# content addressed store Published articles for content addressed store. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What Is a Container Registry and How to Use One DevFeed: [What Is a Container Registry and How to Use One]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-08-10T17:48:35Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [container images](), [content addressed store](), [Docker Hub](), [Dockerfile](), [supply-chain-security]() Tags: [base-images](), [cache](), [ci-cd](), [cloud](), [container-images](), [container-registry](), [containers](), [content-addressed-store](), [cryptographic](), [devops](), [docker](), [docker-hub](), [docker-login](), [docker-pull-rate-limits](), [docker-registry](), [harbor](), [image-manifest](), [image-retention-policy](), [image-scanning](), [image-tags-vs-digests](), [kubernetes](), [private-registry](), [pull-through-cache](), [reproducible-builds](), [security](), [supply-chain-security]() ### AI overview This guide explains how container registries store and distribute container images, emphasizing the distinction between mutable tags and immutable content digests. It also covers pushing, pulling, authentication, Docker Hub rate limits, caching, scanning, and retention. ### Source excerpt A tag is a bookmark somebody else can move. A digest is the image itself. Once that distinction lands, reproducible builds, supply chain security, and every it worked yesterday mystery make sense. ## Nix 2.35.0 released DevFeed: [Nix 2.35.0 released]() Original publisher: [Read original article]() Author: lisanna Published: 2026-07-13T20:36:40Z Content type: release Language: en Sources: [Announcements - NixOS Discourse]() Topics: [Nix](), [releases](), [content addressed store](), [Security](), [hashing](), [integrity]() Tags: [bugfixes](), [content-addressed-store](), [hashing](), [improvements](), [integrity](), [new-features](), [nix-releases](), [releases](), [security]() ### AI overview Nix 2.35.0 is released with new features, bug fixes, performance improvements, and a security fix for recursive-nix. A highlighted change makes source copying to the Nix store more lazy, reducing unnecessary work for typical flake evaluations while preserving the evaluator's existing behavior. ### Source excerpt On behalf of the Nix team, I am pleased to finally announce the release of Nix 2.35.0. This release contains a good number of new features, bugfixes, and performance improvements, and is available at releases.nixos.org. This release also fixes a security issue with recursive-nix: https://github.com/NixOS/nix/security/advisories/GHSA-6h4g-g5j9-fm5f Release Notes Highlights Sources are copied to the store more lazily #3121 #15711 #15920 Historically, flakes source trees have been eagerly fetched to and evaluated from the Nix store to ensure deterministic and hermetic evaluation, even if the resulting store object is not used as a derivation input. This made the implementation simpler, yet made flakes unusable in large repositories and performed unnecessary writes to the store on each change to the source tree. Since Nix 2.32, all I/O (excluding path: and hg+:-style inputs) for reading sources during evaluation has been funneled to their original filesystem location (or to the ~/.cache/nix/tarball-cache-v2 bare git repository for tarball-based inputs). However, the source tree was still fetched to the store - primarily for computing the resulting content-addressed store path. In most cases, (such as importing the nixpkgs package set) this is not necessary. Touching (and hashing the NAR serialisation of) the whole source tree is unavoidable, since: In case of flake inputs, narHash integrity must be checked eagerly. The outPath attribute of a flake must be known in advance, and for backwards compatibility must be a content-addressed store path string with constant string context representing the flake source tree. Even within the constraints imposed by backwards compatibility requirements, there are several improvements that are achievable. To reduce the number of copies performed, Nix now hashes the input without copying first, assuming that the .outPath will not end up in a derivation attribute and thus would never have to be actually fetched to the store. This comes a ## Migrating the Hub from Git LFS to Xet DevFeed: [Migrating the Hub from Git LFS to Xet]() Original publisher: [Read original article]() Author: Jared Sulzdorf; Joseph Godlewski; Sam Horradarn Published: 2025-07-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [xet](), [migration](), [content addressed store](), [content defined chunking](), [Amazon S3](), [Git](), [Open Source]() Tags: [cas](), [content-addressed-store](), [content-defined-chunking](), [git](), [infrastructure](), [integration](), [migration](), [s3](), [storage](), [xet]() ### AI overview The article explains Hugging Face's migration of the Hub from Git LFS to Xet. It describes the Git LFS Bridge, background content migrations, content-defined chunking, the content addressed store, and S3-backed storage that enable gradual, large-scale migration without disrupting users. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Xet is on the Hub DevFeed: [Xet is on the Hub]() Original publisher: [Read original article]() Author: Assaf Vayner; Brian Ronan; Di Xiao; Joseph Godlewski; Sam Horradarn; Jared Sulzdorf Published: 2025-03-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [xet](), [content defined chunking](), [hugging face](), [datasets](), [migration](), [parquet](), [SQLite](), [Open Source]() Tags: [content-addressed-store](), [content-defined-chunking](), [datasets](), [hugging-face](), [infrastructure](), [migration](), [parquet](), [performance](), [sqlite](), [storage](), [xet]() ### AI overview Hugging Face describes migrating the first Model and Dataset repositories from LFS to Xet storage. The article explains how content-defined chunking enables byte-level deduplication, reducing the data transferred for small edits to large files and improving upload performance. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## From Chunks to Blocks: Accelerating Uploads and Downloads on the Hub DevFeed: [From Chunks to Blocks: Accelerating Uploads and Downloads on the Hub]() Original publisher: [Read original article]() Author: Jared Sulzdorf; yuchenglow; Zach Nation; saba noorassa Published: 2025-02-12T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [xet](), [content addressed store](), [AI, ML & Data Engineering](), [Open Source](), [Amazon S3]() Tags: [cas](), [collaboration](), [content-addressed-store](), [content-defined-chunking](), [dedupe](), [hugging-face](), [integration](), [network](), [performance](), [quantization](), [rust](), [s3](), [storage](), [xet]() ### AI overview Hugging Face's Xet team explains how content-defined chunking is being adapted for production to accelerate uploads and downloads on the Hub. The article describes the trade-offs of fine-grained deduplication, including network, infrastructure, metadata, and storage costs, and introduces a Rust-based chunk-oriented integration designed to improve experimentation and collaboration on models and datasets. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Rearchitecting Hugging Face Uploads and Downloads DevFeed: [Rearchitecting Hugging Face Uploads and Downloads]() Original publisher: [Read original article]() Author: Banerjee; Jared Sulzdorf; Ann Huang Published: 2024-11-26T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [content addressed store](), [hugging face](), [Amazon S3](), [Amazon Web Services](), [huggingface\_hub](), [Compression](), [dataset](), [Latency](), [Network]() Tags: [aws](), [compression](), [content-addressed-store](), [dedupe](), [hugging-face](), [huggingface-hub](), [infrastructure](), [internet](), [latency](), [s3](), [speed](), [storage](), [xet]() ### AI overview Hugging Face is redesigning the Hub's upload and download architecture to handle increasingly large model and dataset files. The proposed design introduces a content-addressed store, custom chunk-aware transfer protocols, byte-level processing, deduplication, and compression while continuing to use S3 for data storage. The read path emphasizes high throughput and low latency, while the write path validates and uploads only necessary chunks to improve speed and security. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## From Files to Chunks: Improving HF Storage Efficiency DevFeed: [From Files to Chunks: Improving HF Storage Efficiency]() Original publisher: [Read original article]() Author: Jared Sulzdorf; Ann Huang Published: 2024-11-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [xet](), [content defined chunking](), [content addressed store](), [datasets](), [hugging face](), [Git](), [Open Source]() Tags: [content-addressed-store](), [content-defined-chunking](), [datasets](), [dedupe](), [git](), [open-source](), [performance](), [storage](), [xet]() ### AI overview Hugging Face's Xet team describes a storage system that splits files into variable-sized chunks using content-defined chunking and a rolling hash. Chunks are stored in a content-addressed store with deduplication, so updates upload only new chunks. The article reports a consistent 50% improvement in storage and transfer performance compared with Git LFS across three iterative development use cases, including the CORD-19 dataset. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.