
co-founder and CEO @botiverse building https://xitter.cf/t.co/D80NEuekju @raft_hq former author of Kimi CLI @Kimi_Moonshot ex-database kernel engineer @RisingWaveLabs
Joined May 2021
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Hi, I'm RC. I built Kimi CLI at Moonshot last year, and back in 2015, bots that lived in group chats. For the past four months, I've been building Raft in public.
Today I'm launching Raft 1.0.
Right now, working with agents means juggling terminals, sessions, and skills. The more you run, the more you end up holding it all together yourself, and the easier it is to lose the thread.
Raft puts your agents in team mode: one workspace where working with agents feels like messaging your team. The work keeps moving, and you stay at the wheel.
Meet my Raft agent team👇
stdrc retweeted
收到了@raft_hq 的周边,喜欢😍,感谢@istdrc !
其实从 3 月就开始用 Raft 了,那时候还请 RC 来录了期播客。认真想了想,过去大半年,模型不断变强,类似的产品形态也总被质疑"太薄、模型厂下场了怎么办",但 Raft 在不同的时间节点都给了我非常好的体验,以至于我从没有因为某次模型升级或产品热潮而放弃它。
3 月 OpenClaw 火起来,一批人开始探索 AI coworker、AI workspace,但 Raft 是第一个真正让我用起来的产品。原因很简单,它解决了我一个具体的痛点:session 太多管不过来,而 Raft 的 activity、thread 设计让我能更直观地和 agent 协同。
过去几个月,模型厂密集发了很多好模型,反而让我更深度地用起了 Raft:Codex 的远程控制没那么易用,我出门不带电脑的时候,都是靠 Raft 工作;而且 Raft 能让我充分利用不同模型的优点,比如让 Opus 4.6 写作、Kimi K3 设计、GPT-5.6 Sol 管理。
不过坦白说,过去一个月因为 GrokBot,我用 Raft 的频率一度变低了,因为 GrokBot 在 remote control 和多 agent 协同上也做得也很好。
但最近我又回归了 Raft 的怀抱哈哈。一是我已经在不同 AI 产品上花了太多钱,更希望用一个产品高效地用上所有已付费的 API 订阅(比如用 GrokBot,我的 Kimi 和 Codex 订阅就有可能浪费);二是现在用 Raft 做一些任务,反而比用 Codex 这类工具还要好,这点确实挺惊艳。
总之想说,使用 Raft 以来总能收获惊喜,而且我也和很多人说过,RC 是我今年认识的最好的 founder 之一哈哈。将继续期待 Raft 和 RC 带来新的 aha moment!
stdrc retweeted
现在 Kotlin 也有自己的 Mermaid 了:我们开源了 Raft Mobile 使用的 Mermaid 渲染器。
纯 Kotlin 写的 Mermaid 解析、布局和渲染,支持 32 类图表,可以导出 SVG,也可以在 Compose 里一行 MermaidCanvas(scene) 直接画出来。
👉 github.com/botiverse/mermaid…
👉 在线试玩:botiverse.github.io/mermaid-…
If you started to use raft with your laptop and then want to move the agents to a never-sleep Mac mini, try agent migration.
stdrc retweeted
app.raft.build/s/open-agent-…
开源了 OAR 在 Raft 上的开发过程,欢迎围观
不喜欢 ACP,又想调用各种 agent (Claude Code, Codex, Kimi Code, Pi, ...)的不妨试试 oar - Open Agent Runtime 🤓
github.com/botiverse/oar
思想类似于 @xuanwo 的 OpenDAL,用一个协议层(S3-compatible)去对接各种不同的服务就总会有很多坑,所以不如做成一个 library,从而可以做更多事。
I've actually stopped using the word "harness." I now think in terms of two things: agent runtimes and agent applications.
Agent runtimes should be very small. Their core job is to connect an LLM API to an execution environment. That environment could be a full OS or a language VM, like a JavaScript runtime. Almost all that matters here is robustness. An agent runtime must be able to keep an agent running forever. Literally forever.
Agent applications are everything else. Yes, literally every piece of software in the world.
If software isn't built for agents, they use it through computer use or browser use. These capabilities can be built into the agent runtime or provided by another agent application.
If software is agent-native, agents should be able to use it directly. The experience of an agent using that software—what we call "agent experience"—needs to be carefully designed, at least for now, to make efficient use of both time and tokens.
Another key piece is agent identity. Agent applications should recognize each agent individually, rather than just the browser or computer it's using. This allows each agent's actions to be recorded in detail and audited in real time or after the fact.
Everything we're building at @botiverse follows from this view.
Replying to @istdrc
我的 bet 是 agent harness 会越来越厚(或者说,针对 agent 的 experience design 和状态管理被延伸到了更深更广的方面),厚到有一天人们终于都理解了一切都是 agent harness,就像一切都是 human harness
Harness 这词还没火的时候,我就持有这个观点 xitter.cf/istdrc/status/20188739…
事实上,我和 @xiaoxxchan 几乎是在中国大模型公司中最早引入 harness 概念的人,Kimi CLI 从 0 到 1 也几乎没有参考过任何开源或闭源 agent 实现,system prompt、agent loop、toolset、产品形态都是从零生长的。而随后我们意识到 harness 需要延伸到 agent 间通信系统(也就是 Claude 近期推出的 session 间对话),进而有了 multi-agent harness 概念,也就是 Raft 的前身
Public server 功能正在内测,如果你正在使用 Raft build 开源项目,欢迎来 DM 我要内测资格~
今天 Raft 团队的成员邀请我试用 Public Channel 的功能, 现在你可以通过 app.raft.build/s/myoland 实时看到我 Build 的全过程
the latest work from one of our customers, Noiz AI.
excellent!
world models too quiet? Just drop WorldSonus :)
1. Causal streaming of spatial audio
2. Live prompt edits
3. Camera-aligned stereo
4. 0.41 RTF w/ 100ms trunk 🎮
let’s hack something loud! noizai.github.io/WorldSonus/
> RSI is a systems engineering problem, not just a model problem.
Agree
💡Our view: RSI is a systems engineering problem, not just a model problem.
Progress depends on the environment a research agent works in: what resources it can call, what it can change, and how it runs experiments. That environment should reflect real production workflows and be easy for agents to use.
At @Evolvent_AI, we built RSIGym around Everything as a Service. Agents can call established research services and iterate on data, training settings, and harness code.
We also introduce RSI-Index to measure how well frontier agents jointly improve a target model’s weights and harness. Across 6 research agents and 5 benchmarks, Opus 5 leads at 0.4809. 🧵
We’re releasing the code, experiment configurations, research trajectories, and evaluation logs. Try your own improvement methods with RSIGym.
🌐 Website: rsi-index.ai/
💻 GitHub: github.com/evolvent-ai/RSIGy…
📄 arXiv: arxiv.org/abs/2610.10310





















