The open source training platform for continuously improving agents and models

London & San Francisco
Joined January 2026
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Overmind's eye sees all at @NVIDIAGTC The vision in Europe is clear. The future belongs to companies that build their own intelligence. Overmind brings vision and future into alignment. Stay tuned.
in berlin this week for #nvidiagtc, representing @OvermindLab. the last few years were about building the biggest engine, and the next few will be won by the pit crew. come say hi if you are building agents 🙌
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I used to wonder why there has been rising interest in "open weight models" across the realms... 🌎 Then I realised the @OvermindLab eye sees all.
holy shit who is building this? great segment on @theallinpod @DavidSacks @chamath @Jason @OvermindLab is by far the easiest way to customise small models on your own proprietary data. direct from your coding agent, no in-house research lab required!
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If your traces live in Langfuse, leave them there. Overmind imports them and trains a model on them. Works from Claude Code, Cursor, Codex or OpenCode. Get started: pip install overmind
Top 14 open-source tools for LLM monitoring in 2026 ▪️ Langfuse ▪️ Opik ▪️ Overmind ▪️ MLflow ▪️ LangWatch ▪️ AgentOps ▪️ Helicone ▪️ OpenLIT ▪️ OpenLLMetry ▪️ Evidently ▪️ Weave ▪️ OpenTelemetry Collector ▪️ Grafana OSS stack ▪️ Laminar Bookmark the list to keep it handy! Here is also a practical guide explaining all these observability tools - what each one does, and where it best fits → turingpost.com/p/llm-observa…
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Replying to @jji1jj
🎯
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Select the runs worth learning from and each becomes a training row, so the decisions your agent already makes become the training set. Turn your company into its own AI lab. github.com/overmind-core/ove…
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Own your agent's decisions. Decision models pick an answer instead of writing one. Think Jev, Clef or OpenAI's Decisions API. Overmind trains your agent on those decisions. The weights are yours.
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Replying to @Zai_org
Specialists win on legal. Qwen3.5-9B vs GPT-5.6 Luna: • 7X better at quoting a contract clause word for word • 4X more accurate at reading a research paper • 30X fewer phantom clauses in legal contracts Next step: train one on your own contracts and own the weights.
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Overmind retweeted
Coinbase built this for their employees. Overmind ships it for everyone. Run /overmind finetune in: • Claude Code • Cursor • Codex Open source, and the model is yours.
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.
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Replying to @HeavyDreamscv
Agreed, the eval set is the hard part, so /overmind finetune won’t launch without a separate one. With 500 messy rows our Data Workshop flags answers the inputs don’t support and can generate more training examples. The before/after eval tells you if 500 was enough.
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Replying to @AsifOnWeb3
❤️‍🔥
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Replying to @jackson_felty
Check out @OvermindLab
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Coinbase built this for their employees. Overmind ships it for everyone. Run /overmind finetune in: • Claude Code • Cursor • Codex Open source, and the model is yours.
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Coinbase built this for their employees. Overmind ships it for everyone. Run /overmind finetune in: • Claude Code • Cursor • Codex Open source, and the model is yours.
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.
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