6 ms·

I wonder how much the 'inflection point' is a thing vs marketing. I'm sure the models got somewhat better, but even now when I'm trying to 'vibe code' a game wi

• righthand 5mo ago
  I mean this blog post and many from this author are pure evangelism and marketing. Can you find anything critical or any dissent from this author about LLMs?
• adgjlsfhk1 5mo ago
  It's very real. Just in the past 2 months or so IMO there's been a pretty big improvement in claude for local dev (although I think a lot of that is less model strength and more harness capability). 1m context is a huge difference (~30 min vs 2.5hr between compact significantly increases the scope of what I get the AI to do before it goes stupid). The other biggest difference I've noticed is a better balance of actually doing the work vs pushing back on bad ideas. I want the AI to tell me if it thinks the thing I am telling it is wrong or a bad idea, but if I confirm, I want it to do that anyway. A couple months ago, the claude was a lot more likely to either say "This is too much work I'm not going to do all of it", tell me the idea was genius (and then pretend to do it) or something equally useless.
  • DeathArrow 5mo ago
    >1m context is a huge difference (~30 min vs 2.5hr between compact significantly increases the scope of what I get the AI to do before it goes stupid) I think the smart zone stays within the first 100k tokens, no mater if the context window is 240k or 1 million. I divide the work to fit within that 100k and use subagent for the tasks.
    • danielbln 5mo ago
      In my experience it's more like 400-500k tokens.
• minimaxir 5mo ago
  Opus 4.5 in November 2025 was legitimately, unironically an inflection point and is the sole reason for the current hysteria. GPT 5.5 is a significant improvement over GPT 5.4 but I wouldn't call it an inflection.
  • baq 5mo ago
    5.2 and the first codex model were step function changes in capability
• xbmcuser 5mo ago
  It's real for me as a non coder previously uploading a python script asking it to add this function or that function used to break it now usually it just works at least with Claude and Chat Gpt models. Google Gemini still breaks stuff but rumors are their new flash model that will be announced soon is very good. I am usually working with data in csv files and generating spreadsheet pdf etc and the results for that has improved dramatically.
  • Scoundreller 5mo ago
    That’s me. Built a scraper do dump stuff to a csv of a list of images for further ocr and openCV processing. Now I have a convenient list of hits once I run the batch that used to be a loooot of manual sifting. Once I work out the kinks, I’ll be able to further automate it. Would have taken 10-100x as long for me to build it without AI and the AI version is probably better. But yeah, I have enough knowledge to know what prompts are needed and figure out those “oh, I think it’s running slow or failing because of xyz” and further prompt to improve it based on that what I think it should do instead. And I know where to make slight changes without burning my allotments.
  • LAC-Tech 5mo ago
    "flash" or "fast" AI models are worse than useless at coding for me. they make my codebase much worse. It's a maintenance burden. Gemini Pro on the other hand can be quite a pleasant experience.
• bluegatty 5mo ago
  Paradox - you can get multiple inflection points even as systems start to have dimishing marginal returns in core capability, I think this is due to 'threshold crossing' where something 'becomes good enough for a specific purpose' - it just unlocks capabilities. 'Nail Guns' used to be heavy, required heavy power cords, they were extremely expensive. When they got lighter, cheaper, battery pack ... at some point, they blend seamlessly into the roofers process, and multiply dramatically the work that can be done. Marginal improvements beyond that may not yield the same 'unlocks' because the threshold has been crossed.
  • szundi 5mo ago
    [dead]
  • smackeyacky 5mo ago
    This is a great analogy. Jan/Feb this year was when the models crossed from useful to essential.
  • asdff 5mo ago
    Nitpick but commercial roofers prefer pneumatic over battery.
• halflife 5mo ago
  I feel the change. It went from an autocomplete tool, to an agent running 5 tasks in parallel while I just supervise. The improvement is enormous.
• kvakkefly 5mo ago
  I remember this very clearly myself. Before opus 4.5, I was doing a lot of hand holding and was coding a lot myself, but I have not written code since that day more or less. I did write some stuff myself just to learn how the enigma encryption machine worked, so wrote myself to learn. But professionally, I stopped coding in November.
  • szundi 5mo ago
    [dead]
  • viccis 5mo ago
    How do you justify your salary given that you're just using a tool that any of us could use for $20 an hour in your role?
    • aspenmartin 5mo ago
      Someone competent using them is today a requirement and for awhile will make the marginal utility of skilled workers greater than that of unskilled. The justification is that they are much more productive than they were before.
    • bsder 5mo ago
      Because the tool will happily give you a "solution" that kinda works for a few inputs. It will happily correct itself when you give it more incorrect tests. It will almost never converge on the general solution that will pass tests you haven't given it yet. This is why AI is sooo good at Javascript and related slop. A solution that "kinda works" is good enough 9 times out of 10 and if some tests fail well ... YOLO and the web page will probably render anyway. Contrast that to using Scheme or Lisp where AI will have trouble simply keeping the parentheses balanced.
      • FeepingCreature 5mo ago
        To be fair, take away a human's paren highlighting and see how well they do.
• DeathArrow 5mo ago
  Purely vibe code won't work. You need to define an excellent architecture, have great specs, a solid plan, divide the plan in small phases that fit well in a context window, use TDD and automated code reviews for implementing each phase, do QA and some code review. At any point you need to have agents review, verify and test the other agents output and iterate until the output is perfect. And also, have good e2e tests. IMO, if you don't spend at least a few tens of millions tokens per day, you aren't doing it properly.
  • fluder_tw 5mo ago
    Sounds very self confident to claim such thing. Something like "If you don't do how me is doing, then you are doing it wrong"
  • ssdspoimdsjvv 5mo ago
    At what point is it easier and faster to just code it yourself? I don't trust myself to write better specs than code.
• magicalhippo 5mo ago
  I've "vibed" some non-trivial stuff lately using a combination of Codex with 5.5 and Claude Code with Opus 4.7. Key has been to spend a fair amount of time on initial overall design document, which is split into tangible and limited phases. I go back and forth between them on this document until we're all happy. For each phase an implementation plan is made. At the end, a summary document of what was delivered and what was discovered. This becomes input to next phase. I do check the documents, and what they're doing. I also check the tests, some more thorough. And some spot checks on the code to see if I like the structure. I have mainly used Claude for coding and Codex for design and code review after phases. I ask both to check test coverage after phases. Managed to implement some tools and libraries without writing a single line of code this way, which have been very beneficial to us. Since it's so async I can work on other stuff while they plod along. I think it's not universal though. But stuff that can be tested easily and which you have a firm grasp of what you want to achieve, but not necessarily exactly how, that I've been impressed with.
  • nothinkjustai 5mo ago
    None of it is non-trivial tho. You might think so, but it’s not.
    • magicalhippo 5mo ago
      It wasn't trivial in that I used a lot of my programming and domain knowledge, both when iterating on the design document and skimming implementation plans. I didn't use it often, but when it was needed it was needed.
  • manmal 5mo ago
    That’s not vibing, but waterfall development.
    • whatshisface 5mo ago
      Waterfall was famous for wasting developer time and extending delivery dates in exchange for simplifying management. If Claude time is comparatively inexpensive, but human oversight remains necessary, we will switch back to waterfall because the relative importance of the two resources will invert.
• orrito 5mo ago
  While some people got it to work better, for me vibe coding games still didn't reach the point of regular sites/web apps. Physics, creativity, assets and UI/UX still need a lot of hand handholding with the models. Games that are more interface based like point and click or something like reigns are easier though
• ryanjshaw 5mo ago
  I find it gets you past the starting line but when you dig into the code it’s a mess of duplicated code, muddled responsibilities, poor architecture, 10k line files that eat your tokens, etc. I’m building something using LLMs to scrape websites/socials for unstructured event data from combined text/images and the only way I’ve managed to get 100% consistent results for a reasonable cost is to break the task down into very small pieces that reduce the scope of mistakes significantly. At present, for reasonable complex tasks, Codex/Claude will happily code you into an expensive corner.
  • ben_w 5mo ago
    Indeed. To add to this, the obvious solution (ask the AI to break down the tasks to whatever METR says they'd be capable of 80% of the time) is of limited utility, as the AI are only so-so at estimating task complexity. (Even when they're getting the planning part right, I do also recommend checking the LLM-generated unit tests, because in my experience some of those are "regex the source code" not "execute functions and check outputs").
• iLoveOncall 5mo ago
  It is all marketing. The easiest way to tell is that a year ago the same people said the inflection point was X or Y model. When people claim LLMs just don't work for them, the first question is whether they're using the latest model or not, and if not, dismissing the poster. The thing is that that same question was being asked a year ago, and even a year before that, but with the models that lead to a dismissal today. Just make the experiment yourself, wait 6 months, say LLMs just aren't working for the software engineering that you do, and people will dismiss you if you say that you use Opus 4.5 and not the latest model Claude MegaMind 8.8 pro max gigathinking. Despite this model being touted as the inflection point in this article.
  • harshitaneja 5mo ago
    I think it's because both sides are talking about different things. If you go in expecting it is good enough to make developers obsolete today(reasonable impression to get from the way a lot of people hype it) you would be disappointed and after first couple of tries every few months you would probably not try it much with next generations. Reasonable if it's considered a dichotomy. But a lot of people excited about new generations(including me, now) are not seeing it as a dichotomy but rather a spectrum where models are getting better and indeed once a year or even 6 months at times there comes a sudden growth which feels like an inflection point from what came before. Practically, it's a tool like any other, you evaluate it based on if it's worth the effort and cost for the benefit you get from it and if it is and has a good DX you use it. If the calculation doesn't work for you, it doesn't. For me, it has gone from a novelty, to good for some kind of quick manual search, to I guess it can debug some kind of errors at times in very specific conditions, to hey I think I am getting a bit addicted to autocomplete in IDE provided by them even if I don't use them for anything intelligent but it's becoming indispensable now but only this part, to it's good for areas I lack expertise in, to agentic sucks I will stick with discussing algorithms and architecture with it on greenfield projects, to holy shit it can do agentic decently well now, I am skeptic to give it access more than in limited cases, to now I am getting close to letting it run free on my device in not so distant future I guess. Some of these were big jumps, at each point I was skeptical of growth. Everytime I thought now the growth will slow down from days 2k context window to millions now. From basic chat completion to working on complex adaptive systems, game theoretic modelling, heurestics and constraint modelling and other things I throw at it. I am still needed in the loop, it can be so smart at times and then will do something so stupid, but the frequency of stupidity is rapidly decreasing. I am still needed, I don't think it could accomplish alone all that it has done for me. But I do at times at night remain awake reflecting on my self worth for the potential day when I don't add that value. When I have a harder time keeping up. Also had someone told me not in even 2019 that in 2026 we could have NLP models do what they do today, I would have posited it all as sci-fi and here I am waking up in awe of the world we live in and how quickly we adapt.
• ReptileMan 5mo ago
  Anecdata of 1 but it is real. At the end of last year they passed some invisible threshold and became useful. I don't think it is models themselves, but mostly the much more powerful harnesses and I guess their tool calling abilities. What changed I think was the context harvesting capability of the models. What most programmers did was - debugging and figuring out how something works were the time consuming part - the fix was usually trivial. And now models could do in seconds what took a developer hour or more. If right now we create a smart grep that just takes everything for a piece of code and outlaw llm-s we will not regress to the previous level. The developers needed this context as much as llm-s to do their job.
• sofixa 5mo ago
  Counterpoint, I'm also vibecoding a game, and even before doing the "proper" setup (a good AGENTS.md, skills people have published for my chosen game engine, Godot), mechanically, the game was pretty spot on. It looked boring, so I used Claude Design to create a few mockups to choose from, chose the one I liked the most, and told Claude Code to redo the game UI with it. There have been plenty of small issues like tables not having the columns aligned, or the game menu being a bit offset, or one graph being a placeholder instad of connected to the actual value. And of course I've had to instruct it on all the flavour I want. But honestly, for a simulation strategy game, especially without doing the "proper" setup from the start, it's been _very_ good.
  • QuercusMax 5mo ago
    UI fit and finish is really hard for these models, even in with text-mode UIs. The super fiddly stuff still needs to be done by hand, at least for now.
• vikramkr 5mo ago
  It's very real but probably very domain specific. It got really good at a lot of traditional web dev stuff, bash, sql, and writing one off scripts to accomplish random tasks (hence all the agent stuff taking off). And they got good at staying on task. That may not translate to game dev because from what I understand a lot of these gains are basically around post training methods driven by synthetic data generation etc (with potential caveats on how synthetic that data actually is lol). I wouldn't be surprised if the areas of code the llms are good at now are straight up just product decisions of where to allocate budget for generating those synthetic data sets, and game dev stuff might not be at the top of the list because the customer base for that might not be as big