Comment by Insanity

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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?