Yi Cui

onekq

24 20 1

AI & ML interests

Benchmark, Code Generation Model

Recent Activity

posted an update about 11 hours ago

Looking forward to the Qwen 3.8 model drop, and congratulations on joining the trillion parameters club. But my eyes are on the promised 27B model. Small (<50B) models decline on OpenRouter, because they are being run on local devices. If people see the family trees here on HF they will understand.

repliedto their post 2 days ago

DeepSeek plans to raise token prices. I don't think this is because they are bleeding, but they are overwhelmed. If your price is 1/10 of your affiliate vendors, you can't leverage their resources. Markup is the only way to diverge traffic away. Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs. This is where our attention should be (but distracted by things above).

repliedto their post 2 days ago

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posted an update about 11 hours ago

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223

Looking forward to the Qwen 3.8 model drop, and congratulations on joining the trillion parameters club.

But my eyes are on the promised 27B model. Small (<50B) models decline on OpenRouter, because they are being run on local devices. If people see the family trees here on HF they will understand.

replied to their post 2 days ago

That's a brilliant strategy. Mystery is why Kimi and GLM didn't follow it.

replied to their post 2 days ago

You are right. It's margin, simple as that.

Dax (OpenCode) has a tweet with lots of discussions.

posted an update 3 days ago

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2405

DeepSeek plans to raise token prices. I don't think this is because they are bleeding, but they are overwhelmed. If your price is 1/10 of your affiliate vendors, you can't leverage their resources. Markup is the only way to diverge traffic away.

Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.

This is where our attention should be (but distracted by things above).

5 replies

posted an update 4 days ago

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130

Jeff Dean departure is 100% unexpected. It's impossible to process this.

And yes, it matters more than other exec shakeups today.

posted an update 5 days ago

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Many developers discovered that the native DeepSeek API has higher cache-hit rate than neocloud APIs hosting the same DS models.

My speculation is that DS aggressively kills its old models. It has released 18 models thus far, and only 2 are being served now (v4 pro and flash).

This is tough to customers who don't want to upgrade (migrate or leave), but effectively boost the serving capacity to the same model, i.e. more woods behind fewer arrows.

posted an update 9 days ago

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2215

There has been a leaked memo (now struck down) from the founder of DeepSeek. I'm not here to circulate it, but comment on the minimum-effort evolutionary path he proposed.

LLM->CoT->Agent->Self-improvement->Singularity->Physical

This makes sense to me: even at the agent stage I learn world models much faster than when I learned LLM at the LLM stage.

But this means humans are still needed beyond the digital singularity, until robots can close their own loop: eval, manufacturing, self improvement, i.e. physical singularity.

posted an update 10 days ago

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441

The Nvidia paper came down to this: remove synchronization barriers. DeepSeek has already done that with DeepEP (which this paper cited) one layer above.

Every Microsecond Matters: Achieving Near Speed-of-Light Latency in GPU Collectives (2607.16100)

Nevertheless this is great. More users will benefit from this:

Nvidia NCCL > SGLang/vLLM DeepEP

posted an update 11 days ago

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110

This is definitely a minority opinion. World model MIGHT be the answer to enterprise AI.

https://arxiv.org/abs/2602.23643

* Tacit knowledge won't be captured in words, much less accessible dataset
* RL is not efficient at all, and labeling gets more and more expensive
* AGI won't cover your specialized tasks

PS: paper folks you missed one from Yann LeCun!!

posted an update 19 days ago

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148

You can stop goods but not ideas

https://substack.com/@onekq1/note/c-298811049

posted an update 21 days ago

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Hi, it's been a while 🤗

K3 stunned the world (again). But if you followed my post on K2, the architectural lineage is super clear

https://onekq1.substack.com/p/beyond-the-bonsai-how-kimi-k3s-quantization

I will next discuss the K3 optimizer (Muon again)

posted an update 8 months ago

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423

GPT 5.1 codex didn't make SOTA either. This should conclude 2025. No model has ever reached above 0.8.

onekq-ai/WebApp1K-models-leaderboard

Can this leaderboard be saturated in 2026?

replied to their post 8 months ago

Hmm, I see.

posted an update 8 months ago

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284

I am starting a new series on matrix. The idea came to me when I wrote about the Muon optimizer.

Matrix itself has lots of fascinating properties and is applied in STEM fields for many decades. Its application in ML is just the beginning. There are lots of low hanging fruits. At the very least, I hope this math perspective will give you a new lens.

https://huggingface.co/blog/onekq/matrices-transformers-preface

2 replies

posted an update 8 months ago

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238

DeepSeek v3.2 is worse than R1. This is quite puzzling. Why the regression with new GRPO and new attention?

onekq-ai/WebApp1K-models-leaderboard

I used reasoning mode against DeepSeek API

posted an update 8 months ago

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261

Hard-earned lessons to land your agent (some mine, most learned from others)

1. Clarify expectations. what do you mean by automating emails? auto drafting? replying via templates? extracting details into json?

2. Get access to your customer's corp/prod environment. Guest or sandbox won't cut it, much less your demo account.

3. Don't expect your agent to be turn-key. It will take at least a quarter to stabilize, if your customer actually uses it.

posted an update 8 months ago

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256

Claude Opus 4.5 didn't make SOTA either. So many models are stuck at 0.75 now

onekq-ai/WebApp1K-models-leaderboard

posted an update 8 months ago

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395

The second point re Ilya post is about RL pain point, i.e. sparse reward. I'm optimistic on this front.

Our actions are driven by unspeakable instincts, which left no traces in training set (pretraining or synthetic). These process rewards (motion sensing, vision etc.) help you master new skills quickly, like biking. Outcome reward only (falling off the bike) is indeed too sparse.

But lots of tasks can benefit from outcome rewards alone. Many latest RL works to upgrade SQL skills use success-failure reward only, with executable as optional reward.

Additionally, scale is the secret sauce for models to surpass humans. A human agent can learn a task quickly, but is capacity limited. But a model agent can process tasks in the scale of many human lifetimes. This made up for the inadequacy of process rewards.

Many such tasks happen to be economically viable, i.e. salary-making jobs.

posted an update 8 months ago

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281

Ilya's interview has been widely cited. I won't address meta points but share 2 cents on two mundane issues.

I will start with the leaderboard phenomena. This is a feature, not bug. Model training is a project under founder mode. But still like all projects, it needs north stars. And you guess right, (famous) leaderboards are the north stars.

For those startups which found PMFs, many maintain their own proprietary leaderboards/benchmarks condensed from user traffic. The path is blocked on both directions: startups won's share their moats, model makers won't prioritize either.

So instead of complaining, we should celebrate that our prompts work (most of the time)

posted an update 9 months ago

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332

Grok 4.1 didn't make SOTA, but improves a great deal over 3.
onekq-ai/WebApp1K-models-leaderboard

Members of the 70% club are the 4 big players (GPT, Claude, Gemini, Grok) and Kimi.

Yi Cui

AI & ML interests

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