How do you give an AI system your specific knowledge?
Try a live AI chatbot powered by RAG (Retrieval Augmented Generation) that knows my content. See how AI systems can be grounded with specific data to avoid hallucinations.
AI Engineering Mastery for Teams That Ship
🔥 Free Tutorial: Build a "Chat with My Data" app with LangChain, Pinecone, OpenAI and the Vercel AI SDK.
Join 3,000+ engineers learning to build what actually works
Try the live demo →
Experience the full RAG pipeline in action
Trusted by industry leaders
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🎤 Keynote — DevSecCon 2025
Untethered Software Development
I was the keynote speaker at DevSecCon 2025. This 32‑minute film shows how to orchestrate voice, background agents, and a hardened CI/CD lane so you can think where you think best—while agents safely ship production‑grade code.
Your AI Engineering Blueprint
Transform your team and business with battle-tested architectures, RAG pipelines, and production-grade AI systems — honed at Cloudflare, Pinecone, and Fortune 500s, and distilled into code-first workshops.
Foundations
How do LLMs See Text?
Explore how large language models process and break down text into tokens for understanding.
Key Outcomes
Understand how language models process and interpret text at the token level
Embeddings & Vector Mathematics
Learn how vectors represent data in AI systems and the mathematical principles that power them.
Key Outcomes
Create powerful semantic search capabilities with vector embeddings
Data Science Fundamentals
Learn core data science tools and concepts for effective machine learning and analysis workflows.
Key Outcomes
Accelerate development time and ship AI applications with production-ready architectures
Intermediate & Advanced Topics
AI Implementation Strategies
Discover proven patterns and approaches for designing and implementing robust AI systems.
Key Outcomes
Accelerate development time and ship AI applications with production-ready architectures
Practical Machine Learning
Build real-world machine learning projects through hands-on tutorials and code examples.
Key Outcomes
Accelerate development time and ship AI applications with production-ready architectures
Retrieval Augmented Generation
Learn how to enhance LLMs with external knowledge sources to reduce hallucinations and improve accuracy.
Key Outcomes
Build AI systems that can access and reason with external knowledge sources
Specializations
Fine-tuning LLMs
Master techniques for customizing pre-trained language models to perform specific tasks in your domain.
Key Outcomes
Customize LLMs for specific tasks with higher accuracy and lower costs
Scaling Vector Infrastructure
Design vector database backed applications that efficiently handle millions of queries.
Key Outcomes
Design vector databases that scale to handle millions of queries efficiently
Enterprise Security for AI Systems
Implement fine-grained security controls for RAG systems and document management on AWS.
Key Outcomes
Ensure your AI systems enforce proper access controls to sensitive information
Featured Premium Project
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“Thanks for publishing the tutorial, very helpful.”
Scott McCallum • Full Stack Developer at Intermine
Interactive AI Laboratory
Learn by doing! Explore how AI systems work under the hood through hands-on interactive experiences.
How do LLMs understand meaning in text?
Visualize how text transforms into vectors that capture semantic meaning. See why similar concepts cluster together and understand the foundation of RAG systems.
Why do LLMs have context limits and token costs?
See how language models break text into tokens. Understand why context windows exist, how pricing works, and optimize your prompts for better performance.
Knowledge Collections
Explore my comprehensive library of AI engineering resources and implementation guides.
📚 Deep and Machine Learning Tutorials
Cloud GPU Services for Deep Learning and fine-tuning with Jupyter Notebooks Reviewed: Colab, Paperspace Gradient, Lightning.ai, and more
I tried a handful of services when I last needed to fine-tune an LLM, and I was mostly disappointed...
How to create a custom Alpaca instruction dataset for fine-tuning LLMs
A step by step tutorial with companion notebook.
How to Fine-tune Llama 3.1 on Lightning.ai with Torchtune
One of the better Jupyter Notebooks to GPU-backed environment experiences I've had...
⚙️ Open-source AI / ML / Pipelines Projects
Building a Hand-Drawn Digit Recognizer with PyTorch and MNIST
I trained a neural net to recognize hand-drawn digits, then built a Next.js UI for it
Build a RAG pipeline for your blog with LangChain, OpenAI and Pinecone
You can chat with my writing and ask me questions I've already answered even when I'm not around
Vector Databases in Production for Busy Engineers: RAG Evaluation
I wrote the RAG evaluation chapter for Pinecone's latest book
🤖 AI-assisted Development
Autocomplete is not all you need: Why Cursor and Zed are going to dominate
AI assisted developer tooling is not created equally...
Updated Codeium analysis and review
It's been about a year since I last looked at Codeium - what has changed?
Automations - shell scripts leveraging OpenAI to make your developer workflow buttery smooth and way more fun
I have open sourced my automations project, which is a collection of shell scripts that automatically handle git operations, provide local code reviews, pull requests, and more!
💼 Career Advice
Wash three walls with one bucket
Design your side projects, blog posts and even your fun experiments to triangulate multiple learning paths simultaneously. Then, use them to build out your portfolio.
Run your own tech blog
Control your own destiny, build your personal brand, and master web technologies by running your own tech blog.
You get to keep the neural connections
Going the extra mile only to be unrewarded by your company feels like a personal slight and a waste of your time. It is not.
🎥 Videos
How to build chat with your data using Pinecone, LangChain and OpenAI
I show step by step how to build a RAG chatbot to talk to your data in this easy to follow tutorial for beginners.
How to use ChatGPT in your terminal
Did you know you can use ChatGPT in your terminal? No more copying and pasting...
What is a vector database?
Vector databases explained, featuring clowns, embeddings, neural networks, feature extraction, semantic search and Retrieval Augmented Generation (RAG).
🏗️ Reference Architectures and Demos
Testing Pinecone Serverless at Scale with the AWS Reference Architecture
A step-by-step walkthrough on how to generate arbitrary system load and flex Pinecone Serverless
Announcing the Pinecone AWS Reference Architecture in Pulumi
Deploy production-ready systems using Pinecone in minutes with the AWS Reference Architecture
Pinecone AWS Reference Architecture Technical Walkthrough
An examination of the Reference Architecture components and functionality