jaberjaber23

interesting mix of k and joy. the queue manipulation primitives like -> and => have no equivalent in joy, lets you do things like call/cc in a few lines

seconding the jamis buck book, its one of the few programming books i actually finished. the way he explains each algorithm with visualizations makes it stick

had the same thing, electrician just used whatever was on the truck. swapped the faceplates and got instant gigabit

Six months ago, if you asked me whether an LLM could write a CUDA kernel that actually beats PyTorch's compiler, I would have said no. The optimization space is too complex. Too many hardware details. Too easy to write something that compiles but runs slower than the baseline

I was wrong!!

We're now seeing multi-agent systems that take your PyTorch code and spit out CUDA or Triton kernels with 2x to 14x speedups over torch.compile(mode='max-autotune-no-cudagraphs'). Not on toy benchmarks. On real models like Llama-3.1-8B, Whisper, and Stable Diffusion

amazing paper

Wow man, will give it a try

Impressive work, will give it a try!!

I was using 4 sometimes 6 different tools just to write CUDA. vs code for coding, nsight for profiling, many custom tools for benchmarking and debugging, plus pen to calc the performance "I was cooked"

So I built code editor for CUDA that does it all:

Profile and benchmark your kernels in real-time while you code

Emulate multi-GPU without the hardware

Get AI optimization suggestions that actually understand your GPU "you can use local llm to cost you 0$"

It's free to use if you use your local LLM :D Still needs a lot of refinement, so feel free to share anything you'd like to see in it