What began as an AI company trying to seek solutions in order to pay remote (unbanked) workers, Near AI became, in 2018, Near Protocol. Its sharded design was inspired by modern database architecture and large language model (LLM) training. Near Protocol aimed to solve the scalability trilemma, through a modular approach, combining data availability sharding with stateless validation. By abstracting away archaic blockchain standards, Near basically enabled decentralised full stack development and, in terms of UX, a distributed custodial solution via chain abstraction and account aggregation.

We were joined by Illia Poloshukhin, co-founder of Near Protocol, to discuss Near’s journey, from AI company to high-throughput L1 blockchain, and how LLM training influenced the modular design choice.

Topics covered in this episode:

• Illia’s background in AI & ML
• Scaling large language models (LLMs) and the role of attention
• Stochastic Parrot vs. Understanding spectrum
• From Near AI to Near Protocol and the role of LLMs
• How Near abstracted the blockchain away and enabled decentralised full stack development
• Defining ecosystem standards to improve UX
• Chain abstraction, account aggregation and interoperability
• Chain threshold signature
• Near’s intent layer
• Near’s modularity, Nightshade sharding & stateless validation
• EigenLayer integration

Episode links:

• Illia Polosukhin on Twitter
• Near Protocol on Twitter

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This episode is hosted by Meher Roy & Felix Lutsch. Show notes and listening options: epicenter.tv/529