@JensenHuang put our whole roadmap on one slide at #nvidiagtc berlin: "take control of your intelligence, build your own ai" (the @pollenrobotics microducks waddling around his feet still stole the show!!) nvidia calls them ai factories, with gpus at one end and enterprise value at the other. almost every conversation i had was about some stretch of that chain the best debates were about rl. everyone is racing to own the rl stack, yet every compute provider told me the same thing: enterprises still pick 30-200b general models because big feels safe. a focused 1-3b specialist would often do the job better. that is an education gap, not a tech one. huge thanks to the @nvidia startup team. jessica held the stage with so much warmth, naomi ran everything so smoothly you forgot how hard it was, and howard went from the hardwood to nvidia without losing his court vision. bonus: howard and naomi share my altitude, so for once i got to talk to people eye to eye the future we are building at @OvermindLab: every agent in production supervised by one open-source platform that trains on your own data, runs on infra you own, and ships a better version of itself every single day 🚀

Agent traces are useful only if they feed a better agent. Overmind is an open-source training platform for builders who want to improve specialized AI agents using production traces or their own datasets. It helps you turn those traces into versioned evaluation and training data, then test improvements against the metrics you define. Key features: • Agent context graph – scans a repo for an agent’s capabilities, prompts, tools, and tasks • Trace-based datasets – turns production traces or uploaded files into versioned eval and training datasets • Per-capability evals – measures what “good” means on live traces and in batch • Optimisation experiments – tests prompt, tool, and control-flow changes in your repo; the winner is a git diff • Model workflow – fine-tunes models on your data and serves trained or frontier models through one API The platform is open-source (AGPL-3.0); the SDK and CLI are MIT licensed. Link in the reply 👇

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 🙌

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…