We are interested in the following topics

• Vanilla Python

  • Performance and optimization. Code acceleration, profiling, debugging, and optimization tricks such as improving loop execution time and handling big data.
  • CPython and other interpreters. Features and internals of CPython, PyPy, Jython (is it alive?) and how to use them in different situations.
  • "Back to Basics. Basic Python concepts explained with simple examples. For example, the __slots__ mechanism for saving memory and solving hash collisions in dictionaries, asynchrony in details and errors, etc.
  • The Pythonic Way. Basic principles, tips for turning bad code into good code, examples of the Pythonic approach to solving tasks.
  • Python Standard Library. Facilities, examples of use, features and interesting details.
  • Proprietary libraries and frameworks. Difficulties of development, developers' experiences and best practices.
  • Python under the hood. Interpreter implementation details: GIL, async, how dict works.
  • Developer tools. Linters, batch managers, etc.
• Python testing

  • Basic testing concepts. Unit testing (and when is it unnecessary?), integration and functional testing, and their application to Python. What a developer needs to know to be comfortable.
  • Libraries and tools. Overview of popular libraries and tools for testing: unittest, pytest, nose and others. Their features and best practices.
  • Test-driven development (TDD) in Python. The process of writing tests before implementing functionality.
  • Testing web applications in Python. Application of tools and approaches such as Selenium, Django Testing Framework and Flask Testing.
  • Performance Testing. Tools and techniques for measuring and optimizing the performance of Python applications. Profiling, stress testing and load testing.
  • Testing asynchronous code. Features of asynchronous code testing in Python, using asyncio, Trio, Quart and other libraries.
• ML/MLOps

  • Python-based MLOps tools. Exploiting, completing, and comparing tools such as Airflow, MLFlow, DVC, etc.
  • ML libraries in Python. Useful libraries, their operation, and related problems
  • Deploying ML models using Python. Ways to deploy models. Schedule batch processing, microservices, working with queues
• Backend

  • Monolithic application development
  • Web frameworks
  • Microservice architecture
  • Internal and external API development
  • Asynchronous programming
  • ORM, databases and interaction with them
  • Web application caching and optimization
  • Observability: logging, monitoring and tracing
  • Application configuration
• Security

  • Vulnerabilities in popular Python libraries.
  • Web application vulnerabilities, OWASP.
  • Supply chain dependency attacks.
  • Using Python tools to find vulnerabilities.
  • Fuzzing.
• Other

  • Using Python in hardware. Embedded devices, IoT, robotics, etc.
  • GUI in Python. PyQt, Kiwi, Tkinter, etc.
  • Desktop application development.
  • Bioinformatics, astronomy, astrophysics, and other sciences. Talks on applications of Python to scientific problems
  • Exotic problems and applications of Python. Demoscenes, sizecoding, quine