Gloss

Inspiration

Many AI builders have strong visual instincts but lack the language to turn those instincts into a design system. They can look at two interfaces and immediately know which one feels right. What they cannot easily do is explain why in a way an AI coding agent can carry into the next screen.

Humans see visual patterns. AI reads instructions. Gloss is the translation layer between the two.

What it does

Gloss helps builders turn visual reactions into reusable design guidance through context, curation, and calibration.

Users begin by describing the product they are building and what it needs to communicate. They then curate references and anti-references, choosing visual directions they want to carry forward and ones they want to avoid.

Next, Gloss presents controlled A/B comparisons. Each comparison isolates one design decision, such as whether hierarchy should come from colour or typography, whether a surface should feel spacious or dense, or whether product proof should lead before personality. Users choose the direction that feels right and can explain their reasoning.

Gloss turns those choices into Design DNA: a compact set of principles, trade-offs, anti-principles, and visual tokens. Builders can refine the final theme and export the result as agent-ready guidance.

How we built it

I used ChatGPT as a thought partner to shape the product thesis, define the core workflow, and study adjacent tools. That research helped clarify the gap: existing products can generate a moodboard, extract tokens, or suggest a theme, but they do not help a user confirm the visual decisions an agent should make on future screens.

I then used Codex to prototype the interface and iterate on the product flow. Given the BuildWeek timeline, I focused on proving the core interaction rather than building a full backend: a working visual prototype plus a Codex skill that can apply Gloss’s exported Design DNA to real UI code.

Challenges we ran into

The hardest technical problem is rendering a user’s own uploaded screen in two genuinely different visual directions based on their previous curation. A screenshot does not contain the semantic structure, component states, or layout rules needed for a faithful redesign. Generating those variants also introduces model latency and cost.

For this version, I focused on the more reliable handoff: Gloss captures the user’s decisions, then Codex uses those decisions to create or restyle a real screen in code.

Accomplishments that we’re proud of

I’m proud that Gloss makes an abstract idea visible. The prototype shows that taste does not need to remain a private reaction or a vague prompt. It can become an inspectable system of choices that an agent can use.

I’m also proud of the handoff concept: Gloss does not stop at inspiration. It produces guidance that can shape a screen the user has not seen yet.

What we learned

Taste is not magic, and it is not limited to people with formal design training. It can be broken into decisions about hierarchy, density, grouping, personality, and proof.

The important part is not teaching AI one universal definition of “good taste.” It is helping people teach an agent what feels right for their own product.

What’s next for Gloss

I plan to keep building Gloss and package it as a Codex plugin. The goal is to make the workflow available inside the environment where many AI builders already create products.

A future version will let users bring their product context, references, and calibrated Design DNA into Codex, then apply that guidance directly to their own codebase. It will support a fuller handoff from visual curation to implementation, so builders can create more consistent interfaces without having to translate every design decision into a new prompt.

Built With

• codex