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Space and Time wants to help LLMs and AI agents get on-chain (w/ Scott Dykstra)

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Manage episode 409253597 series 3352318
Content provided by TechCrunch, Yashad Kulkarni, Maggie Stamets, Kell Keller, and Jacquelyn Melinek. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TechCrunch, Yashad Kulkarni, Maggie Stamets, Kell Keller, and Jacquelyn Melinek or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

For this week’s episode, Jacquelyn interviewed Scott Dykstra, CTO and co-founder of Space and Time.

Before diving into web3, Scott spent almost 8 years at the cloud analytics and data platform Teradata and throughout the years he held roles of senior architect, director of cloud solutions and worked his way up to VP of the firm’s global cloud.

As for Space and Time, the company aims to be a verifiable compute layer for web3 that scales zero-knowledge proofs, or ZK proofs, on a decentralized data warehouse. Zero-knowledge proofs are a cryptographic action used to prove something about a piece of data, without revealing the origin data itself.

Space and Time has indexed data both off-chain and on-chain from Ethereum, Bitcoin, Polygon, Sui, Avalanche, Sei and Aptos and is adding support for more chains to power the future of AI x blockchain.

This episode is wrapping up Chain Reaction’s monthly series diving into different topics and themes in crypto. This month’s focused on blockchain and AI integrations.

Jacquelyn and Scott discuss Space and Time’s origin story, how data warehouses work in Web2.0 vs web3 and the importance of data transparency.

They also dive into:

  • Blockchain and AI potential
  • Its OpenAI and blockchain data developments
  • Future use cases for data and on-chain AI
  • Advice throughout the bull and bear markets

Chain Reaction comes out every Thursday at 12:00 p.m. ET, so be sure to subscribe to us on Apple Podcasts, Spotify or your favorite pod platform to keep up with the action.

  continue reading

113 episodes

Artwork
iconShare
 
Manage episode 409253597 series 3352318
Content provided by TechCrunch, Yashad Kulkarni, Maggie Stamets, Kell Keller, and Jacquelyn Melinek. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TechCrunch, Yashad Kulkarni, Maggie Stamets, Kell Keller, and Jacquelyn Melinek or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

For this week’s episode, Jacquelyn interviewed Scott Dykstra, CTO and co-founder of Space and Time.

Before diving into web3, Scott spent almost 8 years at the cloud analytics and data platform Teradata and throughout the years he held roles of senior architect, director of cloud solutions and worked his way up to VP of the firm’s global cloud.

As for Space and Time, the company aims to be a verifiable compute layer for web3 that scales zero-knowledge proofs, or ZK proofs, on a decentralized data warehouse. Zero-knowledge proofs are a cryptographic action used to prove something about a piece of data, without revealing the origin data itself.

Space and Time has indexed data both off-chain and on-chain from Ethereum, Bitcoin, Polygon, Sui, Avalanche, Sei and Aptos and is adding support for more chains to power the future of AI x blockchain.

This episode is wrapping up Chain Reaction’s monthly series diving into different topics and themes in crypto. This month’s focused on blockchain and AI integrations.

Jacquelyn and Scott discuss Space and Time’s origin story, how data warehouses work in Web2.0 vs web3 and the importance of data transparency.

They also dive into:

  • Blockchain and AI potential
  • Its OpenAI and blockchain data developments
  • Future use cases for data and on-chain AI
  • Advice throughout the bull and bear markets

Chain Reaction comes out every Thursday at 12:00 p.m. ET, so be sure to subscribe to us on Apple Podcasts, Spotify or your favorite pod platform to keep up with the action.

  continue reading

113 episodes

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