AI consultant and developer specializing in AI-powered search and AI agents. Focused on building smarter retrieval systems, chatbots, and e-commerce AI with practical, hands-on guides.
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How to create 4 subnets (2 public, 2 private) in different availability zones for high availability
Day 1: Your AI agent's first phone call Day 1 ↗
Day 2: Give your AI agent a real-world mission Day 2 ↗
Day 3: You claimed your territory (VPC) Day 3 ↗
Today: We'll build our neighborhood with subnets
How to create a Virtual Private Cloud (VPC) in AWS - your AI agent's private territory
Days 1-2: Your AI caller agent ran on your laptop.
Today: We're moving it to the cloud
How to give your AI agent a real job: call and book restaurant reservations
Yesterday, your AI agent called you and you had a basic conversation.
Today? We're giving it a real job: booking restaurant reservations.
By the end of today, your AI agent will:
✅ Call a phone number
✅ Act as your personal assistant
✅ Request a dinner reservation
✅ Handle follow-up questions
✅ Sound professional and polite
And you'll test it by answering the phone and roleplaying as the restaurant.
This is where it gets fun.
How to build your first AI phone caller agent in 15 minutes. Your code. Real calls.
Your AI agent is trapped.
It can write emails. Answer questions. Generate code.
But it is stuck in your chat text box. It can't pick up a phone and call someone.
But what if it could?
What if you could say:
"Hey ChatGPT, call the restaurant and book me a table at 7pm"
or
"Hey ChatGPT, call the doctor's office and reschedule my appointment"
What if your AI agent could exist in the REAL world and not just in your browser?
That's what we're building.
How to build and deploy a Chrome extension from scratch that highlights Linkedin posts from people you actually wanted to engage with.
I kept realizing after scrolling that I'd missed Linkedin posts from people I actually wanted to engage with.
Fix: highlight "inner circle" directly in the feed:
MCP (Model Context Protocol) is a new open protocol designed to standardize how applications provide context to Large Language Models (LLMs).
Think of MCP like a USB-C port but for AI agents: it offers a uniform method for connecting AI systems to various tools and data sources.
This post breaks down MCP, clearly explaining its value, architecture, and how it differs from traditional APIs.
LlamaIndex makes it easy to build AI-powered search, but if you're working with multimodal embeddings (text + images), like the AWS Titan multimodal model, you'll notice it's not natively supported.
For e-commerce search, I need embeddings that capture both product descriptions and images to generate more accurate search results.
This guide will show you how to override LlamaIndex's default embedder to use AWS Titan Multimodal.
Jupyter Agent automates Jupyter Notebook creation, making it easy to analyze datasets before vectorizing them for Retrieval-Augmented Generation (RAG).
In this guide, we'll use Jupyter Agent Hugging Face space to explore a fictional shoe store's dataset, uncover key insights, and prepare the data for an AI-powered search system.
Learn how to transform e-commerce product data into numerical vectors using AWS Titan.
This guide show you how to use a multimodal model to create embeddings from both text and images, enabling better search, recommendations, and data analysis.
When an e-commerce customer asks for something like women's black shoes with red details, naive chatbots often struggle to apply multiple color filters simultaneously.
In this post, learn how to build a more advanced AI agent using Python, Pinecone and LlamaIndex, complete with metadata filtering and real-world code examples to handle multi-color product queries seamlessly.