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86 pages · 19 free · 67 Pro

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PA Orientation3Video lessons

4:30

What an AI backend is

Lesson 01 4:30Watch

4:30

Setting up: Python 3.14, uv, and your editor

Lesson 02 4:30Watch

4:32

How to use this course

Lesson 03 4:32Watch

01 Python that reads well8Video lessons

4:29

Values, names, and the REPL

Lesson 01 4:29Watch

4:25

Strings, f-strings, and t-strings

Lesson 02 4:25 PRO

4:45

Lists, dicts, sets, and tuples

Lesson 03 4:45 PRO

4:45

Control flow and comprehensions

Lesson 04 4:45 PRO

4:45

Functions, arguments, and closures

Lesson 05 4:45 PRO

4:08

Modules, packages, and imports

Lesson 06 4:08 PRO

4:44

Errors and exceptions

Lesson 07 4:44 PRO

4:50

Files, JSON, and pathlib

Lesson 08 4:50 PRO

02 Python for real programs8Video lessons

4:24

Classes and dataclasses

Lesson 01 4:24Watch

4:07

Type hints that pay off

Lesson 02 4:07 PRO

4:16

Generators and iterators

Lesson 03 4:16 PRO

4:22

Decorators and context managers

Lesson 04 4:22 PRO

4:21

The standard library you will actually use

Lesson 05 4:21 PRO

4:37

Dependencies and environments with uv

Lesson 06 4:37 PRO

4:04

Testing with pytest

Lesson 07 4:04 PRO

4:28

Project layout and pyproject.toml

Lesson 08 4:28 PRO

03 Async Python5Video lessons

4:19

Why async: waiting is the bottleneck

Lesson 01 4:19Watch

4:24

async, await, tasks, and gather

Lesson 02 4:24 PRO

4:51

Timeouts, cancellation, and semaphores

Lesson 03 4:51 PRO

4:27

httpx: async HTTP clients

Lesson 04 4:27 PRO

4:49

Threads, processes, and when async is wrong

Lesson 05 4:49 PRO

04 Pydantic5Video lessons

4:35

Models and validation

Lesson 01 4:35Watch

4:32

Field constraints and custom validators

Lesson 02 4:32 PRO

4:43

Serialisation and JSON Schema

Lesson 03 4:43 PRO

4:22

Settings and configuration

Lesson 04 4:22 PRO

4:33

TypeAdapter and discriminated unions

Lesson 05 4:33 PRO

05 FastAPI fundamentals9Video lessons

4:27

Your first FastAPI app

Lesson 01 4:27Watch

4:25

Path, query, and body parameters

Lesson 02 4:25Watch

4:16

Response models and status codes

Lesson 03 4:16 PRO

4:21

Dependency injection

Lesson 04 4:21 PRO

4:18

Routers and project structure

Lesson 05 4:18 PRO

4:54

Middleware, CORS, and lifespan

Lesson 06 4:54 PRO

4:37

Errors and exception handlers

Lesson 07 4:37 PRO

4:40

Testing FastAPI

Lesson 08 4:40 PRO

4:53

Auth: API keys, JWT, and OAuth2

Lesson 09 4:53 PRO

06 Databases from Python5Video lessons

4:46

SQLAlchemy 2.0 async basics

Lesson 01 4:46Watch

4:24

Models, sessions, and Alembic migrations

Lesson 02 4:24 PRO

4:31

Repositories and transactions in FastAPI

Lesson 03 4:31 PRO

4:38

Redis for caching, rate limits, and queues

Lesson 04 4:38 PRO

4:41

Background work: BackgroundTasks, ARQ, and Celery

Lesson 05 4:41 PRO

07 Real-time: streaming and WebSockets5Video lessons

4:30

Streaming responses and JSON lines

Lesson 01 4:30Watch

4:35

Server-sent events for token streams

Lesson 02 4:35 PRO

4:38

WebSockets in FastAPI

Lesson 03 4:38 PRO

4:30

Connection managers and broadcasting

Lesson 04 4:30 PRO

5:14

Backpressure, heartbeats, and reconnects

Lesson 05 5:14 PRO

08 LLM APIs from Python9Video lessons

4:38

How an LLM call works: tokens, context, and cost

Lesson 01 4:38Watch

4:35

The OpenAI Responses API

Lesson 02 4:35Watch

4:29

The Anthropic Messages API

Lesson 03 4:29 PRO

4:40

Streaming tokens to the browser

Lesson 04 4:40 PRO

4:41

Structured outputs with Pydantic

Lesson 05 4:41 PRO

4:48

Tool calling by hand: the loop

Lesson 06 4:48 PRO

4:39

Prompts as code: templates, versions, and tests

Lesson 07 4:39 PRO

4:43

Embeddings and a small RAG endpoint

Lesson 08 4:43 PRO

4:49

Retries, timeouts, and rate limits for model calls

Lesson 09 4:49 PRO

09 Agent fundamentals5Video lessons

4:22

What an agent is: loop, tools, state

Lesson 01 4:22Watch

4:11

Designing tools an LLM can use

Lesson 02 4:11 PRO

4:05

Memory: short-term, long-term, and sessions

Lesson 03 4:05 PRO

4:32

Guardrails and human approval

Lesson 04 4:32 PRO

4:37

Evaluating agents

Lesson 05 4:37 PRO

10 OpenAI Agents SDK5Video lessons

4:15

Agent, Runner, and the first run

Lesson 01 4:15Watch

4:20

Function tools and hosted tools

Lesson 02 4:20 PRO

4:45

Handoffs and multi-agent systems

Lesson 03 4:45 PRO

4:43

Guardrails, sessions, and tracing

Lesson 04 4:43 PRO

4:49

Streaming an agent into FastAPI

Lesson 05 4:49 PRO

11 LangChain and LangGraph5Video lessons

4:19

LangChain 1.0: models, tools, and create_agent

Lesson 01 4:19Watch

4:05

LangGraph: state, nodes, and edges

Lesson 02 4:05 PRO

3:59

Persistence and human-in-the-loop

Lesson 03 3:59 PRO

4:25

Streaming and structured responses

Lesson 04 4:25 PRO

4:15

Choosing an agent framework

Lesson 05 4:15 PRO

12 Model Context Protocol3Video lessons

4:28

MCP: servers, clients, and tools

Lesson 01 4:28Watch

4:14

Build an MCP server in Python

Lesson 02 4:14 PRO

4:10

Connect agents to MCP servers

Lesson 03 4:10 PRO

13 Observability with Langfuse5Video lessons

4:23

Why tracing an LLM app is different

Lesson 01 4:23Watch

4:18

Langfuse setup and the OpenAI drop-in

Lesson 02 4:18 PRO

4:14

Tracing FastAPI and agents with @observe

Lesson 03 4:14 PRO

4:33

Sessions, users, scores, and evals

Lesson 04 4:33 PRO

4:41

Prompt management and self-hosting Langfuse

Lesson 05 4:41 PRO

14 Shipping the AI backend6Video lessons

4:30

Configuration, secrets, and environments

Lesson 01 4:30Watch

4:40

Docker for Python services

Lesson 02 4:40 PRO

4:31

Deploying: a VM, a container platform, or serverless

Lesson 03 4:31 PRO

5:07

Cost control: caching, model routing, and budgets

Lesson 04 5:07 PRO

4:27

Security: prompt injection, PII, and tool permissions

Lesson 05 4:27 PRO

5:07

Capstone: a support-agent backend end to end

Lesson 06 5:07 PRO