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 without a Login, a library to make measurement units easier through Pydantic, and a tool to convert if-else code to match statements.

This episode is sponsored by Six Feet Up.

Video Course Spotlight: How to Get Started With Ollama

Learn how to install Ollama, pull local models, and connect them to your Python code using the chat and text generation interfaces.

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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.

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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 analyzer for malicious Python code.

Course Spotlight: Sending Emails Using Python

Learn how to send emails with Python using SMTP and smtplib, attach files, format HTML messages, and personalize bulk emails.

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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.

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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 date for modern Python.

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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.

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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 Rust-based replacement for Python’s json module, and a couple of Python cheat sheet resources.

This episode is sponsored by HydraDB.

Course Spotlight: Exploring Python’s Built-in Functions

Learn Python’s built-in functions for math, data types, iterables, and I/O, and when to use each to write more Pythonic code.

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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.

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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 video courses, using uv in Production, employing Wagtail as Django admin on steroids, managing and measuring Python code quality, a pure-Python implementation of jq, and a project to bring interactivity to plotnine.

This episode is sponsored by AURI by Endor Labs

Course Spotlight: Thread Safety in Python: Locks and Other Techniques

In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe.

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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.

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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 terminal. List their tools, prompts, and resources, then call each one.

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