If you’ve written CUDA before, you know how it goes. You spend hours tweaking memory access, digging through profiler dumps, swapping out intrinsics, and praying it’ll run faster. Most of the time, you're guessing.

We got tired of it. So we built something that just works.

What RightNow AI Actually Does Prompt-based CUDA Kernel Generation Describe what you want in plain English. Get fast, optimized CUDA code back. No need to know the difference between global and shared memory layouts.

Serverless GPU Profiling Run your code on real GPUs without having local hardware. Get detailed reports about where it's slow and why.

Performance Optimizations That Deliver Not vague advice like “try more threads.” We return rewritten code. Our users are seeing 2x to 4x improvements out of the box. Some hit 20x.

Why We Built It We needed it for our own work. Our ML stack was bottlenecked by GPU code we didn’t have time to optimize. Existing tools felt ancient. The workflow was slow, clunky, and filled with trial and error.

We thought: what if I could just say "optimize this kernel for A100" and get something useful?

So we built it.

RightNow AI is live. You can try it for freee: https://www.rightnowai.co/

If you use it and hit something rough, tell us. We’ll fix it.

The project seems very naive. CUDA programming sucks because there's a lot of little gotchas and nuances that dramatically change performance. These optimizations can also significantly change between GPU architectures: you'll get different performances out of Volta, Ampere, or Blackwell. Parallel programming is hard in the first place, and it gets harder on GPUs because of all these little intricacies. People that have been doing CUDA programming for years are still learning new techniques. It takes a very different type of programming skill. Like actually understanding that Knuth's "premature optimization is the root of evil" means "get a profiler" not "don't optimize". All this is what makes writing good kernels take so long. That's even after Nvidia engineers are spending tons of time trying to simplify it.

So I'm not surprised people are getting 2x or 4x out of the box. I'd expect that much if a person grabbed a profiler. I'd honestly expect more if they spent a week or two with the documentation and serious effort. But nothing in the landing page is convincing me the LLM can actually significantly help. Maybe I'm wrong! But it is unclear if the lead dev has significant CUDA experience. And I don't want something that optimizes a kernel for an A100, I want kernelS that are optimized for multiple architectures. That's the hard part and all those little nuances are exactly what LLM coding tends to be really bad at.

Either someone is good at writing CUDA Kernels and a 1-10% perf improvement is impressive, or they're bad at writing CUDA Kernels and a 2x-4x over naïve very often isn't impressive.

What percentage of people who do write custom CUDA kernels are bad at it? How many are so bad at it that they leave 20x on the table as claimed on the website?

What could have helped sell it to me as a concept is an example of a before and after.

EDIT: One of the reviews states "RightNow AI is an innovative tool designed to help developers profile and optimize CUDA code efficiently. Users have praised its ability to identify bottlenecks and enhance GPU performance. For example, one user stated, "RightNow AI is a game-changer for GPU optimization."" I think some of the AI prompt has leaked into the output.