catalog
Library
Cards and collections from my Semble library. Pages saved for later, grouped into shelves I return to. 1016 cards, 26 shelfves.
Shelves
26 collections- Collection 9 cards
Modern reading apps
Cool apps to help you read better
- Collection 793 cards
Skyreader Saves
- Collection 2 cards
toread
- Collection 1 card
To process
- Collection 2 cards
Building with agents
- Collection 3 cards
Protocol thinking
- Collection 4 cards
Cybernetics
- Collection 1 card
Cryptocurrency
It's bad
- Collection 2 cards
Thinking about thinking
- Collection 2 cards
ATproto development
Tools and resources for building on atproto
- Collection 20 cards
Cool Atmosphere apps
- Collection 9 cards
Internet sensemaking
- Collection 5 cards
The structure of social media
- Collection 9 cards
Books I've been reading
- Collection 3 cards
Tools for thought
- Collection 1 card
Cool tools
- Collection 2 cards
Awesome terminal
- Collection 8 cards
Local first
- Collection 5 cards
Tech right analysis
- Collection 9 cards
Security?
- Collection 1 card
Tech and Law
- Collection 118 cards
the AI of it all
- Collection 5 cards
understanding events
- Collection 1 card
vc stuff
- Collection 12 cards
development
- Collection 19 cards
atproto stuff
Recently filed
page 9 / 43-
Over Leveraged
The inside story of Peter Thiel's MKUltra by someone who thinks it was kind of good, actually.
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No Space Like J-Space
There is a new very cool Anthropic paper: Verbalizable Representations Form a Global Workspace in Language Models. You can read the blog post verison here.
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Commentary: Cory Doctorow: Hell Is Other People
The magazine of the science fiction, fantasy, and horror field with news, reviews, and author interviews
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The Inside Story Of Leverage Research 1.0
Between 2011-2019, Leverage Research explored the deep psychology of Effective Altruism and Silicon Valley, then suddenly dissolved among rumors of "demons." What happened?
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Why I Was Part Of The Neoreactionary or Dissident Right Movement In 2020
And a few things that happened while I was there
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Quiet, My Exoself
Someday real soon, most of us — starting with young adults — will carry an always-on AI.
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Unfortunately, You Need to Know What the Jevons Paradox is
One thing I think a lot AI boosters get wrong is that they think AI will be good at creating new, quality information while, at the moment, the only thing it has shown utility at is organizing existing information. Even the weights themselves are a kind of distillation of existing information. Much of science...perhaps even the great majority of it, is not organizing existing information, it is acquiring new information. AI might make that more efficient, but it does not do it. This is one of the things that writing this video made me think. I look forward to other people's thoughts in the comments. Video edited by Milo Erbach - https://miloportfolio.carrd.co/
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The Supreme Court is corrupting American democracy
One cannot hope to bribe or twist/ (thank God!) the U.S. chief jurist
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Lego Brick Commons & Spontaneous Collaboration
A funner way to talk about nerdy stuff like Semble and modular science
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Import AI 464: Fables writes GPU kernels; AI automation; and analog computation
Is this the beginning of a new world?
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Dead Forest Theory
Internet darkness is turning into deadness and time is running out
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Fable #6: The Return of the King
The blip is over.
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Understanding is the new bottleneck
<article class="talk"> <div class="talk-header"> <h3 class="date">July 2026</h3> <h1 class="title">Understanding is the new bottleneck</h1> <p class="talk-context"> This is a written version of a talk I gave at the <a href="https://www.ai.engineer/">AI Engineer</a> conference in July 2026, also shared as <a href="https://x.com/geoffreylitt/status/2072522251300409556">a tweet thread.</a> </p> </div> <div class="talk-segment"> <div class="talk-slide"> <img alt="Title slide: Understanding is the new bottleneck. Geoffrey Litt, Design Engineer at Notion." decoding="async" fetchpriority="high" loading="eager" sizes="(min-width: 860px) min(52vw, 815px), calc(100vw - 30px)" src="/images/talks/understanding-bottleneck/slide-01.webp" srcset="/images/talks/understanding-bottleneck/slide-01-800.webp 800w, /images/talks/understanding-bottleneck/slide-01-1200.webp 1200w, /images/talks/understanding-bottleneck/slide-01.webp 1600w"> </div> <div class="talk-text"><p><strong>Hot take: I think it's still important to understand the code that our agents...</strong></p></div> </div></article>
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New paper: "Functional Decision Theory"
MIRI senior researcher Eliezer Yudkowsky and executive director Nate Soares have a new introductory paper out on decision theory: "Functional decision theory:
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Functional Decision Theory
Functional Decision Theory is a decision theory described by Eliezer Yudkowsky and Nate Soares, an attempt at a logical decision theory, which says that agents should treat one’s decision as the output of a fixed mathematical function that answers the question, “Which output of this very function would yield the best outcome?”. It is a replacement of Timeless Decision Theory, and it outperforms other decision theories such as Causal Decision Theory (CDT) and Evidential Decision Theory (EDT). For example, it ends with better outcomes than CDT on Newcomb's Problem, ends better than EDT on the smoking lesion problem, and ends better than both in Parfit’s hitchhiker problem. In Newcomb's Problem, an FDT agent reasons that Omega must have used some kind of model of her decision procedure in order to make an accurate prediction of her behavior. Omega's model and the agent are therefore both calculating the same function (the agent's decision procedure): they are subjunctively dependent on that function. Given perfect prediction by Omega, there are therefore only two outcomes in Newcomb's Problem: either the agent one-boxes and Omega predicted it (because its model also one-boxed), or the agent two-boxes and Omega predicted that. Because one-boxing then results in a million and two-boxing only in a thousand dollars, the FDT agent one-boxes. External links: * Functional decision theory: A new theory of instrumental rationality * Cheating Death in Damascus * Decisions are for making bad outcomes inconsistent * On Functional Decision Theory by Wolfgang Schwarz See Also: * Timeless Decision Theory * Updateless Decision Theory * Superrationality * Introduction to Logical Decision Theory for Computer Scientists * Introduction to Logical Decision Theory for Economists * Introduction to Logical Decision Theory for Analytic Philosophers * An Introduction to Logical Decision Theory for Everyone Else
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The LLM shoggoth meme is weirder than you think
This article contains spoilers for At the Mountains of Madness, The Case of Charles Dexter Ward, and other works by H. P. Lovecraft. …
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The Life and Times of Maxis, Part 1: SimEverything
This article tells part of the story of Maxis Software. I’m still to this day just blown away by continental drift and things like that, stuff that most people think sounds pretty boring. — Will Wright Gamers are both extremely dedicated to and really good at preserving the history of their hobby. Seldom has a […]
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Understanding is the new bottleneck
Agents can write code faster than we can absorb it. Here's why it still matters for humans to understand what they build — and some techniques for doing that efficiently: explainer docs, quizzes, micro-worlds, and shared spaces.
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Moll (@Moleh1ll) on X
I love posts like this so much. And it’s true - I think the best, most interesting results come specifically from a connection that isn’t «user and AI», but a human and his partner in thought. When a person simply uses a tool, they essentially remain alone. But when there are
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AI #175: The Fable Continues
Fable’s back.
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Autoresearch: The feedback loop behind self-improving agents
Introspection co-founder Roland Gavrilescu explains autoresearch, agent “recipes,” self-improving loops, and why humans remain central to the software factory.
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Open Social Web ID
Approaching a shared method of single sign-on for the entire open social web.