De

Deptry 0.10.0 – detect unused dependencies in your Python project

Hacker News

Deptry 0.10.0 – detect unused dependencies in your Python project

We are happy to share that deptry 0.10.0 has been released! Deptry is a command line tool to check for issues with dependencies in a Python project, such as obsolete or missing dependencies. In this latest release, Some major improvements were added to the way deptry reports issues by [Mathieu Kniewallner]( https://github.com/mkniewallner ). You can find the full release notes [here]( https://github.com/fpgmaas/deptry/releases/tag/0.10.0 ). If you're interested in learning more about deptry, be sure to check out the [Documentation]( https://fpgmaas.github.io/deptry/ ) and the [GitHub repository]( https://github.com/fpgmaas/deptry ). Let us know if you have any questions or feedback!

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TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: notes · Missing: mac, agents, macos
41%41% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
19%19% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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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