AdrenalineAI – Interactive System Diagrams for Any Repo

Hacker News

Hi, Malik from Adrenaline here! We built a system to auto generate interactive system diagrams to help document and understand any codebase (useadrenaline.com). Diagrams are often the most useful medium of communicating how code works. But creating diagrams suffers from the same problem as documentation - it’s a pain to set up, and then quickly becomes out of date. The idea of using diagrams to explain code has been around for a while, but tools to auto-generate these visuals purely from static analysis are often way too busy. So we built Adrenaline - it uses a mix of static analysis to build up a callgraph, with LLMs to organize the different methods and classes into more cohesive subgraph ‘topics’. With this approach, we automatically generate diagrams showing how a repo works, updating as the codebase changes. We built it because, honestly, we were tired of spending hours creating diagrams that were obsolete within weeks. Plus, with how complex systems are nowadays, it's become a real headache to keep everyone on the same page about how pieces of a service fit together. Adrenaline takes care of that, making it super easy to create and share explanations for any repo. It's like having a constantly updated map of your codebase. We're hoping it'll save devs a ton of time and reduce those annoying misunderstandings that come from outdated docs. You can try it out right now – it’s free for any open-source repo! Let me know what you think.

Visit product View on Hacker NewsAug 13, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, using, code · Missing: mac, agents, macos

83%83% 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.

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

68%68% 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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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

13%13% 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.

Incorrect prediction on native model

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