Nexwork – Multi-repo orchestrator with Git worktrees

Hacker News

Hi HN! I'm the creator of Nexwork. I built this to solve a problem my team had: managing features that span multiple microservices (usually 3-5 repos per feature). The Problem: - Manually cloning/branching each repo - Tracking progress across repos in Jira/spreadsheets - AI assistants (Claude/GPT) waste tons of tokens scanning directories - Hard to run tests across all repos at once Nexwork uses Git worktrees to: - Create isolated feature branches in one command - Auto-generate AI context files with complete file trees - Run commands across all repos (e.g., "run npm test" in 5 repos) - Track time, commits, and git statistics Quick try: npm install -g multi-repo-orchestrator multi-repo init multi-repo feature:create It's v1.0.0, so definitely rough around the edges. Built with TypeScript, MIT licensed, works with Node/Python/.NET/Go/Rust/etc. I'm here to answer questions and would love feedback on: - Is this useful for others managing microservices? - What features would make it more valuable? - Any bugs or usability issues? Thanks for checking it out!

Visit product View on Hacker NewsJan 31, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, context · 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, 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.

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

27%27% 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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