• PyCon US 2021: Intro to Pydantic – Run-Time Type Checking For Your Dataclasses

    A talk about the fantastic pydantic library and how it makes your dataclasses better!

  • Python for AI & ML Global Sumit 2021: Intro to Pydantic – Run-Time Type Checking For Your Dataclasses

    A talk about the fantastic pydantic library and how it makes your dataclasses better!

  • Python Web Conf 2021: Intro to Pydantic – Run-Time Type Checking For Your Dataclasses

    A talk about the fantastic pydantic library and how it makes your dataclasses better!

  • BelPy 2021, Keynote: Evolution of Modern Python

    Keynote presentation for BelPy 2021

  • PyCon Sweden 2020, Keynote: Evolution of Modern Python

    Keynote presentation for PyCon Sweden 2020

  • PiterPy 2020: Automatically generate test-cases, Schema-based API testing

    Automatically generate test-cases based on your API-schemas with Schemathesis.

  • EuroPython 2020: Automatically generate test-cases, Schema-based API testing

    Automatically generate test-cases based on your API-schemas with Schemathesis.

  • FlaskCon 2020: Automatically generate test-cases, Schema-based API testing

    Automatically generate test-cases based on your API-schemas with Schemathesis.

  • Python Pizza Remote 2020: Pydantic – Give your python Dataclasses super powers with pydantic

    A quick talk about the fantastic pydantic library and how it makes your dataclasses better!

  • PyCon Sweden 2019: Test Fast, Fix More – Property based in Python testing with Hypothesis

    Did you ever miss that corner case bug? Maybe it was a negative integer, strange timezone conversion behaviour, off by one error or something entirely else. These subtle bugs are often hard to catch and are easily missed in test cases. You like me have probably ran into plenty of code utilising only happy path testing, only to later discover subtle bugs which are easily fixed once pointed out. This is where property based testing comes into the picture. In this talk I will focus on a wonderful Python library called Hypothesis but the concepts apply to other languages as well. Hypethesis is based on the same concept as the famous QuickCheck library for Haskell, which in turn have been ported a large number of languages. Hypothesis uses a wide range of input to find edge cases that you could otherwise easily miss, once it finds these cases it narrows down the input to the minimal breaking example to provide failures which are easier to understand.

  • GothPy 2019: Test Fast, Fix More – Property based in Python testing with Hypothesis

    Did you ever miss that corner case bug? Maybe it was a negative integer, strange timezone conversion behaviour, off by one error or something entirely else. These subtle bugs are often hard to catch and are easily missed in test cases. You like me have probably ran into plenty of code utilising only happy path testing, only to later discover subtle bugs which are easily fixed once pointed out. This is where property based testing comes into the picture. In this talk I will focus on a wonderful Python library called Hypothesis but the concepts apply to other languages as well. Hypethesis is based on the same concept as the famous QuickCheck library for Haskell, which in turn have been ported a large number of languages. Hypothesis uses a wide range of input to find edge cases that you could otherwise easily miss, once it finds these cases it narrows down the input to the minimal breaking example to provide failures which are easier to understand.

  • foss-north 2019: Test Fast, Fix More – Property based in Python testing with Hypothesis

    Did you ever miss that corner case bug? Maybe it was a negative integer, strange timezone conversion behaviour, off by one error or something entirely else. These subtle bugs are often hard to catch and are easily missed in test cases. You like me have probably ran into plenty of code utilising only happy path testing, only to later discover subtle bugs which are easily fixed once pointed out. This is where property based testing comes into the picture. In this talk I will focus on a wonderful Python library called Hypothesis but the concepts apply to other languages as well. Hypethesis is based on the same concept as the famous QuickCheck library for Haskell, which in turn have been ported a large number of languages. Hypothesis uses a wide range of input to find edge cases that you could otherwise easily miss, once it finds these cases it narrows down the input to the minimal breaking example to provide failures which are easier to understand.

  • Data Classes, in Python 3.6 and beyond

    Python 3.7 is here and the @dataclass-decorator is a major new feature simplifying class-creation. In this talk, we will learn to use the power of data classes to make our codebases cleaner and leaner in a pythonic way. We will also learn how to use the back-port in Python 3.6 codebases before upgrading.

  • Python Dataclasses, with Alexander Hultnér – GothPy

    GothPy Meetup, 2018-05-17 Talk about Dataclasses from Python 3.7 showcase of using the backport in Python 3.6

  • Hack The Castle Criteria

    A part of Hack The Castle 2018

  • Hack The Castle Meetup

    A part of Hack The Castle 2018

  • Build a Messenger bot

    A part of Hack The Castle 2018