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Overview
Step-by-step tutorials for developers building AI chatbots and agents. Learn GPT models, embeddings, vector databases, and retrieval-augmented generation (RAG) systems. Solve chatbot issues, handle complex queries, and enhance e-commerce tools with practical AI examples and code.
News
Same prompt. Different teammate.
Same prompt. Different teammate. My 5 cents on Opus 4.8 I'm using Claude Code while creating my "How to deploy an AI agent" course. Don't get me started. I know it's very meta to use AI while building a course about deploying AI apps, but here we are. And
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Stop overbuilding your AI backend
My #1 rule: Deploy the boring loop first. Add intelligence later. Because if the simple loop doesn't work in production, the fancy version won't save you. Your vibe-coded AI app does not need a complicated backend on day one. No RAG No tools No streaming
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Your vibe-coded app is live. Now what?
Getting to a live URL is the easy part now. The harder question is whether you actually own what you built. Can you move it? Can you debug it? Can you explain the stack to a client? Can you protect your API keys? If the answer is "No " or "I'm not sure "
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What is Model Context Protocol (MCP)? How it simplifies AI integrations compared to APIs AI Agents That Work
Model Context Protocol (MCP) is an open standard that connects AI models to tools and data sources efficiently. This guide breaks down MCP's architecture, benefits, and how it differs from traditional APIs
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Vectorizing multimodal e-commerce product data with AWS Titan: a practical guide AI Agents That Work
Learn how to vectorize your e-commerce product data using AWS Titan's multimodal model. This practical guide covers generating embeddings for both images and text, and building vector search using cosine and dot product similarity for improved product rec
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