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Version code, models, & datasets together in GitHub

Hacker News

Version code, models, & datasets together in GitHub

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 .gitattributes file, push pointers to large files in GitHub, and push the raw, large files to us. We have a few distinct features that we’re proud of that improve the user experience: - Our XetData bot comments on your pull requests to provide links to useful dataset views and model diffs. We’re working on rendering these inside GitHub itself using browser extensions. - Git LFS and similar tools only implement file-level deduplication. We created a new technique called block-based deduplication (published in CIDR’23 conference) specifically for data and ML workflows. The ML lifecycle consists of making lots of iterative changes and our technique helps save storage and time spent downloading and uploading changes. - You can mount large repos to your local machine using git-xet mount for exploratory work. Individual files that are needed are streamed in just in time behind the scenes. We open sourced our implementation of mount and it was well received here on HN: https://news.ycombinator.com/item?id=37573679 - To give more users access to your data, just add them to your GitHub repo. This is a beta product and we would love all of your feedback. You can find all instructions to try this out here: https://github.com/apps/xetdata While we’re in beta, our product is completely free to use. We have a Slack you can join or a GitHub issue tracker. - Slack: https://communityinviter.com/apps/xetdata/xet - GitHub: https://github.com/xetdata/xet-tools/issues/

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, slack · Missing: agents, macos, agent
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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