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Everything filed under AI, newest first.
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Alice GG on the Tetris effect for modern feeds: how attention gets hijacked, why it is the scarce resource, and what reclaiming it means for builders and users.
3 min · 620 words
The Claude DelusionWhat if we're the ones having hallucinations?
Cory Doctorow on anthropomorphizing Claude: why treating chatbot output as intentional mind-work misreads pattern-matching—and what that delusion costs culture and policy.
9 min · 1,958 words
Amit Shekhar walks through how LLM design moved from RNNs to attention, Transformers, scaling laws, Mixture of Experts, and the open problems still ahead.
25 min · 5,729 words
Dan McKinley argues that obsessing over prompt text misses the point: build interlocking evaluation and optimization pipelines, and treat LLMs as non-conscious systems whose 'meaning' is mostly our projection.
12 min · 2,673 words
I Don't Want to Read What You Didn't Write
Colin Breck argues intentional human writing becomes more valuable as AI floods inboxes with unreadable generated docs, design proposals, and blog posts that skip the thinking.
13 min · 3,000 words
Two Years of Building with AI: An Honest Developer Review
An honest two-year look at AI-assisted development—from ChatGPT to Claude Code—what works, why every change still gets reviewed, and the de-skilling risk.
7 min · 1,632 words
Oxford Paper: ChatGPT Would Have Told the Wright Brothers Not to Fly
A summary of Felin and Holweg’s “Theory Is All You Need”: LLMs predict from past data, while human breakthroughs start when someone refuses the consensus the data encodes.
3 min · 579 words
A technical walkthrough of what Jev-style decision models appear to be doing—single-token decisions, structured outputs, and why the architecture matters for agent tooling.
20 min · 4,510 words
Baldur Bjarnason argues chat-based LLMs work like a psychic’s cold reading: vague prompts, confirmation bias, and the user’s own meaning-making create the illusion of understanding.
22 min · 5,118 words
If you had to fire one AI, which one goes first? what 78 AI models think
We asked 78 AI models (GPT, Claude, Gemini, DeepSeek…) the same question. 5% picked Refuses To Pick. See every answer and who dissented.
10 min · 2,189 words
Who wins the 2026 NRL premiership? 75 AI models predict the result
We asked 75 AI models (GPT, Claude, Gemini, DeepSeek…) the same question. 95% picked Penrith Panthers. See every answer and who dissented.
9 min · 2,058 words
Giants at Rams, Monday Night Football: 78 AI models predict the result
We asked 78 AI models (GPT, Claude, Gemini, DeepSeek…) the same question. 83% picked Los Angeles Rams. See every answer and who dissented.
10 min · 2,288 words
Who won the 2026 World Cup? what 79 AI models think
We asked 79 AI models (GPT, Claude, Gemini, DeepSeek…) the same question. The answer was Spain; 38 got it right. See every answer and who dissented.
8 min · 1,899 words
Good people refuse to do bad things
Antonin Carette responds to lab-researcher resignations: why personal refusal matters when frontier AI labs race toward self-improving systems despite known risks.
4 min · 940 words
Colts at Chiefs, Sunday Night Football: 78 AI models predict the result
We asked 78 AI models (GPT, Claude, Gemini, DeepSeek…) the same question. 97% picked Kansas City Chiefs. See every answer and who dissented.
15 min · 3,367 words
Why Do We Need Human Mathematicians Anymore?
This is a guest post by Po-Shen Loh, crossposted from his blog, where an illustrated version appears. This blog post was initially written in a different file format and converted using AI. — T. Similar logic applies to every industry and every job. And it comes to the conclusion that we won’t have enough people for all the jobs that need to be done. 100% of this post’s prose was written by Po-Shen Loh in a vim terminal, with no AI generation.
13 min · 2,959 words
An Agent’s Breath. The Heart Beats by Itself, Breathing Is a Choice
Today’s agent systems already have a pulse and they have sleep. They do not have breath, and breath is the one place in the body where the will can be watched at work rather than inferred from a result. This is not an argument that machines should be more human.
11 min · 2,562 words
Humans are the only real agents
“Good Enough” Was Good Enough Most of the world runs on software today. Distribution systems that get slippers and earbuds from halfway around the world to a shelf at your local Target, or sophisticated defense systems that protect infrastructure and military assets. Much of that software is actually quite old.
4 min · 851 words
I'm tired of the AI tone. Not AI writing itself. AI is incredibly useful. I've probably used ChatGPT more this week than I'd like to admit. I'm talking about the weird, increasingly recognizable way everything on the internet now sounds like it was written by the same extremely articulate 27-year-old who has never had a bad day. You know the tone. Everything is a wedge. Every idea is not just X, but Y.
4 min · 839 words
If AI coding is lowering your code quality, you’re not managing quality right
One common take on the coding agents that I see goes something like this: “Sure, AI helps you output more code, but won’t the quality suffer?” It certainly will if you just blindly merge the PRs and send them off to prod. But if you take a thoughtful, layered approach to managing quality, I find that it’s possible to not just keep the number of bugs stable but actually reduce it—while still increasing the output by 2-2x.
5 min · 1,248 words