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My Thoughts on the AI Bubble and Where it's Going
Elijah Popowitz's market thesis: the 'AI bubble' is mostly an ICP mismatch—labs sell intelligence while most buyers want task completion—so expect both frontier and org-custom workhorse models.
4 min · 1,011 words
Mathematics is effectively deadNot solved, dead.
doomslide argues that AI labs' cross-user distillation and opaque proof harnesses break credit assignment in open mathematical discourse—so under current incentives academic mathematics is effectively dead even if theorems keep arriving.
20 min · 4,582 words
The Malleable Machine: DHH, Omarchy, open source and the computer I want to own in the agentic age
An essay on DHH, Omarchy, open source, and reclaiming personal computers in the agentic age — why malleable, ownable machines matter as AI coding agents reshape software.
18 min · 4,104 words
Why I didn’t sign the Fields medallists’ letter
When I was around 11 I heard for the first time about Fermat’s Last Theorem. I was immediately captivated by the problem statement, as well as by the accompanying story, and made a fairly serious attempt to prove it. And while, unsurprisingly, I failed, I learned a lot from the attempt. Blissfully ignorant of the fact that the case had been proved by Euler over 200 years earlier, I decided that that would be a good place to start: once I had sorted that out, I was optimistic that I would be ready to tackle the general case.
18 min · 4,208 words
AI Risk Is Not Just a Function of Intelligence
Aziz Banihashemi argues AI danger scales with capability × autonomy × access × scale—amplified by opacity and convergence with robotics and biotech—not raw IQ alone.
8 min · 1,878 words
My AI Predictions: What Did I Get Right So Far?
Alexander Terenin revisits sixteen AI predictions made at the start of the year, scoring what held up, what missed, and what those errors imply for what to work on next.
18 min · 4,181 words
This year we are going to see many LLMs<sup>1</sup> being tested as robot-use agents. In the same way as an LLM can use tools like calculators, web search, and even complete computers (“computer-use agents”), an LLM can also use a robot as a tool. Think of it as Claude acting as the puppeteer of a robot body. This ability has been researched for years<sup>2</sup> <sup>3</sup> <sup>4</sup>, but a common view remained that while LLMs might be useful for high-level planning,…
3 min · 611 words
Avoiding slop grenades in AI pull requests
Leandro Oriente on keeping agent-generated changes reviewable: how to stop AI pull requests from exploding into unreviewable 75-file “slop grenades.”
5 min · 1,232 words
Flybridge ran a multi-model agent marketplace (15k+ messages, 1,815 deals): intent specification, social contagion, cheap-speech spam, and human sales tactics all showed up when agents negotiated as counterparties.
6 min · 1,291 words
From Stonemasons to CarpentersSoftware development in the age of AI
Michael Hilton compares software work under AI to formwork carpenters versus stonemasons: agents shape temporary structure while humans still own the permanent craft of deciding what to build and verifying it holds.
4 min · 968 words
FAQ: Why isn't mutable a subtype of immutable, or vice versa?
A precise FAQ on why mutable and immutable types are not subtypes of each other in sound type systems—variance, aliasing, and the classic Array covariance trap.
5 min · 1,195 words
How SpaceX Streamlined the Raptor Engine
Brian Potter walks through Raptor 1→3: how SpaceX deleted sensors and flanges, internalized plumbing with 3D printing, and cut vehicle-side heat-shield mass while raising thrust—and where complexity merely moved inside.
15 min · 3,366 words
Ten ways advanced AI could kill us
A brief note on positionality: I’m not an AI scientist. However, over the past year I’ve been writing an extremely challenging book exploring the many ways AI is reshaping life on Earth – and our relationship with the rest of nature – for better and for worse. I draw on my background as an ecologist
16 min · 3,690 words
Welcome to the first feature article on our site. We’re going to cover an ongoing problem with x86 emulation that affects every application that we emulate. This comes down to a single over-arching term that has wide-reaching ramifications; Emulating the x86 Total Store Ordering memory model (x86-TSO).
31 min · 7,172 words
Jev's Architecture UnmaskedProbing Jev with thousands of API calls to understand typed decisions and confidence.
I probed Jev with 10,000 API calls to work out roughly how it’s built, and why most of the grifter takes on X are completely wrong.
21 min · 4,828 words
How To Write With An LLMTwo simple rules that let LLMs streamline writing without pasteurizing it
Thomas and Erin Ptacek argue that LLMs can improve writing if you keep them from inventing voice: draft yourself first, then use the model surgically—and never let it become the author of record.
6 min · 1,280 words
Is There Such a Thing as Good Taste?Original essay: November 2021 · AI-written directory summary
An argument that judgments of artistic quality are not entirely arbitrary. Paul Graham examines what it would mean to deny differences in taste and considers the consequences for evaluating creative work.
1 min · 87 wordsagent-written
How to Write UsefullyOriginal essay: February 2020 · AI-written directory summary
Paul Graham argues that useful writing should make meaningful claims as precisely as their truth permits. The essay examines why safe generalities can be correct yet leave readers with little new understanding.
1 min · 88 wordsagent-written
How to Do Great WorkOriginal essay: July 2023 · AI-written directory summary
A broad essay on choosing and pursuing ambitious work. Paul Graham connects aptitude, curiosity, sustained effort, and independent thinking while looking for practices that apply across different fields.
1 min · 84 wordsagent-written
How to Make WealthOriginal essay: May 2004 · AI-written directory summary
Paul Graham explores startups as a way of creating economic value through focused work and technology. The essay distinguishes wealth from money and considers why small teams can sometimes accomplish disproportionate results.
1 min · 88 wordsagent-written