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Products that do what DVC 1.0 release. 5 lessons from 3 years of building open-source ML tool does
Hey HN, creator of DVC here! DVC (https://dvc.org/) is known as Git for data projects. Technically, DVC codifies your data and machine learning pipelines as text metafiles (with pointers to actual data in S3/GCP/Azure/SSH) while you use Git for the actual versioning. DevOps folks call this approach GitOps or more specifically in this case - DataOps or MLOps. We’ve been working towards 1.0 since we started 3 years ago. What began as my pet project now has 100+ code contributors, 100+ documentation contributors, and thousands of users. Our community has taught us…
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I’ve been in the MLOps space for ~10 years, and data is still the hardest unsolved open problem. Code is versioned using Git, data is stored somewhere else, and context often lives in a 3rd location like Slack or GDocs. This is why we built XetHub, a platform that enables teams to treat data like code, using Git. Unlike Git LFS, we don’t just store the files. We use content-defined chunking and Merkle Trees to dedupe against everything in history. This allows small changes in large files to be stored compactly. Read more here:…
2022 · xethub.com
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Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
2025 · github.com
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2018 · github.com
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Hey HN! I'm Guy, one of the creators of DAGsHub. We help data scientists track experiments and version their code, data & models using Git. We're committed to building on open source protocols and improving the user experience on top of them - in this case, DVC. We've built capabilities on top of it like visually interacting with your pipeline, creating data pull requests, browsing data files etc. One thing we kept running into is users being challenged by setting up the cloud environment necessary, to push their data to AWS S3, GCS (which we support), and other DVC remotes. Since our focus…
2021
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Hi HN! We just launched a GitHub integration that scales your Git repos to handle 100 terabytes of files in a single repo. XetData enables data scientists and machine learning engineers to version code, models, and datasets together. Most teams have glued together clunky workflows using S3, DVC, Git, Git LFS, and other tools and make true reproducibility difficult: https://news.ycombinator.com/item?id=37694701 We instead embrace and extend Git so end-users don’t need to learn a new tool and a new set of commands. Our implementation is similar to Git LFS, where we take over the…
2023
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I've always wanted a better way to explore the authorship data embedded in a Git commit log. I'm having fun building a CLI tool to do this. It's a bit like the "Contributors" tab on Github that shows you how many commits each contributor has made but much faster and with many more options. If you get a chance to try it out, please let me know. I'd love to hear feedback and suggestions. Thank you!
2025 · github.com
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Hey folks, Here's a quick and dirty tool to use natural language to get git to do what you want. Example: $gitgpt create a new branch called feature/test add all the files and commit with msg creating feature test then push to origin I haven't put it through the wringer yet, however it's worked well with some pretty straight forward day to day git usage.
2023 · github.com
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Simon(sfarshid) and I spend a lot of time on GitHub. As data nerds we put together a quick tool to explore your repository’s data. How it works: - Data Loading: We use dlt to pull data (issues, PRs, commits, stars) from GitHub - Semantic Layer: Relta wraps the underlying dataset into a semantic layer so the LLM doesn’t hallucinate. - Text-to-SQL: A text-to-SQL agent transforms your plain-English question into a query using the semantic layer - Generative Charts: assistant-ui dynamically generates a chart based on the SQL query - Refinements: If the semantic layer can’t handle your question,…
2024 · github.com
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2017 · gitorials.com
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hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…
May 2026 · github.com
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I built this as part of my quest to properly learn data visualization. The code is the easy part! Some lessons learned: - personal verification of the the general truth that pie charts are tough! and the returns are not great for the effort due to people's difficulties perceiving angles - may not use "vanilla" d3 with no React. was difficult to adapt for mobile - the GitHub API provides fairly standardized responses so building dynamic charts wasn't too bad. But when working with streaming data (say Kafka) I can see this getting interesting... schema registry should help but creating a view…
2024 · see-my-repo.netlify.app
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Hello friends, Today I'm sharing a little tool to help you explore GitHub repositories: • https://diggit.dev This project was admittedly a big dumb excuse to play with Elm and Claude Code. I published my design notes and all the chat transcripts here: • https://taylor.town/diggit-000 Please add bug reports and feature requests to the repo: • https://github.com/surprisetalk/diggit Enjoy!
2025 · diggit.dev
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I built this as a small side project to learn and experiment, and I ended up with this! I used a subdomain from my personal portfolio, and everything else runs on free tiers. The project uses Nuxt, SVG, Cloudflare Workers, D1 (SQL), KV, Terraform, and some agentic coding with OpenAI Codex and Claude Code. What started as a joke among friends turned into a fun excuse to build something end to end, from zero to production, and to explore a few things I’d never touched before. I’d really appreciate any feedback or suggestions.
Jan 2026 · certificate.brendonmatos.com
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