Explore popular go codebases visually

Hacker News

I've always thought call graphs are a great way to explore a codebase. But they tend to be noisy because programs typically have a lot of control flow going most of which is uninteresting. I'm toying around with a concept to generate hierarchical call graphs to visualize call flow at different levels of granularity like structs and packages. Because at times you want to see the architectural elements, and at times the low level detail. In addition, AI can help weed out some of the "uninteresting" noise and expand with additional semantic information. The result is an interactive diagram of your codebase that you can zoom in and out of to see the level of abstraction you're interested in. I've generated diagrams for some popular go repositories in the link. Would love for you to have a look and hear your feedback on this concept. Also interested in your general thoughts around program comprehension and if you're rethinking this in the era of GenAI!

Visit product View on Hacker NewsJun 24, 2026

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

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% 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

59%59% 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

52%52% 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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

12%12% 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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