Amos Chen

This guide walks you through setting up a local AI development environment using Ollama for running large language models and DevContainers in Visual Studio Code for a consistent, isolated setup. It covers installation, configuration, and making REST API calls to Ollama, with specific instructions for Windows users using WSL 2.

This guide details how to use Amazon Bedrock and AWS Lambda to deploy and invoke the Llama 3.2 1B model in a serverless environment. It covers enabling model access, creating a Python-based Lambda function, configuring IAM permissions, and testing the setup, with step-by-step instructions for seamless AI integration.

This guide explores how to use BeautifulSoup to extract plain text from URLs and build a reusable web scraper for integration into AI-driven applications like AWS Lambda. It includes step-by-step instructions for creating and testing a Python-based scraper with a modular class design.

This guide shows how to create a modular Python application for web scraping and AI summarization using BeautifulSoup and Ollama. It covers organizing scraper and chat services into a reusable module, integrating them in a main script, and generating summaries from online content.

This article guides you through building a modular Python application to leverage Large Language Models (LLMs) with AWS Lambda. Learn to set up AWS CLI with IAM Identity Center, create a Bedrock Chat Service, implement web scraping and summarization, and automate Lambda deployment with a ZIP file. Includes practical code examples and verification steps.