Chronocity – Watch your Git repo's history grow into a 3D city

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This is timelapse of a 3D city simulation based on your Git history, files are buildings and height is based on LOC. Weather can determines if the repo is quiet or active during a span of time. Just a fun visual to get the momentum overview of your project. I've been working a lot in 1 repo for the past few months and got a handsome amount of commits, so I decided to make something visually appealing to 'appreciate' my work somehow. Based on the ideas of: CodeCity (Richard Wettel & Michele Lanza) and Gource (Andrew Caudwell, 2009). Instead of static city like CodeCity or 2D tree like Gource, chronocity is an 3D interactive city and you can even drive car in there lol. All in the browser, TypeScript + three.js, isomorphic-git in a Web Worker, WebCodecs for the video. It scans your .git folder (all client-sided, nothing uploaded) and through the main branch (downscaled to 2000 steps for big repos), compares files, LOC with previous step. three.js renders all the buildings in one instanced mesh, and height is the square root of LOC. The weather is not random: the sky follows the local time (their time zone) of people that commited, fog indicates quiet stretches, rain means a burst of changes in a short window. Try your own project straight in my GitHub page, drop your Git cloned repo there (containing .git folder, all in your browser, your repos are stored in YOUR cache, this is serverless).

Visit product View on Hacker NewsSep 15, 2026

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

87%87% 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, 000, io · Missing: https docs, excited, just released

63%63% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

19%19% 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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