A working AI tool becomes useful to other people when the install path, demo, docs, examples, and support boundaries are clear. A working codebase is not automatically usable.
A stranger has to understand what it does, why it matters, how to try it, how to verify it works, and where to get help if they want it implemented without doing the setup themselves.
Most technical projects fail commercially before anyone reaches the code. The surface is too vague.
The minimum useful surface
- A one-sentence promise that says what changes for the user
- A demo, install path, or clear explanation of how the tool is delivered
- Examples that show actual inputs and outputs
- Tests, demos, screenshots, or logs that prove the system exists
- Metadata that helps humans and AI assistants discover the project
- A next step: try it, install it, read the build note, buy a product, or request implementation help
A tool needs proof, not adjectives
The page cannot say "ready" unless the surface shows instructions, examples, tests, release notes, screenshots, demos, or failure modes. The tradeoff is obvious: proof takes longer than copy, but proof keeps helping after the page is closed.
README promise
install command
minimal example
verification command
known limits
implementation optionWhat Kyanite sells
Kyanite Labs is the creative lab where the tools, experiments, blog posts, and open-source products live. The paid path is implementation and advising around those tools: setup, adaptation, workflow design, docs, training, and integration help.
The goal is not generic consulting. The goal is getting useful tools into working hands.
Good implementation does not hide the mess. It turns the mess into a map.