Created a Python Monorepo with version, release and dependency tooling

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Over at https://github.com/pymedphys/pymedphys I have create a Python Monorepo. It piggy backs of tools found within the nodejs world such as Lerna. It has multiple packages all with interdependencies that are automatically graphed with clickable, navigable svgs https://pymedphys.com/developer/dependencies.html . Bumping the version is as simple as running `yarn bump:minor` for example. This then propagates the newly pinned versions for each python package to each python package. Dependencies are directly extracted from the python code within the packages themselves. It has been quite beautiful merging the Lerna tooling to support a Python multi package, levelled, Monorepo. I'm keen for feedback and advice. Please let me know. Cheers, Simon

Visit product View on Hacker NewsMay 14, 2019

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

55%55% 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: io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% 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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, code · Missing: mac, agents, macos

19%19% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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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