DevSwarm 2.0, fix parallel Claude Code sprawl

Hacker News

Mike, cofounder of DevSwarm here. I’ve been using Claude Code heavily for the last few months (multiple Max plans). Running sessions in parallel across git branches or worktrees is fast, but it breaks down quickly in practice because of sprawl. Terminals everywhere, editor windows everywhere, and you lose track of basics like which agent is on which branch and what is ready for review. DevSwarm is what we built to solve that. The core abstraction is simple: workspace equals branch. Each workspace runs its agent session(s) and keeps the state visible. In 2.0 we added a full VS Code IDE embedded in each workspace, so editing, terminals, diffs, and git controls stay in one window while you jump between parallel branches. I’d love blunt feedback from people doing parallel workflows: • What breaks first for you past 2 parallel sessions? • What would you need for this to be production-grade? Tests, debugging, extensions, PR flow? Video: https://www.youtube.com/watch?v=MqRkSRee6HE Download: https://devswarm.ai/download

Visit product View on Hacker NewsFeb 26, 2026

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

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

66%66% 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: ide, io · Missing: https docs, excited, just released

50%50% 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: video, month, para · Missing: mobile apps, ios, personal

49%49% 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

35%35% 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

30%30% 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

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