abratabia 2mo ago
  [flagged]
• bthornbury 2mo ago
  for me, almost all of the work is specs I am no longer: - reading docs for hours and hours - typing (barely at all) - writing code - manually doing tight debug loops - using an IDE to do this I had to give up reading or even controlling the code and focusing on behavior/design-level control (not superficial, still dictating overall technical architecture) i have agents doing everything from writing the code, verifying the code, hardening, increasing test coverage, analyzing behavior, algorithmic perf improvements, managing/deploying to cloud resources, etc... (pretty much everything) and I am accomplishing projects that would take months or years in a fraction of the time. that's way more than 10x. somehow, this is harder and more cognitively demanding than writing code
  • LearnYouALisp 2mo ago
    How much does it cost in GPU rental?
    • bthornbury 2mo ago
      codex pro plan currently
  • twister2920 2mo ago
    > to do this I had to give up reading or even controlling the code and focusing on behavior/design-level control (not superficial, still dictating overall technical architecture) how do you verify the behavior? are you still writing or at least reading tests or just doing manual testing?
    • bthornbury 2mo ago
      I discuss the testing approach and coverage with the model before and after, sometimes in a fresh thread that does a static analysis. interestingly my input is still pretty important
      • twister2920 2mo ago
        > interestingly my input is still pretty important how do you know?
• amelius 2mo ago
  Just wait until inference becomes 10x faster/cheaper.
• tmsh 2mo ago
  I think it matters a lot to understand the kernel of AI in the past 10 years. It's the only way you can truly appreciate the exponential. It looks flat unless you see the dramatic leap in transformer architecture and scaling laws and reinforcement learning. I know it's been hyped to death but you have to see how those things are truly exponential at their core to appreciate how it's not just walking up one step or two steps faster and how it's walking up buildings etc.
• Alwayshasbeeb 2mo ago
  Sometimes small tools are all you need, even if they're slop. About a month ago I pirated an Argentinian movie and the only subtitles available in my language were out of sync and at a different speed/framerate so adjusting for delays wasn't enough. I was unsuccessful at fixing it with VLC and every other "online tool" I could find. Knowing a srt file is just text with timestamps I vibe coded a python script to take in sample times throughout the movies so it could recalculate the rate and shifts and replace them in the file. It worked on the first try using only the deepseek web chat interface and my terminal. Without AI I theoretically could have sat down with a pen and paper to figure out the math adjustment, then looked up python input handling syntax which I already forgot, typed something out and then hammered it into shape through trial and error over a few hours. But the friction and time investment of doing that would have been so great I would have just given up on watching the movie instead.
• knighthacker 2mo ago
  [flagged]
• SwellJoe 2mo ago
  I was saying it's a 3x multiplier for me during the Opus 4.8/GPT 5.5 era. Now, I don't think I'm notably faster than that, but I'm taking on projects I couldn't have done without LLM assistance, and not just because of time, but because of knowledge. It's become a lever that allows me to learn as I go while tackling problems that are far beyond what I already know. I know a lot, I've programmed for decades in a lot of languages in a lot of domains. But, with the current crop of the best LLMs, I can reach for bigger problems...and actually make progress. I've read books about DSP for audio, and have done little toy projects in the past, but with LLMs, I'm building complex and working synthesis engines in a couple of weekends. That would have required a few months of study and experimentation before. So, that's a huge multiple. Like 100x. For things in my area of expertise? Probably still just 3x, maybe 4x, because it makes fewer mistakes I have to fix in code review. It still writes terrible docs, as the post mentions...they don't understand user desire, so they simply can't write documentation for a user to actually use. You can't prompt them to make really good docs, but I can usually prod their docs into coherence a bit faster than writing it myself. But, it's the lever for doing things I've never done that is such an addictive thing. Which, I imagine is how non-technical people feel shipping their first web app or whatever with these things. For basic work, I think we're at a point where almost anyone can use an LLM to make working software (not necessarily secure or stable software, but working). But, I think we're also at a point where an expert can make that lever really do something, and I hope that means we'll begin to see extremely ambitious new software in addition to all the throwaway junk that's been proliferating at a frightening pace. In short: Maybe don't make another fucking "memory" system for your chatbot so they can be your friend who remembers your birthday, and instead work on something meaningful.
• HarHarVeryFunny 2mo ago
  Even if that 2x were a representative number (although another HN story today says 1.1x - 10% improvement), what really matters is whether companies are overall seeing any AI spending return on the bottom line. There is a lot of reason to suspect that at most companies, especially more established non-startup ones, there will close to zero bottom line benefit, because these companies already have free paid-for developer capacity (developer down time between project cycles) that presumably they would be utilizing if there were reason do to so. In a startup environment where there is zero down time, then productivity may at least show up in reduced time to market. I think Uber's current experiment of limiting AI spend to 10% of salary per developer is interesting since it implies that spending more will not even recoup the extra token cost, although it does remain an experiment. Maybe they will see a revenue increase related to AI spending and choose to tweak that limit up, but it's also entirely possible that all they are doing is reducing developer workload by giving them a productivity tool, and there will be no financial benefit.
  • encyclopedism 2mo ago
    I agree the bottom line ROI largely isn't there and won't be in the future either. The upside for companies is not the bottom line but the potential savings from hiring fewer developers or cutting jobs. As AI based development processes continue to improve, the confidence level employers will have in safely cutting jobs will increase. The longer term impact of AI is HR savings rather than introducing new capabilities into business.
    • HarHarVeryFunny 2mo ago
      > The longer term impact of AI is HR savings rather than introducing new capabilities into business. I tend to agree, although this goes against the Dwarkesh narrative (apparently matching current SV zeitgeist) that there is some insatiable demand for "warehouses full of genius coders". If there really was demand for more coders (especially at the high prices the AI companies are hoping for), then companies would be hiring the unemployed developers available right now, not laying more off. I wonder how many CEOs or CTOs appreciate the massive functional gap between an AI coder like Fable and a human developer - full general intelligence, with continual learning, theory of mind (so they understand what the boss wants, not just what s/he says), etc... all available now, not some 5-10 year AGI/ASI stretch goal (that may in fact take much longer - artificial brain, not just "AGI") ...
• bdcravens 2mo ago
  I'm probably in the 5x-10x range, but we are a very small team, and we've been accumulating a backlog of ideas over the years. I drive the architecture, and have almost complete autonomy over the work I do. I think someone in my shoes will see a greater result than someone trying to do the same thing in large team, with all the usual process and ceremony, only wanting to go faster.
  • RegW 2mo ago
    Where as I'm stuck in a big team with a legacy codebase and lots of business rules, performing small incremental changes. So I spend my days banging the Esc key and shouting "Shut the f**k up!". Luckily I work remote. I'd turn it off again, but my usage is monitored and I don't want to look like a Luddite. So may be .75x to .8x range.
    • xXSLAYERXx 2mo ago
      In my experience a legacy codebase is where LLM shines. What would take days to grok is understood and explained almost immediately. There was a bit of job security with being the one whose worked with IIFEs to manage state. You must know the codebase well if the LLM is slowing you down.
      • bdcravens 2mo ago
        Some of our biggest wins were in a legacy code base, where changes would require a lot of archaeology. I feel like Claude (and probably most coding LLMs) really shine when optimizing fat blocks of SQL, given the complex relational calculus.
• skmurphy 2mo ago
  I can accept the author's premise that LLMs double coding productivity today. Semiconductors following Moore's Law increased their density, which is a reasonable metric for effectiveness, by 40% a year for several decades. Leaving aside projections of AGI in two years, it's still a substantial impact if we can figure out how to increase coding productivity by 40% a year for two or three decades. I don't think that gets us to AGI or the Singularity, but it's similar to the impact of the steam engine, steel, or electricity. These are "normal technologies" that were transformational, and that may be the path we are on with LLMs.
• kazinator 2mo ago
  > LLMs' increased rate of adoption in 2026 is largely due to them becoming reliable enough to run effectively in automated feedback loops Is that really from improvements in LLMs, or from improvements in the feedback loops?
  • sejje 2mo ago
    I think about 80/20, harness vs llm progress
• jwpapi 2mo ago
  I think for small projects you need to scale the coders mental model, which afaik works in the fastest time, when you let ai do the exploration and planning, but coder needs to write the code itself and then ai verifies. That generates the best of world codes and the coder at least has written the code. It’s scalable. In projects that are so big that no single coder or no small group of coders is sufficient to grasp I don’t have personal experience, but my guess would be that they are just a cluster of other small projects.
• legohead 2mo ago
  Totally depends. The real question is what's your average productivity increase? I had a task to completely gut out a codebase to share with a vendor. I gave them my estimate - 2 weeks. Asked Claude to do it, and was done in an hour. Reviewed the changes, and it was perfect. This is an outlier of course, and it was a pretty basic codebase. But it's real world stuff. Overall though, if I had the mental fortitude to work for 8 hours straight, I could easily average 5x my "normal" performance. But most days I can't perform at that level. Also I admit I am not a fast developer, I do a lot of testing and verifying as I'm paranoid.
• resters 2mo ago
  If you're getting 2x you are likely at the limit of your own ability to coordinate or plan intelligent work effort, and you have plateaued such that even when models are twice as powerful as they are today you will still be getting 2x of your own human throughput. We all have a limit after which we lack the attention (or attention to detail, or time, or energy, etc.) to meaningfully manage it. Similarly, a manager may be able to handle a team of two very well but end up poorly managing a team of 20. It used to be that knowing how to write code was a big factor in productivity. Now it is less of a factor compared to the many other cognitive and metacognitive faculties that working with agentic teams demand.
• scelerat 2mo ago
  I recently, with the help of Claude Code, made a tool which involved a database, two separate apps surfacing and interacting with various aspects of the data, and a user base of dozens, with some semi-specific domain knowledge on my part and an intention of re-using and building upon the minimum viable result. OP's description very much mirrors my experience. I was able to do many things much more quickly with the help of the LLM, but there is no substitute for actual users interacting with the tool, saying, "I like this," or "no, this is wrong or needs work," or even, "here's something none of us thought of before, but now this tool makes me think XYZ would help us and might be achievable." That whole interaction takes real time and I don't know how you replace it with coding agents. Secondly, on the matter of code structure, just on a qualitative level, I can see that claude will do things very efficiently on the way to a goal I give it, but it can't read my mind and know that I may want to repeat a specific pattern across two client apps. Or that its shortest-distance solution makes extensibility or broad applicability difficult. That I might want to share code and structure things in a certain way. Not without me saying so or, in many cases after it has built something workable, duplicating refactors I make with an eye for reusability or maintenance. Making those changes in time is important if you don't want to burn tokens later as the LLM tries to unravel its own spaghetti. And again, whether I am coding those intentions directly, or writing out detailed instructions in english, all of that takes time.
  • mindwok 2mo ago
    I’ve been vibe coding apps professionally for the last year and 100% agree with your first take. I really underestimated how much work there is in what I just call the “coordination” of developing software. Users pointing out things that are missing, or me forgetting to tell them I’ve added something, or both of us having a different idea in our minds of what something should do. It’s still extremely time consuming.
• sbinnee 2mo ago
  I wholeheartedly agree on readme. AI generated readme irks me off almost immediately.
• bigbuppo 2mo ago
  There's someone I know that jumped head-first into AI... they have embraced everything agentic. While setting up mail on their new iphone they encountered a problem. They had claude go through and do a bunch of tests against the server... allegedly. It didn't do any actual testing, though it did generate a long-winded report of what it pretended to try, fully blaming the server the whole time. Actual problem: he typed in the wrong password
  • throwuxiytayq 2mo ago
    Not sure what your point is, could you clarify? Are you saying that your friend is stupid? Or do you mean Claude is stupid? Both?
    • chr15m 2mo ago
      Claude lies, like all LLMs today.
      • sejje 2mo ago
        Oh, right. Let's throw it all in the trash, totally useless
        • chr15m 2mo ago
          Why would you say that? It's incredibly useful technology that also lies. It's not binary. There are tradeoffs. Just be careful.
          • bigbuppo 2mo ago
            My point is that far too many people that offload everything to AI tend to just accept the text it generates without thought or verifying what it generates.
• bartleeanderson 2mo ago
  I am able to coax much more of the kind of thing you suggest that LLMS can not do by having it generate deterministic code and then narrate over the results. Hopefully that is not too negative and gives some helpful guidance.
• Ozzie-D 2mo ago
  [flagged]
• spaqin 2mo ago
  I'm pretty sure that just a year or two ago, software engineers were saying that coding was only a small part of the job; the rest is meetings, dealing with requirements, design, testing, maintenance. So, how can we expect a 10x boost in a productivity from improving a small part of the job - especially now if we have to clean up any mistakes the LLM makes?
  • zug_zug 2mo ago
    Yeah that's a good point, in order to see a 10x gain it would require at minimum coding be 90% of your day [7 hours, 20min) (or somehow for AI to replace meetings)
  • 361994752 2mo ago
    Now you actually have very long meetings with AI, if you consider all these long design / explorer / post-poc conversations "meetings".
• sitzkrieg 2mo ago
  maybe i’m an edge case but for 99% of the day to day work i do (embedded, not x86), llms will confidently give me wrong code so many prompts in a row it’s a 0.5x factor. when i venture out to something more normie, like webdev on either side, it does better but still takes more time than if i sat and did it myself once over. i’m not exceptional, but certainly understand everything that has been generated at any rate. i think LLMs are only a useful multiplier if you’re working on the plumbing parts, it’s never useful if you’re doing anything novel really. if this bothers you, i am sorry and i wish you the best with your reddit trained auto complete. i write new code
  • jeffybefffy519 2mo ago
    I do a lot of novel work, that is not setting up a webapp - and totally concur on the novelty point with LLM's failing hard.
• ed_mercer 2mo ago
  > the dark ages of 2025 This made me chuckle. I do think we are seeing exponential improvements over the years and that we don't always notice it happening because we're right in it.
• danans 2mo ago
  > I have not yet taken up woodworking as a contingency plan for "Software Engineer" becoming an extinct profession Between "stable" and "extinct" are many stages of software engineering labor market change, continually more difficult (for labor) as we move toward the latter state. Software engineering is likely to become a skill/tool attached to another harder to automate ability going forward rather than a vocation/craft on its own. To the extent that software engineers are employable, they will likely need some other x-factor adjacent to software skills, whether in the science, business, creative/artistic, or social domain. I've already seen this trend emerging in the startup world.
• Xcelerate 2mo ago
  I have a weird issue with using AI for coding. I can code something entirely by myself at my baseline speed; call it 1x. Or I can use Claude to do it, and it does it in 1/10 - 1/4 of the time. The problem, however, is that to review Claude’s code properly takes 2-3x the amount of time it would have taken me to write it all by hand. So my two choices are basically “YOLO, LGTM” and hope I can revert if it breaks something, or to just write all the code by hand from the start. With the increased pressure for output, I’ve noticed both myself and coworkers tending more toward “commit and hope it works” over time. It’s sort of perverse incentives in a way...
  • taberiand 2mo ago
    The models are good enough these days that I see it like managing a team of juniors. The LLMs need guidance and oversight, and about the same amount of time I'd spend reviewing code from a junior I spend on the LLM output. I think generally it's best practice to work in a way that "commit and hope it works (because it passes all the CI/CD automated testing and verification, and it's a change gated behind feature flags and has an identified and minimal blast radius, etc)" is possible
    • vekker 2mo ago
      I feel like these days it's more like managing a team of seniors who are technically very competent, but may not always have the full domain context of the problem they're solving. And/or they forget parts, if that context is too large. And/or they have a poor temporal dimension (deprecated badly maintained information polluting the context). They also tend to overengineer solutions if not guided well. If you think about it, in that respect it's not that different from managing actual human senior engineers. I think this is why focussing one's limited human attention more the input (defining clear requirements) as well as on validating the output (good CI/CD including end-to-end automated tests) is far more important than manual code reviews and micro-managing the development process.
    • jurgenburgen 2mo ago
      Reviewing LLM code is quite different from junior code. With juniors you tread carefully and give feedback only on important points to encourage growth. With LLMs you channel the inner sailor and nitpick so much that even a senior would start to cry.
• jaxn 2mo ago
  This ignores that non-developers have gone from 0x to 0.5x.
• xbmcuser 2mo ago
  I am not a programmer but for me the return is 100x as I am automating stuff that I knew could be automated but did not know how to but at the same time it was not worth the effort to write it down and pay someone to code it for me. But now I have created so many python and bash scripts that save me hours and hours a month. In my opinion the people that can most take advantage of the programing capabilities are people that least use it for such or know the how they could automate a lot of the repetitive tasks they have to do at work or on their own time.
  • globular-toast 2mo ago
    Now that you only work 3 days a year, what do you do with all the spare time?
    • xbmcuser 2mo ago
      Browse here and reddit and read a lot of fiction and lose a lot of money day trading futres.
• robomartin 2mo ago
  Well, it's interesting. I've been using LLM's for coding for over two years. My findings are somewhat consistent: Feed it jobs in small chunks and it can be a great auto-complete, beyond that and it turns into a mess very quickly. I'm two weeks into a project that has been interesting and also has confirmed the above yet again. I am porting a Windows application written in Python to C# using Avalonia UI. I have never used C# or Avalonia UI. I've written Windows applications in other languages, never C#. I don't know the language, libraries, etc. At first I told Codex: Here's the source code, port it. I just had to run that test. Well, it didn't end well. I'll describe it as a frustrating set of prompts that seemed to result in the implementation going in circles with constant problems being introduced, breaking-fixing-breaking, etc. I then started again with a clean repository and played the role of the architect with full documentation in the form of code. File-by-file, I had it port modules to effectively develop an operational foundation for the classes, methods, properties, abstractions, hardware interfaces, etc. in the original Python program. That went well, was very fast and a good experience. I am running Codex in JetBrains Rider and the integration is excellent. The native OpenAI Codex application is an absolute dog...it pegs all my cores at 100% while doing nothing. Once all the underlying infrastructure was ported and, to the extent possible, individually tested, I threw UI integration at it. This happened quickly and OK from it's-ugly-but-I-can-use-it perspective. Codex seems to be way out of its element when it comes to UI/UX understanding. Funny examples like placing a button on top of an image with the image covering the button because it had a higher z order. Or finally placing the button at the correct z order but making it transparent on-hover. Funny stuff when you are moving slowly and you see it happen. It just proves that there is no understanding whatsoever. With all of that and the experimentation, I'll estimate that a six month project will be cut down to three to four weeks. Another month and it will probably be a much better program with new features and more advanced capabilities. And I have not touched a single line of code. Developing solid prompts is the absolute key, something that you can only really learn by doing and through lots of experimentation. Yes, of course, I fed it working code. I think it could be very different if I were to start something from a blank slate.
  • sejje 2mo ago
    > placing the button at the correct z order but making it transparent on-hover. Funny stuff when you are moving slowly and you see it happen. It just proves that there is no understanding whatsoever. I don't think it proves that. It proves it doesn't run and visually inspect the program. That's because the harness is lacking, not the llm. When you do web dev with the thing, you can have it screenshot the browser and it turns out it has no problem understanding things like "buttons should be visible." You just haven't allowed it to look. You yourself admit that a human makes the same mistake, but sees it. Anyway, have your model build itself a way to take and review screenshots of the app.
• wwind123 2mo ago
  I think it'd take some time for people to figure out what kind of harness or workflow would work best with each individual codebase or team culture. AI certainly can help speed things up a lot, but for now the humans driving the AI need discipline to follow good software-engineering practice, or at least have multiple AI agents critique each other's work, instead of just pushing out whatever one AI agent writes.
• est 2mo ago
  I feel LLM can only build what's known and popular. Sailing into the relms of lesser explored area are painful. It's like an intern but never learns, only getting replaced by better interns.
• chesscoachx 2mo ago
  [flagged]
• devld 2mo ago
  This has been my experience on a recent small project for a client. I took effort to understand and prepare the spec before feeding it to Claude Opus. Yet, the result was slop full of defects, and one that I did not understand well at that. I just did not have a mental model of it. I just thought, there's no way I could ever feel confident about this code, something is missing. It did not take me long to write the important parts myself, from scratch and develop that mental model. Then I would use Claude to make a small fix, write a unit test (write a unit test, not suggest what they unit test should be) or write some less important UI code. It just felt like old school development, with some assistance. I think we may see a shift back to a "copilot" mode and by the way, I still use GitHub Copilot. The $19 subscription includes $30 worth of credits and the autocomplete in VS code that uses some cheap model is completely free. This autocomplete is very good and I would like the market to focus more on IDE integrations. The agentic thing is either ahead of its time or it will never have time. Time will tell. I think the LLM can only be as good as the training data, so it will always excel at small chunks, but to implement entire projects of which there's such variety, I would question that. And also, how using it to implement entire projects means the developer does not hold the program model in the mind, which leads to more issues long term. By the way, in this way of working, I see no difference between Sonnet and Opus. Sonnet is good enough to use as an aid.
• karam_hn 2mo ago
  [flagged]
• asquarer02 2mo ago
  [flagged]
• the-conduit 2mo ago
  When you look outside of coding, the productivity boost and time saving also hits when you're using the Ai as a thinking partner. eg there is a significant different between Opus and Fable when it comes to thinking, data analysis, strategy. I also think Grok 4.5 is extremely underrated, faster than any other model, less verbose, reliable. The human to agent trust factor is also under-considered - how much does the agent "get" you when you give it less.