FUSE (an interface for userpsace programs to export a virtual filesystem to the Linux kernel) is a great tool, and can be used to present many different situations. There are several good examples that are shipped with the bindings for different languages (i.e. almost 20 example programs are available in C for the reference implementation).

Python is a very popular language for teaching, and of course, there are Python bindings available for FUSE PyPI page. Of course, it also has a number of available examples.

With this little project, I want to present a set of examples presenting FUSE's functionality in a stepwise fashion, that is, as a logically ordered set of steps, guiding you from a micro-filesystem that is successful to load and does nothing, to one that gives full interaction and a non-obvious view of a complex set of information. Of course, it is quite possible we will have some forks along the road, as complexity can grow in many different directions 😉

I have started a project to create some stepwise-increasing FUSE filesystems in Python to help my students understand how FUSE works and how to write for it. My project is still in its early stages, but I'd love to show it to the ChiPy community as an example of code I'm having fun with, and probably to get some collaboration from you! 😃

Looking to add new features to your web application? Before diving into the sea of flashy open-source tools, consider this: each new addition can introduce unnecessary complexity and potential pitfalls. What if the solutions you need are already available to you—in your database?

In this talk, we'll explore how leveraging the powerful features of PostgreSQL and SQLAlchemy can simplify your web applications. See some examples of how to implement new functionalities without the overhead of extra services, and unlock the hidden potential of the tools you already use.

QA is more important than ever in software development.  I'll be walking through why and include some real world examples.

Have you thought that generators were only complicated iterators?

Let's explore generators

- To simplify your code and save memory on resource constrained devices.

- To create pipelines,delay execution and create concurrency. 

Generators are a powerful tool to have in your toolbox. In fact you're probably using them and don't even realize it.

“Everything in Python is an object.” This is a profound truth about Python, but what does it mean? Is literally EVERYTHING an object? And what is an object anyway? Are objects the same as instances of a class? How do classes and types really work in Python? And what do metaclasses have to do with anything?

In fact, the answers to these questions are probably not what you think they are - Python’s approach to objects is different from most other languages in sometimes surprising ways.

This talk will use simple live coded examples to explore how objects work in Python and clear up several common misconceptions and misunderstandings about how objects and instances, classes and types, and metaclasses all work together.

Be warned - you are likely to be surprised when you learn the truth about objects in Python.

Have you ever struggled with making your Python scripts reproducible? Do you want another project to be able to use your code without forcing users to copy your files? 

Ever get stumped by these errors:
```
ImportError: No module named MyPackage
ImportError: attempted relative import with no known parent package
```

If yes, then this talk is for you!

We will talk about
- Python module search path basics
- Learn to make a pip-installable package with automated code quality tools. Introducing `pyproject.toml` and `setup.py`.
- Installing a Python package from zip, GitHub, or PyPI
- Introduce a minimal GitHub Actions workflow file and pre-commits. 
- Introduce code quality tools for linting, formatting, type checking, and testing (pytest, black, mypy, ruff). We won't go into the details of each, but we will show how to run these tools in a automated fashion.

In this talk, I hope that you will walk away inspired on ways you might innovate in work or at home. 

I'll discuss a particular personal problem that I had, and how I solved it with a solution using serverless functions and a simple UI. We will dig into the architecture and code. Where I will briefly  show how the solution was solved in Python. 

Finally, we will end with ways to build your innovation muscles and why it matters for you personally and professionally. 

Starting in Python 3.13, CPython includes the option to run with a Just-In-Time Compiler, which emits snippets of machine-code at runtime for added speed. The novel 'copy-and-patch' JIT CPython uses means that the Python runtime doesn't need to actually know anything about compiling this machine-code - it just 'fills in the blanks' of pre-compiled stencils.

This talk will introduce the core concepts of CPython's Tier 2 Interpreter, its copy and patch JIT, and current active areas of exploration. It will also include notes on my own explorations with Superinstructions - sequences of multiple bytecodes compiled together for additional time savings.

This talk explores a new framework for building Artificial Intelligence (AI) inspired by the human brain and leveraging the power of Python. We'll delve into the concept of consciousness as a combination of memory, prediction, and experience. By using hierarchical probabilities, similar to Google's PageRank algorithm, we can build AI systems that are not only powerful but also understandable and aligned with human values. Python will be our toolset as we explore the exciting future of AI development.

What does it mean when people talk about "old-style" vs. "new-style" packaging?  Why do some projects have setup.py, when some have setup.cfg, and others have pyproject.toml?

Let's discuss new vs. old-style packaging, the files involved, and walk through upgrading a sample project from setuptools to hatchling.