Tootfinder

Ummm WAT?! https://github.com/tailwindlabs/tailwindcss.com/pull/2388#issuecomment-3717222957

feat: add llms.txt endpoint for LLM-optimized documentation by quantizor · Pull Request #2388 · tailwindlabs/tailwindcss.com
Add /llms.txt endpoint that serves a concatenated, text-only version of all Tailwind CSS documentation pages optimized for Large Language Model consumption. Extract text from MDX files, removing J...

from my link log —
macmon: sudoless performance monitoring for Apple processors. CPU / GPU / RAM usage, power consumption and temperature.
https://github.com/vladkens/macmon
saved 2026-02-01

GitHub - vladkens/macmon: 🦀⚙️ Sudoless performance monitoring for Apple Silicon processors. CPU / GPU / RAM usage, power consumption & temperature 🌡️
🦀⚙️ Sudoless performance monitoring for Apple Silicon processors. CPU / GPU / RAM usage, power consumption & temperature 🌡️ - vladkens/macmon

“4% of GitHub public commits are being authored by Claude Code right now. At the current trajectory, we believe that Claude Code will be 20% of all daily commits by the end of 2026. While you blinked, AI consumed all of software development.”
Must-read article, even if you can disagree with the analysis

Claude Code is the Inflection Point
What It Is, How We Use It, Industry Repercussions, Microsoft's Dilemma, Why Anthropic Is Winning

If I were to give a prize for the most disruptive workflow change of my life, it will be probably `less` changing home/end key behavior from scrolling to top/bottom (something which I do *all the time*) to disabling line wrapping and scrolling left/right (something I never do).
It's like XKCD#1172, except it's "we've decided spacebar heating is so cool, we've removed all other spacebar functions".
#Gentoo #Linux

👁️ Very glad to present our Art/Perception collaboration with Etienne Rey today!
🔗 https://laurentperrinet.github.io/talk/2026-01-19-art-and-science
What happens today:
We will discuss with Etienne about the emergence of our collaboration, discover th…

A New Look for Convolutional Deep Networks | Next-generation neural computations
By construction, deep convolutional networks treat all pixels equally. Result: they are energy-hungry, fragile to rotations or zooms, and poor at object localization. This contrasts sharply with biological vision, which demonstrates remarkable efficiency. Think of an eagle spotting prey from kilometers away or a human recognizing a face within an eye blink time. We propose that by using a mechanism concentrating processing at the center of gaze, biological vision is optimally suited for object …