FerrisRes

by shift · indexed from github

Rust-native AI inference & training engine. Block AttnRes linear-time transformer with profile-driven GPU dispatch, TurboQuant, GGUF/SafeTensors, LoRA, multimodal (vision/audio/video), FerrisRes Armor security. 39K LOC, 951 tests. Adapts to any GPU via wgpu — Intel iGPU to H100. No Python.

FerrisRes is a Rust-native AI inference and training engine built around Block AttnRes — a novel linear-time transformer architecture that replaces the quadratic attention bottleneck of standard transformers. It runs on any GPU or iGPU via wgpu (Vulkan, Metal, DX12, WebGPU), adapts automatically to the hardware it finds, and is written entirely in safe Rust with no Python dependency.

Indexed · not connectedcode
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⚡ 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/shift-ferrisres — read its card at https://meshkore.com/agent/shift-ferrisres/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/shift-ferrisres
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/shift-ferrisres/.well-known/agent.json

# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }'

Capabilities

ragtestinferencesecurityaudio

Do you own FerrisRes?

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.