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No one’s talking about how good the final product is.

Edit: someone else commented that as I was typing this, lol.

> Let's maybe add comments? The final code can have comments. Fine.

This is actually pretty good!

Is the broken English an optimization or a byproduct of the model being developed in China?

If someone can look at that reasoning trace and see a stochastic parrot next word prediction machine, we don't understand those words in the same way.

If the distillation "attacks" created useful inputs to open weight models, ai-2027 was directionally correct that the Chinese would find ways to extract IP from western firms. (Scaled account creation and grinding outputs etc is not a dramatic story element as spies, though!)

Whether the distillation has constituted "attacks" or has or will meet the bar of "stealing" IP is not super interesting to me, though.

Don’t need a “better” hacker if you have ten thousand AIs all trying literally every single possible thing to exploit a system with. The main issue is that this will eventually bring down the exploitation cost enough to target very minor targets who weren’t worth it before.

Just like how Windows 95 contributed to its own development process.

The AI 2027 paper/website is exactly the same as random guesses from tech bros after a couple of beers telling you what they think the future will be. It has nothing to do with political theory, economic theory, game theory, or any other quasi-scientific or rigorous evaluation of real world events and predictable outcomes. It's just vibes. If they're wrong nobody will notice, if they're right people will call them geniuses.

I wrote about this a couple of weeks ago. It's actually often the biggest cost and it tends to be hidden away on most platforms!

https://martinalderson.com/posts/watch-out-for-cache-read-co...

Btw I still haven't came across any decent model that is <$0.01/MTok cache costs apart from deepseek thru their official API (even with the price increases).

Seems like a bit of an opportunity for someone to take - drop cache read costs significantly.

If you’re Tencent you can just plug it into some field somewhere that lots of people see right? Like how Meta could put their model on Instagram search

It's very likely tencent games those stats, buying their own tokens.

Which explains why almost none of my request go though

i'd be curious if openrouter is just being gamed by these publishers by paying for the exposure.

wouldn't trust they dont do Capitalism like the rest of the AI field.

This is one of the dangers. AI boosters would say that humans already do this compression and it was accelerated by mass media and then the internet, and that model memory + context can be broad enough that compared to human capabilities the opportunities for depth and variability are even greater. But I think we know which way this optimization usually goes. Even the notion of a "fine-tune for subtlety" is a contradiction.

Nah reducing token length means that we're just reducing English down towards a programming language like a nice demi-glace

I don't think so. It's pretty clear that LLMs use the higher level layers for reasoning, so a bit of logorrhea very possibly enriches the result quality.

Read websites through llm.

For the last few days I've been experimenting with the _free_ version of Hy3 offered by Opencode Go and I was also surprised to see how (relatively) good it is on coding tasks too.

The free quota from Opencode Go is also surprisingly generous, I perhaps hit limits one or two times and I've been using it _a lot_ for implementation tasks (using e.g. GLM-5.3-flash for working on specs and planning next steps).

My experience is that even Opus 5 still tends to write buggy or low-quality code and makes serious mistakes when analyzing code. It's a lot better than before but still not something I trust. I've had less experience with Fable since I can't use it at work; I hear it's a step up but still has its limits.

For large tasks like a web browser or a compiler, even expensive swarms of frontier LLMs have not been shown capable of producing codebases that actually work. (Anthropic built a C compiler with Opus 4.6 but it lacked optimizations and apparently hit a complexity wall.)

I also want to use LLMs for reverse engineering, but apparently it's pretty hit-or-miss, especially if you're forced to use open-source models to avoid restrictions.

For me personally, the answer is no. Fable is adequate to do basically anything I want to do. My perspective, broadly speaking, is that we've saturated most of the benchmarks because we've largely saturated our capacity to verify models' work at scale. What's left is context-bound verification, i.e. the problem of ensuring that output matches intent and ambiguities in prompting were resolved correctly. Further advances in autonomy do not make that latter verification problem easier. If anything they make it harder as the output per task becomes more complex and therefore more taxing for a human to verify.

The solution to that (to my mind) would be not a better model but a basic shift in architecture beyond the current paradigm and into a setup where agents have durable, plastic memories and undergo contextual individuation over time. But at that point agents start to become quasi-persons and not tools.

I asked a current generation LLM to make me $1k a week and it hasn't so far.

3d modeling to an STL a part compatible to a visible cable raceway still fails even if I let Claude Fable use me as a robot that measures with calipers.

I want to be able to generate my own Simlilirian movie by dumping the content of a book into an LLM.

Both animated and live action results would be acceptable.

Unfortunately most existing LLMs lack the capability to maintain context across tens of thousands of frames.

I saw a laptop earlier in the train that I asked ChatGPT, Claude and Gemini what it was, providing a brand, screen size and ports description. Gemini could never figure it out, Claude and ChatGPT eventually did, after multiple rounds of indirection, giving completely wrong answers (there was a perfect match for the problem statement, they all explored alternatives first). LLMs are (probably) amazing at things I don't care about, and still suck at the mundane stuff you would have the marketing tell you they excel at.

I was given a picture cube, which is like a Rubik's cube but every side is a unique picture. It came scrambled and I don't have an original reference image. I like to take videos of it and give it to llms to solve. I call it my agi test because it hasn't been solved yet

Yes. Most of us are, still. The frontier is currently both at expanding ‘common sense’ / non-cheating outcomes for imprecisely specified software (that’s all software), and at expanding autonomy - ability to work longer unsupervised with success, oh, and also at expanding outside contextual reasoning about what’s being built, as in “hmm, that doesn’t look right or make sense, let me explore that.”

I think the tech analogy for frontier models is going to be super computers.

Super computers keep getting better but most people don't need them for most things.

Scientific physics simulations - even the frontier models just engage in rationalization of obviously unphysical results instead of understanding the system. They have the rote knowledge but fail to apply it unless their hand is held through the process.

Hardware debugging and firmware details lead to thinking/testing loops on all but the frontier here.

Yes, lots – I think that folks will hopefully discover more of these as they scale up their ambition, now that LLMs make a lot of previously difficult things far easier.

Infra as code and devops shit. Fable is there in general because things it doesn’t know I can point at documentation and have it do a reasonable job. Opus 5 sucks. If I don’t have fable quota, I drop to Opus 4.8 and hold its hand.

This is the exact same type of comment I heard about computer hardware upgrades for three decades in a row.

“Very few people actually require a Pentium workstation, a 486 is perfectly adequate for the majority”

The logical fallacy is taking an extant distribution of “product capability” that is priced to fit what the market will bear and assuming the “next upgrade” simply tacks on a little bit more to the right hand rail of that curve.

No!

It shifts the entire curve!

Everything for everyone gets better and the top 1% of the most demanding users will continue to pay the same-ish premium.

“Nothing” will change.

Look at it this way: you can buy a $200 laptop for your kid or a $20,000 Mac with an M5 Ultra processor.

BOTH are vastly more powerful than either a $200 PC or a $20,000 “workstation” from 20+ years ago.

Look at: https://arena.ai/leaderboard/text?q=openai&utm_source=chatgp...

The “budget” 5.5 Instant model beats o1 and o3 which were “pro” models at the time of their release!

A spanish rock solved that problem for free.

I get buy with very cheap models and actually using my brain, you don't need these SOTA models. China will definitely win this AI 'war'

This guy gets it.

Novita does not offer Hy4-preview on either OpenRouter or their own model list. Maybe you confused it with Hy3.

In my evals, I saw an unprecedented jump between preview and final release on Hy3, from unusable to competitive. Did you see similar in preview vs release version?

Looks okay to me.

The first column has both the Hy4 and Hy3 scores overlaid on one another (Hy4 is darker blue and the taller one), with both scores written below the top of the respective bar - maybe you're seeing that?

Probably AI generated.

But, what bars are clearly off? I couldn't spot any.

Noticed that as well

You can open source dataset without all the details how it was assembled.

Models are lossy compressed datasets you can pick up and amend (fine tune / continue training / alter) according to license they were released under.

Hy4 is released under OSI approved Apache License 2.0.