tastemaker

# Tastemaker ## The problem this solves Ask an LLM to build a UI and it defaults to the same handful of patterns: indigo-to-purple gradients, the same rounded card with a soft shadow, the same generic hero layout. This isn't a prompting failure — it's what happens when a model has to invent visual taste from scratch, from a text description, with no grounding and no memory of what the person asking actually likes. Most "design skill" approaches try to fix this by handing the model a bigger catalog of canned styles and palettes to pick from. That helps a little, but it's still generic — a library of canned options, not *your* taste, and it forgets everything the moment the session ends. Tastemaker works differently, on three ideas: 1. **Ground in real pixels, not descriptions.** If the user gives you references (images, screenshots, URLs), extract tokens from the actual reference — deterministically, with a script — instead of writing a text summary of the vibe and generating from that summary. Text-mediated style transfer is lossy; that's most of why AI UI looks generic even when the prompt describes something specific. 2. **Remember, don't re-derive.** Once a project's style is established, lock it and reuse it for every subsequent screen or component in that project. Across projects, keep a lightweight personal profile of what this specific developer keeps vs. rejects, so returning users start warm instead of from zero. 3. **Scope to what's actually being built.** If a...