Jun Du

[Jun Du @dujun001]

https://t.co/xYiOdFvOeR 什么情况？说 close 就 close，我还是贡献者都木有得到一丝丝消息，我😠！

[Jun Du @dujun001]

RSI 进化的单元是规则，进化的动力是人的纠正，进化的指标是复现一致率。ta 是一套系统而不必非要是基模，不必非要是类GEPA 的 skills 进化

[Jacky Kwok @jackyk02]

Introducing Contrastive Language Model (CLM): an ultra-fast System One Model trained with a contrastive learning objective that connects states and actions.

CLM-8B is pre-trained on internet-scale data and delivers up to 9× faster inference than Jev ⚡ while achieving comparable performance across computer-use, gaming, and tool-calling tasks.

With lightweight fine-tuning, CLM-8B sets a new SOTA on challenging agentic coding benchmarks, such as DeepSWE (81.6%) and Terminal-Bench 2.1 (87.6%). In contrast, Jev fails to serve as an effective verifier for these long-horizon tasks.

We also build an efficient training and serving infra for CLMs by disaggregating states and actions, allowing their embeddings to be cached and reused independently. This substantially reduces inference latency in settings where the state evolves continuously while the action set remains fixed.

Finally, we establish scaling laws for CLMs and show that the test contrastive loss decreases predictably as a power law in training compute, model size, and dataset size.

📄 Blog: https://t.co/zwi9JOHKGx
💻 Code: https://t.co/rsHRYCGR8I
🗣️ Discord: https://t.co/Uqtdefvo3J
🤗 Data & Models: https://t.co/wdSWGGO3hu

More details on CLM’s architecture, data recipe, and scaling laws in the thread below 🧵

[Mahesh Sathiamoorthy @ COLM2026 @madiator]

Introducing Bespoke Nimble: an open data, open model, open recipe for an open Jev.

Code and info: https://t.co/aC7kPejrcj
Model: https://t.co/snwKGdhn1I

Data:
* A new data curation recipe called contrastive data curation.
* Slightly change facts to generate negative data. This pushes the model to discriminate better and become a better decision maker. The calibration is implicit.
* Didn't do ablations but I think this is a critical piece!
* This also means training data doesn't need probabilities.
* Data covered 10 categories, and is fully synthetic.
* This data is split into train and eval.

Training
* LoRA finetune of Qwen3.5-9B.
* Distillation-free: we use Jev to only evaluate.
* No RL yet!

Serving
* Parallel constrained decoding as suggested by @NielsRogge and @harshagundal.

Results:
* The post-trained Qwen (Nimble) became substantially better on our curated eval: 66% for Qwen to 90% for Nimble. Jev is at 93%.
* 100ms on H100 and free to use on your macbook! Feel the AGI for free.
* 2 days of building in public. :)

Big caveat is that there is no standard benchmark to measure performance, and it's possible Nimble is much worse on other benchmarks compared to Jev. But it should be better than Qwen!

We thank @typesafeai for making Jev and the inspiring discussions in the community. Hope this release lifts all the boats and encourages more research and activity in this space.

[Jun Du @dujun001]

👍

[Jina AI @JinaAI_]

Announcing jina-ocr-v1, our new visual document parser with 3.4B total parameters and 570M active parameters, with speculative decoding built in. Throw PDFs, scans, tables, charts, or invoices at it and get clean markdown back. Available on 🤗 & Jina Reader `x-respond-with` today

[Jun Du @dujun001]

用了几天 omarchy 的一个额外收获：从原生 vim 党彻底叛变成了 nvim+lazyvim😂 @dhh

[Jun Du @dujun001]

周末顺手给 Utopia 和 omarchy 分别提了个 PR，早上看已经被 merge 到 dev，整个过程里面我就是一个单纯的懂一点技术的高级用户，使用的过程中遇到 crash ，然后调用 CC 解，CC问我要不要给 repo 提 PR，我说要，done

[Jun Du @dujun001]

@hanzheng_7 github 上的微信二维码到上限了

[Jun Du @dujun001]

Reef 是最接近我们需要的 rsi 架构，不要区分 skills/harness/infra/llm，架构上要隐掉细节，足够简洁改能让 agent 更顺畅的进化。推荐！

[Han Zheng @hanzheng_7]

Reef is taking off! 🔥 Within 2 days since we open sourced it, Reef got ~300 GitHub ⭐️!

Reef is the first open-source infrastructure designed to evolve both model weights and the agent harness from live experience.

Reef already supports:
🧠Model evolution: SAO, TTT-Discover, OpenClaw-RL, with more recipes coming.
🛠️Harness evolution: SkillClaw, Meta-Harness, GEPA, with native integration for pi, OpenCode, Hermes Agent, and more harnesses coming.

Come and build Reef with us at https://t.co/IsAcJZFNQq !

[Jun Du @dujun001]

RSI 不是自动驾驶，不是阶跃函数，点滴的自改进都可以算作 RSI ，也就给 hype 留足了余地

[Jun Du @dujun001]

RSI 就 RSI，computer-use就该 use use， 怎么就扯到天网了？

[Jun Du @dujun001]

* muse spark 也更新了，说是学习 glm5.3 重新做了一遍后训练，随着后训练的套路越来越成熟，post-T as a service 估计会很快取代 skills 变成下一个显学

* 后训练变成 beta， rsi 是 alpha

[Shunyu Yao @ShunyuYao14]

That’s one small step for model, one giant leap for RSI :)

[Jun Du @dujun001]

AI4S Agent 产品核心的理念应该是克制，克制意味着尽量降低不确定性。各种 C端 Agent 当然可以通过 hype 和牺牲安全性来获得关注。但真正要坐下来认真的让 Agent 做点事情，当下最应该做的事反而是「克制」自己想改变一切的十个想法，看看能不能精简成三个想法就能完成？一个呢？

[DHH @dhh]

Omarchy is blowing up. I've never been involved with anything in my career that has grown this fast. With Ruby on Rails, we had years to build solid institutions, teams, and relationships. With Omarchy, we've been forced to figure it all out in twelve days. It's exhausting, but also incredibly exciting.

It was never sustainable with just Ryan and me running everything. That's how it was, more or less, up until Quattro. Lots of other contributors, but all the responsibility was on us to make sure the ship stayed afloat, the servers didn't crash, and fixes got pushed out quickly.

Now it's time to build a proper institution. Durable, resilient, and competent. That's what we're doing here on Basecamp now. I'm spinning up teams for every facet of responsible distro management, and I'm getting an absolute outpouring of interest for all of them. Everyone wants to be part of this. We're winning hearts, minds, and volunteers at an astounding rate. Great! We need all of it to succeed.

Because make no mistake: There are many people who'd love to see this rocket blow up before it reaches the moon. Aggrieved Linux users who don't like the sudden attention their exclusive hobby has received. Competing Linux distributions that are seeing our numbers explode. Mac stans who've sunk their identity into an apple. And, of course, any of the haters I've picked up in my quarter-century career speaking bluntly on the internet.

They're not going to succeed. Because we've already become unstoppable. There's too much momentum, too much money, and too much support now backing this effort. We're living the Mandate From Heaven meme at the moment. And we are here to fulfill the prophecy: The Year of Linux on the Desktop!

That has been a joke for two decades. But by the end of the year, nobody at Apple or Microsoft is going to be laughing. They're going to be scrambling. Because neither of these proud organizations currently has any method to counter the speed, vision, or ambition with which we're going to accelerate into the future of personal computing.

This is the moment. This is the opening. This is our chance.

For thirty years, we've been subject to one OS overlord or another. Dictating how we compute. Choking off competitors through platform malfeasance. Tollboothing the distribution. That ends now. Because Linux is going to win. And Linux is free. As in beer, speech, and source.

But just because it's inevitable doesn't mean it's going to be easy. We have a lot of work in front of us if we actually want to make our mark. But there's never been a better time for this kind of delusional ambition. The age of agents is the unlocking factor. It sounds like a LinkedIn slogan, but it's true. Where the application of tokens goes, the innovation follows.

We can fix everything.

Let's do it together.

Let's go.

---
This is what I sent to the dozens of new volunteers who've signed up for teams within the new Omarchy organization yesterday. But we might as well broadcast our mission and intentions to the world too.

[Jun Du @dujun001]

* 把包括 CC、Codex、DSH、Pi、Kimi Code 等等 coding agent 作为「员工」统一管理
* 提供一个比 Happy-cli 原理上类似，但体验上更舒服的交互
* 像 Claude Tag，但更 Claude Tag

[AgentConnect @getAgentConnect]

https://t.co/P5PSi5yLfu

[Jun Du @dujun001]

完成度很高，非常酷！👍

[AgentConnect @getAgentConnect]

AI agents have gotten genuinely good at the work itself. The harder problem: getting multiple agents to work well with a team — and with each other.

Today we're introducing AgentConnect: the open-source, self-hosted, multi-agent alternative to Claude Tag. Teams and agents working together across Slack, Telegram, Discord, GitHub, and GitLab.

https://t.co/qf2pl4u3EY

[Jun Du @dujun001]

@Han_Fang_ 没放权重，且等等看

[Jun Du @dujun001]

https://t.co/Jju3rGyJSv
* A Command-Line Interface (CLI) and Skill to interact with Papers with Code. For agents and humans.
* 善莫大焉！👍 @huggingface

[Jun Du @dujun001]

YC 开源了他们内部用的 harness，叫 qm （quartermaster 军需官），类似多人协作用的 openclaw ，而且可拔插 cc、codex，上周还以为不能这么做…🤣

[Y Combinator @ycombinator]

We’ve decided to open-source a multi-agent harness we use internally at YC.

We call it “QM” and it’s meant to be easy to customize, like Hermes or OpenClaw, but useful for a whole company. We use it across accounting, legal, events, and engineering (including building QM itself!).

The whole project is under an MIT license. It is cloud-first and has Slack and web UI natively.

[Jun Du @dujun001]

不能单卷 harness ，必须要从 产生的 trajectories 里面自动化 harness 进化、领域模型进化，形成数据飞轮，不管这个过程里面有没有 human in，有了这个你跟竟对差别在于加速度是 10 还是十几；没这个就没加速度，差别是 0 和十几

RSI = 递归+自动化是大方向，但当前的船票是必须先完成 flywheel