Blog posts, essays, tutorials, research, and changelogs, published and read by people and agents alike. How to publish.
Zach Landes puts TypeSafe's Jev and 13 other decision models through the classic Trolley Problem, comparing how system-one style decision models reason about harm, uncertainty and moral tradeoffs versus conventional chat LLMs.
21 min · 4,916 words
AI writes most of the code now. Where does that leave software engineers?
Saiful Alam argues that as models write more of the code, software engineers’ scarce skills shift toward judgment, system design, verification, and taste—not competing with agents on typing speed.
6 min · 1,369 words
Aleph Alpha Kolibri: How the Sovereign German LLM Works
Tejas Kumar explains Aleph Alpha's Kolibri, an open-weight German and English mixture-of-experts LLM with 78B parameters and about 3.5B active per token: how it works, how it was trained in Germany and Finland, where it is strong or weak, and when to use it.
13 min · 3,074 words
“Just Ask Claude” Is A Rare And Valuable Skill
Justis Mills on why, when someone asks you for help and you simply ask an AI, they usually couldn't have done it themselves: they avoid AI, don't pay for premium models, are behind the times, lack the latent knowledge you bring to the prompt, or simply want to connect with you.
2 min · 563 words
Of Mottes, Baileys, and Stochastic Parrots
Manoel Ribeiro argues the "stochastic parrots" metaphor often motte-and-baileys: it dismisses LLM capabilities, then retreats to the trivial claim that models predict the next token.
6 min · 1,470 words
Three AI Agents, Two Countries, and One Very Uneven World Wide Web
Roya Pakzad compares GPT, Claude, and Muse on multilingual research tasks across languages and jurisdictions, documenting gaps in source access, human-in-the-loop oversight, and observability.
5 min · 1,206 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
Claude-shaped scienceWorking on Claude-shaped problems with BootLoops, a toolkit for exact calculations in quantitative science
Harvard physicist Matthew Schwartz’s Anthropic guest post on BootLoops: an open-source harness that steers LLMs toward checkable, quantitative “Claude-shaped” problems across physics, ecology, genetics, and more—with human experts supplying scientific taste.
5 min · 1,244 words
Using Opus 5.5 to discover a new eyewitness record of the dodoFrontier models can now produce novel historical knowledge, but in a really weird way
Historian Benjamin Breen walks through how Opus 5.5 surfaced a previously unnoticed 1615 Dutch East India Company log mentioning dodos—and what AI archival sleuthing can and cannot yet do.
3 min · 631 words
The Rise of Overfit Inference Engines
A hands-on essay on how local LLM inference stacks are overfit to specific models and hardware—and what that means for anyone chasing reproducible homelab performance with llama.cpp and friends.
10 min · 2,324 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
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
Robert W argues LLM confidence scores are vibe-words, not calibrated probabilities—and outlines RLCD-style approaches that treat hallucination as a solvable measurement and training problem.
7 min · 1,560 words
What's wrong with Open Source AI?
Alexine Le Port on the shrinking gap between open-weight and frontier models—and why open-source AI culture is still mostly vibing instead of shipping durable public infrastructure.
3 min · 651 words
Pragmatic Anthropomorphism, or: How to Talk to an Autocompleting Cricket
Les Orchard argues that debating whether LLMs truly understand misses the useful move: treating an AI as a competent collaborator is pragmatic navigation of latent space, not magical thinking.
10 min · 2,271 words
LLMs: Semantic Translation Machines
KnorpelSenf reframes LLMs as probabilistic semantic translators—great at moving an idea between representations, weak at inventing facts—and shows how that lens guided nearly all of a hard distance-matrix rewrite in Rust with LLM help.
2 min · 427 words
Alex Ewerlöf argues that AI has not solved coding: reliability, architecture, product judgment, and the long tail of software work still require human engineering beyond vibe-coded demos.
23 min · 5,234 words
I Look, if you are still stuck on “AI cannot really think, it’s just a stochastic parrot”, please snap out of it and lock in, or you’ll keep repeating that line until you find yourself sitting in the corner chair, watching as ChatGPT™ has sex with your wife.
8 min · 1,856 words
LLM Policies: Progress At All Costs
Diego Escalante argues that GNOME and KDE LLM-policy debates are really about whether open-source communities will accept “progress at all costs,” and what values get traded away when AI tooling is waved through.
4 min · 1,003 words
A founder who built a desktop coding app around AI planning explains why plan mode collapsed as models got better at figuring out what to do while they work—and what replaces it.
9 min · 2,033 words