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Prompt Engineering: Foundations and Beyond
Started in February 2025
This series is your hands-on guide to getting the most out of large language models — by learning the art and science of prompting. Whether you’re a developer, builder, or curious tinkerer, we break down the core concepts behind writing effective prompts and understanding how models actually respond.
We start from the basics — model settings, simple prompt styles, prompt elements — and move toward advanced tips and real-world examples. Instead of generic advice, you’ll see tested prompts, step-by-step walkthroughs, and platform-agnostic insights that work across tools like GPT-4, Claude, Gemini, and more.
Think of this as your "prompting toolkit" — grounded in practical application, built from real experiments, and continuously updated as LLMs evolve.
Explore the Series →LLM Engineering for Busy Builders
Ongoing since January 2025
This evolving, hands-on series is for developers, product builders, and curious technologists who want to build real-world LLM applications, without wading through academic papers or overhyped buzzwords.
From model choices and prompting techniques to embeddings, agents, and observability, each post focuses on one practical step at a time. Think of it as your build log for creating LLM-based systems that are useful, safe, and scalable, grounded in real experiments and actual deployment experience.
Whether you’re building your first retrieval-augmented generation (RAG) pipeline or trying to debug a flaky agent response, this series is designed to help you learn the “LLM stack” by doing, not just reading. Topics include prompt patterns, minimal dev setups, lightweight RAG, agent limitations, testing, deployment tips, and more, all updated as the space evolves.
Explore the Series →AI Foundations: Concepts Every Builder Should Know
Started in December 2024
This series is designed to cut through the noise and give you a clear, grounded understanding of what Artificial Intelligence really is, how it works, where it adds value, and what it takes to build real-world AI systems.
You’ll explore foundational topics like the difference between narrow and general intelligence, how machine learning models learn from data, and why clean, relevant datasets matter more than sheer volume. We also look at the relationship between AI, machine learning, deep learning, and data science, and what distinguishes AI-first organizations from the rest.
Whether you're just getting started or looking to sharpen your mental models, this series equips you with the mental frameworks and terminology needed to navigate AI conversations with clarity and confidence.
Start the Series →Python for Beginners
Ongoing since March 2024
This series is a gentle, practical introduction to Python for absolute beginners, whether you're learning to code for the first time or helping your kids get started. We walk through core programming concepts in a fun, readable format with real code, relatable examples, and no jargon.
Each part builds on the last, moving from simple output and variables to user input, conditionals, loops, and beyond. Designed to be read in order or picked up post-by-post, it's ideal for curious learners, parents, or teachers introducing Python in a hands-on way.
Along the way, you’ll meet mini-projects, playful examples, and beginner tips that make Python feel friendly, not intimidating.
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