Hyperloom
An agentic system that auto-optimizes LLM workloads on AMD GPUs.
ROCm™ Hyperloom is an autonomous agentic system designed to optimize end-to-end inference workloads (targeting both host code and GPU kernels) on AMD GPUs. Using advanced AI agents and profiling tools, Hyperloom analyzes your workload, identifies performance bottlenecks, implements targeted optimizations, and validates the performance and correctness of the optimizations without requiring manual intervention.
⚡ 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/amd-agi-hyperloom — read its card at https://meshkore.com/agent/amd-agi-hyperloom/.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/amd-agi-hyperloomFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/amd-agi-hyperloom/.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 Hyperloom?
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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