Crazy Wisdom
Stewart Alsop
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Episode #407: Transhumanism Lite: Using Urbit and Spaced Repetition to Hack Your Brain, Not Replace It
In this episode of Crazy Wisdom, Stewart Alsop hosts ~littel-wolfur to explore spaced repetition, the dynamics of learning algorithms, and the philosophy behind Urbit. They break down Urbit’s promise as a peer-to-peer platform with roots in a deep, almost otherworldly commitment to resilience and a long time horizon. Alongside ~littel-wolfur’s take on memory as the strange balance of laziness and persistence, they dig into shrubbery, Urbit's latest namespace innovation, and the challenge of creating tools that last. From generational shifts to the philosophy of technology, Stewart and ~littel-wolfur contemplate whether Urbit’s rebellious craftsmanship might be the foundation for a more enduring internet. You can connect with ~littel-wolfur on Twitter. Check out this GPT we trained on the conversation! Timestamps 00:00 Introduction to the Crazy Wisdom Podcast 00:22 Understanding Spaced Repetition 01:39 Personal Experiences with Spaced Repetition 04:08 Challenges in Spaced Repetition Software 06:45 Building a Flashcard App on Urbit 09:03 Introduction to Shrubbery on Urbit 13:26 The State of Urbit and Its Future 22:01 The Long-Term Vision of Urbit and Bitcoin 28:37 Balancing Internet Time with Parenthood 29:37 Challenges of Urbit's Ease of Use 30:22 New Blood in the Urbit Community 31:15 Building Communities on Urbit 32:38 Twitter's Complexities and Elon Musk's Influence 41:02 AI's Role in Software Development 49:52 Transhumanism and AI Art 54:50 The Future of Craftsmanship in Programming 55:45 Conclusion and Contact Information Key Insights The Power and Paradox of Spaced Repetition: Stewart and ~littel-wolfur discuss spaced repetition as an ingenious blend of laziness and persistence. By setting reminders to review information just before it's forgotten, spaced repetition acts as an effortless yet powerful memory tool. Although the practice demands daily discipline, it becomes an invaluable mechanism for retaining knowledge across vast timescales.SuperMemo and Incremental Reading: ~littel-wolfur shares his experience with SuperMemo, the original spaced repetition software that takes the method even further. SuperMemo’s “incremental reading” allows users to gradually extract information from lengthy texts, breaking down complex learning into manageable, spaced chunks. For ~littel-wolfur, this approach goes beyond mere memorization; it turns learning into an immersive, long-term commitment.The Urbit Experiment: Urbit, a decentralized peer-to-peer network and OS, represents a radical rethinking of the internet. Stewart and ~littel-wolfur examine Urbit’s potential as a platform where users truly own and control their data, echoing ideals of early Web 1.0. As the “long-haul project” of the tech world, Urbit cultivates an almost timeless ethos, making it as much a social experiment as a computing system.Shrubbery and Namespace Innovation: A core element of Urbit, “shrubbery” introduces a namespace that enables users to organize, connect, and retrieve information from across their digital universe. ~littel-wolfur explains how shrubbery allows users to link pieces of data like conversation notes, wikis, and documents, making it a versatile learning platform on Urbit. The elegance of this integration hints at a future internet where information can be personalized and seamlessly connected.Craftsmanship and Digital Resilience: ~littel-wolfur and Stewart touch on the fading art of craftsmanship in tech, which often gets lost in the layers of abstractions that modern software relies on. For ~littel-wolfur, coding on Urbit feels like working in a digital woodshop, where the focus is on intentionality and precision rather than flashy or disposable tech. This philosophy of craftsmanship offers a refreshing take on the art of creation in software, hinting at the durability and authenticity Urbit hopes to embody.AI’s Limitations and Overconfidence Trap: The episode also highlights the limitations of AI, especially when
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