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Some of you asked: What happens when you disable system-wide animations, transparency effects, and set File Explorer's default location to a folder or "This PC"? Result: It's way faster now with preloading enabled. However, it is still a bit slower than Windows 10.

16,104 görüntüleme • 8 ay önce •via X (Twitter)

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If you think this is just another silly demo made with AI, read this post. You might change your mind, because this demo is about MATH. What you see on the screen is not a render from Blender (obviously, it’s not that good). It’s a three.js app built with Toolcraft. Available on the web and rendered in real time(link in the comments). But Blender still has a lot to do with it. Blender has Geometry Nodes - a powerful node-based system for creating and manipulating procedural geometry. In other words, it’s math. And math is a universal language. And who do you think is pretty good at math? >>> AI. Now you can download or buy Blender files from marketplaces, and when they contain Geometry Nodes for procedural animations, objects, surfaces, or effects, you can transfer that logic to the web. Make it real-time, make it interactive. Materials are a separate story, of course. They can still suck unless you use the right tricks: PBR, HDRIs, material blending, displacement, and faked surface relief. So why is Blender important here? Blender is open source, and many tools around it are open source too. An AI trained on their code. That means it can translate the math from one environment to another quite accurately. If you’ve been struggling to reproduce some idea with AI that you had in your head or seen in some references, and it has something to do with Geometry Nodes, and you can find that idea or a close one in the Blender ecosystem - it means you can transfer it to the web. Thank me later.

Alex Barashkov

28,300 görüntüleme • 1 ay önce

Taste is invisible until you try to write it down. This is probably my biggest lesson with AI building as of late. At Sundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM. Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?" It's really humbling work! You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again. I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it! This gap *is* taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't. Being good at something is not the same as being able to articulate why you're good at it. I now see two bottlenecks to making something better than today's generic AI: 1. Can you *see* what better looks like in the first place? 2. Even if you can see, can you *articulate* what that is in a way that the LLM can understand and systemize? #2 is now a new craft, the art of distilling the art. The people who can do it well are the ones building standout products.

Julie Zhuo

17,582 görüntüleme • 3 ay önce