Launching today
Overmind
Open-source platform for continuous learning
74 followers
Open-source platform for continuous learning
74 followers
Overmind is the model training platform for AI teams. Turn your production traces into specialised models you own, trained, benchmarked, and served.
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Launch Team / Built With
Framer AI AgentsDesign and publish professional sites with AI
Promoted
Heyyy PH, Frey here, founding engineer at Overmind.
When most teams hear "build your own AI", they picture a research lab, a GPU budget and a year of hiring. We think it should feel more like flipping a switch. So we built one open-source platform that watches every agent you have in production, learns from your own data, trains models made for those agents and keeps making them better well after launch. All of it runs on your own infrastructure, so your data never leaves your hands. The traffic you see today turns into tomorrow's training set, and your agent gets a little better every day.
We kept one test in our heads the whole time: can someone who has never fine-tuned a model go from one prompt in their coding agent to their own model in production? Making the answer yes is what the whole team spent the last few months on.
It is all open source, so if something looks off, open an issue or come find me in the comments. Happy to go as deep as you want on the technical side!
Toodaloo 👋
@freyam7 toodaloo!
@freyam7 looks amazing. Great work team and congrats on big launch momentum already.
@hissingkitty Thanks!! The team can't wait to see everybody start "creating" AI rather than just consuming it!
Hey Product Hunt 👋
I'm Tyler, co-founder and CEO of Overmind.
Overmind is the open-source platform for AI agent training. What we're working towards is giving every product its own AI lab. 🚀
Most AI products run on the same few frontier models. A frontier lab works differently, collecting data, building evals, training, testing. Ship and repeat.
To get started: Connect our MCP, and paste one prompt into Cursor, Claude Code, Codex or OpenCode.
What you get:
🔸 Tracing: OpenTelemetry traces, scored as they arrive and matched to the part of your agent that produced them
🔸 Datasets: production traces turned into versioned eval and training sets
🔸 Evals: you define what "good" means for each capability, and Overmind measures it on live traffic
🔸 Optimisers: prompt, tool and code experiments run in your repo, and the winner comes back as a git diff
🔸 Fine-tuning: small specialised models trained on your data and benchmarked against the model you run today, with weights you can download and export
🔸 Inference: your own models and frontier models behind one OpenAI-compatible API
Why are we doing this? 💭
Today, millions of developers can build with AI, but creating and improving the models behind it still requires expertise concentrated in relatively few teams.
We believe that gap will close. Every application, business, device and individual has different needs. Meeting those needs will require more people to be able to create, test and run specialized models.
Computing became widespread as processors became cheap enough to build into almost anything, and development tools made them useful to far more people. We believe intelligence needs a similar expansion in who can build it and where it can run.
Overmind is our contribution to that: putting the work of an AI lab into tools any developer can use, so they can create, own and improve intelligence for their own tasks.
Our mission is to make intelligence as easy to create as it is to consume.
The team is here all day. What would you train first?
The humans at Overmind ❤️ 🌎
The versioned eval sets are a useful part of this. I'd want to be able to replay the same cases when a new model starts behaving differently.
Hey @oleksii_sekundant 👋 That's exactly what we built for. You turn your real production traffic into a test set and freeze it. Then when a new model comes along (a frontier one or one you fine-tuned with us), you replay the same cases on it next to your current model. You see what got better and what broke before your users do. We'd love for you to point Overmind at one of your agents and tell us how it feels.
@oleksii_sekundant adding to frey's answer: the eval set is pinned to a dataset version, so when you replay it on a new model you're comparing on the exact same rows, not "roughly the same traffic". we also split by conversation rather than by row, so a model can't score well just because it saw other turns of the same chat in training
A big shoutout to the guy who designed the website ❤️
@vijayanandkailash our second proudest asset (after the platform) :)
@vijayanandkailash The brick-breaker easter egg is my favourite! https://www.newgenre.studio/ and we had a blast designing our website!
@vijayanandkailash brand is the new moat! Totally agree.
This is legitimately really cool! Agent training, evals, observations, etc is such a fragmented and clunky process and I loveee what Overmind has done to make so much simpler! 🚀
@parthkoshti thanks for the support Parth!! AI has its own interface, so we thought training AI needs one too - as easily accessible and usable :)
@parthkoshti Loveee to hear it! Clunky was the exact word in our heads when we started. Now it is one prompt in your coding agent and you are off. Would love for you to take it for a spin and tell us where it still feels clunky, we will fix it 🙌
Super happy to be launching Overmind today 🔥 want to get started? Paste this onboarding prompt into the coding agent of your choosing and let it work:
"Set me up on Overmind https://console.overmindlab.ai/setup.md"
@samsmallmind Bish bash bosh!
All the best!
@preetmishra Thank you Preet!! Means a lot 🙌
@preetmishra Appreciate it Preet! ❤️🔥