3Blue1Brown does an amazing job of making math interesting to learn in itself. As opposed to the hundreds of millions of kids told “You just have to learn it or you’ll be dumb. Keep up or you’ll fall behind forever. Go!”

The vid calls out explicitly that math education is a primarily a motivation problem and I agree. One way or another, we need a lot more focus on that.

Meanwhile, writing an OpenGL-on-Vulkan layer has been a plan for deprecating/simplifying driver support for OpenGL for quite a while now. Might as well focus efforts on that.

Lua is great for high-level control of a program written in C/C++. When setting that up, I’ve often appreciated that I could removed features like file I/O from the Lua standard library and the rest keeps on trucking.

Lua is terrible for writing programs by itself. People keep trying to make it happen. But, for anything non-trivial, it’s a fool’s errand.

https://www.lunarg.com/the-state-of-vulkan-on-apple-oct-2026...

Now we need Mesa’s OpenGL-on-Vulkan to be production-ready when running on KosmicKrisp

https://docs.mesa3d.org/drivers/zink.html

The tech specifics covered have been out of use for decades. But, it's still a very fun read about thinking through the process of optimization.

Next we need a "Verbose useless content" detector and we'll be set up to GAN the whole internet into https://xkcd.com/810/ "Mission Accomplished".

Avoiding surgery involved months of finger stretches, forearm massage, 2 keyboards and 2 mice at different elevations, typing with my hands hanging slack like a zombie. It worked. But, even typing this out 20 years later I can still feel the RSI hiding in the shadows. Since then I've avoided RSI by spending 10x more time thinking about code than typing it.

All that is setup for: There have been many times recently where I've thought "I know exactly what I want this code to look like. But, damn. That's going to be a lot of keypresses..." The effort is nothing. Risking nerve damage is the concern.

In those cases I have been thrilled be able to tell AI exactly what to make, how to refactor it, how to clean it up, clean it up more, yet more, still not done. All in less than 10% of the keypresses that would have been required to type it by hand.

I write a lot of high-performance code. To do that, I had to learn the details of how devices (drives/NICs/GPUs) work, how buses (PCI) works, how RAM, CPU caches, instruction pipelining work. Now I know how to structure data flow to work well with the machine instead of against it.

I write a lot of APIs. In doing that, I had to learn the hard way about how different API trade-offs drive client decisions. How to foresee problems in the future stemming from interfaces I'm writing now. How to argue with clients to get them what they need in the long term and not just want they want for the next milestone.

At all of these levels, I had to learn how each level works by doing it. By trying it lots of different ways. And, by optimizing it all the way down from "More productive client discussions" to "More performant assembly instructions" :P

Now whenever I do vibe-code, it's because I know exactly what I want and I can get AI to type it faster and with less RSI (carpal tunnel). Or, I know I want to prototype a few options rapidly before committing to implementing one design for realz. In neither of these cases am I telling the AI "Make it performant and easy to use." Because I know how I want to structure the code to make it performant. And, I know how to set up the API to make it easy to use. So, I'm telling the AI how to structure the data flow and how to set up the API. Because, if I didn't, I'd just be generating a big ball of noisy mud that's neat to gawk at, but has no long-term value.

So, what should you learn? You need to learn how the stuff you are interested in actually works under the hood by experimenting with it manually. Not just "Hey chat, make it work somehow..." You need to focus on architecture and systems thinking because otherwise you won't know how to prevent AI from holding your hand deep into a maze neither of you can escape from. And, you should be spending the majority of your tokens on step-by-step refactoring, never one-shotting. One-shotting is for AI tech demos. Not for learning or production.

You want to learn web dev? Start typing HTML and JS. You want to learn how to use AI? Start typing specs and prompts. You'll need to learn both. But, you can't learn web dev by typing specs and prompts.

You can learn using AI as a research assistant, a tutor, a reviewer, a critic. You can use it to bang out quick tools, prototypes, deal with the hassles that are not your focus at the moment. But, whatever you are trying to learn, you need to do the actual implementation manually. There is no such thing as passive learning.

In the GeForce3 era, the registers got complicated enough they resembled tiny "pixel shaders", but under the hood it was still a small struct held in registers. Vertex shaders were 1 to 128 asm instructions executed strictly linearly.

In the G80 era we got "general purpose shaders." But, they still depend heavily on the fix function pipeline for their dispatch/scheduling and I/O.

These days, everything is basically a dressed-up compute shader. The shared-memory SRAM is front-and-center in your attention. Dispatch and scheduling are manual and complicated. GPUs are transitioning into tensor evaluators.

So, maybe today we can start considering talking about planning committee meetings about stability. But, what I've observed is that this has been a request for a few decades now. And, in hindsight it would not have worked out in the past. Moving forward, maybe it would work out OK today for a while. But, I don't see the rate of change in GPUs slowing down any time soon. Wouldn't be surprised if we're racing towards some Cerebras + Tensor Cores + FPGA near future.

https://news.ycombinator.com/item?id=49736660

https://www.reddit.com/r/LocalLLaMA/comments/1wjieap/made_th...

Papers: https://arxiv.org/abs/2503.23303 https://arxiv.org/abs/2510.01237

Model: https://huggingface.co/DeepMostInnovations/sales-conversion-...

Dataset: https://huggingface.co/datasets/DeepMostInnovations/saas-sal...

I can't compare it to changes I've seen in CPU architecture since then. Maybe like: Compare the NES with its 6502 and per-cartridge mappers vs. a IBM 386 PC. Now repeat that shift 2 or 3 more times.

A flood of VR mods for popular games have been coming out lately.

That can't recognize what you say. But, it can ID where you are in a specific show out of zillions of hours of shows.