Deploy an AI calling agent on AWS, utilizing Docker, ECR, Fargate, and Twilio to make real phone calls.
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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.
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The blog post details the process of building a Chrome extension that highlights LinkedIn posts from selected users, referred to as the 'inner circle.' It outlines the design goals, architecture, and coding steps involved in creat...
A secure backend for AI calling is created using AWS Lambda, API Gateway, and Cognito to prevent unauthorized access and financial losses.
This blog post provides a step-by-step guide on building an AI phone caller agent using OpenAI's technology and Twilio. It outlines the prerequisites, including Python, Twilio account, and OpenAI API key, and details the setup pro...
This blog post provides a step-by-step guide on how to create an AI agent capable of making restaurant reservations over the phone. It outlines the necessary code changes, the importance of specific prompts for AI behavior, and ho...
Learn how to set up an Application Load Balancer in AWS to securely route internet traffic to private AI containers, ensuring reliable access and management.
Learn how to create AWS route tables to connect private subnets to a NAT Gateway and public subnets to an Internet Gateway for effective network traffic management.
Learn how to create a NAT Gateway in AWS for secure one-way internet access from private subnets, ensuring no inbound traffic is allowed.
A comprehensive guide on validating VPC infrastructure by testing connectivity and configurations before deployment to ensure a production-ready network.
Learn to create AWS Security Groups to secure your load balancer and AI agent containers, ensuring controlled access and production-ready security.
Learn how to set up a custom domain with AWS Route 53 to create a professional URL for your Application Load Balancer.
A guide on creating four subnets in a VPC, detailing public and private configurations for high availability and security in AI applications.
Learn to build a secure frontend with Vite, React, and AWS Cognito, deploying it to S3 and CloudFront to protect against unauthorized access.
Learn how to secure your application with a free SSL certificate from AWS and enable HTTPS on your Application Load Balancer for enhanced security and user trust.
Setting up a Virtual Private Cloud (VPC) in AWS is crucial for securely hosting your AI agent and controlling network access.
The Model Context Protocol (MCP) is a new open protocol designed to standardize how applications provide context to Large Language Models (LLMs). It simplifies how AI models interact with data, tools, and services by acting as a s...
The text provides a guide on how to build a custom embedder in LlamaIndex using the AWS Titan multimodal model. It explains the need for custom embeddings, the errors encountered, and the steps to create a custom embedding class. ...
The text is a practical guide on using Jupyter Agent for data exploration, focusing on the importance of data exploration in building a RAG system. It explains what Jupyter Agent is, why data exploration matters, and how to use Ju...
This is a practical guide on how to use AWS Titan to transform e-commerce product data into numerical vectors. It includes steps to generate embeddings using AWS Titan multimodal model, and how to use these vectors for better prod...
The text is a step-by-step guide on how to build an AI agent with LlamaIndex that can handle multiple color requirements. It includes the process of generating embeddings, creating LlamaIndex Document objects, and initializing Pin...
The text discusses how AI agents handle budget-focused searches, highlighting the limitations of naive chatbots and the advantages of AI agents. It provides a scenario, the response of a naive chatbot, and the solution provided by...
The text discusses how AI agents handle multiple product requests in one query, highlighting the limitations of naive chatbots and the advantages of AI agents. It provides a scenario where a user wants 'red heels' and 'blue men's ...
The text highlights the limitations of naive chatbots in handling numeric height queries and how AI agents excel in this area. It explains how AI agents use numeric filtering to find all matching options, improving accuracy and us...
The text discusses the limitations of naive chatbots in understanding and fulfilling customer requests for special occasions, such as gala nights, and how AI agents use metadata filtering to provide suitable products. It also prov...