Experiments, comparisons, and explorations in the world of AI.
Predicting text and being helpful are very different things. Reinforcement Learning from Human Feedback is the technique that transformed raw language models into assistants with something that looks like values — here's exactly how it works.
ChatGPT Plus, Claude Pro, Gemini Advanced — all $20/month. The marketing is deliberately vague. Here's an honest breakdown of what you actually get, what it means in practice, and who should pay.
From keystroke to streaming text — the full technical journey of a ChatGPT message: tokenization, transformer layers, attention, sampling, and why the response appears word by word.
Prompting guides a model at runtime without changing anything. Fine-tuning updates its weights permanently. Here's when each approach makes sense — and the common mistake of reaching for fine-tuning when a better prompt would do.
The context window is everything an AI can see at once. Here's what tokens are, why limits exist, what "lost in the middle" means, and why the jump from 4K to 1M tokens is such a big deal for real work.
AI only knows what it was trained on — but RAG lets it answer questions about your documents, current events, and private data without retraining. Here's how the retrieval pipeline actually works.
These three terms get used interchangeably in every tech headline. Here's the plain-English breakdown of how they actually relate to each other — and why the distinction matters.
ChatGPT, Claude, Gemini — they're all "large language models." Here's a plain-English explanation of what that means, how they work, and what they can and can't do.
AI governance is the new IT governance headache. How organizations are managing spend, risk, and policy — and why chat tools and coding tools demand completely different approaches.
AI coding tools don't end software engineering — they raise the floor, just like high-level languages did before them. An interactive look at what that shift actually means for humans in the loop.
Concrete, specific techniques for using ChatGPT, Claude, and other AI tools at work — not vague productivity advice, but actual things you can do immediately with example prompts.
Most people who feel disappointed by AI get mediocre results because of bad prompts, not bad models. Here are seven concrete techniques that make a real difference.
AI has moved from generating text to taking actions in the world. What are agents, what changed to make them possible, and what can they actually do today?
An honest, opinionated comparison of the three leading AI assistants — what each is genuinely best at, with a radar chart breakdown and specific use-case verdicts.
They share a common origin but follow radically different philosophies. Here's the story behind each company, their competing theories of how to build AI safely, and what that means for the products they ship.
AI models confidently state things that aren't true — not as a bug, but as a consequence of how they fundamentally work. Here's what's actually happening and how to reduce it in practice.
DALL-E, Midjourney, and Stable Diffusion all use diffusion — starting from pure noise and iteratively refining toward an image. Here's how that process actually works, with an interactive demo.
Exploring bias, data privacy, and the impact on jobs — the ethical challenges that come with advanced AI systems.
How ChatGPT is transforming customer support, content generation, tutoring, and personal assistants.
Charting the progress from early rule-based systems to machine learning, transformers, and the rise of GPT models.
Tokenization, attention mechanisms, and the transformer architecture — unpacking how ChatGPT understands and generates language.
15 minutes to build a data visualization from scratch with AI help — a pie chart, a ChatGPT conversation, and surprisingly working code.
I gave both AI assistants the same prompt to help build this website. The difference in quality was striking.