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Cafe Website Localization (EN → JA) — Full website localization of a specialty coffee shop site for the Japanese market. Adapted layout, menu pricing (USD → JPY), cultural tone, and SEO-optimized Japanese copy. Includes responsive design and natural, professional Japanese text.
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Logo explorations for a Korean pop-up store 🇰🇷🧋
Stunning!
I built something wild for the Paper Challenge🚀
It turns any GitHub repo into a visual case study — instantly.
No writing. No designing.
Just drop a repo link… and the canvas builds itself.
That’s CodeSage💯
Most devs have solid projects…
but they’re buried in README files nobody reads.
CodeSage changes that.
It reads your actual code — architecture, stack, commits —
and transforms it into a clean, dark, presentation-ready case study.
The real unlock?
Paper MCP.
I connected my AI agent directly to the canvas.
So instead of designing manually…
the agent builds everything:
• Sections
• Layout
• Insights
• Structure
All generated directly on the canvas.
No drag. No drop.
Just output.
Pick any repo → get 3 analysis modes:
*Each one gives
→ Complexity score
→ PR velocity
→ Language breakdown
→ Full Refactor Roadmap
Try CodeSage: https://codesage-871524277866.us-central1.run.app/
Tech stack:
Paper MCP · Gemini API · GitHub API · Antigravity
Great concept 🔥
Building a Zero-Touch Design Feedback Loop with Slack and the @Paper MCP
What if you never had to manually process a minor design revision again? As designers and developers, we easily waste 30% of our week fulfilling fast client requests—changing button colors, adjusting typography, or tweaking padding. I decided to completely automate this process.
In this project, I engineered an autonomous AI-driven workflow that physically alters native UI designs based on conversational Slack messages, proving the massive potential of the Paper MCP.
✨ The Power of the Paper MCP: Historically, AI agents have struggled to actually "see" or edit design files. By deploying the Paper MCP, I gave a raw Large Language Model the ability to programmatically traverse a 2D design canvas.
The Setup: I design the initial UI (like a Login Page) using Paper and post the screenshot to the client's Slack channel.
The Trigger: The client requests changes natively in Slack by mentioning a custom @DesignBot (e.g., "Make the login button green and change the title to 'Welcome'").
The Execution: Instead of guessing layout geometry, the AI uses the Paper MCP to dynamically query the canvas, isolate the precise target node, and duplicate the master to create a protected variant. It aligns the new design on the canvas grid and updates the styling properties natively.
The Delivery: The MCP server intercepts the final render state and pushes a flawlessly formatted screenshot right back into the Slack thread.
🛠️ Technical Stack Used:
Paper MCP: The absolute core of the project, enabling rigorous JSON-RPC access to native design layers, text bounds, and styling hooks.
Slack Bolt (Socket Mode): For threading logic, instant reaction processing, and conversational memory.
Agentic LLM Engine: Used native OpenAI tool-calling to map the complex MCP schemas directly into the AI’s brain so it never hallucinates commands.
The Result: Clients experience magical, instantaneous visual revisions directly inside their communication channels, while designers and developers win back hours of completely uninterrupted, deep-focus work.
Check out the video demo below to see the AI leverage Paper MCP to parse a layer tree and dynamically manipulate the UI!
This is the kind of workflow that quietly kills 30% of design busywork 👀
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