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Dependabot – automated dependency updates for Ruby, JavaScript and PHP

Hacker News

Dependabot – automated dependency updates for Ruby, JavaScript and PHP

Link: https://dependabot.com A friend and I have spent the last couple of months building Dependabot. We want to make it as useful as possible, so we'd love any feedback - does this match your ideal flow for keeping dependencies up-to-date? If not, what would you like to see us do differently? Things we'd particularly like guidance on are: - Is one PR per dependency update the right choice? They're easier to review/merge, but it's nosier than submitting a PR with multiple updates. - What should we do about sub-dependencies (dependencies of your dependencies)? - Is the "compatibility score" useful? Is there a way you'd rather see this information? ( https://dependabot.com/compatibility-score ) - What additional languages would you like to see? Any feedback very gratefully received!

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62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
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47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
34%34% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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