Ooookay?

I'm skeptical, as tests go, I think that's burned out now. They could easily be training specifically to get a better pelican...

GPT-OSS was by far the worst local model in 2026. I have the luxury of plenty of H100's to try them, and Gpt-oss was atrocious with a coding agent. Gemma4 and Deepseek 4 are the best for coding, Qwen for images.

I think it's just their version of "Designed by Apple in California"

Tencent seems to have subsidiaries: https://en.wikipedia.org/wiki/Tencent#Subsidiaries

Also they have large European / South African shareholders.

Some things are better left unsaid.

I will vouch for Simon. I do not know him personally. To me his posts are honest and inspirational.

> I have been overly critical and arguing in bad faith about your writing in the past

I think you are just critical without a stated valid reason. Arguing in bad faith seems to be a thing if HN history is the judge.

And this is coming from a critical thinker, who is a bit tired of people firing off "human slop" comments. The Internet is full of a lot of people, but even when a few are bad apples, it spoils the lot. Maybe that is the intent. Maybe you are just grumpy for your life's situation.

It is 100% possible to build software entirely with AI. If you don't do that, that's great! I still code by hand from time to time, and I'm reading a lot of Rust nowadays, and learning the ropes. I come from a strong Python and Javascript background, plus networking and operations, which I'm a whiz at. I don't do it anymore, but I know how to inform it is done properly.

With this power, I can build things nobody wants to build, but me. Doesn't mean one has to put it into production, or it has to pass some security test, although with me driving it probably will. It only need be what is important to the end user, the prompter, to matter.

I think Simon helps people with this mindset be better at what they love to do. And for that, we should all be grateful.

Do people really use 100B+ models for writing? I am no writer but to me it seems like writing is one of the easiest tasks with barely any logic or reasoning and as long as its not longer than a handful of pages I expect even 8B models to perform great.

I have been telling people exactly this the last few months.

We have not seen the full power of deepseek v4 yet.

Having heavily evaluated both antirez’s ds4 flash and Qwen 3.6 27B at FP8 and Q8: it depends. The quantised Flash is better in a number of tasks despite running much slower on my DGX Spark-alike.

27B is amazing for its size but has some surprising limits when used for longer agentic coding sessions, especially if you’re using tools that are outside the stock standard web tech stuff: it really isn’t good at Relay, for example.

I think its good advice to test both on your own evals for sure, but the MoE parameters are already natively FP4 in ds4. Dropping to 2bpw isn't as big of a loss as it seems (and as corroborated by antirez's work).

Its also only 13B active, so your decode speed would be nearly 2x that of Qwen3.6-27B. So there are other latent benefits as well.

For most coding or agentic tasks, Qwen 3.6 27B likely outperforms, yes.

For 'general intelligence', DS4 Flash seems to be a noticeable step up still.

I suspect it would depend on the task. DS4-flash does, as previously mentioned, handle quantization very well. Even at 2-bit it's still very coherent.

Isn't Q8 way overkill these days? I see many graphs showing Q4 or Q5 having less than %1 deviation. Nvidia's NVFP4 Qwen quantization should be even better due to its better training methods.

qwen 27b at q8 is slower and worse than ds4 at q2 in my experience.

I'm dusting off the local models thing after being too busy for a while and it is crazy how wrong the benchmarks and general narrative is.

First, vLLM is like, we can do better than this, we need a better default target. It parses capability wrong, silently falls back on sm_80 xmma kernels when cutlass3x_sm120_bstensorprop_... CASK kernels are available, has mad Python in the hot path, emits dubious tool call syntax, and just managed to do something so off-road it tripped a driver bug that managed to wedge MMIO so bad the EC couldn't get an SBR out so the fan was going literally max until I hard pulled power. They say AMD is worse and I'll take their word for it because patching the driver and Inductor both in the same day is plenty of grief for me.

But the amazing thing and why your comment prompted me to write all this is that Qwen3.6 is insanely good, so good I am seriously questioning the MoE dogma. I was running the NVFP4 quant with the BF16 drafter, 100+ tokens a second of clean, legible reasoning trace and flawless tool calls. It's small and you can tell it hasn't memorized half the internet, but it's reasoning is like, better than most frontier. Opus has cleaner reasoning, GPT 5.x and Gemini 3.x Pro do not. If someone scaled that boi up by 5x? I get the feeling DeepSeek did so many arch innovations in one release that they just didn't quite have the convergence, this is like, the fundamentals as artistry. It's wayyyyyy stronger than GPT-4o at over a trillion parameters.

The other thing is I was using it on OpenRouter and it was all janky in the traces, stuttering and going in circles. On another day I would have been like "what do you expect it's the size of an iPhone". I wonder how many other people have drawn that conclusion too.

I'm not going to call it a conspiracy because it's explained by neglect, but we haven't even scratched the surface of the local model ceiling. With a harness that kept the data fresh, scale up the parameter count a bit, stretch context out a bit, and write an LLM serving engine that isn't hobbyist Python jank wrapped around fuckin inductor/triton jank?

That's Claude Code Opus experience on an expensive gaming box.

Lower context window notwithstanding, Hy3's coding benchmarks hold their own against DeepSeek v4 Pro & MiMo v2.5 Pro. That's quite something for a model priced like DeepSeek v4 Flash & MiMo v2.5 (for non-cached tokens), which are 3x cheaper than their respective Pro variants.

299B for Hy3 vs 284B* for Flash

Edit: fixed, got bad info

The current Deepseek V4 Pro is still just their initial preview AFAIK, with the "real" model release rumored to come later this month. GLM-5.2 might be outperforming simply because it's had more post-training on top of the GLM-5 base.

Yeah i shouldve been more clear, a model of this size could run on 2 dgx sparks so out of the range of a lot of the typical consumer sure, but I think there is definitely a market for that size

For context Hy is 295B, roughly on par with DeepSeek V4 Flash (284B).

I haven't tested it yet so I cannot comment on the quality (nor the comparison with 10x smaller (!) models)

Why not! And free hot showers during the summer. Just looking at the progress made since 2023, I don't think the LLM architecture we have today is the most efficient. We need creative game developers to start making LLMs.

used to heat my dorm room with a Pentium D that I overclocked and pointed a small fan at. Could open a window in winter, turn off the room radiator, and keep it cozy.

perhaps at-home LLMs will bring me back to that. fun days of hacking and thermodynamics.

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Yeah I meant gpt-5.4-mini, but GLM 5.2 is pretty close to gpt-5.4 base, and much better than it when it comes to design stuff.

GLM 5.2 already matches GPT-5.4 easily.

Er, actually, Pro is a big 1T model (which makes more sense given how well it does, sometimes). Regular MiMo is small, and I haven't tested it.

If they can't tell me what it is or how to use it then they can't explain to me how to install it or anything else properly so forget it. I'm not wasting my time trying to figure it out.