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Auto-Generated C4 Architecture Diagrams in Go

Hacker News

Auto-Generated C4 Architecture Diagrams in Go

Hey HackerNews! One of the most common problems in IT projects is the problem of out-of-date documentation or lack of documentation. Architecture diagrams are an essential part of it. C4 Model is trying to help with that by providing a tool that helps to standardize a way to create clear architecture diagrams on multiple levels. But having a standard practice to follow is not enough to have this documentation up-to-date. Because of that, Krzysztof from my team created a script that can make these diagrams directly from the code. Unfortunetly we can't share our company code. Fortunately, we have Wild Workouts DDD example that is a perfect use case to show the power of that tool: https://github.com/ThreeDotsLabs/wild-workouts-go-ddd-exampl... . The linked article describes all the needed steps to create these diagrams. Feel free to play with it and try to add it to your project. If you want to know more about Wild Workouts DDD example, they are already 14 articles that describe the project in detail: https://threedots.tech/series/modern-business-software-in-go... .

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
72%72% 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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, new, code · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
21%21% 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
11%11% 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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