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Result: 40x content throughput. Zero additional headcount.
Before: Property Filter produced one member success story per month, maximum. Every step was manual: interview, writing, editing brief, blog article, social copy. Short-form video: zero. SEO-optimised articles: zero.
What I built: A multi-stage AI production system using Claude. One coordinator types a single command after each member interview. The system generates: a timestamped video editing blueprint for the editor; a full SEO and GEO-optimised blog article using a validated canonical template; a publishing checklist for the web team; platform-specific social copy for 4-5 short-form clips. HubSpot MCP pulls the member's data in automatically. Automated Slack messages route each output to the right person in the right channel.
Not automation replacing humans. An intelligence layer that makes every person in the pipeline perform at a higher level.
After: 10+ stories per week. 4-5 short-form clips per story (previously zero). Substantially better video quality because editing briefs are precise and timestamped. Zero additional headcount.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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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 🔥
Claude Design shipped today.
Making a landing page, app or deck is basically free now.
Knowing what to put on it isn’t.
Positioning and taste just became the whole game.:
This just got me SO excited 🙌
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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