That does it. I am officially switching back to “Statistician”.

a smarter alternative to "always use plan mode": always frame your task as a question, so that the model is invited to push back and rate the quality of the idea/suggest alternatives, rather than blindly execute what you SAID to do (which is often not precisely what you MEANT) literally just appending "?" to the end of your prompt often does it

Always remember that when an LLM prints the beginning of a text, it has no idea what the end will be. Therefore, when it says "The answer is yes, and this is why:" the text after "why" would most likely be a very elaborate lie combined with gaslighting in case "yes" was the wrong answer.

Okay here's my argument for exactly how I think the "AI uses a bottle of water" statistic looks like it was miscalculated. It's a little in the weeds. Link below

Is there way on iOS to automatically hide notifications that begin with “A gunman”…?

What happens when you post a real Monet and say it’s AI? The coolest art social experiment I’ve seen in a while. Thank you @SHL0MS

I have not laughed this hard in a very long time. Hang up your meeting and listen to this 🤣🤣🤣 “DIRECTIONALLY DIRECTIONALLY VERY BAD” 🎵

This is a must-read for anyone concerned about the expanding lawlessness of the USA. (Gift link.) nytimes.com/2026/05/01/opini…

It’s been a tough stretch with GitHub availability. The feedback isn’t wrong. People depend on us, and we haven’t met that bar. Just know there are humans on the other side of this, reading all your comments and working as hard as we can to improve it. github.blog/news-insights/co…

Why the weird discontinuities at the :00 and :30 times? Physiology doesn’t care about clocks, so is it an artefact of training?

Four photos of Wimbledon’s Centre Court show how tennis has shifted: from grass worn across the whole court (serve-and-volley era) to damage almost only at the baseline today. A slower, power-driven game played from the back. The 1970 picture (top left) shows the grass played to a pulp all over the court, from baseline to net. In 1980 (top right), turf damage is visible in a T-shaped zone beyond the baseline. This damage to “volley valley” largely evaporates by 2005 (bottom left). By 2023 (bottom right), the service boxes appear entirely untouched, with only the baseline area showing wear and tear. Source: bigthink.com/strange-maps/do…

Excited to announce the new preview for Microsoft Foundry Agents 🎉! You can now build, run, and deploy your agent using any model, any framework, any harness in the cloud 🧑‍💻 - check out the demo below This is not just any cloud compute environment; it's an agent-optimized platform with: 🖥️ Persistent microVMs - securely scale up and down without losing context 🛠️ Built-in tools (1000 ) 👀 Observability and evaluations 👷 Guardrails 🔐 Private networking... and more

Got 4th on the video game leaderboard and won this light cube. @slace didn’t even make 10th. #githubSydney

This is so good. The art of talking to people you disagree with —>

Funny how “God” worked it out for the octopus but not humans. Huh.

Rebooting my iPhone to make it connect to my AirPods like it’s fuckin Windows 95

Saw the EML paper blowing up yesterday so I thought: “What if I build a full LLM inference engine with it?” So I did. Meet emilio — an end-to-end inference engine where every single multiplication is replaced by the EML primitive: eml(x, y) = exp(x) - ln(y) No *. No . Just this one wild operator (plus the constant 1) powering all the math: linear layers, attention, everything. It runs Qwen2.5-0.5B-Instruct (494M params) completely in EML and actually generates coherent text. → ~5.5 tok/s on CPU → ~30 tok/s on Apple Silicon GPU Not competing with llama.cpp on speed. This is pure proof-of-concept: showing the math actually works at transformer scale. A celebration of mathematical minimalism in the LLM era. Repo: github.com/videlalvaro/emili… My paper (how I built optimized it): github.com/videlalvaro/emili… If you like weird math, extreme minimalism, or just watching someone go “hold my beer” after reading a paper… star it ✨ #LLM #MachineLearning #Math #Rust

The New York Times falls victim to the No True Scotsman fallacy. nytimes.com/2026/04/14/us/po…

"Publish this PDF to a live URL." That's the whole prompt. @GitHub Copilot CLI handles the rest. Converts the file, spins up a repo, deploys to GitHub Pages, & drops you the link. PDF, PPTX, HTML, Google Slides. Also In awesome-copilot. andreagriffiths11.github.io/…

there’s a crime scene on fashion ave

My favourite map Discovery from 1519 to 1901 Zoom for detail By H.E.C Robinson 1927