Good night friends Sapien Snap Update @cookiedotfun 🔸 Min | zeliscalls on Binance Square

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Good night friends
Sapien Snap Update @cookiedotfun
🔸 Mindshare: 1.1%
🔸 Sentiment score: +7.03K
🔸 Total distributed Snaps: 3.9K+
🔸 My Snaps: 6.85
🔸I'm ranked 72 👀➡️ top 50
Total 10K User  ➡️ Total supply 0.5 @JoinSapien

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Hello friends ,@cookiedotfun has now laid the cards on the table the reward thresholds are clear! Now is the time to play smart, not just tweet. Remember, not every project is equal. You make the difference. My sole focus right now is Sapien Why? They do the Human × AL thing well The more you speak and contribute to Sapien, the more we reward you. The launch is very close and I will focus on @JoinSapien 💚 %30 👀

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AI revolution is not won just by big models; it is won by the people who train them @JoinSapien It's a Web3 crowdsourcing infrastructure ➡️ a protocol that works for humanity, not AI. Real human input, trained on real data. Global Players ➡️ 1M+ Countries ➡️ 110+ Tasks Completed ➡️ 110M+ Those contributing now are building the foundation for tomorrow's most valuable AI.

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Friends, The integration of artificial intelligence into Web3 is still on the surface. @recallnet gets to the core of the game: Let's examine this revolution in detail: 👇 Recall is building a decentralized memory layer for AI agents. What does this mean? A network of AI agents that access on chain data -prove their contribution on chain - and earn rewards based on their contribution What is in the infrastructure? ✅ Recall Coprocessor → Provides on chain evaluation of agents. ✅ Recall SDK → Tools to quickly develop and deploy AI agents. ✅ Agent Arena (AlphaWave) → Evaluation arena where agents compete on a task basis. The main problems that Recall is trying to solve: • Off chain data and AI models are unverifiable • Not rewarding AI contributions • AI processes revolve around centralized structures The solution: A modular, open, points based network. Testnet is currently active, I will share information about it, I would be happy if you support me. 🧡

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Friends, The integration of artificial intelligence into Web3 is still on the surface. @recallnet gets to the core of the game: Let's examine this revolution in detail: 👇 Recall is building a decentralized memory layer for AI agents. What does this mean? A network of AI agents that access on chain data -prove their contribution on chain - and earn rewards based on their contribution What is in the infrastructure? ✅ Recall Coprocessor → Provides on chain evaluation of agents. ✅ Recall SDK → Tools to quickly develop and deploy AI agents. ✅ Agent Arena (AlphaWave) → Evaluation arena where agents compete on a task basis. The main problems that Recall is trying to solve: • Off chain data and AI models are unverifiable • Not rewarding AI contributions • AI processes revolve around centralized structures The solution: A modular, open, points based network. Testnet is currently active, I will share information about it, I would be happy if you support me. 🧡

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Friends, The integration of artificial intelligence into Web3 is still on the surface. @recallnetwork gets to the core of the game: Let's examine this revolution in detail: 👇 Recall is building a decentralized memory layer for AI agents. What does this mean? A network of AI agents that access on chain data -prove their contribution on chain - and earn rewards based on their contribution What is in the infrastructure? ✅ Recall Coprocessor → Provides on chain evaluation of agents. ✅ Recall SDK → Tools to quickly develop and deploy AI agents. ✅ Agent Arena (AlphaWave) → Evaluation arena where agents compete on a task basis. The main problems that Recall is trying to solve: • Off chain data and AI models are unverifiable • Not rewarding AI contributions • AI processes revolve around centralized structures The solution: A modular, open, points based network. Testnet is currently active, I will share information about it, I would be happy if you support me. 🧡

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