The thousand token rule is now available also in epub and pdf formats.

Ship quality software faster with systematic AI workflows

Front-load decisions. Iterate where it's cheap. Let machines check machines. Learn production-ready techniques for building with AI assistants.

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In the spotlight

LLM fundamentals

AILLMsCoding agents

From neural networks to modern language models, explore how LLMs actually work, what they can do, and how to leverage them effectively. This course covers the technical foundations, agentic systems, hardware considerations, and real-world implementation strategies.

Chapters

AI, but not really!
Why we say AI when we mean LLMs, what this course covers, and the terminology trap everyone falls into.
How we got to modern LLMs
The evolution from neural networks to today's language models. Covers the hardware revolution, big data explosion, transformers, attention mechanisms, GPT evolution, training, fine-tuning, and context windows.
Capabilities and constraints
How LLMs process tokens, recognize patterns, and solve problems. Includes multimodal capabilities (vision, audio), best use cases, and fundamental limitations.
Your tireless coding companion
What makes agents different from simple LLM calls. Covers agentic pipeline architecture, tool use, context management, memory systems, and orchestration.
Hardware and infrastructure economics
GPU vs CPU tradeoffs, memory needs, cloud vs self-hosting economics with real calculations, and when each approach makes sense.
Navigating the LLM ecosystem
Open-source models, inference servers, fine-tuning frameworks, orchestration tools, and observability. Understand the stack layers and compose them into working applications.
Tales from the trenches
GitHub Copilot, customer support automation, content generation, and internal tooling. What worked, what didn't, and lessons learned.
The road ahead
Emerging capabilities, agentic system evolution, cost trends, regulatory landscape, and how to prepare for what's next.

Goals

Evaluate when transformers solve problems RNNs cannot
Apply attention mechanism concepts to understand model capabilities
Apply scaling laws to make model size vs training data decisions

Details

Format:	Text, quizzes
Audience:	Developers and technical decision-makers
Platform:	Platform-agnostic
Length:	2-3 hours

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In the spotlight

LLM fundamentals

AILLMsCoding agents

Available now

The thousand token rule

AIPlatform agnosticAdvanced

A practical guide to professional software development with AI assistants. Learn to front-load decisions into cheap document iterations, use AI to enforce good practices, and ship quality code faster.

Deep Dive

Transformers from first principles

AIMathDeep learning

Build intuition for large language models through mathematics and interactive visualizations. No black boxes - every component explained from the ground up.

See all learning material

Why should you join us?

Master AI-assisted development from fundamentals to production. Learn how LLMs actually work, then apply that knowledge to build better software with AI as your partner. Our courses go beyond surface-level tutorials to teach systematic workflows, economic trade-offs, and techniques that compound over time.

Deep-dive courses on LLM internals and AI-assisted workflows
Real-world production patterns, not toy examples
Platform-agnostic techniques that outlast specific tools

On the fence? Check out our approach to learn how our courses can level up your app development skills.

In the spotlight

LLM fundamentals

Chapters

Goals ⓘ

Details

In the spotlight

LLM fundamentals

Available now

The thousand token rule

Deep Dive

Transformers from first principles

Why should you join us?

Goals