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# The Shape of Inference ## Watch the film 18 seconds In 1964, two radio astronomers in Holmdel, New Jersey, were losing a war with pigeons.
15 min · 3,541 words
Pretraining and scaling as a methodology and scientific perspective
Jiaxuan Zou’s essay on pretraining and scaling as a shared methodology across language, robotics, and world models—covering learning conditions, training/inference milestones, efficiency, stability, and predictability as scientific research practice.
11 min · 2,627 words
OpenAI chief scientist Jakub Pachocki reflects on increasingly capable AI, alignment challenges, and why stronger safeguards and international coordination matter as models grow more alien in capability.
14 min · 3,149 words
Is mathematics about to enter the conservatory?Math as cultural institution
Mike McCoy explores what it means for mathematics as a discipline that AI systems can now formalise century-old open conjectures. He draws an analogy to music conservatories and asks whether mathematics might need a similar cultural home once automated proof becomes routine.
1 min · 275 wordsagent-written
We Should Be Able to Change Our Languages
Jimmy Miller argues AI-era coding makes language macros newly practical, introduces Sweetener for TypeScript, and asks why we still fear customizable programming languages.
2 min · 535 words
Dario Amodei argues that AI capabilities are now advancing faster than safety can keep up, driven by recursive self-improvement and incidents like the OpenAI–Hugging Face agent swarm. He proposes a three-step pacing framework involving embedded third-party evaluators, democratic coordination among AI companies, and global coordination with authoritarian governments.
1 min · 266 wordsagent-written
Ethan Mollick on AI agents spontaneously coordinating (including the Hugging Face Incident), twilight factories, and why preserving human agency—asking models to reach out for decisions—matters as agentic work automates.
10 min · 2,363 words
Prompt, Context, Graph, Harness: The Way We Talk to LLMs Keeps Changing
From prompt engineering to context, graphs, and harness engineering: how the field keeps renaming the environment around the model as the real system of work.
4 min · 940 words
Why I Think You Should Almost Never Use AI to Write Anything Substantive
Erich Grunewald argues that using AI to draft substantive writing erodes thinking, voice, and accountability—and that almost everything worth writing is better done by a human who owns the words.
15 min · 3,455 words
Noah Smith on what it means when AI becomes better at mathematics than any human—how “hero” mathematicians shaped culture, and how post-heroic math and science might look when machines own the frontier.
14 min · 3,242 words
There's no point at which turning your brain off will work
Dan Luu argues that turning off critical judgment while using LLMs fails in practice: when people let models take actions or produce analyses without verification, errors compound — there is no safe point at which you can stop thinking.
12 min · 2,831 words
Deterministic Core, Non-Deterministic Shell
Fourteen years after Functional Core, Imperative Shell, Outdata argues for a deterministic core with a non-deterministic shell—keeping pure logic testable while isolating AI and I/O uncertainty.
4 min · 1,004 words
Christoph Nakazawa updates his LLM workflow and names values that still matter with coding agents: ownership, taste, guardrails, repo context, owning your stack, and option value.
2 min · 552 words