AIStore (AIS) is a high-performance, distributed object storage system engineered for AI/ML workloads. It operates as a cluster of proxy (gateway) and target (storage) nodes, providing S3-compatible API, multi-cloud backend support (AWS, GCP, Azure, OCI), and advanced data management features such as erasure coding, mirroring, LRU caching, and distributed resharding (dsort). The system is designed for linear scale-out and consistent performance, with a rich set of extended actions (xactions) for batch operations like copy-bucket, archive, blob download, and ETL transformations. It includes a full-featured CLI (`ais`), a Python SDK with PyTorch dataset integration, and a standalone authentication server (AuthN). The codebase is primarily a production-grade application with strong software engineering practices: modular architecture, extensive unit and integration tests, code generation for high-performance serialization (msgp), and build-tag-based feature gating. It demonstrates non-obvious craftsmanship in areas like memory management (slab allocators, SGL), intra-cluster transport (stream bundles), and probabilistic data structures (dynamic cuckoo filters). While the core is a storage system, it has significant data/research features (ETL pipelines, distributed sorting, shard indexing) that make it a hybrid tool for AI data pipelines. Key entry points include `cmd/aisnode/main.go` (server), `cmd/cli/main.go` (CLI), `cmd/authn/main.go` (authentication), and `python/aistore/sdk/` (Python SDK). The system relies on cloud SDKs, Kubernetes client, and various Go libraries. It is actively maintained with frequent commits, comprehensive CI/CD, and a large test suite.