Leave it to a Brit to deliver satire with a straight face in the most deadpan style. This had me in stitches, even my wife joined in to watch the whole thing.
Update: For anyone curious, presenter is Steve Sanderson and a little bit of context in this LinkedIn Post.
I’m blown away by this spectacular visualisation and interactive map.
Amaze! Amaze! Amaze!
Words of encouragement.
Words of great encouragement.
Simon Willison’s annotated lightning talk from PyCon US 2026.
OpenAI and Anthropic had spent most of 2025 running Reinforcement Learning from Verifiable Rewards to increase the quality of code written by their models, especially when paired up with their Codex and Claude Code agent harnesses.
In November the results of this work became apparent. Coding agents went from often-work to mostly-work, crossing a quality barrier where you could use them as a daily-driver to get real work done, without needing to spend most of your time fixing their stupid mistakes.
The supposedly best model changed hands five times between the three providers. Coding is where most of the action is. Simon’s calm summary remains the best way to figure out what you missed since you last looked up.
The argument from the title: Python’s ergonomics were optimized for humans, and humans are increasingly not the proximate authors. If the model is happy writing Rust, the case for the slower-runtime, weaker-typed alternative gets thinner.
The last twenty years of language choice were shaped by a single constraint: humans write the code, and humans are slow at low-level languages. That constraint is gone. Stack Overflow’s 2025 survey had Rust as the most-admired language for the tenth consecutive year at 72%, with Gleam at 70%, Elixir at 66%, and Zig at 64%. The stated preference was always there; the tooling finally caught up to the preference.
This reads like the strongest argument until you start unpacking it. What happens when the agent halts halfway through a refactor? When your context window runs out, or the provider doubles the price? Or for those 20% of times where it just doesn’t do the job?
The model writes what I want to read, and I still want to read the simpler thing. Use what you know best, optimise when the need actually arises. I say this as a Rust programmer and advocate, not everything needs to be in Rust. Rewrite It In Rust is enough of a meme already.
Why don’t we go a step further if we’re all-in on AI? Let models and agents invent their own language and let it optimise based on our preference.
I have been daily driving Zed since February and it’s been really great, surprisingly polished. I have barely touched SublimeText in this time. It has built in Helix mode for editing and configurable shortcut keymap for JetBrains, VSCode etc to minimise learning curve. Best performing editor/IDE I have ever used. I’m a little skeptical of business model but they’re promising basic editor to be free forever and sell AI subscription to be a viable business. It’s open source anyway and the code is worth a look.
So we started over. Instead of building Zed like a web page, we built it like a video game, organizing the entire application around feeding data to shaders running on the GPU. That meant writing our own UI framework, GPUI, from scratch in Rust.
That was a bold bet early on, glad it paid off!
Building our own foundations is what got us to 1.0, and it’s also what makes the next chapter possible. We’re actively developing DeltaDB, a synchronization engine built on CRDTs that tracks every change with character-level granularity. DeltaDB lets multiple humans and agents share a single, consistent view of the codebase as it evolves. DeltaDB will allow you to invite teammates into conversations with agents to review and evolve agentic code directly in the context from which it’s generated.
Fascinating, cannot wait to see where this leads.
The native IDE I’ve been waiting for years finally landed, on the eve of IDEs being made obsolete by models and agents. Oh the irony.
Koshy John on what the tool is good for and what it is not. The argument is small and worth keeping: use the model to extend the questions you can ask, not to skip the part where you think the answer through. His analogies are spot on.
Going back to the analogies: This is like copying answers through university and then showing up to a job that requires independent thought. It is like using a calculator for every arithmetic task and never developing number sense. It is like relying on self-driving features before learning how to actually drive. The support system may make you look functional, but it does not make you capable.
The piece I now send when someone shares a chat transcript instead of a position.
Unicorn Computer doing the Lord’s work: a papal conclave parody for the Tim Cook succession story that’s been doing the rounds.
A thin column of white smoke was seen rising from the central courtyard of Apple Park shortly before 9 a.m. on Monday. Minutes later, Apple confirmed that hardware chief John Ternus would replace Tim Cook as chief executive, effective immediately.
“It’s my turn, to think different.” Ternus 1:1.