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Codemap AI: Visualize and explore large codebases with AI

Hacker News

Codemap AI: Visualize and explore large codebases with AI

Hi HN! I’m a solo technical founder and just launched the first public version of Codemap AI ( https://code-map.com ) - a tool to help developers instantly understand large codebases. Codemap AI currently: - Parses large codebases (Java for now) - Generates interactive relationship diagrams - Lets you chat with an AI about your code - Lets you chat with dev agents (developer clones) about code changes - Tracks security issues and project statistics - Helps with onboarding, debugging, and documentation I built this because I’ve personally spent weeks trying to understand unfamiliar and legacy code in large projects. My goal is to make codebases as explorable as possible - like having a "Google Maps" for your code. At this stage, it’s not a live SaaS - I have a landing page and a demo video here: - Landing page: https://code-map.com - Demo video: https://youtu.be/kMy-zwsApxI Would love feedback from the HN community: - Does this solve a real pain for you? - What features would make it indispensable? - Any suggestions for improvements or integrations? Thanks — happy to answer any questions!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, google · Missing: mac, macos, cursor
82%82% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, google · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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 · Strong signals: saas, active · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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