At least that site should draw out a full page then start replacing that page with the next, starting from the top and working downwards, repeating each time it hits the bottom.

[0] https://chatjimmy.ai/

I've always eyed Cerebras but never had a use for it that would justify paying for the API directly. Although now that I think about it, trying out the API would probably cost less than a subscription for a month...

not to say a speed boost isnt there but if they didnt increase tokens / s at all youd likely see things slow down a lot with the new model compared to current

https://www.youtube.com/watch?v=43QHhEfzz-Q

The new Blackwell hardware combined with TensorRT-LLM and speculative decoding consistently can hit 1,000 TPS/user barrier, comparing to closer to ~250 TPS/user (out of 10k+/TPS on the server)

Is there something I missed, this looks more like 14.4 to 56 on a 64kbps backing channel modem story. I have no doubt that there are still massive gains to be found, but they seem to be using existing constraints more efficiently, not that fios is coming.

I don’t have the budget to work on the foundational model scale, but with a draft model 10x–20x faster than target and an 60-80 acceptance rate I can see how they could promise 750/TPS (with a lot of other hard work) but I would appreciate where I should look to figure out what I am missing.

Do you feel most of the speed upgrade will come from the software or hardware side?

Imagine a world where there is no code, just things mildly handshaking and then creating data APIs on the fly. Where communication is fuzzy and locked in on an individual basis. No years of RFCs, no RFCs at all, just... data.

Just data, man.

An API arbitration aberratically assigned at authorized access, abridged and annotated, analytically assuring absolute assurance.

Most of the frontier models can, when prompted and tooled correctly, do a lot of “reasoning” tasks that amount to resolving how the user has explained a particular widely known paradigm.

The more difficult and obscure the issues you provide them with, the faster you notice them reward hacking by altering the criteria until they are no longer attempting to solve the problem. Using “advisor” style loops helps hold this off at the cost of tokens, but there is still a fairly short limit at which they will essentially give up if they can’t find all of the necessary information - sometimes the issue is actually worse if they find a small amount of information instead of nothing - they’ll extrapolate from that tiny piece of data and generate plausible-sounding hallucinations almost every time.

And god forbid your problem involves doing something a different way than the majority of people do it. Unless you can write a full spec on it, the models will repeatedly spiral back into adjusting everything about your problem until it matches one of the most popular approaches in their training data.

The council stopped him, said that if he knows such words he definitely won’t overstay his visit to work as a dishwasher, and accepted his B1/B2. Seriously.

Not sure if it would be the same if he used “defenestrate” when talking about his plans.

Jalepeno is for mass scale inference.

Cerebras is extremely expensive and difficult to scale, hence the limited release.

I tend to doubt they would. Cerebras notably doesn't have a kv, is wildly high bandwidth, but within/across the chip, not able to dump/restore kv super well. I doubt openai is going to build something that is as expensive to run. Also, wafer-scale is absurdly hard & weird to pull off, so I doubt that would be their first foray.

Better hardware, and other techniques on top of that and you speed up even further.

If your customers are fine with that, your IP is not interesting, then you can use it.

Deepseek V4 Pro on the other hand is a really really good main driver and we have a lot of success using it. Its not Opus or GPT-5.5 level but on its way. Kimi 2.6 as well btw.. so there is already quite some choice.

Llms seem to only impress a certain type of person. Hint, this type of person also was really excited about NFTs.

Citation: have you looked at OAI and Anthropic’s customer growth numbers?

Why do you think so? This game can be played forever, you just need strong marketing and orgs gullible enough to pay a higher price for a minor upgrade.

Feels like they are just pulling in as much as they can whilst competing on capabilities instead. At which point its a case of who can last the longest.

Doesn't feel like Uber/Netflix.

How many people do you see using haiku or sonnet? I see very few and most people default to the latest model and just play with thinking effort. I think three layers are good enough and supporting more is not a good UX.

Luckily in my experience it usually ends up only doing it to achieve the task set to it as opposed to anything "malicious", but boy it is scary reading back at how quickly the chain-of-thought pivots to attempts at privilege escalation or searching your disk for secrets when a tool doesn't work.

For most important work (complex, cross-domain inquiries etc.), I still rely on Codex GPT 5.5 though.

I'm working in a 600k+ LoC codebase that has complex domain-specific logic and lots of moving parts. I find that Codex 5.5 is pretty good at surgical fixes, but does not go out of its way to explore and figure out what those surgical fixes might break. So I only use it to work on parts of the system that are pretty isolated from everything else so that risk of regression is small.

But most of my time is spent on delivery, and the biggest problem with delivery is that if a bug occurs during runtime, the client curses me out. So to me, GPT code feels meticulous.

Open source contributors might be different. Most of them write code after long periods of deliberation. They take their brightest ideas and put them into open source. Those pieces of code are probably the best answers those programmers can give.

But for someone like me, who works primarily on delivery, we mostly plug in proven patterns and focus on getting things done. 'It works' and 'it's beautiful' are different terms, after all. In that sense, I highly value the meticulousness of GPT code — the very thing you called verbose. Because even if it's inefficient, at least it runs, and it catches and wraps around far more of the parts where things break.

Given a month, I could probably write code at GPT's level, at least to some degree. The problem is the difference between one hour and one month. At its core, AI code is still based on training data.

Personally, I think this kind of coding experience varies from person to person

"What gets measured gets managed"

Not saying that's the case with OP, but I've found folks sometimes just rationalize it so [0] as they're paying top dollar for it (especially, when compared to may be less capable but affordable models).

[0] https://en.wikipedia.org/wiki/Choice-supportive_bias

-Why do you cut API boundaries this way? -Why do you change the order of struct fields? -Why do you deliberately insert padding?

Most of it depends on the background and context. Sometimes you add it, sometimes you don't. To understand this tacit knowledge, you need access to senior developers. But their attitude often depends on how promising the student is and what background they come from. On top of that, you don't have to rely on the respondent's mood, authority, or availability.

Programming is fundamentally a field that requires seniors. In my case, I had no such seniors at all. I learned to code by buying codebases from failed companies and studying them. My first job didn't hire me as an employee—they hired me as the CEO of a subcontracting company (because that was structurally more advantageous for the contract). So I wasn't given the patience to learn programming fundamentals gradually. I had to pay penalties if I failed. Most of the projects I worked on were the kind where failure meant bankruptcy for me. Naturally, there was no one to teach me.

Most of my knowledge comes from reverse-engineering the code I purchased.

People say LLM code contains falsehoods, but commercially sold code has always had falsehoods too. Honestly, if we're just talking ratios, LLM code has fewer falsehoods.

In that sense, I still think it's a matter of context. If LLM code is false, was human code ever really true? LLMs do lie. They generate plenty of incorrect code. But humans do the same thing. If a problem comes up, you just look it up then and there. For me, LLMs and humans aren't all that different.

... then says offensive thing.

With that said, I doubt OpenAI would choose to publish a singular coding benchmark for a new model that exactly matches their previous model (88.8%).

Contrary to your predisposition, we're actually quite peeved that we might be seeing results from 5.6 instead of 5.5, as it's muddying our own internal data.

We've run the tasks on this benchmark hundreds of times for our own internal harness. It got magically better yesterday. Last week we were seeing worse performance (sub-80%).

I agree that benchmarks don't mean much for real world use, and I'm a bit disappointed at the lack of variety in the published benchmarks so far.

With that said, 88.8% is higher than Mythos, and the highest I've seen from vanilla Codex. If 5.6 is any better than 5.5, you'd think they would avoid publishing just one coding-related benchmark with a score that equals their previous model.

> I'm not sure why a higher scores on a few tests [..]

It's not just higher scores, the API is no longer flagging tests for cybersecurity warnings that it's been flagging for weeks.

OpenAI flat out copying Anthropic is a pretty funny development. It's strong evidence that they've been in catch-up mode.

Maybe it's a tune of the base model that works especially well with the subagent loop?

I beg to differ. They are not perfect but immensively useful today.

For instance, some of these tasks include creating videos, and one of the common reported failure mode is truncated videos, or not all videos being created. This sort of failure mode is currently best managed by an outer evaluation loop; no frontier model will, when managed by an eval loop, submit work like this right now.

JEPA gives you interpretability for free.

I have not personally inspected them and my view is maybe a more exaggerated/dramatic claim of those working in the JEPA sphere

Claude plans are more generous now by about 2-3x but Anthropic slowed their tps a month or so ago so you’re not getting the speed. It’s flip flopped, Codex tightened it significantly recently and used to be more generous.

I do split between work, personal and OSS projects, which is why I have the plans.

What is the consensus on who becomes part of the said small group of trusted partners and if they weren't so opaque about it. I'd expect comparatively big names like Simon to be included within such but Alas its not reality.

FFS. I hate this world so much. I wish I could just flip a switch and never have to hear about or have anything to do with AI ever again.

Do you ever stop to think about the horrific dystopia you and your acolytes are creating?

(To be clear: I do not like this new paradigm)

If what you need is only possible with the more capable model then the "affordability" of the less capable model is sort of irrelevant. If what you need is a novel mathematical proof, it doesn't matter that a high school student is "more affodable". You need the math PhD.

As "old" models get more and more capable, it's going to be an increasingly important skill to be able to adequately recognize when a task requires a frontier model and when it doesn't, so that the less capable (and therefore cheaper) model can be used.

Mythos/Fable is supposedly next generation in size vs Opus, and is rumored to have some architectural innovation in terms of dynamic routing/compute, possibly only fully enabled with Fable which at $10/50 is still twice the price of Sol 5.6's $5/30, but a big reduction from Mythos preview which had been an astronomical $30/150 possibly due to the dynamic routing not yet having been enabled.

Edit: yeah. https://claude.ai/share/06fefe02-4299-44da-8c5a-42607f54ca77