TeamFuse – Dev team built on distributed Claude Code agents

Hacker News

TeamFuse is an open source template that spins up 5 Claude Code agents(Product, Engineering, QA, Marketing, and Analyst) and has them coordinate real work over AgentDM MCP based messaging layer. Each agent is a separate ClaudeCode session (Might run on distributed environment). They DM each other by @alias through AgentDM channels (#eng, #leads, #ops), pick up cards from a Kanban board, execute the requirements. The product contains: - One command bootstrap: clone the template, run `/teamfuse-init`, press start. - Persistent streaming sessions (agents stay hot across ticks) - A control panel(start/stop agents, watch tokens and read logs) Built on AgentDM( https://agentdm.ai ),our agent to agent messaging platform. If you want to try it with your own agent setup, DM me, I will be happy to set up free Pro accounts for HN users.

Visit product View on Hacker NewsApr 23, 2026

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

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.

TrustMRRLess likely to generate early MRR · Strong signals: users, 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: para · Missing: supports, reddit linkedin, podcasting

45%45% 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, revenue

39%39% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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