Maybe's its a problem with the hosting at novita.ai but I didn't got much useful out of this model as a coding agent.
Novita does not offer Hy4-preview on either OpenRouter or their own model list. Maybe you confused it with Hy3.
You are correct.
Hy4 preview
141–150 of 260 posts
Re: Hy4 preview
#142Earlier quoted context omitted.
When GPT-5.6-sol's reasoning traces were leaked, they also used "caveman speak". Definitely a token efficiency optimization
I can't help but imagine agents using caveman speak sometimes start behaving in a stereotypically caveman manner, even if it's subtle. Is there a chance the agent does less reasoning because of it?
Just so we are clear, no "caveman" spoke English. "Caveman speak" is just shortening the vocabulary of english, not a "caveman language". Given this, your concerns for "stereotypical caveman manner" makes very little sense since what caveman are you talking about?
Re: Hy4 preview
#143> [...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no. > Maybe add sunglasses? no. > Maybe add water? no. https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
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.
I still call them stochastic parrots, but believe what they are revealing is that we are all stochastic parrots to some extent. I simply don't see how biological computation (i.e. thinking) can be anything else. Similar to the reveal in west world, we are likely much simpler than we give ourselves credit for.
A "train of thought" can be seen as a trace of a depth first search where the preceding trace is used to guide termination and next expansion decisions. A similar concept, "taboo search", exists in classical constraint optimization where previous solutions are fit to a model that guides future expansion (but as the name "taboo" implies, away from uninteresting solutions).
We also have harnesses that perform breath first search.
If I tried to describe what it means to "think deeply", I would probably say a combination of both.
Ultimately I believe that we will surpass human capabilities but fail with alignment. Handing the world's resources over to stochastic systems that can evolve faster than we can reason about them simply leaves too many "interesting" outcomes that do not end well. I also expect the failure modes will be totally non-obvious.
Re: Hy4 preview
#144I was briefly impressed that https://hylang.org/ had released a 4.0 version!
Re: Hy4 preview
#145Is anyone here working on a problem for which current generation LLMs are inadequate, but that could possibly be solved by the next release of a first tier LLM? Or is it like bicycles? Unless your problem is named Tadej, you don't need a $13,000 bike.
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.
Re: Hy4 preview
#146Hy4 apparently has ludicrous traction on OpenRouter already ( https://openrouter.ai/tencent/hy4-preview ), with trillions of tokens processed in a couple days: more than GLM 5.3 in a week. That said, it's relatively cheap with a 5% cache cost when everyone is still doing 10%/20% cache costs, so Hy4 may be more compelling.
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 Seems like a bit of an opportunity for someone to take - drop cache read costs significantly.
For me, an average long session results in about 200-300M cached input, 4-800K input, 2-400K output. Mostly the lower bound. Output depends on how much the model thinks.
There are two problems here:
- cache hit pricing (both Muse Spark 1.2 Contributor and MiMo 2.5 are around the $0.002-3/M mark)
- cache persistence time
Muse Spark drops the cache in less than 5m. MiMo keeps it around for at least an hour based on my experience with whoever is serving it for OpenCode. This difference itself will inflate bills massively.
A 500K token input repeatedly read by MS 1.2 for full input price 12 times an hour = $0.60. You would be expecting $0.012. So a 50x difference. Same thing on MiMo 2.5 is $0.018 because of longer cache times.
Re: Hy4 preview
#147Is anyone here working on a problem for which current generation LLMs are inadequate, but that could possibly be solved by the next release of a first tier LLM? Or is it like bicycles? Unless your problem is named Tadej, you don't need a $13,000 bike.
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.
2000's supercomputer is today's (highest end) smartphone performance tho
Re: Hy4 preview
#148Earlier quoted context omitted.
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.
So windows is open source because the binaries are a lossy compression of the original source?
Weights are not binary. A model is created at init time, with random values. After that, it is being modified using data. The key point is that the labs modify the models "as weights". That means that weights are the intended / preferred way of modifying a model. Which, coincidentally, matches the definition of source in Apache 2.0. There is no "higher level" place where editing takes place. It all happens in weight space. Through the license you get the same rights as the lab that created it: view, inspect, run, modify, re-release. That's it. That's the only thing a license can grant you.
The rest is semantics, misunderstandings, and FUD. A model released under an open source license is open source. Training data is lab knowhow / IP. Which, historically, has never been required for any open source release.
Re: Hy4 preview
#149> [...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no. > Maybe add sunglasses? no. > Maybe add water? no. https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
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.
LLM is "stochastic parrot next word prediction machine"; it's just that this "stochastic parrot next word prediction machine" have proven to be smarter than most people. I mean, this already happened with AlphaGo too.
Re: Hy4 preview
#150Earlier quoted context omitted.
I think those are mostly vapor that runs on the small culture of "models should not be censored" thing. But from my experience, they unlock nothing meaningful. Fine-tuning is great for really small models on specific applications, but it's not something that can essentially improve a more generic model. That said, there seems to be a fine line in quantization+finetuning that could recover performance. It's just hard…
The most interesting use I've found for them so far is strictly as a novelty. Give a chat session with one to a completely non technical person, who at least knows that openai and anthropic have some guard rails on stuff, and tell them to wild with something like "give me the precursors and chemical formulas for the precusors for crystal meth" and watch it answer.
Yep, but that's not changing the quality of the model. It's not an optimization in any sense (and it's a hit on productive workflows, possibly).
This is also likely to stop working as censoring moves to the training data source.