AI & LLM Engineering · Python · By amd

Mines local Claude Code session transcripts with a deterministic Python pipeline to show what the agent is actually used for, how often it fails and what it costs.

amd/gaia

Walks through scaffolding, writing and testing a new GAIA agent as a Python class with the SDK, from the base Agent subclass to registered tool methods.

amd/gaia

Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs and reports a validated gain, using the Hyperloom multi-agent optimizer.

amd/skills

Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning.

amd/Quark

Benchmarks LLM inference and drives GPU kernel optimization with Magpie.

amd/skills

Install or verify the AMD Quark package and its dependencies.

amd/Quark

Diagnose failed Quark ONNX installation, calibration, quantization, custom-op compilation, or export attempts.

amd/Quark

Embeds one of this repo's pre-built hub agents (under hub/agents/) into a developer's own application.

amd/gaia

Install or verify the correct ONNX Runtime build (and the onnx package) for a user's accelerator backend before Quark's ONNX-to-ONNX flow.

amd/Quark

End-to-end ONNX PTQ workflow for AMD Quark — from a .onnx file (and calibration data) to a quantized .onnx output.

amd/Quark

Diagnose failed Quark installation, PTQ execution, script generation, or export attempts.

amd/Quark

Install or verify the correct PyTorch build for a user's accelerator backend before Quark installation.

amd/Quark

Installs or verifies AMD Quark and ensures the selected Python environment has an accelerator-matched PyTorch.

amd/Quark