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Panoptisch – A recursive dependency scanner for Python projects

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Panoptisch – A recursive dependency scanner for Python projects

Hello all, Very excited to share this project with you all! Panoptisch scans your Python file or module to find it's imports (aka dependencies) and recursively does so for all dependencies and sub-dependencies. It then generates a dependency tree in JSON for you to parse and enforce import policies. Supply chain attacks are no joke, and this is one way to transparently analyze your dependencies to see if any malicious imports are taking place. For example, your yaml parser, nor it's sub-dependencies should import socket, or sys. Panoptisch is in early stages, with known limitations (for now). I welcome feedback, testing and contributions. Also, happy to answer any questions!

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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% 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.
TrustMRRLess likely to generate early MRR · Strong signals: way, scanner · Missing: mobile apps, ios, personal
44%44% 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 · Missing: https docs, just released, exist
34%34% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.
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.

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