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Created a dashboard for a fashion brand.
Running a small fashion brand means checking every number yourself. With an AI agent that runs the checks, this make management much easier.
Analytics: ask what needs attention and get a ranked list.
Sals: click a day on the chart and the whole page follows.
History: every question is saved with a snapshot of the page.
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Connect with clients, get discovered, and run your business 100% commission-free
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A fashion storefront needs to show the outfit and make its individual pieces easy to find.
For FORM, I paired a warm, editorial lookbook with clearly grouped product names, prices, and size choices.
Cream surfaces, charcoal typography, and consistent product cards connect the homepage with the desktop and mobile screen concepts.
One useful design principle from this project: give campaign imagery and shopping details their own readable zones. The collection can have a strong visual presence while the next product decision stays clear.
Explore the complete design case study:
https://contra.com/p/rNPRqooQ-form-fashion-e-commerce-website-design
When exploring a clothing collection, what helps you most: a styled outfit or an individual product grid?
Adorable
Did a motion for the Cockpit concept ⁕ 🔈 ON
Design & Creative Direction: Me
Motion & Sound: Claude Code
Cockpit is an AI agent management platform concept that helps teams:
- Set boundaries
- Monitor activity
- Manage approvals
- Track knowledge sources
- Measure performance and costs
It brings together five key AI UX challenges:
👉 Autonomy
👉 Observability
👉 Human Approvals
👉 Context
👉 Analytics
The idea is simple:
As AI agents become more capable, the experiences we design around them need to make their actions more transparent, predictable, and manageable.
Thanks!
Human approvals is the part most agent UIs skip. Good call making it one of the five challenges.
I’ve been working on a small skill called Manual Orchestrator to make longer projects with AI agents easier to manage.
The problem started with a familiar situation: one chat would accumulate plans, implementation details, corrections, and test results. By the time I was ready for the next phase, I wanted a fresh conversation without losing the decisions we’d already made.
Manual Orchestrator gives that process some structure. One chat acts as the orchestrator: it breaks the work into stages and writes a prompt for the next one. I run that prompt in a new session, then bring the result back. The orchestrator reviews it against the stage’s criteria before preparing the next prompt. If something needs fixing, I go back to the same worker session.
The skill also keeps a record in the project with the current stage, decisions, evidence, and next steps. That makes it easier to pause, resume, or hand the work to another session.
The handoff is manual. I choose when to open a session and carry the result back. The orchestrator and worker sessions can also use different agent CLIs, with the prompt adapted to the capabilities available.
It’s open source and still a work in progress. I’m using it to learn what helps in practice and where the workflow needs to improve. Installation has been checked for Codex, Claude Code, and Gemini CLI, but the full workflow hasn’t been tested across every CLI or version.
If you try it, I’d be interested in hearing what works and where it gets in your way.
