Workz – Zoxide for Git worktrees (auto node_modules and .env, AI-ready)

Hacker News

workz fixes the #1 pain with git worktrees in 2026: When you spin up a new worktree for Claude/Cursor/AI agents you always end up: • Manually copying .env* files • Re-running npm/pnpm install (or cargo build) and duplicating gigabytes workz does it automatically: • Smart symlinking of 22 heavy dirs (node_modules, target, .venv, etc.) with project-type detection • Copies .env*, .npmrc, secrets, docker overrides • Zoxide-style fuzzy switch: just type `w` → beautiful skim TUI + auto `cd` • `--ai` flag launches Claude/Cursor directly in the worktree • Zero-config for Node/Rust/Python/Go. Custom .workz.toml only if you want Install: brew tap rohansx/tap && brew install workz # or cargo install workz Demo in README → https://github.com/rohansx/workz Feedback very welcome, especially from people running multiple AI agents in parallel!

Visit product View on Hacker NewsFeb 25, 2026

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

80%80% 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.

TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Acquire.com, 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

22%22% 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

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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