Full-Stack Developer | MCP Specialist | Building AI-Powered Applications on Cloudflare Workers
I'm a software engineer specializing in Model Context Protocol (MCP) servers, RAG systems, and edge computing. I build production AI applications using Cloudflare Workers AI, Vectorize, and semantic search at global scale.
MCP Server Architecture on Cloudflare Workers
- Built 3 production MCP implementations (local stdio, hybrid, full edge HTTP)
- Published comprehensive guides on DEV.to (2K+ views)
- Sub-50ms semantic search globally using Workers AI + Vectorize
- Open-source implementations demonstrating MCP sampling patterns
FPL Hub - Fantasy Premier League Platform
- Serves 2,000+ users with 99.9% uptime
- Processes 500K+ daily API calls
- Real-time data pipeline with ML-powered predictions
- Built with React, Node.js, MongoDB, Cloudflare Workers
AI & Edge Computing:
- Model Context Protocol (MCP) server development
- RAG (Retrieval Augmented Generation) systems
- Semantic search with vector databases (Vectorize)
- Workers AI integration (embeddings, LLMs)
- Edge deployment & serverless architecture
Backend & Infrastructure:
- Cloudflare Workers, Pages, KV, R2, D1
- High-performance APIs (500K+ daily requests)
- Real-time data processing
- TypeScript, Node.js, Express.js
Frontend:
- React, Next.js, TypeScript
- Modern UI/UX with Tailwind CSS
AI/ML: Cloudflare Workers AI, Vectorize, RAG patterns, Semantic search, Embedding models
Cloud: Cloudflare Workers, Pages, KV, R2, D1, Serverless
Backend: Node.js, TypeScript, Express.js, RESTful APIs
Frontend: React, Next.js, TypeScript, Tailwind CSS
Database: MongoDB, PostgreSQL, SQLite, Vector databases
DevOps: Docker, CI/CD, Git, Performance optimization
Technical Articles on DEV.to:
- MCP Sampling on Cloudflare Workers - Making tools intelligent without managing LLMs
- Building MCP Servers on Cloudflare Workers - Edge deployment with semantic search
- AI-Powered FAQ System - Production RAG implementation
Combined views: 3K+ | Shared to 200K+ developers
HTTP-based MCP server deployed to Cloudflare's edge
- Semantic search with Workers AI + Vectorize
- Sampling context for intelligent tool responses
- Sub-50ms global latency
- Production-ready with CORS, error handling
Local MCP server bridging to Workers backend
- stdio transport for Claude Desktop
- True sampling implementation
- Hybrid architecture pattern
Fantasy Premier League analytics platform
- 2,000+ active users, 99.9% uptime
- 500K+ daily API calls
- ML-powered price predictions
- Real-time match tracking
AI-powered FAQ with RAG
- Workers AI + Vectorize integration
- Semantic search + answer generation
- Admin dashboard with React
- Production deployment patterns
Cobalt.tools - Contributed to media downloading tool (50K+ stars)
- International collaboration
- Large-scale codebase serving millions globally
- 💼 Upwork: Daniel Nwaneri
- 📝 DEV.to: @dannwaneri
- 📧 Email: danielnwaneri41@gmail.com
- 🐦 Twitter: @dannwaneri
- 📍 Location: Port Harcourt, Nigeria
- MCP server development and architecture consulting
- Cloudflare Workers AI implementations
- RAG system design and deployment
- Semantic search and vector database integration
- Edge computing and serverless architecture
- Technical writing and developer advocacy
✅ Built production MCP servers deployed globally on Cloudflare's edge ✅ Published technical guides with 3K+ views, shared to 200K+ developers ✅ Engineered platform serving 2,000+ users with 99.9% uptime ✅ Processes 500K+ daily API calls with sub-second response times ✅ Achieved 60% page load reduction through optimization ✅ Open-source contributor to high-impact projects (50K+ stars)
"Every complex problem can be solved by breaking it down into fundamental subsets."
I focus on building scalable, production-ready solutions that combine clean architecture with cutting-edge AI capabilities. Whether it's deploying MCP servers at the edge, designing RAG systems, or optimizing high-traffic APIs, I prioritize both technical excellence and user experience.
⭐️ Open to work on exciting AI and edge computing projects!
Check out my pinned repos below for production MCP implementations and technical deep-dives.