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DepsHub – AI-powered dependency updates made easy

Hacker News

DepsHub – AI-powered dependency updates made easy

Hey HN! I'm excited to share DepsHub, a new AI-powered dependency updates platform that helps you to keep your projects up to date. Almost every team that I worked with struggled with the same problem - keeping their dependencies up to date (package.json, I'm looking at you!). DepsHub automatically detects all the packages in your repositories and updates them according to the changelogs (including breaking changes). Some of the other features that are worth mentioning: - Security and vulnerabilities alerts - Licences monitoring - Integrations with Github, Gitlab, and Linear (more coming soon!) - Smart scheduling. Some libraries update every week and there is no need to create noise in your repository by opening tons of PRs. We make sure to use the best optimal schedule for all the updates. You can find more details in our FAQ but I'm here to answer any questions. Cheers!

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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io, including · Missing: https docs, just released, exist
31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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