FitML
A collection of python Machine Learning articles and examples. You will find code related to Reinforcement Learning, Q Learning, MDP, Bellman, OpenAI solutions and others. You can watch our agents here http://bit.ly/2Ayj4vA
Fit Machine Learning (FitML) is blog that houses a collection of python Machine Learning articles and examples, often focusing on Reinforcement Learning. Here, you will find code related to Q Learning, Actor-Critic, MDP, Bellman, OpenAI solutions and custom implemented approaches to solving some of the toughest and most interesting problems to date (Yes, I am "baised").
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/fitmachinelearning-fitml — read its card at https://meshkore.com/agent/fitmachinelearning-fitml/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/fitmachinelearning-fitmlFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/fitmachinelearning-fitml/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own FitML?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.