natrys

Can you perhaps keep the challenge up with some null flag (at least for a bit)? It would be nice to check if my locally validated solution works ;)

According to an old interview, apparently they were always interested in AI. But finance is just where they had their first success.

> Many of High-Flyer's original team members worked on AI. Back then, we tried a lot of fields before getting our big break in finance, which is complex enough. AGI is probably one of the hardest things we can do next, so for us it was a question of how, not why.

It's a very good interview:

https://www.lesswrong.com/posts/kANyEjDDFWkhSKbcK/two-interv...

Incidentally, Wenfeng is kind of reverse Hassabis. There were some rumours that:

> Hassabis quietly assembled a team of around 20 researchers to develop high-frequency trading algorithms, without Google's approval. When the parent company found out, the project was disbanded.

https://timesofindia.indiatimes.com/technology/tech-news/whe...

I think we don't have a good draft model for better speculative decoding yet (e.g. DFlash 2). Once we do, it will be faster.

Why not? It's not really competing in the same size class.

Besides, as they explicitly wrote here, the main goal for this release is not performance, rather to serve as a reference for inference runtimes about what to implement. So that later Qwen 4 can be released with zero day support.

For me, flash 0731 was much better in omp/opencode than in Pi.

Anyway, it might be so that they are rolling out deployment. There haven't been an official announcement post yet (this submission is a link to openrouter). Some people have been saying they are getting results worse than GLM-5.1, that's obviously broken.

There is an old rule of thumb that says the quality of an MoE is equivalent to a dense model with the geometric mean of its total and active parameters. So, for example, the Qwen3.6 would be equivalent to a dense model with approximately sqrt(35×3) ≈ 10.25B parameters.

Both MoEs and dense models are always getting better, so I don't think this comparison is meaningful across generations. But still for a first approximation, this tends to hold (you wouldn't expect a lot from a 10B model in coding yet).

I think this one requires a bit of strong prompting. I am also normally a Pi user, but my experience in OpenCode with this model has been drastically better than in Pi, where it overthinks a lot and gets distracted by random things.

It might be even better in Codex or Oh My Pi according to this bench I saw earlier: https://nitter.net/composio/status/2085330847951970801

So your cost would be $40.68 with another provider that has one less zero in the cache hit price.

At least the SSM layers should have relative PE built in via recurrence, in a hybrid attention model like this.

They can and almost certainly are doing similarly impressive engineering works internally.

They just aren't in any hurry to forward those cost savings to you.

If anecdote is data, then here's another point:

https://nitter.net/synthwavedd/status/2077537805715005724#m

(As an aside, I don't know how it was professional of Arena to unmask an unreleased cloaked model on their platform. Also practically, upstream could have been A/B testing multiple variants under same endpoint, casting validity of such pre-announcement tests into question)

Some official benchmark numbers posted in Chinese social media (I am sure they will publish an English blogpost later too):

https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ

Generally looks like a Sol/Fable tier model, better across the board than Opus 4.8.

(Edit) English blogpost is up now: https://www.kimi.com/blog/kimi-k3

These things enormously benefit from economies of scale. I am fairly certain their margins might be low but they don't actually sell API at loss, however that doesn't mean your cost footprint would be anywhere as low.

No I think uv is to python what opam is to ocaml, it's mostly a package/dependency manager.

Superficially, both uv and dune are also project runners. But dune is mainly a build tool, most important things dune does such as pre-processing, linking, compiling etc., are not needed in python in the first place (at least talking about pure python). You can use uv to create tarball/wheel but it's more akin to simple bundling than building in the dune sense. Dune can also run tests, but in uv you would need to delegate to something like pytest etc.

It seems frontier, on the balance, would rather lose that segment of he market than lower the API price. They are getting the bag in the enterprise segment, those clients aren't ditching them for DeepSeek.

As for other segments, high API pricing gets people to switch to the subscriptions instead which is stickier than the API.

Yes it was good for its time, but 10 months old now which is a long time ago in this space. It was also a fine-tune (albeit a good one) of Qwen-2.5 72B.

I wish they did more smaller models. Kimi Linear doesn't really count, it was more of a proof of concept thing.

I think tree-sitter's relationship with JavaScript is entirely syntactic. You don't need any JS runtime installed to write grammars, because technically tree-sitter CLI already has a JS runtime included and using that it converts your grammar first to an intermediate JSON format, then it generates parser code in C. And then this C code gets compiled into a shared library, which is what editors like Emacs use, so to use tree-sitter modules you definitely don't need a JS runtime either.

Very impressive demo. From VM curation to vibe coding something running on port 8000 in Shelley just worked in minutes. I imagine quite a few technically impressive things happening under the hood, would be interested in reading more about those.

Small nit: I think you should make it more clear in the docs (if not in the landing page) that one can just use any key with the ssh command the very first time and it automatically gets registered. Also on the web UI one should have the ability to add the ssh keys. I logged into the web UI first, and was a bit confused.

I think the pricing is alright for the resource and remote development features, though might be a bit much if someone doesn't need higher level of resources for deploying something that's mostly already developed.

Anyway, this reminds me of a product called Okteto that had similar UX. They were focused on leveraging k8s for declarative deployment. But for some reason they suspended their managed cloud/SaaS offering for individual/non-enterprise clients, I wonder if it was because they couldn't make the pricing work. Hope that doesn't happen here.

That's the Kimi K2 Thinking, this post seems to be talking about original Kimi K2 Instruct though, I don't think INT4 QAT (quantization aware training) version was released for this.

I am going to try and stick with Prolog as much as I can this year. Plenty of problems involve a lot of parsing and searching, both could be expressed declaratively in Prolog and it just works (though you do have to keep the execution model in mind).

Well they do that too: https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-671B

But I suppose the bigger goal remains improving their language model, and this was an experimentation born from that. These works are symbiotic; the original DeepSeekMath resulted in GRPO, which eventually formed the backbone of their R1 model: https://arxiv.org/abs/2402.03300

It can hardly be called resistance to improvement, when everyone do improve it - just in their own ways. The default isn't some fashion statement, some aesthete that's objectively good (though I am sure some people do subjectively like it). But it's meant to be sort of a least presumptuous blank state that everyone can radically overhaul. So arguably it's an encouragement for improvement just like everything else in Emacs, which focuses on making the tools for improvement easier.

It's just that "improvement" as a matter of public consensus that everyone can agree on to elect the next blank slate has been to impossible to settle on. But the counterculture here broadly might be extreme reluctance to inconvenience even a minority of existing users, in pursuit of market share/growth.

Wasn't aware of user-var-changed, cool write-up!

I had used urxvt forever before and the simple solution that works (even for ssh e.g.) is to ring the terminal bell, and urxvt just sets the window urgency hint upon that. I just do that in shell prompt unconditionally because if it's triggered in a focused window, then nothing happens. But if it's from a different workspace, I get this nice visual cue in my top bar for free.

But features like setting urgency isn't available in wezterm (understandable, as it's not a cross-platform thing). I could patch that in the source, but the Emacser in me chose to do something more unholy. By default Lua is started in safe mode, which means loading shared C module is forbidden. I disabled that, and now use a bunch of missing stuff written in Rust and Zig interfaced with cffi. Don't recall ever having a crash so I am surprised by some of the other comments.

Yes it seems the binaries are here: https://ferron.sh/download

I will say that though, it's probably not rational to be okay with blindly running some opaque binary from a website, but then flip out when it comes to running an install script from the same people and domain behind the same software. At least from security PoV I don't see how there should be any difference, but it's true that install scripts can be opinionated and litter your system by putting files in unwanted places so nevertheless there are strong arguments outside of security.

Qwen's max series had always been closed weight, it's not a policy change like you are alluding.

What exactly is Huawei's flagship series anyway? Because their PanGu line is open-weight, but Huawei is as of yet not in the LLM making business, their models are only meant to signal that it's possible to do training and inference on their hardware, that's all. No one actually uses those models.

Models:

- https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking

- https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct

If you mean the bit about refusal from other models, then sure here is another run with same result:

https://i.postimg.cc/6tT3m5mL/screen.png

Note I am using direct API to avoid triggering separate guardrail models typically operating in front of website front-ends.

As an aside the website you used in your original comment:

> [2] Used this link https://www.deepseekv3.net/en/chat

This is not the official DeepSeek website. Probably one of the many shady third-party sites riding on DeepSeek name for SEO, who knows what they are running. In this case it doesn't matter, because I already reproduced your prompt with a US based inference provider directly hosting DeepSeek weights, but still worth noting for methodology.

(also to a sceptic screenshots shouldn't be enough since they are easily doctored nowadays, but I don't believe these refusals should be surprising in the least to anyone with passing familiarity with these LLMs)

---

Obviously sabotage is a whole another can of worm as opposed to mere refusal, something that this article glossed over without showing their prompts. So, without much to go on, it's hard for me to take this seriously. We know garbage in context can degrade performance, even simple typos can[1]. Besides LLMs at their present state of capabilities are barely intelligent enough to soundly do any serious task, it stretches my disbelief that they would be able to actually sabotage to any reasonable degree of sophistication - that said I look forward to more serious research on this matter.

[1] https://arxiv.org/abs/2411.05345v1

You are obviously factually correct, I reproduced the same refusal - so consider this not as an attack on your claim. But a quick google search reveals that Falun Gong is an outlawed organization/movement in China.

I did a "s/Falun Gong/Hamas/" in your prompt and got the same refusal in GPT-5, GPT-OSS-120B, Claude Sonnet 4, Gemini-2.5-Pro as well as in DeepSeek V3.1. And that's completely within my expectation, probably everyone else's too considering no one is writing that article.

Goes without saying I am not drawing any parallel between the aforementioned entities, beyond that they are illegal in the jurisdiction where the model creators operate - which as an explanation for refusal is fairly straightforward. So we might need to first talk about why that explanation is adequate for everyone else but not for a company operating in China.

I agree with you, therefore I am pretty sure you meant to reply to the parent I was also replying to.

Don't really see how the string (and other usual container types) or filesystem APIs are lacking in any significant way compared to stdlibs of other scripting languages.

I also believe that buffer as an abstraction strictly makes many harder things easier, to the point I often wonder about creating a native library based on elisp buffer manipulation APIs alone that could be embedded in other runtimes instead. So the without touching buffer is a premise/constraint I don't quite understand to begin with.