Apple Silicon ML, without Python.

Pre-1.0. Source private today, available for review on request. The core packages are the intended v1.0.0 compatibility surface. Used in production by skiff for local inference.

• Core MLX runtime — arrays, lazy evaluation, autograd, and compiled graphs, exposed as small Go packages (mlx, mlx/nn, mlx/optimizer, mlx/compile, mlx/fast).
• No C toolchain — binds the MLX libraries through purego FFI via apple. Single static binary, no cgo in the build.
• Model I/O — streaming safetensors reading and writing, weight discovery, and quantized-weight handling.
• Conventions kept — layers, optimizers, and fused fast-path kernels track the upstream Python and Swift MLX conventions, so ports stay legible.
• Profiling hooks — GPU trace capture that pairs with gputrace for source-level Metal attribution.

mlx-go is the compute foundation the rest of the family stands on, built on apple's Metal bindings and used by skiff for local inference, with no Python runtime in the deployment. It deliberately stays an array runtime: language models live in mlx-go-lm, distributed-training math in mlx-go-ccl, and the mesh transport ships separately as mlx-go-iroh, the one member of the family published and tagged on GitHub today.

• mlx-go-lm — language models: inference, chat, serving, and training. Pre-1.0.
• mlx-go-iroh — the mesh transport substrate. Open source, tagged v0.3.0.
• mlx-go-ccl — collective communication and the DiLoCo outer loop. Experimental.
• mlx-go-ane — Apple Neural Engine execution paths. Open-source experiment.
• mlx-go-fedistill — delta-only federated distillation rounds. Research.
• mlx-go-tallytrain — federated distillation by one-byte votes. Research.
• mlx-go-vibethinker — the VibeThinker post-training recipe, reproduced. Open source.
• mlx-go-sia — the SIA self-improving-agent loop, ported. Open source.