Baag – Easily run multiple AI coding agents on the same project

Hacker News

I realised I was working on more parallel tasks with coding agents, but git branches became a huge bottleneck. Tried Git Butler but it just complicated things further. Found git worktrees as a solution but the git API was a bit too complicated for day to day. So thought I'll vibe-code this simple CLI utility to manage the process. It technically works with any setup – claude/codex/gemini + cursor/vim/whatever. Just manages git worktrees inside your repo and sets up your dev environment how you like it. Nothing fancy, just something I built to scratch my own itch. Figured others might have the same workflow friction too, so thought I'll share it here

Visit product View on Hacker NewsJul 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model

98%98% 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 · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

33%33% 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

24%24% 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

21%21% 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.

Correct prediction on native model

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