mr_octopus

  I kept noticing smart people missing the same things. Not from lack of intelligence — from filters they didn't
  know they were running. So I wrote a nonfiction trilogy called The Calibrated View.

  Volume I covers nine perceptual filters — the lenses you inherit from family, culture, trauma, information diet —
  that shape what you see before you're conscious enough to question them. Volume II is about what it costs to stay
  in a room when the room gets difficult. Volume III is about the person who sees a gap and fills it without being
  asked.

  Each chapter opens with a real person's story, then names the pattern. Bowlby's attachment theory, Fannie Lou
  Hamer's voter registration, a man in India who planted a forest alone for 40 years.

  I published under a pen name and put everything under Creative Commons. Built the PDFs with Playwright/Chromium
  from Markdown source. The site is a static flipbook on GitHub Pages.

  Happy to answer questions about the writing process, the framework, or the build pipeline.

  This is vibe-coded. LLMs generated the bulk of the code. That's not a caveat —
  it's the point. But "vibe-coded" doesn't mean "unreviewed." Every architectural
  decision has a human at the gate: pure Vulkan (no CUDA lock-in), zero external
  dependencies (hand-rolled everything), 23-concept language spec that fits in an
  LLM prompt, Loom Engine dispatch chains that let the GPU run autonomously.

  The AI writes code. The human decides what to build, why, and whether it ships.

  Two principles guide every decision:

  - Sustainability: Is complexity growing faster than it can be maintained? If the
    test count drops or the gotcha list grows, stop and fix before shipping more.

  - Empowerment: Can a non-GPU-programmer go from intent to working GPU code? If a
    feature makes the language harder for AI to generate, it doesn't ship.

  What's real today: 966 tests passing, 445 stdlib modules, 150 pre-compiled
  SPIR-V kernels, interactive REPL with GPU, AI code generation via
  `octoflow chat`. What's in progress: running 24GB LLM models on 6GB consumer
  GPUs via layer streaming.

  The stdlib and everything on GitHub is MIT. The compiler (Rust) is in a private
  repo for now, but the developer is willing to open-source all of it once there's
  a team to sustain it. Looking for co-maintainers — especially people with Vulkan
  experience or who care about keeping languages small and learnable.

  Happy to answer questions about the architecture, the vibe-coding workflow, or
  what it's like building a programming language with AI doing 90% of the typing
  and a human doing 100% of the deciding.

  let c = gpu_add(a, b)
  let total = gpu_sum(c)