Mubit 推出用智能体 API 调用训练自有小模型的服务
Your API calls are now training data you own, a great launch for owning your intelligence stack.
Mubit 读取智能体已发给 OpenAI 或 Anthropic 的 API 调用，找出重复任务并训练用户自有小模型，让这部分工作成本更低、等待更短，其余任务仍交给前沿模型。集成只需替换 base URL 并保留原有客户端代码，也可直接接入已收集的 traces；公司称模型每隔几分钟刷新一次。作者认为其思路是把经验写进权重而非提示词，因此不同公司的智能体会训练出不同的小模型。
查看原帖我们在阿伯丁创办了Mubit。没有网络，没有实验室，没人认识我们。我们只是想要真正管用的AI系统。 如今我们与世界上一些最优秀的公司和工程师合作。 而我们不断看到的一点是：你和你的竞争对手运行着同样的AI。它本该属于你自己。🧵 在接下来的48小时内评论“SAPLING”，即可获得100美元免费额度。企业客户首月免费。
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We started Mubit in Aberdeen. No network, no lab, nobody knew us. We just wanted AI systems that actually work. Today we work with some of the best companies and engineers in the world. And the thing we kept seeing: you and your competitor run the same AI. It should be yours. 🧵 Comment "SAPLING" in the next 48 hours for $100 free credits. Enterprises get the first month free.
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Your API calls are now training data you own, a great launch for owning your intelligence stack.
Mubit reads the calls your agents already send to OpenAI or Anthropic, finds the tasks that repeat, and trains small models that you own.
so you pay less and wait less while the big model handles the rest.
> The company says they refresh every few minutes. > The integration is one line. You swap the base URL and keep the same client code. If you’d rather not touch that, you can connect the traces you already collect. > I like is the split. Narrow, repeated work moves to a model trained on your own traffic. Anything outside those tasks still goes to the frontier model.
All these means, two companies on Mubit end up with different small models, because each is shaped by its own agents’ work.
Remembering feedback in the prompt is unpredictable. So what Mubit is doing that instead of putting lessons into the prompt, it trains small models on the tasks your agents repeat, so the improvement lives in the weights.