The open-source alternative to Claude Tag

Hacker News

Hi there HN! Been working on agent-swarm since Nov last year, and talking with a lot of companies on the exact same narrative that Claude Tag and Karpathy are pushing as "breaking change" and "the new UX era of AI". We believe that as you move to an AI-native approach in your company you should be in control of the models, harnesses, context, and memory. It should not be a blackbox as Claude is doing. Hence the swarm is trying to solve this problem: and open project that let you define a team of agents that meet you where you work. Just @agent-swarm in any channel and you good to go. Want to continue the convo in email? You can. It is FOSS and we aim to always be. AI models might be closed, but how you integrate it should be open. https://github.com/desplega-ai/agent-swarm Cheers,

Visit product View on Hacker NewsJun 29, 2026

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

99%99% 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 · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% 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 · Strong signals: arr · Missing: mrr, revenue, profit

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

18%18% predicted probability of success on AppSumo, 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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