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Open-source version control for game developers

Hacker News

Open-source version control for game developers

Hi! I'm developing an open-source version control system to address some of the issues that game developers have with current version control systems. Current closed-source systems, like Perforce and PlasticSCM, are expensive and complex while current open-source systems, like Git/GitLFS or SVN, are slow and do not handle large files efficiently. Game developers currently use Git/GitLFS in smaller teams, but tend to switch to Perforce when their teams get larger since Git/GitLFS lacks features critical for game development, such as file locking and handling large repositories. Eventually, I plan on this being similar to closed-source internal tools like Google's Piper and Facebook's Sapling to enable work on large monorepos efficiently. jamhub.dev is a hosted version of the source. Let me know if you have any questions!

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, open · Missing: mac, agents, macos
89%89% 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: pipe, io · Missing: https docs, excited, just released
83%83% 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 · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, efficient · Missing: plus, platform, intuitive
26%26% 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
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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