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.

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. 

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.

Join us for an insightful talk where we delve into Kubernetes, the industry-standard deployment technology. Experience firsthand the deployment of a Python application as we transform the chipy.org website to operate on Kubernetes. Explore essential concepts including Nodes, Deployments, Jobs, Services, Ingress, PV/PVCs, Operators, ServiceAccounts, ConfigMaps, Secrets, and more!

The farmer emoji (👩🏾‍🌾) is a bit of a mystery.

In Python, its length is 4.

Same in Ruby. In JavaScript, its length is 7. It's 15 in Go and 12 in Java. There's just one character here...shouldn't they all have the same length: 1?

To understand this madness, you need to understand a little about Unicode. Many developers, myself included, get intimidated by Unicode. What's "UTF-8"? What's a "code point"? What does "U+1F937" mean?

In this talk, I'll try to answer these questions so that the next time someone gets confused by the length of the farmer emoji, you can help.

First time at PyCon? Not sure what to do while you are there? How do you network? Will it hurt?

All your questions about about America's biggest Python conference will be answered in a way that will probably help anyone at any conference.

One shortfall of example-based unit tests is that they only test known examples. Property-based testing lets you test against randomized inputs if you can specify properties that must be true of the code's behavior ("invariants"). You also test your function against extreme-values (aka, fuzzing).

In this talk, will review some examples of property-based tests using the Hypothesis library. We will demo automated test generation ("ghostwriting" tests) to make writing tests easier. We will demo stateful testing to confirm that all possible states are valid in a program.  Lastly, we will end with parting thoughts on how to specify properties. Often, the tricky part with PBT is knowing what to test! Since we are using randomized input, we need to specify properties that should hold true across all outputs.

Golly is an open source, multiplatform tool for exploring various cellular automata (such as the game of life) that allows Python scripts to study and interact with the cellular automata. First we will look at very basic operation of the rule by studying the game of life and also inputting a new initial conditions such as gliders, still lifes and spaceships. Then we will switch over to study my cellular automata I created which I call  https://conwaylife.com/wiki/OCA:SnowLife and use Python scripts to analyze my cellular automata.

The calculation of π to an insane number of digits is something that has an interesting history.  This talk will look at historic algorithms for the calculation π and implementation of the algorithms in Python.  And we will meditate upon how lucky we are to have computers to do the calculation. In doing this we will see things in the Python standard library that make it possible to calculate the crazy values needed in modern algorithms (e.g. one over a factorial cubed). The final demonstration is the calculation of π to 100 significant digits.