https://x.com/haider1/status/2073695124220006575, https://xcancel.com/haider1/status/2073695124220006575
there seems to be very big misunderstanding about what the "ultra" is, so let me explain it basing on the codex source code:
it's similar to Claude code ultracode.
there is no ultra effort level implemented on the backend. it's just alias in the codex to max effort setting and single line addition to prompt to use subagents proactively. that's all
as far as we know pro models work differently. for once those are backend implementations and they probably run multiple parallel reasonings for any chunk and use some judgement model to pick best version as persistent one. but that's what I believe is most popular guess, because this is openai secret sauce.
there is still no way to use pro models from codex, or at leat so far there is no trace of it anywhere.
I'm working in large US corporation. And I see that I already have access to 5.6-Sol Ultra on my corporate account.
I haven't really used it yet.
2 months ago management was showing us scoreboards, praising leaders who used most tokens. Last few weeks, we're getting weekly emails, telling us that whenever we can - we should use cheaper models, and that we should watch the page which shows our tokens usage.
The craziest to me was someone saying “we are using AI in daily processes, now we need to automate”.
But of course to some asshole non-technical people it meant asking for their vibe coded bullshit to be merged into production without review and fighting about it.
The craziest for me is companies that sticking stochastic agents into automated business processes and expecting stable/reliable outcomes. Businesses want deterministic processes in the vast majority of cases.
People are stochastic. You build reliable processes out of unreliable parts with feedback and self-correcting mechanisms. AI is not actually magically special in this regard. It has higher variance and we're still figuring out how to get all the tradeoffs right.
People live in very stochastic and volatile environments and they manage that in ways no LLMs currently ever can. (ie: imagine sending an LLM all the data - sensory/auditory/etc… - that a human receive)
People’s job is to partially reign in this volatile environment by creating processes with stable output.
I don't know of any modern workflows that rely on "we'll tell the person not to do it again", though. There's a reason that companies have adopted blameless postmortems, because if your response to the DB going down is "It's fine, Kevin learns and next time he won't misuse the prod credentials", you are guaranteeing prod will go down again in the same way at some point.
> AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file, or you can add skills to deal with specific prompts, or you provide a better default harness.
The whole idea of coding agents is their harness makes a big difference vs a pure raw model.
> However AI cannot meaningfully handle feedback and learn
How do you think models are created? They are trained on feedback and learn.
Its not cheap but you can post train models. This is how custom models are mode, that deal with specific tasks more efficiently and accurately.
Example ... Composer? Its base Kimi v2.5 model that has been post-trained 2 weeks, to create Composer 2.5, what is a much better coding model.
Its literally trained to make less mistakes by feeding it correct data. Hell, a lot of the models you are using, are often the same base model, where v2.0 was the initial released model but the model keeps training, so when they release v2.1, its still the same model, but with more training time on feedback provided to v2.0.
LLM Models are not a cake you cook one time and they are done, and you start from zero again. If you have the money, and a powerful server setup, you can take a model like GLM 5.2 and post-train it, to reduce specific errors. Sure, you need a ton of money because its a large model.
But people have been doing this with 5M, 100M, 1B, 5B models for a long time already. To the point that some of the small models can do specific tasks, almost or better then some of the huge more general trained models.
> The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
I feel like this line of thinking is kind of an unfair comparison. I'm not saying LLMs are magical beings that can suddenly learn by themselves after getting something wrong, but your "person making mistake then being corrected" assumes you do tell the person about the mistake and tell them to avoid doing the same mistake in the future, but for the "LLM making mistake" example you then intentionally avoid letting the prompt being changed in response to the mistake, which would be the "then being corrected" part on the LLM side of the comparison.
Similarly, if you just let a person make a mistake and don't let them know about the mistake, they might keep making that same mistake over and over again.
If you update how you use the LLM as you discover what mistakes it does, just like you'd correct a person, then you can use an LLM and also the LLM can "be taught to not make that mistake again".
"AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck."
please take a look at the error(s) made in the prior run. what could've been done better? create or modify an existing skill to emphasize this, or suggest additional language in AGENTS.md.
Indeed. Any meaningful AGI/ASI will have to have a form of memory / continual learning. Sam Altman said last year that this will be the focus for GPT-6.
The whole "soul.md" stuff today is a poor approximation to that. But I wonder whether it will grow into it, like chain of thought prompting grew into reasoning models.
I didn't put them on the same level.
At the same time, one should acknowledge that not all tasks are on the same level.
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I'm struggling with the assertion that these models cannot provide reasonably deterministic guarantees.
I am using gpt to populate JSON objects conforming to a list of natural language constraints for purposes of generating fake customers. I am finding that gpt5+ never fucks up. Not even a little bit. I've ran this test hundreds of times with 20+ constraints and it's been perfect every time.
Stable information yields stable control flow. Humans are much more likely to forget one of the many constraints during testing. This happy mistake may incidentally cover an edge but it also means we lose coverage elsewhere.
I think whether or not the LLM should be allowed to directly author deterministic control flow (code) is mostly the same thing. If you have a lot of constraints you want to satisfy all at the same time, this can give you a hit very close to the ideal target very quickly. Not knowing exactly what you want is when the LLM takes you for a ride.
We could probably debate this ad nauseam, so I'll just give you my most compelling arguments.
1) Writing code the "old fashioned" way (i.e., a Python program that does X, Y, Z) allows you to arrive at a battle tested solution that will not change over time. From a risk assessment perspective, the behavior is essentially immutable, allowing a business to guarantee consistent behavior over long periods of time.
2) Just because something hasn't happened to you do, does not mean that it will not happen. LLM are opaque. If you stay on the "happy path", you may see consistent behavior for long periods of time, but there's always potential for an edge case where something goes catastrophically wrong. This is without even opening the can of worms regarding prompt injection and intentional sabotage of a working system.
3) There are plenty of real world examples of an LLM spontaneously deleting data from a DB (or the entire DB) or otherwise going completely off the rails. These might seem hyperbolic, but it happened at our company (to a test DB, not production). The severity of errors that occur can be existential to a business' survival without the proper guard rails.
4) There's no concrete way to truly confirm understanding between an LLM and a human. It can tell you that it completely understands what you want, and then it can do exactly the opposite. Followed by, "my bad" (Claude's new favorite catch phrase). Code can be audited and even proven to be correct given the appropriate level of time and energy.
My best results have been gleaned in using LLM to produce deterministic systems. I recognize everyone has different use cases and needs, but this seems to be the best use of the technology in my experience.
> I'm struggling with the assertion that these models cannot provide reasonably deterministic guarantees.
LLMs are probabilistic by design so running the same prompt multiple times will give you different results.
Otherwise, we wouldn’t needed LLMs and could replace it all with Postgres
I can argue that by applying multiple stochastic processes, with a human in the loop, that you will (may) converge on something that is deterministic. You use tests/test vectors to prove this.
We're no different to AI. The code we write to solve a particular problem can (and probably does) change from day to day, depending on your "mood", what you had for breakfast, if you've been fighting with your significant other, other problems/human emotions.
the humans only there to take the blame. you arn't not goingto be a cyborg, nor is your idea of determinism ever going to last the cycle of the agents, no matter how many memorrry layers, skills and other context guardrails you place.
You're there for blame, not much else. The systems are still going to churn garbage, but because it's a business, that business will rather pay less for garbage they can sell than pay you a living wage, eventuially.
People really need to read Dijkstras Go to statement considered harmful letter [1]. If the obscurity of go to for static analysis of the code was too much, of course bringing in a literal ai black box is harmful for stable processes.
[1] https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.p...
"Business processes" can also mean "building power point decks" and other things.
But your point stands: for critical business processes that need predictability, we indeed need determinism.
Yeah because "works many times in a row" = "deterministic" to many people.
That's kinda hilarious. Pretty soon they might just ask people to write code themselves.
I’m in Finance and learned pretty quickly that to point out the implicit future cost raises based on the cost the LLM-providers need to recoup was unpopular at best (STFU better describes the situation). Running full force into a bear trap.
I've seen that mentality and gone to bat to convince a boardroom that it's the wrong approach, when people were star-struck by the possibilities. Luckily I'm in a position as CTO of a (very non-tech, brick and mortar) company that entrusts me to manage their budget for new features, and prevent erosion of our software/logistics over the long term. And I've come down decidedly on the side of not having LLMs fuck with any schema or architecture changes or anything in the codebase that would touch upon business logic. When your code actually encapsulates business logic, which is often counterintuitive and full of weird exceptions, 90% of the code work is done by prior planning to map out all possible branches and the algorithms to assist employee decision making. The 10% that's actually writing code needs to be done by someone who understands the entire stack and business model perfectly. Some nice HTML/CSS fluff here and there is great to hand off to an LLM, and you don't need frontier models for that.
I shot down similar arguments in favor of outsourcing overseas for years. Outsourcing any critical logic to an LLM is even worse.
Other than the delay time, I'm not sure I see the difference. You're removing your primary from the job of writing code, putting them into an editorial role, which removes responsibility and agency and actual hands-on understanding. The quality of the code is beside the point. More friction (language barriers, time zone difference) is actually better if you want to maintain institutional knowledge, because it requires more intellectual engagement. Accepting the LLM answer is too easy and leads to a decay of the systems knowledge and of the thought process.
[edit] as the sibling points out, decayed system knowledge leads to relying on the LLM to fix the bugs the LLM introduced, which causes further decay in the institutional ability to reason with the business logic in code.
It can be as similar to or as far from outsourcing as you allow it to be.
The "if AI code breaks, who will fix it? -> "AI will fix it" exchange has (anecdotally) been very common among executives, which is much closer to outsourcing than programming.
No need for the downvotes imho. Can’t speak for u/noduerme but in putting outsourcing (labor, LLM/AI) in the same basket I don’t see a category mistake, but a dry way of looking at business. What are the risks, what are the rewards, what future skills are we at risk of losing (the business logic part) if we go in direction XYZ.
Something completely different (but with the same logic): do you outsource legal, hire your own team of business lawyers or will you let customer services use AI for legal problems (and only hire a lawyer for a day in court)? I think all three solutions are currently active in different firms. From a risk perspective I would always want a lawyer on my team. Insource those learnings. But perspectives vary.
You know… up until your comment I’ve never even considered there are companies right now outsourcing overseas to people who will just vibecode it for a quick buck, my those companies live in interesting times
You’re in finance and you should know better… it’s not about recouping costs.
It’s about pumping up revenues, earnings and eventually cash flows (since they can’t keep raising money constantly) to support a nonsensical valuation.
This is wrong. Token costs for the same model rapidly collapse over a year. Hardware inflation is a thing but not bad enough to outweigh the massive impact of software optimizations.
Tokens billed at API prices are profitable for openAI and anthropic today and it only get more lucrative every month for them as their inference costs fall. If it weren’t for continuous massive training runs taking larger and larger capex, these companies would be massively profitable
The sad part is that you work in FINANCE of all things and this happens there.
Like: What competence do decision makers in FINANCE have, when they are this oblivious to economics?
>> Going squarely against the market sentiment is not the way to gain and keep confidence
I don't think this is axiomatic. I usually invest my time, energy and money in things that go squarely against the market's momentary sentiment.
A better question would be how anyone who thinks about economic fundamentals could get a job in finance (or stay in it).
[no offense intended to the parent post, I'm on their side]
oh no! what about all these demos which product management vibe coded?
> Pretty soon they might just ask people to write code themselves.
GPT-10 Human Ultra
Same here. It's insane that big, conservative tech companies would sign contracts saying "allow our engineers to use however much they want, we'll pay the bill later, no matter what it costs". In any other domain my company would insist on prior permission, and soft usage caps, and hard usage caps, and real-time tracking of actual dollar amounts (not just opaque tokens/credits, not just an after-the-fact view on a dashboard).
The AI companies' salespeople must be the greatest geniuses in the history of the world.
This is the first time in history that I have seen the shovel-sellers convince a supermajority of the gold-miners that "amount of gold mined out of the ground" is less of an indicator of profit than "amount of shovels purchased".
I’ve switched to a more precise use of agents where I give less autonomy but I have much higher trust the output will be what I expect.
And I do more hand coding to guide the agent to useful patterns I want.
I feel like that’s how I used less capable agents a year ago. But I’m finding even with high quality agents, the slop creeps in.
I want more control. I want to save money. Hence going back to a more 80/20 agent to human LoC split.
We are using this brand new hammer to build everything!
Please stop using this brand new hammer for glassmaking.
The leader boards based on token usage happened in our org for a month. Then we managed to convince the board that what matters is the reliable software shipped. Now we are back to DORA metrics.
Apologies for sounding like a billboard, but this is exactly why we built https://flowstate.inc/
Dog! Use it and tell us how it is! Stop with this token maxing moaning. You have access to a new powerful tool.
Are corporate employees not allowed to use personal subscriptions?
Sounds like a bad idea in general. Any data use agreements get lost, shadow-IT brews and nobody knows what tools to use, oh and it's against the service terms.
Generally not, since the corporate account has all the privacy knobs turned up. I use my personal account on my open source projects, where code leaks aren’t exactly an issue.
Unless you get written permission you can be sued for publishing their trade secrets. This can end in jail time if your employer is particularly uncaring.
Ymmv, do whatever you want . It's your life.
If you want your company's code to be used in training, then yes.
I pitched this to management as a way to get a very expensive fine tuning job done for free. They're not quite across that line yet but we're getting closer every day.
> 2 months ago management was showing us scoreboards, praising leaders who used most tokens.
Everyone is insane.
It‘s a collective psychosis. There is not other explanation.
Everyone is falling over themselves out of FOMO.
It’s 100% FOMO from investors, board members and C-levels disconnected from the daily work.
The thing with language models is they are tailor made to fool managerial types into thinking it’s the holy grail.
Just like many managers, the appearance of productivity is all that counts. And LLMs shine at giving the appearance of having solved all of the managers problems, and all they have to do to use it is spend on tokens.
This isn’t to say that LLMs aren’t truly useful, they absolutely are. But they’re very nature is one of simulating intelligence through next word prediction.
The chat modes and models are by their nature supremely attractive to management layers, because they give answers that sound so damn plausible even when they are complete fictions, and uttered with such confidence how could they be anything but the singularity.
I'm using local models in Ollama for most things and only use the paid corporate account when my local model gets stuck.
for code as well? which local model are u using for coding
interesting, our enterprise account here in Australia doesn't have access to it yet.
I wonder if clues like this will be what are written in history books as the beginning of the bubble bursting.
I don't think that people relying on the tools too much is the first sign I would identify to mean the bubble is popping
The market is priced at expecting AGI levels of breakthroughs. Just a very useful tool for programming is definitely not enough to keep the music playing.
What does "AGI" mean to you such that we have not had it for years already? Whenever I see people mention AGI it's in the future tense, as if we don't have artificial machines that can apply intelligence to general domains.
The problem is that it's too expensive. It can technically do parts of my job for me, but at a variable cost that's higher than mine right now.
No, the clue is people realizing that relying on the tools costs too damn much. The tools may be fine-ish for what they do, but there might be a bubble built on unrealistically high expectations of revenues.
[dead]
wow almost as if they needed to incentivise people to use and then tame it down to keep it in sustainable levels. shocking!
For context:
> Additionally, we’re introducing a new ultra mode that goes beyond the capabilities of a single agent by leveraging subagents to accelerate complex work.
https://openai.com/index/previewing-gpt-5-6-sol/
Can someone explain how this compares with Pro? I thought Pro was already something similar.
For pro mode the agents worked independently and only when they all finished did a new agent take a look at everything to merge the work into a single response. The new thing involves subagents that have been trained to cooperatively pursue a task and are allowed to communicate with each other along the way.
I tried a pro model out the other day and thought there must have been a bug in Pi’s cost calculations. But no, it’s absolutely fucking insane. Wasn’t even any better at the task.
I really suspect that the models are basically the same below, it’s all in the prompt. The way I use them, surgically, they seem to perform about the same. Fable certainly hasn’t blow my socks off.
This is where I think you see the distinction between two classes of LLM users:
1. Managers: those who generally know what needs to be done, and want it done faster, so they provide a lot of instructions and context (where many developers fall)
2. Executives: those who vaguely know the end goal, but are clueless about the process, and are willing to burn resources and cycles on a black box to get the result
> Fable certainly hasn’t blow my socks off.
Same. I suspect they'll get better at taking in terrible prompts over time though... Maybe that's what Fable does better, reminds me of Sora 2, it would take my crappy prompt and expound upon it. I told it once to generate a video of someone working at some company that changed its name, but the old name had historic relevance, it referred to the new company name without me telling it to, by virtue of me wanting a video of TODAY with a 90s icon.
Some of it feels boiled down to "opus works better when told not to be dumb, fable's prompt tells it not to be dumb."
If they know much of what the tool is used for, they can customize prompts to "do that usage right" even if the user doesn't know exactly how to ask for it.
> Fable certainly hasn’t blow my socks off. Same. Its not so much perf increase as cost increase justified by ambiguous perf increase.
Do you have a source for this, or just rumors?
The responses I get from pro don't feel like ensembles. They are often very one directional.
This can be because the summary model just picked the output from one of the sub agents.
oops
> makes use of parallel test time compute
Any idea what that means exactly? I vaguely remember that ChatGPT Pro was originally called "deep thought", just like Geminis "deep thought" feature (or "deep think"?), so it seems likely they are using the same approach.
I imagine this is something like Anthropic's dynamic workflows where a JS file is created to make a little AI harness on the spot
Wow, I hadn't heard of this!
const audits = await pipeline(found.files, file =>
agent(`Audit ${file} for missing authentication checks.`, { label: file }),
)
Apparently in the web chat (and also in Claude Code?[0] Though I haven't tried yet) they can call the Anthropic API and your subscription key gets auto-magicked into the requests somehow.
Those are two separate things of course (aside from the key-injection) but I guess there's no reason it couldn't run completely in the front-end... hmm...
My understanding is that in the generated JS code, the subagents are invoked as headless Claude, equivalent to Claude -p, or the agents SDK.
I feel like it's not crazy to run Javascript in the browser... We've come so far I almost forgot where it all started.
> In the beginning, the Universe was created. This had made many people very angry and has been widely regarded as a bad move.
How is this any different than what we have already? We've had this ability for ages (6+ months, decades in the AI world), you can literally today easily prompt CC or Codex to use subagents to accomplish tasks and they'll do it well. My entire workflow is one top level orchestrator chat creating tickets to dispatch to subagents to implement, and other subagents to verify. Why is this being sold as a new thing? Have HN users never tried tried asking CC or Codex to use subagents?
I'd love to read more about how to use this workflow. What kind of top level instructions does this actually work with? Is there an article out there with some concrete examples of how to do this effectively?
in opencode, if you directly referencee a subagent keyword or the name of a defined agent, it'll often spawn the agent.
If you don't mention it directly, it's 50/50 whether any given request will invoke a subagent.
The same with tools, skills, etc. No matter how smart these LLMs appear, they rarely do thinking as you expect.
So basically: learn how the harnesses operate, and know the names of the tools they have.
The top comment on the thread explains this will involve subagent to subagent comms.
To what effect I don’t know… I thought subagents were useful because they were explicitly single purpose and bound to a narrow context
I assume this is ~equivalent to ultracode in Claude Code, which can deploy a tree of hundreds of nested subagents and was just released experimentally 5 weeks ago IIRC.
Because most people need complexity to be wrapped in a simple UI/UX. Most people just want the one-two button press and be on their way.
Pro also makes you ask it to use sub-agents instead of just doing it when useful.
Hopefully, 5.6 will automatically spawn sub-agents without needing to ask.
i would believe this will be matched with something like orchestrator-focused model: https://news.ycombinator.com/item?id=48624782
I wonder if it's related that that OpenAI has found a way to cut inference costs by half, according to The Information.
https://www.theinformation.com/newsletters/ai-agenda/openai-...
"However, these inference optimizations, which rival Anthropic refers to as “compute multipliers,” are a big focus for all the labs. Anthropic CEO Dario Amodei has been publicly talking about the concept since at least mid-2023, when he said on a podcast that the company limits “the number of people who are aware of a given compute multiplier” because it could give other AI labs a leg up if they were to be able to replicate them. (Compute multipliers can also refer to efficiency optimizations in the model-training phase.)"
Yes, on a world with finite resources where your industry is singlehandedly siphoning ALL THE RESOURCES - hoard general efficiency optimizations and treat them as trade secrets - winning is all that matters, normal people and other species and the planet be damned.
Everything I hear about Dario these days makes me like him less and less. He sure did seem to speed run the 'tech leader with scruples' to 'tech villain' path! I guess all the cycles are compressing as we approach the singularity..
It gives them a massive advantage because they can cut the cost per token by a lot and eat Anthropic's market share.
In what universe is any company going to give that advantage away?
In any case if they take away a lot of market share it's basically the same in the end - most people will be using these optimisations.
Deepseek gave it away with R1. They did it again with V4 and DSpark. You don't use it because it's Chinese
Companies and people worldwide just use whatever Americans use, because they read English but can't write it. Americans don't use Chinese models because they're Chinese.
Ok I’m not sure I follow your point here. Isn’t all that he’s saying that if they find some optimization techniques, that gives them an edge? And that makes sense?
How is this suddenly evidence of him being a villain?
The evidence is that unlike Deepseek he does not publish his compute multipliers. Under that argument Deepseek should not publish any of their research either.
It has got to be one of the most insane takes I've read on HN, which to be fair has been trending towards "unhinged" when it comes to Anthropic and AI safety.
Compute multipliers are like a quant firm's trading algorithms. They're the crown jewels, the whole alpha of the lab. If you leak them, the lab dies.
Protecting them does not make Dario a villain, it's literally his job. It's also Sam's job, Denis's job, Mira's job, etc. Every lab guards these multipliers closely because they represent the entire worth of the lab.
So how can Deepseek publish them without killing themselves?
The fact that they don’t release model weights for free to download on huggingface means that Sam, Mira, Dario, etc are ontologically evil and may they all reincarnate as either durian fruits or cockroaches, ideally as durian fruit infested with cockroaches…
Not sure I know where I fall regarding your point: Yes to trade secrets, but also science and AI should be for the good of all.
OpenAI seems to be trading roles back with Anthropic becoming misanthropic. I hope they both start heading in the direction of how the AI field was prior to LLMs.
Collaboration and benefit for all should always be the primary motivator.
> Collaboration and benefit for all should always be the primary motivator.
Of all the things to never happen, this is never going to happen the most.
That train left the station for good once hundreds of billions to trillions of dollars were involved.
On the bright side, in the long run I suspect the vast majority of the value of AI will not be captured by the model making labs and the vast investments in them are going to implode, so...
They're in a price war with the People's Republic of China running flat out with the full backing of a government that literally does not care if they ever see a financial return on the investment, they just want to drive the value of LLM training and inference to zero because we banked the market on it being arbitrarily high margin forever. China was like hold my beer.
They have a staggering surplus of grid capacity and can bring more online without any difficulty. We couldn't get a serious nuclear project done if Jeffrey Epstein was offering private flights to the ribbon cutting.
In the United States at any given time more than half of the FLOPs are badly misallocated, Meta has like, a double digit percentage of the total capacity going down the drain every day and has for years. That's a conspicuous example but on OpenRouter rankings it's rare to see more than one or two American vendors in the top 10, sometimes the top 20. But 3rd, 4th, and 5th place are all merrily burning half the compute duplicating effort and missing key innovations because we stopped publishing real results. In China if DeepSeek makes a breakthrough it's at Zhupai and Moonshot and MiniMax and MiMo and Qwen that week.
Our only lever, export restrictions, seems to do nothing but breed multiply antibiotic resistant super hackers who just get more efficient and immediately propagate all of those efficiencies to the rest of the Chinese AI industry.
At the beginning of 2026 there was one Chinese lab with a model that had any real relevance fielding modern tool users. Today in July there are like, eight lagging the absolute frontier by maybe 3-6 months. Barring some massive bend in some curve 3-4 of the top 5 and 6-8 of the top 10 will be Chinese and open weight by January.
The great irony in all of this is that our current playbook is straight out of the 1960s USSR, and the PRC's current playbook is straight out of 1960s USA. We're the ones with the opaque decision making and gross resource misallocation driven by the personal agendas of a shadowy cabal of frenemies wired back channel into government in the form of the individuals rather than the offices. They're the ones with a thriving marketplace of ideas powered by robust public/private partnership and a paved path running bidirectionally to the university system.
It's going to implode because the Kruschev system does. Theirs is going to thrive because the Kennedy system puts a man on the moon before the decade is out.
> the company limits “the number of people who are aware of a given compute multiplier” because it could give other AI labs a leg up if they were to be able to replicate them.
I wonder if that makes sense if the orgs within the industry are starting to shift their mindset towards "Tokens are expensive, we should use AI less." which feels like an existential threat to the status quo, if those AI providers can't find ways to keep costs affordable for their clients. Otherwise those orgs would just be using GLM 5.2 or DeepSeek V4 Pro but it seems like what they're doing instead is trying to use AI just less, period.
And Anthropic sure reads and applies all the open research.
This 2023 thread about this issue is prescient: https://old.reddit.com/r/MachineLearning/comments/11sboh1/d_... (just add Anthropic to OpenAI)
i really hope it's just what Deepseek V4 does. Deepseek V4 is very cheap and highly performant
OpenAI tried to pull off the same trade secret thing with RL when they announced o1 and o3, aka "Compute time scaling". Then Deepseek revealed it with Deepseek R1.
Could also be something like Deepseek DSpark. Or using diffusion like DiffusionGemma as a draft model. The timing between the release of those, and this article, makes me think its maybe one or both of those things
deep down, i suspect they're all just drafting on implementations to llamacpp.
I agree that Dario is pretty annoying, but I think the "tech villain" archetype is essentially survivorship bias. The tech leaders who don't act that way are not nearly as visible because they're not nearly as successful.
How are the Chinese doing it then? It's not a zero sum game is it?
HN is just a massive Anthropic hate fest now, probably funded/manipulated by OAI's $8B PR budget.
OP phrases it as a bad thing that Dario is keeping compute multipliers to Anthropic. How naive can one be? Compute multipliers are the whole business. Those are the trade secrets every lab is built on. It is the alpha of the business. How does protecting this make Dario evil?
This website is getting out of hand with the uninformed hot takes. I wish when HN was still people that knew what they were talking about.
I'm saying I would do the same thing if I were Dario. I don't think he's evil. I just think his hero complex is annoying.
Hey! I'm an on-again-off-again Anthropic hater and I may be guilty of uninformed hot takes but I'm not paid for by OpenAI[0]
People have different opinions than you, it happens..
[0] @sama if you're reading this we can fix that...
I'm not as conspiratorial as you, but it does seem the tide of opinion here is turning against Amodei, for no particularly obvious external reason. At the same time, there does seem to be at least some evidence of adversarial attempts to oppose data-centers by America's competitors.
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Dario tells the truth. If you look at everything through their safe AGI mission it all makes sense. They are not bs'ing about that. Also I think most people just read headlines or 10 second clips and make false extrapolations from there.
(BTW Anthropic only exists because Sam Altman is a liar, Dario admitted this.)
> If you look at everything through their safe AGI mission it all makes sense.
Except for, you know, all the outside investors and the forthcoming IPO.
"if I don't do it, someone worse will"
Of course that's what Dario thinks because that's what every tech CEO thinks. Dario, Sam, Sundar, probably many Chinese CEOs as well. It's what everyone thinks. That's why they're competing so fiercely with one another. That's why they basically make all the same decisions. That's why we need properly open source AI.
You expect AGI to be built without additional investor money?
Keep in mind, these tech leaders have deluded themselves with the infinite jest of "effective altrusim" which can effective ignore any problem today for some imagined future problem that they're solving. So if they have to enslave the human race because there's a super killer asteroid 10 million lightyears away heading towards us, they'll do it, because obviously, saving future humanity is the only thing that makes sense (because only their genius can save the day)!
> He sure did seem to speed run the 'tech leader with scruples' to 'tech villain' path!
What kind of rosy-eyed chump believes in the "tech leader with scruples" bullshit? It always lies.
Did some people just ignore Mark Zuckerberg and Tim Cook's sociopathy, somehow? Did anyone buy into their "privacy is a human right" nonsense?
Zuckerberg never had scruples, and everyone knew that from the start.
I find this kind of cynicism fascinating tbh. On the one hand, it seems so relatable in some ways, because there is something uncomfortable about being seen as naive, in a way that being seen as cynical or negative doesn't seem to carry. I guess it's just self-protective, almost like some kind of perverse Pascal's wager: it's better to think everyone is horrible and be wrong than to think the opposite and be taken advantage of?
The thing I can't quite square is that it doesn't really fit my lived experience. I have known sincere, genuine people in the types of positions that I'm sure someone like you would declare to be sociopathic.
But beyond that, I just don't know why it would actually be true that everyone at the top is a villain. Why couldn't someone like Dario (or even Altman, gasp) be sincere? Because if he is, it does seem like a lot of the moves he's made would make sense given his worldview.
But if you assume he's just a villain, then you can twist any of those moves to just be further evidence of that which you already believe.
I don't know, I just find cynicism interesting, and a little sad.
OpenAI's agreement with the Pentagon was "No use of OpenAI technology to direct autonomous weapons systems".
And about the mass surveillance, I don't see why the military should not use AI to do surveillance abroad.
> how far can I push myself to see multiple facets of a story?
Please, I'm dying to hear the optimist's take on Mark Zuckerberg's career. It wouldn't happen to be embarassingly foolish, would it?
It's a lot easier to sound smart on the internet if you're a bitter cynic.
Lots of nerds for some reason have made cynicism a personality trait. They think optimism/honesty is hopelessly naive, therefor cynicism is the correct default.
> I just don't know why it would actually be true that everyone at the top is a villain
History.
Also, nobody said 'everyone' or 'villain'. How Paul Graham of you.
IDK, only one company has held their two red lines in open conflict with the government, while all the others capitulated to "all lawful use."
simple math. Money == Power.
Power corrupts, absolute power corrupts absolutely.
Given enough money and increasingly perverse incentives to gain even more has a very high potential to corrupt.
Did they start out as corrupt, or was it the influence of the power that came with the obscene amount of money?
It's really a chicken and egg level of calculus.
Doesn't matter which came first, either way you get feathers and chickenshit all over the yard.
Do some very rich people still seem very nice in person? Sure. Of course they must, because otherwise no one would willingly work for or with them. As the total amount of money goes up, the incentives to remain 'seemingly nice' go down and either you get to see who they really are, or who they became through the choices to make that much more money. Doesn't matter which is true.
The examples of non-villainous billionare are rare.
Of non-villainous multi-billionaire; lets see there's about eight of them that stand out for giving significant amount of the massive wealth to helping the world around them, who live normal lives like the people in the communities where they reside, and who participate at the companies they own by eating in the company cafeteria among the people who earn the wealth they enjoy.
Thats 8/3400 global billionaires ... about a quarter of a percent.
And of the 'pledges' like Giving Pledge by the billionaire class, the actual amount delivered - not parked in a family or private trusts for tax deductions; but actually delivered to the front lines of any global crisis amounts to 0.18%, less than one fifth of one percent of the $20.1 TRILLION dollars held by that class of owners. thats less than $2.00 on every $1000.
That's not to say that donating to public needs is 1:1 for non-heinous behavior, but it seems like a basic tool for distinction. the 'can make a significant difference in global suffering : chooses not to' ratio as a surrogate for villain may be useful metric and doesn't require cynicism as the underlying rationale for calling someone's behavior as unkind in general or mean in particular.
The only reason Chinese labs release their research is because they are behind. They lose nothing by doing so because the alpha has already been discovered.
This is not hard to understand. Do you really think DeepSeek would publish their algorithms if they led the American companies? Lmao.
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I wonder if AI labs are actively manipulating the narrative (and thus investor sentiment) by airing problems, and then solving them weeks to months later. I wouldn't be surprised if they have a lot of stuff figured out that is not included in the current version, just to make a steady product cycle with years of tangible improvements from one version to another (this is a common practice in the industry).
For example, if inference isn't too expensive, but they figure out how to cut costs, then price goes down. After all, why pay OpenAI when a smaller datacenter can give you similar models?
But, if they make a huge issue about how inference is too expensive, they engineer a crisis of their own creation - then, once they deploy the solution (which they might already have), then they're back on top.
Semi-related, has anyone noticed their GPT 5.5 usage in Codex being cut in half as of a couple days ago? I got a lot more mileage out of my session usage yesterday for the same workload.
I've noticed less quota and 5.5 intelligence degrading. I didn't run the analysis like the post the other day, but I had noticed decreasing ability to complete tasks, more laziness. Switched back to 5.4 and it's much better. Maybe they're getting ready to launch 5.6?
What’s the technique? And did they buy it from thinking machines?
Maybe cache similar answers from others. Surprised if this is not already being done.
They're already doing this under the name "fast answers"
[1]: https://www.reddit.com/r/OpenAI/comments/1stsxvc/new_feature...
Like google search, this does not work because of how common long tail use is.
What you think could be a big chunk, is more likely to be a fraction of a percent of queries.
And what use is similar query caching - so you (very often! if actually cost effective, maybe half the time) get a response to a query that was different from yours. Including for when you have a lot of context input already. You’re going to get trash.
If it were constrained to only very common initial prompts, and somehow the long tail did not actually dominate as it does with Google search (can't find the reference at the moment but it was a famous article some years ago), it also wouldn't account for serious enough cost savings. Long context is what is expensive.
This might only work in constrained domains like customer service where there’s tolerance for generic answers and escalation paths. For technical work? For general purpose use, with secretly canned responses charged at full price?
Questions like that cost a tiny fraction of a cent. "What's the capital of Sri Lanka?" cost a fifth of a cent at GPT 5.5 API price, and would cost a fraction of that if the question were routed to a more suitable, cheaper model. The output was 78 tokens.
By contrast, when coding, devs typically have hundreds of thousands of tokens in the context window, and may use many millions of input tokens per day.
Caching requires the full prefix to match exactly. If a single word differs near the beginning of the prompt, nothing after that can share the cache. So this type of caching would save a few queries that cost virtually nothing, but wouldn't help with the stuff where cost matters.
How is that cheaper? You now need to have a database of millions of possibly gigabyte sized rows. Also, transformers have quadratic complexity, so short queries cost practically nothing.
The only optimization that makes sense is per user prefix caching, because you are often sending the same system prompt over and over again or are continuing a conversation.
I wouldn't
I don't understand how this fits LLM architecture at all
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I’m really looking forward to see non contaminated benchmarks. In this space its obvious that every day is just another day of a race. Cross fingers we get better opus for lower price
Hope this forces Anthropic to be less stingy with Fable.
Not including their best model in a max subscription would otherwise be truly a good reason for once to consider going back to openai for me. I'll at least try it.
At least Anthropic have a max subscription for corporate. Codex is only pay-as-you-go pricing beyond the base plan. Hence I'm stuck with Opus for work for the foreseeable future.
Apparently not? At least our it guy said after 150 people or so you have to pay for enterprise which is pay per token for everyone.
> Codex is only pay-as-you-go pricing beyond the base plan
I don't think so, I'm on a ~$200 subscription (guessing that counts as way beyond "base plan") and have no pay-as-you-go pricing at all, using the OpenAI APIs would be way too expensive for me.
For corporate *
You can't use sub pricing with orgs w/ OpenAI.
It is not because they want to but because they literally don't have the capacity to.
Why haven’t we seen any queues or the like over the past week then? If it’s truly a capacity limitation why not just boot subscription users to a lower priority queue or limit usage to outside peak hours?
There could be a whole host of reasons. It may be during launch that compute is re-allocated from training to inference so that all users can try Fable. Soon that compute will re-allocate back to training until they can get more compute.
What a suprise, to make better models, they just making them larger and larger, then they can barely run it, after a round of LARP-ing that they invented some dangerous LLM?
I thought that was solved after the SpaceX deal? Claude is rarely down since that and they have a 1.5x usage promo until 13th July.
Has anyone already tried 5.6 Sol in their day to day coding/development activities?
How does it compare to GPT-5.5?
Has it been released yet? I'm not seeing GPT 5.6 anywhere in the selectors, nor any announcement about it, but you're talking about it as it's already been released?
There's something like 20 companies with access.
Isn't it like 99% sure all those companies got that access to it together with NDAs and other goodies?
Some of them will publicly break it for whatever reason, an NVIDIA engineer (@blelbach) recently made some posts on X about his results using 5.6 Sol Ultra to optimize stuff and then deleted them shortly after (presumably got yelled at).
Recently, I've been so eager to get new model releases in Codex. I'm hooked. I hope this accelerates development. Shows how dependant I have become to Codex.
the naming convention has reached the point where i am pretty sure the next one will just be called "GPT: The Reckoning"
Will it have similar limited access like Fable? It is an interesting timeline, as general access for Fable (without using extra credits) is coming to an end :(
It is like with drug dealers, you get some free dose, then cheap one, then you pay an arm and a leg.
Somebody has to finaly pay for these heaps of accelerator hardware.
Are people excited about the capabilities of 5.6 over competing LLMs?
The full conversation https://xcancel.com/haider1/status/2073695124220006575#m
Here's the correct link, not sure what happened with that first one https://xcancel.com/thsottiaux/status/2073933490513752151
It doesn't load. It uselessly keeps showing this in a loop:
> This process is automatic. Your browser will redirect to your requested content shortly. Please allow up to 0 second…
Remove the #m at end of the link. That's causing the redirect. Here is a cleaner link: https://xcancel.com/haider1/status/2073695124220006575
are you, by any chance, a bot?
Not at all. I was controlling the browser myself as a human, not via any bot.
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Only children want to read social media threads without logging in?
You would be surprised to hear that some of us don't have X because we don't use social media, not for any kind of cultural war you're imagining.
I made (repurposed an existing) an extension for firefox to cancel xcancel: https://addons.mozilla.org/en-US/firefox/addon/xcancel-to-x/
Which will be the uses cases for this model?
Nice! It never made sense to me that Pro Extended wasn't in the Codex app.
can't wait. been maxing Fable out, if Sol Ultra turns out to be as good and in Codex - that's a paradigm shift
Related:
Previewing GPT‑5.6 Sol: a next-generation model
I still don't know why OpenAI doesn't put gpt-5.5-pro in Codex. It's one hell of a model and easily parallels Fable/Mythos. Sure, it'll use up your quota much faster but that's the price some users are willing to pay for absolutely high quality responses.
I think gpt-5.5-pro runs 12x parallel gpt-5.5 agents behind the scene and uses OpenAI's secret sauce to synthesize their answers into one insanely good response.
API pricing ends up being something like 20x more expensive for GPT 5.5 Pro than GPT 5.5 for actual work, even though the token cost is "only" 6x. On benchmarks where I've run both, I saw $1.12 mean per task with 5.5 and nearly $23 per task with 5.5 Pro, I guess it chews longer and harder on the problem.
If that's at all reflective of what it costs them to run it, I imagine they're in the same boat as Anthropic with Fable; they probably can't afford to offer it at subscription prices given current cost to operate it.
If 5.6 Sol Ultra has efficiency improvements (at one or more layers), and it allows OpenAI to offer a model that's competitive with Fable on the subscription plans, I'll guess a lot of folks will switch.
Fable is notably better than what came before. I watched it figure out stuff on its own over and over, on extremely hard problems, that I previously needed to guide a model to an understanding about, or work with them back and forth for several turns to figure it out together. Like, I've been reverse engineering a hardware device lately, and I've tried to tackle it a few times in the past with both some version of GPT and a couple of versions of Opus (most recently 4.7). In all cases, I barely made progress...would have gotten there eventually, probably, as I'm stubborn, but there were roadblocks constantly, with me and the models getting stumped and going around in circles in the end on every prior attempts.
Fable figured out other ways to find out what's happening, it dug into config files, found and extracted Boost-serialized data, compared that data to the observed behavior, built tools to compare the observed data with our emulated behavior, without being prompted. Would I have gotten there? Eventually, maybe. All prior models didn't; they mostly just tried the things I suggested and stopped at "well, that didn't work" or declared success after seeing results that matched their misunderstanding of the problem. I guess it's possible my prior attempts with other models had "loosened the lid" on the problem; we did already have a long list of documented "this didn't work" and a pile of tools for finding out if something worked. But, even so, I was impressed.
There probably will still be a "OK, let's rewrite this so it's not using lookup tables to precisely simulate the hardware behavior in software, because we don't need the noise, too" stage of the process...but, in one day with Fable, it solved a problem that I'd banged on for at least a week or too in the past with very little real progress. I don't think the models write exceedingly good code, even the best ones, but it sure does figure shit out quick.
many of us and myself have been using chatgpt pro from codex cli for months now
I recently have been testing ChatGPT business at work and the quota seems to disappear almost instantly even using weaker models. Unless they dramatically increase their quotas it’ll be unusable.
I don’t know how anyone can realistically use the “business” plans - you blow through your quota so quickly. I use a consumer Pro account ($100 a month) and don’t hit the usage limits nearly as quickly. 5.5 Pro is so slow that it’s not a big deal to paste big prompts into it and come back and check on it an hour later.
My solution for the ones stuck with that: use 5.5 for planning and 5.3-mini for the grunt work. 5.3 is remarkably useful still but you need to hold its hand.
I actually meant 5.4-mini..
Is it as good as Fable..? Fable is the first model that mostly writes without the AI slop format for me, and so I can comfortably actually copy and paste most of what it spits out.
OpenAI models have always been the worst in my experience for verbose, slop formatted responses, with each generation increasing in sloppiness.
Copy and paste...? In mid-2026? Why on earth would you copy and paste code instead of having the cli tool to the coding end to end?
I haven't opened an IDE in 8 months or so and have no plans to go back.
> Fable is the first model that mostly writes without the AI slop format for me
I'm not that impressed by Fable's writing to be honest, still has the AI giveaways like em dash.
The em-dash is not a "AI giveaway", it's just correct writing. Actual AI giveaways are in the writing style itself.
Humans use em dash as well.
I hate that I have had to remove it from my writing style because people assume it’s AI generated. But I think that ship has sailed. I’ll have to do without now.
Parens are ok for short asides (like this) but unreadable for longer asides and not usable for compound sentences like the emdash. Unfortunately, neither ellipses nor semicolons can exactly replace the compounding ability of the emdash, I find the best option without it is often to just split a sentence in two.
Macos, ios, google docs, and microsoft word will autocorrect two hyphens to an em dash, which is how I normally type it. On a mac you can also type an em dash with option-shift-hyphen.
Many word processors (Microsoft Word, LibreOffice Writer, etc.) and some online editors will auto convert double hyphens "--" as they are typed into an em dash.
Some text editors replace the -- (two separate dashes) with a proper em-dash. Literate people - who understand why em dash exists - have been using it all the time. Thats, after all, how the models learned to use it.
At least in macOS there is a key for that on the keyboard, Shift + Option + Hyphen (-). This information is a quick internet search away.
Being Hacker News, a lot of us use programmable IDEs & keyboards. I added em dash support to both Emacs and Dygma keyboard.
OMG — I'm a robot.
dash dash "--" on a lot of systems and word processors turns it into the em-dash automatically
i cant reply to hn_user2, but i have the same experience, i find myself never using emdash where i would have before
Why can't you reply to hn_user2? I just did. Did you say something mean to hn_user2 and now you have a restraining order and can't go near them within one reply?
when will it be available? do we know? I don't have X, not sure if the thread mentions it.
No Twitter, what’s he responding to?
Corrected link https://xcancel.com/thsottiaux/status/2073933490513752151
Check out LibRedirect or Predirect (MV3), it automatically redirects youtube, X, etc links to privacy-respecting frontends.
https://news.ycombinator.com/item?id=44344246
107 comments, 1 year ago.
Will individual subscribers have access?
I would assume yes - their goal is to capture consumer subscribers. Claude are going to take Fable away, and they're going to swoop in and give it to us.
This is why I don't think Fable will be taken away. Not for long anyway.
Still avialable through the API. According to people that have tried both Fable nad 5.6, Fable is clearly better at coding. So i expect a lot of people to pay extra for it.
But if 5.6 is better than Opus, Claude Code Max plan users will switch to OpenAI Codex en masse. Using Fable at API pricing is expensive
Every large enterprise?
Who do you think pays for your subscription's actual usage?
Most enterprises are already paying API prices as Anthropic are forcing them to above a certain company size.
Every company above 150 employees has to pay API prices as Anthropic won't give you subscriptions.
I love how competition is great for customers!
They really try to force everyone into AI.
Next logical step: mandatory age sniffing but it can only be done if you have AI. Those not complying will be denied access to the www.
Back to terrible naming from Open AI.
Bruh when did understanding chatbots become like following pokemon? Wtf does any of this this mean. Tf is sol? Tf is ultra? Tf is codex? Tf happened to descriptive nomenclature?
Maybe you jest, but I'll bite.
> Tf is sol?
It's a proper noun: the name given to their latest and greatest model. Means "Sun" in Latin. Similar to how Anthropic has been naming its models Fable, Opus, Sonnet etc. Their other models are called Terra (Latin: Earth) and Luna (Latin: Moon) [0].
> Tf is ultra?
The name of the "harness" around the model. It'll use deeper thinking, subagents and all that jazz in response to a prompt. Other options include max, high, medium etc I suppose.
> Tf is codex?
"Coding agent" similar to Claude Code [1], something with a more descriptive name.
> Tf happened to descriptive nomenclature?
Something like GLM-4-32B-0414-128K (not made up [2]) doesn't quite roll off the tongue I suppose.
[0]: https://openai.com/index/previewing-gpt-5-6-sol/
Of course I understand all this, but why the cuteness? Why not just say "<whatever the hell sol is supposed to imply> 2 agent?
> Something like GLM-4-32B-0414-128K (not made up [2]) doesn't quite roll off the tongue I suppose.
Surely it would sell better though if they could communicate what they're selling?
> Coding agent" similar to Claude Code [1], something with a more descriptive name.
Does this really need branding at all? Surely claude should sell itself if it can work as an agent. And if it is a specific model, what tasks is it trained to do? can we see the fucking training to make this money worth it? Or am I just another sucker buying nikes?
All of this hand waving makes me nauseated. People should buy value, not vibes.
It's a post on twitter, not a press release.
Branding is far more subsistent and meaningful than either lol. They just want to paint themselves as gods. But they are producing software, not magical beings, and we should see them as salesmen, not gods. Not to mention they'd probably sell more software if people could understand their product lineup
It's to create an in-group, and you are in the out-group.
If you follow AI news you'd know what these are easily, or even just look at the top posts in the last month on HN to see.
https://hn.algolia.com/?dateRange=pastMonth&page=2&prefix=fa...
This wouldn't be necessary with descriptive product names
they better get that out fast, it will become totally meaningless when the next GLM gets there first.
more competition is always good for consumers.
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Given that it is #1 on the front page now, people most definitely care.
Also, from the Guidelines:
> Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.
Seems rather random though that it's #1. Fable had way more upvotes.
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You.
All these names mean squat
Gamechanger..
I’m all in on Anthropic. How good is frontier openAI models for coding and things?
I can see they have inherited their poor product naming from Microslop
> single line addition to prompt to use subagents proactively.
This misses an important detail. In Claude Code [1], ultracode suggests the agent create a JavaScript code to deterministically orchestrate sub agents. This is different from just having the main agent launch sub agents and (non-deterministically) manage them.
The resulting workflow is called “dynamic” because CC creates this orchestration script dynamically, “on the fly”.
[1] https://claude.com/blog/introducing-dynamic-workflows-in-cla...
Another useful thing about dynamic workflows is you can ask Claude to make them durable as skills (or slash command) that can be invoked later.
I believe inside Google they have a similar concept called “deterministic workflows”.
I find ultracode extremely useful. Of course you have to watch how your 5 hour and weekly session usage percentages are getting used. So I had Claude make a status-line with 3 progress bars: for context window, 5h session, 7d session:
https://pchalasani.github.io/claude-code-tools/tools/statusl...
Btw, the /loop “dynamic workflow” is so beyond broken/not working.
It’s sad to see folks like Karpathy make a big deal about looping, than to find that the loop command is broken and it’s crap vibe coded documentation isn’t even accurate on the Claude docs.
This whole dynamic workflow idea is on face bad. It’s all done as a massive cope for the fact that real determinism (I.e using structured outputs to enforce control flow of tools deterministically) is bad for alignment/safety so they can’t let you have access to those tools anymore…
sources for easy confirmation:
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
The nomenclature in this industry is all over the place.
ultracode in Claude Code kicks off a dynamic workflow.
It's similar in that it also pins you at xhigh effort _combined_ with the workflows (which one might say isn't far off from proactive use of agents)