You have to split your code so numerically intense parallel routines are offloaded to the GPU and serial work remains on the CPUs. The GPU is a distinct device, and you have to move data back and forth between these devices. The latter takes energy, the former takes money. Perhaps with GenAI coding assistants, this hybrid computing model can be made easier to implement.

Stop for one second and ask yourself a simple question. Where do your words come from?

When you speak, what comes first, the idea or the word? Do you first feel a thought inside you, and only after that go searching for the right word to wrap around it? I think we all do. The word is never the start. The word is just the skin. The idea, the consciousness, is the thing sitting under it.

Now ask the same question about an LLM. For an LLM, it is exactly the opposite. And I think this one small difference explains almost everything about where we are heading.

• User wants to do X.
• User doesn't know how to do X, but thinks they can fumble their way to a solution if they can just manage to do Y.
• User doesn't know how to do Y either.
• User asks for help with Y.
• Others try to help user with Y, but are confused because Y seems like a strange problem to want to solve.
• After much interaction and wasted time, it finally becomes clear that the user really wants help with X, and that Y wasn't even a suitable solution for X.

We show you colors. You recreate them from memory. Challenge friends to beat your score. It's harder than you think. Play free at dialed.gg.

Weekend at Bernie’s showed that a good chunk of the most-depended-on open source packages are dead, and there are a lot of different ways for a project to end up that way.

In the event of an imminent nuclear apocalypse, we suspect that many people who have access to private jets will immediately take to the skies and escape city centers. This site tracks this indicator in realtime. The current emergency level is reported on a scale of 1 to 5, with 5 being an indicator of a likely imminent apocalypse.

Email is like those creaking old Terminators from the ’70s which continue to function without complaining. Designed for a world that doesn’t exist anymore, it has optional encryption, no built-in auth, three⁺ retrofitted security layers bolted on top, an unstandardized filtering layer and many more quirks. Yet billions of emails arrive correctly every single day.

Email is not elegant but nonetheless it is Lindy7. In the new age of agentic AI8, we can only expect it to metamorphose into another dimension.

In April 2026, Andrej Karpathy published a GitHub gist. Not code. Not a library. A markdown document describing a pattern: an LLM-maintained folder of wiki pages that compounds across sessions and beats RAG.

LuaJIT finishes a computational benchmark in 23.29 seconds. C finishes in 22.29. That's a 4.5% gap between a dynamically typed scripting language and the fastest compiled language on earth. Python, doing the same test? 416.55 seconds. Eighteen times slower.

I thought I was seeing fewer arXiv papers on the front page of Hacker News (HN) these days, and I wanted to check if that was real.

So I asked Claude to run a quick analysis: track the share of arXiv stories on HN over time. It queried the BigQuery HN dataset, bucketed the stories by month.

This is the 16th year we’ve been teaching the Stanford Lean LaunchPad class. This year, from the first hour of the first class, we realized we were seeing something extraordinary happen. It was both the end and beginning of a new era.

Teams showed up to the first day of class with MVPs (Minimal Viable Products) looking like finished products that previous classes had taken weeks or months to build. After the class, as the instructors sat processing what just happened, we realized there’s no going back.

I’ve been writing about how AI is going to change startups, but the shock of seeing 8 teams actually implementing it was mind blowing. And not a single team thought they were doing anything extraordinary.