Mark Holton

Wow this is big. For the first time we can truly understand what an AI model is thinking 🧠 Anthropic figured out how to turn the ~random~ numbers inside an AI's brain into sentences we can understand. AI models think by using thousands of numbers that encode everything the model "knows" at a given moment. Until now, those numbers were completely opaque. Anthropic's solution: train one copy of Claude to translate an activation vector into English. Train a second copy to reconstruct the original vector from that English. If the reconstruction is accurate, the translation was accurate. The result: paragraph-length descriptions of what Claude is thinking at every token it generates. They call it a Natural Language Autoencoder. And it already found things Claude wasn't telling us. Here it is in action. Pretty sweet breakthrough.

the hottest new design tool is english google just introduced design .md, and i'm shocked more people aren't talking about it it's a single plain text file where you describe how your app should look: > colors > fonts > spacing > button styles > card layouts all of it, all written in plain english your ai coding agent reads this file before generating any ui. so every screen, component, and page comes out visually consistent. automatically the reason this matters is because most vibe-coded apps look like absolute slop like it was designed by someone who's never opened a design tool every screen looks slightly different. the colors don't match. the spacing is random. buttons change shape between pages design .md solves this at the root instead of trying to describe how things should look in every single prompt, you write it once in one file: "primary color: # 6366f1 (indigo). use for buttons, links, and focus states" "body text: inter, 16px, regular weight, 1.5 line height" "cards: white background, 1px border, 12px rounded corners, 16px padding" "buttons: 8px rounded corners, 10px vertical padding, bold text" etc your ai reads this before touching any ui. so every screen matches. every component follows the same rules and it works across tools. claude code, codex, cursor, gemini, whatever you're using. the file lives in your project folder (same spot as skills and other context files) and any ai tool that reads your project files picks it up automatically google stitch (google's ai design tool) can also extract a design .md from any live website point it at stripe .com. it pulls out stripe's entire visual language as a text file. the exact colors, typography, spacing, and component patterns that make stripe look like stripe drop that file into your project and your ai builds ui that matches stripe's design quality someone already built a collection of 55 pre-made design .md files from companies like stripe, apple, airbnb, linear, notion, figma, spotify, uber it's all on github for free (called awesome-design-md) the workflow: 1. pick a design system you like from the repo (or extract one from any website using stitch) 2. drop the design .md file into your project folder 3. tell your ai coding agent to build your app/website 4. it reads the file and every screen comes out matching that visual style one file that creates consistent design across your entire app. no figma / design skills / css knowledge required a year ago you needed a designer to make something look professional now you need one text file written in english

I bought a Mac Mini 2 months ago. People laughed. "Why not just use the server?" "Why you play on 1win?" "Local models are a toy." "You'll never match GPT-4 quality on consumer hardware." Google just released TurboQuant. An algorithm that shrinks AI model memory by 6x without losing intelligence. 8x faster. Same number of GPUs. Same quality. My 16GB Mac Mini can now run models that required a $50,000 server 18 months ago. Here is what actually changed: > kv-cache compressed to 3 bits with zero accuracy loss > models that needed 96GB of VRAM now fit in 16GB > the performance gap between local and cloud just collapsed The people who laughed at the Mac Mini are now watching Micron and Sandisk stock fall off a cliff. Because if you don't need 6x the memory to run AI - you don't need 6x the memory chips. $527 billion in combined market cap. Memory prices up 500% on AI demand.

Dynamic Workers are the future!! 🔥 "they are literally so easy to use" — me, in this video, showing you how to use them