This free CUDA course is worth more than most CS degrees.
12 hours that separate library users from GPU engineers.
I watched senior devs struggle with concepts taught in hour 3.
What makes it different:
No hand-waving. No "just use this library."
You build an MLP trainer FOUR times: → PyTorch (the easy way) → NumPy (getting harder) → C (now we're cooking) → CUDA (chef's kiss)
Same model. Same dataset. Four implementations.
By the end, you understand WHY PyTorch is fast.
The curriculum nobody else teaches:
➡️ GPU architecture (not just "it's parallel")
➡️ Writing kernels that don't suck
➡️ Profiling at kernel AND system level
➡️ When cuBLAS helps (and when it doesn't)
➡️ CUDA vs Triton (the comparison you need)
➡️ PyTorch extensions (actually useful ones)
Real talk:
➡️ After this course, you'll read PyTorch source code and understand it.
➡️ You'll optimize models other engineers can't touch.
➡️ You'll be the person teams hire to make things fast.
12 hours. Free. No excuses.
Who's starting this weekend?
(I will put the details in the comments.)
♻️ Repost to save someone $$$ and a lot of confusion.
✔️ You can follow @techNmak , for more insights.
Alibaba dropped a new image model called Z-Image.
is it good? Well, let me make it easy for you (especialy if you're looking for a specific things like me 😆)
Top: Grok imagine
Bottom: Z-Image
My personal take:
Z-Image is ok, but stuck in the middle, committing to nothing, vibing in permanent mid.
This is why i double down on Grok Imagine. When it decides to go feral, it actually goes feral. Z-Image feels like they wants applause without taking risks.
Anyway, that’s just my personal opinion. What matters is that you test it for yourself.
O RJ foi palco de um genocídio, se é traficante, tem que ser julgado e preso, não existe pena de morte no Brasil, isso é obra de um verdadeiro ASSASSINO!