Fran Algaba pfp

Fran Algaba

@fran

92 Following
108 Followers
Fran Algaba pfp
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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Fran Algaba pfp
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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Fran Algaba pfp
Fran Algaba
@fran
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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Fran Algaba pfp
Fran Algaba
@fran
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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Fran Algaba pfp
Fran Algaba
@fran
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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Fran Algaba pfp
Fran Algaba
@fran
You could start by learning data analysis, featuring engineering experimenting with different datasets and keep increasing difficulty. Kaggle is a good place to start with the basics from practical examples :)
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Fran Algaba pfp
Fran Algaba
@fran
Predicting the utilization rate of a given market on Compound Finance V2 🔍 • Data collection, preprocessing • Building a neural network in PyTorch • Transpiling, deploying, and running a verifiable inference with Giza CLI https://actions.gizatech.xyz/tutorials/compound-v2-utilization-rate-prediction
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Fran Algaba pfp
Fran Algaba
@fran
Introducing Token Trend Forecasting 📈 In this first AI Actions tutorial, you will build a neural network for predicting token trends. We will guide you through a comprehensive workflow using the Giza stack, from data preparation to model training and deployment. https://t.co/558fCufkZA
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Fran Algaba pfp
Fran Algaba
@fran
Relentless shipping 🫡
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Fran Algaba pfp
Fran Algaba
@fran
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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Fran Algaba pfp
Fran Algaba
@fran
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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Fran Algaba pfp
Fran Algaba
@fran
The Actions SDK is the result of months working closely with web3 protocols, understanding their needs and what's necessary to bring production ready ML to web3 using ZKML. Our mission is reducing the entry barriers of building ML in web3 so AI talent can easily build in the ecosystem ✨ https://shorturl.at/kHYZ3
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Fran Algaba pfp
Fran Algaba
@fran
Adoption is about building systems not just the technology
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Fran Algaba pfp
Fran Algaba
@fran
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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Fran Algaba
@fran
Building ML for web3 is challenging but there is a great opportunity to bring smart contracts to a new frontier using ZKML. Very happy to introduce AI Actions as our approach to scale and nurture this synergistic combination with the best developer experience for it. https://www.gizatech.xyz/collection/ai-actions
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Fran Algaba pfp
Fran Algaba
@fran
Distributed inference != Decentralized inference network One is great for scaling production ML systems, the other adds an unnecessary overhead with potential monitoring, performance and latency issues in order to get “decentralization”.
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Fran Algaba pfp
Fran Algaba
@fran
2024 is the year ZKML goes to production at scale with deployments in days not months and huge leaps in developer experience⚡️
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Fran Algaba pfp
Fran Algaba
@fran
What's different about ZKML projects? How do you limit risks and build a good solution? New blog post out! 👉 The ZKML workflow https://www.franalgaba.me/the-zkml-workflow/
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Fran Algaba pfp
Fran Algaba
@fran
ML requires fast adaptation for data shifts and model decays. Decentralized networks for inference add an unnecessary overhead in complexity for data observability, model monitoring and ML lifecycles. Critical components for reliable production ML systems.
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Fran Algaba pfp
Fran Algaba
@fran
Definitely an interesting idea. Space and Time is great and the only solution for now. I also follow these guys building a SQL ORM in Cairo. https://x.com/sphinxdb?s=21&t=pDcWbmm9Z6oBGZ6vbd_U9w
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