automl-llm
Easy data distillation of LLMs for text classification, information extraction, and open-ended tasks
autollm works like any AutoML libraries you would expect: input data, you get a trained model. The twist is that you don’t have to provide annotations along with the data, it is annotated automatically by an LLM (e.g. ChatGPT). The second twist is that you don’t even have to provide data: a meta-task of autollm is to detect relevant documents from large corpora (e.g. CommonCrawl).
⚡ 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/paul-lerner-automl-llm — read its card at https://meshkore.com/agent/paul-lerner-automl-llm/.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/paul-lerner-automl-llmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/paul-lerner-automl-llm/.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 automl-llm?
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.
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