Separate the story from the runtime
Use the page to explain the workflow, inputs, outputs, and limitations. Keep model weights, local endpoints, credentials, and private data on the machine that runs them.
SaveKit use case
Local AI is attracting builders who care about privacy, cost, latency, or control over their data. A static explainer or interface mockup is often the fastest way to show how the workflow feels without exposing a local machine or model endpoint.
A good fit for: Local model demos, privacy-first workflows, hardware experiments, AI tool comparisons, and technical explainers.
Use the page to explain the workflow, inputs, outputs, and limitations. Keep model weights, local endpoints, credentials, and private data on the machine that runs them.
Create a visual walkthrough, sample interaction, or architecture diagram as HTML. Use fictional or public sample data so the page is safe to share.
Upload the finished HTML or ZIP, preview it, and share the URL with a specific question about clarity, setup cost, or the next experiment.
Before you publish
These checks prevent the most common surprises after a folder becomes a public URL.
Common questions
A public static page should not depend on a private local service. Use the page for a safe explanation or connect it to a separately secured backend.
Show the user goal, the data boundary, the model or tool step, and a representative result. A clear boundary is more useful than a generic chatbot mockup.
Do not publish private prompts, files, logs, or personal information. Use synthetic or properly authorized examples instead.
Upload the HTML or ZIP build, preview it, and get a link you can send to the next person who needs to see it.
Start publishing