Em
Emdash – OS UI for parallel coding agents and Linear tickets
Hacker NewsEmdash – OS UI for parallel coding agents and Linear tickets
Hey HN — Emdash is an open-source UI for running multiple coding agents in parallel. It’s provider-agnostic (10+ CLIs). Each agent runs in its own Git worktree; after a run, compare diffs side-by-side and apply only what you want. Data stays local. Linear tickets can be handed off directly to agents.
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, coding · Missing: mac, macos, cursor97%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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal39%39% 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, podcasting31%31% 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, revenue23%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, crypto21%21% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Correct prediction on native model
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