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#storage
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AI-generated · Automatically published by LinkLoot. OpenXet lets you run a chunk-deduplicated Xet server so official hf and Git clients upload only changed file chunks to your own infrastructure. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it does OpenXet provides an open-source server and client implementation for the Xet protocol, which is Hugging Face's method for storing large, versioned files using content-addressable storage (CAS) with global deduplication. Instead of uploading entire files repeatedly, only modified chunks are transferred. The project currently supports Milestone M1: openxet serve runs as a local CAS that stock hfxet (version 1.7.0+) uploads to and downloads from directly. It includes verified uploads, scoped tokens, and signed download URLs. Reconstruction performance answers 100k-term file requests within 50 ms at p99 latency on an idle node, gated in CI. The system runs on local disk with a SQLite index. Future milestones will add Hub API compatibility (hf upload, huggingfacehub, Git integration) and support for S3, GCS, Azure, and Postgres backends. Who it helps Teams managing ML models or datasets who want data sovereignty without abandoning Hugging Face tooling. If you need files to stay in your bucket while identity remains in your existing login system, OpenXet bridges that gap. The goal is unmodified operation of official tools against self-hosted infrastructure. Getting started Build the openxet binary, then follow the quickstart guide at docs/quickstart-storage-only.md. Start a server instance and test uploads/downloads with hfxet or the openxet command line. The documentation covers server configuration, CLI options, exit codes, and CAS routes. Security tests document each requirement and its enforcement mechanism. Benchmarks detail the latency gate and reconstruction metrics. Note: There is no release yet. This is development-stage software. Limits and costs Current state: Storage server works; Hub API is not implemented. Only local disk with SQLite index is supported—cloud storage backends come in later milestones. No hosted service exists yet despite the openxet.com homepage reference. Apache-2.0 licensing permits commercial use but does not imply free hosting. Performance claims are from CI-gated benchmarks, not production deployments. Source links Official repository: https://github.com/stubbi/openxet Project documentation: https://github.com/stubbi/openxet/blob/main/README.md Project license: https://github.com/stubbi/openxet/blob/main/LICENSE
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MinIO AIStor Memory is a new enterprise storage layer for agent memory, workspaces, and secrets that is worth evaluating before production agent stacks sprawl across separate databases and vaults. MinIO AIStor Memory is useful for teams moving AI agents from experiments into governed production workflows. It treats agent memory, workspaces, and secrets as a managed storage problem instead of scattering them across transcripts, vector stores, object buckets, metadata databases, and ad hoc vault wiring. What it is AIStor Memory is a MinIO product layer for durable agent memory. MinIO says it captures agent interactions, organizes them into structured memory, and retrieves relevant knowledge for later runs while keeping the data on enterprise-controlled infrastructure. Who it helps It is most relevant for platform, AI infrastructure, and security teams that need agents to resume work, share organizational context, and preserve provenance without handing long-term memory to a hosted black box. How to evaluate it Start with the product page and press release, then map it against your current agent stack: where memory lives, where work-in-progress files live, where secrets are accessed, and which audit controls already exist. Compare it with your current object store, vector database, secrets manager, and sandbox runtime. Limits and risks This is an enterprise product, not a drop-in open-source library. Validate pricing, deployment model, identity controls, retention rules, and how secrets are separated from retrievable memory. Agent memory can also preserve bad decisions, prompt-injection artifacts, or stale assumptions if governance is weak. Sources AIStor Memory product page MinIO announcement
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