Simon Eskildsen is a learning machine.

I first interviewed him in 2020 about how he leveled up from being an intern at Shopify to becoming the company’s director of production engineering by reading and applying insights from hundreds of books.

A lot has changed over the last four years. Language models have made it possible to access and contextualize information faster, easier, and more cheaply than ever before—and in this episode, Simon and I talk about how this changes the way he learns.

Simon is now the cofounder and CEO of AI startup turbopuffer, which is building a search engine that makes vector search—an approach to information retrieval that uses machine learning to gather context—easy and affordable to run at scale.

We spent an hour talking about how he leverages LLMs’ contextual intelligence to supercharge his learning, such as helping him pick up new words as a non-native English speaker, do odd jobs to maintain his rural cabin in Quebec, and articulate technical concepts in legalese. As we talk, we screenshare through his Anki setup, including the flashcard template he finds most useful, and the custom AI commands he’s created in productivity software Raycast. Simon tells me about the clutch of AI tools he experiments with for journaling, writing, and coding, as well as his thoughts on how language models will fundamentally reshape the way we learn. Here’s a link to the transcript of this episode.

This is a must-watch for note-taking aficionados and anyone who wants to supercharge their learning with AI.

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Links to resources mentioned in the episode:

Simon Eskildsen: @Sirupsen

Simon’s startup, turbopuffer: turberpuffer.com, @turbopuffer

My first interview with Simon in 2020: https://every.to/superorganizers/how-to-build-a-learning-machine-299655

The productivity tool through which Simon uses LLMs, Raycast: https://www.raycast.com/

The other AI tools that Simon is experimenting with: voice-to-text tool superwhisper, copilot for developers Supermaven, code editor Cursor

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