Intermediate

Defining Artificial Intelligence

AI is a label that covers many things. Let's narrow it down so you can tell marketing hype from the real computer science underneath.

The Supervised Learning Loop

Most modern AI is trained on a loop of guess, check, and adjust. Understand the loop and you understand the heart of machine learning.

Tokens and Embeddings: How AI Reads Words

AI does not read letters. It reads tokens, which live as vectors in a space of meaning. Learn how text becomes numbers you can do math on.

Neural Networks, Actually Explained

You have heard the term a thousand times. Now let's actually look inside: neurons, weights, activations, and what happens in a single pass.

Where Training Data Actually Comes From

You cannot understand modern AI without understanding its diet. Let's map where the data comes from, how it gets cleaned, and what that means.

Benchmarks, Leaderboards, and Their Limits

Every new model claims a new high score. Before you trust a leaderboard, learn what benchmarks actually measure — and what they miss.

Is the Model Reasoning or Pattern Matching?

The line between deep reasoning and clever pattern recognition is blurry. Here's how researchers try to tell them apart.

Scaling Laws: Why Bigger Worked

The past decade of AI progress came from a simple, ruthless law: more compute and more data, predictable improvements. Here is the math behind it.

Emergence: When Abilities Appear Out of Nowhere

As models scale, some skills do not gradually improve — they just snap into existence. Let's look at what emergence really means and why it scares people.

A Short History: From Expert Systems to Transformers

AI did not start in 2022. It has decades of wrong turns and breakthroughs. Knowing the history helps you spot hype from real progress.

What Is Intelligence, Really? A Working Framework

Before we can judge whether an AI is intelligent, we need a framework for what intelligence even means. Draw on Chollet, Dennett, and modern evals.

The Full Machine Learning Pipeline

From raw bytes to deployed model, every ML system follows the same ten-stage pipeline. Master it and you can read any architecture paper.

Transformers Under the Hood

Attention, positional encoding, residual streams. A walk through the architecture that powers every frontier language model today.

The Economics and Ethics of Training Data

Data is the strategic asset of AI. Understand the supply chain, the legal fight, and the philosophical stakes before you build anything on top.

Scaling Laws and Compute-Optimal Training

Dive into the equations that governed the last five years of AI progress, and the fresh questions they raise now that pure scaling is hitting walls.

Emergence, Capability Forecasting, and Safety

Emergent abilities make AI both more exciting and more dangerous. How do labs forecast what the next model will do — and what happens when they are wrong?

Narrow, General, AGI, ASI: What We Mean and Why It Matters

The terminology ladder of AI capability is loaded. Clarify your definitions and you clarify your whole view of the field.

Probabilistic Systems: Why LLMs Do Not Act Like Code

Writing software on top of an LLM is not like writing software on top of a database. Treat it as a stochastic system or it will bite you.

Open vs. Closed Models: Philosophy and Strategy

Open-source AI is both a technical movement and a political one. Understand the arguments so you can pick a stack and defend it.

The Three Ingredients: Data, Compute, Algorithms (Capstone)

Every AI breakthrough of the past decade rests on three interacting ingredients. Synthesize everything you have learned into one working model.

Anthropic's Prompt Engineering Patterns

Anthropic publishes detailed prompt engineering guidance. Master the core patterns — Be Direct, Let Claude Think, and Chain Complex Prompts — to write production-grade prompts.

Claude's XML Tag Superpower

Claude was trained heavily with XML-tagged examples. Using tags to separate inputs, instructions, and expected outputs is one of the highest-leverage Claude-specific techniques.

Prefill Attacks and Defenses

An attacker can inject text that looks like part of the AI's own response, tricking it into behaviors it would otherwise refuse. Understand the attack vector and how to defend.