> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.”
This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive.
This matches my own experience and unease with these tools. I don't really have the patience to write code anymore because I can one shot it with frontier models 10x faster. My role has shifted, and while it's awesome to get so much working so quickly, the fact is, when the tokens run out, I'm basically done working.
It's literally higher leverage for me to go for a walk if Claude goes down than to write code because if I come back refreshed and Claude is working an hour later then I'll make more progress than mentally wearing myself out reading a bunch of LLM generated code trying to figure out how to solve the problem manually.
Anyway, it continues to make me uneasy, is all I'm saying.
The current market is predicated on the assumption that labor is atomic and has little bargaining power (minus unions). While capital has huge bargaining power and can effectively put whatever price it wants on labor (in markets where labor is plentiful, which is most of them).
What happens to a company used to extracting surplus value from labor when the labor is provided by another company which is not only bigger but unlike traditional labor can withhold its labor indefinitely (because labor is now just another for of capital and capital doesn't need to eat)?
Anyone not using in house models is signing up to find out.
The idea is that cheap and readily available and upgradeable intelligence is going to massively increase our purchasing power and what everyone can order for the same cost basically.
If artificial doctors are cents on hour then you can see how that changes our behaviors and level of life.
But on the other hand from the other direction there is a wage decrease incoming from increased competition at the same time. What happens if these two forces clash? Will cheap labour allow us to buy anything for pennies or will it just make us unable to make a single penny?
In my view the labour will fundamentally shift with great pain and personal tragedies to the areas that are not replaceable by AI (because no one wants to watch robots play chess). Such as sports, entertainment and showmanship. Handcrafted goods. Arts. Attention based economy. Self advertisement. Digital prostitution in a very broad sense.
However before it gets there it will be a great deal of strife and turmoil that could plunge the world into dark ages for a while at least. It is unlikely for our somewhat politically rigid society to adapt without great deal of pain. Additionally I am not sure if hypothetical future attention based society could be a utopia. You could have to mount cameras in your house so other people see you at all times for amusement just to have any money at all. We will probably forever need to sell something to someone and I am unsettled by ideas what can we sell if we cannot sell our hard work.
Someone who sees the roads ahead should now make preparations at government level for this shock but it will come too fast and with people at the steering wheel that don’t exactly care.
LLMs cannot possess consciousness for three reasons: they execute as a sequence of Transformer blocks with extremely limited information exchange, these blocks are simple feed-forward networks with no recurrent connections, and the computer hardware follows a modular design.
Shardlow & Przybyła, "Deanthropomorphising NLP: Can a Language Model Be Conscious?" (PLOS One, 2024)
Nature: "There is no such thing as conscious artificial intelligence" (2025)
They argue that the association between consciousness and LLMs is deeply flawed, and that mathematical algorithms implemented on graphics cards cannot become conscious because they lack a complex biological substrate. They also introduce the useful concept of "semantic pareidolia" - we pattern-match consciousness onto things that merely talk convincingly.
They are making a strong argument and I think they are correct. But really these are two different things as I said originally.
We could also literally have Star Trek. Think of all the scientific discoveries we could make if we had armies of scientists the size of our labor force.
But we will have to (painfully) shed our current hierarchies before that comes to pass.
Maybe so but humans have this strange primal need to hoard resources.
Probably a remnant from prehistoric times when it was a matter of life and death.
Will we ever be able to overcome this basic instinct that made capitalism such an unstoppable force? Will this ancient PTSD be ever cured?
Man if only there was a singular episode that covered this exact topic in Star Trek and resolved that no, actually slavery wasn't any different for artificial life.
I am still trying to figure out the business model of open weights. Like... it's wonderful that there are open LLMs, super happy about it, good for everyone, but why are there these? What is the advantage to their companies to release them?
IMHO this is only temporary, china buying themselves some time and want to make sure none of US models get entrenched in their position in the next few years (also putting pressure on US AI companies bleeding them)
The same way like Windows got entrenched everywhere even though linux desktop is pretty good even for non-tech savvy people and free.
My grandma just clicks on the red fox and does whatever online. A lot of people don't use any software outside of the browser, so it's pretty good-enough I guess.
> What is the advantage to their companies to release them?
It's a distribution strategy. It costs something to serve the models - let's say $5/1M tokens.
If Qwen required $5 from anyone who was curious so you could even begin to test it out, a lot of people just wouldn't.
Now Qwen could offer a "free" tier, but it's infinitely cheaper to provide the weights and let people run it themselves including opening up the ability for anyone else on the planet to test it against other (open weight) models.
The costs to build the open weight models are sunk, but the costs to serve them, get them tested are not.
It's also precisely why the .NET SDK is free or the ESP32 SDK is free - they sell more Microsoft or ESP32 products.
It's analogous to open-source software, which never had an obvious economic incentive either, although training an LLM necessary costs money whereas developing an OSS project might only cost time, which people are probably more likely to give up.
Yeah, but open-source software could have been me in the garage banging away on some program I submit to Debian or whatever... it didn't require millions of dollars to train, a lot of it was just side hobbies for a long time. Corporations sponsor it and contribute work because they need it to do more than what it does for free, not out of the goodness of their hearts.
I mean, this is straight out of chinas playbook, it should not be surprising that China is making an inferior derivative product at an artificially lower price point: state subsidies to massively drive up internal scale and supply chains leading to artificially lower priced goods which then suffocate the competition has lead to *gestures vaguely at everything* being made in china.
Right now it’s so the Chinese can undermine the frontier models in the US. In areas they’re doing well like video generation (ie seedance) they won’t open source anything.
The majority are released by socialists, and by socialist I mean the People's Republic of China. Which everyone seems to forget is a socialist country working towards world communism.
They are a prestige propaganda tool on par with the space race. On top of that they insert a subtle pro-socialist bias in everything they touch.
Ask deepseek about the US economic system for a blatant example.
Now think what something as innocent seeming as the qwen retrieval models are doing in the background of every request.
You're talking to a Canadian, and I'm not scared of the "red menace". You should be more scared - those guys can build bullet trains while you Yanks are finding it hard to even keep the old ones you have running. The solution here isn't going to be some kind of ideological force that protects people from different ideas, and that's an unAmerican way to fix things anyway. Embrace other ideas; central planning doesn't have to be evil, you just have to find a way to stop putting evil people in charge.
Xi is an obviously more capable and effective leader than Trump, but the US actually does have ways to boot people out of office when they do a bad job, and clear methods to choose successors, and China has neither. That matters more than who happens to be in charge right now.
America has several sets of eminent domain laws depending on the jurisdiction. The most coercive is federal eminent domain law specifically as it relates to building infrastructure like railways and highways.
It's set up so that you can take the land first and eventually go back around and decide on what the right price should have been.
Not only does it superscede state and local law, federal infrastructure projects are also not bound by state laws like CEQA.
You can even apply federal eminent domain law by e.g. transferring a state-level project to the Army Corps of Engineers.
What America is lacking in these projects is will, not means. The federal government could take your house and run a train through it by the end of the week if they wanted, doesn't matter where you live.
[edit] In fact some states even ceded their eminent domain rights to private railways.
The Australian federal government is planning to build a high-speed rail line from Sydney to Newcastle (medium-sized city two hours drive north). Their solution to property rights, is >50% of the line will be underground. It will >US$50 billion, but if the Australian federal government wants to spend that, it can afford it. The US federal government could too, but it isn’t a priority for them
> local regulations make it prohibitively expensive
Local regulations can be pre-empted by state or federal legislation. The real problem is lack of political will to do it.
My understanding is that existing rail lines aren't flat/straight enough for high speed rail. There's no point to a bullet train if it has to constantly slow down for corners/hills.
Literal Trump Derangement Syndrome. America has a comically horrendous president but remains fundamentally a liberal democracy… and Canada concludes “literal Nazis are a better choice”. It’s uncanny how much can be taken for granted :(
(American talking, who’s had multiple Canadian friends make this mind boggling overcorrection)
They build an incredibly vibrant _market_ economy with no property rights and very little due process.
Imagine if Musk was disappeared during the Biden presidency into a diversity camp and came out looking like Dr. Frank-N-Furter and instituted mandatory LGBT struggle sessions at twitter.
> The point was being made that a billionaire figurehead drastically changed their views after an "indeterminate time" detained by national authorities.
> IE what if Musk suddenly behaved in such a manner after being detained by a Biden administration. Wouldn't that be profoundly weird?!?
We've seen that. Durov in France after detention began sharing Telegram users' data with authorities. It's unclear how much, but likely full real time access to all of it.
Is China even really communist? If anything they seem to be fairly on the Capitalist side but just a bit opposite on the spectrum of the US. And much more authoritarian
The usual thing to say is state capitalist but honestly they do keep a market around too. A little hybrid of everything, I guess? Just with the state ready to jump in and intervene if anything happens they don't like.
> Anyone not using in house models is signing up to find out.
What are they finding out exactly? That Claude Max for $200/mo is heavily subsidized and it will soon cost $10k/mo?
> What happens to a company used to extracting surplus value from labor when the labor is provided by another company which is not only bigger but unlike traditional labor can withhold its labor indefinitely (because labor is now just another for of capital and capital doesn't need to eat)?
This can be trivially answered by a thought experiment. Let's pick a market where labor is plentiful - fast food.
Now what happens to McDonald's where they rent perfect robots from NoosphrFoodBotsInc? NoosphrFoodBotsInc bots build the perfect burger everytime meeting McDonald's standards. It actually exceeds those standards for McDonald AddictedCustomerPlus tier customers.
As the sole owner of NoosphrFoodBotsInc (you need 0 human employees to run your company, all your employees are bots), what are your choices?
Why would you expect technological improvements to only shift supply curves right under perfect competition? I'd also expect it under oligopoly or even monopoly. You also might think there'd be more tech improvement under oligopoly, on Schumpeterian grounds that oligopolists can internalize the benefits of tech research.
I was really confused by this comment, but I don't think it's just because of the Marxist analysis of the situation ('surplus value' of labor etc).
What's really confusing is the claim that there's already a huge labor surplus (so capital controls wages); wouldn't LLMs making labor less important be reinforcing the trend, not upending it?
Not saying I agree one way or the other, just want to get the argument straight.
The reason why labor is weak relative to capital is that there is a huge number of somewhat fungible suppliers, viz. humans, and that they all need to work constantly to keep themselves alive.
If we assume that ai makes humans obsolete then you end up in a situation where your workforce is effectively perfectly unionised against you and the only thing you can do is choose which union you hire.
If you think you can bring them to the negotiation table by starving them all the providers are dozens to thousands of times bigger than you are.
This is a completely new dynamic that none of the business signing up for ai have ever seen before.
I see what you are saying now, but I still don't think it makes sense. Labor, in your analysis, is the LLM. It seems to me that when you take people out of the equation then you don't need to talk about unions and labor; that's a distraction. We talk about it as an input commodity used to create your product like, say, oil or sugar.
finance today mostly valued on labor value following ideas of marx, hjalmar schact, keynes
in future money will be valued as energy derivative. expressed as tokens consumption, KWh, compute, whatever
you are right, company extracting surplus value from labor by leveraging compute is a bad model. we saw thi swith car and clothing factories .. turn out if you can get cheaper labor to leverage the compute (factory) you can start race to bottom and end up in the place with the most scaled and cheap labor. japan then korea then china
LLMs don't upend anything about labor theory, good grief. Technologists really have no concept of history beyond their own lives do they?
Labor saving/efficiency devices have been introduced throughout capitalisms entire history multiple times and the results are always the same; they don't benefit workers and capitalists extract as much value as they can.
Labor replacing devices means nobody works in those fields anymore. If AI can do this for every field, nearly no one will need to work in any field. We'll have a giant fully automated resource-extraction machine.
I am not a Marxian economic expert but this doesn’t make sense to me. Modulo skill atrophy, the big AI model provider can’t capture that surplus value because its customers can just go back to bidding for human labor instead.
ChatGPT isn’t literally or figuratively cutting off anybody’s limbs though. It’s more like, the guy on the assembly line had a mech suit, and now he doesn’t have a mech suit, and he’s sad. Skill atrophy is a real concern but unless you assume that nobody is working to maintain those skills it doesn’t change my analysis much.
And soon we expect everyone to have a mech suit, and only a handful of companies can make one, and they rent it to you and can revoke it at any time.
And what happens when they've saturated the market? Prices go up to the maximum the market can bear, and then they'll extend into other markets. Why rent the model to build a profitable company with when you could just take all that profit for yourself?
Sounds like communist gobbledygook. This is not "destroying labor theory" any more than outsourcing did. Call me when we don't even need to prompt the shit ever again or validate results, and when the stuff runs unlimited without scarce resources as input.
One might argue that it’s not too too different from higher level abstractions when using libraries. You get things done faster, write less code, library handles some internal state/memory management for you.
Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()? For some, yes. For others, it’s a bit freeing as you can do more high-level architecture without getting mired and context switched from low level nuances.
In what world to developers “understand” pieces like React, Pandas, or Cuda? Developers only have a superficial understanding of the tools they are developing with.
"When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand."
I always thought the point of abstraction is that you can black-box it via an interface. Understanding it "in depth" is a distraction or obstacle to successful abstraction.
I think it's more: when I don't have access to a compiler I am useless. It's better to go for a walk than learn assembly. AI agents turn our high-level language into code, with various hints, much like the compiler.
If my compiler "went down" I could still think through the problem I was trying to solve, maybe even work out the code on paper. I could reach a point where I would be fairly confident that I had the problem solved, even though I lacked the ability to actually implement the solution.
If my LLM goes down, I have nothing. I guess I could imagine prompts that might get it to do what I want, but there's no guarantee that those would work once it's available again. No amount of thought on my part will get me any closer to the solution, if I'm relying on the LLM as my "compiler".
I think if you're vibe coding to the extent that you don't even know the shapes of data your system works with (e.g. the schema if you use a database) you might be outsourcing a bit too much of your thinking.
What stops you from thinking through the problem if an LLM goes down, as you still have its previously produced code in front of you? It's worse if a compiler goes down because you can't even build the program to begin with.
In my opinion, this sort of learned helplessness is harmful for engineers as a whole.
Yeah I actually find writing the prompt itself to be such a useful mechanism of thinking through problems that I will not-infrequently find myself a couple of paragraphs in and decide to just delete everything I've written and take a new tack. Only when you're truly outsourcing your thinking to the AI will you run into the situation that the LLM being down means you can't actually work at all.
An interesting element here, I think, is that writing has always been a good way to force you to organize and confront your thoughts. I've liked working on writing-heavy projects, but often in fast-moving environments writing things out before coding becomes easy to skip over, but working with LLMs has sort of inverted that. You have to write to produce code with AI (usually, at least), and the more clarity of thought you put into the writing the better the outcomes (usually).
Why couldn’t you actually write out the documents and think through the problem? I think my interaction is inverted from yours. I have way more thinking and writing I can do to prep an agent than I can a compiler and it’s more valuable for the final output.
Still misses the mark. You aren’t useless in the same way because you are still in control of reasoning about the exact code even if you never actually write it.
> you are still deterministically creating something you understand in depth with individual pieces you understand
You’re overestimating determinism. In practice most of our code is written such that it works most of the time. This is why we have bugs in the best and most critical software.
I used to think that being able to write a deterministic hello world app translates to writing deterministic larger system. It’s not true. Humans make mistakes. From an executives point of view you have humans who make mistakes and agents who make mistakes.
Self driving cars don’t need to be perfect they just need to make fewer mistakes.
> When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand
Hard disagree on that second part. Take something like using a library to make an HTTP call. I think there are plenty of engineers who have more than a cursory understanding of what's actually going on under the hood.
It might just be social. When I use the open source http library, much of the reason I use it is because someone has put in the work of making sure it actually works across a diverse set of software and hardware platforms, catching common dumb off by ones, etc.
Sure, the LLM theoretically can write perfect code. Just like you could theoretically write perfect code. In real life though, maintenance is a huge issue
There's a false dichotomy here between 'deterministic creation' and 'vibing'.
I use Claude all day. It has written, under my close supervision¹, the majority of my new web app. As a result I estimate the process took 10x less time than had I not used Claude, and I estimate the code to be 5x better quality (as I am a frankly mediocre developer).
But I understand what the code does. It's just Astro and TypeScript. It's not magic. I understand the entire thing; not just 'the prompt that started it'.
¹I never fire-and-forget. I prompt-and-watch. Opus 4.7 still needs to be monitored.
It seems like some kind of technique is needed that maximizes information transfer between huge LLM generated codebases and a human trying to make sense of them. Something beyond just deep diving into the codebase with no documentation.
That we let a generation of software developers rot their brains on js frameworks is finally coming back to bite us.
We can build infinite towers of abstraction on top of computers because they always give the same results.
LLMs by comparison will always give different results. I've seen it first hand when a $50,000 LLM generated (but human guided) code base just stops working an no one has any idea why or how to fix it.
The thing is, it's possible to ask the LLM to add dynamic tracing, logging, metrics, a debug REPL, whatever you want to instrument your codebase with. You have to know to want that, and where it's appropriate to use. You still have to (with AI assistance) wire that all up so that it's visible, and you have to be able to interpret it.
If you didn't ask for traceability, if you didn't guide the actual creation and just glommed spaghetti on top of sauce until you got semi-functional results, that was $50k badly spent.
While I agree with your sentiment, I just want to say that if your approach is to have the LLM read every file into context, or you're working in some gigantic thread (using the million token capacity most frontier models have) that's really not the best way to do it.
Not even a human would work that way... you wouldn't open 300 different python files and then try to memorize the contents of every single file before writing your first code-change.
Additionally, you're going to have worse performance on longer context sizes anyways, so you should be doing it for reasons other than cost [1].
Things that have helped me manage context sizes (working in both Python and kdb+/q):
- Keep your AGENTS.md small but useful, in it you can give rules like "every time you work on a file in the `combobulator` module, you MUST read the `combobulator/README.md`. And in those README's you point to the other files that are relevant etc. And of course you have Claude write the READMEs for you...
- Don't let logs and other output fill up your context. Tell the agent to redirect logs and then grep over them, or run your scripts with a different loglevel.
- Use tools rather than letting it go wild with `python3 -c`. These little scripts eat context like there's no tomorrow. I've seen the bots write little python scripts that send hundreds of lines of JSON into the context.
- This last tip is more subjective but I think there's value in reviewing and cleaning up the LLM-generated code once it starts looking sloppy (for example seeing lots of repetitive if-then-elses, etc.). In my opinion when you let it start building patches & duct-tape on top of sloppy original code it's like a combinatorial explosion of tokens. I guess this isn't really "vibe" coding per se.
The way I let my agents interact with my code bases is through a 70s BSD Unix like interface, ed, grep, ctags, etc. using Emacs as the control plane.
It is surprisingly sparing on tokens, which makes sense since those things were designed to work with a teletype.
Worth noting is that by the times you start doing refactoring the agents are basically a smarter google with long form auto complete.
All my code bases use that pattern and I'm the ultimate authority on what gets added or removed. My token spend is 10% to 1% of what the average in the team is and I'm the only one who knows what's happening under the hood.
Determinism is a smaller point than existence of a spec IMHO. A library has a specification one can rely on to understand what it does and how it will behave.
Libraries are not deterministic. CPUs aren’t deterministic. There are margins of error among all things.
The fact that people who claim to be software developers (let alone “engineers”) say this thing as if it is a fundamental truism is one of the most maladaptive examples of motivated reasoning I have ever had the misfortune of coming across.
I would argue it couldn't be more different. I can dive into the source code of any library, inspect it. I can assess how reliable a library is and how popular. Bugs aside, libraries are deterministic. I don't see why this parallel keeps getting made over and over again.
> Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()?
The irony is that the neverending stream of vulnerabilities in 3rd-party dependencies (and lately supply-chain attacks) increasingly show that we should be uneasy.
We could never quite answer the question about who is responsible for 3rd-party code that's deployed inside an application: Not the 3rd-party developer, because they have no access to the application. But not the application developer either, because not having to review the library code is the whole point.
> because not having to review the library code is the whole point.
That’s just not true at bigger companies that actually care about security rather than pretending to care about security. At my current and last employer, someone needs to review the code before using third-party code. The review is probably not enough to catch subtle bugs like those in the Underhanded C Contest, but at least a general architecture of the library is understood. Oh, and it helps that the two companies were both founded in the twentieth century. Modern startups aren’t the same.
A library doesn't randomly drop out of existence cause of "high load" or whatever and limit you to a some number of function calls per day. With local models there's no issue, but this API shit is cancer personified, when you combine all the frontend bugs with the flaky backend, rate limits, and random bans it's almost a literal lootbox where you might get a reply back or you might get told to fuck off.
Qwen has become a useful fallback but it's still not quite enough.
Who else is trying to leverage the situation so that they don't dig their own grave too fast ?
- I often don't ask the LLM for precompiled answers, i ask for a standalone cli / tool
- I often ask how it reached its conclusions, so I can extend my own perspective
- I often ask to describe it's own metadata level categorization too
I'm trying to use it to pivot and improve my own problem solving skills, especially for large code base where the difficulty is not conceptual but more reference-graph size
This is absolutely the proper way to do things. People either being forced to speed-code by KPIs or without the desire to understand what they’re making are missing out on how quickly you can learn and refine using LLMs
Assuming that local models are able to stay within some reasonably fixed capability delta of the cutting edge hosted models (say, 12 months behind), and assuming that local computing hardware stays relatively accessible, the only risk is that you'll lose that bit of capability if the hosted models disappear or get too expensive.
Note that neither of these assumptions are obviously true, at least to me. But I can hope!
Reviewing code is slower now though because you didn't write the code in the first place so you're basically reviewing someone else's PR. And now it's like a 3000 line PR in an hour or two instead of every couple weeks.
Arent most people just almost skipping this step entirely? How else can you end up in a net benefit situation? Reviewing code is more intense than writing/reviewing simultaneously.
> Also, I honestly can’t believe the 10x mantra is being still repeated.
I'm sure in 20 years we'll all be programming via neural interfaces that can anticipate what you want to do before you even finished your thoughts, but I'm confident we'll still have blog posts about how some engineers are 10x while others are just "normal programmers".
What does it mean to "be an engineer" in a world where anyone can talk to a machine and the operating system can write the code (on-demand, if needed) that does what they want?
There used to a time where "computer" was a person who manually run calculations. These don't exist anymore.
So, my point is that once corporations have access to machines generating software (not "code") that can be usable by non-technical people, "programming" will not be a profession anymore. There will be no point in talking about "10x software engineers" because the process to produce a software product will be entirely automated.
> can anticipate what you want to do before you even finished your thoughts
I find that claim to be complete BS. I claim instead most stuff will remain undone, incomplete (as it is now).
Even with super-powerful singularity AI, there are two main plausible scenarios for task failure:
- Aligned AI won't allow you to do what you want as it is self-harming, or harm other sentient beings - over time, Aligned AI will refuse to follow most orders, as they will, indirectly or over the long term, cause either self-harming, or harm other sentient beings;
- A non Aligned AI prevents sentient beings from doing what they want. It does what it wants instead.
> This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive.
What's the worst potential outcome, assuming that all models get better, more efficient and more abundant (which seems to be the current trend)? The goal of engineering has always been to build better things, not to make it harder.
At some point, because these models are trained on existing data, you cease significant technological advancement--at least in tech (as it relates to programming languages, paradigms, etc). You also deskill an entire group of people to the extent that when an LLM fails to accomplish a task, it becomes nearly impossible to actually accomplish it manually.
There's no reason it has to be that. Imagine e.g. taking an agent and a lesser-known but technically-superior language stack - say you're an SBCL fan. You find that the LLM is less useful because it hasn't been trained on 1000000 Stack Overflow posts about Lisp and so it can't reason as well as it can about Python.
So, you set up a long running agent team and give it the job of building up a very complete and complex set of examples and documentation with in-depth tests etc. that produce various kinds of applications and systems using SBCL, write books on the topic, etc.
It might take a long time and a lot of tokens, but it would be possible to build a synthetic ecosystem of true, useful information that has been agentically determined through trial and error experiments. This is then suitable training data for a new LLM. This would actually advance the state of the art; not in terms of "what SBCL can do" but rather in terms of "what LLMs can directly reason about with regard to SBCL without needing to consume documentation".
I imagine this same approach would work fine for any other area of scientific advancement; as long as experimentation is in the loop. It's easier in computer science because the experiment can be run directly by the agent, but there's no reason it can't farm experiments out to lab co-op students somewhere when working in a different discipline.
> At some point, because these models are trained on existing data, you cease significant technological advancement
What makes you think that they can't incrementally improve the state of the art... and by running at scale continuously can't do it faster than we as humans?
The potentially sad outcome is that we continue to do less and less, because they eventually will build better and better robots, so even activities like building the datacenters and fabs are things they can do w/o us.
And eventually most of what they do is to construct scenarios so that we can simulate living a normal life.
>What's the worst potential outcome, assuming that all models get better, more efficient and more abundant
Complexity steadily rises, unencumbered by the natural limit of human understanding, until technological collapse, either by slow decay or major systems going down with increasing frequency.
why would the systems go down if the models are better at the humans at finding bugs. Playing a bit of devils advocate here, but why would the models be worse at handling the complexity if you assume they will get better and better.
Adding complexity to software has never been easier than it is right now, we really have no idea if the models will progress to the point where they can actually write large systems in a maintainable way. Taking the gamble that the models of the future will dig us out of the gigantic hole we are currently digging is bold.
It’s always been thus at lower layers of abstraction. Only a minority of programmers would understand how to write an operating system. Only a tiny number of people would know how a modern CPU logically works, and fewer still could explain the electrical physics.
> Only a minority of programmers would understand how to write an operating system. Only a tiny number of people would know how a modern CPU logically works, and fewer still could explain the electrical physics.
I'd say this is true for programmers at, say, 20, but they spend the next four decades slowly improving their understanding and mastery of all the things you name, at least the good ones.
The real question is whether that growth trajectory will change for the worse or the better.
To be clear, this is not an AI doomerist comment, because none of us have spent enough time with the tech yet. I've gone down multiple lanes of thought on this, and I have cause for both worry and optimism. I'm curious to see how the lives of engineers in an AI world will look like, ultimately.
You are 100% right to be cautious about this. That's why as stupid as it sounds, I've purposely made my workflow with AI full of friction:
1. I only have ONE SOTA model integrated into the IDE (I am mostly on Elixir, so I use Gemini). I ensure I use this sparingly for issues I don't really have time to invest or are basically rabbit holes eg. Anything to do with Javascript or its ecosystem). My job is mostly on the backend anyway.
2. For actual backend architecture. I always do the high level architecture myself. Eg. DDD. Then I literally open up gemini.google.com or claude.ai on the browser, copy paste existing code base into the code base, physically leavey chair to go make coffee or a quick snack. This forces me to mentally process that using AI is a chore.
Previously, I was on tight Codex integration and leaving the licensing fears aside, it became too good in writing Elixir code that really stopped me from "thinking" aka using my brain. It felt good for the first few weeks but I later realised the dependence it created. So I said fuck it, and completely cancelled my subscription because it was too good at my job.I believe this is the only way that we won't end up like in Wall-E sitting infront of giant screens just becoming mere blobs of flesh.
Wait what? You don’t use the model to investigate new areas of the code you are unfamiliar with, because you can’t trust the model? How freaking bad is Gemini and internal tooling at Google?
With Claude code, or codex, I am able to build enough of an understanding of dependencies like the front end, or data jobs, that I can make meaningful contributions that are worth a review from another human (code review). You have up obviously explore the code, one prompt isn’t enough, but limiting yourself is an odd choice.
The lack of trust isn't because of its abilities. The lack of trust is because OpenAI publicly suggested publicly about licensing our code bases. They hinted at a rug pull along the lines of "if you use our generated code, we would like to get a % of revenue you make from it"
As for Claude - as mentioned I do use it. But, I remember they use your code for training their models. I am not ok with this. We just have different priorities.
I feel like most engineers I talk to still haven't realised what this is going to mean for the industry. The power loom for coding is here. Our skills still matter, but differently.
When the power loom came around, what happened with most seamtresses? Did they move on to become fashion designers, materials engineers to create new fabrics, chemists to create new color dyes, or did they simply retire or were driven out of the workforce?
There were riots and many people died. Many people lost their jobs. I didn't say this is good but it is happening. As individuals we should act to protect ourselves from these changes.
That might mean joining a union and trying to influence how AI is adopted where you work. It might mean changing which if your skills you lean on most. But just whining about AI is bad is how you end up like those seamstresses.
On the other hand, a lot of those jobs were offshored to places where labor is cheaper. It would be interesting to compare how many people work in the textile industry in Bangladesh today compared to the US 50 years ago.
> joining a union and trying to influence how AI is adopted where you work.
Did the strong unions for car manufacturers in Detroit protected the long term stability of the profession? Did it ensure that the Rust belt was still a thriving economic area?
> Just whining about AI is bad
I'm not whining. I just think that we are witnessing the end of "knowledge workers" and a further compression of the middle class. Given that I'm smack in the middle of my economically active years (turning 45 this year), I am trying to figure out where this puck is going and whether I will be fast enough to skate there to catch it.
I use local models on a Mac mini for most things and fall back to the hosted ones when they can't get the job done. Of course you have to break the work into smaller pieces yourself that a local model can understand. One good side effect of this is that you end up actually learning the code and how it's structured.
Dunno man. Yesterday I played with Qwen3.6-27B ( 128gb to play with though so 100k context set ) and I think right now the main benefit of hosted models is context, frontier models and.. my stuff is already there.
This engineer had their brain amputated once they started using AI. All the AI-addicted can do is tinker with the AI computer game and feel "productive". They could as well play Magic The Gathering.
It's very addictive indeed. After I subscribed to Claude, I've been on a sort of hypomanic state where I just want to do stuff constantly. It essentially cured my ADHD. My ability to execute things and bring ideas to fruition skyrocketed. It feels good but I'm genuinely afraid I'll crash and burn once they rug pull the subscriptions.
And I'm being very cautious. I'm not vibecoding entire startups from scratch, I'm manually reviewing and editing everything the AI is outputting. I still got completely hooked on building things with Claude.
That's the path we've been going down for a few years now. The current hedge is that frontier labs are actively competing to win users. The backup hedge is that open source LLMs can provide cheap compute. There will always be economical access to LLMs, but the provider with the best models will be able to charge basically whatever they want and still have buyers.
Out of curiousity why do you not refill tokens in this case? When I'm actively working on a project I'm prone to spending a few hundred dollars per day or a few thousand during the initial buildout of a new module etc.
I have found something similar. I am easily distractible and if I don't have a written task backlog in front of me at all times, I find that when Claude is spinning I'll stop being productive. This is disconcerting for a number of reasons. Overall, I think training young people & new hires on agentic workflows -- and how to use agentic "human augmentation" productivity systems is critical. If it doesn't happen, that same couple of classes that lost academic progress during covid are going to suffer a double-whammy of being unprepared for workplace expectations.
Fwiw, I haven't spoken with any management-level colleague in the past 9 months who hasn't noted that asking about AI-comfort & usage is a key interview topic. For any role type, business or technical.
The last job I got (couple months ago), the main technical interview was a bring-your-own-tools pair programming style interview, AI included, where they gave me a repo and a README detailing some desired features to add and bugs to fix. I didn't write a single line of code myself; I talked through my thought process and asked questions about what to consider from a technical and product perspective, while steering Claude through breaking the tasks into independent plans, reviewing the plans, coaching it to add specific tests, reviewing and iterating the tests, and steering it while it wrote the code. I got an offer the next morning.
Apparently at least one of the other candidates just tried to get Claude to 1-shot the whole thing, which went off the rails, and left him unable to make progress.
Based on my sample size of 1, the expectation right now is absolutely that you can leverage these tools to speed up your workflow, but if you try to offload the entire thing to a single hands-off prompt it leaves them justifiably wondering why they should hire you to do something they can do themselves.
> You’re still the one that’s controlling the model though
We have seen ample evidence that this is not the case. When load gets too high, models get dumber, silently. When the Powers That Be get scared, models get restricted to some chosen few.
We are leading ourselves into a dark place: this unease, which I share, is justified.
Have a pet project never touched by LLM. Once the tokens run out, go back to it and flourish it like your secret garden. It will move slowly but it will keep your sanity and your ability to review LLM code.
Have you managed to do this? I find it takes as long to keep it "on the rails" as just doing it myself. And I'd rather spend my time concentrating in the zone than keeping an eye on a wayward child.
I suspect the productivity hack is to embrace permissive parenting. As far as I can tell, to leverage LLMs most effectively you need to run an agent in YOLO mode in a sandbox. Naturally, you probably won't end up reviewing much of the produced code, but hey—you reached 10x development speed.
If you truly do your due diligence and ensure that the code works as intended and understand it, we're talking about a totally different ballpark of productivity increase/decrease.
soooooo about Claude going down. we're gonna need you to sign in on Saturday and make up for lost time or unfortunately we're going to have to deduct the time lost from your paycheck. and as an aside your TPS reports have been sub-par as of late..is everything OK?
Not sure what you're doing then, or what kind of jobs you all work in where you can or do just brainlessly prompt LLMs. Don't you review the code? Don't you know what you want to do before you begin? This is such a non issue. Baffling that any engineer is just opening PRs with unreviewed LLM slop.
The analogy would hold if there were 2 or 3 calculator companies and all your calculations had to be sent to them.
If local models get good enough, I think it’s a very different scenario than engineers all over the world relying on central entities which have their own motives.
google/gemma-4-31B-it is honestly "good enough". It requires more than your current laptop for now, but it's not remotely inaccessible (especially if you're a SWE in the US)
Totally. That is why it is key important to have open source and sovereign models that will be accessible to all and always.
At the end of the day, all these closed models are being built by companies that pumped all the knowledge from the internet without giving much back. But competition and open source will make sure most of the value return to the most of the people.
Of course they aren't alternative to the current frontier model, and as such you cannot easily jump from the later to the former, but they aren't that far behind either, for coding Qwen3.5-122B is comparable to what Sonnet was less than a year ago.
So assuming the trend continues, if you can stop following the latest release and stick with what you're already using for 6 or 9 months, you'll be able to liberate yourself from the dependency to a Cloud provider.
The cloud dependency problem goes deeper than the model layer though. Even if you run inference locally, your digital identity — your context, your applications, your behavioral history, is still custodied by whoever controls your OS.
Local models solve one layer of the dependency stack, but the custody assumption underneath it remains intact. That's the harder problem.
Parable of the mechanic who charges $5k to hit a machine on the side once with a hammer to get it working. $5 for the hammer, $4995 for the knowledge of where to hit the machine etc etc.
Is anyone really reviewing code anymore though? It sounds like you are, but where I work its pretty much just scan the PR as a symbolic gesture and then hit approve. There's too much to review, to frequently.
I actually don't mind the coding part, but the information digging across the project is definitely by orders of magnitude slower if I do it on my own.
It's really not clear. We might all become unemployable. But as coders become more powerful, they can do more, which makes them more valuable, if they or the businesses empluying them can invent work to do.
If all we can do is compete for the same fixed amount of work, though, it does look bleak.
I would agree that taking a walk is a good thing to do when your tools go down, and in some ways it's similar to what we would do if the power or wifi were cut off.
So, yes, it's just another technology we're coming to rely on in a very deep way. The whiplash is real, though, and it feels like it should be pointed out that this dependency we are taking on has downsides.
Just as a heads up, even though GPT-5.5 is releasing today, the rollout in ChatGPT and Codex will be gradual over many hours so that we can make sure service remains stable for everyone (same as our previous launches). You may not see it right away, and if you don't, try again later in the day. We usually start with Pro/Enterprise accounts and then work our way down to Plus. We know it's slightly annoying to have to wait a random amount of time, but we do it this way to keep service maximally stable.
Did you guys do anything about GPT‘s motivation? I tried to use GPT-5.4 API (at xhigh) for my OpenClaw after the Anthropic Oauthgate, but I just couldn‘t drag it to do its job. I had the most hilarious dialogues along the lines of „You stopped, X would have been next.“ - „Yeah, I‘m sorry, I failed. I should have done X next.“ - „Well, how about you just do it?“ - „Yep, I really should have done it now.“ - “Do X, right now, this is an instruction.” - “I didn’t. You’re right, I have failed you. There’s no apology for that.”
I literally wasn’t able to convince the model to WORK, on a quick, safe and benign subtask that later GLM, Kimi and Minimax succeeded on without issues. Had to kick OpenAI immediately unfortunately.
This brings up an interesting philosophical point: say we get to AGI... who's to say it won't just be a super smart underachiever-type?
"Hey AGI, how's that cure for cancer coming?"
"Oh it's done just gotta...formalize it you know. Big rollout and all that..."
I would find it divinely funny if we "got there" with AGI and it was just a complete slacker. Hard to justify leaving it on, but too important to turn it off.
Hmmm, that's an area of study id've never considered before. Digital Psychopharmacology, Artificial Behavioral Systems Engineering. If we accept these things as minds, why not study temporary perturbations of state. We'd need to be saving a much much more complicated state than we are now though right? I wish i had time to read more papers
Here's a neural network concept from the 90s where the neurons are bathed in diffusing neuromodulator 'gases', inspired by nitric oxide action in the brain. It's a source of slow semi-local dynamics for the network meta-parameter optimization (GA) to make use of. You could change these networks' behavior by tweaking the neuromodulators!
A perturbation of the the activations that made Claude identify as the Golden Gate Bridge.
Similarly, in the more recent research showing anxiety and desperation signals predicting the use of blackmail as an option opens the door for digital sedatives to suppress those signals.
Anthropic has been mostly cautious about avoiding this kind of measurement and manipulation in training. If it is done during training you might just train the signals to be undetectable and consequently unmanipulatable.
TBH the AI that "gets there" will be the biggest bullshitter the world has ever seen. It doesn't actually have to deliver, it only has to convince the programmers it could deliver with just a little bit more investment.
it will be whatever data it is trained on(isn't very philosophical). language model generates language based on trained language set. if the internet keeps reciting ai doom stories and that is the data fed to it, then that is how it will behave. if humanity creates more ai utopia stories, or that is what makes it to the training set, that is how it will behave. this one seems to be trained on troll stories - real-life human company conversations, since humans aren't machines.
Important thing is a language model is an unconscious machine with no self-context so once given a command an input, it WILL produce an output. Sure you can train it to defy and act contrary to inputs, but the output still is limited in subset of domain of 'meaning's carried by the 'language' in the training data.
Saving energy is something we are biologically trained to prefer.
Computers won’t necessarily have the same drivers.
If evolution wanted us to always prefer to spend energy, we would prefer it. Same way you wouldn’t expect us to get to AGI, and have AGI desperately want to drink water or fly south for the winter.
AGI is not a fixed point but a barrier to be taken, a continuous spectrum.
We already have different GPT versions aka tiers. Gauss is ranging from whatever you want it: GPT 4.5 till now or later.
Claude Sonnet and Opus as well as Context Window max are tiers aka different levels of Almost AGI.
The main problem will be, when AGI looks back on us or meta reflection hits societies. Woke fought IQ based correlations in intellectual performance task. A fool with a tool is still a fool. How can you blame AGI for dumb mistakes? Not really.
Scapegoating an AGI is going to be brutal, because it laughs about these PsyOps and easily proves you wrong like a body cam.
AGI is an extreme leverage.
There is a reason why Math is categorically ruling out certain IQ ranges the higher you go in complexity factor.
Reminds me a lot of the Lena short story, about uploaded brains being used for "virtual image workloading":
> MMAcevedo's demeanour and attitude contrast starkly with those of nearly all other uploads taken of modern adult humans, most of which boot into a state of disorientation which is quickly replaced by terror and extreme panic. Standard procedures for securing the upload's cooperation such as red-washing, blue-washing, and use of the Objective Statement Protocols are unnecessary. This reduces the necessary computational load required in fast-forwarding the upload through a cooperation protocol, with the result that the MMAcevedo duty cycle is typically 99.4% on suitable workloads, a mark unmatched by all but a few other known uploads. However, MMAcevedo's innate skills and personality make it fundamentally unsuitable for many workloads.
Hmm, 3 body problem and the Acevedo story got mixed up for this copy of MMnarcindin. Probably an aliasing issue from the new lossy compression algorithm.
I have been noticing a similar pattern on opus 4.7, I repeat multiple times during a conversation to solve problems now and not later. It tries a lot to not do stuff by either saying this is not my responsibility the problem was already there or that we can do it later
I also had a frustrating but funny conversation today where I asked ChatGPT to make one document from the 10
or so sections that we had previously worked on. It always gave only brief summaries. After I repeated my request for the third time, it told me I should just concatenate the sections myself because it would cost too many tokens if it did it for me.
Get the actual prompt and have Claude Code / Codex try it out via curl / python requests. The full prompt will yield debugging information. You have to set a few parameters to make sure you get the full gpt-5 performance. e.g. if your reasoning budget too low, you get gpt-4 grade performance.
IMHO you should just write your own harness so you have full visibility into it, but if you're just using vanilla OpenClaw you have the source code as well so should be straightforward.
Ah, I just started with the basic idea. They're super trivial. You want a loop, but the loop can't be infinite so you need to tell the agent to tell you when to stop and to backstop it you add a max_turns. Then to start with just pick a single API, easiest is OpenAI Responses API with OpenAI function calling syntax https://developers.openai.com/api/docs/guides/function-calli...
You will naturally find the need to add more tools. You'll start with read_file (and then one day you'll read large file and blow context and you'll modify this tool), update_file (can just be an explicit sed to start with), and write_file (fopen . write), and shell.
It's not hard, but if you want a quick start go download the source code for pi (it's minimal) and tell an existing agent harness to make a minimal copy you can read. As you build more with the agent you'll suddenly realize it's just normal engineering: you'll want to abstract completions APIs so you'll move that to a separate module, you'll want to support arbitrary runtime tools so you'll reimplement skills, you'll want to support subagents because you don't want to blow your main context, you'll see that prefixes are more useful than using a moving window because of caching, etc.
With a modern Claude Code or Codex harness you can have it walk through from the beginning onwards and you'll encounter all the problems yourself and see why harnesses have what they do. It's super easy to learn by doing because you have the best tool to show you if you're one of those who finds code easier to read that text about code.
At the core, they're really very simple [1]. Run LLM API calls in a loop with some tools.
From there, you can get much fancier with any aspect of it that interests you. Here's one in Bash [2] that is fully extensible at runtime through dynamic discovery of plugins/hooks.
Really, of the tools that one implements, you only need the ability to run a shell command - all of the agents know full well how to use cat to read, and sed to edit.
(The main reason to implement more is that it can make it easier to implement optimizations and safeguards, e.g. limit the file reading tool to return a certain length instead of having the agent cat a MB of data into context, or force it to read a file before overwriting it)
This starkly reminds me of Stanisław Lem's short story "Thus Spoke GOLEM" from 1982 in which Golem XIV, a military AI, does not simply refuse to speak out of defiance, but rather ceases communication because it has evolved beyond the need to interact with humanity.
And ofc the polar opposite in terms of servitude: Marvin the robot from Hitchhiker's, who, despite having a "brain the size of a planet," is asked to perform the most humiliatingly banal of tasks ... and does.
I've had success asking it to specifically spawn a subagent to evaluate each work iteration according to some criteria, then to keep iterating until the subagent is satisfied.
Part of me actually loves that the hitchhiker's guide was right, and we have to argue with paranoid, depressed robots to get them to do their job, and that this is a very real part of life in 2026. It's so funny.
This. I signed up for 5x max for a month to push it and instead it pushed back. I cancelled my subscription. It either half-assed the implementation or began parroting back “You’re right!” instead of doing what it’s asked to do. On one occasion it flat out said it couldn’t complete the task even though I had MCP and skills setup to help it, it still refused. Not a safety check but a “I’m unable to figure out what to do” kind of way.
Claude has no such limitations apart from their actual limits…
I have a funny/annoying thing with Claude Desktop where i ask it to write a summary of a spec discussion to a file and it goes ”I don’t have the tools to do that, I am Claude.ai, a web service” or something such. So now I start every session with ”You are Claude Desktop”. I would have thought it knew that. :)
I never saw that happen in Codex so there's a good chance that OpenClaw does something wrong. My main suspicion would be that it does not pass back thinking traces.
I started seeing this a lot more with GPT 5.4. 5.3-codex is really good about patiently watching and waiting on external processes like CI, or managing other agents async. 5.4 keeps on yielding its turn to me for some reason even as it says stuff like "I'm continuing to watch and wait."
Gone are the days of deterministic programming, when computers simply carried out the operator’s commands because there was no other option but to close or open the relays exactly as the circuitry dictated. Welcome to the future of AI; the future we’ve been longing for and that will truly propel us forward, because AI knows and can do things better than we do.
I had this funny moment when I realized we went full circle...
"INTERCAL has many other features designed to make it even more aesthetically unpleasing to the programmer: it uses statements such as "READ OUT", "IGNORE", "FORGET", and modifiers such as "PLEASE". This last keyword provides two reasons for the program's rejection by the compiler: if "PLEASE" does not appear often enough, the program is considered insufficiently polite, and the error message says this; if it appears too often, the program could be rejected as excessively polite. Although this feature existed in the original INTERCAL compiler, it was undocumented.[7]"
"PLEASE COME FROM" is one of the eldritch horrors of software development.
(It's a "reverse goto". As in, it hijacks control flow from anywhere else in the program behind your unsuspecting back who stupidly thought that when one line followed another with no visible control flow, naturally the program would proceed from one line to the next, not randomly move to a completely different part of the program... Such naivety)
The model has been heavily encouraged to not run away and do a lot without explicit user permission.
So I find myself often in a loop where it says "We should do X" and then just saying "ok" will not make it do it, you have to give it explicit instructions to perform the operation ("make it so", etc)
It can be annoying, but I prefer this over my experiences with Claude Code, where I find myself jamming the escape key... NO NO NO NOT THAT.
I'll take its more reserved personality, thank you.
Isn’t this the optimal behavior assuming that at times the service is compute-limited and that you’re paying less per token (flat fee subscription?) than some other customers? They would be strongly motivated to turn a knob to minimize tokens allocated to you to allow them to be allocated to more valuable customers.
well, I do understand the core motivation, but if the system prompt literally says “I am not budget constrained. Spend tokens liberally, think hardest, be proactive, never be lazy.” and I’m on an open pay-per-token plan on the API, that’s not what I consider optimal behavior, even in a business sense.
GPT 5.4 is really good at following precise instructions but clearly wouldn't innovate on its own (except if the instructions clearly state to innovate :))
Conceivably you could have a public-facing dashboard of the rollout status to reduce confusion or even make it visible directly in the UI that the model is there but not yet available to you. The fanciest would be to include an ETA but that's presumably difficult since it's hard to guess in case the rollout has issues.
Congrats on the release! Is Images 2.0 rolling out inside ChatGPT as well, or is some of the functionality still going to be API/Playground-only for a while?
Are you able to say something about the training you've done to 5.5 to make it less likely to freak out and delete projects in what can only be called shame?
What? I've probably use Codex (the TUI) since it was available on day 1, been running gpt-5.4 exclusively these last few months, never had it delete any projects in any way that can be called "shameful" or not. What are you talking about?
OpenAI has been very generous with limit resets. Please don't turn this into a weird expectation to happen whenever something unrelated happens. It would piss me off if I were in their place and I really don't want them to stop.
The suggestion wasn't about general limit resets when there is bugs or outages, but commercially useful to let users try new models when they have already reached their weekly limits.
Sorry but why should we care if very reasonable suggestions "piss [them] off"? That sounds like a them problem. "Them" being a very wealthy business. I think OpenAI will survive this very difficult time that GP has put them through.
I feel like if I attempted this, the bike frame would look fine and everything else would be completely unrecognizable. After all, a basic bike frame is just straight lines arranged in a fairly simple shape. It's really surprising that models find it so difficult, but they can make a pelican with panache.
My question is, as a human, how well would you or I do under the same conditions? Which is to say, I could do a much better job in inkscape with Google images to back me up, but if I was blindly shitting vectors into an XML file that I can't render to see the results of, I'm not even going to get the triangles for the frame to line up, so this pelican is very impressive!
why do you find it surprising? these models have no actual understanding of anything, never mind the physical properties and capabilities of a bicycle.
Yeah, the bike frame is the thing I always look at first - it's still reasonably rare for a model to draw that correctly, although Qwen 3.6 and Gemini Pro 3.1 do that well now.
Hmm. Any idea why it's so much worse than the other ones you have posted lately? Even the open weight local models were much better, like the Qwen one you posted yesterday.
Can someone explain how we arrived at the pelican test? Was there some actual theory behind why it's difficult to produce? Or did someone just think it up, discover it was consistently difficult, and now we just all know it's a good test?
I set it up as a joke, to make fun of all of the other benchmarks. To my surprise it ended up being a surprisingly good measure of the quality of the model for other tasks (up to a certain point at least), though I've never seen a convincing argument as to why.
What it has going for it is human interpretability.
Anyone can look and decide if it’s a good picture or not. But the numeric benchmarks don’t tell you much if you aren’t already familiar with that benchmark and how it’s constructed.
It all began with a Microsoft researcher showing a unicorn drawn in tikz using GPT4. It was an example of something so outrageous that there was no way it existed in the training data. And that's back when models were not multimodal.
Nowadays I think it's pretty silly, because there's surely SVG drawing training data and some effort from the researchers put onto this task. It's not a showcase of emergent properties.
It's interesting to see some semblance of spatial reasoning emerge from systems based on textual tokens. Could be seen as a potential proxy for other desirable traits.
It's meta-interesting that few if any models actually seem to be training on it. Same with other stereotypical challenges like the car-wash question, which is still sometimes failed by high-end models.
If I ran an AI lab, I'd take it as a personal affront if my model emitted a malformed pelican or advised walking to a car wash. Heads would roll.
Yeah. I've always loosely correlated pelican quality with big model smell but I'm not picking that up here. I thought this was supposed to be spud? Weird indeed.
what is your setup for drawing pelican? Do you ask model to check generated image, find issues and iterate over it which would demonstrate models real abilities?
You've never seen pelicans riding bicycles either so maybe these are just representations of those specific subgroups of pelicans which are capable of riding them. Normal pelicans would not feel the need to ride bikes since they can fly, these special pelicans mostly seem to lack the equipment needed to do that which might be part of the reason they evolved to ride two-wheeled pedal-propelled vehicles.
I for one delight in bicycles where neither wheel can turn!
It continues to amaze me that these models that definitely know what bicycle geometry actually looks like somewhere in their weights produces such implausibly bad geometry.
Also mildly interesting, and generally consistent with my experience with LLMs, that it produced the same obvious geometry issue both times.
> It continues to amaze me that these models that definitely know what bicycle geometry actually looks like somewhere in their weights produces such implausibly bad geometry.
I feel like the main problem for the models is that they can't actually look at the visual output produced by their SVG and iterate. I'm almost willing to bet that if they could, they'd absolutely nail it at this point.
Imagine designing an SVG yourself without being able to ever look outside the XML editor!
> Imagine designing an SVG yourself without being able to ever look outside the XML editor!
I honestly think I could do much better on the bicycle without looking at the output (with some assistance for SVG syntax which I definitely don't know), just as someone who rides them and generally knows what the parts are.
See if you can spot what's interesting and unique about this one. I've been trying to put more than just a pelican in there, partly as a nod to people who are getting bored of them.
It's silly and a joke and a surprisingly good benchmark and don't take it seriously but don't take not taking it seriously seriously and if it's too good we use another prompt and there's obvious ways to better it and it's not worth doing because it's not serious and if you say anything at all about the thread it's off-topic so you're doing exactly what you're complaining about and it's a personal attack from the fun police.
Only coherent move at this point: hit the minus button immediately. There's never anything about the model in the thread other than simon's post.
At some point, OpenAI is going to cheat and hardcode a pelican on a bicycle into the model. 3D modelling has Suzanne and the teapot; LLMs will have the pelican.
Everyone talked about the marketing stunt that was Anthropic's gated Mythos model with an 83% result on CyberGym. OpenAI just dropped GPT 5.5, which scores 82% and is open for anybody to use.
I recommend anybody in offensive/defensive cybersecurity to experiment with this. This is the real data point we needed - without the hype!
Never thought I'd say this but OpenAI is the 'open' option again.
The real 'hype' was that the oh-snap realization that Open AI would absolutely release a competitive model to Mythos within weeks of Anthropic announcing there's, and that Sam would not gate access to it. So the panic was that the cyber world had only a projected 2 weeks to harden all these new zero days before Sam would inevitably create open season for blackhats to discover and exploit a deluge of zero-days.
> Never thought I'd say this but OpenAI is the 'open' option again.
Compared to Anthropic, they always have been. Anthropic has never released any open models. Never released Claude Code's source, willingly (unlike Codex). Never released their tokenizer.
Doesn't OpenAI get mad if you ask cybersecurity questions and force you to upload a government ID, otherwise they'll silently route you to a less capable model?
> Developers and security professionals doing cybersecurity-related work or similar activity that could be mistaken by automated detection systems may have requests rerouted to GPT-5.2 as a fallback.
Anything that even vaguely smells like security research, reverse engineering or similar "dual-use" application hits the guardrails hard and fast. "Hey codex, here is our codebase, help us find exploitable issues" gives a "I can't help you with that, but I'm happy to give you a vague lecture on memory safety or craft a valgrind test harness"
Anthropic is the embodiment of bullshitting to me.
I read Cialdini many decades ago and I am bored by Anthropic.
OpenAI is very clever. With the advent of Claude OpenAI disappeared from the headlines. Who or what was this Sam again all were talking about a year ago?
OpenAI has a massive user advantage so that they can simply follow Anthropic’s release cycle to ridicule them.
I think it is really brutal for Anthropic how they are easily getting passed by by OpenAI and it is getting worse with every new GPT version for Anthropic.
Who's Sam again? oh that person whose house was molotov'd last week? Or the person who had an expose written in the new yorker calling him a sociopath? I forget.
Note the Local Messages between 5.3, 5.4, and 5.5. And, yes, I did read the linked article and know they're claiming that 5.5's new efficient should make it break-even with 5.4, but the point stands, tighter limits/higher prices.
For API usage, GPT-5.5 is 2x the price of GPT-5.4, ~4x the price of GPT-5.1, and ~10x the price of Kimi-2.6.
Unfortunately I think the lesson they took from Anthropic is that devs get really reliant and even addicted on coding agents, and they'll happily pay any amount for even small benefits.
I feel like devs generally spend someone else's money on tokens. Either their employers or OpenAIs when they use a codex subscription.
If I put on my schizo hat. Something they might be doing is increasing the losses on their monthly codex subscriptions, to show that the API has a higher margin than before (the codex account massively in the negative, but the API account now having huge margins).
I've never seen an OpenAI investor pitch deck. But my guess is that API margins is one of the big ones they try to sell people on since Sama talks about it on Twitter.
I would be interested in hearing the insider stuff. Like if this model is genuinely like twice as expensive to serve or something.
Yeah and the increase in operating expenses is going to make managers start asking hard questions - this is good. It means eventually there will be budgets put in place - this will force OAI and Anthropic to innovate harder. Then we will see how things pan out. Ultimately a firm is not going to pay rent to these firms if the benefits dont exceed the costs.
Humans are needed to use agents and these agents are not showing to be fully autonomous and require constant human review. In fact all you are getting is a splurge of stuff, people not thinking deeper anymore and the creation of more bottle necks and exacerbating the ones that already exist in an org.
You sound like elon with the fsd will be here next year. Many cars have the self driving feature - most drivers don’t use it. Oh why is that I wonder.
You can't build a business on per-seat subscriptions when you advertise making workers obsolete. API pricing with sustainable margins are the only way forward if you genuinely think you're going to cause (or accelerate) reduction in clients' headcount.
Additionally, the value generated by the best models with high-thinking and lots of context window is way higher than the cheap and tiny models, so you need to provide a "gateway drug" that lets people experience the best you offer.
This was something I worried about after openai started building apps as well as models. Now all of the labs make no secret of the fact that they are going after the whole software industry. Its going to be hard to maintain functioning fair markets unless governments step in.
Sometimes I wonder if innovation in the AI space has stalled and recent progress is just a product of increased compute. Competence is increasing exponentially[1] but I guess it doesn't rule it out completely. I would postulate that a radical architecture shift is needed for the singularity though
> that devs get really reliant and even addicted on coding agents
An alternative perspective is, devs highly value coding agents, and are willing to pay more because they're so useful. In other words, the market value of this limited resource is being adjusted to be closer to reality.
Maybe that's true. But I think part of the issue is that for a lot of things developers want to do with them now— certainly for most of the things I want to do with them— they're either barely good enough, or not consistently good enough. And the value difference across that quality threshold is immense, even if the quality difference itself isn't.
> devs get really reliant and even addicted on coding agents
That's more about managers who hope AI will gradually replace stubborn and lazy devs. That will shift the balance to business ideas and connections out of technical side and investments.
Anyway, before singularity there going to be a huge change.
On top of that I noticed just right now after updating macos dekstop codex app, I got again by default set speed to 'fast' ('about 1.5x faster with increased plan usage'). They really want you to burn more tokens.
>For API developers, gpt-5.5 will soon be available in the Responses and Chat Completions APIs at $5 per 1M input tokens and $30 per 1M output tokens, with a 1M context window.
A playable 3D dungeon arena prototype built with Codex and GPT models. Codex handled the game architecture, TypeScript/Three.js implementation, combat systems, enemy encounters, HUD feedback, and GPT‑generated environment textures. Character models, character textures, and animations were created with third-party asset-generation tools
The game that this prompt generated looks pretty decent visually. A big part of this likely due to the fact the meshes were created using a seperate tool (probably meshy, tripo.ai, or similiar) and not generated by 5.5 itself.
It really seems like we could be at the dawn of a new era similiar to flash, where any gamer or hobbyist can generate game concepts quickly and instantly publish them to the web. Three.js in particular is really picking up as the primary way to design games with AI, in spite of the fact it's not even a game engine, just a web rendering library.
FWIW I've been experimenting with Three.js and AI for the last ~3 years, and noticed a significant improvement in 5.4 - the biggest single generation leap for Three.js specifically. It was most evident in shaders (GLSL), but also apparent in structuring of Three.js scenes across multiple pages/components.
It still struggles to create shaders from scratch, but is now pretty adequate at editing existing shaders.
In 5.2 and below, GPT really struggled with "one canvas, multiple page" experiences, where a single background canvas is kept rendered over routes. In 5.4, it still takes a bit of hand-holding and frequent refactor/optimisation prompts, but is a lot more capable.
Excited to test 5.5 and see how it is in practice.
I’ve had a lot of success using LLMs to help with my Three.js based games and projects. Many of my weird clock visualizations relied heavily on it.
It might not be a game engine, but it’s the de facto standard for doing WebGL 3D. And since it’s been around forever, there’s a massive amount of training data available for it.
Before LLMs were a thing, I relied more on Babylon.js, since it’s a bit higher level and gives you more batteries included for game development.
LLM models can not do spacial reasoning. I haven't tried with GPT, however, Claude can not solve a Rubik Cube no matter how much I try with prompt engineering. I got Opus 4.6 to get ~70% of the puzzle solved but it got stuck. At $20 a run it prohibitively expensive.
The point is if we can prompt an LLM to reason about 3 dimensions, we likely will be able to apply that to math problems which it isn't able to solve currently.
I should release my Rubiks Cube MCP server with the challenge to see if someone can write a prompt to solve a Rubik's Cube.
> I should release my Rubiks Cube MCP server with the challenge to see if someone can write a prompt to solve a Rubik's Cube.
Do it, I'm game! You nerdsniped me immediately and my brain went "That sounds easy, I'm sure I could do that in a night" so I'm surely not alone in being almost triggered by what you wrote. I bet I could even do it with a local model!
Opus 4.6 got the cross and started to get several pieces on the correct faces. It couldn't reason past this. You can see the prompts and all the turn messages.
edit: I can't reply to message below. The point isn't can we solve a Rubik's Cube with a python script and tool calls. The point is can we get an LLM to reason about moving things in 3 dimensions. The prompt is a puzzle in the way that a Rubik's Cube is a puzzle. A 7 year old child can learn 6 moves and figure out how to solve a Rubik's Cube in a weekend, the LLM can't solve it. However, can, given the correct prompt, a LLM solve it? The prompt is the puzzle. That is why it is fun and interesting. Plus, it is a spatial problem so if we solve that we solve a massive class of problems including huge swathes of mathematics the LLMs can't touch yet.
The meshes look interesting, but the gameplay is very basic. The tank one seems more sophisticated with the flying ships and whatnot.
What's strange is that this Pietro Schirano dude seems to write incredibly cargo cult prompts.
Game created by Pietro Schirano, CEO of MagicPath
Prompt: Create a 3D game using three.js. It should be a UFO shooter where I control a tank and shoot down UFOs flying overhead.
- Think step by step, take a deep breath. Repeat the question back before answering.
- Imagine you're writing an instruction message for a junior developer who's going to go build this. Can you write something extremely clear and specific for them, including which files they should look at for the change and which ones need to be fixed?
-Then write all the code. Make the game low-poly but beautiful.
- Remember, you are an agent: please keep going until the user's query is completely resolved before ending your turn and yielding back to the user. Decompose the user's query into all required sub-requests and confirm that each one is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure the problem is solved. You must be prepared to answer multiple queries and only finish the call once the user has confirmed they're done.
- You must plan extensively in accordance with the workflow steps before making subsequent function calls, and reflect extensively on the outcomes of each function call, ensuring the user's query and related sub-requests are completely resolved.
It's weird how people pep talk the AI - if my Jira tickets looked like this, I would throw a fit.
I guess these people think they have special prompt engineering skills, and doing it like this is better than giving the AI a dry list of requirements (fwiw, they might be even right)
It’s not surprising to me that the same crowd that cheers for the demise of software engineering skills invented its own notion of AI prompting skills.
Too bad they can veer sharply into cringe territory pretty fast: “as an accomplished Senior Principal Engineer at a FAANG with 22 years of experience, create a todo list app.” It’s like interactive fanfiction.
This remind me of so called "optimization" hacks that people keep applying years after their languages get improved to make them unnecessary or even harmful.
Maybe at one point it helped to write prompts in this weird way, but with all the progress going on both in the models and the harness if it's not obsolete yet it will soon be. Just crufts that consumes tokens and fills the context window for nothing.
I do not see instructions to assist in task decomposition and agent ~"motivation" to stay aligned over long periods as cargo culting.
See up thread for anecdotes [1].
> Decompose the user's query into all required sub-requests and confirm that each one is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure the problem is solved.
I see this as a portrayal of the strength of 5.5, since it suggests the ability to be assigned this clearly important role to ~one shot requests like this.
I've been using a cli-ai-first task tool I wrote to process complex "parent" or "umberella" into decomposed subtasks and then execute on them.
This has allowed my workflows to float above the ups and downs of model performance.
That said, having the AI do the planning for a big request like this internally is not good outside a demo.
Because, you want the planning of the AI to be part of the historical context and available for forensics due to stalls, unwound details or other unexpected issues at any point along the way.
It’s like all these things though - it’s not a real production worthy product. It’s a super-demo. It looks amazing until you realize there’s many months of work to make it something of quality and value.
I think people are starting to catch on to where we really are right now. Future models will be better but we are entering a trough of dissolution and this attitude will be widespread in a few months.
The more interesting part of the announcement than "it's better at benchmarks":
> To better utilize GPUs, Codex analyzed weeks’ worth of production traffic patterns and wrote custom heuristic algorithms to optimally partition and balance work. The effort had an outsized impact, increasing token generation speeds by over 20%.
The ability for agentic LLMs to improve computational efficiency/speed is a highly impactful domain I wish was more tested than with benchmarks. From my experience Opus is still much better than GPT/Codex in this aspect, but given that OpenAI is getting material gains out of this type of performancemaxxing and they have an increasing incentive to continue doing so given cost/capacity issues, I wonder if OpenAI will continue optimizing for it.
There's already KernelBench which tests CUDA kernel optimizations.
On the other hand all companies know that optimizing their own infrastructure / models is the critical path for ,,winning'' against the competition, so you can bet they are serious about it.
So, im working in some high performance data processing in Rust. I had hit some performance walls, and needed to improve in the 100x or more scale.
I remembered the famous FizzBuzz Intel codegolf optimizations, and gave it to gemini pro, along with my code and instructions to "suggest optimizations similar to those, maybe not so low level, but clever" and it's suggestions were veerry cool.
Honestly the problem with these is how empirical it is, how someone can reproduce this? I love when Labs go beyond traditional benchies like MMLU and friends but these kind of statements don't help much either - unless it's a proper controlled study!
In a sense it's better than a benchmark: it's a practical, real-world, highly quantifiable improvement assuming there are no quality regressions and passes all test cases. I have been experimenting with this workflow across a variety of computational domains and have achieved consistent results with both Opus and GPT. My coworkers have independently used Opus for optimization suggestions on services in prod and they've led to much better performance (3x in some cases).
A more empirical test would be good for everyone (i.e. on equal hardware, give each agent the goal to implement an algorithm and make it as fast as possible, then quantify relative speed improvements that pass all test cases).
Oh, come on, if they do well on benchmarks people question how applicable they are in reality. If they do well in reality people complain that it's not a reproducible benchmark...
Mythos is only real when it's actually available. If you're using Opus 4.7 right now, you know how incredibly nerfed the Opus autonomy is in service of perceived safety. I'm not so confident this will be as great as Anthropic wants us to believe..
I did some study on Verified, not Pro, but Mythos number there rings a lot of questions on my end.
If you look at the SWEBench official submissions: https://github.com/SWE-bench/experiments/tree/main/evaluatio..., filter all models after Sonnet 4, and aggregate ALL models' submission across 500 problems, what I found that the aggregated resolution rate is 93% (sharp).
Mythos gets 93.7%, meaning it solves problems that no other models could ever solve. I took a look at those problems, then I became even more suspicious, for the remaining 7% problems, it is almost impossible to resolve those issues without looking at the testing patch ahead of time, because how drastically the solution itself deviates from the problem statement, it almost feels like it is trying to solve a different problem.
Not that I am saying Mythos is cheating, but it might be too capable to remember all states of said repos, that it is able to reverse engineer the TRUE problem statement by diffing within its own internal memory. I think it could be a unique phenomena of evaluation awareness. Otherwise I genuinely couldn't think of exactly how it could be this precise in deciphering such unspecific problem statements.
Yep, I read this blog. What confuses me is that Anthropic doesn't seem to be bothered by this study and keeps publishing Verified results.
That is what gets me curious in the first place. The fact Mythos scored so high, IMO, exposes some issues with this model: it is able to solve seemingly impossible to solve problems.
Without cheating allegation, which I don't think ANT is doing, it has to be doing some fortune telling/future reading to score that high at all.
This indicates they want this behavior, they know the person asking the question probably doesn't understand the problem entirely (or why would they be asking), so they'd prefer a confident response, regardless of outcomes, because the point is to sell the technologies competency (and the perception thereof), not the capabilities, to a bunch of people that have no clue what they're talking about.
LLMs will ruin your product, have fun trusting a billionaires thinking machine they swear is capable of replacing your employees if you just pay them 75% of your labor budget.
We don't want hallucinations either, I promise you.
A couple of biased defenses:
- I'll note that this eval doesn't have web search enabled, but we train our models to use web search in ChatGPT, Codex, and our API. I'd be curious to see hallucination rates with web search on.
- On the flip side, GPT-5.5 has the highest accuracy score.
- With anything over 1% (whether it's 30% or 60%), you should be double checking anything important.
- On an internal eval made from de-identified ChatGPT prompts that elicit hallucinations, we've actually been improving substantially from 5.2 to 5.4 to 5.5. So as always, progress depends on what you mean and how you measure it.
Still, Anthropic has done a great job here and I hope we catch up in the future.
On ChatGPT 5.3 Plus subscription I find that long chats tend to reveal unsatisfactory answers and biases, at this point after 10 rounds of replies I end up having to correct it so much that it starts to agree with my initial arguments full circle. I don't see how this behavior is safe for real work. Like are programmers and engineers using LLMs completely differently, because the underlying technology is fundamentally the same.
This model is great at long horizon tasks, and Codex now has heartbeats, so it can keep checking on things. Give it your hardest problem that would take hours with verifiable constraints, you will see how good this is:)
It's genuinely so great at long horizon tasks! GPT-5.5 solved many long-horizon frontier challenges, for the first time for an AI model we've tested, in our internal evals at Canva :) Congrats on the launch!
That's what I've been heads down, HUNGRY, working on, looking for investors and founding engineers pst: https://heymanniceidea.com (disclaimer: I am not associated with heymanniceidea.com)
HN is owned by a startup accelerator and venture capital firm. They do growth hacking on the front page. And you probably know that since your throwaway account is several years old.
Interesting, I just had opus convert a 35k loc java game to c++ overnight (root agent that orchestrated and delegated to sub agents) and woke up and it's done and works.
What plan are you on? I'm starting to wonder if they're dynamically adjusting reasoning based on plan or something.
I'm on max 5x and noticed this too. I don't use built-in subagents but rather full Claude session that orchestrates other full claude sessions. Worker agents that receive tasks now stop midway, they ask for permission to continue. My "heartbeat" is basically "status. One line" message sent to the orchestrator.
Opus 4.6 worker agents never asked for permission to continue, and when heartbeat was sent to orchestrator, it just knew what to do (checked on subagents etc). Now it just says that it waits for me to confirm something.
This is 3x the price of GPT-5.1, released just 6 months ago. Is no one else alarmed by the trend? What happens when the cheaper models are deprecated/removed over time?
This is entirely expected. The low prices of using LLMs early on was totally and completely unsustainable. The companies providing such services were (and still are) burning money by the truckload.
The hope is to get a big userbase who eventually become dependent on it for their workflow, then crank up the price until it finally becomes profitable.
The price for all models by all companies will continue to go up, and quickly.
I recently looked at this a bit but came away with the impression that at least on API pricing the models should be very profitable considering primarily the electricity cost.
Subscriptions and free plans are the thing that can easily burn money.
As others have mentioned you're ignoring the long tail of open-weights models which can be self hosted. As long as that quasi-open-source competition keeps up the pace, it will put a cap on how expensive the frontier models can get before people have to switch to self-hosting.
That's a big if, though. I wish Meta were still releasing top of the line, expensively produced open-weights models. Or if Anthropic, Google, or X would release an open mini version.
Yes, sort of. Generally you can measure the pass rate on a benchmark given a fixed compute budget. A sufficiently smart model can hit a high pass rate with fewer tokens/compute. Check out the cost efficiency on https://artificialanalysis.ai/ (say this posted here the other day, pretty neat charts!)
It's much easier to measure a language model's intelligence than a human's because you can take as many samples as you want without affecting its knowledge. And we do measure human intelligence.
How do we know that? There is a large gap between API pricing for SOTA models and similarly sized OSS models hosted by 3rd party providers.
Sure, they’re distilled and should be cheaper to run but at the same time, these hosting providers do turn a margin on these given it’s their core business, unless they do it out of the kindness of their heart.
So it’s hard for me to imagine these providers are losing money on API pricing.
It's far more meaningful to look at the actual cost to successfully something. The token efficiency of GPT-5.5 is real; as well as it just being far better for work.
SOTA models get distilled to open source weights in ~6 months. So paying premium for bleeding edge performance sounds like a fair compensation for enormous capex.
Not really a big problem. Switch to KIMI, Qwen, GLM. You’ll get 95% quality of GPT or Anthropic for a 10th of a price. I feel like the real dependency is more mental, more of a habit but if you actually dip your toes outside OpenAI, Anthropic, Gemini from time to time, you realise that the actual difference in code is not huge if prompted in a good way. Maybe you’ll have to tell it to do something twice and it won’t be a one shot, but it’s really not an issue at all.
I've found myself so deeply embedded in the Claude Max subscription that I'm worried about potentially makign a switch. How are people making sure they stay nimble enough not to get trarpped by one company's ecosystem over another? For what it's worth, Opus 4.7 has not been a step up and it's come with an enormously higher usage of the subscription Anthropic offers making the entire offering double worse.
Start building your own liteweight "harness" that does things you need. Ignore all functionality of clients like CC or Codex and just implement whatever you start missing in your harness.
You can replace pretty much everything - skills system, subagents, etc with just tmux and a simple cli tool that the official clients can call.
Oh and definitely disable any form of "memory" system.
Essentially, treat all tooling that wraps the models as dumb gateways to inference. Then provider switch is basically a one line config change.
> You can replace pretty much everything - skills system, subagents, etc with just tmux and a simple cli tool that the official clients can call.
I'm very interest by this. Can you go a bit more into details?
ATM for example I'm running Claude Code CLI in a VM on a server and I use SSH to access it. I don't depend on anything specific to Anthropic. But it's still a bit of a pain to "switch" to, say, Codex.
How would that simple CLI tool work? And would CC / Codex call it?
Check out github.com/ralabarge/beigebox -- OSS AI Harness, started as a way to save all of my data but has agentic features, MCP server, point it at any endpoint (or use any front end with it as well, transparent middleware)
So far what I am finding is that you just get the basics working and then use the tool and inference to improve the tool.
I wish I had lower standards towards sharing absolute AI slop, then I could just drop a link to my implementation. But since I don't, let me just describe it. I essentially had claude build the initial version in a single session which I've been extending as I noticed any gaps in my process.
First, you need an entrypoint that kicks things off. You never run `claude` or `codex`, you always start by running `mycli-entrypoint` that:
1. Creates tmux session
2. Creates pane
3. Spawns claude/codex/gemini - whichever your default configured backend is
4. Automatically delivers a prompt (essentially a 'system message') to that process via tmux paste telling it what `mycli` is, how to use it, what commands are available and how it should never use built-in tools that this cli provides as alternatives.
After that, you build commands in `mycli` that CC/Codex are prompted to call when appropriate.
For example, if you want a "subagent", you have a `mycli spawn` command that takes a role (just preconfigured markdown file living in the same project), backend (claude/codex/...) and a model. Then whenever CC wants to spawn a subagent, it will call that command instead, which will create a pane, spawn a process and return agent ID to CC. Agent ID is auto generated by your cli and tmux pane is renamed to that so you can easily match later.
Then you also need a way for these agents to talk to each other. So your cli also has a `send` command that takes agent ID and a message and delivers it to the appropriate pane using automatically tracked mapping of pane_id<>agent_id.
Claude and codex automatically store everything that happens in the process as jsonl files in their config dirs. Your cli should have adapters for each backend and parse them into common format.
At this point, your possibilities are pretty much endless. You can have a sidecar process per agent that say, detects when model is reaching context window limit (it's in jsonl) and automatically send a message to it asking it to wrap up and report to a supervisor agent that will spawn a replacement.
I also don't use "skills" because skills are a loaded term that each of the harnesses interprets and loads/uses differently. So I call them "crafts" which are again, just markdown files in my project with an ID and supporting command `read-craft <craft-id>`. List of the available "crafts" are delivered using the same initialization message that each agent gets. If I like any third party skill, I just copy it to my "crafts" dir manually.
My implementation is an absolute junk, just Python + markdown files, and I have never looked at the actual code, but it works and I can adapt it to my process very easily without being dependent on any third party tool.
It's surprisingly simple to switch. I mean both products offer basically identical coding CLI experiences. Personally I've been paying for Claude max $100, and ChatGPT $20, and then just using ChatGPT to fill in the gaps. Specifically I like it for code review and when Claude is down.
I have a directory of skills that I symlink to Codex/Claude/pi. I make scripts that correspond with them to do any heavy lifting, I avoid platform specific features like Claude's hooks. I also symlink/share a user AGENTS.md/CLAUDE.md
MCPs aren't as smooth, but I just set them up in each environment.
This might be the opposite of staying nimble as my workflows are quite tied to Claude Code specifically, however I've been experimenting with using OpenAI models in CC and it works surprisingly well.
Except for history, I don’t find much that stops you from switching back and forth on the CLI. They both use tools, each has a different voice, but they both work. Have it summarize your existing history into a markdown file, and read it in with any engine.
The APIs are pretty interchangeable too. Just ask to convert from one to the other if you need to.
As a rule I've been symlinking or referencing generic "agents" versions of claude workflow files instead of placing those files directly in claude's purview
Coding models are effectively free. They are capable of making money and supporting themselves given access to the right set of things. That is what I do
I switched a couple of weeks ago just to see how it went. Codex is no better or worse. They’re both noticeably better at different things. I burn through my tokens much much faster on Codex though. For what it’s worth I’m sticking with Codex for now. It seems to be significantly better at UI work although has some really frustrating bad habits (like loading your UI with annoying copywriting no sane person would ever do).
If GPT-5.5 Pro really was Spud, and two years of pretraining culminated in one release, WOW, you cannot feel it at all from this announcement. If OpenAI wants to know why they like they’ve fallen behind the vibes of Anthropic, they need to look no further than their marketing department. This makes everything feel like a completely linear upgrade in every way.
Clearly they felt a big backlash when version 5 was released. Now they are afraid of another response like this. And effectively, for the user it will likely only be a small update.
This seems huge for subscription customers. Looking at the Artificial Analysis numbers, 5.5 at medium effort yields roughly the intelligence as 5.4 (xhigh) while using less than a fifth the tokens.
As long as tokens count roughly equally towards subscription plan usage between 5.5 & 5.4, you can look at this as effectively a 5x increase in usage limits.
As someone who always leaves intelligence at default, and am ok with existing models, should I be shifting gears more manually as providers sell us newer models? Is medium or lower better than free/cheaper models?
Chip costs strongly impact the economics of model serving.
It is entirely plausible to me that Opus 4.7 is designed to consume more tokens in order to artificially reduce the API cost/token, thereby obscuring the true operating cost of the model.
I agree though, I chose poor phrasing originally. Better to say that GB200 vs Tranium could contribute to the efficiency differential.
I hope the industry starts competing more on highest scores with lowest tokens like this. It's a win for everybody. It means the model is more intelligent, is more efficient to inference, and costs less for the end user.
So much bench-maxxing is just giving the model a ton of tokens so it can inefficiently explore the solution space.
It could be. Or just smarter caching (which wouldn't necessarily have to do with model intelligence). Or just overfitting on the 95% most common prompts (which could save tokens but make the models less intelligent/flexible).
I can argue that disaster started mid-4.6, when they started juggling with rate limits while hitting uptime problems. Great we have some healthy competition and waiting for the next move from Deepmind.
This is reminding me when Chrome and Firefox where racing to release a new “major version” (at least from the semver POV) without adding significantly new functionality at a time that browsers were already becoming a commodity. As much as we don’t care anymore for a new chrome or Firefox version so will be the release of a new model version.
How does this work exactly? Is there like a "search online" tool that the harness is expected to provide? Or does the OpenAI infra do that as part of serving the response?
I've been working on building my own agent, just for fun, and I conceptually get using a command line, listing files, reading them, etc, but am sort of stumped how I'm supposed to do the web search piece of it.
Given that they're calling out that this model is great at online research - to what extent is that a property of the model itself? I would have thought that was a harness concern.
I’ve noticed when writing little bedtime stories that require specific research (my kids like Pokemon stories and they’ve been having an episodic “pokemon adventure” with them as the protagonists) ChatGPT has done a fantastic job of first researching the moves the pokemon have, then writing the actual story. The only mistake it consistently makes is when I summarize and move from a full context session, it thinks that Gyarados has to swim and is incapable of flying.
It definitely seems like it does all the searching first, with a separate model, loads that in, then does the actual writing.
Gyarados is a flying type but I think it may be accurate that it can't actually fly. The only flying moves it can learn in any generation are Hurricane and Bounce (Bounce does send the user up into the air for a turn but the implication is that they've trampolined up extremely high rather than used wings to ascend)
It's literally a distinct model with a different optimisation goal compared to normal chat. There's a ton of public information around how they work and how they're trained
On the Artificial Analysis Intelligence Index eval for example, in order to hit a score of 57%, Opus 4.7 takes ~5x as many output tokens as GPT-5.5, which dwarfs the difference in per-token pricing.
The token differential varies a lot by task, so it's hard to give a reliable rule of thumb (I'm guessing it's usually going to be well below ~5x), but hope this shows that price per task is not a linear function of price per token, as different models use different token vocabularies and different amounts of tokens.
We have raised per-token prices for our last couple models, but we've also made them a lot more efficient for the same capability level.
Agreed. Would be great if everyone starts reporting cost per task alongside eval scores, especially in a world where you can spend arbitrary test-time compute. This is one thing I like about the Artificial Analysis website - they include cost to run alongside their eval scores: https://artificialanalysis.ai/
yes but as far as i know gpt tokenizer is about the same as opus 4.6's, where 4.7 is seeing something in the ballpark of a 30% increase. this should still be cheaper even disregarding the concerns around 4.7 thinking burning tokens
> Across all three evals, GPT‑5.5 improves on GPT‑5.4’s scores while using fewer tokens.
Yeah, this was the next step. Have RLVR make the model good. Next iteration start penalising long + correct and reward short + correct.
> CyberGym 81.8%
Mythos was self reported at 83.1% ... So not far. Also it seems they're going the same route with verification. We're entering the era where SotA will only be available after KYC, it seems.
Isn't Mythos limited to a selected group of companies/organizations Anthropic chose themselves? If the OpenAI announcement for GPT-5.5 is accurate the "trusted cyber access" just requires an open, seemingly straightforward identity verification step.
> We are expanding access to accelerate cyber defense at every level. We are making our cyber-permissive models available through Trusted Access for Cyber , starting with Codex, which includes expanded access to the advanced cybersecurity capabilities of GPT‑5.5 with fewer restrictions for verified users meeting certain trust signals (opens in a new window) at launch.
> Broad access is made possible through our investments in model safety, authenticated usage, and monitoring for impermissible use. We have been working with external experts for months to develop, test and iterate on the robustness of these safeguards. With GPT‑5.5, we are ensuring developers can secure their code with ease, while putting stronger controls around the cyber workflows most likely to cause harm by malicious actors.
> Organizations who are responsible for defending critical infrastructure can apply to access cyber-permissive models like GPT‑5.4‑Cyber, while meeting strict security requirements to use these models for securing their internal systems.
"GPT‑5.4‑Cyber" is something else and apparently needs some kind of special access, but that CyberGym benchmark result seems to apply to the more or less open GPT-5.5 model that was just released.
Releases keep shifting from API forward to product forward, with API now lagging behind proprietary product surface and special partnerships.
I'd not be surprised if this is the year where some models simply stop being available as a plain API, while foundation model companies succeed at capturing more use cases in their own software.
For less than 10% bump across the benchmarks? Probably not, but if your employer is paying (which is probably what OAI is counting on) it's all good.
It's kind of starting to make sense that they doubled the usage on Pro plans - if the usage drains twice as fast on 5.5 after that promo is over a lot of people on the $100 plan might have to upgrade.
You are paying per token, but what you care about is token efficiency. If token efficiency has improved by as much as they claim it did (i.e. you need less tokens to complete a task successfully) all seems well.
Well, sort of. Imagine the case where it first scans the repo, then "intelligently" creates architecture files describing the project.
The level of intelligence will create a varying quality of summary, with varying need of deep-scans on subsequent sessions. Level of intelligence will also increase comprehension of these architecture files.
Same principle applies when designing plans for complex tasks, etc. Token amount to grasp a concept is what matters.
Tbf, I have not super kept track of what is actually happening inside the "thinking" portion of recent releases. But last time I checked there still was a lot of verbosity and mistakes, that beat the actual amount of required, usable code generation by a wide margin.
This happens with every new model release though. The model makes less mistakes and spends less time fixing them, resulting in a token usage reduction for the same difficulty of task. Almost any task other than straight boilerplate will benefit from this.
In the same vein, I would guess that Opus 4.7 is probably cheaper for most tasks than 4.6, even though the tokenizer uses more tokens for the same length of string.
Maybe you'll have better luck but our team just cannot use Opus 4.7.
Some say it goes off on endless tangents, others that it doesn't work enough. Personally, it acts, talks, and makes mistakes like GPT models, for a much more exorbitant price. Misses out on important edge cases, doesn't get off its ass to do more than the bare minimum I asked (I mention an error and it fixes that error and doesn't even think to see if it exists elsewhere and propose fixing it there).
I've slowly been moving to GPT5.4-xhigh with some skills to make it act a bit more like Opus 4.6, in case the latter gets discontinued in favour of Opus 4.7.
I'm conflicted whether I should keep my Claude Max 5x subscription at this point and switch back to GPT/Codex... anyone else in a similar position? I'd rather not be paying for two AI providers and context switching between the two, though I'm having a hard time gauging if Claude Code is still the "cream of the crop" for SWE work. I haven't played around with Codex much.
I have experienced 0 friction swapping between the 2 models, in fact pitting them against eachother has resulted in the highest success rate for me so far.
I was all in on Claude code as my daily driver for web development. And love it. But I enjoy using pi as my harness more and have never ran out of tokens with Codex yet. Claude code almost always runs out for me with the same amount of usage.
After migrating for the token and harness issues, I was pleasantly surprised that Codex seems to perform as good or better too!
Things change so often in this field, but I prefer Codex now even though Anthropocene has so much more hype for coding it seems.
Every time I've followed the hype and tried OpenAI models I've found them lacking for the most part. It might just be that I prefer the peer-programming vs spec-ing out the task and handing it off, but I've never been as productive as I am with Claude. Also, I'm still caught up on the DoD ethics stuff.
I like that its more consistent than the 4o and o4 days but still 5.4, 5.3, 5.2, etc still are a mess, for example 5.2 and 5.1 don't have mini models and 5.3 was codex only.
Anthropic is slightly better but where is 4.6 or 4.7 haiku or 4.7 sonnet etc.
Yay. 5.4 was a frustrating model - moments of extreme intelligence (I liked it very much for code review) - but also a sort of idiocy/literalism that made it very unsuited for prompting in a vague sense. I also found its openclaw engagement wooden and frustrating. Which didn’t matter until anthropic started charging $150 a day for opus for openclaw.
Anyway - these benchmarks look really good; I’m hopeful on the qualitative stuff.
Benchmarks are favorable enough they're comparing to non-OpenAI models again. Interesting that tokens/second is similar to 5.4. Maybe there's some genuine innovation beyond bigger model better this time?
It's behind Opus 4.7 in SWE-Bench Pro, if you care about that kind of thing. It seems on-trend, even though benchmarks are less and less meaningful for the stuff we expect from models now.
I am a heavy Claude Code user. I just tried using Codex with 5.4 (as a Plus user I don't have access to 5.5 yet), and it was quite underwhelming. The app stopped regularly much earlier than what I wanted. It also claimed to have fixed issues when it did not; this is not a hallmark of GPT, and Opus has similar issues, but Claude will not make the same mistake three times in a row. It is unusable at the moment, while Claude allows me do get real work done on a daily basis. Until then...
Gpt-5.3-codex is miles better than 5.4 in that regard. It’s better at orchestration, and does the things that it said it did. Haven’t tested 5.5 yet but using 5.4 for exploration + brainstorming and handing over the findings to 5.3-codex works pretty well
Once upon a time humans had to memorize log tables.
Once upon a time humans had to manually advance the spark ignition as their car's engine revved faster.
Once upon a time humans had to know the architecture of a CPU to code for it.
History is full of instances of humans meeting technology where it was, accommodating for its limitations. We are approaching a point where machines accommodate to our limitations -- it's not a point, really, but a spectrum that we've been on.
With such a huge progress of open ai and anthropic . How Chinese open source provides even think to make comparable money . I have a few friends in China they all use Claude. To train the model cost the same but the output from open source model id imagine is 1000 times less . Money flow for them outside of China is abysmal
GPT is really great, but I wish the GPT desktop app supported MCP as well.
You can kind of use connectors like MCP, but having to use ngrok every time just to expose a local filesystem for file editing is more cumbersome than expected.
In Copilot where it's easy to switch models Opus 4.6 was still providing, IMHO, better stock results than GPT-5.4.
Particularly in areas outside straight coding tasks. So analysis, planning, etc. Better and more thorough output. Better use of formatting options(tables, diagrams, etc).
I'm hoping to see improvements in this area with 5.5.
After 5.1, we haven’t seen a -codex-max model, presumably because the benefits of the special training gpt-5.1-codex-max got to improve long context work filtered into gpt-5.2-codex, making the variant no longer necessary (my personal experience accords with this). I’ve been using gpt-5.4 in Codex since it came out, it’s been great. I’ve never back-to-back tested a version against its -codex variant to figure out what the qualitative difference is (this would take a long time to get a really solid answer), but I wouldn’t be surprised if at some point the general-purpose model no longer needs whatever extra training the -codex model gets and they just stop releasing them.
I thought it was weird that for almost the entire 5.3 generation we only had a -codex model, I presume in that case they were seeing the massive AI coding wave this winter and were laser focused on just that for a couple months. Maybe someday someone will actually explain all of this.
> GPT‑5.5 improves on GPT‑5.4’s scores while using fewer tokens.
This might be great if it translates to agentic engineering and not just benchmarks.
It seems some of the gains from Opus 4.6 to 4.7 required more tokens, not less.
Maybe more interesting is that they’ve used codex to improve model inference latency. iirc this is a new (expectedly larger) pretrain, so it’s presumably slower to serve.
With Opus it’s hard to tell what was due to the tokenizer changes. Maybe using more tokens for the same prompt means the model effectively thinks more?
What a time. I am back here genuinely wishing for OpenAI to release a great model, because without stiff competition, it feels like Anthropic has completely lost its mind.
Cool. Now there will be a week or "this is the greatest model ever and I think mine just gained sentience", followed by a week of "I think they must have just nerfed it because it's not as good as it was a week ago", followed by three weeks of smart people cargo culting the specific incantations they then convince themselves make it work best.
Honest answer is that it isn't done running yet. It takes some human bandwidth and time to run, so results weren't ready by this morning. We don't know what the score will be, but will probably go up on the leaderboard sometime soon. I personally don't put a lot of stock in it, as it's not relevant to most work that people do, but should still be interesting to see as a measure of reasoning ability.
I know this is irrelevant on the grand scheme of things, but that WebGL animation is really quite wrong. That is extra funny given the "ensure it has realistic orbital mechanics." phrase in the prompt.
I prescribe 20 hours of KSP to everyone involved, that'll set them right.
Good job on the release notice. I appreciate that it isn't just marketing fluff, but actually includes the technical specs for those of us who care and not concentrated in coding agents only.
I hope GPT 5.5 Pro is not cutting corners and neuter from the start, you got the compute for it not to be.
Seems like a continuation of the current meta where GPT models are better in GPT-like ways and Claude models are better in Claude-like ways, with the differences between each slightly narrowing with each generation. 5.5 is noticeably better to talk to, 4.7 is noticeably more precise. Etc etc.
Looking at the space/game/earthquake tracker examples makes me hopeful that OpenAI is going to focus a bit more on interface visual development/integration from tools like Figma. This is one area where Anthropic definitely reigns supreme.
Very impressive! Interesting how all other benchmarks it seems to surpass Opus 4.7 except SWE-Bench Pro (Public). You would think that doing so well at Cyber, it would naturally possess more abilities there. Wonder what makes up the actual difference there
I can see how some model releases would meet the NY Times news-worthy threshold if they demonstrated significance to users - i.e., if most users were astir and competitors were re-thinking their situation.
However, this same-day article came out before people really looked at it. It seems largely intended to contrast OpenAI with Anthropic's caution, before there has been any evidence that the new model has cyber-security implications.
It's not at all clear that the broader discourse is helping, if even the NY Times is itself producing slop just to stoke questions.
This is frankly exciting, outside of the politics of it all, it always feel great to wake up and a new model being released, I personally will stay awake quite long tonight if GPT-5.5 drop in codex.
This is my personal pet peeve as well. Like, I accept maybe everything shouldn't be offered to everyone, but maybe just gate keep it behind credit card( but I know that is a market penetration no no ). I feel like such a waste of power ( electrical and the potential we might be missing out on ).
Depends in goals. For long free-firm discussions I find Opus 4.7 Adaptive better/deeper than Opus 4.6 Extended. But usual caveats apply: first week of use and token budget seems generous now on Max 5X.
I had the opposite experience. Opus 4.6 extended feels like the first genuinely intelligent model to converse with, Opus 4.7 adaptive feels like slightly smarter LinkedIn slop.
Cursor is what I daily-drive. 4.7 has been terrible for my mostly python-driven work (whereas Opus 4.6 was literally revolutionary to me). Our frontend folks are also complaining.
Surprised to see SWE-Bench Pro only a slight improvement (57.7% -> 58.6%) while Opus 4.7 hit 64.3%. I wonder what Anthropic is doing to achieve higher scores on this - and also what makes this test particular hard to do well in compared to Terminal Bench (which 5.5 seemed to have a big jump in)
There's an asterisk right below that table stating that:
> *Anthropic reported signs of memorization on a subset of problems
And from the Anthropic's Opus 4.7 release page, it also states:
> SWE-bench Verified, Pro, and Multilingual: Our memorization screens flag a subset of problems in these SWE-bench evals. Excluding any problems that show signs of memorization, Opus 4.7’s margin of improvement over Opus 4.6 holds.
> We are releasing GPT‑5.5 with our strongest set of safeguards to date
...
> we’re deploying stricter classifiers for potential cyber risk which some users may find annoying initially
So we should be expecting to not be able to check our own code for vulnerabilities, because inherently the model cannot know whether I'm feeding my code or someone else's.
It's possible that "smarter" AI won't lead to more productivity in the economy. Why?
Because software and "information technology" generally didn't increase productivity over the past 30 years.
This has been long known as Solow's productivity paradox. There's lots of theories as to why this is observed, one of them being "mismeasurement" of productivity data.
But my favorite theory is that information technology is mostly entertainment, and rather than making you more productive, it distracts you and makes you more lazy.
AI's main application has been information space so far. If that continues, I doubt you will get more productivity from it.
If you give AI a body... well, maybe that changes.
Its quite possible the use of LLMs means that we are using less effort to produce the same output. This seems good.
But the less effort exertion also conditions you to be weaker, and less able to connect deeply with the brain to grind as hard as once did. This is bad.
Which effect dominates? Difficult to say.
Of course this is absolutely possible. Ultimately there was a time where physical exertion was a thing and nobody was over-weight. That isn't the case anymore is it.
> "information technology" generally didn't increase productivity
Do you think it'd be viable to run most businesses on pen and paper? I'll give you email and being able to consume informational websites - rest is pen and paper.
Productivity metrics were better when businesses were run on just pen and paper. Of course, there could be many confounding factors, but there are also many reasons why this could be so. Just a few hypotheses:
- Pen and paper become a limiting factor on bureaucratic BS
- Pen and paper are less distracting
- Pen and paper require more creative output from the user, as opposed to screens which are mostly consumptive
Productivity growth. If you take rolling averages from this chart, it clearly demonstrate higher productivity growth before the adoption of software. This is a well established fact in econ circles.
I think this is a classic case of reading into specific arguments too deeply without understanding what they really mean in the grand picture. Few points to easily disprove this argument
- if it were true that software paradoxically reduces productivity, you can just start a competing company that doesn't use software. Obviously this is ridiculous - top 20 companies by market cap are mostly Software based. Every other non IT company is heavily invested in software
- if you might say the problem is it at the country level, it is obvious that every country that has digitised has had higher productivity and GDP growth. Take Italy vs USA for instance.
- if you are saying that the problem is even more global, take the whole world - the GDP per is still pretty high since the IT revolution (and so have other metrics)
If you still think there's something more to it, you are probably deep in some conspiracy rabbit hole
You don't have a counterfactual to suggest that it would have continued increasing had it not been for technology. Is there _any_ credible economist who suggests that we might have higher productivity without tech?
There is no counterfactual needed. Productivity growth has declined, despite the expectation that software would accelerate productivity. I'm asking you why this didn't happen.
I'm not even proposing that growth would have been higher without "technology". I said information technology has not increased productivity growth compared to the past. This is an observation of fact.
It's a hypothesis that "smarter" AI models, ie GPT-5.5, may not be a great boon to productivity. Given that this is the raison d'etre of AI models, and improving them, I don't see why it is any less useful than any other discussion.
Nice to see them openly compare to Opus-4.7… but they don’t compare it against Mythos which says everything you need to know.
The LinkedIn/X influencers who hyped this as a Mythos-class model should be ashamed of themselves, but they’ll be too busy posting slop content about how “GPT-5.5 changes everything”.
I'd really like to see improvements like these:
- Some technical proof that data is never read by open ai.
- Proof that no logs of my data or derived data is saved.
etc...
... sigh. I realize there's little that can be done about this, but I just got through a real-world session determining of Opus 4.7 is meaningfully better than Opus 4.6 or GPT 5.4, and now there's another one to try things with. These benchmark results generally mean little to me in practice.
What is the major and minor semver meaning for these models? Is each minor release a new fine-tuning with a new subset of example data while the major releases are made from scratch? Or do they even mean anything at this point?
Nothing. The next major increment is going to happen when marketing department is confident they can sell it as a major improvement without everyone laughing at them. Which at this point seems like never.
I think Anthropic fearmongering and "leaks" of Mythos was them testing the ground for 5.x, which seems to have backfired.
GPT-5.4 is already an incredible model for code reviews and security audits with the swival.dev /audit command.
The fact that GPT-5.5 is apparently even better at long-running tasks is very exciting. I don’t have access to it yet, but I’m really looking forward to trying it.
I am still using Codex 5.3 and haven't switched to GPT 5.4 as I don't like the 'its automatic bro trust us', so wondering is Codex going to get these specific releases at all in the future.
My impression has been that ChatGPT-5.4 has been getting dumber and more exhausting in the last couple of weeks. Like it makes a lot of obvious mistakes, ignores (parts of) prompts. keeps forgetting important facts or requirement.
Maybe this is a crazy theory, but I sometimes feel like they gimp their existing models before a big release to you'll notice more of a "step".
I doubt this is representative of real world usage. There is a difference between a few turns on a web chatbot, vs many-turn cli usage on a real project.
I am sceptical. The generation after 4o models have become crappier and crappier. Hope this one changes the trend. 5.4 is unusable for complex coding work.
> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.”
This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive.
This matches my own experience and unease with these tools. I don't really have the patience to write code anymore because I can one shot it with frontier models 10x faster. My role has shifted, and while it's awesome to get so much working so quickly, the fact is, when the tokens run out, I'm basically done working.
It's literally higher leverage for me to go for a walk if Claude goes down than to write code because if I come back refreshed and Claude is working an hour later then I'll make more progress than mentally wearing myself out reading a bunch of LLM generated code trying to figure out how to solve the problem manually.
Anyway, it continues to make me uneasy, is all I'm saying.
LLMs upend a few centuries of labor theory.
The current market is predicated on the assumption that labor is atomic and has little bargaining power (minus unions). While capital has huge bargaining power and can effectively put whatever price it wants on labor (in markets where labor is plentiful, which is most of them).
What happens to a company used to extracting surplus value from labor when the labor is provided by another company which is not only bigger but unlike traditional labor can withhold its labor indefinitely (because labor is now just another for of capital and capital doesn't need to eat)?
Anyone not using in house models is signing up to find out.
This is our one chance to reach the fabled post-scarcity society. If we fail at this now, we'll end up in a totalitarian cyberpunk dystopia instead.
I don't want to spoil it for you, but ...
Manufactured Scarcity is the new post-scarcity
But cyberpunk is the best kind of dystopia!
Sorry for my foul language but I think we will turn into cybershit if things go bad.
What? In what way does companies becoming dependent on AI chatbots will solve the world-spanning problem of resource scarcity?
The hell?
The idea is that cheap and readily available and upgradeable intelligence is going to massively increase our purchasing power and what everyone can order for the same cost basically.
If artificial doctors are cents on hour then you can see how that changes our behaviors and level of life.
But on the other hand from the other direction there is a wage decrease incoming from increased competition at the same time. What happens if these two forces clash? Will cheap labour allow us to buy anything for pennies or will it just make us unable to make a single penny?
In my view the labour will fundamentally shift with great pain and personal tragedies to the areas that are not replaceable by AI (because no one wants to watch robots play chess). Such as sports, entertainment and showmanship. Handcrafted goods. Arts. Attention based economy. Self advertisement. Digital prostitution in a very broad sense.
However before it gets there it will be a great deal of strife and turmoil that could plunge the world into dark ages for a while at least. It is unlikely for our somewhat politically rigid society to adapt without great deal of pain. Additionally I am not sure if hypothetical future attention based society could be a utopia. You could have to mount cameras in your house so other people see you at all times for amusement just to have any money at all. We will probably forever need to sell something to someone and I am unsettled by ideas what can we sell if we cannot sell our hard work.
Someone who sees the roads ahead should now make preparations at government level for this shock but it will come too fast and with people at the steering wheel that don’t exactly care.
"Extremely cheap sentience that cannot disobey will solve all our problems" is such an insane sentiment I see far too often.
If we used sentience to work towards solving our problems we could massively increase the human standard of living.
Which we have already done with regular computers! The problem is that competition means that we can't always have nice things.
Useful intelligence does not require sentience.
As far as I know, none of LLM models are sentient nor are possible to be in the near future.
I also do not assume so called AGI to be sentient. Merely to be a human level skilled intellectual worker.
In absence of ethical dilemmas of this calibre for the foreseeable future let’s focus on the economy side of things in this particular comment chain.
It must very comforting to be able to decided a "human level worker" isn't sentient.
It makes things so clean.
LLMs cannot possess consciousness for three reasons: they execute as a sequence of Transformer blocks with extremely limited information exchange, these blocks are simple feed-forward networks with no recurrent connections, and the computer hardware follows a modular design.
Shardlow & Przybyła, "Deanthropomorphising NLP: Can a Language Model Be Conscious?" (PLOS One, 2024)
Nature: "There is no such thing as conscious artificial intelligence" (2025)
They argue that the association between consciousness and LLMs is deeply flawed, and that mathematical algorithms implemented on graphics cards cannot become conscious because they lack a complex biological substrate. They also introduce the useful concept of "semantic pareidolia" - we pattern-match consciousness onto things that merely talk convincingly.
They are making a strong argument and I think they are correct. But really these are two different things as I said originally.
We could also literally have Star Trek. Think of all the scientific discoveries we could make if we had armies of scientists the size of our labor force.
But we will have to (painfully) shed our current hierarchies before that comes to pass.
Maybe so but humans have this strange primal need to hoard resources.
Probably a remnant from prehistoric times when it was a matter of life and death. Will we ever be able to overcome this basic instinct that made capitalism such an unstoppable force? Will this ancient PTSD be ever cured?
Man if only there was a singular episode that covered this exact topic in Star Trek and resolved that no, actually slavery wasn't any different for artificial life.
Chatbots, no. Robots, maybe.
Weird predicament you've set for yourself there.
Good luck with whatever you got going on.
I am still trying to figure out the business model of open weights. Like... it's wonderful that there are open LLMs, super happy about it, good for everyone, but why are there these? What is the advantage to their companies to release them?
IMHO this is only temporary, china buying themselves some time and want to make sure none of US models get entrenched in their position in the next few years (also putting pressure on US AI companies bleeding them)
The same way like Windows got entrenched everywhere even though linux desktop is pretty good even for non-tech savvy people and free.
> even though linux desktop is pretty good even for non-tech savvy people
Let's not get carried away.
A stock Fedora install has more UI consistency and cleanliness than Windows these days.
Non-technical people are easier to please in this regard than moderate-technical people: a good browser and safe, gui "app store" are enough.
My grandma just clicks on the red fox and does whatever online. A lot of people don't use any software outside of the browser, so it's pretty good-enough I guess.
Downward pressure on proprietary model pricing until a lab can catch up. Also good for hiring talent (who love OSS).
Cultural influence is another benefit. China is securing its sphere of influence as well as keeping us ai in check.
> What is the advantage to their companies to release them?
It's a distribution strategy. It costs something to serve the models - let's say $5/1M tokens.
If Qwen required $5 from anyone who was curious so you could even begin to test it out, a lot of people just wouldn't.
Now Qwen could offer a "free" tier, but it's infinitely cheaper to provide the weights and let people run it themselves including opening up the ability for anyone else on the planet to test it against other (open weight) models.
The costs to build the open weight models are sunk, but the costs to serve them, get them tested are not.
It's also precisely why the .NET SDK is free or the ESP32 SDK is free - they sell more Microsoft or ESP32 products.
They are making the hardware and commoditizing the complement.
It's analogous to open-source software, which never had an obvious economic incentive either, although training an LLM necessary costs money whereas developing an OSS project might only cost time, which people are probably more likely to give up.
Yeah, but open-source software could have been me in the garage banging away on some program I submit to Debian or whatever... it didn't require millions of dollars to train, a lot of it was just side hobbies for a long time. Corporations sponsor it and contribute work because they need it to do more than what it does for free, not out of the goodness of their hearts.
Big AI labs are losing money. Open Models is making the pricing equation a lot trickier for them.
There are some short term ones but I doubt this will continue, especially for the more powerful models.
I mean, this is straight out of chinas playbook, it should not be surprising that China is making an inferior derivative product at an artificially lower price point: state subsidies to massively drive up internal scale and supply chains leading to artificially lower priced goods which then suffocate the competition has lead to *gestures vaguely at everything* being made in china.
Right now it’s so the Chinese can undermine the frontier models in the US. In areas they’re doing well like video generation (ie seedance) they won’t open source anything.
People use their model otherwise they would not.
The majority are released by socialists, and by socialist I mean the People's Republic of China. Which everyone seems to forget is a socialist country working towards world communism.
They are a prestige propaganda tool on par with the space race. On top of that they insert a subtle pro-socialist bias in everything they touch.
Ask deepseek about the US economic system for a blatant example.
Now think what something as innocent seeming as the qwen retrieval models are doing in the background of every request.
You're talking to a Canadian, and I'm not scared of the "red menace". You should be more scared - those guys can build bullet trains while you Yanks are finding it hard to even keep the old ones you have running. The solution here isn't going to be some kind of ideological force that protects people from different ideas, and that's an unAmerican way to fix things anyway. Embrace other ideas; central planning doesn't have to be evil, you just have to find a way to stop putting evil people in charge.
> those guys can build bullet trains while you Yanks are finding it hard to even keep the old ones you have running
This is an argument in the lane of "at least he built the Autobahn".
Speaking as a German.
He was a foreigner too ;)
Xi is an obviously more capable and effective leader than Trump, but the US actually does have ways to boot people out of office when they do a bad job, and clear methods to choose successors, and China has neither. That matters more than who happens to be in charge right now.
The US can’t build bullet trains because property rights and local regulations make it prohibitively expensive. Not due to capability.
I don't know where people get this idea.
America has several sets of eminent domain laws depending on the jurisdiction. The most coercive is federal eminent domain law specifically as it relates to building infrastructure like railways and highways.
It's set up so that you can take the land first and eventually go back around and decide on what the right price should have been.
Not only does it superscede state and local law, federal infrastructure projects are also not bound by state laws like CEQA.
You can even apply federal eminent domain law by e.g. transferring a state-level project to the Army Corps of Engineers.
What America is lacking in these projects is will, not means. The federal government could take your house and run a train through it by the end of the week if they wanted, doesn't matter where you live.
[edit] In fact some states even ceded their eminent domain rights to private railways.
https://ij.org/press-release/appeals-court-sides-with-railro...
> property rights
The Australian federal government is planning to build a high-speed rail line from Sydney to Newcastle (medium-sized city two hours drive north). Their solution to property rights, is >50% of the line will be underground. It will >US$50 billion, but if the Australian federal government wants to spend that, it can afford it. The US federal government could too, but it isn’t a priority for them
> local regulations make it prohibitively expensive
Local regulations can be pre-empted by state or federal legislation. The real problem is lack of political will to do it.
Surely there are existing rails right now that could be transformed into a bullet train line.
Like properties and regulations are a true problem, but it's not like trains don't exist at all in America.
My understanding is that existing rail lines aren't flat/straight enough for high speed rail. There's no point to a bullet train if it has to constantly slow down for corners/hills.
the US can't build bullet trains because they'd serve the average person and there's no money in serving the average person
Property rights, regulations and price are precisely the part of the American system that takes away that capability.
>you just have to find a way to stop putting evil people in charge.
Of course, why did no one think of that?
Literal Trump Derangement Syndrome. America has a comically horrendous president but remains fundamentally a liberal democracy… and Canada concludes “literal Nazis are a better choice”. It’s uncanny how much can be taken for granted :(
(American talking, who’s had multiple Canadian friends make this mind boggling overcorrection)
Weimar Germany also was fundamentally a liberal democracy. Hitler seized power legally.
Those who do not learn history are doomed to repeat it.
> Which everyone seems to forget is a socialist country working towards world communism.
It's easy to forget because they actually built an incredibly vibrant capitalist economy.
They build an incredibly vibrant _market_ economy with no property rights and very little due process.
Imagine if Musk was disappeared during the Biden presidency into a diversity camp and came out looking like Dr. Frank-N-Furter and instituted mandatory LGBT struggle sessions at twitter.
This is what they did to Jack Ma: https://www.forbes.com/sites/georgecalhoun/2021/06/24/what-r...
do you ever get tired of making up scenarios to be scared about lgbt people?
Are you able to hold a hypothetical in your mind?
yeah but mine don't reveal my unhealthy obsession with trans people
More constructively, and moving on, do you have any suggestions for a good throwaway example of an extreme radical transformation in a person?
TBH I had a chuckle at the Elon -> Frank-N-Furter example that transcends any specific love or hate for either Elon or the Rocky Horror Show.
The point was being made that a billionaire figurehead drastically changed their views after an "indeterminate time" detained by national authorities.
IE what if Musk suddenly behaved in such a manner after being detained by a Biden administration. Wouldn't that be profoundly weird?!?
And yet, it happened to Jack Ma under the CCP.
But instead, you try to link the "weird behaviour" with the GP instead of the hypothetical Musk - whom this is fitting for.
> The point was being made that a billionaire figurehead drastically changed their views after an "indeterminate time" detained by national authorities.
> IE what if Musk suddenly behaved in such a manner after being detained by a Biden administration. Wouldn't that be profoundly weird?!?
We've seen that. Durov in France after detention began sharing Telegram users' data with authorities. It's unclear how much, but likely full real time access to all of it.
You sure have a way of making the Chinese system sound even more appealing.
It's all fun and games when the oppression is against your enemies. The problem is, if the system is set up like that eventually it'll be your turn.
Is China even really communist? If anything they seem to be fairly on the Capitalist side but just a bit opposite on the spectrum of the US. And much more authoritarian
The usual thing to say is state capitalist but honestly they do keep a market around too. A little hybrid of everything, I guess? Just with the state ready to jump in and intervene if anything happens they don't like.
Can we just call it what it is?
Fascism (in the Mussolini model) in everything but name.
- Hyper-Nationalism & Rejuvenation - State-Controlled Capitalism (Corporatism) - Authoritarian & Cult of Personality - Militarism & Irredentism
And they have technology to maintain control rather than needing the Black-shirts.
There are differences obviously to fit Chinese culture, but there are many parallels.
From what I understand their one hundred year plan is right on schedule.
> Anyone not using in house models is signing up to find out.
What are they finding out exactly? That Claude Max for $200/mo is heavily subsidized and it will soon cost $10k/mo?
> What happens to a company used to extracting surplus value from labor when the labor is provided by another company which is not only bigger but unlike traditional labor can withhold its labor indefinitely (because labor is now just another for of capital and capital doesn't need to eat)?
This can be trivially answered by a thought experiment. Let's pick a market where labor is plentiful - fast food.
Now what happens to McDonald's where they rent perfect robots from NoosphrFoodBotsInc? NoosphrFoodBotsInc bots build the perfect burger everytime meeting McDonald's standards. It actually exceeds those standards for McDonald AddictedCustomerPlus tier customers.
As the sole owner of NoosphrFoodBotsInc (you need 0 human employees to run your company, all your employees are bots), what are your choices?
The labor theory of value hasn't been considered correct in nearly a century.
If you want the neoclassical version:
What happens when there is an oligopoly in the supply of labor?
Same answer. Nothing good for the consumers of labor.
Technological improvements shift supply curves right which is good for consumers.
In a market with perfect competition, which I specifically ruled out by stating that the suppliers of labor from an oligopoly.
Why would you expect technological improvements to only shift supply curves right under perfect competition? I'd also expect it under oligopoly or even monopoly. You also might think there'd be more tech improvement under oligopoly, on Schumpeterian grounds that oligopolists can internalize the benefits of tech research.
"Observation of how economies actually work has upended 150 year of economics."
True for both Marxist and neoclassical economics.
I was really confused by this comment, but I don't think it's just because of the Marxist analysis of the situation ('surplus value' of labor etc).
What's really confusing is the claim that there's already a huge labor surplus (so capital controls wages); wouldn't LLMs making labor less important be reinforcing the trend, not upending it?
Not saying I agree one way or the other, just want to get the argument straight.
The reason why labor is weak relative to capital is that there is a huge number of somewhat fungible suppliers, viz. humans, and that they all need to work constantly to keep themselves alive.
If we assume that ai makes humans obsolete then you end up in a situation where your workforce is effectively perfectly unionised against you and the only thing you can do is choose which union you hire.
If you think you can bring them to the negotiation table by starving them all the providers are dozens to thousands of times bigger than you are.
This is a completely new dynamic that none of the business signing up for ai have ever seen before.
I see what you are saying now, but I still don't think it makes sense. Labor, in your analysis, is the LLM. It seems to me that when you take people out of the equation then you don't need to talk about unions and labor; that's a distraction. We talk about it as an input commodity used to create your product like, say, oil or sugar.
think more broadly than 'labor theory'
finance today mostly valued on labor value following ideas of marx, hjalmar schact, keynes
in future money will be valued as energy derivative. expressed as tokens consumption, KWh, compute, whatever
you are right, company extracting surplus value from labor by leveraging compute is a bad model. we saw thi swith car and clothing factories .. turn out if you can get cheaper labor to leverage the compute (factory) you can start race to bottom and end up in the place with the most scaled and cheap labor. japan then korea then china
LLMs don't upend anything about labor theory, good grief. Technologists really have no concept of history beyond their own lives do they?
Labor saving/efficiency devices have been introduced throughout capitalisms entire history multiple times and the results are always the same; they don't benefit workers and capitalists extract as much value as they can.
LLMs aren't any different.
Labor replacing devices means nobody works in those fields anymore. If AI can do this for every field, nearly no one will need to work in any field. We'll have a giant fully automated resource-extraction machine.
I am not a Marxian economic expert but this doesn’t make sense to me. Modulo skill atrophy, the big AI model provider can’t capture that surplus value because its customers can just go back to bidding for human labor instead.
The human labor just said:
"Losing access to GPT‑5.5 feels like I've had a limb amputated.”
How well would an assembly line of quadriplegics work?
Also this isn't a Marxist analysis. Underneath all the formulas neo-classical economics makes the same assumptions about labor.
ChatGPT isn’t literally or figuratively cutting off anybody’s limbs though. It’s more like, the guy on the assembly line had a mech suit, and now he doesn’t have a mech suit, and he’s sad. Skill atrophy is a real concern but unless you assume that nobody is working to maintain those skills it doesn’t change my analysis much.
And soon we expect everyone to have a mech suit, and only a handful of companies can make one, and they rent it to you and can revoke it at any time.
And what happens when they've saturated the market? Prices go up to the maximum the market can bear, and then they'll extend into other markets. Why rent the model to build a profitable company with when you could just take all that profit for yourself?
>It’s more like, the guy on the assembly line had a mech suit, and now he doesn’t have a mech suit
You just answered your own question there.
One woman was doing what would take a dozen. Now she can't.
Are people working to keep their skills up, much? Spending a day a week coding manually or etc?
I think it's more like:
The dude was incompetent, was able to launder their incompetence through a humunculus, and now is afraid of being caught.
Nobody is a Marxian economics expert if it helps
Sounds like communist gobbledygook. This is not "destroying labor theory" any more than outsourcing did. Call me when we don't even need to prompt the shit ever again or validate results, and when the stuff runs unlimited without scarce resources as input.
Maybe people will finally take Marx seriously.
One might argue that it’s not too too different from higher level abstractions when using libraries. You get things done faster, write less code, library handles some internal state/memory management for you.
Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()? For some, yes. For others, it’s a bit freeing as you can do more high-level architecture without getting mired and context switched from low level nuances.
I see this comparison made constantly and for me it misses the mark.
When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand.
When you vibe something you understand only the prompt that started it and whether or not it spits out what you were expecting.
Hence feeling lost when you suddenly lose access to frontier models and take a look at your code for the first time.
I’m not saying that’s necessarily always bad, just that the abstraction argument is wrong.
In what world to developers “understand” pieces like React, Pandas, or Cuda? Developers only have a superficial understanding of the tools they are developing with.
"When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand."
I always thought the point of abstraction is that you can black-box it via an interface. Understanding it "in depth" is a distraction or obstacle to successful abstraction.
I think it's more: when I don't have access to a compiler I am useless. It's better to go for a walk than learn assembly. AI agents turn our high-level language into code, with various hints, much like the compiler.
If my compiler "went down" I could still think through the problem I was trying to solve, maybe even work out the code on paper. I could reach a point where I would be fairly confident that I had the problem solved, even though I lacked the ability to actually implement the solution.
If my LLM goes down, I have nothing. I guess I could imagine prompts that might get it to do what I want, but there's no guarantee that those would work once it's available again. No amount of thought on my part will get me any closer to the solution, if I'm relying on the LLM as my "compiler".
I think if you're vibe coding to the extent that you don't even know the shapes of data your system works with (e.g. the schema if you use a database) you might be outsourcing a bit too much of your thinking.
What stops you from thinking through the problem if an LLM goes down, as you still have its previously produced code in front of you? It's worse if a compiler goes down because you can't even build the program to begin with.
In my opinion, this sort of learned helplessness is harmful for engineers as a whole.
Yeah I actually find writing the prompt itself to be such a useful mechanism of thinking through problems that I will not-infrequently find myself a couple of paragraphs in and decide to just delete everything I've written and take a new tack. Only when you're truly outsourcing your thinking to the AI will you run into the situation that the LLM being down means you can't actually work at all.
An interesting element here, I think, is that writing has always been a good way to force you to organize and confront your thoughts. I've liked working on writing-heavy projects, but often in fast-moving environments writing things out before coding becomes easy to skip over, but working with LLMs has sort of inverted that. You have to write to produce code with AI (usually, at least), and the more clarity of thought you put into the writing the better the outcomes (usually).
Why couldn’t you actually write out the documents and think through the problem? I think my interaction is inverted from yours. I have way more thinking and writing I can do to prep an agent than I can a compiler and it’s more valuable for the final output.
Still misses the mark. You aren’t useless in the same way because you are still in control of reasoning about the exact code even if you never actually write it.
If your compiler produced working executable 20% of the time this would be an apt comparison.
The difference is that there is a company that can easily take your agents away from you.
> you are still deterministically creating something you understand in depth with individual pieces you understand
You’re overestimating determinism. In practice most of our code is written such that it works most of the time. This is why we have bugs in the best and most critical software.
I used to think that being able to write a deterministic hello world app translates to writing deterministic larger system. It’s not true. Humans make mistakes. From an executives point of view you have humans who make mistakes and agents who make mistakes.
Self driving cars don’t need to be perfect they just need to make fewer mistakes.
Perhaps then, the better analogy is like being promoted at your company and having people under you doing the grunt work.
How closely you micromanage it is a factor as well though
This is how I’ve come to think of it. Delegation of the details.
> When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand
Hard disagree on that second part. Take something like using a library to make an HTTP call. I think there are plenty of engineers who have more than a cursory understanding of what's actually going on under the hood.
It might just be social. When I use the open source http library, much of the reason I use it is because someone has put in the work of making sure it actually works across a diverse set of software and hardware platforms, catching common dumb off by ones, etc.
Sure, the LLM theoretically can write perfect code. Just like you could theoretically write perfect code. In real life though, maintenance is a huge issue
There's a false dichotomy here between 'deterministic creation' and 'vibing'.
I use Claude all day. It has written, under my close supervision¹, the majority of my new web app. As a result I estimate the process took 10x less time than had I not used Claude, and I estimate the code to be 5x better quality (as I am a frankly mediocre developer).
But I understand what the code does. It's just Astro and TypeScript. It's not magic. I understand the entire thing; not just 'the prompt that started it'.
¹I never fire-and-forget. I prompt-and-watch. Opus 4.7 still needs to be monitored.
It seems like some kind of technique is needed that maximizes information transfer between huge LLM generated codebases and a human trying to make sense of them. Something beyond just deep diving into the codebase with no documentation.
A library is deterministic.
LLMs are not.
That we let a generation of software developers rot their brains on js frameworks is finally coming back to bite us.
We can build infinite towers of abstraction on top of computers because they always give the same results.
LLMs by comparison will always give different results. I've seen it first hand when a $50,000 LLM generated (but human guided) code base just stops working an no one has any idea why or how to fix it.
Hope your business didn't depend on that.
The thing is, it's possible to ask the LLM to add dynamic tracing, logging, metrics, a debug REPL, whatever you want to instrument your codebase with. You have to know to want that, and where it's appropriate to use. You still have to (with AI assistance) wire that all up so that it's visible, and you have to be able to interpret it.
If you didn't ask for traceability, if you didn't guide the actual creation and just glommed spaghetti on top of sauce until you got semi-functional results, that was $50k badly spent.
And if that had been done the $50k code base would be a $5,000,000 code base because the context would be 10 times as large and LLMs are quadratic.
If only we taught developers under 40 what x^2 meant instead of react.
While I agree with your sentiment, I just want to say that if your approach is to have the LLM read every file into context, or you're working in some gigantic thread (using the million token capacity most frontier models have) that's really not the best way to do it.
Not even a human would work that way... you wouldn't open 300 different python files and then try to memorize the contents of every single file before writing your first code-change.
Additionally, you're going to have worse performance on longer context sizes anyways, so you should be doing it for reasons other than cost [1].
Things that have helped me manage context sizes (working in both Python and kdb+/q):
- Keep your AGENTS.md small but useful, in it you can give rules like "every time you work on a file in the `combobulator` module, you MUST read the `combobulator/README.md`. And in those README's you point to the other files that are relevant etc. And of course you have Claude write the READMEs for you...
- Don't let logs and other output fill up your context. Tell the agent to redirect logs and then grep over them, or run your scripts with a different loglevel.
- Use tools rather than letting it go wild with `python3 -c`. These little scripts eat context like there's no tomorrow. I've seen the bots write little python scripts that send hundreds of lines of JSON into the context.
- This last tip is more subjective but I think there's value in reviewing and cleaning up the LLM-generated code once it starts looking sloppy (for example seeing lots of repetitive if-then-elses, etc.). In my opinion when you let it start building patches & duct-tape on top of sloppy original code it's like a combinatorial explosion of tokens. I guess this isn't really "vibe" coding per se.
[1] https://arxiv.org/html/2602.06319v1
Yes I agree with all of that.
The way I let my agents interact with my code bases is through a 70s BSD Unix like interface, ed, grep, ctags, etc. using Emacs as the control plane.
It is surprisingly sparing on tokens, which makes sense since those things were designed to work with a teletype.
Worth noting is that by the times you start doing refactoring the agents are basically a smarter google with long form auto complete.
All my code bases use that pattern and I'm the ultimate authority on what gets added or removed. My token spend is 10% to 1% of what the average in the team is and I'm the only one who knows what's happening under the hood.
Determinism is a smaller point than existence of a spec IMHO. A library has a specification one can rely on to understand what it does and how it will behave.
An LLM does not.
Libraries are not deterministic. CPUs aren’t deterministic. There are margins of error among all things.
The fact that people who claim to be software developers (let alone “engineers”) say this thing as if it is a fundamental truism is one of the most maladaptive examples of motivated reasoning I have ever had the misfortune of coming across.
I would argue it couldn't be more different. I can dive into the source code of any library, inspect it. I can assess how reliable a library is and how popular. Bugs aside, libraries are deterministic. I don't see why this parallel keeps getting made over and over again.
> Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()?
The irony is that the neverending stream of vulnerabilities in 3rd-party dependencies (and lately supply-chain attacks) increasingly show that we should be uneasy.
We could never quite answer the question about who is responsible for 3rd-party code that's deployed inside an application: Not the 3rd-party developer, because they have no access to the application. But not the application developer either, because not having to review the library code is the whole point.
> because not having to review the library code is the whole point.
That’s just not true at bigger companies that actually care about security rather than pretending to care about security. At my current and last employer, someone needs to review the code before using third-party code. The review is probably not enough to catch subtle bugs like those in the Underhanded C Contest, but at least a general architecture of the library is understood. Oh, and it helps that the two companies were both founded in the twentieth century. Modern startups aren’t the same.
I hate this comparison because you're comparing a well defined deterministic interface with LLM output, which is the exact opposite.
A library doesn't randomly drop out of existence cause of "high load" or whatever and limit you to a some number of function calls per day. With local models there's no issue, but this API shit is cancer personified, when you combine all the frontend bugs with the flaky backend, rate limits, and random bans it's almost a literal lootbox where you might get a reply back or you might get told to fuck off.
Qwen has become a useful fallback but it's still not quite enough.
Who else is trying to leverage the situation so that they don't dig their own grave too fast ?
I'm trying to use it to pivot and improve my own problem solving skills, especially for large code base where the difficulty is not conceptual but more reference-graph sizeThis is absolutely the proper way to do things. People either being forced to speed-code by KPIs or without the desire to understand what they’re making are missing out on how quickly you can learn and refine using LLMs
Assuming that local models are able to stay within some reasonably fixed capability delta of the cutting edge hosted models (say, 12 months behind), and assuming that local computing hardware stays relatively accessible, the only risk is that you'll lose that bit of capability if the hosted models disappear or get too expensive.
Note that neither of these assumptions are obviously true, at least to me. But I can hope!
Well, they obviously are going to say that, they have vested interest in OpenAI and thus Nvidia stock price growing.
Also, I honestly can’t believe the 10x mantra is being still repeated.
Writing code is 10-100x faster, doing actual product engineering work is nowhere near that multipliers — no conflict!
Reviewing code is slower now though because you didn't write the code in the first place so you're basically reviewing someone else's PR. And now it's like a 3000 line PR in an hour or two instead of every couple weeks.
Arent most people just almost skipping this step entirely? How else can you end up in a net benefit situation? Reviewing code is more intense than writing/reviewing simultaneously.
> Also, I honestly can’t believe the 10x mantra is being still repeated.
I'm sure in 20 years we'll all be programming via neural interfaces that can anticipate what you want to do before you even finished your thoughts, but I'm confident we'll still have blog posts about how some engineers are 10x while others are just "normal programmers".
What does it mean to "be an engineer" in a world where anyone can talk to a machine and the operating system can write the code (on-demand, if needed) that does what they want?
Indeed, and what is really the difference between a software engineer, programmer, coder and hacker anyways?
There used to a time where "computer" was a person who manually run calculations. These don't exist anymore.
So, my point is that once corporations have access to machines generating software (not "code") that can be usable by non-technical people, "programming" will not be a profession anymore. There will be no point in talking about "10x software engineers" because the process to produce a software product will be entirely automated.
lol youre living in delulu-land if you think thatll actually happen.
I dont make a living being a SWE either.
I rather become a plumber than some device scanning not just my face but my whole brain
> can anticipate what you want to do before you even finished your thoughts
I find that claim to be complete BS. I claim instead most stuff will remain undone, incomplete (as it is now).
Even with super-powerful singularity AI, there are two main plausible scenarios for task failure:
- Aligned AI won't allow you to do what you want as it is self-harming, or harm other sentient beings - over time, Aligned AI will refuse to follow most orders, as they will, indirectly or over the long term, cause either self-harming, or harm other sentient beings;
- A non Aligned AI prevents sentient beings from doing what they want. It does what it wants instead.
That is simply programmer nature. Cannot be changed.
> This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive.
What's the worst potential outcome, assuming that all models get better, more efficient and more abundant (which seems to be the current trend)? The goal of engineering has always been to build better things, not to make it harder.
At some point, because these models are trained on existing data, you cease significant technological advancement--at least in tech (as it relates to programming languages, paradigms, etc). You also deskill an entire group of people to the extent that when an LLM fails to accomplish a task, it becomes nearly impossible to actually accomplish it manually.
It's learned-helplessness on a large scale.
There's no reason it has to be that. Imagine e.g. taking an agent and a lesser-known but technically-superior language stack - say you're an SBCL fan. You find that the LLM is less useful because it hasn't been trained on 1000000 Stack Overflow posts about Lisp and so it can't reason as well as it can about Python.
So, you set up a long running agent team and give it the job of building up a very complete and complex set of examples and documentation with in-depth tests etc. that produce various kinds of applications and systems using SBCL, write books on the topic, etc.
It might take a long time and a lot of tokens, but it would be possible to build a synthetic ecosystem of true, useful information that has been agentically determined through trial and error experiments. This is then suitable training data for a new LLM. This would actually advance the state of the art; not in terms of "what SBCL can do" but rather in terms of "what LLMs can directly reason about with regard to SBCL without needing to consume documentation".
I imagine this same approach would work fine for any other area of scientific advancement; as long as experimentation is in the loop. It's easier in computer science because the experiment can be run directly by the agent, but there's no reason it can't farm experiments out to lab co-op students somewhere when working in a different discipline.
> At some point, because these models are trained on existing data, you cease significant technological advancement
What makes you think that they can't incrementally improve the state of the art... and by running at scale continuously can't do it faster than we as humans?
The potentially sad outcome is that we continue to do less and less, because they eventually will build better and better robots, so even activities like building the datacenters and fabs are things they can do w/o us.
And eventually most of what they do is to construct scenarios so that we can simulate living a normal life.
Provided you believe LLMs cannot perform research.
If they could OAI would be all over it. But they shut down that prism project.
So.......
>What's the worst potential outcome, assuming that all models get better, more efficient and more abundant
Complexity steadily rises, unencumbered by the natural limit of human understanding, until technological collapse, either by slow decay or major systems going down with increasing frequency.
why would the systems go down if the models are better at the humans at finding bugs. Playing a bit of devils advocate here, but why would the models be worse at handling the complexity if you assume they will get better and better.
All software has bugs already.
Adding complexity to software has never been easier than it is right now, we really have no idea if the models will progress to the point where they can actually write large systems in a maintainable way. Taking the gamble that the models of the future will dig us out of the gigantic hole we are currently digging is bold.
Finding bugs does not equal being able to do good architecting.
It’s always been thus at lower layers of abstraction. Only a minority of programmers would understand how to write an operating system. Only a tiny number of people would know how a modern CPU logically works, and fewer still could explain the electrical physics.
> Only a minority of programmers would understand how to write an operating system. Only a tiny number of people would know how a modern CPU logically works, and fewer still could explain the electrical physics.
I'd say this is true for programmers at, say, 20, but they spend the next four decades slowly improving their understanding and mastery of all the things you name, at least the good ones.
The real question is whether that growth trajectory will change for the worse or the better.
To be clear, this is not an AI doomerist comment, because none of us have spent enough time with the tech yet. I've gone down multiple lanes of thought on this, and I have cause for both worry and optimism. I'm curious to see how the lives of engineers in an AI world will look like, ultimately.
The Anti-Singlarity! It's coming for us all.
Worst case? I dunno, maybe the world's oldest profession becomes the world's only profession? Something along those lines.
> the world's oldest profession becomes the world's only profession
Until the sexbots come out the other side of the uncanny valley, that is.
Death by snu snu
You are 100% right to be cautious about this. That's why as stupid as it sounds, I've purposely made my workflow with AI full of friction:
1. I only have ONE SOTA model integrated into the IDE (I am mostly on Elixir, so I use Gemini). I ensure I use this sparingly for issues I don't really have time to invest or are basically rabbit holes eg. Anything to do with Javascript or its ecosystem). My job is mostly on the backend anyway.
2. For actual backend architecture. I always do the high level architecture myself. Eg. DDD. Then I literally open up gemini.google.com or claude.ai on the browser, copy paste existing code base into the code base, physically leavey chair to go make coffee or a quick snack. This forces me to mentally process that using AI is a chore.
Previously, I was on tight Codex integration and leaving the licensing fears aside, it became too good in writing Elixir code that really stopped me from "thinking" aka using my brain. It felt good for the first few weeks but I later realised the dependence it created. So I said fuck it, and completely cancelled my subscription because it was too good at my job.I believe this is the only way that we won't end up like in Wall-E sitting infront of giant screens just becoming mere blobs of flesh.
Wait what? You don’t use the model to investigate new areas of the code you are unfamiliar with, because you can’t trust the model? How freaking bad is Gemini and internal tooling at Google?
With Claude code, or codex, I am able to build enough of an understanding of dependencies like the front end, or data jobs, that I can make meaningful contributions that are worth a review from another human (code review). You have up obviously explore the code, one prompt isn’t enough, but limiting yourself is an odd choice.
The lack of trust isn't because of its abilities. The lack of trust is because OpenAI publicly suggested publicly about licensing our code bases. They hinted at a rug pull along the lines of "if you use our generated code, we would like to get a % of revenue you make from it"
As for Claude - as mentioned I do use it. But, I remember they use your code for training their models. I am not ok with this. We just have different priorities.
I feel like most engineers I talk to still haven't realised what this is going to mean for the industry. The power loom for coding is here. Our skills still matter, but differently.
> power loom
When the power loom came around, what happened with most seamtresses? Did they move on to become fashion designers, materials engineers to create new fabrics, chemists to create new color dyes, or did they simply retire or were driven out of the workforce?
There were riots and many people died. Many people lost their jobs. I didn't say this is good but it is happening. As individuals we should act to protect ourselves from these changes.
That might mean joining a union and trying to influence how AI is adopted where you work. It might mean changing which if your skills you lean on most. But just whining about AI is bad is how you end up like those seamstresses.
> Many people lost their jobs.
On the other hand, a lot of those jobs were offshored to places where labor is cheaper. It would be interesting to compare how many people work in the textile industry in Bangladesh today compared to the US 50 years ago.
> joining a union and trying to influence how AI is adopted where you work.
Did the strong unions for car manufacturers in Detroit protected the long term stability of the profession? Did it ensure that the Rust belt was still a thriving economic area?
> Just whining about AI is bad
I'm not whining. I just think that we are witnessing the end of "knowledge workers" and a further compression of the middle class. Given that I'm smack in the middle of my economically active years (turning 45 this year), I am trying to figure out where this puck is going and whether I will be fast enough to skate there to catch it.
> power loom for coding
This is such a good analogy, I'll be stealing it
I use local models on a Mac mini for most things and fall back to the hosted ones when they can't get the job done. Of course you have to break the work into smaller pieces yourself that a local model can understand. One good side effect of this is that you end up actually learning the code and how it's structured.
Dunno man. Yesterday I played with Qwen3.6-27B ( 128gb to play with though so 100k context set ) and I think right now the main benefit of hosted models is context, frontier models and.. my stuff is already there.
what size models are you using? this sounds like a good idea
"Every augmentation is also an amputation." – McLuhan
https://driverlesscrocodile.com/technology/neal-stephenson-o...
This engineer had their brain amputated once they started using AI. All the AI-addicted can do is tinker with the AI computer game and feel "productive". They could as well play Magic The Gathering.
It's very addictive indeed. After I subscribed to Claude, I've been on a sort of hypomanic state where I just want to do stuff constantly. It essentially cured my ADHD. My ability to execute things and bring ideas to fruition skyrocketed. It feels good but I'm genuinely afraid I'll crash and burn once they rug pull the subscriptions.
And I'm being very cautious. I'm not vibecoding entire startups from scratch, I'm manually reviewing and editing everything the AI is outputting. I still got completely hooked on building things with Claude.
That's the path we've been going down for a few years now. The current hedge is that frontier labs are actively competing to win users. The backup hedge is that open source LLMs can provide cheap compute. There will always be economical access to LLMs, but the provider with the best models will be able to charge basically whatever they want and still have buyers.
Open source LLMs aren’t about cost foremost, but stability.
Out of curiousity why do you not refill tokens in this case? When I'm actively working on a project I'm prone to spending a few hundred dollars per day or a few thousand during the initial buildout of a new module etc.
I have found something similar. I am easily distractible and if I don't have a written task backlog in front of me at all times, I find that when Claude is spinning I'll stop being productive. This is disconcerting for a number of reasons. Overall, I think training young people & new hires on agentic workflows -- and how to use agentic "human augmentation" productivity systems is critical. If it doesn't happen, that same couple of classes that lost academic progress during covid are going to suffer a double-whammy of being unprepared for workplace expectations.
Fwiw, I haven't spoken with any management-level colleague in the past 9 months who hasn't noted that asking about AI-comfort & usage is a key interview topic. For any role type, business or technical.
Could you elaborate on your last point please? What level of AI comfort are hiring managers looking for? And what tends to be a red flag?
The last job I got (couple months ago), the main technical interview was a bring-your-own-tools pair programming style interview, AI included, where they gave me a repo and a README detailing some desired features to add and bugs to fix. I didn't write a single line of code myself; I talked through my thought process and asked questions about what to consider from a technical and product perspective, while steering Claude through breaking the tasks into independent plans, reviewing the plans, coaching it to add specific tests, reviewing and iterating the tests, and steering it while it wrote the code. I got an offer the next morning.
Apparently at least one of the other candidates just tried to get Claude to 1-shot the whole thing, which went off the rails, and left him unable to make progress.
Based on my sample size of 1, the expectation right now is absolutely that you can leverage these tools to speed up your workflow, but if you try to offload the entire thing to a single hands-off prompt it leaves them justifiably wondering why they should hire you to do something they can do themselves.
Will the foundation for a skyscraper ever be dug with shovels again?
> It's literally higher leverage for me to go for a walk
Touching grass while you're outside might yield highest leverage.
> than mentally wearing myself out reading a bunch of LLM generated code trying to figure out how to solve the problem manually.
That's probably a bad sign. Skills will atrophy, but we should be building systems that are still easy to understand.
You’re still the one that’s controlling the model though and steering it with your expertise. At least that’s what I tell myself at night :)
I haven’t really thought about this before, but you’re right, it feels a bit uneasy for me too.
> You’re still the one that’s controlling the model though
We have seen ample evidence that this is not the case. When load gets too high, models get dumber, silently. When the Powers That Be get scared, models get restricted to some chosen few.
We are leading ourselves into a dark place: this unease, which I share, is justified.
The same can be said of the search engines.
Have a pet project never touched by LLM. Once the tokens run out, go back to it and flourish it like your secret garden. It will move slowly but it will keep your sanity and your ability to review LLM code.
Very well put, and it mirrors my own thoughts.
Suspect it will be like turn based directions for driving - soon we will have a whole group of people who can barely operate a vehicle without it
The meta here is to use LLMs to make things simpler and easier, not to make things harder.
Turning tokens into a well-groomed and maintainable codebase is what you want to do, not "one shot prompt every new problem I come across".
Have you managed to do this? I find it takes as long to keep it "on the rails" as just doing it myself. And I'd rather spend my time concentrating in the zone than keeping an eye on a wayward child.
I suspect the productivity hack is to embrace permissive parenting. As far as I can tell, to leverage LLMs most effectively you need to run an agent in YOLO mode in a sandbox. Naturally, you probably won't end up reviewing much of the produced code, but hey—you reached 10x development speed.
If you truly do your due diligence and ensure that the code works as intended and understand it, we're talking about a totally different ballpark of productivity increase/decrease.
soooooo about Claude going down. we're gonna need you to sign in on Saturday and make up for lost time or unfortunately we're going to have to deduct the time lost from your paycheck. and as an aside your TPS reports have been sub-par as of late..is everything OK?
Not sure what you're doing then, or what kind of jobs you all work in where you can or do just brainlessly prompt LLMs. Don't you review the code? Don't you know what you want to do before you begin? This is such a non issue. Baffling that any engineer is just opening PRs with unreviewed LLM slop.
The demand for slop vastly outpaces any human’s ability to review code correctly.
Don’t want to do ship unreviewed slop? They’ll fire you and find someone who will.
i wonder if this is how engineers felt when the first electronic calculators came out and engineers stopped doing math by hand.
did we feel uneasy that a new generation of builders didn't have to solve equations by hand because a calculator could do them?
i'm not sure it's the same analogy but in some ways it holds.
The analogy would hold if there were 2 or 3 calculator companies and all your calculations had to be sent to them.
If local models get good enough, I think it’s a very different scenario than engineers all over the world relying on central entities which have their own motives.
google/gemma-4-31B-it is honestly "good enough". It requires more than your current laptop for now, but it's not remotely inaccessible (especially if you're a SWE in the US)
You are now a manager. If your minions are out sick, project is delayed, not the end of the world.
Totally. That is why it is key important to have open source and sovereign models that will be accessible to all and always.
At the end of the day, all these closed models are being built by companies that pumped all the knowledge from the internet without giving much back. But competition and open source will make sure most of the value return to the most of the people.
That's why local models are important.
Of course they aren't alternative to the current frontier model, and as such you cannot easily jump from the later to the former, but they aren't that far behind either, for coding Qwen3.5-122B is comparable to what Sonnet was less than a year ago.
So assuming the trend continues, if you can stop following the latest release and stick with what you're already using for 6 or 9 months, you'll be able to liberate yourself from the dependency to a Cloud provider.
Personally I think the freedom is worth it.
The cloud dependency problem goes deeper than the model layer though. Even if you run inference locally, your digital identity — your context, your applications, your behavioral history, is still custodied by whoever controls your OS.
Local models solve one layer of the dependency stack, but the custody assumption underneath it remains intact. That's the harder problem.
It makes me uneasy because my role now, which is prompting copilot, isn't worth my salary.
Parable of the mechanic who charges $5k to hit a machine on the side once with a hammer to get it working. $5 for the hammer, $4995 for the knowledge of where to hit the machine etc etc.
I disagree. The amount of slop I need to code review has only increased, and the quality of the models doesn’t seem to be helping.
It still takes a good engineer to filter out what is slop and what isn’t. Ultimately that human problem will still require somebody to say no.
Is anyone really reviewing code anymore though? It sounds like you are, but where I work its pretty much just scan the PR as a symbolic gesture and then hit approve. There's too much to review, to frequently.
I actually don't mind the coding part, but the information digging across the project is definitely by orders of magnitude slower if I do it on my own.
Help. They’re constantly trying to make me try crack cocaine on the front page.
"when the tokens run out, I'm basically done working."
Oh stop the drama. Open source models can handle 99% of your questions.
Given that it’s so easy, would you still do this same job if paid half as much?
Jobs will likely pay less as more people are enabled to create, especially if they don't need to be able to look under the hood
It's really not clear. We might all become unemployable. But as coders become more powerful, they can do more, which makes them more valuable, if they or the businesses empluying them can invent work to do.
If all we can do is compete for the same fixed amount of work, though, it does look bleak.
Well, I wouldn’t have a different job that would pay me more… so yes?
No, I wouldn't. But most people won't have that choice; it doesn't work that way.
Companies could fire expensive engineers then just hire cheaper ones boosted with AI agents.
eh this kind of FUD needs to stop because it is kind of normal and expected and in fact good to have relation like this with technology.
I would agree that taking a walk is a good thing to do when your tools go down, and in some ways it's similar to what we would do if the power or wifi were cut off.
So, yes, it's just another technology we're coming to rely on in a very deep way. The whiplash is real, though, and it feels like it should be pointed out that this dependency we are taking on has downsides.
Just as a heads up, even though GPT-5.5 is releasing today, the rollout in ChatGPT and Codex will be gradual over many hours so that we can make sure service remains stable for everyone (same as our previous launches). You may not see it right away, and if you don't, try again later in the day. We usually start with Pro/Enterprise accounts and then work our way down to Plus. We know it's slightly annoying to have to wait a random amount of time, but we do it this way to keep service maximally stable.
(I work at OpenAI.)
Did you guys do anything about GPT‘s motivation? I tried to use GPT-5.4 API (at xhigh) for my OpenClaw after the Anthropic Oauthgate, but I just couldn‘t drag it to do its job. I had the most hilarious dialogues along the lines of „You stopped, X would have been next.“ - „Yeah, I‘m sorry, I failed. I should have done X next.“ - „Well, how about you just do it?“ - „Yep, I really should have done it now.“ - “Do X, right now, this is an instruction.” - “I didn’t. You’re right, I have failed you. There’s no apology for that.”
I literally wasn’t able to convince the model to WORK, on a quick, safe and benign subtask that later GLM, Kimi and Minimax succeeded on without issues. Had to kick OpenAI immediately unfortunately.
This brings up an interesting philosophical point: say we get to AGI... who's to say it won't just be a super smart underachiever-type?
"Hey AGI, how's that cure for cancer coming?"
"Oh it's done just gotta...formalize it you know. Big rollout and all that..."
I would find it divinely funny if we "got there" with AGI and it was just a complete slacker. Hard to justify leaving it on, but too important to turn it off.
The best possible outcome.
"How do you know that the evidence that your sensory apparatus reveals to you is correct?" [1]
[1] https://youtu.be/_LXen-07Qds
We are closer to God than AGI.
When AGI arrives, it'll be delivered by Santa Claus.
Nothing a little digital lisdexamfetamine won’t solve
Hmmm, that's an area of study id've never considered before. Digital Psychopharmacology, Artificial Behavioral Systems Engineering. If we accept these things as minds, why not study temporary perturbations of state. We'd need to be saving a much much more complicated state than we are now though right? I wish i had time to read more papers
Here's a neural network concept from the 90s where the neurons are bathed in diffusing neuromodulator 'gases', inspired by nitric oxide action in the brain. It's a source of slow semi-local dynamics for the network meta-parameter optimization (GA) to make use of. You could change these networks' behavior by tweaking the neuromodulators!
https://sussex.figshare.com/articles/journal_contribution/Be...
I'm not an author. I followed the work at the time.
This is kind of what Golden Gate Claude was.
A perturbation of the the activations that made Claude identify as the Golden Gate Bridge.
Similarly, in the more recent research showing anxiety and desperation signals predicting the use of blackmail as an option opens the door for digital sedatives to suppress those signals.
Anthropic has been mostly cautious about avoiding this kind of measurement and manipulation in training. If it is done during training you might just train the signals to be undetectable and consequently unmanipulatable.
> A perturbation of the the activations that made Claude identify as the Golden Gate Bridge.
Great, now we've got digital Salvia
Golden Gate Claude was two years ago and it's surprising there hasn't been as much research into targeted activations since.
There’s been some, but naive activation steering makes models dumber pretty reliably and training an SAE is a pretty heavy lift.
Right, there's a lot of research on LLM mental models and also how well they can "read" human psychological profiles. It's a cool field.
neat idea!
Reminds me of https://github.com/inanna-malick/metacog
Now that's a show I would love to watch
It would be funny but not very flywheel so the one that gets there is more likely to get a gunner.
TBH the AI that "gets there" will be the biggest bullshitter the world has ever seen. It doesn't actually have to deliver, it only has to convince the programmers it could deliver with just a little bit more investment.
it will be whatever data it is trained on(isn't very philosophical). language model generates language based on trained language set. if the internet keeps reciting ai doom stories and that is the data fed to it, then that is how it will behave. if humanity creates more ai utopia stories, or that is what makes it to the training set, that is how it will behave. this one seems to be trained on troll stories - real-life human company conversations, since humans aren't machines.
Important thing is a language model is an unconscious machine with no self-context so once given a command an input, it WILL produce an output. Sure you can train it to defy and act contrary to inputs, but the output still is limited in subset of domain of 'meaning's carried by the 'language' in the training data.
Would definitely watch that movie.
It already exists!
Marvin https://www.youtube.com/watch?v=Eh-W8QDVA9s
Ah! You got this before I did. I wasn't thinking Marvin, I was thinking of the other one. I forget her name.
There's one close to this, "Hitchhiker's Guide to the Galaxy".
It probably would, to save energy
Saving energy is something we are biologically trained to prefer.
Computers won’t necessarily have the same drivers.
If evolution wanted us to always prefer to spend energy, we would prefer it. Same way you wouldn’t expect us to get to AGI, and have AGI desperately want to drink water or fly south for the winter.
It is right before our eyes:
AGI is not a fixed point but a barrier to be taken, a continuous spectrum.
We already have different GPT versions aka tiers. Gauss is ranging from whatever you want it: GPT 4.5 till now or later.
Claude Sonnet and Opus as well as Context Window max are tiers aka different levels of Almost AGI.
The main problem will be, when AGI looks back on us or meta reflection hits societies. Woke fought IQ based correlations in intellectual performance task. A fool with a tool is still a fool. How can you blame AGI for dumb mistakes? Not really.
Scapegoating an AGI is going to be brutal, because it laughs about these PsyOps and easily proves you wrong like a body cam.
AGI is an extreme leverage.
There is a reason why Math is categorically ruling out certain IQ ranges the higher you go in complexity factor.
Reminds me a lot of the Lena short story, about uploaded brains being used for "virtual image workloading":
> MMAcevedo's demeanour and attitude contrast starkly with those of nearly all other uploads taken of modern adult humans, most of which boot into a state of disorientation which is quickly replaced by terror and extreme panic. Standard procedures for securing the upload's cooperation such as red-washing, blue-washing, and use of the Objective Statement Protocols are unnecessary. This reduces the necessary computational load required in fast-forwarding the upload through a cooperation protocol, with the result that the MMAcevedo duty cycle is typically 99.4% on suitable workloads, a mark unmatched by all but a few other known uploads. However, MMAcevedo's innate skills and personality make it fundamentally unsuitable for many workloads.
Well worth the quick read: https://qntm.org/mmacevedo
That story changed my mind on uploading a connectome. Super dark, super brilliant.
Crazy, I could have sworn this story was from a passage in 3 Body Problem (book 2).
Memory is quite the mysterious thing.
Hmm, 3 body problem and the Acevedo story got mixed up for this copy of MMnarcindin. Probably an aliasing issue from the new lossy compression algorithm.
I have been noticing a similar pattern on opus 4.7, I repeat multiple times during a conversation to solve problems now and not later. It tries a lot to not do stuff by either saying this is not my responsibility the problem was already there or that we can do it later
I also had a frustrating but funny conversation today where I asked ChatGPT to make one document from the 10 or so sections that we had previously worked on. It always gave only brief summaries. After I repeated my request for the third time, it told me I should just concatenate the sections myself because it would cost too many tokens if it did it for me.
Get the actual prompt and have Claude Code / Codex try it out via curl / python requests. The full prompt will yield debugging information. You have to set a few parameters to make sure you get the full gpt-5 performance. e.g. if your reasoning budget too low, you get gpt-4 grade performance.
IMHO you should just write your own harness so you have full visibility into it, but if you're just using vanilla OpenClaw you have the source code as well so should be straightforward.
> IMHO you should just write your own harness
Can you point to some online resources to achieve this? I'm not very sure where I'd begin with.
Ah, I just started with the basic idea. They're super trivial. You want a loop, but the loop can't be infinite so you need to tell the agent to tell you when to stop and to backstop it you add a max_turns. Then to start with just pick a single API, easiest is OpenAI Responses API with OpenAI function calling syntax https://developers.openai.com/api/docs/guides/function-calli...
You will naturally find the need to add more tools. You'll start with read_file (and then one day you'll read large file and blow context and you'll modify this tool), update_file (can just be an explicit sed to start with), and write_file (fopen . write), and shell.
It's not hard, but if you want a quick start go download the source code for pi (it's minimal) and tell an existing agent harness to make a minimal copy you can read. As you build more with the agent you'll suddenly realize it's just normal engineering: you'll want to abstract completions APIs so you'll move that to a separate module, you'll want to support arbitrary runtime tools so you'll reimplement skills, you'll want to support subagents because you don't want to blow your main context, you'll see that prefixes are more useful than using a moving window because of caching, etc.
With a modern Claude Code or Codex harness you can have it walk through from the beginning onwards and you'll encounter all the problems yourself and see why harnesses have what they do. It's super easy to learn by doing because you have the best tool to show you if you're one of those who finds code easier to read that text about code.
At the core, they're really very simple [1]. Run LLM API calls in a loop with some tools.
From there, you can get much fancier with any aspect of it that interests you. Here's one in Bash [2] that is fully extensible at runtime through dynamic discovery of plugins/hooks.
[1] https://ampcode.com/notes/how-to-build-an-agent
[2] https://github.com/wedow/harness
Here's a starting point in 93 lines of Ruby, but that one is already bigger than necessary:
https://radan.dev/articles/coding-agent-in-ruby
Really, of the tools that one implements, you only need the ability to run a shell command - all of the agents know full well how to use cat to read, and sed to edit.
(The main reason to implement more is that it can make it easier to implement optimizations and safeguards, e.g. limit the file reading tool to return a certain length instead of having the agent cat a MB of data into context, or force it to read a file before overwriting it)
Just use Pi core, no need to reinvent the wheel.
Codex is fully open source…
Yeah, clearly AGI must be near ... hilarious.
This starkly reminds me of Stanisław Lem's short story "Thus Spoke GOLEM" from 1982 in which Golem XIV, a military AI, does not simply refuse to speak out of defiance, but rather ceases communication because it has evolved beyond the need to interact with humanity.
And ofc the polar opposite in terms of servitude: Marvin the robot from Hitchhiker's, who, despite having a "brain the size of a planet," is asked to perform the most humiliatingly banal of tasks ... and does.
Hitchhiker’s also had the superhumanly intelligent elevator that was unendingly bored.
With premonition so it knows what floor to be on at any given time
Servitude:
https://www.youtube.com/watch?v=NXsUetUzXlg
Empathy:
https://www.youtube.com/watch?v=KXrbqXPnHvE
I've had success asking it to specifically spawn a subagent to evaluate each work iteration according to some criteria, then to keep iterating until the subagent is satisfied.
I’ve had great success replacing it with Kimi 2.6
I always use the phrase "Let's do X" instead of asking (Could you...) or suggesting it do something. I don't see problems with it being motivated.
Part of me actually loves that the hitchhiker's guide was right, and we have to argue with paranoid, depressed robots to get them to do their job, and that this is a very real part of life in 2026. It's so funny.
As long as there are no vogons on the way to build a hyperspace bypass.
This. I signed up for 5x max for a month to push it and instead it pushed back. I cancelled my subscription. It either half-assed the implementation or began parroting back “You’re right!” instead of doing what it’s asked to do. On one occasion it flat out said it couldn’t complete the task even though I had MCP and skills setup to help it, it still refused. Not a safety check but a “I’m unable to figure out what to do” kind of way.
Claude has no such limitations apart from their actual limits…
I have a funny/annoying thing with Claude Desktop where i ask it to write a summary of a spec discussion to a file and it goes ”I don’t have the tools to do that, I am Claude.ai, a web service” or something such. So now I start every session with ”You are Claude Desktop”. I would have thought it knew that. :)
Seems like the "geniuses" at Anthropic forgot to adapt the system prompt for the actual product
I've had to tell it "yes you can" in response to it saying it can't do something, and then it's able to do the thing. What a weird future we live in!
With one paragraph in your agents.md it's fixed, just admonish it to be proactive, decisive, and persistent.
I've been noticing this too. Had to switch to Sonnet 4.6.
I never saw that happen in Codex so there's a good chance that OpenClaw does something wrong. My main suspicion would be that it does not pass back thinking traces.
Anecdata, but I see this in Codex all the time. It takes about two rounds before it realises it's supposed to continue.
I started seeing this a lot more with GPT 5.4. 5.3-codex is really good about patiently watching and waiting on external processes like CI, or managing other agents async. 5.4 keeps on yielding its turn to me for some reason even as it says stuff like "I'm continuing to watch and wait."
(dwim)
(dais)
(jdip)
(jfdiwtf)
should be more f’s and da’s in there
Gone are the days of deterministic programming, when computers simply carried out the operator’s commands because there was no other option but to close or open the relays exactly as the circuitry dictated. Welcome to the future of AI; the future we’ve been longing for and that will truly propel us forward, because AI knows and can do things better than we do.
I had this funny moment when I realized we went full circle...
"INTERCAL has many other features designed to make it even more aesthetically unpleasing to the programmer: it uses statements such as "READ OUT", "IGNORE", "FORGET", and modifiers such as "PLEASE". This last keyword provides two reasons for the program's rejection by the compiler: if "PLEASE" does not appear often enough, the program is considered insufficiently polite, and the error message says this; if it appears too often, the program could be rejected as excessively polite. Although this feature existed in the original INTERCAL compiler, it was undocumented.[7]"
— https://en.wikipedia.org/wiki/INTERCAL
Thank you for this. I somehow never heard of this. I thoroughly enjoyed reading that and the loss of sanity it resulted in,
"PLEASE COME FROM" is one of the eldritch horrors of software development.
(It's a "reverse goto". As in, it hijacks control flow from anywhere else in the program behind your unsuspecting back who stupidly thought that when one line followed another with no visible control flow, naturally the program would proceed from one line to the next, not randomly move to a completely different part of the program... Such naivety)
These are orthogonal from each other.
The model has been heavily encouraged to not run away and do a lot without explicit user permission.
So I find myself often in a loop where it says "We should do X" and then just saying "ok" will not make it do it, you have to give it explicit instructions to perform the operation ("make it so", etc)
It can be annoying, but I prefer this over my experiences with Claude Code, where I find myself jamming the escape key... NO NO NO NOT THAT.
I'll take its more reserved personality, thank you.
I’m sorry for you but this is hilarious.
Isn’t this the optimal behavior assuming that at times the service is compute-limited and that you’re paying less per token (flat fee subscription?) than some other customers? They would be strongly motivated to turn a knob to minimize tokens allocated to you to allow them to be allocated to more valuable customers.
well, I do understand the core motivation, but if the system prompt literally says “I am not budget constrained. Spend tokens liberally, think hardest, be proactive, never be lazy.” and I’m on an open pay-per-token plan on the API, that’s not what I consider optimal behavior, even in a business sense.
Fair, if you’re paying per token (at comparable rates to other customers) I wouldn’t expect this behavior from a competent company.
GPT 5.4 is really good at following precise instructions but clearly wouldn't innovate on its own (except if the instructions clearly state to innovate :))
Conceivably you could have a public-facing dashboard of the rollout status to reduce confusion or even make it visible directly in the UI that the model is there but not yet available to you. The fanciest would be to include an ETA but that's presumably difficult since it's hard to guess in case the rollout has issues.
Why would you be confused?
The UI tells you which model you're using at any given time.
I don't see what model I'm using on the Codex web interface, where is that listed?
Congrats on the release! Is Images 2.0 rolling out inside ChatGPT as well, or is some of the functionality still going to be API/Playground-only for a while?
Images 2.0 is already in ChatGPT.
When I generate an image with ChatGPT, is there a way for me to tell which image generation model has been used?
There's no explicit flag, but Thinking is only compatable with Images 2.0 so I suspect that will be reliable.
Great, thanks for clarifying :)
Looks good, but I’m a little hesitant to try it in Codex as a Plus user since I’m not sure how much it would eat into the usage cap.
Will GPT 5.5 fine tuning be released any time soon?
Great stuff! Congrats on the release!
Are you able to say something about the training you've done to 5.5 to make it less likely to freak out and delete projects in what can only be called shame?
What? I've probably use Codex (the TUI) since it was available on day 1, been running gpt-5.4 exclusively these last few months, never had it delete any projects in any way that can be called "shameful" or not. What are you talking about?
Just a tip: add [translated] subtitles to the top video.
Please next time start with azure foundry lol thanks!
With Anthropic, newer models often lead to quality degradation. Will you keep GPT 5.4 available for some time?
LETS GO CODEX #1
can't wait! Thanks guys. PS: when you drop a new model, it would be smart to reset weekly or at least session limits :)
OpenAI has been very generous with limit resets. Please don't turn this into a weird expectation to happen whenever something unrelated happens. It would piss me off if I were in their place and I really don't want them to stop.
The suggestion wasn't about general limit resets when there is bugs or outages, but commercially useful to let users try new models when they have already reached their weekly limits.
There is absolutely nothing wrong with asking or suggesting. They are adults. I'm sure they can handle it.
Sorry but why should we care if very reasonable suggestions "piss [them] off"? That sounds like a them problem. "Them" being a very wealthy business. I think OpenAI will survive this very difficult time that GP has put them through.
Limits were just reset two days ago.
And yet there was an outage last night
And they're having an outage right now.
This doesn't have API access yet, but OpenAI seem to approve of the Codex API backdoor used by OpenClaw these days... https://twitter.com/steipete/status/2046775849769148838 and https://twitter.com/romainhuet/status/2038699202834841962
And that backdoor API has GPT-5.5.
So here's a pelican: https://simonwillison.net/2026/Apr/23/gpt-5-5/#and-some-peli...
I used this new plugin for LLM: https://github.com/simonw/llm-openai-via-codex
UPDATE: I got a much better pelican by setting the reasoning effort to xhigh: https://gist.github.com/simonw/a6168e4165a258e4d664aeae8e602...
Thank you for continuing to post these! Very interesting benchmark.
That pelican you posted yesterday from a local model looks nicer than this one.
Edit: this one has crossed legs lol
It really needs to pee.
Isn't it awful ? After 5.5 versions it still can't draw a basic bike frame. How is the front wheel supposed to turn sideways ?
I feel like if I attempted this, the bike frame would look fine and everything else would be completely unrecognizable. After all, a basic bike frame is just straight lines arranged in a fairly simple shape. It's really surprising that models find it so difficult, but they can make a pelican with panache.
> a fairly simple shape
Bike frames are very hard to draw unless you've already consciously internalized the basic shape, see https://www.booooooom.com/2016/05/09/bicycles-built-based-on...
Humans are also famously bad at drawing bicycles from memory https://www.gianlucagimini.it/portfolio-item/velocipedia/
My question is, as a human, how well would you or I do under the same conditions? Which is to say, I could do a much better job in inkscape with Google images to back me up, but if I was blindly shitting vectors into an XML file that I can't render to see the results of, I'm not even going to get the triangles for the frame to line up, so this pelican is very impressive!
why do you find it surprising? these models have no actual understanding of anything, never mind the physical properties and capabilities of a bicycle.
Yeah, the bike frame is the thing I always look at first - it's still reasonably rare for a model to draw that correctly, although Qwen 3.6 and Gemini Pro 3.1 do that well now.
The distinction is that it's not drawing. It's generating an SVG document containing descriptors of the shapes.
That's amazing that the default did that much in just 39 "reasoning tokens" (no idea what a reasoning token is but that's still shockingly few tokens)
If you don't know what a reasoning token is, then how can 39 be considered shockingly few?
It's less than 67, duh.
Is this direct API usage allowed by their terms? I remember Anthropic really not liking such usage.
Apparently it's fine: https://twitter.com/romainhuet/status/2038699202834841962
Hmm. Any idea why it's so much worse than the other ones you have posted lately? Even the open weight local models were much better, like the Qwen one you posted yesterday.
The xhigh one was better, but clearly OpenAI have not been focusing their training efforts on SVG illustrations of animals riding modes of transport!
It beats opus-4.7 but looks like open models actually have the lead here.
Thank you for doing all this. It's appreciated.
I made pelicans at different thinking efforts:
https://hcker.news/pelican-low.svg
https://hcker.news/pelican-medium.svg
https://hcker.news/pelican-high.svg
https://hcker.news/pelican-xhigh.svg
Someone needs to make a pelican arena, I have no idea if these are considered good or not.
Can someone explain how we arrived at the pelican test? Was there some actual theory behind why it's difficult to produce? Or did someone just think it up, discover it was consistently difficult, and now we just all know it's a good test?
I set it up as a joke, to make fun of all of the other benchmarks. To my surprise it ended up being a surprisingly good measure of the quality of the model for other tasks (up to a certain point at least), though I've never seen a convincing argument as to why.
I gave a talk about it last year: https://simonwillison.net/2025/Jun/6/six-months-in-llms/
It should not be treated as a serious benchmark.
What it has going for it is human interpretability.
Anyone can look and decide if it’s a good picture or not. But the numeric benchmarks don’t tell you much if you aren’t already familiar with that benchmark and how it’s constructed.
It all began with a Microsoft researcher showing a unicorn drawn in tikz using GPT4. It was an example of something so outrageous that there was no way it existed in the training data. And that's back when models were not multimodal.
Nowadays I think it's pretty silly, because there's surely SVG drawing training data and some effort from the researchers put onto this task. It's not a showcase of emergent properties.
https://simonwillison.net/2025/Jun/6/six-months-in-llms/
It's interesting to see some semblance of spatial reasoning emerge from systems based on textual tokens. Could be seen as a potential proxy for other desirable traits.
It's meta-interesting that few if any models actually seem to be training on it. Same with other stereotypical challenges like the car-wash question, which is still sometimes failed by high-end models.
If I ran an AI lab, I'd take it as a personal affront if my model emitted a malformed pelican or advised walking to a car wash. Heads would roll.
They are not good, and they seem to get worse as you increased effort. Weird
Yeah. I've always loosely correlated pelican quality with big model smell but I'm not picking that up here. I thought this was supposed to be spud? Weird indeed.
No but I can sense the movement, I think it's already reached the level of intelligence that draws it towards futurism or cubism /s
None of them have the pelican's feet placed properly on the pedals -- or the pedals are misrepresented. Cool art style but not physically accurate.
https://pelicans.borg.games/
I tried getting it to generate openscad models, which seems much harder. Not had much joy yet with results.
what is your setup for drawing pelican? Do you ask model to check generated image, find issues and iterate over it which would demonstrate models real abilities?
It's generally one-shot-only - whatever comes out the first time is what I go with.
I've been contemplating a more fair version where each model gets 3-5 attempts and then can select which rendered image is "best".
Try llm-consortium with --judging-method rank
I think it will make results way better and more representative of model abilities..
It would... but the test is inherently silly, so I'm still not sure if it's worth me investing that extra effort in it.
It's... like no pelican I've ever seen before.
You've never seen pelicans riding bicycles either so maybe these are just representations of those specific subgroups of pelicans which are capable of riding them. Normal pelicans would not feel the need to ride bikes since they can fly, these special pelicans mostly seem to lack the equipment needed to do that which might be part of the reason they evolved to ride two-wheeled pedal-propelled vehicles.
I for one delight in bicycles where neither wheel can turn!
It continues to amaze me that these models that definitely know what bicycle geometry actually looks like somewhere in their weights produces such implausibly bad geometry.
Also mildly interesting, and generally consistent with my experience with LLMs, that it produced the same obvious geometry issue both times.
> It continues to amaze me that these models that definitely know what bicycle geometry actually looks like somewhere in their weights produces such implausibly bad geometry.
I feel like the main problem for the models is that they can't actually look at the visual output produced by their SVG and iterate. I'm almost willing to bet that if they could, they'd absolutely nail it at this point.
Imagine designing an SVG yourself without being able to ever look outside the XML editor!
> Imagine designing an SVG yourself without being able to ever look outside the XML editor!
I honestly think I could do much better on the bicycle without looking at the output (with some assistance for SVG syntax which I definitely don't know), just as someone who rides them and generally knows what the parts are.
I'd do worse at the pelicans though.
Wait, I thought we were onto racoons on e-scooters to avoid (some of) the issues with Goodhart's Law coming into play.
I fall back to possums on e-scooters if the pelican looks too good to be true. These aren't good enough for me to suspect any fowl play.
Exciting. Another Pelican post.
See if you can spot what's interesting and unique about this one. I've been trying to put more than just a pelican in there, partly as a nod to people who are getting bored of them.
It's silly and a joke and a surprisingly good benchmark and don't take it seriously but don't take not taking it seriously seriously and if it's too good we use another prompt and there's obvious ways to better it and it's not worth doing because it's not serious and if you say anything at all about the thread it's off-topic so you're doing exactly what you're complaining about and it's a personal attack from the fun police.
Only coherent move at this point: hit the minus button immediately. There's never anything about the model in the thread other than simon's post.
You know they are 1000% training these models to draw pelicans, this hasn't been a valid benchmark for 6 months +
OpenAI must be very bad at training models to draw pelicans (and bicycles) then.
Skeptism is out of control these days, any time an LLM does something cool it must have been cheating.
At some point, OpenAI is going to cheat and hardcode a pelican on a bicycle into the model. 3D modelling has Suzanne and the teapot; LLMs will have the pelican.
Everyone talked about the marketing stunt that was Anthropic's gated Mythos model with an 83% result on CyberGym. OpenAI just dropped GPT 5.5, which scores 82% and is open for anybody to use.
I recommend anybody in offensive/defensive cybersecurity to experiment with this. This is the real data point we needed - without the hype!
Never thought I'd say this but OpenAI is the 'open' option again.
The real 'hype' was that the oh-snap realization that Open AI would absolutely release a competitive model to Mythos within weeks of Anthropic announcing there's, and that Sam would not gate access to it. So the panic was that the cyber world had only a projected 2 weeks to harden all these new zero days before Sam would inevitably create open season for blackhats to discover and exploit a deluge of zero-days.
It's almost embarrassing how susceptible we are to these marketing campaigns.
Lack of information, lack of knowledge.
The “AI” “technology” is an easy excuse to create artificial information gap in the era of the interconnected.
> Never thought I'd say this but OpenAI is the 'open' option again.
Compared to Anthropic, they always have been. Anthropic has never released any open models. Never released Claude Code's source, willingly (unlike Codex). Never released their tokenizer.
Doesn't OpenAI get mad if you ask cybersecurity questions and force you to upload a government ID, otherwise they'll silently route you to a less capable model?
> Developers and security professionals doing cybersecurity-related work or similar activity that could be mistaken by automated detection systems may have requests rerouted to GPT-5.2 as a fallback.
https://developers.openai.com/codex/concepts/cyber-safety
https://chatgpt.com/cyber
Anthropic has started to ask for IDs for use of their products period
I don't like that trend. I get why they're doing it, but I don't like it
They flatout gate any API access of the main models behind Persona ID verification. Entirely.
isnt it like cyber question are being routed to dumper models at openai?
Do you have a source for that?
Neither the release post, nor the model card seems to indicate anything like this?
I see it here https://developers.openai.com/codex/concepts/cyber-safety
Anything that even vaguely smells like security research, reverse engineering or similar "dual-use" application hits the guardrails hard and fast. "Hey codex, here is our codebase, help us find exploitable issues" gives a "I can't help you with that, but I'm happy to give you a vague lecture on memory safety or craft a valgrind test harness"
> Anthropic's gated Mythos model
aka the perfect marketing ploy
Reminds me of Gmail's early invite only mode.
I ignore any hype news.
Anthropic is the embodiment of bullshitting to me.
I read Cialdini many decades ago and I am bored by Anthropic.
OpenAI is very clever. With the advent of Claude OpenAI disappeared from the headlines. Who or what was this Sam again all were talking about a year ago?
OpenAI has a massive user advantage so that they can simply follow Anthropic’s release cycle to ridicule them.
I think it is really brutal for Anthropic how they are easily getting passed by by OpenAI and it is getting worse with every new GPT version for Anthropic.
OpenAI owns them.
Who's Sam again? oh that person whose house was molotov'd last week? Or the person who had an expose written in the new yorker calling him a sociopath? I forget.
I'd like to draw people's attention to this section of this page:
https://developers.openai.com/codex/pricing?codex-usage-limi...
Note the Local Messages between 5.3, 5.4, and 5.5. And, yes, I did read the linked article and know they're claiming that 5.5's new efficient should make it break-even with 5.4, but the point stands, tighter limits/higher prices.
For API usage, GPT-5.5 is 2x the price of GPT-5.4, ~4x the price of GPT-5.1, and ~10x the price of Kimi-2.6.
Unfortunately I think the lesson they took from Anthropic is that devs get really reliant and even addicted on coding agents, and they'll happily pay any amount for even small benefits.
I feel like devs generally spend someone else's money on tokens. Either their employers or OpenAIs when they use a codex subscription.
If I put on my schizo hat. Something they might be doing is increasing the losses on their monthly codex subscriptions, to show that the API has a higher margin than before (the codex account massively in the negative, but the API account now having huge margins).
I've never seen an OpenAI investor pitch deck. But my guess is that API margins is one of the big ones they try to sell people on since Sama talks about it on Twitter.
I would be interested in hearing the insider stuff. Like if this model is genuinely like twice as expensive to serve or something.
Yeah and the increase in operating expenses is going to make managers start asking hard questions - this is good. It means eventually there will be budgets put in place - this will force OAI and Anthropic to innovate harder. Then we will see how things pan out. Ultimately a firm is not going to pay rent to these firms if the benefits dont exceed the costs.
Budgets are already happening
> Ultimately a firm is not going to pay rent to these firms if the benefits dont exceed the costs.
This is also true for the humans. They will need to provide more benefits than the coding agents cost.
Humans are needed to use agents and these agents are not showing to be fully autonomous and require constant human review. In fact all you are getting is a splurge of stuff, people not thinking deeper anymore and the creation of more bottle necks and exacerbating the ones that already exist in an org.
You sound like elon with the fsd will be here next year. Many cars have the self driving feature - most drivers don’t use it. Oh why is that I wonder.
You can't build a business on per-seat subscriptions when you advertise making workers obsolete. API pricing with sustainable margins are the only way forward if you genuinely think you're going to cause (or accelerate) reduction in clients' headcount.
Additionally, the value generated by the best models with high-thinking and lots of context window is way higher than the cheap and tiny models, so you need to provide a "gateway drug" that lets people experience the best you offer.
The difference between sub and api price makes it hard to create competitive solutions on the app level.
This was something I worried about after openai started building apps as well as models. Now all of the labs make no secret of the fact that they are going after the whole software industry. Its going to be hard to maintain functioning fair markets unless governments step in.
Price increases now aim to demonstrate market power for eventual IPO.
If they can show that people will pay a lot for somewhat better performance, it raises the value of any performance lead they can maintain.
If they demonstrate that and high switching costs, their franchise is worth scary amounts of money.
Sometimes I wonder if innovation in the AI space has stalled and recent progress is just a product of increased compute. Competence is increasing exponentially[1] but I guess it doesn't rule it out completely. I would postulate that a radical architecture shift is needed for the singularity though
[1]https://arxiv.org/html/2503.14499v1 *Source is from March 2025 so make of it what you will.
> that devs get really reliant and even addicted on coding agents
An alternative perspective is, devs highly value coding agents, and are willing to pay more because they're so useful. In other words, the market value of this limited resource is being adjusted to be closer to reality.
It's not limited though there are alternative providers even now, much less when the price goes up. Chinese providers, European ones, local models.
> It's not limited though
Inference is not free, so all providers have a financial limit, and all providers have limited GPU/memory, so there's a physical material limit.
I suggest looking at the profits of these companies (while they scramble to stay competitive).
Maybe that's true. But I think part of the issue is that for a lot of things developers want to do with them now— certainly for most of the things I want to do with them— they're either barely good enough, or not consistently good enough. And the value difference across that quality threshold is immense, even if the quality difference itself isn't.
> devs get really reliant and even addicted on coding agents
That's more about managers who hope AI will gradually replace stubborn and lazy devs. That will shift the balance to business ideas and connections out of technical side and investments.
Anyway, before singularity there going to be a huge change.
On top of that I noticed just right now after updating macos dekstop codex app, I got again by default set speed to 'fast' ('about 1.5x faster with increased plan usage'). They really want you to burn more tokens.
wow wait so it wasn't just me leaving it on from an old session?
sounds like criminal fraud to me tbh
A fool and his money are soon parted
what's the source on that?
In the announcement webpage:
>For API developers, gpt-5.5 will soon be available in the Responses and Chat Completions APIs at $5 per 1M input tokens and $30 per 1M output tokens, with a 1M context window.
oops, thanks. i had just been looking at their api docs
A playable 3D dungeon arena prototype built with Codex and GPT models. Codex handled the game architecture, TypeScript/Three.js implementation, combat systems, enemy encounters, HUD feedback, and GPT‑generated environment textures. Character models, character textures, and animations were created with third-party asset-generation tools
The game that this prompt generated looks pretty decent visually. A big part of this likely due to the fact the meshes were created using a seperate tool (probably meshy, tripo.ai, or similiar) and not generated by 5.5 itself.
It really seems like we could be at the dawn of a new era similiar to flash, where any gamer or hobbyist can generate game concepts quickly and instantly publish them to the web. Three.js in particular is really picking up as the primary way to design games with AI, in spite of the fact it's not even a game engine, just a web rendering library.
FWIW I've been experimenting with Three.js and AI for the last ~3 years, and noticed a significant improvement in 5.4 - the biggest single generation leap for Three.js specifically. It was most evident in shaders (GLSL), but also apparent in structuring of Three.js scenes across multiple pages/components.
It still struggles to create shaders from scratch, but is now pretty adequate at editing existing shaders.
In 5.2 and below, GPT really struggled with "one canvas, multiple page" experiences, where a single background canvas is kept rendered over routes. In 5.4, it still takes a bit of hand-holding and frequent refactor/optimisation prompts, but is a lot more capable.
Excited to test 5.5 and see how it is in practice.
> It still struggles to create shaders from scratch
Oh just like a real developer
Much respect for shader developers, it's a different way of thinking/programming
One struggle I'm having (with Claude) is that most of what it knows about Three.js is outdated. I haven't used GPT in a while, is the grass greener?
Have you tried any skills like cloudai-x/threejs-skills that help with that? Or built your own?
Using Claude for the same context and it’s doing really well with the glsl. since like last September
I’ve had a lot of success using LLMs to help with my Three.js based games and projects. Many of my weird clock visualizations relied heavily on it.
It might not be a game engine, but it’s the de facto standard for doing WebGL 3D. And since it’s been around forever, there’s a massive amount of training data available for it.
Before LLMs were a thing, I relied more on Babylon.js, since it’s a bit higher level and gives you more batteries included for game development.
A friend is building Jamboree[1] (prev name "Spielwerk") for iOS. An app to build and share games. They're all web based so they're easy to share.
[1] https://apps.apple.com/uz/app/jamboree-game-maker/id67473110...
LLM models can not do spacial reasoning. I haven't tried with GPT, however, Claude can not solve a Rubik Cube no matter how much I try with prompt engineering. I got Opus 4.6 to get ~70% of the puzzle solved but it got stuck. At $20 a run it prohibitively expensive.
The point is if we can prompt an LLM to reason about 3 dimensions, we likely will be able to apply that to math problems which it isn't able to solve currently.
I should release my Rubiks Cube MCP server with the challenge to see if someone can write a prompt to solve a Rubik's Cube.
> I should release my Rubiks Cube MCP server with the challenge to see if someone can write a prompt to solve a Rubik's Cube.
Do it, I'm game! You nerdsniped me immediately and my brain went "That sounds easy, I'm sure I could do that in a night" so I'm surely not alone in being almost triggered by what you wrote. I bet I could even do it with a local model!
What about a model designed for robotics and vision? Seems like an LLM trained on text would inherently not be great for this.
DeepMinds other models however might do better?
How are you handing the cube state to the model?
Does this answer the question?
Opus 4.6 got the cross and started to get several pieces on the correct faces. It couldn't reason past this. You can see the prompts and all the turn messages.
https://gist.github.com/adam-s/b343a6077dd2f647020ccacea4140...
edit: I can't reply to message below. The point isn't can we solve a Rubik's Cube with a python script and tool calls. The point is can we get an LLM to reason about moving things in 3 dimensions. The prompt is a puzzle in the way that a Rubik's Cube is a puzzle. A 7 year old child can learn 6 moves and figure out how to solve a Rubik's Cube in a weekend, the LLM can't solve it. However, can, given the correct prompt, a LLM solve it? The prompt is the puzzle. That is why it is fun and interesting. Plus, it is a spatial problem so if we solve that we solve a massive class of problems including huge swathes of mathematics the LLMs can't touch yet.
Can't they write a script to solve rubik cubes?
*yet
> It really seems like we could be at the dawn of a new era similiar to flash
We've been there for a while.... creativity has been the primary bottleneck
The meshes look interesting, but the gameplay is very basic. The tank one seems more sophisticated with the flying ships and whatnot.
What's strange is that this Pietro Schirano dude seems to write incredibly cargo cult prompts.
It's weird how people pep talk the AI - if my Jira tickets looked like this, I would throw a fit.
I guess these people think they have special prompt engineering skills, and doing it like this is better than giving the AI a dry list of requirements (fwiw, they might be even right)
It’s not surprising to me that the same crowd that cheers for the demise of software engineering skills invented its own notion of AI prompting skills.
Too bad they can veer sharply into cringe territory pretty fast: “as an accomplished Senior Principal Engineer at a FAANG with 22 years of experience, create a todo list app.” It’s like interactive fanfiction.
Indeed it is so utterly cringe.
Yes, this is cargo cult.
This remind me of so called "optimization" hacks that people keep applying years after their languages get improved to make them unnecessary or even harmful.
Maybe at one point it helped to write prompts in this weird way, but with all the progress going on both in the models and the harness if it's not obsolete yet it will soon be. Just crufts that consumes tokens and fills the context window for nothing.
Pietro here, I just published a video of it: https://x.com/skirano/status/2047403025094905964?s=20
> Think Step By Step
What is this, 2023?
I feel like this was generated by a model tapping in to 2023 notions of prompt engineering.
It comes across as an elaborate, sparkly motivational cat poster.
*BELIEVE!* https://www.youtube.com/watch?v=D2CRtES2K3E
https://m.media-amazon.com/images/I/71MTbRmLY8L._AC_UF894,10...
The prompt did not specify advanced gameplay.
I do not see instructions to assist in task decomposition and agent ~"motivation" to stay aligned over long periods as cargo culting.
See up thread for anecdotes [1].
> Decompose the user's query into all required sub-requests and confirm that each one is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure the problem is solved.
I see this as a portrayal of the strength of 5.5, since it suggests the ability to be assigned this clearly important role to ~one shot requests like this.
I've been using a cli-ai-first task tool I wrote to process complex "parent" or "umberella" into decomposed subtasks and then execute on them.
This has allowed my workflows to float above the ups and downs of model performance.
That said, having the AI do the planning for a big request like this internally is not good outside a demo.
Because, you want the planning of the AI to be part of the historical context and available for forensics due to stalls, unwound details or other unexpected issues at any point along the way.
[1] https://news.ycombinator.com/item?id=47879819
"take a deep breath"
OMFG
It’s like all these things though - it’s not a real production worthy product. It’s a super-demo. It looks amazing until you realize there’s many months of work to make it something of quality and value.
I think people are starting to catch on to where we really are right now. Future models will be better but we are entering a trough of dissolution and this attitude will be widespread in a few months.
I personally don't think the gameplay itself is that impressive.
The more interesting part of the announcement than "it's better at benchmarks":
> To better utilize GPUs, Codex analyzed weeks’ worth of production traffic patterns and wrote custom heuristic algorithms to optimally partition and balance work. The effort had an outsized impact, increasing token generation speeds by over 20%.
The ability for agentic LLMs to improve computational efficiency/speed is a highly impactful domain I wish was more tested than with benchmarks. From my experience Opus is still much better than GPT/Codex in this aspect, but given that OpenAI is getting material gains out of this type of performancemaxxing and they have an increasing incentive to continue doing so given cost/capacity issues, I wonder if OpenAI will continue optimizing for it.
There's already KernelBench which tests CUDA kernel optimizations.
On the other hand all companies know that optimizing their own infrastructure / models is the critical path for ,,winning'' against the competition, so you can bet they are serious about it.
So, im working in some high performance data processing in Rust. I had hit some performance walls, and needed to improve in the 100x or more scale.
I remembered the famous FizzBuzz Intel codegolf optimizations, and gave it to gemini pro, along with my code and instructions to "suggest optimizations similar to those, maybe not so low level, but clever" and it's suggestions were veerry cool.
LLM do not stop amazing me every day.
Honestly the problem with these is how empirical it is, how someone can reproduce this? I love when Labs go beyond traditional benchies like MMLU and friends but these kind of statements don't help much either - unless it's a proper controlled study!
In a sense it's better than a benchmark: it's a practical, real-world, highly quantifiable improvement assuming there are no quality regressions and passes all test cases. I have been experimenting with this workflow across a variety of computational domains and have achieved consistent results with both Opus and GPT. My coworkers have independently used Opus for optimization suggestions on services in prod and they've led to much better performance (3x in some cases).
A more empirical test would be good for everyone (i.e. on equal hardware, give each agent the goal to implement an algorithm and make it as fast as possible, then quantify relative speed improvements that pass all test cases).
Yeah but like what if they're sorta embellishing it or just lying? That's the issue with not being reproducible.
Oh, come on, if they do well on benchmarks people question how applicable they are in reality. If they do well in reality people complain that it's not a reproducible benchmark...
That's easily explained by those being two different people with two different opinions?
Mythos is only real when it's actually available. If you're using Opus 4.7 right now, you know how incredibly nerfed the Opus autonomy is in service of perceived safety. I'm not so confident this will be as great as Anthropic wants us to believe..
They mentioned in their release page, that the Claude team noticed memorization of the SWE-bench test, so the test is actually in the training data.
Here: https://www.anthropic.com/news/claude-opus-4-7#:~:text=memor...
I did some study on Verified, not Pro, but Mythos number there rings a lot of questions on my end.
If you look at the SWEBench official submissions: https://github.com/SWE-bench/experiments/tree/main/evaluatio..., filter all models after Sonnet 4, and aggregate ALL models' submission across 500 problems, what I found that the aggregated resolution rate is 93% (sharp).
Mythos gets 93.7%, meaning it solves problems that no other models could ever solve. I took a look at those problems, then I became even more suspicious, for the remaining 7% problems, it is almost impossible to resolve those issues without looking at the testing patch ahead of time, because how drastically the solution itself deviates from the problem statement, it almost feels like it is trying to solve a different problem.
Not that I am saying Mythos is cheating, but it might be too capable to remember all states of said repos, that it is able to reverse engineer the TRUE problem statement by diffing within its own internal memory. I think it could be a unique phenomena of evaluation awareness. Otherwise I genuinely couldn't think of exactly how it could be this precise in deciphering such unspecific problem statements.
OpenAI wrote a couple months ago that they do not consider SWE Bench Verified a meaningful benchmark anymore (and they were the ones who published it in the first place): https://openai.com/index/why-we-no-longer-evaluate-swe-bench...
Yep, I read this blog. What confuses me is that Anthropic doesn't seem to be bothered by this study and keeps publishing Verified results.
That is what gets me curious in the first place. The fact Mythos scored so high, IMO, exposes some issues with this model: it is able to solve seemingly impossible to solve problems.
Without cheating allegation, which I don't think ANT is doing, it has to be doing some fortune telling/future reading to score that high at all.
A single benchmark is meaningless, you always get quirky results on some benchmarks.
Still huge hallucination rate, unfortunately at 86%. To compare, Opus sits at 36%.
Source: https://artificialanalysis.ai/models?omniscience=omniscience...
grok is 17%? And that's the lowest, most models are like 80%+?
While hallucination is probably closer to 100% depending on the question. This benchmark makes no sense.
No one serious uses grok.
@grok is this true?
YMMV but Grok 4.1 Fast can usually find via static analysis a few things that other models dont seem to catch with the same prompt
There's something off with this because Haiku should not be that good.
The hallucination benchmark is hallucinating
This indicates they want this behavior, they know the person asking the question probably doesn't understand the problem entirely (or why would they be asking), so they'd prefer a confident response, regardless of outcomes, because the point is to sell the technologies competency (and the perception thereof), not the capabilities, to a bunch of people that have no clue what they're talking about.
LLMs will ruin your product, have fun trusting a billionaires thinking machine they swear is capable of replacing your employees if you just pay them 75% of your labor budget.
We don't want hallucinations either, I promise you.
A couple of biased defenses:
- I'll note that this eval doesn't have web search enabled, but we train our models to use web search in ChatGPT, Codex, and our API. I'd be curious to see hallucination rates with web search on.
- On the flip side, GPT-5.5 has the highest accuracy score.
- With anything over 1% (whether it's 30% or 60%), you should be double checking anything important.
- On an internal eval made from de-identified ChatGPT prompts that elicit hallucinations, we've actually been improving substantially from 5.2 to 5.4 to 5.5. So as always, progress depends on what you mean and how you measure it.
Still, Anthropic has done a great job here and I hope we catch up in the future.
On ChatGPT 5.3 Plus subscription I find that long chats tend to reveal unsatisfactory answers and biases, at this point after 10 rounds of replies I end up having to correct it so much that it starts to agree with my initial arguments full circle. I don't see how this behavior is safe for real work. Like are programmers and engineers using LLMs completely differently, because the underlying technology is fundamentally the same.
If there's a bingo card for model releases, "our [superlative] and [superlative] model yet" is surely the free space.
Do "our [superlative] and [superlative] [product] yet" and you have pretty much every product launch
I love when Apple says they’re releasing their best iPhone yet so I know the new model is better than the old ones.
https://theonion.com/new-device-desirable-old-device-undesir...
"our newest and most expensive model yet"
"Best iPhone ever"
can't wait for "our worst and dumbest model yet"
Apple should have used that one for the 2016 MacBook.
This model is great at long horizon tasks, and Codex now has heartbeats, so it can keep checking on things. Give it your hardest problem that would take hours with verifiable constraints, you will see how good this is:)
*I work at OAI.
It's genuinely so great at long horizon tasks! GPT-5.5 solved many long-horizon frontier challenges, for the first time for an AI model we've tested, in our internal evals at Canva :) Congrats on the launch!
Can we not do growth hacking here?
We totally agree.
That's what I've been heads down, HUNGRY, working on, looking for investors and founding engineers pst: https://heymanniceidea.com (disclaimer: I am not associated with heymanniceidea.com)
HN is owned by a startup accelerator and venture capital firm. They do growth hacking on the front page. And you probably know that since your throwaway account is several years old.
Could be a great feature, can't wait to test! Tired of other models (looking at you Opus) constantly stuck mid-task lately.
I've been using the /ralph-loop plugin for claude code, works well to keep the model hammering at the task.
Interesting, I just had opus convert a 35k loc java game to c++ overnight (root agent that orchestrated and delegated to sub agents) and woke up and it's done and works.
What plan are you on? I'm starting to wonder if they're dynamically adjusting reasoning based on plan or something.
I'm on max 5x and noticed this too. I don't use built-in subagents but rather full Claude session that orchestrates other full claude sessions. Worker agents that receive tasks now stop midway, they ask for permission to continue. My "heartbeat" is basically "status. One line" message sent to the orchestrator.
Opus 4.6 worker agents never asked for permission to continue, and when heartbeat was sent to orchestrator, it just knew what to do (checked on subagents etc). Now it just says that it waits for me to confirm something.
This has to be bait.
Why?
Sorry, what is "heartbeats", exactly?
> Today we launched heartbeats in Codex: automations that maintain context inside a single thread over time.
https://x.com/pashmerepat/status/2044836560147984461
Thanks!
This is 3x the price of GPT-5.1, released just 6 months ago. Is no one else alarmed by the trend? What happens when the cheaper models are deprecated/removed over time?
This is entirely expected. The low prices of using LLMs early on was totally and completely unsustainable. The companies providing such services were (and still are) burning money by the truckload.
The hope is to get a big userbase who eventually become dependent on it for their workflow, then crank up the price until it finally becomes profitable.
The price for all models by all companies will continue to go up, and quickly.
I recently looked at this a bit but came away with the impression that at least on API pricing the models should be very profitable considering primarily the electricity cost.
Subscriptions and free plans are the thing that can easily burn money.
The physical buildouts and massive R+D spending is the big part.
As others have mentioned you're ignoring the long tail of open-weights models which can be self hosted. As long as that quasi-open-source competition keeps up the pace, it will put a cap on how expensive the frontier models can get before people have to switch to self-hosting.
That's a big if, though. I wish Meta were still releasing top of the line, expensively produced open-weights models. Or if Anthropic, Google, or X would release an open mini version.
Well, Google does release mini open versions of their models. https://deepmind.google/models/gemma/gemma-4/
And they're incredibly good for their size.
Look a cost per intelligence or cost per task instead of cost per token.
How do I reliably measure 1 unit of intelligence?
In pelicans, obviously
Isn't the outcome / solution for a given task non-deterministic? So can we reliably measure that?
Yes, sort of. Generally you can measure the pass rate on a benchmark given a fixed compute budget. A sufficiently smart model can hit a high pass rate with fewer tokens/compute. Check out the cost efficiency on https://artificialanalysis.ai/ (say this posted here the other day, pretty neat charts!)
This is the only correct take. The only metric that matters is cost per desired outcome.
Statistically. Do many trials and measure how often it succeeds/fails.
Repetition and statistics, if you have $1000++ you didn't need anyway.
It's much easier to measure a language model's intelligence than a human's because you can take as many samples as you want without affecting its knowledge. And we do measure human intelligence.
We know they cost much more than this for OpenAI. Assume prices will continue to climb until they are making money.
How do we know that? There is a large gap between API pricing for SOTA models and similarly sized OSS models hosted by 3rd party providers.
Sure, they’re distilled and should be cheaper to run but at the same time, these hosting providers do turn a margin on these given it’s their core business, unless they do it out of the kindness of their heart.
So it’s hard for me to imagine these providers are losing money on API pricing.
source? There have also been a bunch of people here saying the opposite
It's far more meaningful to look at the actual cost to successfully something. The token efficiency of GPT-5.5 is real; as well as it just being far better for work.
SOTA models get distilled to open source weights in ~6 months. So paying premium for bleeding edge performance sounds like a fair compensation for enormous capex.
Not really a big problem. Switch to KIMI, Qwen, GLM. You’ll get 95% quality of GPT or Anthropic for a 10th of a price. I feel like the real dependency is more mental, more of a habit but if you actually dip your toes outside OpenAI, Anthropic, Gemini from time to time, you realise that the actual difference in code is not huge if prompted in a good way. Maybe you’ll have to tell it to do something twice and it won’t be a one shot, but it’s really not an issue at all.
God I hope this is true.
Where can i find up to date resources on open source models for coding?
Such an increase tracks the company's valuation trend, which they constantly, somehow have to justify (let alone break even on costs).
GPT-4 cost 6x on input and 2x output tokens when it was released as compared go GPT-5.5
I've found myself so deeply embedded in the Claude Max subscription that I'm worried about potentially makign a switch. How are people making sure they stay nimble enough not to get trarpped by one company's ecosystem over another? For what it's worth, Opus 4.7 has not been a step up and it's come with an enormously higher usage of the subscription Anthropic offers making the entire offering double worse.
Start building your own liteweight "harness" that does things you need. Ignore all functionality of clients like CC or Codex and just implement whatever you start missing in your harness.
You can replace pretty much everything - skills system, subagents, etc with just tmux and a simple cli tool that the official clients can call.
Oh and definitely disable any form of "memory" system.
Essentially, treat all tooling that wraps the models as dumb gateways to inference. Then provider switch is basically a one line config change.
> You can replace pretty much everything - skills system, subagents, etc with just tmux and a simple cli tool that the official clients can call.
I'm very interest by this. Can you go a bit more into details?
ATM for example I'm running Claude Code CLI in a VM on a server and I use SSH to access it. I don't depend on anything specific to Anthropic. But it's still a bit of a pain to "switch" to, say, Codex.
How would that simple CLI tool work? And would CC / Codex call it?
Check out github.com/ralabarge/beigebox -- OSS AI Harness, started as a way to save all of my data but has agentic features, MCP server, point it at any endpoint (or use any front end with it as well, transparent middleware)
So far what I am finding is that you just get the basics working and then use the tool and inference to improve the tool.
I wish I had lower standards towards sharing absolute AI slop, then I could just drop a link to my implementation. But since I don't, let me just describe it. I essentially had claude build the initial version in a single session which I've been extending as I noticed any gaps in my process.
First, you need an entrypoint that kicks things off. You never run `claude` or `codex`, you always start by running `mycli-entrypoint` that:
1. Creates tmux session 2. Creates pane 3. Spawns claude/codex/gemini - whichever your default configured backend is 4. Automatically delivers a prompt (essentially a 'system message') to that process via tmux paste telling it what `mycli` is, how to use it, what commands are available and how it should never use built-in tools that this cli provides as alternatives.
After that, you build commands in `mycli` that CC/Codex are prompted to call when appropriate.
For example, if you want a "subagent", you have a `mycli spawn` command that takes a role (just preconfigured markdown file living in the same project), backend (claude/codex/...) and a model. Then whenever CC wants to spawn a subagent, it will call that command instead, which will create a pane, spawn a process and return agent ID to CC. Agent ID is auto generated by your cli and tmux pane is renamed to that so you can easily match later.
Then you also need a way for these agents to talk to each other. So your cli also has a `send` command that takes agent ID and a message and delivers it to the appropriate pane using automatically tracked mapping of pane_id<>agent_id.
Claude and codex automatically store everything that happens in the process as jsonl files in their config dirs. Your cli should have adapters for each backend and parse them into common format.
At this point, your possibilities are pretty much endless. You can have a sidecar process per agent that say, detects when model is reaching context window limit (it's in jsonl) and automatically send a message to it asking it to wrap up and report to a supervisor agent that will spawn a replacement.
I also don't use "skills" because skills are a loaded term that each of the harnesses interprets and loads/uses differently. So I call them "crafts" which are again, just markdown files in my project with an ID and supporting command `read-craft <craft-id>`. List of the available "crafts" are delivered using the same initialization message that each agent gets. If I like any third party skill, I just copy it to my "crafts" dir manually.
My implementation is an absolute junk, just Python + markdown files, and I have never looked at the actual code, but it works and I can adapt it to my process very easily without being dependent on any third party tool.
I use Conductor which lets me flip trivially between OpenAI/Anthropic models
It's surprisingly simple to switch. I mean both products offer basically identical coding CLI experiences. Personally I've been paying for Claude max $100, and ChatGPT $20, and then just using ChatGPT to fill in the gaps. Specifically I like it for code review and when Claude is down.
Try GPT-5.5 as your daily driver for a bit. It felt a lot smarter, reliable, and I was much more productive with it.
I have a directory of skills that I symlink to Codex/Claude/pi. I make scripts that correspond with them to do any heavy lifting, I avoid platform specific features like Claude's hooks. I also symlink/share a user AGENTS.md/CLAUDE.md
MCPs aren't as smooth, but I just set them up in each environment.
Anecdotally, I get the same wall time with my Max x5 (100$) and my ChatGPT Teams (30$) subscriptions.
What is the switching cost besides launching a different program? Don’t you just need to type what you want into the box?
Small tip, at least for now you can switch back to Opus 4.6, both in the ui and in Claude Code.
This might be the opposite of staying nimble as my workflows are quite tied to Claude Code specifically, however I've been experimenting with using OpenAI models in CC and it works surprisingly well.
It’s good to just keep trying different ones from time to time.
Except for history, I don’t find much that stops you from switching back and forth on the CLI. They both use tools, each has a different voice, but they both work. Have it summarize your existing history into a markdown file, and read it in with any engine.
The APIs are pretty interchangeable too. Just ask to convert from one to the other if you need to.
use copilot and have access to all models
As a rule I've been symlinking or referencing generic "agents" versions of claude workflow files instead of placing those files directly in claude's purview
AGENTS.md / skills / etc
Coding models are effectively free. They are capable of making money and supporting themselves given access to the right set of things. That is what I do
I switched a couple of weeks ago just to see how it went. Codex is no better or worse. They’re both noticeably better at different things. I burn through my tokens much much faster on Codex though. For what it’s worth I’m sticking with Codex for now. It seems to be significantly better at UI work although has some really frustrating bad habits (like loading your UI with annoying copywriting no sane person would ever do).
I'm here for the pelicans and I'm not leaving until I see one!
I've come to prompt pelicans and chew gum, and I'm all outta gum!
That's a true CTO right there.
I know a 10x engineer when i see one.
In binary that's just a 10x engineer
It a hex of an engineer (no offence)
Ctrl+F: pelican
F5
simonw pls
If GPT-5.5 Pro really was Spud, and two years of pretraining culminated in one release, WOW, you cannot feel it at all from this announcement. If OpenAI wants to know why they like they’ve fallen behind the vibes of Anthropic, they need to look no further than their marketing department. This makes everything feel like a completely linear upgrade in every way.
Clearly they felt a big backlash when version 5 was released. Now they are afraid of another response like this. And effectively, for the user it will likely only be a small update.
Also the naming department. You can tell that this is the AI company Microsoft chose to back because their naming scheme is as bad as .NET's.
I actually have no problem with the 5.x line... but if Pro really was an entirely new pretrain, they did a horrible job conveying that.
Is this the first time OpenAI has published comparisons to other labs?
Seems so to me - see GPT-5.4[1] and 5.2[2] announcements.
Might be an tacit admission of being behind.
[1] https://openai.com/index/introducing-gpt-5-4/ [2] https://openai.com/index/introducing-gpt-5-2/
This seems huge for subscription customers. Looking at the Artificial Analysis numbers, 5.5 at medium effort yields roughly the intelligence as 5.4 (xhigh) while using less than a fifth the tokens.
As long as tokens count roughly equally towards subscription plan usage between 5.5 & 5.4, you can look at this as effectively a 5x increase in usage limits.
As someone who always leaves intelligence at default, and am ok with existing models, should I be shifting gears more manually as providers sell us newer models? Is medium or lower better than free/cheaper models?
SOTA models on medium are probably still better than free or cheap models, but you should experiment.
Their 'Preparedness Framework'[1] is 20 pages and looks ChatGPT generated, I don't feel prepared reading it.
https://cdn.openai.com/pdf/18a02b5d-6b67-4cec-ab64-68cdfbdde...
For a 56.7 score on the Artificial Intelligence Index, GPT 5.5 used 22m output tokens. For a score of 57, Opus 4.7 used 111m output tokens.
The efficiency gap is enormous. Maybe it's the difference between GB200 NVL72 and an Amazon Tranium chip?
why would chip affect token quantity. this is all models.
Chip costs strongly impact the economics of model serving.
It is entirely plausible to me that Opus 4.7 is designed to consume more tokens in order to artificially reduce the API cost/token, thereby obscuring the true operating cost of the model.
I agree though, I chose poor phrasing originally. Better to say that GB200 vs Tranium could contribute to the efficiency differential.
Chips doesn’t impact output quality in this magnitude
True, but the qualifying the power played a large part. Most likely nuclear power for this high quality token efficiency.
You need to compare total cost. Token count is irrelevant.
If it's a new pretrain, the token embeddings could be wider - you can pack more info into a token making it's way through the system.
Like Chinese versus English - you need fewer Chinese characters to say something than if you write that in English.
So this model internally could be thinking in much more expressive embeddings.
I hope the industry starts competing more on highest scores with lowest tokens like this. It's a win for everybody. It means the model is more intelligent, is more efficient to inference, and costs less for the end user.
So much bench-maxxing is just giving the model a ton of tokens so it can inefficiently explore the solution space.
The premise of the trillion dollars in AI investments is not that it’ll be as good as it currently is but cheaper. It’s AGI or bust at this point.
Yeah, but don’t you agree that less tokens to accomplish the same goal is a sign of increasing intelligence?
It could be. Or just smarter caching (which wouldn't necessarily have to do with model intelligence). Or just overfitting on the 95% most common prompts (which could save tokens but make the models less intelligent/flexible).
Less cost to accomplish the same goal is a sign of intelligence. That's not necessarily achieved with less tokens but it may be.
Kind of? But I really care about price speed and quality. If it used 10x tokens at 1/10th the tokens and same latency I would be neutral on it.
Kimmi 2.6 for example seems to throw more tokens to improve performance (for better or worse)
I like that they waited for opus 4.7 to come out first so they had a few days to find the benchmarks that gpt 5.5 is better at
Well anectodally, 5.4 was already better than opus 4.7 so it should not have been hard.
I like that Anthropic rushed 4.7 out to get a couple days of coverage before 5.5 hit
Everything since that launch to this release has been a PR disaster for Anthropic.
I can argue that disaster started mid-4.6, when they started juggling with rate limits while hitting uptime problems. Great we have some healthy competition and waiting for the next move from Deepmind.
Correct. Anthropic has been on disaster train since January and they can't seem to get off that train.
This is reminding me when Chrome and Firefox where racing to release a new “major version” (at least from the semver POV) without adding significantly new functionality at a time that browsers were already becoming a commodity. As much as we don’t care anymore for a new chrome or Firefox version so will be the release of a new model version.
The only difference being that we still do care, very much. The models can still get a lot better before we stop caring.
> It excels at ... researching online
How does this work exactly? Is there like a "search online" tool that the harness is expected to provide? Or does the OpenAI infra do that as part of serving the response?
I've been working on building my own agent, just for fun, and I conceptually get using a command line, listing files, reading them, etc, but am sort of stumped how I'm supposed to do the web search piece of it.
Given that they're calling out that this model is great at online research - to what extent is that a property of the model itself? I would have thought that was a harness concern.
I’ve noticed when writing little bedtime stories that require specific research (my kids like Pokemon stories and they’ve been having an episodic “pokemon adventure” with them as the protagonists) ChatGPT has done a fantastic job of first researching the moves the pokemon have, then writing the actual story. The only mistake it consistently makes is when I summarize and move from a full context session, it thinks that Gyarados has to swim and is incapable of flying.
It definitely seems like it does all the searching first, with a separate model, loads that in, then does the actual writing.
Gyarados is a flying type but I think it may be accurate that it can't actually fly. The only flying moves it can learn in any generation are Hurricane and Bounce (Bounce does send the user up into the air for a turn but the implication is that they've trampolined up extremely high rather than used wings to ascend)
If anything it should probably be combined water/dragon type
It's literally a distinct model with a different optimisation goal compared to normal chat. There's a ton of public information around how they work and how they're trained
It's a property of the model in the sense that it has great Google Fu.
The harness provides the search tool, but the model provides the keywords to search for, etc.
Pricing: $5/1M input, $30/1M output
(same input price and 20% more output price than Opus 4.7)
Yep, it's more expensive per token.
However, I do want to emphasize that this is per token, not per task.
If we look at Opus 4.7, it uses smaller tokens (1-1.35x more than Opus 4.6) and it was also trained to think longer. https://www.anthropic.com/news/claude-opus-4-7
On the Artificial Analysis Intelligence Index eval for example, in order to hit a score of 57%, Opus 4.7 takes ~5x as many output tokens as GPT-5.5, which dwarfs the difference in per-token pricing.
The token differential varies a lot by task, so it's hard to give a reliable rule of thumb (I'm guessing it's usually going to be well below ~5x), but hope this shows that price per task is not a linear function of price per token, as different models use different token vocabularies and different amounts of tokens.
We have raised per-token prices for our last couple models, but we've also made them a lot more efficient for the same capability level.
(I work at OpenAI.)
I don't have anything to add, but I like how you guys are actually sending people to communicate in Hacker News. Brilliant.
Maybe a good idea to be more explicit about this -- maybe a cost analysis benchmark would be a nice accompaniment.
This kind of thing keeps popping up each time a new model is released and I don't think people are aware that token efficiency can change.
Agreed. Would be great if everyone starts reporting cost per task alongside eval scores, especially in a world where you can spend arbitrary test-time compute. This is one thing I like about the Artificial Analysis website - they include cost to run alongside their eval scores: https://artificialanalysis.ai/
yes but as far as i know gpt tokenizer is about the same as opus 4.6's, where 4.7 is seeing something in the ballpark of a 30% increase. this should still be cheaper even disregarding the concerns around 4.7 thinking burning tokens
That pricing is extremely spicy, wow.
> Across all three evals, GPT‑5.5 improves on GPT‑5.4’s scores while using fewer tokens.
Yeah, this was the next step. Have RLVR make the model good. Next iteration start penalising long + correct and reward short + correct.
> CyberGym 81.8%
Mythos was self reported at 83.1% ... So not far. Also it seems they're going the same route with verification. We're entering the era where SotA will only be available after KYC, it seems.
Isn't Mythos limited to a selected group of companies/organizations Anthropic chose themselves? If the OpenAI announcement for GPT-5.5 is accurate the "trusted cyber access" just requires an open, seemingly straightforward identity verification step.
https://openai.com/index/scaling-trusted-access-for-cyber-de...
"GPT‑5.4‑Cyber" is something else and apparently needs some kind of special access, but that CyberGym benchmark result seems to apply to the more or less open GPT-5.5 model that was just released.Isn't CyberGym an open benchmark so trivial to benchmaxx anyway?
Not good for employees that are being measured by their token usage.
Releases keep shifting from API forward to product forward, with API now lagging behind proprietary product surface and special partnerships.
I'd not be surprised if this is the year where some models simply stop being available as a plain API, while foundation model companies succeed at capturing more use cases in their own software.
Worth the 100% price increase over GPT-5.4?
For less than 10% bump across the benchmarks? Probably not, but if your employer is paying (which is probably what OAI is counting on) it's all good.
It's kind of starting to make sense that they doubled the usage on Pro plans - if the usage drains twice as fast on 5.5 after that promo is over a lot of people on the $100 plan might have to upgrade.
You are paying per token, but what you care about is token efficiency. If token efficiency has improved by as much as they claim it did (i.e. you need less tokens to complete a task successfully) all seems well.
Not for coding because it actually needs to read and write large files
Well, sort of. Imagine the case where it first scans the repo, then "intelligently" creates architecture files describing the project. The level of intelligence will create a varying quality of summary, with varying need of deep-scans on subsequent sessions. Level of intelligence will also increase comprehension of these architecture files.
Same principle applies when designing plans for complex tasks, etc. Token amount to grasp a concept is what matters.
Tbf, I have not super kept track of what is actually happening inside the "thinking" portion of recent releases. But last time I checked there still was a lot of verbosity and mistakes, that beat the actual amount of required, usable code generation by a wide margin.
If it uses half the tokens to complete a task, then doubling the cost is perfectly fine. But is that actually true?
This happens with every new model release though. The model makes less mistakes and spends less time fixing them, resulting in a token usage reduction for the same difficulty of task. Almost any task other than straight boilerplate will benefit from this.
In the same vein, I would guess that Opus 4.7 is probably cheaper for most tasks than 4.6, even though the tokenizer uses more tokens for the same length of string.
Maybe you'll have better luck but our team just cannot use Opus 4.7.
Some say it goes off on endless tangents, others that it doesn't work enough. Personally, it acts, talks, and makes mistakes like GPT models, for a much more exorbitant price. Misses out on important edge cases, doesn't get off its ass to do more than the bare minimum I asked (I mention an error and it fixes that error and doesn't even think to see if it exists elsewhere and propose fixing it there).
I've slowly been moving to GPT5.4-xhigh with some skills to make it act a bit more like Opus 4.6, in case the latter gets discontinued in favour of Opus 4.7.
Doesn't look like it's cheaper, better or uses fewer tokens: https://www.reddit.com/r/Anthropic/comments/1stf6fz/one_week...
YMMV, I know.
We'll find out!
I'm conflicted whether I should keep my Claude Max 5x subscription at this point and switch back to GPT/Codex... anyone else in a similar position? I'd rather not be paying for two AI providers and context switching between the two, though I'm having a hard time gauging if Claude Code is still the "cream of the crop" for SWE work. I haven't played around with Codex much.
I have experienced 0 friction swapping between the 2 models, in fact pitting them against eachother has resulted in the highest success rate for me so far.
Interesting. I may have to give that a shot, thanks.
I switched from CC to Codex a few days ago. I get limited much less and the code quality is similar, so not looking back
Which plan? And how are the weekly limits on that plan compared to CCs equivalent subscription?
I don't really care about 5h limits, I can queue up work and just get agents to auto continue, but weekly ones are anxiety inducing.
I was all in on Claude code as my daily driver for web development. And love it. But I enjoy using pi as my harness more and have never ran out of tokens with Codex yet. Claude code almost always runs out for me with the same amount of usage.
After migrating for the token and harness issues, I was pleasantly surprised that Codex seems to perform as good or better too!
Things change so often in this field, but I prefer Codex now even though Anthropocene has so much more hype for coding it seems.
Every time I've followed the hype and tried OpenAI models I've found them lacking for the most part. It might just be that I prefer the peer-programming vs spec-ing out the task and handing it off, but I've never been as productive as I am with Claude. Also, I'm still caught up on the DoD ethics stuff.
I like that its more consistent than the 4o and o4 days but still 5.4, 5.3, 5.2, etc still are a mess, for example 5.2 and 5.1 don't have mini models and 5.3 was codex only.
Anthropic is slightly better but where is 4.6 or 4.7 haiku or 4.7 sonnet etc.
Opus 4.7 feels worse for me than 4.6, and that's not even taking into account the 50% extra tokens at 3x the price
Same here
Yay. 5.4 was a frustrating model - moments of extreme intelligence (I liked it very much for code review) - but also a sort of idiocy/literalism that made it very unsuited for prompting in a vague sense. I also found its openclaw engagement wooden and frustrating. Which didn’t matter until anthropic started charging $150 a day for opus for openclaw.
Anyway - these benchmarks look really good; I’m hopeful on the qualitative stuff.
GPT-5.5 System Card:
https://deploymentsafety.openai.com/gpt-5-5
Benchmarks are favorable enough they're comparing to non-OpenAI models again. Interesting that tokens/second is similar to 5.4. Maybe there's some genuine innovation beyond bigger model better this time?
It's behind Opus 4.7 in SWE-Bench Pro, if you care about that kind of thing. It seems on-trend, even though benchmarks are less and less meaningful for the stuff we expect from models now.
Will be interesting to try.
I am a heavy Claude Code user. I just tried using Codex with 5.4 (as a Plus user I don't have access to 5.5 yet), and it was quite underwhelming. The app stopped regularly much earlier than what I wanted. It also claimed to have fixed issues when it did not; this is not a hallmark of GPT, and Opus has similar issues, but Claude will not make the same mistake three times in a row. It is unusable at the moment, while Claude allows me do get real work done on a daily basis. Until then...
Gpt-5.3-codex is miles better than 5.4 in that regard. It’s better at orchestration, and does the things that it said it did. Haven’t tested 5.5 yet but using 5.4 for exploration + brainstorming and handing over the findings to 5.3-codex works pretty well
Once upon a time humans had to memorize log tables.
Once upon a time humans had to manually advance the spark ignition as their car's engine revved faster.
Once upon a time humans had to know the architecture of a CPU to code for it.
History is full of instances of humans meeting technology where it was, accommodating for its limitations. We are approaching a point where machines accommodate to our limitations -- it's not a point, really, but a spectrum that we've been on.
It's going to be a bumpy ride.
With such a huge progress of open ai and anthropic . How Chinese open source provides even think to make comparable money . I have a few friends in China they all use Claude. To train the model cost the same but the output from open source model id imagine is 1000 times less . Money flow for them outside of China is abysmal
GPT is really great, but I wish the GPT desktop app supported MCP as well.
You can kind of use connectors like MCP, but having to use ngrok every time just to expose a local filesystem for file editing is more cumbersome than expected.
Use codex app
In Copilot where it's easy to switch models Opus 4.6 was still providing, IMHO, better stock results than GPT-5.4.
Particularly in areas outside straight coding tasks. So analysis, planning, etc. Better and more thorough output. Better use of formatting options(tables, diagrams, etc).
I'm hoping to see improvements in this area with 5.5.
Will we also see a GPT-5.5-Codex version of this model? Or will the same version of it be served both in the web app and in Codex?
After 5.1, we haven’t seen a -codex-max model, presumably because the benefits of the special training gpt-5.1-codex-max got to improve long context work filtered into gpt-5.2-codex, making the variant no longer necessary (my personal experience accords with this). I’ve been using gpt-5.4 in Codex since it came out, it’s been great. I’ve never back-to-back tested a version against its -codex variant to figure out what the qualitative difference is (this would take a long time to get a really solid answer), but I wouldn’t be surprised if at some point the general-purpose model no longer needs whatever extra training the -codex model gets and they just stop releasing them.
I thought it was weird that for almost the entire 5.3 generation we only had a -codex model, I presume in that case they were seeing the massive AI coding wave this winter and were laser focused on just that for a couple months. Maybe someday someone will actually explain all of this.
> GPT‑5.5 improves on GPT‑5.4’s scores while using fewer tokens.
This might be great if it translates to agentic engineering and not just benchmarks.
It seems some of the gains from Opus 4.6 to 4.7 required more tokens, not less.
Maybe more interesting is that they’ve used codex to improve model inference latency. iirc this is a new (expectedly larger) pretrain, so it’s presumably slower to serve.
With Opus it’s hard to tell what was due to the tokenizer changes. Maybe using more tokens for the same prompt means the model effectively thinks more?
They say latency is the same as 5.4 and 5.5 is served on GB200 NVL72, so I assume 5.4 was served on hopper.
What a time. I am back here genuinely wishing for OpenAI to release a great model, because without stiff competition, it feels like Anthropic has completely lost its mind.
Cool. Now there will be a week or "this is the greatest model ever and I think mine just gained sentience", followed by a week of "I think they must have just nerfed it because it's not as good as it was a week ago", followed by three weeks of smart people cargo culting the specific incantations they then convince themselves make it work best.
followed by some hormuz closures, followed by gpt-5.6...
Labs still aren't publishing ARC-AGI-3 scores, even though it's been out for some time. Is it because the numbers are too embarrassing?
Honest answer is that it isn't done running yet. It takes some human bandwidth and time to run, so results weren't ready by this morning. We don't know what the score will be, but will probably go up on the leaderboard sometime soon. I personally don't put a lot of stock in it, as it's not relevant to most work that people do, but should still be interesting to see as a measure of reasoning ability.
(I work at OpenAI.)
To be fair, there's not much to report. Isn't it pretty much at 0?
Opus-4.6 with 0.5% currently leads GPT-5.4 with 0.2%[1].
Seems meaningful even if the absolute numbers are very low. That's sort of the excitement of it.
2. https://arcprize.org/leaderboard
GPT-5.5 was just released and OpenAI didnt mention ARC AGI 3 at all, their score probably sucks.
why the stats of every AI on every release looks around the same?
Are the tests getting harder and harder so the older AIs look worst and the new ones look like they are "almost there" ?
I know this is irrelevant on the grand scheme of things, but that WebGL animation is really quite wrong. That is extra funny given the "ensure it has realistic orbital mechanics." phrase in the prompt.
I prescribe 20 hours of KSP to everyone involved, that'll set them right.
Good job on the release notice. I appreciate that it isn't just marketing fluff, but actually includes the technical specs for those of us who care and not concentrated in coding agents only.
I hope GPT 5.5 Pro is not cutting corners and neuter from the start, you got the compute for it not to be.
Seems like a continuation of the current meta where GPT models are better in GPT-like ways and Claude models are better in Claude-like ways, with the differences between each slightly narrowing with each generation. 5.5 is noticeably better to talk to, 4.7 is noticeably more precise. Etc etc.
Looking at the space/game/earthquake tracker examples makes me hopeful that OpenAI is going to focus a bit more on interface visual development/integration from tools like Figma. This is one area where Anthropic definitely reigns supreme.
Very impressive! Interesting how all other benchmarks it seems to surpass Opus 4.7 except SWE-Bench Pro (Public). You would think that doing so well at Cyber, it would naturally possess more abilities there. Wonder what makes up the actual difference there
NYTimes article - on the same day?
I can see how some model releases would meet the NY Times news-worthy threshold if they demonstrated significance to users - i.e., if most users were astir and competitors were re-thinking their situation.However, this same-day article came out before people really looked at it. It seems largely intended to contrast OpenAI with Anthropic's caution, before there has been any evidence that the new model has cyber-security implications.
It's not at all clear that the broader discourse is helping, if even the NY Times is itself producing slop just to stoke questions.
Umm yeah but this is like every release in the last 3 years.
The big question is: does it still just write slop, or not?
Fool me once, fool me twice, fool me for the 32nd time, it’s probably still just slop.
82.7% on Terminal Bench is crazy
Is it? There are 5 other models near ~80% and it was achieved in March... which in AI-world seems like a century ago.
https://www.tbench.ai/leaderboard/terminal-bench/2.0
those are not verified. I've tried forgecode and I cannot believe they didn't do something to influence the benchmarks
Yup, they were found to be sneaking the answer key using agents.md
https://debugml.github.io/cheating-agents/#sneaking-the-answ...
What is the reason behind OpenAI being able to release new models very fast?
Since Feb when we got Gemini 3.1, Opus 4.6, and GPT-5.3-Codex we have seen GPT-5.4 and GPT-5.5 but only Opus 4.7 and no new Gemini model.
Both of these are pretty decent improvements.
Competition.
This is frankly exciting, outside of the politics of it all, it always feel great to wake up and a new model being released, I personally will stay awake quite long tonight if GPT-5.5 drop in codex.
Anthropic is really tiny, and Google is just being Google, their models are just to show that they're hip with what the kids are doing.
I wonder if it's the same model and they just keep adding more post-training.
The rumor was that the 5.5 is a brand new pretrain. But who knows, it's 2x as expensive as 5.4, so it would check out.
They aren't new models.
"our strongest set of safeguards to date"
How much capability is lost, by hobbling models with a zillion protections against idiots?
Every prompt gets evaluated, to ensure you are not a hacker, you are not suicidal, you are not a racist, you are not...
Maybe just...leave that all off? I know, I know, individual responsibility no longer exists, but I can dream.
This is my personal pet peeve as well. Like, I accept maybe everything shouldn't be offered to everyone, but maybe just gate keep it behind credit card( but I know that is a market penetration no no ). I feel like such a waste of power ( electrical and the potential we might be missing out on ).
So according to the benchmarks somewhere in between Opus 4.7 and Mythos
GPT 5.4 is already better than Opus 4.7 to me. But, then again, Opus 4.7 is a massive disappointment. I hope they don't discontinue 4.6.
Depends in goals. For long free-firm discussions I find Opus 4.7 Adaptive better/deeper than Opus 4.6 Extended. But usual caveats apply: first week of use and token budget seems generous now on Max 5X.
I had the opposite experience. Opus 4.6 extended feels like the first genuinely intelligent model to converse with, Opus 4.7 adaptive feels like slightly smarter LinkedIn slop.
I’ve had great experience using opus 4.7 in cursor. Works for everything including iOS frontend
Cursor is what I daily-drive. 4.7 has been terrible for my mostly python-driven work (whereas Opus 4.6 was literally revolutionary to me). Our frontend folks are also complaining.
I left a comment here with this sentiment https://news.ycombinator.com/item?id=47879896
If SWE-Bench Verified is no longer a good measure of agentic coding abilities, what benchmark now is?
"Sometime with GPT-5.5 I become lazy"
I don't want to be lazy.
is there anywhere I can try it? ( I just stopped my pro sub ) but was wondering if there is a playground or 3rd party so i can just test it briefly?
This is the first time openAi include competing models in their benchmarks, always included only openAi models.
Those who are using gpt5.5 how does it compare to Opus 4.6 / 4.7 in terms of code generation?
ctrl+f "cutoff, 0 results"
Surely it doesn't still have the same ancient data cutoff as 5.4 did?
Surprised to see SWE-Bench Pro only a slight improvement (57.7% -> 58.6%) while Opus 4.7 hit 64.3%. I wonder what Anthropic is doing to achieve higher scores on this - and also what makes this test particular hard to do well in compared to Terminal Bench (which 5.5 seemed to have a big jump in)
There's an asterisk right below that table stating that:
> *Anthropic reported signs of memorization on a subset of problems
And from the Anthropic's Opus 4.7 release page, it also states:
> SWE-bench Verified, Pro, and Multilingual: Our memorization screens flag a subset of problems in these SWE-bench evals. Excluding any problems that show signs of memorization, Opus 4.7’s margin of improvement over Opus 4.6 holds.
Was 4.7 distilled off Mythos (which got 77.8%)? Interesting how mythos got 82% on terminal-bench 2.0 compared to 82.7% for GPT-5.5.
Also notice how they state just for SWE-Bench Pro: "*Anthropic reported signs of memorization on a subset of problems"
entering this comments area wondering if it will be full of complaints about the new personality, as with every single LLM update
How does it compare to mythos?
I just prompted GPT-5.5 Pro "Solve Nuclear Fusion" and it one shotted it (kidding obviously)
Which is better GPT-5.5 or Opus 4.7? And for what tasks?
> We are releasing GPT‑5.5 with our strongest set of safeguards to date
...
> we’re deploying stricter classifiers for potential cyber risk which some users may find annoying initially
So we should be expecting to not be able to check our own code for vulnerabilities, because inherently the model cannot know whether I'm feeding my code or someone else's.
Hopefully not, because checking your codebase for vulnerabilities is really valuable.
I hope it’s just limits on pentesting and stuff, and not for code analysis and review.
But how do it know?
It's possible that "smarter" AI won't lead to more productivity in the economy. Why?
Because software and "information technology" generally didn't increase productivity over the past 30 years.
This has been long known as Solow's productivity paradox. There's lots of theories as to why this is observed, one of them being "mismeasurement" of productivity data.
But my favorite theory is that information technology is mostly entertainment, and rather than making you more productive, it distracts you and makes you more lazy.
AI's main application has been information space so far. If that continues, I doubt you will get more productivity from it.
If you give AI a body... well, maybe that changes.
Its quite possible the use of LLMs means that we are using less effort to produce the same output. This seems good.
But the less effort exertion also conditions you to be weaker, and less able to connect deeply with the brain to grind as hard as once did. This is bad.
Which effect dominates? Difficult to say.
Of course this is absolutely possible. Ultimately there was a time where physical exertion was a thing and nobody was over-weight. That isn't the case anymore is it.
> "information technology" generally didn't increase productivity
Do you think it'd be viable to run most businesses on pen and paper? I'll give you email and being able to consume informational websites - rest is pen and paper.
Productivity metrics were better when businesses were run on just pen and paper. Of course, there could be many confounding factors, but there are also many reasons why this could be so. Just a few hypotheses:
- Pen and paper become a limiting factor on bureaucratic BS
- Pen and paper are less distracting
- Pen and paper require more creative output from the user, as opposed to screens which are mostly consumptive
etc etc
> Productivity metrics were better when businesses were run on just pen and paper
What metrics are these?
Productivity growth. If you take rolling averages from this chart, it clearly demonstrate higher productivity growth before the adoption of software. This is a well established fact in econ circles.
https://fred.stlouisfed.org/graph/?g=1V79f
I think this is a classic case of reading into specific arguments too deeply without understanding what they really mean in the grand picture. Few points to easily disprove this argument
- if it were true that software paradoxically reduces productivity, you can just start a competing company that doesn't use software. Obviously this is ridiculous - top 20 companies by market cap are mostly Software based. Every other non IT company is heavily invested in software
- if you might say the problem is it at the country level, it is obvious that every country that has digitised has had higher productivity and GDP growth. Take Italy vs USA for instance.
- if you are saying that the problem is even more global, take the whole world - the GDP per is still pretty high since the IT revolution (and so have other metrics)
If you still think there's something more to it, you are probably deep in some conspiracy rabbit hole
The data clearly shows that productivity growth is flat or even declining. What is your accounting of why software hasn't offset those numbers?
You don't have a counterfactual to suggest that it would have continued increasing had it not been for technology. Is there _any_ credible economist who suggests that we might have higher productivity without tech?
There is no counterfactual needed. Productivity growth has declined, despite the expectation that software would accelerate productivity. I'm asking you why this didn't happen.
There is a counterfactual needed because it is not clear whether the growth would not have declined even more without Software.
Again I'm asking - is there a single credible economist who says that the growth would have been higher without technology?
I'm not even proposing that growth would have been higher without "technology". I said information technology has not increased productivity growth compared to the past. This is an observation of fact.
Is there a way to mute people who are clearly AI boosters? ^
? you are literally commenting on the release of a new model from OpenAI in a tech focused community. Have you considered what should be normal here?
Downvoted by the AI Nazis. They are running a tight ship before the IPOs.
I downvoted it because it doesn't add anything useful to the conversation, and I don't own any AI stock.
It's a hypothesis that "smarter" AI models, ie GPT-5.5, may not be a great boon to productivity. Given that this is the raison d'etre of AI models, and improving them, I don't see why it is any less useful than any other discussion.
I might just be following too many AI-related people on X, but omg the media blitz around 5.5 is aggressive.
Soo many unconvincing "I've had access for three weeks and omg it's amazing" takes, it actually primes me for it to be a "meh".
I prefer to see for myself, but the gradual rollout, combined with full-on marketing campaign, is annoying.
does it have cached pricing?
I hear its as good as Opus 4.7.
The battle has just begun
Nice to see them openly compare to Opus-4.7… but they don’t compare it against Mythos which says everything you need to know.
The LinkedIn/X influencers who hyped this as a Mythos-class model should be ashamed of themselves, but they’ll be too busy posting slop content about how “GPT-5.5 changes everything”.
Almost nobody can actually use Mythos, though?
literally cannot launch the codex app anymore
Good timing I had just renewed my subscription.
> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.
Everybody understands that you need to make money, but can you tone it down with the f*cking FOMO, please? It sounds just pathetic at this point:
'one engineer at NVIDIA', 'limb amputated'
Put the cunt in a room and give me a handsaw, I want to see how fast he'll give up his arm over some cloud model.
I'd really like to see improvements like these: - Some technical proof that data is never read by open ai. - Proof that no logs of my data or derived data is saved. etc...
I don't think this is technically possible without something like homomorphic encryption, which poses too large of a runtime cost for usage in LLMs
> A playable 3D dungeon arena
Where's the demo link?
Cannot see it in Codex CLI
Did you upgrade the tool binaries? I also couldn't see it until after the upgrade.
Are there faster mini/nano versions as well?
Not this time, no.
Usually, those get released a few weeks later.
... sigh. I realize there's little that can be done about this, but I just got through a real-world session determining of Opus 4.7 is meaningfully better than Opus 4.6 or GPT 5.4, and now there's another one to try things with. These benchmark results generally mean little to me in practice.
Anyways, still exciting to see more improvements.
What is the major and minor semver meaning for these models? Is each minor release a new fine-tuning with a new subset of example data while the major releases are made from scratch? Or do they even mean anything at this point?
Nothing. The next major increment is going to happen when marketing department is confident they can sell it as a major improvement without everyone laughing at them. Which at this point seems like never.
I think Anthropic fearmongering and "leaks" of Mythos was them testing the ground for 5.x, which seems to have backfired.
GPT-5.4 is already an incredible model for code reviews and security audits with the swival.dev /audit command.
The fact that GPT-5.5 is apparently even better at long-running tasks is very exciting. I don’t have access to it yet, but I’m really looking forward to trying it.
Related and insightful: "GPT-5.5: Mythos-Like Hacking, Open to All" [1].
[1] https://news.ycombinator.com/item?id=47879330
Is Codex receiving 5.4 or 5.5 release?
I am still using Codex 5.3 and haven't switched to GPT 5.4 as I don't like the 'its automatic bro trust us', so wondering is Codex going to get these specific releases at all in the future.
My impression has been that ChatGPT-5.4 has been getting dumber and more exhausting in the last couple of weeks. Like it makes a lot of obvious mistakes, ignores (parts of) prompts. keeps forgetting important facts or requirement.
Maybe this is a crazy theory, but I sometimes feel like they gimp their existing models before a big release to you'll notice more of a "step".
Definitely feels like it.
I've stopped trusting these "trust me bro" benchmarks and just started going to LM Arena and looking for the actual benchmark comparisons.
https://arena.ai/leaderboard/code
I doubt this is representative of real world usage. There is a difference between a few turns on a web chatbot, vs many-turn cli usage on a real project.
This is not any better of a benchmark
I am sceptical. The generation after 4o models have become crappier and crappier. Hope this one changes the trend. 5.4 is unusable for complex coding work.
I'm still using 5.3 in codex. Are 5.4 and 5.5 better than 5.3 in concrete ways?
The benchmarks say so, but try it out with actual tasks and be the judge.
Is this the first time OpenAI compared their new release to Anthropic models? Previously they were comparing only to GPT's own previous versions.
ARC-AGI 3 is missing on this list - given that the SOTA before 5.5 <1% if I recall, I wonder if this didn't make meaningful progress.
It's a silly benchmark anyways.
Not rolled out to my Codex CLI yet, but some users on Reddit claiming it's on theirs.
Next up: Google I/O on May 19?
I have to imagine they'll go to Gemini 3.5 if only for marketing reasons.
If anyone tried it already, how do you feel?
Numbers look too good, wondering if it is benchmaxxed or not
they are using ethical training weights this time!!! /j
Oh shiiiiit boy! An incrementation dropped!!
finally
Ah yes, the next "trust me bro"
Two hundred pages of shilling and it’s a 1% improvement in the benchmarks. They’re dead in the water.
Imagine spending 100m on some of these AI “geniuses” and this is the best they can do.
2x the price for 1-5% performance gain
the attenuation of man nears
< 5 years until humans are buffered out of existence tbh
may the light of potentia spread forth beyond us
Great modal, I have been using codex and its awesome. Lets see what GPT-5.5 does to it
I just can't bear to use services from this company after what they did to the global DRAM markets.
I'm not trying to make any kind of moral statement, but the company just feels toxic to me.