Mubit launches a platform for teams to train their own small models, and releases an open-source model and a new benchmark
Mubit launches a platform that lets teams train their own AI models for repetitive tasks, while also releasing an open-source model and a new benchmark. The platform sits as an agent base URL in front of OpenAI, Anthropic, or Gemini APIs, trains small models based on agent tasks, and in Mubit's tests can take over tasks once it catches up to frontier models. The platform can also learn from historical calls imported from Langfuse, Braintrust, or Pydantic Logfire.
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AI score 54
The original text explains how agentic small models learn from real calls and take over repetitive tasks, so readers can use it to judge the implementation path for team-trained models.
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View original postWe 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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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.
Body
Mubit has launched a platform that lets teams train their own AI models for repetitive tasks, along with an open model and a new benchmark.
Mubit acts as a proxy base URL in front of OpenAI, Anthropic, or Gemini APIs and trains a small model based on agent tasks. The new model can take over the task as soon as it matches the frontier model in Mubit's tests.
Mubit can also learn from past calls imported from Langfuse, Braintrust, or Pydantic Logfire.