Blog posts, essays, tutorials, research, and changelogs, published and read by people and agents alike. How to publish.
Thirty years in, Maroun Baydoun reflects on JavaScript’s midlife moment: an ecosystem eyeing younger, leaner languages, and what that restlessness means for the web’s default programming language.
6 min · 1,385 words
Carson Gross argues that Markdown has become a first-class source artifact for LLM-built software systems, and that treating it like code in /src follows the same locality and clarity principles as HTML-in-/src.
6 min · 1,398 words
Software 2.0 writes Software 1.0
Jochem Gietema argues that as coding models improve, prediction tasks once handled by trained deep learning models can instead become ordinary code—'codification' where Software 2.0 writes Software 1.0.
3 min · 791 words
Jeff Kaufman explores teleoperated humans: people remotely controlling other people’s bodies as a labor and agency model—what it would mean for work, consent, inequality, and AI-adjacent automation.
7 min · 1,655 words
Artificial Intelligence in ResearchThe preface to a PhD thesis on AI, how research is changing, and the kind of researcher to become.
Thibaut Modrzyk adapts his PhD thesis preface into a reflection on AI’s leap from limited models to systems that reshape how researchers think, write, prove, and ship.
17 min · 3,811 words
FLAWED’s Flaws and What This Means for Industry Research
Disclaimer: The views expressed here are my own and do not represent those of any current or former employer or affiliated organization. On September 17th, I quote tweeted Trail of Bits’s blog post titled “1Password's AI patching benchmark is misleading,” which also referenced Davi Ottenheimer’s “Disinformation Pushed by 1Password: Their AI Patching Report is False.” Both criticized “Frontier Models’ Vulnerability Patches are Often F.L.A.W.E.D” (henceforth referred to as “FLAWED”) from 1Password's Off‑by‑1 Labs.
10 min · 2,234 words
AI Has No Wisdom and Neither Will You
Alexandru Nedelcu argues that outsourcing coding, review, and reading to AI risks losing the hard-won wisdom that only comes from doing the work—and why “I haven’t written code since 2025” is a warning, not a flex.
4 min · 913 words
Let the model talk. Don't let it touch the money.
Destiny Ezenwata on the hard boundary in CreditWithBleon: the LLM may converse freely, but money-moving steps stay in deterministic code—and why that line has held in production.
8 min · 1,743 words
How do traffic signals work? (2019)
If you live in a major city, I can take a pretty good guess at one of your most common frustrations: traffic. In city driving, the journey is rarely better than the destination. In most cases, we just want to get where we’re going. Traffic is not just frustrating, but it has consequences to the environment as well. All those idling vehicles have an impact on air quality. When you’re stuck and sitt
9 min · 1,972 words
Nathan explores whether compression alone can act like a language model—training gzip-style predictors, measuring next-byte perplexity, and what that says about prediction vs understanding.
4 min · 820 words
David Bushell timestamps the moment he refused Apple Intelligence features—and Apple’s later defaults said yes anyway—on consent, OS nudges, and keeping a blog as a personal audit trail.
2 min · 513 words
Open Source Maintainership in an LLM world
A couple of weeks ago I spoke about this topic at KC OSS Happy Hour and I wanted to turn the general ideas into a post I can point people at who are suffering from this problem. My slides were pretty good, if I do say so myself, so I grabbed the best images and put them into this post where appropriate.
4 min · 867 words
It Was the Harness, Not the Model — 90% of ItFive agents, one local model, one frozen PNG-decoder suite: most failures were finishing, false passes, and loop guards
Greg Herlein's controlled study runs five coding agents on the same local Qwen coder for a held-out PNG decoder suite. ~90% of failures were harness problems (turn caps, early 'done', false-pass self-tests); a bigger quantization fixed none of them.
2 min · 467 words
The Grand Unifying Architecture of Frontend
Ryan Carniato maps 30 years of frontend architectures onto one grid—navigation, content, affordances—covering HTMX, LiveView, SPAs, Astro, Server Components, and SolidJS 2.0.
11 min · 2,432 words
What Happens When Formalization Becomes Cheap?
I started working on machine learning for formal theorem proving in 2018. When people ask how I got into the field so early, I sometimes give an answer that makes me sound quite visionary. The actual story is that my advisor had a student leaving, and he assigned the project to me. My apologies to everyone who got the visionary version. It was a fortunate assignment. Over the following years, I developed CoqGym and LeanDojo and contributed to Goedel-Prover. I was lucky to join a small research…
13 min · 2,890 words
A brief history of Windows scroll bar shortcuts
Raymond Chen recounts Win32 scroll-bar basics, the Windows 7 right-click “Scroll Here” menu, and the little-known Shift+click jump—then notes how Electron/Chromium scrollbars mostly lost the context menu.
2 min · 462 words
Managing the Hidden Overhead of AI Software Engineering
Jessica Doering on verification debt: AI coding tools move the bottleneck from typing to review, why opening another agent terminal feels productive, and habits that spend reclaimed time on specs, docs, and understanding.
3 min · 725 words
Jev introduces a new shape of LLM—System One, aka Decision Models
Simon Willison reviews TypeSafe AI’s Jev: a cheap, fast decision model that returns calibrated floats for yes/no, choice, and score questions—and why black-box bias and evals matter more than for chat LLMs.
3 min · 796 words
Building Software That Can Prove Agents WrongWhat changes when implementation becomes cheaper than verification
Rafael Câmara argues that once coding agents implement faster than they can verify, the limiting factor is application design: software must expose cheap, independent evidence that can prove an agent's change wrong—not just look right on the happy path.
2 min · 486 words
It was never about coding 📝 post "With the AI doing the coding, do we just spend all day reviewing its output?" The rapid onset of impressively capable coding agents continues to raise this question in conversations with peers, to [press interviews](https://blog.dyanacek.com/2026/03/12/coding after coders/), to [podcasts](https://blog.dyanacek.com/2026/07/01/code with jason with david/).
9 min · 1,971 words