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    Technology

    The Real Python Podcast

    A weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community.

    The show covers a wide range of topics including Python programming best practices, career tips, and related software development topics. Join us every Friday morning to hear what’s new in the world of Python programming and become a more effective Pythonista.

    Advertise

    Copyright: © 2020 Real Python

    • Apple Podcasts
    • Google Play
    • Spotify

    Latest Episodes:
    Improving Your GitHub Developer Experience Feb 06, 2026
    Show notes

    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

    What is Git, what is GitHub, and what’s the difference? Learn the basics of Git and GitHub from the perspective of a Pythonista in this step-by-step video course.

    Topics:

    • 00:00:00 – Introduction
    • 00:02:20 – Why the focus on developer experience?
    • 00:03:41 – Process of writing the book
    • 00:06:26 – Filling in the gaps of knowledge
    • 00:11:52 – GitHub-Flavored Markdown
    • 00:16:00 – Sponsor: Honeybadger
    • 00:16:47 – Acronym glossary
    • 00:25:18 – GitHub command palette
    • 00:28:35 – What questions did you want to answer?
    • 00:29:42 – Whether to cover Copilot or not
    • 00:36:14 – Video Course Spotlight
    • 00:37:50 – Advice on working with coding agents
    • 00:40:46 – Defining the scope
    • 00:48:07 – GitHub pages and codespaces
    • 00:50:46 – Finding the hidden features
    • 00:51:53 – Data-oriented Django series
    • 00:53:59 – How to find the book
    • 00:54:51 – What are you excited about in the world of Python?
    • 00:57:27 – What do you want to learn next?
    • 00:58:00 – How can people follow your work online?
    • 00:58:22 – Thanks and goodbye

    Show Links:

    • Adam Johnson’s Website
    • Boost Your GitHub DX
    • Boost Your Git DX
    • GitHub-Flavored Markdown (GFM) Spec
    • GitHub CLI (gh)
    • GitHub Command Palette - GitHub Docs
    • Keyboard shortcuts - GitHub Docs
    • GitHub’s Guide on Writing Great Issues
    • GitHub Pull Request Templates
    • GitHub Pages
    • GitHub Codespaces
    • Data-Oriented Django - DjangoCon 2022 - YouTube
    • Data-Oriented Django Deux - DjangoCon Europe 2024 - YouTube
    • Data-Oriented Django Drei - DjangoCon Europe 2025 - YouTube
    • Tachyon — Statistical profiler — Python 3.15.0a5 documentation
    • Adam Johnson (@adamj.eu) — Bluesky
    • Adam Johnson (@adamchainz@fosstodon.org) - Fosstodon

    Level up your Python skills with our expert-led courses:

    • Introduction to Git and GitHub for Python
    • How to Set Up a Django Project
    • Python Continuous Integration and Deployment Using GitHub Actions

    Support the podcast & join our community of Pythonistas


    Testing Python Code for Scalability & What's New in pandas 3.0 Jan 30, 2026
    Show notes

    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 offline reverse geocoding library, and a library for auto-generating CLIs from any Python object.

    Our live Python cohorts start February 2, and we’re down to the last few seats. There are two tracks: Python for Beginners or Intermediate Deep Dive. Eight weeks of live instruction, small groups, and real accountability. Grab your seat at realpython.com/live.

    This episode is sponsored by Honeybadger.

    Course Spotlight: Intro to Object-Oriented Programming (OOP) in Python

    Learn Python OOP fundamentals fast: master classes, objects, and constructors with hands-on lessons in this beginner-friendly video course.

    Topics:

    • 00:00:00 – Introduction
    • 00:03:28 – Python 3.15.0 Alpha 4 Released
    • 00:03:50 – Django Bugfix Release: 5.2.10, 6.0.1
    • 00:04:22 – PEP 819: JSON Package Metadata
    • 00:04:41 – PEP 820: PySlot: Unified Slot System for the C API
    • 00:04:59 – PEP 822: Dedented Multiline String (d-String)
    • 00:06:04 – What’s New in pandas 3.0
    • 00:13:11 – pandas 3.0.0 documentation
    • 00:13:44 – Sponsor: Honeybadger
    • 00:14:30 – Unit Testing Your Code’s Performance
    • 00:17:51 – Introducing tprof, a Targeting Profiler
    • 00:23:03 – Video Course Spotlight
    • 00:24:31 – Django Quiz 2025
    • 00:24:56 – 8 Versions of UUID and When to Use Them
    • 00:29:17 – 10 hard-to-swallow truths they won’t tell you about software engineer job
    • 00:44:02 – gazetteer: Offline Reverse Geocoding Library
    • 00:46:13 – python-fire: A library for automatically generating command-line interfaces
    • 00:47:40 – Thanks and goodbye

    News:

    • Python 3.15.0 Alpha 4 Released
    • Python 3.15.0a5 Alpha 5 Released
    • Django Bugfix Release: 5.2.10, 6.0.1
    • PEP 819: JSON Package Metadata (Draft)
    • PEP 820: PySlot: Unified Slot System for the C API (Draft)
    • PEP 822: Dedented Multiline String (d-String) (Draft)

    Show Links:

    • What’s New in pandas 3.0 – Learn what’s new in pandas 3.0: pd.col expressions for cleaner code, Copy-on-Write for predictable behavior, and PyArrow-backed strings for 5-10x faster operations.
    • What’s new in 3.0.0 (January 21, 2026) — pandas 3.0.0 documentation
    • Unit Testing Your Code’s Performance – Testing your code is important, not just for correctness but also for performance. One approach is to check performance degradation as data sizes go up, also known as Big-O scaling.
    • Introducing tprof, a Targeting Profiler – Adam has written tprof, a targeting profiler for Python 3.12+. This article introduces you to the tool and why he wrote it.
    • Django Quiz 2025 – Last month, Adam held another quiz at the December edition of Django London. This is an annual tradition at the meetup, and now you can take it yourself or just skim the answers.
    • 8 Versions of UUID and When to Use Them – RFC 9562 outlines the structure of Universally Unique Identifiers (UUIDs) and includes eight different versions. In this post, Nicole gives a quick intro to each kind so you don’t have to read the docs, and explains why you might choose each.
    • uuid — UUID objects according to RFC 9562 — Python 3.14.2 documentation

    Discussion:

    • 10 hard-to-swallow truths they won’t tell you about software engineer job

    Projects:

    • gazetteer: Offline Reverse Geocoding Library
    • python-fire: Python Fire is a library for automatically generating command-line interfaces (CLIs) from absolutely any Python object

    Additional Links:

    • Gary Gnu Sings “No Gnews Is Good Gnews Song” - The Great Space Coaster - YouTube
    • plasma-umass/bigO: Measures empirical computational complexity (in both time and space) of functions
    • Episode #172: Measuring Multiple Facets of Python Performance With Scalene

    Level up your Python skills with our expert-led courses:

    • Testing Your Code With pytest
    • Investigating Quasar Data With Polars and Interactive marimo Notebooks
    • Intro to Object-Oriented Programming (OOP) in Python

    Support the podcast & join our community of Pythonistas


    Continuing to Improve the Learning Experience at Real Python Jan 23, 2026
    Show notes

    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: Honeybadger
    • 00:14:38 – Live courses
    • 00:23:20 – Podcast visibility and interconnection
    • 00:32:42 – Spotlight
    • 00:33:35 – Editorial standards and goals for 2026
    • 00:36:28 – Building the team
    • 00:37:52 – Real Python for teams
    • 00:40:56 – Feedback and listening to users
    • 00:43:23 – Thanks and goodbye

    Show Links:

    • Reference – Real Python
    • Cohort-Based Live Python Courses – Real Python
    • Episode #246: Learning Intermediate Python With a Deep Dive Course
    • Episode #279: Coding Python With Confidence: Beginners Live Course Participants
    • The Real Python Podcast – Real Python
    • Python Learning Paths – Real Python
    • Editorial Guidelines – Real Python
    • Meet Our Team – Real Python
    • Team Memberships – Real Python
    • Feedback – Real Python

    Level up your Python skills with our expert-led courses:

    • Python Decorators 101
    • Python Basics: Code Your First Python Program
    • Intro to Object-Oriented Programming (OOP) in Python

    Support the podcast & join our community of Pythonistas


    Considering Fast and Slow in Python Programming Jan 16, 2026
    Show notes

    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 website with FastAPI using Jinja2, Python numbers every programmer should know, a discussion of spec-driven development and whether waterfall is back, a tool to detect whether a PDF has a bad redaction, and a CLI for measuring HTTP request phases.

    Course Spotlight: Jinja Templating

    With Jinja, you can build rich templates that power the front end of your web applications. But you can also use Jinja without a web framework running in the background. Whenever you need to generate text files with dynamic content, Jinja provides a flexible and powerful solution.

    Topics:

    • 00:00:00 – Introduction
    • 00:02:53 – Top Python Libraries of 2025
    • 00:04:43 – 2025 Python Year in Review
    • 00:04:58 – PyPI in 2025: A Year in Review
    • 00:05:36 – Join in the PSF Year-End Fundraiser & Membership Drive!
    • 00:06:10 – How uv Got So Fast
    • 00:12:29 – Why Python’s deepcopy Can Be So Slow
    • 00:17:03 – Serve a Website With FastAPI Using HTML and Jinja2
    • 00:23:19 – The Uselessness of “Fast” and “Slow” in Programming
    • 00:27:06 – Python Numbers Every Programmer Should Know
    • 00:28:17 – Video Course Spotlight
    • 00:29:43 – Spec-Driven Development: The Waterfall Strikes Back
    • 00:49:21 – x-ray: A Tool to Detect Whether a PDF Has a Bad Redaction
    • 00:52:20 – httptap: CLI Measuring HTTP Request Phases
    • 00:54:19 – Thanks and goodbye

    2025 Top List Roundup

    • Top Python Libraries of 2025 – Explore Tryolabs’ 11th annual Top Python Libraries roundup, featuring two curated Top 10 lists: one for General Use and one for AI/ML/Data tools.
    • 2025 Python Year in Review – Talk Python interviews Barry Warsaw, Brett Cannon, Gregory Kapfhammer, Jodie Burchell, Reuven Lerner, and Thomas Wouters, and the panel discusses what mattered for Python in 2025.
    • PyPI in 2025: A Year in Review – Dustin summarizes all the happenings with the Python Packaging Index in 2025, including 130,000 new projects and over 2.5 trillion requests served.
    • Join in the PSF Year-End Fundraiser & Membership Drive!

    Topics:

    • How uv Got So Fast – uv’s speed comes from engineering decisions, not just Rust. Static metadata, dropping legacy formats, and standards that didn’t exist five years ago.
    • Why Python’s deepcopy Can Be So Slow – “Python’s copy.deepcopy() creates a fully independent clone of an object, traversing every nested element of the object graph.” That can be expensive. Learn what it’s doing and how you can sometimes avoid the cost.
    • Serve a Website With FastAPI Using HTML and Jinja2 – Use FastAPI to render Jinja2 templates and serve dynamic sites with HTML, CSS, and JavaScript, then add a color picker that copies hex codes.
    • The Uselessness of “Fast” and “Slow” in Programming – “One of the unique aspects of software is how it spans such a large number of orders of magnitude.” The huge difference makes the terms “fast” and “slow” arbitrary. Read on to discover how this affects our thinking as programmers and what mistakes it can cause.
    • Python Numbers Every Programmer Should Know – Ever wonder how much memory an empty list takes? How about how long it takes to add two integers in Python? This post contains loads of performance data for common Python operations.

    Discussion:

    • Spec-Driven Development: The Waterfall Strikes Back
    • Coding has never been the bottleneck - Rob Bowley

    Projects:

    • x-ray: A Tool to Detect Whether a PDF Has a Bad Redaction
    • httptap: CLI Measuring HTTP Request Phases

    Additional Links:

    • Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv – The Real Python Podcast
    • Primer on Jinja Templating – Real Python
    • Get Started With FastAPI – Real Python
    • Teach Yourself Programming in Ten Years

    Level up your Python skills with our expert-led courses:

    • Python REST APIs With FastAPI
    • Jinja Templating
    • Creating a Scalable Flask Web Application From Scratch

    Support the podcast & join our community of Pythonistas


    Coding Python With Confidence: Beginners Live Course Participants Jan 09, 2026
    Show notes

    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 friendly, interactive cohort. Build confidence writing code and understand the “why” behind Python’s core concepts.

    Topics:

    • 00:00:00 – Introduction
    • 00:01:40 – Instructor Stephen Gruppetta
    • 00:02:42 – Designing the course
    • 00:09:01 – Introducing mini-projects early
    • 00:13:22 – How have the questions changed for Python beginners?
    • 00:20:23 – Taking advantage of Real Python resources
    • 00:24:07 – More courses for 2026
    • 00:25:40 – Spotlight: Python for Beginners
    • 00:26:39 – Python for Beginners participants
    • 00:27:50 – Louis’ background in programming
    • 00:30:46 – Andrew’s background in programming
    • 00:37:43 – Starting to use the knowledge with mini-projects
    • 00:42:52 – What were challenges with the language?
    • 00:54:15 – Working on the larger final project
    • 00:59:45 – What advantages did the cohort-style course provide?
    • 01:03:56 – Are you ready for that blank page?
    • 01:12:00 – How do you see yourself using these new skills?
    • 01:17:39 – Thanks and goodbye

    Show Links:

    • Python for Beginners: Code With Confidence – Real Python
    • Episode #246: Learning Intermediate Python With a Deep Dive Course
    • Intermediate Python Deep Dive Course – Real Python
    • Cohort-Based Live Python Courses – Real Python
    • MI6 chief: We’ll be as fluent in Python as we are in Russian - The Register

    Level up your Python skills with our expert-led courses:

    • Python Basics: Setting Up Python
    • Python Basics: Code Your First Python Program
    • Python Basics: Lists and Tuples

    Support the podcast & join our community of Pythonistas


    PyCoder's Weekly 2025 Top Articles & Hidden Gems Jan 02, 2026
    Show notes

    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 – Django vs. FastAPI, An Honest Comparison
    • 00:05:46 – How to Use Loguru for Simpler Python Logging
    • 00:06:47 – Narwhals: Unified DataFrame Functions
    • 00:08:32 – Observations and statistics for the year of articles
    • 00:13:23 – Data Validation Libraries for Polars (2025 Edition)
    • 00:18:53 – Video Course Spotlight
    • 00:20:25 – Create Temporary Files and Directories in Unittest
    • 00:22:27 – Capture Stdout and Stderr in Unittest
    • 00:24:59 – I don’t like NumPy
    • 00:26:34 – Python performance myths and fairy tales
    • 00:31:05 – Congratulations on making through 2025!

    News:

    • Django 6.0 released - Django Weblog

    Top PyCoders Links 2025:

    • The Inner Workings of Python Dataclasses Explained – Discover how Python dataclasses work internally! Learn how to use __annotations__ and exec() to make our own dataclass decorator!
    • Episode #249: Going Beyond requirements.txt With pylock.toml and PEP 751 – What is the best way to record the Python dependencies for the reproducibility of your projects? What advantages will lock files provide for those projects? This week on the show, we welcome back Python Core Developer Brett Cannon to discuss his journey to bring PEP 751 and the pylock.toml file format to the community.
    • Django vs. FastAPI, An Honest Comparison – David has worked with Django for a long time, but recently has done some deeper coding with FastAPI. As a result, he’s able to provide a good contrast between the libraries and why/when you might choose one over the other.
    • How to Use Loguru for Simpler Python Logging – Real Python – In this tutorial, you’ll learn how to use Loguru to quickly implement better logging in your Python applications. You’ll spend less time wrestling with logging configuration and more time using logs effectively to debug issues.
    • Narwhals: Unified DataFrame Functions for pandas, Polars, and PySpark – Narwhals is a lightweight compatibility layer between DataFrame libraries. You can use it as a common interface to write reproducible and maintainable data science code which supports pandas, Polars, DuckDB, PySpark, PyArrow, and more.

    Featured Links:

    • Data Validation Libraries for Polars (2025 Edition) – Pointblank - Given that Polars is so hot right now and that data validation is an important part of a data pipeline, this post explores five Python data validation libraries that support Polars DataFrames. Through contrasting and comparison, the post puts forward which of them are best for specific use cases.
    • Python: Create Temporary Files and Directories in Unittest – Sometimes, tests need temporary files or directories. You can do this in Python’s unittest with the standard library tempfile module. This post looks at some recipes to do so within individual tests and setUp().
    • Python: Capture Stdout and Stderr in Unittest – When testing code that outputs to the terminal through either standard out (stdout) or standard error (stderr), you might want to capture that output and make assertions on it.
    • I don’t like NumPy – This opinion piece talks about why NumPy gets difficult fast. Two dimensions to your array? No problem, the calc is mostly self evident. Add a couple more dimensions and it gets messy fast. See also the associated HN discussion, which also includes possible solutions.
    • Python performance myths and fairy tales – This post summarizes a talk by Antonio Cuni who is a long time contributor to PyPy, the alternate Python interpreter. The talk spoke about the challenges and limits of performance in Python and how the flexibility of dynamic languages comes at a cost. See also the associated HN discussion.

    Additional Links:

    • Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
    • Episode #272: Michael Kennedy: Managing Your Own Python Infrastructure
    • Episode #510 - 10 Polars Tools and Techniques To Level Up Your Data Science
    • ty
    • waelstow: Python unittest utilities. Includes output capture, test discovery, and directory relocation.
    • PyPy
    • spylang · GitHub

    Level up your Python skills with our expert-led courses:

    • Filtering Iterables With Python
    • NumPy Techniques and Practical Examples
    • Using Functional Programming in Python

    Support the podcast & join our community of Pythonistas


    Moving Towards Spec-Driven Development Dec 19, 2025
    Show notes

    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 start today.

    Topics:

    • 00:00:00 – Introduction
    • 00:02:41 – How did you get involved in open source?
    • 00:07:23 – How would you describe spec-driven development?
    • 00:10:49 – Balancing the desire to start coding with defining the project
    • 00:13:06 – What does this documentation look like?
    • 00:18:27 – Declarative vs imperative programming
    • 00:24:13 – Infrastructure as part of the design
    • 00:27:03 – Getting started with a small project
    • 00:29:05 – Committing the spec files along with the code
    • 00:31:08 – What is steering?
    • 00:34:17 – How to get better at distilling specifications?
    • 00:38:59 – What are anti-patterns in spec-driven development?
    • 00:41:08 – Should you avoid third-party libraries?
    • 00:43:16 – Real Python Resource Spotlight
    • 00:44:39 – Getting started with Kiro
    • 00:51:00 – Neuro-symbolic AI
    • 00:55:41 – What are you excited about in the world of Python?
    • 00:58:36 – What do you want to learn next?
    • 01:00:18 – How can people follow your work online?
    • 01:00:57 – Thanks and goodbye

    Show Links:

    • Kiro and the future of AI spec-driven software development - Kiro
    • Marc Brooker’s Blog - Marc’s Blog
    • Kiro: The AI IDE for prototype to production
    • Beyond Prompts: The Future of AI-Assisted Development | Marc Brooker - YouTube
    • Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl
    • Declarative programming - Wikipedia
    • Behaviour-Driven Development - Cucumber
    • Steering - Docs - Kiro
    • Best practices - Docs - Kiro
    • CLI - Kiro
    • Does your code match your spec? - Kiro
    • Amazon takes on AI’s biggest nightmare: Hallucinations - Fast Company
    • Neuro-symbolic AI - Wikipedia
    • Spec-Driven Development: The Waterfall Strikes Back
    • G-code - Wikipedia
    • Marc Brooker (@MarcJBrooker) / X
    • Marc Brooker (@marcbrooker@fediscience.org)
    • Marc Brooker - LinkedIn

    Level up your Python skills with our expert-led courses:

    • Building a Neural Network & Making Predictions With Python AI
    • First Steps With LangChain
    • Getting Started With Claude Code

    Support the podcast & join our community of Pythonistas


    Exploring Quantum Computing & Python Frameworks Dec 05, 2025
    Show notes

    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 algebra
    • 00:10:32 – What is a quantum computer?
    • 00:14:52 – Superconducting devices
    • 00:17:29 – Looking for ways to advance computing
    • 00:19:17 – Superposition of qubits and entanglement
    • 00:22:43 – Video Course Spotlight
    • 00:24:27 – Potential areas of research
    • 00:26:45 – IBM quantum computing & Qiskit
    • 00:29:43 – Describing superposition as a spinning coin
    • 00:30:41 – Other types of quantum computers
    • 00:32:08 – Qiskit Global Summer School 2025
    • 00:32:48 – Qiskit Advocate
    • 00:33:49 – What do you see in the near future?
    • 00:37:42 – What are the type of HPCs?
    • 00:40:05 – Additional resources to learn more
    • 00:43:32 – Answering to skeptics
    • 00:47:51 – What are you excited about in the world of Python?
    • 00:48:24 – What do you want to learn next?
    • 00:49:04 – What’s the best way to follow your work online?
    • 00:49:25 – Thanks and goodbye

    Show Links:

    • Quantum Computing Basics With Qiskit
    • Quantum mechanics - Wikipedia
    • Qubit - Wikipedia
    • Quantum superposition - Wikipedia
    • Quantum entanglement - Wikipedia
    • Quantum supremacy - Wikipedia
    • Linear Algebra in Python: Matrix Inverses and Least Squares – Real Python
    • Linear Algebra - Khan Academy
    • Quantum Echoes: Towards real world applications - YouTube
    • Meet Willow, our state-of-the-art quantum chip
    • IBM and Vanguard explore quantum optimization for finance
    • Register today for Qiskit Global Summer School 2025
    • Aqora: Explore Quantum Computing for Real-World Applications
    • Qiskit - IBM Quantum Computing
    • IBM Quantum Platform
    • Cirq - Google Quantum AI
    • Xanadu - PennyLane
    • Classiq - Quantum Computing Software - Limitless Development |
    • Computer architecture - Wikipedia
    • NumPy
    • uv
    • About Negar Vahid – Real Python
    • The Path Integral
    • Negar Vahid - LinkedIn

    Level up your Python skills with our expert-led courses:

    • NumPy Techniques and Practical Examples
    • Profiling Performance in Python
    • Python Project Management With uv

    Support the podcast & join our community of Pythonistas


    Building a FastAPI Application & Exploring Python Concurrency Nov 21, 2025
    Show notes

    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, a project to transform functions into a web interface, and a Python disk-backed cache.

    Course Spotlight: Python Descriptors

    Learn what Python descriptors are, how the descriptor protocol works, and when descriptors are useful, with practical, hands-on examples.

    Topics:

    • 00:00:00 – Introduction
    • 00:02:18 – Django Security Release
    • 00:02:46 – Django Is Now a CVE Numbering Authority (CNA)
    • 00:03:53 – An Annual Release Cycle for Django
    • 00:04:12 – PEP 810: Explicit Lazy Imports (Accepted)
    • 00:04:27 – PSF Board Office Hour Sessions for 2026
    • 00:05:42 – PyCon US 2026: Call for Proposals Open
    • 00:06:15 – PSF Fundraiser campaign for 2025
    • 00:10:12 – A Close Look at a FastAPI Example Application
    • 00:16:36 – Speed Up Python With Concurrency
    • 00:21:08 – __dict__: Where Python Stores Attributes
    • 00:25:59 – Video Course Spotlight
    • 00:27:17 – 10 Smart Performance Hacks for Faster Python Code
    • 00:29:56 – FuncToWeb: Transform Python Functions Into a Web Interface
    • 00:32:48 – python-diskcache: Python Disk-Backed Cache
    • 00:34:07 – Thanks and goodbye

    News:

    • Django Security Release: 5.2.8, 5.1.14, and 4.2.26
    • Django Is Now a CVE Numbering Authority (CNA)
    • An Annual Release Cycle for Django - Buttondown
    • PEP 810: Explicit Lazy Imports (Accepted)
    • PSF Board Office Hour Sessions for 2026
    • PyCon US, Long Beach CA, 2026: Call for Proposals Open
    • PSF Fundraiser campaign for 2025
    • Connecting the Dots: Understanding the PSF’s Current Financial Outlook

    Show Links:

    • A Close Look at a FastAPI Example Application – Set up a FastAPI example app, add path and query parameters, and handle CRUD operations with Pydantic for clean, validated endpoints.
    • 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.
    • __dict__: Where Python Stores Attributes – Most Python objects store their attributes in a __dict__ dictionary. Modules and classes always use __dict__, but not everything does.
    • 10 Smart Performance Hacks for Faster Python Code – Some practical optimization hacks, from data structures to built-in modules, that boost speed, reduce overhead, and keep your Python code clean.

    Projects:

    • FuncToWeb: Transform Python Functions Into a Web Interface
    • python-diskcache: Python Disk-Backed Cache

    Additional Links:

    • Become a Member of the PSF - Python Software Foundation
    • FastAPI
    • Welcome to Pydantic
    • Quiz: A Close Look at a FastAPI Example Application – Practice FastAPI basics with path parameters, request bodies, async endpoints, and CORS. Build confidence to design and test simple Python web APIs.
    • Diskcache, more than caching - Bite code!

    Level up your Python skills with our expert-led courses:

    • Speed Up Python With Concurrency
    • Python REST APIs With FastAPI
    • Python Descriptors

    Support the podcast & join our community of Pythonistas


    Preparing Data Science Projects for Production Nov 14, 2025
    Show notes

    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 – Using Polars
    • 00:26:03 – Advantages of marimo notebooks
    • 00:34:21 – Video Course Spotlight
    • 00:35:44 – Shipping a project in data science
    • 00:42:10 – Advice on testing
    • 00:49:50 – Creating importable parameter values
    • 00:53:55 – Seeing the commit diff of a notebook
    • 00:55:12 – What are you excited about in the world of Python?
    • 00:56:04 – What do you want to learn next?
    • 00:56:52 – What’s the best way to follow your work online?
    • 00:58:28 – Thanks and goodbye

    Show Links:

    • Production Ready Data Science by Khuyen Tran - CodeCut
    • CodeCut
    • Top 6 Python Libraries for Visualization: Which One to Use? - CodeCut
    • Ruff
    • uv
    • Cookiecutter
    • marimo - a next-generation Python notebook
    • Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python
    • Polars — DataFrames for the new era
    • Episode #260: Harnessing the Power of Python Polars
    • Narwhals
    • Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
    • pytest documentation
    • nbdime: Tools for diffing and merging of Jupyter notebooks.
    • LangChain
    • Build Production-Ready LLM Agents with LangChain 1.0 Middleware - CodeCut
    • Build an LLM RAG Chatbot With LangChain
    • Khuyen Tran - LinkedIn
    • Khuyen Tran (@KhuyenTran16) - X

    Level up your Python skills with our expert-led courses:

    • Working With Python Polars
    • Getting Started With marimo Notebooks
    • Python Project Management With uv

    Support the podcast & join our community of Pythonistas


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