The Real Python Podcast

How does Python manage memory behind the scenes? What are the common ways to lay out a Python project? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. Christopher shares an article titled “First Steps into Memory Management in Python.” With recent updates to the garbage collector and the ongoing work toward removing the Global Interpreter Lock (GIL), the article covers the fundamentals of how Python allocates, tracks, and frees memory. We also share other articles and projects from the Python community, including recent releases, why Parquet dominates modern data engineering, choosing the right Python project layout, developing the most cursed + operator in all of Python, finding which dependencies block a Django project upgrade, and a Django performance benchmark profiler. Spotlight: Codex for Python Developers: Hands-On Agentic Coding Course Most Python developers use AI as fancy autocomplete. This 2-day live course teaches you to build entire…

How is the Python ecosystem adapting to the modern landscape of software security and supply chain defense? This week on the show, we sit down with Seth Larson, the Python Software Foundation’s Security Developer in Residence. Seth shares his background in open source and how he got involved in security engineering. We discuss the Python Security Response Team and how a recent PEP formalized its processes for reporting and triage. Seth breaks down how large language models are affecting vulnerability discovery and increasing the volume to triage. We also cover the security advantages of trusted publishing on PyPI, how Software Bill of Materials (SBOMs) under PEP 770 help track dependencies, and some advice on improving your CI/CD security. Course Spotlight: Testing Your Code With Python’s unittest Learn how to use Python’s unittest framework to write unit tests for your code, including test cases, fixtures, and test suites. Free Bonus: Python Security Checklist Get a Python Security Checklist (PDF) and keep…

What are common mistakes that Python beginners make, and how do you avoid them? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We dig into an article about common pitfalls and mistakes new Python developers make. Some of the mistakes are due to Python doing exactly what you asked it to do. We also talk about Real Python resources for getting started with the language and building your development environment. We also share other articles and projects from the Python community, including recent releases, a collection of new PEPs, and the results of two recent Python elections. We cover creating temporary files in Python, decoupling pure logic from Django’s ORM, a Python functions reference, a lightning-fast logger, and an online development environment for creating games. Course Spotlight: How to Use Python: Taking Your First Steps Take your first steps with Python. Install it, run your first program, and work with variables, data types, loops,…

How do you assemble the core pieces of a coding agent? How are developers defining projects through conversation and introspection? This week on the show, Geir Arne Hjelle returns to talk about his recent project to demonstrate how coding agents work under the hood. Geir Arne was my first guest on this program and has returned often over the years. He’s been exploring how coding agents work and has built a demonstration tool to explain them. We also discuss how he is using LLM tools in his work. We talk about moving beyond Spec-Driven Development to using tools that help you build architecture documents through thorough questioning sessions. Course Spotlight: Modern Python Linting With Ruff Ruff is a blazing-fast, modern Python linter with a simple interface that can replace Pylint, isort, and Black—and it’s rapidly becoming popular. Topics: 00:00:00 – Introduction 00:01:52 – Codemoo - Demonstrating how coding agents work under the hood 00:06:39 – Exploring Spec-Driven Development 00:09:38 – Breaking into…

Python 3.15 is (almost*) here! Christopher Trudeau and Bartosz Zaczyński return to discuss the new version. Bartosz coordinated the series of preview articles with members of the Real Python team and wrote the showcase tutorial, “Python 3.15: Cool New Features for You to Try.” Christopher’s video course “What’s New in Python 3.15” covers the topics from the article and shows the new features in action. Christopher and Bartosz dug into the release to create code examples of the new features for the tutorial and course. We cover the new built-in types, unpacking in comprehensions, and developer experience enhancements. We also look into the performance improvements, including lazy imports, the upgraded JIT compiler, and a new sampling profiler. We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.15. Course Spotlight: What’s New in Python 3.15 Explore the new features in Python 3.15, including lazy imports,…

How should you manage AI contributions to an open-source project? How do you measure the impact of using AI tools for development, and what generative features do end users want from a content management system? This week on the show, we speak with Thibaud Colas and Meagen Voss from Wagtail about the complex considerations software organizations are currently facing. Meagen is the Wagtail community manager, and Thibaud is a member of the core team. We discuss the impact of AI on a popular open-source project. We reference multiple articles they’ve written about managing these challenges. We dig into wrangling AI-assisted contributions to the project. They share how they research options for open-weight AI models and inference providers. We also discuss building AI features into the Wagtail CMS. Finally, we touch on the recent release of Wagtail 8.0. Course Spotlight: Using the Claude API in Python Learn how to use the Claude API in Python to send prompts, control responses with system instructions, and get…

What are the latest trends uncovered in the 2026 Django Developers Survey? How are Django users employing LLMs in their development process? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We discuss a summary by Will Vincent about this year’s Django Developers Survey. The survey draws on responses from nearly 3,500 developers across more than 40 countries. It provides a look at how developers are adapting to AI workflows and which tools they’re embracing. We dig into a post from Brett Cannon about reproducible builds in Python. It covers the importance of creating an independently verifiable, repeatable build of CPython and other associated tools. The post explains why Python isn’t there yet and outlines the direction of the work still needed. We also share other articles and projects from the Python community, including recent releases, upcoming PSF elections, Polars vs SQL differences nobody is talking about, creating Django unsubscribe links…

How do you plan for the performance of your Python applications? What does a performance budget entail, and where should you spend your resources? This week on the show, we speak with Den Odell about his new book “Fast by Default: Practical Performance Engineering.” Den has 25 years of experience in building web systems for companies with hundreds of millions of users. Working on public-facing tools has honed his skills in understanding where performance is vital and in guiding decisions based on data from actual users. Den shares details of his performance framework that works across any platform or stack. We discuss the need for budgeting performance during the planning phase, measuring performance through consistent profiling, and keeping systems fast even as your codebase and user base grow. Course Spotlight: Profiling Performance in Python Learn to profile Python programs with built-in and popular third-party tools, and turn performance insights into faster code. Topics: 00:00:00 – Introduction 00:01:39…

What are the key characteristics of complex systems, and what are practical patterns for tackling complex coding problems? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. Christopher covers an article about working on complex systems. The piece explores the distinction between complicated and complex problems. Each requires a fundamentally different approach to solve. We dig into a couple of tutorials about creating repeatable data science pipelines. One covers validating DataFrames and tables with the pointblank library to build into a continuous integration workflow. The other explores how to use configuration files to build modular pipelines that avoid the pitfalls of hardcoded paths and parameters. We also share other articles and projects from the Python community, including recent releases, three Python Enhancement Proposals (PEPs), the difficulties with breaking up lines of text, a static site generator for marimo notebooks, and a static…

Why do AI systems silently fail? How can you set up a system that produces results while also reviewing and validating the work? This week on the show, Calvin Hendryx-Parker returns to discuss his recent talk “Orchestrate Agentic AI: Context, Checklists, and No-Miss Reviews.” Calvin describes how silent failures can occur when handing a large document to an LLM for parsing. He describes it as the tragedy of context, where the LLM is confident, but you have no idea what it didn’t read. We discuss issues with file formats, dropped attachments, and silent truncation. Calvin shares the example project from his talk, which includes Markdown files for the agents, hooks, skills, and scripts. He also discusses a variety of coding agents, skill frameworks, and CLI tools. Course Spotlight: Coding With OpenCode: AI-Assisted Python Learn how to use OpenCode for AI-assisted Python coding, using a free Gemini API key to analyze and refactor code right in your terminal. Topics: 00:00:00 – Introduction 00:02:19 – Co-hosting…

What bottlenecks were preventing NumPy from scaling on free-threaded Python? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. Christopher shares a recent article on the work done in NumPy to make multi-threaded workloads scale on the free-threaded build of CPython. It covers how removing the Global Interpreter Lock (GIL) is not enough on its own and how profiling revealed hidden bottlenecks in NumPy and CPython. We also share other articles and projects from the Python community, including recent releases, a pair of announcements from PyPI, a tool for crawling your Django project, SIMD in pure Python, managing wildcard imports, a publication-quality Python Data Visualization library, and a tool to review your Django schema. Spotlight: Modern Object-Oriented Python Book Master classes, Python’s data model, and object-oriented design with Modern Object-Oriented Python. The best of Real Python’s OOP tutorials, curated into one book and fully up to…

How can you move from manually writing prompts for an LLM application toward defining them programmatically? This week on the show, Brett Kennedy returns to discuss his new book “Building LLM Applications with DSPy.” With large language models becoming embedded in production software, manual prompt engineering becomes brittle, time-consuming, and difficult to maintain. Brett shares how DSPy replaces manually writing and adjusting prompts with structured prompt programming. We dig into how DSPy uses declarative signatures to define inputs and outputs. We also discuss how developers programmatically compile, tune, and optimize prompts for specific LLM-based tasks within an application. Course Spotlight: Using LlamaIndex for RAG in Python Learn how to set up LlamaIndex, load your data, build and persist an index, and run queries to get grounded answers with RAG in Python. Topics: 00:00:00 – Introduction 00:01:50 – Working on a new book 00:02:53 – Writing with a partner 00:04:16 – Building LLM Applications with…

Should you understand the entirety of your codebase? How familiar are you with Python’s built-in functions? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We discuss a recent article by Sean Goedecke titled “In Defense of Not Understanding Your Codebase.” We dig into the differences in the scale of software projects and the factors that can obscure understanding of an entire codebase. We cover how opinions and practices people often argue for are based on the development practices from decades ago. Christopher shares his recent video course that explores Python’s built-in functions. The course is divided into sections to help you find the right built-in for tasks involving math, data types, iterables, and I/O. We discuss which of these functions are frequently used in our code. We also share other articles and projects from the Python community, including recent releases, querying with f-expressions in Django, replacing if-else chains, a…

Which is more important, the model or the “harness” around an LLM? What are ways to assemble an efficient agentic developer workflow? This week on the show, Ayan Pahwa joins us to discuss harnessing, web scraping, and self-hosting Python applications. Ayan is a developer advocate at Zyte and an experienced project builder. We discuss a recent article he wrote about creating an extension for the web scraping tool Scrapy. He also digs into his self-hosting setup for Python applications and tools. Our discussion extends to the complexities of developing effective harnesses. Ayan shares his setup and how he navigated shifting from prompt engineering to context and loop engineering. This episode is sponsored by HydraDB. Course Spotlight: Introduction to Web Scraping With Python In this video course, you’ll learn all about web scraping in Python. You’ll see how to parse data from websites and interact with HTML forms using tools such as Beautiful Soup and MechanicalSoup. Topics: 00:00:00 – Introduction 00:02:01 –…

How many attempts have been made to remove Python’s Global Interpreter Lock (GIL)? How do they compare to the current approach? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. Christopher shares a recent article about Thomas Wouters’ talk at PyCon US 2026. The talk, titled “Free-threaded Python: past, present, and future,” covers the efforts to remove the GIL starting in 1996. He explains that threads are complex, but they allow multiple tasks to run concurrently within a single process and its address space. The GIL is how CPython implements threading. The GIL protects Python objects and their reference counts, which determine the current objects in use. The talk also looks forward and shares the current work to remove the GIL, now named free-threaded Python, and the goals for the near future. We also share other articles and projects from the Python community, including community announcements, a roundup of recent Real Python tutorials and…

How can you improve your LLM agent systems through specification enrichment? What are the advantages of having an LLM act as a judge within an agent system? This week on the show, Senior IEEE Member and Quality Engineer Suneet Malhotra joins us to discuss building and evaluating agentic architecture. Suneet Malhotra is an independent practitioner-researcher with 18 years of experience in Quality Engineering (QE) and test automation for consumer-scale platforms. He discusses building specification-enrichment loops, monitoring performance, and using Cohen’s kappa to measure agreement between LLM judgments. Suneet is currently publishing multiple papers that are under peer review on these topics. He also provides links to his work and GitHub projects if you want to experiment with these concepts and methods yourself. Quick Survey: Get more out of the podcast show notes Video Course Spotlight: Testing MCP Servers With a Python MCP Client Learn how to build a Python MCP client that tests MCP servers from your…

How do you avoid the risk of running a Python application locally that could be malicious, break your code, or leak private data? How can you create a sandboxed local environment using WASM and MicroPython? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We cover a recent article by previous guest Simon Willison titled “Running Python code in a sandbox with MicroPython and WASM.” Simon has been experimenting for years on how to run Python code in a sandbox to reduce the risk of trying out new software, untrusted libraries, and wild ideas. He’s developed a solution using WASM and MicroPython and is sharing it as an alpha package on PyPI. We also share other articles and projects from the Python community, including new releases, community announcements, a roundup of recent Real Python tutorials and video courses, a plugin case study using Pluggy, a look at whether you’re expected to run five type-checkers now, wrapping programs using the…

Do you feel like your Python skills are atrophying after using LLM coding tools? How do you add the right kind of friction into your coding routine to keep your developer instincts sharp? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We discuss a recent article by previous guest Bob Belderbos about developers keeping their instincts when AI is writing the code. He stresses the importance of the right kind of friction for maintaining skills and scheduling a deliberate coding practice routine. We also share other articles and projects from the Python community, including new releases, a roundup of recent Real Python tutorials and video courses, sending emails with Python, libraries to enhance your Python Polars workflows, exploring Django Integrity-Policy, a next-generation HTTP client for Python, and a project to detect lazy imports incompatibilities. This episode is sponsored by AURI by Endor Labs Course Spotlight: Accessing Multiple AI Models…

What’s happening at EuroPython 2026? The conference celebrates its 25th anniversary this year in Kraków, Poland. This week on the show, organizers Mia Bajić and Daria Linhart Grudzien join me to discuss this year’s conference. Mia serves as the Vice Chair of the EuroPython Society, and Daria leads the EuroPython communications team. We dig into the details of the conference, including the wide variety of tracks, the reasoning for selecting the location, and the additional activities surrounding the event. We talk about volunteering, organizing, and continuing support for conferences. Mia and Daria also share the talks they’re excited to check out and how they use Python currently. Course Spotlight: Building Command Line Interfaces With argparse In this step-by-step Python video course, you’ll learn how to take your command line Python scripts to the next level by adding a convenient command line interface that you can write with argparse. Topics: 00:00:00 – Introduction 00:02:27 – EuroPython 2026 details…

How can you easily reduce the size of a Python Docker container? What are the exceptions you should catch in your code? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We cover a tutorial by Khuyen Tran at CodeCut about shrinking the size of a Python Docker container. The piece explores SlimToolKit, which analyzes a container at runtime, identifies what files are used, and then builds a minimal image with only those dependencies. We dig into a recent piece by Trey Hunner about Python exceptions. When trying to determine a strategy to handle potential errors, which exceptions should you catch and which should you leave unhandled? We also share other articles and projects from the Python community, including recent releases, two PEPs that have been deferred to 3.16, a critical vulnerability in an open-source ASGI framework, resolving a lazy import manually, a project to anonymize sensitive PII data, and a tool for loading Django settings from a…

Have you ever been confused by the naming of modules you’re importing from a package? Is there a standard way to organize and name your Python virtual environments? This week on the show, Brett Cannon returns to discuss the Python Enhancement Proposals (PEPs) he’s been working on recently. We start with PEP 794, which extends the metadata fields for Python packages to specify the import names a project provides. The metadata will help developers identify the correct project to install when they know the import name or the importable module names a project provides once installed. We dive back into WebAssembly to discuss PEP 816, which specifies the WASI support in CPython releases. We also wade into the controversy around PEP 832, which proposes standards around naming and the discovery of virtual environments. Brett shares his motivation for being a prolific author and supporter of PEPs. We discuss his promotion of standards and protocols to simplify the Python ecosystem for current and future developers.…

How can you avoid schema problems in your Polars data pipeline when adding new columns? How can you quickly examine a GitHub user’s profile to decide how much to invest in their contributions? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. Christopher shares a recent article about handling schema issues in Python Polars. The piece covers ways that a schema change can break your data pipeline. It provides strategies to avoid and resolve issues. We cover a recent project from previous guest Eric Matthes to investigate GitHub profiles of a potential contributor. With the rise of AI-slop pull requests, the tool can provide valuable background on the user. We also share other articles and projects from the Python community, including recent releases, exploring four different “get” special methods, what’s new in Pip 26.1, inverse Sapir-Whorf and programming languages, and a Python scripting framework for CAD. This episode is sponsored by Six Feet Up.…

What are the limitations of using a file-based agent workflow? Why do massive context windows tend to collapse? This week on the show, Mikiko Bazeley from MongoDB joins us to discuss agentic architecture and context engineering. Mikiko is an applied AI engineer. She helps developers and organizations build AI and ML applications using MongoDB. We dig into the debate of files versus a database. What are some of the limitations of building an agent with just a folder of files? We explore the surprising limitations of massive context windows and strategies for fixing them. Mikiko also shares advice and resources to help you get up to speed on building your own agent skills. Our conversation touches on multiple topics in the current development landscape. This episode is sponsored by SerpApi. Video Course Spotlight: Building Type-Safe LLM Agents With Pydantic AI Build type-safe LLM agents in Python with Pydantic AI using structured outputs, function calling, and dependency injection. Topics: 00:00:00 –…

What if you could build charts in Python by describing what your data means, instead of scripting every visual detail? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We cover a recent Real Python article about the data visualization library Altair. Most tools require you to write detailed boilerplate code to set up the axis and figure. Altair follows a declarative approach where you specify which columns go to which axis, the type of chart or plot, and what should be interactive. We also share other articles and projects from the Python community, including recent releases, clarifying the differences between iterators and iterables, decoupling your business logic from the Django ORM, comparing an LLM-based tool for web scraping against Playwright, a neural network emulator for guitar amplifiers, and a CLI tool to generate ASCII art of the current moon phase. This episode is sponsored by Build Your Own Coding Agent. Video Course Spotlight: Use…

How do you add agent skills to your data science workflow? How can a coding agent assist with data wrangling and research? This week on the show, Trevor Manz from marimo joins us to discuss marimo pair. Trevor is a founding engineer at marimo, where he’s been working on integrating LLM tools with marimo. We discuss the balancing act of building a skill and determining how to give an agent access to all the variables in a notebook. He shares how they built a specialized reactive REPL that eliminates hidden state and allows the agent to continue constructing a reproducible Python program. We dig into installing and getting started with marimo pair. Trevor also covers several of the tasks an agent can tackle in a data science workflow. Video Course Spotlight: Getting Started With marimo Notebooks Discover how marimo notebook simplifies coding with reactive updates, UI elements, and sandboxing for safe, sharable notebooks. Topics: 00:00:00 – Introduction 00:02:26 – Trevor’s role at marimo 00:03:08 – Current AI…

How can learning Rust help make you a better Python Developer? How do techniques required by a compiled language translate to improving your Python code? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We discuss a recent article by Bob Belderbos titled “Learning Rust Made Me a Better Python Developer.” Bob has been on a journey learning to program in Rust, which has made him rethink how he’s been writing Python. The compiler forced him to confront things he’d been ignoring. We also share other articles and projects from the Python community, including recent releases, a boatload of PEPs, NumPy as a synth engine, firing and forgetting with Python’s asyncio, managing state with signals in Python, a documentation site generator for Python packages, and a tool to explain your Python environment. This episode is sponsored by AgentField. Video Course Spotlight: Using Loguru to Simplify Python Logging Learn how to use Loguru for simpler Python…

What are the current techniques being employed to improve the performance of LLM-based systems? How is the industry shifting from post-training towards context engineering and multi-agent orchestration? This week on the show, Jodie Burchell, data scientist and Python Advocacy Team Lead at JetBrains, returns to discuss the current AI coding landscape. In our last conversation, Jodie covered how LLMs were approaching the limits of scaling laws. This time, we recap last year’s big focus on reasoning models and a post-training method called “reinforcement learning from verifiable rewards” (RLVR). We also cover test-time compute, where models spend more time reasoning through steps and considering multiple approaches to solve a problem. We touch on Agent Context Protocol (ACP), agent orchestration layers, and context engineering. We also share some concerns about the hype cycle, maintaining all that code being generated, and running local models. Course Spotlight: Vector Databases and Embeddings With ChromaDB…

What goes into managing a major project? What techniques can you employ for a project that’s in crisis? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We discuss an article by Ben Kuhn titled, “How I’ve Run Major Projects.” We dig into the skills required for project management, and provide advice for when projects fall into crisis. We cover how the field’s terminology has been updated. However, the time investment, sober communication, and planning still remain at the core of successful projects. We also share other articles and projects from the Python community, including recent releases and announcements, exploring an IDE for data science development, using Python set comprehensions, making friendly classes, using atexit for cleanup, a high-performance caching library for Python written in Rust, and a curated list of awesome marimo things. This episode is sponsored by PropelAuth. Video Course Spotlight: Using Data Classes in Python When…

With the mountains of Python code that it’s possible to generate now, how’s your code review going? What are the limitations of human review, and where does machine review excel? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We discuss a recent piece from Glyph titled, “What Is Code Review For?” We dig into the limitations of human review and where software tools like linters and formatters can help you. We cover the challenges developers and open-source maintainers face with the rise of LLM-generated code and pull requests. We also share other articles and projects from the Python community, including a collection of recent releases and announcements, creating publication-ready tables from DataFrames, choosing the right Python task queue, mastering context managers, statically checking Python dicts for completeness, an open-source inventory management system, and an ORM-based backend for Django tasks. This episode is sponsored by SerpApi.…

How do you quickly get an understanding of what’s inside a new set of data? How can you share an exploratory data analysis with your team? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We discuss a recent Real Python tutorial about YData Profiling. This library lets you quickly generate an exploratory data analysis (EDA) report with a few lines of code. The report provides column-level analysis, visualizations, and summary statistics that can be exported to HTML to share with others. We cover an article by Trey Hunner about building your own Python comprehensions. Python includes list, dictionary, and set comprehensions. But what if you want to create ones for other collections, such as a tuple, frozenset, or a Counter? We also share other articles and projects from the Python community, including a collection of recent releases and PEPs, using the Django ORM as a standalone module, a history of attempts to eliminate programmers, a discussion…

What goes into creating the tutorials you read at Real Python? What are the steps in the editorial process, and who are the people behind the scenes? This week on the show, Real Python team members Martin Breuss, Brenda Weleschuk, and Philipp Acsany join us to discuss topic curation, review stages, and quality assurance. We start by sharing the multiple roles our panel of guests perform across the editorial process. They describe the phases a tutorial passes through, including layers of reviews, from technical accuracy to educational effectiveness. We also discuss our editorial independence, external authors, and the continuous feedback loop with our readers. This episode is sponsored by AgentField. Spotlight: Claude Code Course: Stop Copy-Pasting From ChatGPT Most Python developers use AI as fancy autocomplete. This 2-day course teaches you to build entire projects with an AI agent inside your codebase. Topics: 00:00:00 – Introduction 00:03:33 – Martin’s role at Real Python 00:04:35 – Philipp’s role at Real…

Do you have complex logic and unpredictable dependencies that make it hard to write reliable tests? How can you use Python’s mock object library to improve your tests? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. Christopher shares details about his recent Real Python video course, “Improving Your Tests With the Python Mock Object Library.” He describes how mocking in Python with unittest.mock allows you to simulate complex logic or unpredictable dependencies, such as responses from external services. He covers how the Mock class can imitate real objects, and the patch() function lets you temporarily substitute mocks for real objects in your tests. We also share other articles and projects from the Python community, including a collection of recent releases, using open source AI at Wagtail, updates from the inaugural PyPI Support Specialist, a lightweight OS for microcontrollers in MicroPythonOS, why match-case is not necessarily switch-case…

How can you move your MCP tools beyond plain text? How do you add interactive UI components directly inside chat conversations? This week on the show, Den Delimarsky from Anthropic joins us to discuss MCP Apps and interactive UIs in MCP. Den is a member of the MCP Steering Committee and a Core Maintainer focusing on auth and security. He explains how MCP acts as a universal bridge, providing AI models with the real-time context they need. He shares insights on working with MCP Apps and moving beyond simple text to render web-based user interfaces directly in your chat window. Den previously worked on GitHub Spec Kit, and we discuss taking advantage of spec-driven development and how to use “Skills” to remove the toil from your workflow. We also talk briefly about his podcast and his conversations about navigating careers in technology. This episode is sponsored by AgentField. Course Spotlight: Getting Started With Google Gemini CLI Learn how to use Gemini CLI to bring Google’s AI-powered coding assistance…

Would you like to learn how to work with LLMs locally on your own computer? How do you integrate your Python projects with a local model? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. We cover a recent Real Python step-by-step tutorial on installing local LLMs with Ollama and connecting them to Python. It begins by outlining the advantages this strategy offers, including reducing costs, improving privacy, and enabling offline-capable AI-powered apps. We talk through the steps of setting things up, generating text and code, and calling tools. We also share other articles and projects from the Python community, including the 2026 Python Developers Survey, creating callable instances with Python’s .__call__(), creating maps and projections with GeoPandas, ending 15 years of subprocess polling, discussing backseat software, a retry library that classifies errors, and a peer-to-peer encrypted CLI chat project. This episode is sponsored by…

What are ways to improve how you’re using GitHub? How can you collaborate more effectively and improve your technical writing? This week on the show, Adam Johnson is back to talk about his new book, “Boost Your GitHub DX: Tame the Octocat and Elevate Your Productivity”. Adam has written a series of books about improving developer experience (DX). In this episode, we dig into his newest book, which focuses on GitHub and how to get the most out of its features—from settings and keyboard shortcuts to hidden tools, CLI commands, and the command palette. Adam also shares insights on the best ways to communicate on the platform. We discuss the nuances of GitHub-Flavored Markdown (GFM), best practices for effective communication in open source, the importance of maintaining civility in issue reports, and why he included a glossary of acronyms to help developers decode common shorthand like LGTM and FTFY. This episode is sponsored by Honeybadger. Course Spotlight: Introduction to Git and GitHub for Python Developers…

How do you create automated tests to check your code for degraded performance as data sizes increase? What are the new features in pandas 3.0? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. Christopher digs into an article about building tests to make sure your software is fast, or at least doesn’t get slower as it scales. The piece focuses on testing Big-O scaling and its implications for algorithms. We also discuss another article covering the top features in pandas 3.0, including the new dedicated string dtype, a cleaner way to perform column-based operations, and more predictable default copying behavior with Copy-on-Write. We share several other articles and projects from the Python community, including a collection of recent releases and PEPs, a profiler for targeting individual functions, a quiz to test your Django knowledge, when to use each of the eight versions of UUID, the hard-to-swallow truths about being a software engineer, an…

If you haven’t visited the Real Python website lately, then it’s time to check out a great batch of updates on realpython.com! Dan Bader returns to the show this week to discuss improvements to the site and more ways to learn Python. Dan details changes to the website, including our Python reference area. This tool provides a quick reference for both beginners and experienced developers looking for concise definitions and refreshers on Python’s features. We discuss the expansion of our live courses to include a beginner course and an intermediate deep dive. Dan also shares how we’re growing the team and highlights the ongoing commitment to our editorial standards. Spotlight: Python for Beginners: Code With Confidence Learn the fundamentals of Python step-by-step in a friendly, interactive cohort. Build confidence writing code and understand the “why” behind Python’s core concepts. Topics: 00:00:00 – Introduction 00:02:54 – Real Python Reference 00:08:31 – Integration with search 00:13:51 – Sponsor:…

How often have you heard about the speed of Python? What’s actually being measured, where are the bottlenecks—development time or run time—and which matters more for productivity? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article titled “The Uselessness of ‘Fast’ and ‘Slow’ in Programming.” It digs into how the different aspects of software performance span a wide range of orders of magnitude, and how developers can obsess over irrelevant performance details, often losing more time working in suboptimal environments than building what they need with tools they already know. We also discuss an article about why uv is fast, which explains how most of its speed comes from engineering decisions rather than just being written in Rust. We then share several other articles and projects from the Python community, including a roundup of 2025 year-end lists, an explanation of why Python’s deepcopy can be so slow, serving a…

Are you looking for that solid foundation to begin your Python journey? Would the accountability of scheduled group classes help you get through the basics and start building something? This week, two members of the Python for Beginners live course discuss their experiences. We speak with course instructor Stephen Gruppetta about building a course where the participants start using their knowledge as soon as possible. He describes how he’s evolved his teaching techniques over years of working with beginners. We explore the advantages of having a curated collection of written tutorials, video courses, and a forum for asking those nagging questions. We also speak with students Louis and Andrew about their experiences learning Python through the course. They discuss learning how to apply their new skills, employing them in their careers, and building confidence to continue their Python learning journey. Spotlight: Python for Beginners: Code With Confidence Learn the fundamentals of Python step-by-step in a…

PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2025. Christopher Trudeau is back on the show this week to help wrap up everything by sharing some highlights and uncovering a few hidden gems from the pile. We share the top links explored by PyCoder’s readers. We also dig into trends across all the articles and stories this year. We highlight a few gems that we didn’t cover on the show and a couple that explore the overall themes of the year. We hope you enjoy this review! We look forward to bringing you an upcoming year full of great Python news, articles, topics, and projects. Course Spotlight: Using Functional Programming in Python Boost your Python skills with a quick dive into functional programming: what it is, how Python supports it, and why it matters. Topics: 00:00:00 – Introduction 00:01:52 – Django 6.0 released 00:02:58 – The Inner Workings of Python Dataclasses Explained 00:03:40 – Going Beyond requirements.txt With pylock.toml and PEP 751 00:04:58 –…

What are the advantages of spec-driven development compared to vibe coding with an LLM? Are these recent trends a move toward declarative programming? This week on the show, Marc Brooker, VP and Distinguished Engineer at AWS, joins us to discuss specification-driven development and Kiro. Marc describes the process of developing an application by writing specifications, which outline what a program should do and what needs it should meet. We dig into a bit of computer science history to explore the differences between declarative and imperative programming. We also discuss Kiro, a new integrated development environment from Amazon, built around turning prompts into structured requirements. We examine the various types of documents used to specify tasks, requirements, design, and steering. Real Python Resource Spotlight: Python Coding With AI - Learning Path Explore tools and workflows for AI in Python: coding partners, prompt engineering, RAG, ChromaDB, FastAPI chatbots, and MCP integrations. Stay current and…

What are the recent advances in the field of quantum computing and high-performance computing? And what Python tools can you use to develop programs that run on quantum computers? This week on the show, Real Python author Negar Vahid discusses her tutorial, “Quantum Computing Basics With Qiskit.” Negar digs into the fundamentals of quantum computers, describing qubits, superposition, entanglement, and interference. We discuss the concept of quantum advantage and the fields of exploration where quantum computing promises speed-ups over classical computers. She also shares tools for designing quantum circuits with Python. Course Spotlight: Profiling Performance in Python Learn to profile Python programs with built-in and popular third-party tools, and turn performance insights into faster code. Topics: 00:00:00 – Introduction 00:01:31 – Writing for Real Python 00:02:37 – What drew you to quantum computing? 00:04:27 – What is quantum advantage? 00:07:10 – Quantum computing basics article 00:09:32 – Linear…

What are the steps to get started building a FastAPI application? What are the different types of concurrency available in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a recent Real Python step-by-step tutorial about programming a FastAPI example application. You practice installing FastAPI, building your first endpoints, adding path and query parameters, and validating endpoints using Pydantic. Christopher covers updates to his Real Python video course about concurrency in Python. The course digs into what concurrency means in Python and why you might want to incorporate it in your code. He describes the different methods and demonstrates how to approach coding using threading, asyncio, and multiprocessing. We also share several other articles and projects from the Python community, including a news roundup, the PSF fundraiser campaign for 2025, where Python stores attributes, performance hacks for faster Python code,…

How do you prepare your Python data science projects for production? What are the essential tools and techniques to make your code reproducible, organized, and testable? This week on the show, Khuyen Tran from CodeCut discusses her new book, “Production Ready Data Science.” Khuyen shares how she got into blogging and what motivated her to write a book. She shares tips on how to create repeatable workflows. We delve into modern Python tools that will help you bring your projects to production. Course Spotlight: Python Project Management With uv Create and manage Python projects with uv, a blazing-fast package and project manager built in Rust. Learn setup, workflow, and best practices. Topics: 00:00:00 – Introduction 00:01:27 – Recent article about top six visualization libraries 00:02:19 – How long have you been blogging? 00:03:55 – What do you cover in your book? 00:07:07 – Potential issues with notebooks 00:11:40 – Structuring data science projects 00:15:12 – Reproducibility and sharing notebooks 00:20:33…

What are techniques for writing maintainable Python code? How do you make your Python more readable and easier to refactor? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a recent article about writing code that is easy to maintain. We cover writing comments, creating meaningful names, avoiding magic numbers, and preparing code for your future self. We also share several other articles and projects from the Python community, including release news, modifying the REPL, differences between Polars and pandas, generating realistic test data in Python, investigating quasars with Polars and marimo, creating simple meta tags for Django objects, and a GUI toolkit for grids of buttons. Course Spotlight: Modern Python Linting With Ruff Ruff is a blazing-fast, modern Python linter with a simple interface that can replace Pylint, isort, and Black—and it’s rapidly becoming popular. Topics: 00:00:00 – Introduction 00:01:53 – PyTorch 2.9…

How do you deploy your Python application without getting locked into an expensive cloud-based service? This week on the show, Michael Kennedy from the Talk Python podcast returns to discuss his new book, “Talk Python in Production.” Michael runs multiple Python applications online, including a training site, blog, and two podcasts. While searching for the best solution for hosting his business, he documented his findings in a book. We talk about containerizing Python applications, generating static sites, preparing for traffic spikes, and avoiding cloud service lock-in. Course Spotlight: Speed Up Python With Concurrency Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing. Topics: 00:00:00 – Introduction 00:01:28 – Welcome back! 00:03:05 – Is this your first book? 00:04:13 – A book that reads like a blog 00:06:15 – Incentives to keep you locked in 00:09:20 – Following the…

How does Python 3.14 perform under a few hand-crafted benchmarks? Does the performance of asyncio scale on the free-threaded build? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. At the top of the show, we have a mountain of release news and Python Enhancement Proposals to cover. Then, we dig into a couple of articles covering the performance of Python 3.14. The first is a benchmarking comparison of the last several Python versions and their variations, including JIT and free-threaded mode. The second explores the changes in 3.14 that enable asyncio to scale on CPython’s free-threaded build. We also share several other articles and projects from the Python community, including an introduction to NiceGUI, a free-threaded Python library compatibility checker, an exploration of what is “good taste” in software engineering, HTML templating with t‑strings, and a tool for testing Sphinx docs instantly in the browser. Course Spotlight:…

How is teaching young students Python changing with the advent of LLMs? This week on the show, Kelly Schuster-Paredes from the Teaching Python podcast joins us to discuss coding and AI in the classroom. Kelly shares her current thoughts on teaching Python to young students. She stresses that the earliest classes still need to cover the fundamentals. We also discuss how coding instruction is evolving toward reading and reviewing code more than writing it. Course Spotlight: Building a Python GUI Application With Tkinter In this video course, you’ll learn the basics of GUI programming with Tkinter, the de facto Python GUI framework. Master GUI programming concepts such as widgets, geometry managers, and event handlers. Then, put it all together by building two applications: a temperature converter and a text editor. Topics: 00:00:00 – Introduction 00:01:28 – Starting to use AI in the classroom 00:05:17 – How did students react to using NLP and NLTK? 00:06:40 – Teaching Python beginners now 00:12:31 – Code…

Python 3.14 is here! Christopher Trudeau returns to discuss the new version with Real Python team member Bartosz Zaczyński. This year, Bartosz coordinated the series of preview articles with members of the Real Python team and wrote the showcase tutorial, “Python 3.14: Cool New Features for You to Try.” Christopher’s video course, “What’s New in Python 3.14”, covers the topics from the article and shows the new features in action. Christopher and Bartosz dug into the new release to create code examples showcasing the new features for the tutorial and course. We look at the enhanced and more colorful REPL experience, better error messages, safer hooks for live debugging, and deferred annotation evaluation. We also discuss template strings, Zstandard compression, and multiple performance improvements. We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.14. Course Spotlight: What’s New in Python 3.14 Covers…

What’s changed about learning Python over the last few years? What new techniques and updated advice should beginners have as they start their journey? This week on the show, Stephen Gruppetta and Martin Breuss return to discuss beginning to learn Python. We share techniques for finding motivation, building projects, and learning the fundamentals. We provide advice on installing Python and not obsessing over finding the perfect editor. We also examine incorporating LLMs into learning to code and practicing asking good questions. Stephen shares details about our upcoming eight-week live course, Python for Beginners: Code With Confidence. Check out realpython.com/live to learn more and reserve your spot. Course Spotlight: 11 Beginner Tips for Learning Python In this course, you’ll see several learning strategies and tips that will help you jumpstart your journey towards becoming a successful Python programmer. Topics: 00:00:00 – Introduction 00:02:36 – Martin’s teaching background 00:02:57 – Stephen’s teaching…