Co

Cogeol – Align your projects Python versions by cog and endoflife.date

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Cogeol – Align your projects Python versions by cog and endoflife.date

Hello, cogeol is a small tool which allows you to manage Python versions of your projects (usually libraries) by utilizing cog's ( https://github.com/nedbat/cog ) static code generation and https://endoflife.date/ data. For example - say you want to always support three latest latest Python versions, no more, no less (according to Scientific Python SPEC0: https://scientific-python.org/specs/spec-0000/ ). Currently that would be Python version 3.13, 3.12 and 3.11. When 3.14 officially drops, you would have to move your library manually to 3.14, 3.13, 3.12. This is what cogeol automates, see the usage example: https://github.com/open-nudge/cogeol?tab=readme-ov-file#usag... . Also works with other files, see example in the README, thanks in advance! If you want to follow for other projects, check out the opennudge GitHub here: https://github.com/open-nudge (or associated links at the top)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released
44%44% 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: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code, open · Missing: mac, agents, macos
31%31% 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
30%30% predicted probability of success on AppSumo, 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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