Most languages treat GPU as "write a kernel, dispatch it, copy results back." OctoFlow flips it — data lives on
  the GPU by default. The CPU handles I/O and nothing else.

  let a = gpu_fill(1.0, 10000000)
  let b = gpu_scale(a, 2.0)
  let c = gpu_add(a, b)
  print("sum: {gpu_sum(c)}")

  10 million elements. Data never leaves VRAM between operations.

  It's early — there's a lot to improve — but it works today and I'd love feedback from people who try it.

  What you can do right now:

  - GPU compute with arrays up to 10M+ elements
  - Statistical analysis, ML (regression, clustering, neural net primitives)
  - CSV/JSON data processing, HTTP client
  - Stream pipelines for image processing
  - Interactive REPL with GPU access
  - Import from 51 stdlib modules across 11 domains

  What you need: any GPU with a Vulkan driver and the 2.2 MB binary. That's it.

  I've been working on this solo and would genuinely appreciate people kicking the tires. What works, what breaks,
  what's missing — all useful.

  https://github.com/octoflow-lang/octoflow