Zuse" One agent coordinating 20 Linear issues in worktrees

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I’m building Zuse, an open source workspace for coding agents I gave one lead agent 20 Linear issues. It created separate workspaces and started a different coding agent in each one. Each agent made its changes, ran tests, tested the app in a browser, and took screenshots. I reviewed every result before merging anything. In about two hours, 18 issues were completed and two needed my help. Demo: https://x.com/swarajb/status/2087535598672429334?s=20 GitHub: https://github.com/swarajbachu/zuse I’d love feedback on how you’d want to review this much parallel agent work and you can fork or add your own changes to the github app

Visit product View on Hacker NewsAug 23, 2026

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

89%89% 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.

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

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

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: open source · Missing: https docs, excited, just released

34%34% 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

34%34% 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

19%19% 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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