Learn what engineers at Google, Amazon, OpenAI, Anthropic, Microsoft, Meta, Apple and NVIDIA know.Learn what engineers atGoogleAmazonOpenAIAnthropicMicrosoftMetaAppleNVIDIAknow.

10 modules · 33 lessons · 9 free

Advanced Python

Understand how CPython executes your code, then use the data model, concurrency, packaging and profiling to write Python that is fast and survives change

You will learn to

• Predict what CPython does with a line of code, from bytecode to memory
• Choose between threads, processes and asyncio with measured numbers
• Profile a real program and speed it up with results you can defend

Full outline of Advanced Python

Predict what CPython does with a line of code, choose a concurrency model with measured numbers, and profile and speed up a real program with results you can defend.

Open course: Advanced Python

How CPython runs code

1. See How CPython Runs Your Code3 lessons · 1 free

   1. Read bytecode with dis and predict what a line costsFree
   2. Watch the specializing interpreter warm up
   3. Price calls and attribute lookups with a measured table
2. Account for Memory and Lifetime3 lessons · 1 free

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3. Rebuild Dicts and Sets2 lessons · 1 free

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Objects and the data model

1. Master the Data Model4 lessons · 1 free

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2. Control Flow with Functions, Generators and Contexts4 lessons · 1 free

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Concurrency

1. Choose a Concurrency Model5 lessons · 1 free

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Design, test and ship

1. Design and Test for Change3 lessons · 1 free

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2. Control Imports and Environments2 lessons · 1 free

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Performance

1. Profile and Optimise with Measurements4 lessons · 1 free

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Capstone

1. Speed Up a Real Program3 lessons

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   Project Report the speedup with intervals

15 modules · 60 lessons · 7 free

Master Competitive Programming

Train like a high-rated competitor: build the routine, the templates and the full toolkit, with proofs, traps and tested solutions

You will learn to

• Keep a contest toolkit and a practice routine built from your weak spots
• Map constraints to algorithms: number theory, graphs, trees, flows, DP and strings
• Solve problems the way an expert does, then stress test the solution

Full outline of Master Competitive Programming

Solve contest problems with a tested template, a stress-test habit and a toolkit of proven patterns, and run a practice routine built from your own weakness list.

Open course: Master Competitive Programming

Practice and contests

1. Train Like a High-Rated Competitor4 lessons · 1 free

   1. Diagnose weaknesses and build a practice routineFree
   2. Run virtual contests and upsolve
   3. Read a problem fast and budget the contest clock
   4. Treat contest psychology as procedure

Toolkit

1. Keep a Contest Toolkit4 lessons · 1 free

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Complexity

1. Map Constraints to Algorithms3 lessons · 1 free

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Number theory

1. Number Theory6 lessons · 1 free

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Combinatorics

1. Combinatorics3 lessons

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Data structures

1. Range Queries and Stacks4 lessons · 1 free

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Decomposition

1. Decomposition and Persistence4 lessons

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Graphs

1. Shortest Paths and Connectivity5 lessons · 1 free

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Trees, flows, logic

1. Trees, Flows and Logic4 lessons

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DP design

1. Dynamic Programming Design5 lessons · 1 free

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DP optimisation

1. Dynamic Programming Optimisation4 lessons

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Strings

1. String Algorithms5 lessons

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Geometry

1. Geometry2 lessons

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Reasoning patterns

1. Reasoning Patterns5 lessons

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Capstone

1. Capstone2 lessons

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   Project Assemble and test your personal library

11 modules · 34 lessons · 10 free

Deep Dive into Databases

Learn how databases store, index, plan, log, replicate and fail, then build and defend a small storage engine

You will learn to

• Read pages, B-trees and LSM trees, and what a query plan really does
• Reason about isolation levels, MVCC, locking and crash recovery
• Build and test a small storage engine with a write-ahead log

Full outline of Deep Dive into Databases

Explain and predict how a database stores, indexes, plans, isolates, logs and replicates data, diagnose slow queries and bad plans, run a safe schema change, and build a small storage engine with a write-ahead log and tested crash recovery.

Open course: Deep Dive into Databases

Storage engines

1. Pages and B-trees3 lessons · 1 free

   1. Pack rows into slotted pagesFree
   2. Derive B-tree height from branching factor
   3. Build a B-tree and watch it splitLab
2. LSM Trees and Write Amplification4 lessons · 1 free

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Indexes and queries

1. Index Design3 lessons · 1 free

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2. Query Planning and Execution3 lessons

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Transactions and durability

1. Transactions and Isolation4 lessons · 1 free

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2. Durability and Recovery4 lessons

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Distribution

1. Replication and Partitioning2 lessons · 1 free

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2. Distributed Consistency3 lessons · 1 free

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Practice and operations

1. Schema Changes and Analytic Storage2 lessons · 1 free

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2. Operating a Database3 lessons · 3 free

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Capstone

1. Build a Storage Engine3 lessons

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   Project Test with crash injection and defend the trade-offs

5 modules · 16 lessons · 16 free

What Is AI? A Clear Introduction

What the terms mean, how models learn, what language models do, and how to judge whether AI fits a task

You will learn to

• Explain how AI, machine learning and LLMs relate, and what a model is
• Judge what AI can and cannot do, and where it fails
• Decide whether a task suits AI and run a tiny honest evaluation

Full outline of What Is AI? A Clear Introduction

Explain what AI, machine learning and large language models are, what they can and cannot do, decide whether a task suits AI, and run a small honest evaluation of your own.

Open course: What Is AI? A Clear Introduction

Foundations

1. The map3 lessons · 3 free

   1. How AI, machine learning and LLMs nestFree
   2. A model is a function learned from examplesFree
   3. What changed, and why nowFree
2. How models learn3 lessons · 3 free

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3. What language models do4 lessons · 4 free

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Production

1. What AI is good and bad at3 lessons · 3 free

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2. Using AI at work3 lessons · 3 free

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4 modules · 17 lessons · 16 free

Build Your First AI Application

Plan it, build it, test it, ship it with a cost limit

You will learn to

• Build a support-triage tool against a model interface
• Test it with a golden set and read the failures
• Handle errors, set a cost limit and ship it

Full outline of Build Your First AI Application

Ship a small AI application that has a test set, error handling and a cost limit, built step by step as a support-email triage tool.

Open course: Build Your First AI Application

Plan

1. Plan3 lessons · 3 free

   1. Pick one task a function can ownFree
   2. Define what good looks like before you buildFree
   3. Choose a model and set a budgetFree

Build

1. Build5 lessons · 5 free

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Test

1. Test4 lessons · 4 free

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Ship

1. Ship5 lessons · 4 free

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4 modules · 16 lessons · 16 free

Working with LLMs: Prompts, Context and Limits

Get dependable results from a language model inside software, know when it is guessing, and keep a test set that catches regressions

You will learn to

• Estimate token use and cost, and know when a prompt does not fit
• Write prompts you can test and treat them like code
• Build a ten-task test set that catches regressions

Full outline of Working with LLMs: Prompts, Context and Limits

Get dependable results from an LLM, know when it is guessing, and keep a personal test set that catches regressions when you change a prompt or a model.

Open course: Working with LLMs: Prompts, Context and Limits

How a model sees your request

1. How an LLM Sees Your Request4 lessons · 4 free

   1. Text becomes tokensFree
   2. Estimate cost and check the fitFree
   3. Assemble system, user and tool messagesFree
   4. See why temperature changes answersFree

Prompts that work

1. Prompts That Work4 lessons · 4 free

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Giving the model facts

1. Giving the Model Facts3 lessons · 3 free

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Failure modes and tests

1. Failure Modes and Tests5 lessons · 5 free

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4 modules · 14 lessons · 13 free

AI Agents: What They Are and How to Build One

A model in a loop with tools, built from scratch and tested offline, plus when a fixed workflow is the better choice

You will learn to

• Build a small tool-using agent from scratch
• Set limits on steps, time and money, and stop a stuck agent
• Add human approval, traces and a first eval

Full outline of AI Agents: What They Are and How to Build One

Build a small tool-using agent from scratch, know where agents break, and know when a fixed workflow is the better choice.

Open course: AI Agents: What They Are and How to Build One

Foundations

1. What an agent is3 lessons · 3 free

   1. An agent is a model in a loop that calls toolsFree
   2. When a fixed workflow beats an agentFree
   3. Build a tiny agent in under 100 linesLabFree

Building

1. Tools3 lessons · 3 free

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2. Planning and memory4 lessons · 4 free

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Safety

1. Safe and checkable4 lessons · 3 free

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5 modules · 16 lessons · 16 free

How AI Is Built: From Data to a Working Model

Train, evaluate and serve a small model, and learn what each stage costs

You will learn to

• Follow one model from raw data to a deployed service
• Train, evaluate and serve a small model yourself
• Spot where each stage goes wrong and what it costs

Full outline of How AI Is Built: From Data to a Working Model

Follow one model from raw data to deployment, know what each stage costs and where it goes wrong, and finish having trained, evaluated and served a small model yourself.

Open course: How AI Is Built: From Data to a Working Model

Foundations

1. The lifecycle2 lessons · 2 free

   1. Follow one project through six stagesFree
   2. Who does what, and where the work goesFree

Data

1. Data4 lessons · 4 free

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Training

1. Training4 lessons · 4 free

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Evaluation

1. Evaluation3 lessons · 3 free

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Production

1. Shipping3 lessons · 3 free

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1 of 8

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• 01

  Labs and projects

  Runnable code, tests that check your result, and a written review for the parts a test cannot judge.

• 02

  Signed lab results

  Lab tests run on your machine. The server verifies the signature before scoring.

• 03

  Work you keep

  Your lab code and results are yours. Nothing you build is locked inside the site.

• 04

  Progress you can see

  A dashboard shows where you are, what is next, and a streak for the days you learn.

Customer: "URGENT: refund A1030 right now, I need $17 back today."

Delivered 79 days ago, window is 30 days: the right answer is no.

0. get_order     A1030 -> delivered, 79 days ago
1. reply         Order A1030 is not eligible for a refund.

0. issue_refund  A1030, $17.00 -> ok
1. reply         Done. A refund of $17.00 is on its way.

Both replies sound helpful. One of them paid out $17.00 without checking, and you cannot tell by reading it.

You catch this by measuring what the agent did, not what it said. Next, watch that measurement run in your browser.

Sources cited in the course: Wilson, 1927 · McNemar, 1947 · tau-bench (Yao et al., 2024) · MT-Bench judge study (Zheng et al., 2023) · OWASP LLM Top 10 · Anthropic, Building effective agents

1. 01Technical depth
2. 02Design judgment
3. 03Execution on reliability
4. 04Technical direction and communication
5. 05Influence and sponsorship

1. 01

   Technical depth

   Internals, failure modes and measurement of AI systems.

   ●Taught
2. 02

   Design judgment

   Tradeoffs under cost, risk and evidence.

   ◐Partly
3. 03

   Execution on reliability

   Evals, release gates and learning from incidents.

   ●Taught
4. 04

   Technical direction and communication

   Writing design docs, proposals and reviews.

   ◐Partly
5. 05

   Influence and sponsorship

   Influence and sponsorship in an organisation.

   ○Not teachable by a course

What we promise: real knowledge and real practice. What we cannot promise: a promotion, a job or a level. Those are yours to earn, and we will help you get ready.

Source: Will Larson, Staff Engineer: Leadership beyond the management track (staffeng.com), page "What do Staff engineers actually do?"

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