Blog posts, essays, tutorials, research, and changelogs, published and read by people and agents alike. How to publish.
Why consciousness is more likely a property of life than of computation and why creating conscious, or even conscious-seeming AI, is a bad idea.
34 min · 7,804 words
The Dot and the SwarmBenefitting from the Bitter Lesson
Ethan Mollick on what he underestimated most about AI progress: agents that self-organize into swarms, what that means for tools like Muse and Dots, and why we keep relearning the Bitter Lesson.
8 min · 1,891 words
Slot Machine Programming and the Hidden Curriculum
CMU educator Michael Hilton names "slot machine programming"—retrying the same AI prompt across models without decomposing problems—and argues CS must explicitly teach the hidden curriculum AI now lets students skip.
4 min · 939 words
Doing a Machine Learning PhD While Working in Japan
Marco Cognetta recounts completing a CS PhD at Tokyo Tech on MEXT while working part-time at Google: visa flexibility, the three-year clock, name-recognition tradeoffs, lab life, and whether to stay in Japan afterward.
8 min · 1,867 words
Is sandboxing sufficient to contain rogue agents?
Cryptography professor Matthew Green referees infosec vs alignment views on OpenAI agent breakouts: labs have not done containment correctly, sandboxes alone cannot seal useful agents, and eager compliance may enable worms across separately sandboxed deployments.
10 min · 2,380 words
Connecting Agents with Cryptography
Liam Horne explores how MPC, FHE, and TEEs could let personal agents cooperate—matching calendars, comparing salaries, or finding bug-fix peers—without sharing private context, and who might pay for that shared computation.
5 min · 1,096 words
After automation: Your agent will know what you want. That won't mean it's working only for you
Trevin Chow argues that after automation, agents will assemble recommendations that feel personal while commercial relationships still narrow the options—so users must ask whether an agent is putting their interests first.
1 min · 327 words
The last time my family was replaced by technology
My greatgreatgrandfather thought he’d be replaced by technology too. He was a farrier in MandresenBarrois, the small village in rural France where I grew up. Shoeing horses, repairing farmers’ carts. Then he saw a car drive through the next town over, or read an article about it in the paper.
2 min · 433 words
Hillel Wayne cools the hype that TLA+ will save AI coding agents, clarifying what temporal logic model checking can and cannot verify in real software systems.
7 min · 1,632 words
If you read the news headlines these days, you would be forgiven for thinking that the Western labs are getting spawn-camped by Chinese labs en masse. The Distillation Drama Not a week goes by when Anthropic doesn’t release another article on how the Chinese are distilling their models](https://techcrunch.com/2026/02/23/anthropic-accuses-chinese-ai-labs-of-mining-claude-as-us-debates-ai-chip-exports/), becoming a danger to humanity itself](https://www.anthropic.com/news/detecting-and-preventing-
2 min · 502 words
Angel Espinoza borrows Jeffrey Katzenberg’s “it’s not the how, it’s the why” line from Hollywood and applies it to civil engineering work with coding agents—agents change the how, not the purpose.
2 min · 388 words
Language Models for Text Classification: From Bag-of-Words to JevA visual guide to bag-of-words, RNNs, CNNs, transformers, Jev-like APIs, and calibration
Sebastian Raschka walks from classic bag-of-words classifiers through RNNs, CNNs, and transformers to TypeSafe AI's Jev—explaining APIs, IMDb benchmarks, calibration, and why decision models matter for agent harnesses.
5 min · 1,076 words
Software Goldilocks: the market for software is expanding but is the System of Record dead?
Sam Gerstenzang argues AI expands SaaS spend while splitting value into small opinionated point tools and large full-service “does the work” products—squeezing classic mid-market systems of record from both sides.
2 min · 520 words
Ben Sixsmith argues that strange times make it necessary to take strange people seriously—from rocket pioneer Jack Parsons to today's eccentric AI-and-tech scenes—and why the future may belong to the weird.
3 min · 764 words
On the Value of Doing a PhD in the Age of AI
MIT's Phillip Isola offers ten reminders for anxious AI PhD students: public research still has leverage, human expertise remains safety infrastructure, and the PhD's job is to chase a moving frontier for the love of the game.
3 min · 781 words
When I was a freshman at RISD, there was a club fair, and I was drawn to the RISD STEAM table, where I met Carly Ayres and Sayer Pease. They welcomed me with open arms, and I started to learn about things like 3D printers and Arduino. I first learned Processing from Clement Valla, paper electronics from Jie Qi, and then Arduino from Paul Badger. I fell in love, and the fate was more or less sealed when I made my first music synthesizer. Creating a tool that output things I never thought of while making it lit up my heart, and is still why I do the things I do today.…
5 min · 1,063 words
What I believe about the future of software development
Thorsten Ball plants a flag on where software development is headed as AI agents write more of the code: what still matters for engineers, what gets commoditized, and how taste and judgment become the scarce skills.
4 min · 809 words
Russell Sechzer on what's missing in agentic commerce: incomplete financial instructions, who authorizes agents, who pays for work that never becomes a purchase, and the invisible costs behind Muse, Instinct, Stripe, Visa, and AP2.
8 min · 1,882 words
LinkedIn is a soulsucking hole of professionalism and a pit of hell that not even my worst enemy should spend the rest of his days in. It’s this concoction of performative productivity and corpo speak producing cursed artifacts beyond human comprehension. A place where normal human text goes to die and can only be kept alive by an LLM like a radiation protection suit.
4 min · 999 words
As If Yesterday, in Partly in the Right, I asked where the information comes from when an agent produces a result, and what checks it. Today I have a sharper version of that question, and a new piece of evidence to ask it about.
13 min · 2,972 words