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Strict interfaces and dep management for Python, written in Rust

Hacker News

Strict interfaces and dep management for Python, written in Rust

Tach is a pip package that lets you define module boundaries, automatically detect all cross-module dependencies, and then validate and enforce those dependencies. It also supports strict interfaces for modules by overloading `__all__`. The core static analysis is done in Rust, so all Tach commands run quickly. The goal of Tach is to help eng teams maintain velocity while scaling quickly, something we've seen break down a number of times. Give it a try!

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: supports · Missing: reddit linkedin, podcasting, created
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
41%41% 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 · Strong signals: interface · Missing: plus, platform, intuitive
27%27% 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
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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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