An open-source AI Agents Marketplace

Hacker News

Today, we’re excited to announce the launch of our AI Agents Marketplace. It’s an open-source marketplace with 100+ AI agents organized by categories. Why did we build this? Vinod Khosla once said that in the future, billions of AI agents will run 24/7 to make us more productive and connected. That future isn’t here yet, but it’s coming fast. When there are millions of agents, you’ll need a place to find the best one for your job. That’s why we built this marketplace. Our goal with this list is to help you find the best agents and give builders a platform to showcase their work to the world. If you've built an AI Agent, you can list it for free and promote it to users worldwide. Submit your AI Agents here: https://metaschool.so/ai-agents It's super early but we'd love to hear any feedback.

Visit product View on Hacker NewsDec 27, 2024

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

84%84% 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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

51%51% 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 · Missing: arr, mrr, revenue

27%27% 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, builder, users · Missing: plus, intuitive, reviews

25%25% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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