Ta

Tach – Visualize and untangle your Python codebase

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Tach – Visualize and untangle your Python codebase

Hey everyone! We're Evan and Caelean, the authors of Tach. Tach lets you visualize the architecture of your Python codebase, and gives you the tools to incrementally improve it. It uses module boundaries to give teams the benefits of microservices without the deployment complexity. If your code has been getting tangled up as your team and codebase grows, Tach helps you move back in the right direction, incrementally and quickly. You can use Tach to incrementally adopt a "modular monolith" architecture [1], for better local reasoning and smoother feature development. Since our last Show HN ( https://news.ycombinator.com/item?id=41359181 ) we've shipped support for layers, third party dependencies, visualizations, and more. Tach is: * Open source (MIT) * completely free * fast (written in Rust) * in use by teams at NVIDIA, PostHog, and more. One way Tach differs from existing systems that handle this problem (build systems, import linters, etc) is the ability to be incrementally adopted. Also, runtime speed. If you struggle with dependencies, onboarding new engineers, or a massive codebase, Tach is for you! We built it with developers in mind - with clean integrations into Git, CI/CD, and IDEs, and the performance for it to be effective in any form factor. [1] https://www.milanjovanovic.tech/blog/what-is-a-modular-monol...

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual, code · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: visualize, way · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% 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
17%17% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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