wt

wt – lightweight Git worktree orchestrator for parallel coding agents

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wt – lightweight Git worktree orchestrator for parallel coding agents

I built wt to manage the coordination overhead of running multiple AI coding agents (Claude Code, Codex, etc.) concurrently on the same repository. The problem: I'd spin up 3-4 agents working on different features simultaneously, then conflict on files, and resolving those conflicts burns agent context. Git worktrees solve the isolation problem but the native CLI is verbose, lacks primitives for managing multiple sessions, and I'd have to manage persistence (folders to store the trees) separately. wt wraps git worktree in an interface designed for this workflow: wt new feature/auth # creates worktree + spawns subshell (wt:feature/auth) $ claude exit git merge feature/auth Also integrates with tmux to coordinate agent sessions—wt session watch shows which agents are actively processing vs idle by monitoring pane output buffers. There's a /do skill for Claude Code that implements issue-driven workflows: /do gh 123 fetches the GitHub issue, creates a worktree with a branch derived from the issue, and populates the agent context with the description. Written in Rust. Binaries for macOS/Linux. https://github.com/pld/wt Blog post with more detail: https://peet.ldee.org/general/2026/01/26/wt-git-worktree-orc...

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, model, apple
97%97% 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 HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting
41%41% 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
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
27%27% 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 · Strong signals: active · Missing: arr, mrr, revenue
23%23% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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