Fugi
Designing a visual workspace to collect references, organize them, and find the right one later

Fugi is a visual workspace to collect references, organize them, and find the right one later. I designed and built it on my own with AI, around one rule: save first, organize later. What users save stays easy to find again from a visual memory, and easy to bring back into their work.
Context
Turn scattered design references into one visual workspace
The Problem
I realized saving references was easy.
Finding them again wasn’t.
Competitive Analysis
Each tool did one step well.
None covered the whole path.
Milanote · Web clips wait until I place them on a board.
No search by color or shape.
Figma · A freeform canvas to compare side by side.
No one-click saving from the browser.
Eagle · A library I can search by color, shape and tag.
No canvas to lay references out freely.
Core Idea
Save the idea first.
Organize it later.

From Milanote
Save without deciding
One click. It waits in Unplaced.

From Figma
Organize anytime
Move, group or split as ideas change.

From Eagle
Find it by how it looked
Search the canvas by color, shape or tag. Plus find similar.
Building with AI
From Markdown rules to a working product
Showcase
I designed Fugi.
Then I built it.
/ 01
Save from any page in one click

/ 02
Group references, or break them apart

/ 03
Tag references in seconds

/ 04
Find it by color or shape, not by name

/ 05
Always one click from the source

/ 06
Cards that adapt to the content

/ 07
Stack links from the same platform

/ 08
Copy a selection to Figma in one click

What I learned
Agents move fast. Clear rules keep them on track.
In this project, I learned that agents need the full context before execution. So I align with AI on the requirements, define each role's responsibilities and boundaries, and document key decisions, which reduces misunderstandings and rework.

18 button styles
1 shared button
Design system drift
Enforce the design rules
AI agents built one-off components, so I learned to set rules for reusing the same ones.

Loose roles
Clear contracts
Agent sprawl
Limit new agents
More AI agents split the context, so I learned to reuse existing ones before adding new ones.

Local tests
Real pages
Real-world testing gaps
Test in real conditions
Local tests missed bugs, so I learned to test early in real environments.












