Clang-uml – C++ to UML diagram generator based on Clang

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Hi, clang-uml is an open-source C++ to UML diagram generator, driven by YAML configuration files. The main idea behind the project is to easily maintain up-to-date diagrams within a code-base or document legacy code. The configuration file for clang-uml defines the types and contents of each generated diagram. The diagrams can be currently generated in PlantUML and JSON formats. Main features: - class, sequence, package and include diagrams - up to C++17 with support for C++20 concepts - visualization of template specialization relationships - declarative diagram content filtering based on namespaces, elements and relationships - relationship inference from C++ containers, smart pointers and custom templates - customizable interactive links in diagrams (SVG output only) - generation of UML packages from namespaces or directories - JSON output containing intermediate diagram model representation for custom processing More features, usage information and examples are in README at https://github.com/bkryza/clang-uml and online docs at https://clang-uml.github.io In particular, checkout diagrams generated from test cases here: https://clang-uml.github.io/md_docs_2test__cases.html

Visit product View on Hacker NewsJun 26, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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