The testing system you and your AI agents need to stop second-guessing and ship with confidence
"Jan's courses are a beacon of clarity, presenting complex concepts in an easily digestible format that doesn't neglect the depth needed to build robust software. It's rare to find educational material that strikes such a perfect balance between accessibility and comprehensive coverage."
Giacomo Miolo
Chief Data Officer @ Volteras
"Jan has earned his staff engineer title, and is a great mentor on top of that. When you go through one of his courses, you're getting his expertise bottled up in an intermediate dev-friendly manner."
Zev Averbach
Senior Python Developer @ Vitol
"Excellent educator, both in his public courses and consulting sessions – that's Jan. His knowledge helps me tons to organize the infrastructure for my SaaS project, and I'm grateful for all the help he provides."
Alex Lebedev
Co-Founder @ vidscope.ai
"I was working on a project and I watched one of Jan's talks on Python Web Conference 2023. The topic he discussed was about AWS ECS and Celery. It was so insightful – his explanation on the topic, tips and best practices. Straight to the point and quite easy to understand."
Kevin Jake Casala
DevOps Engineer @ Macquarie Group
"Courses written by Jan are exceptionally well-designed, ensuring accessibility and comprehension for learners of all levels. The content effectively simplifies complex topics, optimises code, and implements best practices in development, thereby enhancing overall productivity."
Vishwa Nageshwar Kodumagulla
Identity Engineer @ Tecnics Integration Technologies
Let Claude Code and Cursor move fast without breaking things. Write tests you and your AI tools can trust.
Solve one small piece of a problem at once. Come a step closer to the final solution with every new test.
Avoid updating lots of tests due to signature or implementation changes. Keep tests easy to maintain.
Drive your implementations with tests. Build a safety net out of the box. Deliver solutions faster, with more confidence.
Implement test-doubles to replace I/O code (database, APIs, …). Experiment in production with feature flags.
Ensure simple test setup, resistance to changes in implementation, and readability. Change only what's necessary.
"Software is of high quality when you can safely change it faster than business can change their mind!"
Chapter 5
"Factory fixtures are fixtures that return function as a result instead of a static value. Using that, we can build fixtures that will return created objects while requiring minimal data to be provided from the test itself."
Chapter 12
"We've used responses to test the API interaction. This way we can test the interaction with the external system without actually sending the data there."
Get the Complete Python Testing Guide – real-world patterns, AI-ready workflows, and tests you can trust.
Modern Python testing works best with modern tools. The right tools make testing significantly simpler. For example, FastAPI's dependency injection can be used to make your tests faster while validating the same behavior.
"Passed test must give you confidence things are working.
Failed test must give you confidence things are not working."
Jan is currently a Staff Software Engineer at ren.co, where he's leading backend engineering efforts. Prior to that, he was co-founder and lead software engineer at typless.com. Along the way, he gained deep experience developing high-quality software used by some of the largest enterprises.
His in-depth Python courses and tutorials on testdriven.io teach techniques and tools needed for effectively developing real-world applications. From setting up VPCs and ECS clusters on AWS with Terraform to deploying ML models on AWS Lambda, developing FastAPI APIs, and setting up CI/CD pipelines. He's also a speaker at Python conferences and a Python/AWS/TDD consultant.
"High-quality tests make your nights and weekends peaceful!"
"Test behaviour, not implementation details!"
I worked on codebases where 100% code coverage was required. Codebases where all methods but the tested one had to be mocked. Believe me, I know what it means to update 100 tests for a single line change inside the implementation. I contributed to projects where we were chasing our own tail – one bug was fixed, and three new bugs were added to production. I was in setups where we just restarted failed test jobs and hoped that they'd pass the next time. I know what it means to have automated tests that work against you. You lose time writing, executing, and maintaining them. Despite that, bugs keep popping up everywhere. I've also seen what happens when AI tools write code without solid tests to guide them – fast output, but nothing you can trust. So believe me when I say: I know the automated tests struggle.
Despite all these experiences, I never gave up on automated testing. Instead, I decided to experiment as long as needed – until I found the way I'd been reading about. The way that allows you to ship to production whenever tests pass. The way where tests work for you, not against you. At this point in my career, I can say I found it. I'm part of the team that ships to production ten times per day. I run Claude Code with multiple agents at the same time, each working on different parts of the codebase – and because the tests are solid, I trust what they produce. Deployment is a non-event. If pipelines are green, we ship to production.
I've read quite a few books about software testing. I followed tutorials and courses. One thing that was always missing was – "How do I apply this to a real-world project?". Examples were usually very trivial and far from real-world problems (e.g., business logic was implemented, but there was no database interaction). So I had to figure things out on my own. I'm not saying they didn't help – they helped me a lot. But I just couldn't find a way to apply those ideas in my day-to-day work.
Over the past years, I helped quite a few developers set up their projects, improve Python testing skills, set up AWS environments, etc. Over and over again, I had to help them with testing. Many times, code design didn't allow effective testing. And lately, AI tools were making things worse – generating code fast, but without proper tests to catch mistakes, they created more problems than they solved. I did help, but many times, a major rewrite would be needed to simplify testing. So I decided to write the Complete Python Testing Guide. To show developers how to build applications using real-world examples (e.g., syncing users to CRM). So the next time they need to test something, they can just reference this guide. (That's what I've been doing with all my courses in my day-to-day job for years.)
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