exo is a distributed inference system for running large language models (LLMs) and image generation models across clusters of devices, primarily targeting Apple Silicon hardware via the MLX framework. It enables running models larger than a single device's memory by sharding them across multiple nodes using pipeline and tensor parallelism. The system provides an OpenAI-compatible API, a web dashboard, and a native macOS application. The architecture is event-sourced, with a master node managing state and workers executing inference tasks. Communication is handled by a custom Rust networking layer built on Zenoh, supporting peer discovery and RDMA over Thunderbolt. The codebase demonstrates strong engineering in several areas, including immutable state models (FrozenModel), typed pub/sub routing, and sophisticated KV-cache management for LLM inference. While the core architecture is sound, the project carries significant technical debt through custom forks of critical dependencies (zenoh, mlx-lm), extensive monkey-patching to work around upstream library limitations, and a highly complex Nix-based build system. The codebase is tightly coupled to the Apple Silicon ecosystem, limiting portability but enabling deep optimization. It is a high-quality, ambitious project under active development, but its complexity and ecosystem coupling present challenges for long-term maintainability and external contribution.