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Need OpenClaw Setup Help? Complete OpenClaw Installation & Configuration Support
Are you looking for a professional OpenClaw setup expert? I provide complete OpenClaw installation, configuration, troubleshooting, and production deployment support for individuals, developers, startups, and businesses.
With my OpenClaw setup service, you can get a fully configured self-hosted AI agent platform running securely on your Linux VPS or dedicated server.
OpenClaw Setup & Configuration Services Include:
✅ OpenClaw installation on Ubuntu Linux server
✅ OpenClaw dashboard and gateway configuration
✅ Telegram bot integration and setup
✅ AI API integration (Z.AI, OpenAI-compatible APIs, and other LLM providers)
✅ Custom domain and subdomain configuration
✅ NGINX reverse proxy setup
✅ SSL certificate installation with Let's Encrypt
✅ Production deployment and security hardening
✅ Workspace, agent, and model configuration
✅ OpenClaw configuration troubleshooting and error fixing
✅ Server optimization and maintenance support
Whether you are facing OpenClaw installation errors, gateway connection problems, API configuration issues, SSL errors, or Telegram integration problems, I can help you deploy OpenClaw correctly and securely.
If you need reliable OpenClaw support, ping me inbox for complete installation, customization, migration, troubleshooting, and ongoing technical assistance.
🚀 Get your OpenClaw AI agent platform deployed professionally and ready for production.
#OpenClaw #OpenClawAI #OpenClawSetup #OpenClawInstallation #OpenClawSetup
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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Title:
I built a design system generator inside Figma Make. Here's the workflow.
The problem I kept running into: every new client project meant re-explaining the brand to every AI tool from scratch. Figma Make didn't know the colors. Cursor didn't know the tokens. Claude didn't know the rules. I was the shared memory — and I kept losing things.
So I built Design System Gen. Entirely inside Figma Make. Powered by Claude API.
The workflow:
Upload brand references → Claude API analyzes them across 8 dimensions (palette, mood, typography, composition, lighting, texture, motion, UI DNA) → structured data, not vibes → generates 11 files, each formatted for a specific tool in your stack.
figma-make.md → paste once, Figma Make knows your entire design system
design-system.css → all tokens as CSS custom properties
tokens.json → W3C format, imports directly into Figma Variables
starter.tsx → React starter with tokens already bound
.cursorrules → Cursor knows your system from root
Claude.md → Claude reads it automatically every session
+ 5 more for docs, LLMs, prompts, components
Every tool gets its own context file. All consistent with each other.
The part I'm most proud of: I'm a designer. I don't write code. The entire tool — UI, API integration, file generation logic — was built and shipped inside Figma Make. No external hosting. No separate codebase.
Live at: vibecodingdsgen.figma.site
Add your Claude API key and try it with your own moodboard.
#FigmaMake #DesignSystem #VibeCoding #AI #Claude #UXUI #DesignSystems #ConfigMakeathon
As someone who constantly jumps between Figma, Claude, ChatGPT, Cursor, Lovable, and other AI tools, this problem feels very familiar 😅
The tools are getting smarter, but keeping context consistent is still surprisingly manual. Love seeing people tackle that gap.
Figma Make isn't just a prototyping tool. I used it to ship a Claude-powered design system generator.
Most designers treat Figma Make as a quick vibe-coding sketchpad. I wanted to show it's something deeper — a full vibe-coding platform that integrates with external APIs.
The connection nobody writes about
There are three ways Claude connects to Figma today. Figma MCP — Claude reads and writes Figma files via MCP server. Figma's built-in AI — Figma calls its own models internally. And mine — a React app inside Figma Make that calls Claude API directly from the browser. Claude responds to the code, not to Figma. Figma Make is the hosting.
The new pipeline
Old approach: get brandbook and references from client → generate moodboard → align → write prompts for UI Kit. Separate steps, human in the center every time.
My approach: collapse all of that into one step. Run an AI analysis and output structured data — input for machines, not a result for humans.
We don't ask AI how the brand looks. We run an MRI on it and get structured data that machines use to build code.
What gets generated
11 files, all consistent with each other because they share one analysis. For Figma specifically:
tokens.json — W3C DTCG 3-tier format (primitive → semantic → component), imports directly into Figma Variables via Tokens Studio or Figma Make prompt
design-system.css — all tokens as CSS custom properties, ready to import
starter.tsx — paste into Figma Make, full design system renders instantly
figma-make.md — session context, paste once and the AI knows the entire design system
Add a content plan on top — generate a full design in one run with minimal token spend.
The entire tool was built and shipped inside Figma Make. No external hosting. No separate codebase.
#ConfigMakeathon #FigmaMake #DesignSystem #VibeCoding #ClaudeAPI #BuildInPublic
tNot gonna lie, I opened this out of curiosity and left with more questions than answers 😅 But that's usually a good sign. Really interesting exploration of what design workflows might look like in the AI era. And by the way, this link doesn't work figma-make.md
AgentOps Deal Desk
AgentOps Deal Desk is an AI-assisted dashboard that helps freelancers and service operators convert scattered opportunities into a ranked action pipeline. It scores each deal by fit, urgency, budget, readiness and risk, then turns that signal into a proposal angle, bid range and delivery checklist.
Category: Dashboard.
What it does: the app turns freelance and consulting opportunities into a deal pipeline. Each opportunity has a win score, readiness score, budget, deadline, category and status. Selecting a deal opens a structured view with the client pain, proposal angle, bid range, delivery checklist and risk flags.
How it builds on the Bubble AI baseline: I started with a Bubble AI-generated app foundation from a detailed product prompt covering the dashboard category, opportunity scoring, proposal angles, bid ranges, delivery checklists, risk flags and the core navigation. I then refined the product concept around decision speed: helping an operator know which deal to chase, how to position it and what proof is needed before sending a proposal.
Demo video and AI baseline proof are attached.
Great attention to detail. The result feels very refined.
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