What I found particularly noteworthy is the emphasis on building specialized models, rather than relying solely on general purpose ones. This can be highly effective for teams that require greater control over performance, costs and model behaver.
The pinned dataset version detail stood out to me — replaying the exact same rows, split by conversation rather than by row, is what would make me trust a before/after comparison. The Qwen 0.8B example going from 36.7% to 84% on message classification is striking. How do you guard against the eval set going stale as the agent's tools or prompts change underneath it? Do you version the tool and prompt snapshot alongside the dataset, or flag cases that no longer match the current agent setup?
multiple years of AI experience told me turn any data into training data is very hard tbh. not data format itself, how to define the label, how to collect enough signal, which data is good to use through hundreads of thousands of table, also you need to have a really deep understanding for the business domain as well. Also curious how you handle entire mlops, how to minotor the drifting and model retraining, and infrerence cost
Happy launch team! Sounds like interesting project, I would like to check it out. But why not adding more information in the description field? Most people here don't really check the screenshots or videos ;)
As a marketer, I'm not new to using AI, but I am new to actually building and running AI agents. Overmind seems like it would be able to help me get there much quicker than I anticipated, and without hiring an entire ML team, too 😁 Best of luck with the launch!
Follow you guys for some time and this one looks crazy! how do you know the small model's good enough to swap in for what I’m using right now? do I guess right you test it against the current one?
The idea of turning production traces into a model for a specific task is interesting. Teams already have a lot to learn from how their agents perform in the real world. Congrats on the launch!
Hey team, this sounds crazy, I always had this mental friction in my head that training model is a tedious task, and needs some decent technical bandwidth. But this sounds like it'll remove that friction.
Fine tuning on the traces your agent already produces is the most honest training data there is, and shipping it as a Cursor plugin means I can try it without wiring up a whole new pipeline first.
hey PH, pritam here, one of the founding engineers at overmind 👋
the part i've been living in is fine-tuning and evals. the thing that surprised me most building it: the eval is what makes the training worth anything. we had a run recently where every model, big and small, scored ~67% on a yes/no task. no amount of tuning fixes that, the data had no signal. overmind showed it in the before/after numbers instead of letting us ship a "fine-tuned" model that was really a coin flip.
the happy path looks more like our support demo: qwen 3.5 0.8b went from 36.7% to 84.0% on messages it had never seen, in 23 minutes from launch to live.
if you've got an agent in prod making the same kind of call over and over (routing, classification, tool choice), that's where i'd start. ask me anything about how the evals work
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.
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! 🚀
About Overmind on Product Hunt
“Open-source platform for continuous learning”
Overmind launched on Product Hunt on October 27th, 2026 and earned 297 upvotes and 75 comments, earning #2 Product of the Day. 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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What I found particularly noteworthy is the emphasis on building specialized models, rather than relying solely on general purpose ones. This can be highly effective for teams that require greater control over performance, costs and model behaver.