Fran Algaba (fran)

The future of Web3 is agentic. Presenting Giza Agents: A modular framework for integrating trust-minimized ML into on-chain strategy and action. https://www.gizatech.xyz/collection/introducing-giza-agents-framework

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Introducing Token Volatility Forecasting tutorial! 📈 Using the complete Giza stack, we predict the volatility of various tokens, compare these predictions against a benchmark, and execute the AI action. https://t.co/KuLdf2QAmF

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Aave Liquidation Prediction Tutorial is live! Learn how to use Giza Datasets and the Actions SDK to develop a Verifiable ML Model for forecasting the liquidation of debt positions in Aave V2 and V3 protocols. https://t.co/LS5gE743XF

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Calling all ML developers, data scientists, and ZK builders! Join Gizathon and Build ML Models for Web3, with Giza’s tools for ZKML. 📅February 19th - 26th 🏆$10,000 prize pool 📍Online Register here: https://764trmbrrb1.typeform.com/to/ApNFyqgm https://twitter.com/gizatechxyz/status/1755986306625089550

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The future of Web3 is agentic. Presenting Giza Agents: A modular framework for integrating trust-minimized ML into on-chain strategy and action. https://www.gizatech.xyz/collection/introducing-giza-agents-framework

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Starting today, I'm rolling out a series of articles on ZKML. I'll be exploring this topic with content that haven't been discussed much. First one is simple: 👉Introduction to ZKML for blockchains https://www.franalgaba.me/introduction-to-zero-knowledge-machine-learning-for-blockchains/

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If we want ML to thrive in web3, we need the right tools and products for developers to build ML solutions that create value in protocols. Giza Datasets makes easy to build ML models for web3 use cases using curated and labeled data. https://www.gizatech.xyz/collection/introducing-giza-datasets

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