Gwt – conflictless AI sessions in parallel (terminal)

Hacker News

Opencode, claude code, aider and other terminal ai assistants are great! But I don't think they cover a case where I want to have several sessions working in isolation on separate features without changes/files conflicts. So very often I create a separate git worktree and spin up claude code or opencode. But managing git worktrees and spinning up ai assistant is adding a bit of friction (I'm bad at remembering commands). This is why this little simple minimalistic tool was created. It originally only supported claude code, but now you decide what ai assistant it should spin up. Demo where I simulate running 2 sessions (quasar, quantum): https://raw.githubusercontent.com/slowestmonkey/gwt/51f6bc21... Try and let me know. Feedback appreciated!

Visit product View on Hacker NewsJan 31, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, user, code · Missing: mac, agents, macos

92%92% 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: created, para · Missing: supports, reddit linkedin, podcasting

50%50% 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

45%45% 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

33%33% predicted probability of success on AppSumo, 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

28%28% 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

24%24% 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.

Correct prediction on native model

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