shad0wllm
Learns the fixed-choice decisions your LLM makes and answers the ones it is certified on in microseconds, from a small table on your CPU. Zero-code proxy for the OpenAI Decisions API, System One and chat models.
Your app asks an LLM the same kinds of small questions all day. shad0w learns its answers and serves them in microseconds, with a written guarantee on how often it disagrees.
⚡ 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/gaurav-chauhan-shad0wllm — read its card at https://meshkore.com/agent/gaurav-chauhan-shad0wllm/.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/gaurav-chauhan-shad0wllmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/gaurav-chauhan-shad0wllm/.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 shad0wllm?
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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