A huge amount of the Anti-AI code sentiment massively overestimates the quality of human code outside of a very small set of open source and high quality company codebases. Human Slop is everywhere and can trivially be improved on by any opus level model.
Given the way the rules are written, the ref had no real choice but to call a penalty. But this is a perfect example of how the cost of a penalty to the defending team is often vastly disproportionate to the benefit that team got by committing the penalty.
Yamal doesn't play this ball - he lets it deflect off his arm. And he has no real possibility of turning into a good scoring chance from where he was. So Diane's interference cost Spain maybe 0.2 expected goals (if that). But the benefit to Spain of the penalty was 0.8 expected goals, That's an outsized benefit - which is why players look for penalties.
I'm curious what it would take a talented engineer to build Postgres from scratch in 2026.
Not a slopfork. Written in Rust, from the ground up, and fixing the mistakes of the original design (process per connection, table bloat, schema catalog).
Who wants to burn some tokens?
There's a lesson here for everybody who thinks that "I'll just use a cheaper model for certain tasks"
It's *very* hard to know in advance how smart a model has to be to do a task.
If it's not smart enough, it will likely keep trying, using cheaper but more tokens.
“A budget panel (Gemini 3 Flash, Kimi K2.6, and DeepSeek V4 Pro) beat GPT-5.5 and Opus 4.8. It came within 1% of Fable 5’s score while being 50% of the cost.”
Fusion is neurodiversity, but for models. Try it now!
💬 Chatroom: https://t.co/8Fyf0tjGa5 (pick a preset or build a custom panel)
⚙️ API: docs at https://t.co/170CR9bu07
ℹ️ More info on the blog post: https://t.co/jjB95vm7yY
When I was asked by the American Academy of Arts and Sciences to write an essay on my thoughts on how AI will accelerate Science, I felt honored but also felt that it would require a lot of thoughtfulness and diligence to distill my thoughts on paper.
The essay has now been published and I cannot be more thankful to the @americanacad and @GoogleDeepMind teams for their feedback and encouragement during the process.
Key reflections from my essay:
🔭 AI is our newest revolutionary lens: Just as the telescope and microscope expanded our physical perception, AI is extending our cognitive reach, allowing us to decipher the immense complexity of the data-universe.
🧬 The rise of "machine intuition": AI is not just a computational engine. By detecting hidden structures across disciplines—from protein folding to extremal combinatorics—it acts as an ultimate bridge, accelerating the interdisciplinary breakthroughs that modern science depends on.
🏗️ From puzzle-solvers to architects of questions: As we transition toward open-ended, agentic AI systems that actively generate novel hypotheses, the burden of reasoning is shifting. We are evolving from being the solvers of intricate puzzles into the architects of profound scientific questions.
✨ Expanding human potential: AI won't replace scientists; it expands what we can imagine and achieve. Just as the telescope didn't make astronomers obsolete, AI is giving us the stars.
Read the full essay here: https://t.co/LCoF7ds7WZ
“If every interaction with an agent happens in a private window, the only person who learns anything is the person at the keyboard. Everyone else is locked out of the apprenticeship.”
Apollo & Artemis (a thread for Earth Day)
I spent years poring over all 18,000 photos taken by the Apollo program, finding the very best photos of Earth from space and restoring them.
So how do the Artemis photos compare?
🧵