The Real Python Podcast

By Real PythonTechnology

The Real Python Podcast episodes

October 02Python 3.15: Exploring the New Features
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, frozendict, sentinel values, a sampling profiler, and a faster JIT.
Topics:
00:00:00 – Introduction
00:00:02 – * NOTE: Python 3.15 release delayed!
00:03:35 – Real Python 3.15 Preview Articles and Video Course
00:04:40 – Sentinel Values
00:08:55 – Lazy Imports
00:14:48 – Unpacking in Comprehensions
00:17:12 – Mathematical functions
00:20:07 – Sampling Profiler
00:27:43 – Video Course Spotlight
00:29:18 – frozendict
00:33:12 – The speed round of features
00:36:04 – Continued work on free-threaded Python and upgrading the JIT
00:39:34 – Upgrading to 3.15
00:44:35 – Thanks and goodbye
Show Links:
* Python 3.15: following tradition, let’s have a surprise rc3! - Core Development - Discussions on Python.org
Python 3.15: Cool New Features for You to Try – Real Python
Python 3.15 Preview: Sentinel Values – Real Python
Python 3.15 Preview: Lazy Imports – Real Python
Unpacking in Comprehensions -What’s new in Python 3.15 — Python 3.15 documentation
math — Mathematical functions — Python 3.15 documentation
Python 3.15 Preview: Sampling Profiler
PEP 799 – A dedicated profiling package for organizing Python profiling tools
Python 3.15 Preview: frozendict
PEP 814 – Add frozendict built-in type
More color - What’s new in Python 3.15
Improved error messages - What’s new in Python 3.15
Python 3.15 Preview: UTF-8 by Default
PEP 803: Stable ABI for free-threaded builds - What’s new in Python 3.15
Python 3.15 Preview: Upgraded JIT Compiler
Additional Links:
PEP 810 – Explicit lazy imports
Implement native PEP 0810 lazy loading by hebaalazzeh - google-cloud-python
PEP 798 – Unpacking in Comprehensions
PEP 448 – Additional Unpacking Generalizations
Tachyon: Python 3.15’s sampling profiler is faster…- Pablo Galindo Salgado, Laszlo Kiss Kollar - YouTube
The amazing and varied life of Tachyon particles in Star Trek TNG - YouTube
Episode #128: Using a Memory Profiler in Python & What It Can Teach You
Episode #172: Measuring Multiple Facets of Python Performance With Scalene
Upgraded experimental JIT compiler - What’s new in Python 3.15
What’s new in Python 3.15 — Python 3.15 documentation
Level up your Python skills with our expert-led courses:
Understanding Python List Comprehensions
What's New in Python 3.14
What's New in Python 3.15

Support the podcast & join our community of Pythonistas
45 min
September 25Navigating AI in Open Source: Insights From Wagtail
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 structured output.
Topics:
00:00:00 – Introduction
00:01:49 – What is Wagtail?
00:08:35 – Flood of AI submissions to open-source projects
00:11:38 – Guidance for AI-led contributions
00:20:43 – Security submissions
00:21:56 – Ethical decisions about internal use of AI
00:26:31 – Resurgence of web sites
00:27:41 – Measuring benefits and performance
00:32:49 – Models understanding your framework
00:38:05 – Video Course Spotlight
00:39:42 – Adding AI features to Wagtail for creators
00:48:47 – Balancing the use of AI internally and the cost of inference
00:55:29 – Advice for other open-source projects
01:00:00 – Wagtail 8.0
01:02:10 – What are you excited about in the world of Python?
01:04:10 – What do you want to learn next?
01:05:56 – How can people follow your work online?
01:06:43 – Thanks and goodbye
Show Links:
Wagtail CMS - Django Content Management System
Wagtail Space 2026 - Wagtail CMS
Showcase - Wagtail CMS
Guidance for AI-led contributions - Wagtail CMS
What I learned from two days of hanging out with AI experts - Wagtail CMS
AI saved me 10 minutes on a blog post: Was it worth it? - Wagtail CMS
What AI tools get right and wrong with Wagtail - Wagtail CMS
Comparing open weight AI models and providers - Wagtail CMS
AI in the CMS: steering the ecosystem - Wagtail CMS
The carbon footprint of Wagtail AI - Wagtail CMS
Open source AI we use to work on Wagtail - Wagtail CMS
Results of the 2026 Wagtail DX with AI survey - Wagtail CMS
open-code-review: Secure, fast, efficient, battle-tested - hybrid architecture code review tool
CMS with AI, not AI CMS: Wagtail 8.0’s new API
Roadmap - Wagtail CMS
Executive Director Search Extended to September 22 - Django Weblog
git-worktree Documentation
Wagtail newsletter
Meagen Voss (@[email protected]) - Fosstodon
Thibaud Colas (@[email protected]) - Fosstodon
Level up your Python skills with our expert-led courses:
How to Set Up a Django Project
Getting Started With Google Gemini CLI
Using the Claude API in Python

Support the podcast & join our community of Pythonistas
1 hr 8 min
September 11Django Developers Survey Results & Reproducible Python Builds
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.
Topics:
00:00:00 – Introduction
00:02:46 – Python 3.15.0 candidate 2 is here!
00:03:10 – Django 6.1.1 release notes
00:03:20 – Wagtail 8.0 release notes
00:04:27 – RISC-V Is Now Officially Supported by CPython
00:05:19 – PEP 843: Export Statement for DRY Re-Exports
00:06:27 – 2026 PSF Board Election Interviews
00:07:07 – Up and Running with Rust Course
00:07:56 – Guido Comment on PEP 805: Safe Parallel Python
00:09:56 – The Polars vs SQL Differences Nobody Is Talking About
00:16:55 – Sponsor: Six Feet Up
00:17:50 – Unsubscribe Links Without a Login: Django Signing
00:20:56 – What’s Missing to Have Reproducible Builds on PyPI
00:27:09 – Video Course Spotlight
00:28:44 – The Python Community’s Institutional Response to the Astral Acquisition Has Begun
00:29:57 – The State of Django 2026: Boring Is So Back
00:40:13 – matchify: Converts if-else Code to match Statements
00:43:14 – pydantic-pint: Pydantic Pint Quantities
00:45:16 – Thanks and goodbye - Submit topics to PyCoders Link
News:
Python 3.15.0 candidate 2 is here! - Python Insider
Django 6.1.1 release notes - Django documentation
CMS With AI, Not AI CMS: Wagtail 8.0’s New API – Wagtail 8 includes a new API built on Django Ninja and Pydantic, to automate common admin tasks with and without agents. 50+ operations derived from projects’ existing Python/Django code, mimicking the admin panel but via endpoints and an official CLI.
Wagtail 8.0 release notes — Wagtail Documentation 8.0 documentation
RISC-V Is Now Officially Supported by CPython
PEP 843: Export Statement for DRY Re-Exports (Draft)
PEP 842: Module Exports (Withdrawn)
2026 PSF Board Election Interviews – This is a collection of interviews of the various candidates running for the Python Software Foundation Board. Many of the posts also include links to AMA sessions.
Python Software Foundation News: The 2026 PSF Board Election is Open!
Python Software Foundation News: Inaugural Python Packaging Council Election: Voting is now open!
Up and Running with Rust Course
Guido Comment on PEP 805: Safe Parallel Python
Show Links:
The Polars vs SQL Differences Nobody Is Talking About – Some problems can be attacked with either SQL or Polars, but subtle differences in how the two mechanisms work can run you into trouble. Learn more about these potential gotchas.
Unsubscribe Links Without a Login: Django Signing – Django has a signing module that makes it easy to build an unsubscribe link that works with no login and no session: token = signing.dumps(recipient.pk, salt=UNSUBSCRIBE_SALT). The token itself is the credential, and it ships with Django out of the box.
What’s Missing to Have Reproducible Builds on PyPI – A reproducible build is a way of creating an independently verifiable, repeatable build of CPython and other associated tools. This post explains why Python isn’t there yet and why it is important.
The Python Community’s Institutional Response to the Astral Acquisition Has Begun – Brett Cannon posted on discuss.python.org (March 23): a PEP is coming, the python/prebuilt-cpython repo already exists, and the PSF has been building an official prebuilt relocatable CPython distribution since October 2025. Covers what’s actually being built, what it means for uv/ruff/python-build-standalone, and why the Astral upstream patches and PSF alternative aren’t in conflict.
The State of Django 2026: Boring Is So Back – A summary of this year’s State of Django report which draws on responses from nearly 3,500 developers across more than 40 countries: from students in their first year to veterans with decades of experience.
Projects:
matchify: Converts if-else Code to match Statements
pydantic-pint: Pydantic Pint Quantities
Additional Links:
Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
Mastering DuckDB when you’re used to pandas or Polars
Episode #297: Improving Python Through PEPs and Protocols
PEP 770 – Improving measurability of Python packages with Software Bill-of-Materials
Phantom Dependencies: is your requirements.txt haunted? - Seth Michael Larson - YouTube
prebuilt-cpython - GitHub
htmx - high power tools for html
Pint: makes units easy — pint 0.25.3rc1.dev1+g5e79411e1 documentation
Exploring Astrophysics in Python With pandas and Matplotlib – Real Python
PyCoder’s Weekly - Submit a Link
Level up your Python skills with our expert-led courses:
Exploring Astrophysics in Python With pandas and Matplotlib
Connecting LLMs to Your Data With Python MCP Servers
How to Get Started With Ollama

Support the podcast & join our community of Pythonistas
47 min
September 04Performance Engineering: Profiling and Making Apps Fast by Default
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 – Modern systems run across multiple languages
00:03:52 – What lead you toward performance engineering?
00:12:54 – Frameworks and balancing performance against developer experience
00:17:45 – Measuring performance and profiling
00:24:28 – What is Fast by Default?
00:36:34 – Video Course Spotlight
00:38:16 – Breaking into the methodology
00:42:10 – Performance budget
00:48:27 – End user pain points
00:51:21 – Microservices and monoliths
00:54:01 – Performance questionnaire
01:00:31 – What is easier about performance planning upfront?
01:01:50 – Discoveries during the writing process
01:07:01 – Do LLMs write performant code?
01:12:37 – What are you excited about in the world of Python?
01:13:41 – What do you want to learn next?
01:15:02 – How can people follow your work online?
01:15:43 – Thanks and goodbye
Show Links:
Fast by Default - Den Odell - Manning Discount Link
Fast by Default
Constraints and the Lost Art of Optimization - Den Odell
Python 3.15 Preview: Sampling Profiler
Tachyon: High frequency statistical sampling profile
Profiling in Python: How to Find Performance Bottlenecks
Episode #128: Using a Memory Profiler in Python & What It Can Teach You
Episode #172: Measuring Multiple Facets of Python Performance With Scalene
Running Python code in a sandbox with MicroPython and WASM
Syntorial: The Ultimate Synthesizer Course
Den Odell - LinkedIn
Den Odell – Author. Staff Web Engineer.
Level up your Python skills with our expert-led courses:
Speed Up Python With Concurrency
Profiling Performance in Python
Testing Your Code With Python's unittest

Support the podcast & join our community of Pythonistas
1 hr 17 min
August 28Exploring Complex Systems & Maintainable Data Science Pipelines
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.
Topics:
00:00:00 – Introduction
00:02:24 – Python 3.12.14, 3.11.16 and 3.10.21 Released
00:03:02 – Django Is Moving to an Annual Release Cycle
00:04:17 – PEP 828: Supporting ‘Yield From’ in Asynchronous Generators
00:04:56 – PEP 841: Adding Frozen Syntax to Optimize Immutable Types
00:07:04 – PEP 844: public and private Builtins
00:10:09 – Validating Data With Pointblank in Python
00:18:22 – Working on Complex Systems: Patterns I Learned at Google
00:22:14 – Video Course Spotlight
00:23:58 – Hydra for Python Configuration: Build Modular and Maintainable Pipelines
00:29:29 – Breaking Up (Lines) Is Hard to Do
00:35:00 – marimo-book: Static Site Generator for marimo Notebooks
00:38:06 – marimo-lsp: A language server and VS Code extension for marimo
00:38:31 – hexora: Static analysis of malicious Python code
00:40:01 – Thanks and goodbye
News:
Python 3.12.14, 3.11.16 and 3.10.21 Released
Django Is Moving to an Annual Release Cycle
PEP 828: Supporting ‘Yield From’ in Asynchronous Generators (Accepted)
PEP 841: Adding Frozen Syntax to Optimize Immutable Types (Draft)
PEP 844: public and private Builtins (Draft)
Show Links:
Validating Data With Pointblank in Python – Learn how to validate data in Python with Pointblank: declare quality checks, split clean from failing rows, and rerun validation plans from YAML.
Working on Complex Systems: Patterns I Learned at Google – Explore the key characteristics of complex systems and learn practical patterns for tackling complex problems
The Coder Cafe - Teiva Harsanyi – The Coder Cafe introduces vital software engineering concepts that will upgrade your day-to-day practice, regardless of your skill level. Discount Code (CODERCAFE40) Expires November 21st 2026.
Hydra for Python Configuration: Build Modular and Maintainable Pipelines – Hardcoded paths and parameters can quickly drift out of sync across a data science project’s scripts. This article shows how to move them into Hydra configuration files, access values with dot notation, override settings from the command line, swap entire configuration groups, and run experiment sweeps with a single multi-run flag.
Breaking Up (Lines) Is Hard to Do – Here’s a seemingly simple question: given a chunk of multi-line text, how do you split it and return an array? Unicode makes everything harder than it might first seem.
Projects:
marimo-book: Static Site Generator for marimo Notebooks
marimo-lsp: A language server and VS Code extension for marimo
hexora: Static analysis of malicious Python code
Additional Links:
pointblank - PyPI
Episode #274: Preparing Data Science Projects for Production With Khuyen Tran
marimo-book
DartBrains - How can we understand how the brain works?
marimo - Visual Studio Marketplace
Level up your Python skills with our expert-led courses:
Getting Started With marimo Notebooks
Managing Imports With Python's all
Sending Emails Using Python

Support the podcast & join our community of Pythonistas
42 min
August 21Navigating Silent Failures in AI: Strategies for Effective Oversight
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 Python Bytes
00:03:43 – Why AI Fails Silently talk
00:11:07 – Digging into silent failures and an audit trail
00:18:44 – How would you define hooks?
00:20:48 – Document extraction, embeddings, and Cowork
00:30:38 – Stripping the noise from file formats
00:34:43 – Things that are now baked into models
00:45:53 – Video Course Spotlight
00:47:23 – Leveraging agents to pick models
00:51:44 – GitHub repo for the project
00:54:57 – The current market around tokens
01:00:12 – What are you excited about in the world of Python?
01:03:02 – What do you want to learn next?
01:04:21 – The content the LLMs are trained on
01:07:36 – Upcoming events
01:08:38 – Thanks and goodbye
Show Links:
Why AI Fails Silently and How to Fix It
Claude Cowork - Claude by Anthropic
List of All Oblique Strategies - Matt Rickard
Pi Coding Agent
goose - Your open source AI agent
Codex CLI - ChatGPT Learn
2026 All Things Open AI: Orchestrate Agentic AI: Context, Checklists, and No-Miss Reviews - GitHub
scaf: A template manager that simplifies bootstrapping and updating projects.
Tau - Learn how coding agents are built.
superpowers: An agentic skills framework & software development methodology that works.
Hermes Agent — Open-Source AI Agent That Grows With You
PyOhio 2026 - PyOhio 2026
AWS re:Invent 2026 - Nov 30-Dec 4, Las Vegas
Python Bytes - Python News Podcast
beats the hell out of me - painfully - YouTube
Level up your Python skills with our expert-led courses:
Getting Started With Claude Code
Use Codex CLI to Enhance Your Python Projects
Coding With OpenCode: AI-Assisted Python

Support the podcast & join our community of Pythonistas
1 hr 10 min
August 14Improving NumPy Performance on Free-Threaded Python
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.
Topics:
00:00:00 – Introduction
00:02:26 – Python 3.15.0 candidate 1 is here!
00:02:47 – Python 3.14.7 and 3.13.15 are now available!
00:03:00 – Django 6.1 released
00:03:39 – Planned Updates to the PyPI User Interface
00:04:13 – PyPI Releases Now Reject New Files After 14 Days
00:05:07 – PEP 837 – Extensible JSON serialization
00:06:20 – PEP 842: Module Exports
00:07:51 – Introducing django-crawl
00:12:02 – SIMD in Pure Python
00:18:29 – Managing Imports With Python’s __all__
00:29:34 – Spotlight: Modern Object-Oriented Python Book
00:30:53 – Scaling NumPy on Free-Threaded Python
00:37:53 – cnsplots: Python Data Visualization for Complex Datasets
00:42:19 – django-orm-lens: Django Schema Review
00:45:14 – Thanks and goodbye
News:
Python 3.15.0 candidate 1 is here! - Python Insider
Python 3.14.7 and 3.13.15 are now available! - Python Insider
Django 6.1 released - Weblog - Django
Planned Updates to the PyPI User Interface – Over the next few months a new user interface will be rolled out for the Python packaging website, PyPI. The rollout will be done in phases to make sure it is rock solid and to get community feedback. This post talks about the history of PyPI’s UI and what is changing.
PyPI Releases Now Reject New Files After 14 Days – “The Python Package Index (PyPI) now rejects new files being uploaded to releases that are older than 14 days. This restriction was put in place to prevent old and long-stable releases from being poisoned”
PEP 837 – Extensible JSON serialization
PEP 842: Module Exports
Show Links:
Introducing django-crawl – During a recent site migration, Adam used the Django test harness to crawl his site looking for missing security headers. In the process he uncovered seven other bugs for a project that had 100% code coverage. He has consolidated the crawling technique for testing into a library: django-crawl.
SIMD in Pure Python – SIMD is Single Instruction, Multiple Data, an approach that does calculations with vectors of data sets. Python doesn’t support it natively, but libraries like NumPy allow you to code this way.
Thoughts on “SIMD in Pure Python” - purplesyringa’s blog
Managing Imports With Python’s __all__ – Learn how Python’s dunder all variable controls wildcard imports and shapes the public API your packages and modules expose.
Scaling NumPy on Free-Threaded Python – A recap on the work done in NumPy and CPython to make multi-threaded NumPy workloads scale on the free-threaded build of CPython.
Projects:
cnsplots: Python Data Visualization for Complex Datasets
django-orm-lens: Django Schema Review
Additional Links:
What’s up Python? __json__, __export__ and Astral stuff
Episode #97: Improving Your Django and Python Developer Experience
Python’s __all__: Packages, Modules, and Wildcard Imports – Tutorial
Quiz: Managing Imports With Python’s __all__
Smoke - The Amazing Hamster, Storing Food - YouTube
Modern Object-Oriented Python Book – Real Python
Level up your Python skills with our expert-led courses:
Python Modules and Packages: An Introduction
NumPy Techniques and Practical Examples
Managing Imports With Python's all

Support the podcast & join our community of Pythonistas
47 min
August 07Programmatically Developing LLM Prompts With DSPy
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 DSPy
00:05:22 – What is DSPy?
00:06:58 – Working with signatures and typing
00:09:53 – Prompt engineering failing to work for a new model
00:13:43 – What is an optimizer?
00:19:04 – Moving toward a new level of abstraction in prompting
00:23:19 – Prompts for the LLM within a consumer facing application
00:28:26 – Video Course Spotlight
00:30:21 – Example of using with RAG
00:38:45 – Example of a business plan tool
00:43:06 – The Bitter Lesson
00:46:03 – Structure of the book
00:49:55 – Advice on starting with DSPy
00:54:15 – Example of a transformed prompt
00:59:37 – What are you excited about in the world of Python?
01:01:47 – What do you want to learn next?
01:04:11 – What’s the best way to follow your work online?
01:04:40 – Thanks and goodbye
Show Links:
Building LLM Applications with DSPy - Serj Smorodinsky and Brett Kennedy
DSPy
DSPy: The framework for programming—not prompting—language models - GitHub
Tutorials Overview - DSPy
GEPA optimization - DSPy
Let the LLM Write the Prompts: An Intro to DSPy in Compound AI Pipelines - YouTube
Episode #208: Detecting Outliers in Your Data With Python
Tabular Models Benchmark: Performance Across 19 Datasets 2026
Brett Kennedy on Manning
Serj Smorodinsky on Manning
W Brett Kennedy – Medium
Brett Kennedy - LinkedIn
Level up your Python skills with our expert-led courses:
Building Type-Safe LLM Agents With Pydantic AI
Accessing Multiple AI Models With the OpenRouter API
Using LlamaIndex for RAG in Python

Support the podcast & join our community of Pythonistas
1 hr 6 min
July 31Should You Understand Your Entire Python Codebase?
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.
Topics:
00:00:00 – Introduction
00:02:29 – Christopher’s Python News Song
00:03:18 – Python 3.15.0 Beta 4 Released
00:03:39 – PEP 838: Adding python-version to pyvenv.cfg
00:04:59 – PEP 840: Name Resolution in Class Namespaces
00:06:42 – Stop Using if-else Chains
00:13:46 – Sponsor: HydraDB
00:14:44 – Nifty Django Feature: F Expressions
00:20:23 – In Defense of Not Understanding Your Codebase
00:34:13 – Exploring Python’s Built-in Functions
00:45:26 – Video Course Spotlight
00:46:57 – Python strftime/strptime Directive Cheat Sheet
00:50:22 – Itertools Cheatsheet
00:52:59 – Introducing django-orjson
00:55:33 – Thanks and goodbye
News:
Python 3.15.0 Beta 4 Released
PEP 838: Adding python-version to pyvenv.cfg (Added)
PEP 840: Name Resolution in Class Namespaces (Added)
Show Links:
Stop Using if-else Chains – Learn a cleaner, more extensible way to dispatch logic in Python using dictionaries and function pointers instead of long if-else chains.
Nifty Django Feature: F Expressions – Django’s F-Expression provides a way of querying fields from the ORM. They’re particularly handy to traverse relationships in more complex queries.
F() Expressions - Django Documentation
In Defense of Not Understanding Your Codebase – In this opinion piece, Sean argues that there is a difference in the thought process between maintaining smaller software projects vs larger ones, and that the former is over represented in engineering discussion in the internet.
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.
Projects:
Python strftime/strptime Directive Cheat Sheet
Itertools Cheatsheet – Cheatsheet with visual diagrams that explain how the iterables from itertools work.
Introducing django-orjson – orjson is a Rust-based replacement for Python’s json module. So what would Adam Johnson do with it? Make it easier to use in Django of course.
orjson: Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
django-orjson documentation
Additional Links:
Primer on Python Decorators - Registering Plugins
Python Built-in Functions: A Complete Guide
Python Cheat Sheet – Real Python
What’s in which Python - Ned Batchelder
Level up your Python skills with our expert-led courses:
Using Python's datetime Module
Python Basics: Conditional Logic and Control Flow
Exploring Python's Built-in Functions

Support the podcast & join our community of Pythonistas
58 min
July 24Configuring a Versatile LLM Harness & Scraping the Web With Scrapy
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 – Scrapy and building an extension
00:08:46 – Zyte and the web scraping API
00:11:19 – Sponsor: HydraDB
00:12:22 – noalgotube project
00:15:49 – Homelab & self hosting projects
00:22:19 – What goes into a harness?
00:32:33 – Where did you start exploring LLM tools?
00:36:13 – Local models & edge computing
00:39:31 – ExtractPod and discussing Apple’s AI
00:43:35 – Video Course Spotlight
00:44:54 – Managing token use and tools
00:52:37 – What are you excited about in the world of Python?
00:55:10 – What do you want to learn next?
00:56:38 – What is the best way to follow your work online?
00:56:58 – Thanks and goodbye
Show Links:
How to build your first Scrapy extension
Web Scraping API - All-in-one Web Scraper - Zyte API
Web Scraping With Scrapy and MongoDB – Real Python
noalgotube: I Built My Own YouTube Feed Because the Algorithm Stopped Working for Me - CodeNSolder
noalgotube: A personal content aggregator for YouTube channels and blog RSS feeds
Why homelab? Building a proper self-hosted setup from scratch - CodeNSolder
OPNsense: Open source, feature rich firewall and routing platform, offering cutting-edge network protection
Proxmox - Powerful open-source server solutions
Pi-hole – Network-wide Ad Blocking
omni-tools: Self-hosted collection of powerful web-based tools for everyday tasks
Frigate NVR
Harness Engineering, part 1: What is an agent harness and why it matters
My agentic coding setup: Claude Code, multi-agent orchestration, and how I actually work
ExtractPod EP07 - AI Harnesses, our model usage and a Scottish dinner staple. - YouTube
OpenCode - The open source AI coding agent
OpenRouter
Gemma 4 — Google DeepMind
LM Studio Bionic - Agent for Open Models
GLM-5.2: Built for Long-Horizon Tasks
caveman: 🪨 why use many token when few token do trick — Claude Code skill
ponytail: Makes your AI agent think like the laziest senior dev in the room
Episode #301: Running Python Locally in a Sandbox
Aillio – Bullet R2 - Coffee Roaster
CodeNSolder
Ayan Pahwa - Zyte
HydraDB
Level up your Python skills with our expert-led courses:
Introduction to Web Scraping With Python
Getting Started With Claude Code
Testing MCP Servers With a Python MCP Client

Support the podcast & join our community of Pythonistas
59 min