Training course · Windsurf · Python Development

Windsurf for Python Developers

Develop with the Windsurf AI IDE and its Cascade agent: flows, memories, rules, workflows and safe use across the team.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Windsurf for Python Developers” is designed for roles such as Python back-end developers working with Django, Flask or FastAPI, Automation and scripting specialists in IT and business units and Data and ML engineers maintaining production Python code and shows step by step how to use Windsurf productively, safely and transparently in Python Development – from the fundamentals to advanced workflows.

Windsurf is an AI-powered development environment built around its agent, Cascade. Cascade understands the structure of your project, follows your actions in the editor and terminal, and works with you according to the flow principle: you and the agent take turns on the same task without having to re-explain context over and over. In this two-day course you will learn how to use Cascade effectively for features, debugging and refactoring, and how to make the most of intelligent completion in your daily development work.

To keep results consistent across a team, you steer Cascade with global and workspace rules, rely on memories for lasting project knowledge and automate recurring procedures with workflows that run from a slash command. You will connect external tools through MCP, control how terminal commands are executed and decide which data Cascade is allowed to see. Finally, you will define rules for code review, security and data protection so that Windsurf can be used responsibly within your organisation and its existing processes.

Python powers web back ends, automation, data pipelines and AI applications – often inside the same organisation, which makes codebases notoriously diverse. AI coding assistants and agents have a particularly visible impact here: they write pytest suites around your existing fixtures, add type hints until mypy passes cleanly, migrate legacy scripts to modern packaging with pyproject.toml and walk you through an unfamiliar Django or FastAPI project in minutes rather than days. The key is steering these tools with the right context: project conventions, Ruff rules, test commands and architectural decisions belong in instruction files and prompts. Once you master that, you can delegate routine work with confidence and focus on design, review and business logic.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Python back-end developers working with Django, Flask or FastAPI
  • Automation and scripting specialists in IT and business units
  • Data and ML engineers maintaining production Python code
  • Tech leads responsible for code quality and review processes
  • Developers modernising legacy Python codebases

Typical challenges we address

  • Ageing codebases with no type hints and patchy test coverage
  • Dependency sprawl across requirements.txt, Poetry, uv and virtual environments
  • Time-consuming reviews and inconsistent style despite linters and formatters
  • Onboarding into unfamiliar projects that rely on undocumented tribal knowledge

What you will be able to do

  • You use Cascade productively in flow for features, debugging and refactoring.
  • You steer Windsurf’s context precisely and keep sensitive content protected.
  • You maintain memories and rules that reliably enforce your project conventions.
  • You automate recurring procedures with workflows and connect external tools via MCP.
  • Have AI agents reliably deliver pytest suites, type hints and refactorings across your Python codebase.
  • Encode project conventions, tooling such as Ruff, mypy and uv, and test commands so the assistant follows them consistently.
  • Review generated Python code systematically for correctness, security and maintainability before it is merged.

Course outline

Day 1

Module 1 Getting to know Windsurf and Cascade
  • Installation, importing existing editor settings and basic configuration
  • Cascade in code and chat mode: when the agent edits and when it only advises
  • The flow principle: how Cascade takes your editor and terminal actions into account
  • Intelligent completion and inline commands in everyday development
Module 2 Steering context
  • Understanding codebase indexing and referencing relevant files or directories
  • Bringing documentation, web sources and terminal output into context
  • Excluding sensitive content from context with ignore files
  • Structuring sessions and starting afresh when the topic changes
Module 3 Memories and rules for consistent results
  • Reviewing and maintaining auto-generated and manually created memories
  • Creating and versioning global and workspace rules
  • Expressing architecture, style and testing guidelines as effective rules
  • Activating rules selectively by file pattern or occasion
Module 4 Workflows and automation
  • Defining recurring procedures as workflows and launching them via slash command
  • Building workflows for release preparation, testing or code maintenance
  • Connecting MCP servers for issue trackers, databases or internal services
  • Controlling terminal command execution with allow and deny lists

Day 2

Module 5 Quality, review and governance
  • Tracing Cascade’s changes, accepting them partially or reverting them
  • Using a test-driven approach and preview features for verification
  • Team settings, data protection options and handling of secrets
  • Defining review processes and accountability for AI-generated code
Module 6 Practice lab: Python Development
  • Generating pytest tests
  • Type hints and mypy clean-up
  • Django migrations and ORM refactoring
  • FastAPI endpoints with Pydantic
  • Modernising legacy scripts
  • Understanding and documenting a codebase
Module 7 Data protection, quality & transfer to daily work
  • Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating pytest tests

The assistant inspects functions and existing fixtures, writes parametrised pytest tests covering edge cases and mocks for external services, then runs them iteratively until they pass and reach the coverage you are aiming for.

Type hints and mypy clean-up

Untyped code is annotated module by module with type hints, TypedDicts and Protocols. The agent runs mypy or Pyright, interprets the output and fixes issues step by step without changing runtime behaviour.

Django migrations and ORM refactoring

Model changes are planned together with data migrations, makemigrations conflicts are resolved and N+1 queries are fixed with select_related or prefetch_related. The AI flags risks to existing production data before you deploy.

FastAPI endpoints with Pydantic

From a functional description or an OpenAPI draft, the assistant produces Pydantic models, routers, dependency injection and validation logic, complete with error handling and matching tests using the TestClient.

Modernising legacy scripts

Old scripts become proper packages with pyproject.toml, global state is removed, argparse or Click interfaces are added and Ruff rules applied. The agent documents every change clearly in the commit history.

Understanding and documenting a codebase

The assistant explores an unfamiliar repository, produces architecture overviews, explains data flows between modules and fills in missing docstrings in Google or NumPy style so new team members become productive faster.

Possible hands-on projects

Log analysis CLI

A Typer-based command-line tool that reads application logs, clusters error patterns and outputs a concise summary as Markdown or JSON – shipped with tests and proper packaging.

FastAPI microservice with a database

A small REST service with SQLAlchemy, Alembic migrations, Pydantic validation and a Docker setup, planned, implemented and tested end to end by an AI agent.

Dependency audit script

A script that lists all project dependencies, checks for outdated or vulnerable packages against public advisory databases and produces a prioritised upgrade report.

Prerequisites

You program regularly in at least one language and are familiar with Git and a modern IDE. Solid Python skills plus hands-on experience with Git, virtual environments and at least one framework such as Django, Flask or FastAPI.

Technical requirements

Your own laptop with the Windsurf editor, Git and your usual toolchain installed; a Windsurf account with access to Cascade is required.

Data protection & compliance

Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Stuttgart Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Online Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Berlin Request
  • Tue, 16 February 2027 – Wed, 17 February 2027 Online Request
  • Tue, 2 March 2027 – Wed, 3 March 2027 Munich Request

Frequently asked questions

What does Windsurf mean by flows?

Flows describe the close collaboration between you and Cascade. The agent observes what you do in the editor and terminal and takes those actions into account in its next steps. As a result you do not have to keep explaining where you are, and you can move smoothly between doing the work yourself and delegating it to the agent.

How do memories, rules and workflows differ?

Memories store knowledge that Cascade has gathered while working or that you have explicitly recorded. Rules are guidelines you write that shape the agent’s behaviour permanently. Workflows are reusable sequences of steps for recurring tasks, launched with a slash command. Together they keep results consistent across the whole team.

Can I use Windsurf with my existing projects and languages?

Yes. Windsurf is language-agnostic and works with your existing toolchain, including build tools, package managers and test frameworks. Whether you work on back end, front end, data processing or infrastructure code, what matters is giving Cascade good rules and clear context. Each course variant adds the specific focus for your field.

Does AI-assisted coding work in large Django monoliths?

Yes, provided the agent gets the right context. On the course you learn to create instruction files describing architecture rules, app structure and test commands, and to scope tasks so the assistant works on individual apps or modules. Changes stay manageable and reviewable even when the overall project is very large.

Will AI replace my code reviews?

No. AI assistants speed up writing code and can prepare reviews by pointing out missing tests or risky sections, but functional and security accountability stays with your team. We show how to combine review checklists, static analysis and tests so that generated code meets exactly the same quality bar as hand-written code.

How much does “Windsurf for Python Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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