Python Bytes

Topics covered in this episode:

Better Python tests with inline-snapshot

jolt Battery intelligence for your laptop

Markdown code formatting with ruff

act - run your GitHub actions locally

Extras

Joke

Watch on YouTube

About the show

Sponsored by us! Support our work through:

Our courses at Talk Python Training

The Complete pytest Course

Patreon Supporters
Connect with the hosts

Michael: @[email protected] / @mkennedy.codes (bsky)

Brian: @[email protected] / @brianokken.bsky.social

Show: @[email protected] / @pythonbytes.fm (bsky)
Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too.
Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

Brian #1: Better Python tests with inline-snapshot

Alex Hall, on Pydantic blog

Great for testing complex data structures

Allows you to write a test like this:

from inline_snapshot import snapshot
def test_user_creation():
user = create_user(id=123, name="test_user")
assert user.dict() == snapshot({})

Then run pytest --inline-snapshot=fix

And the library updates the test source code to look like this:

def test_user_creation():
user = create_user(id=123, name="test_user")
assert user.dict() == snapshot({
"id": 123,
"name": "test_user",
"status": "active"
})

Now, when you run the code without “fix” the collected data is used for comparison

Awesome to be able to visually inspect the test data right there in the test code.

Projects mentioned

inline-snapshot

pytest-examples

syrupy

dirty-equals

executing

Michael #2: jolt Battery intelligence for your laptop

Support for both macOS and Linux

Battery Status — Charge percentage, time remaining, health, and cycle count

Power Monitoring — System power draw with CPU/GPU breakdown

Process Tracking — Processes sorted by energy impact with color-coded severity

Historical Graphs — Track battery and power trends over time

Themes — 10+ built-in themes with dark/light auto-detection

Background Daemon — Collect historical data even when the TUI isn't running

Process Management — Kill energy-hungry processes directly

Brian #3: Markdown code formatting with ruff

Suggested by Matthias Schoettle

ruff can now format code within markdown files

Will format valid Python code in code blocks marked with python, py, python3 or py3.

Also recognizes pyi as Python type stub files.

Includes the ability to turn off formatting with comment [HTML_REMOVED] , [HTML_REMOVED] blocks.

Requires preview mode

[tool.ruff.lint]
preview = true

Michael #4: act - run your GitHub actions locally

Run your GitHub Actions locally! Why would you want to do this? Two reasons:

Fast Feedback - Rather than having to commit/push every time you want to test out the changes you are making to your .github/workflows/ files (or for any changes to embedded GitHub actions), you can use act to run the actions locally. The environment variables and filesystem are all configured to match what GitHub provides.

Local Task Runner - I love make. However, I also hate repeating myself. With act, you can use the GitHub Actions defined in your .github/workflows/ to replace your Makefile!

When you run act it reads in your GitHub Actions from .github/workflows/ and determines the set of actions that need to be run.

Uses the Docker API to either pull or build the necessary images, as defined in your workflow files and finally determines the execution path based on the dependencies that were defined.

Once it has the execution path, it then uses the Docker API to run containers for each action based on the images prepared earlier.

The environment variables and filesystem are all configured to match what GitHub provides.

Extras

Michael:

Winter is coming: Frozendict accepted

Django ORM stand-alone

Command Book app announcement post

Joke: Plug ‘n Paste

Topics covered in this episode:

Command Book App

uvx.sh: Install Python tools without uv or Python

Ending 15 years of subprocess polling

monty: A minimal, secure Python interpreter written in Rust for use by AI

Extras

Joke

Watch on YouTube

About the show

Sponsored by us! Support our work through:

Our courses at Talk Python Training

The Complete pytest Course

Patreon Supporters
Connect with the hosts

Michael: @[email protected] / @mkennedy.codes (bsky)

Brian: @[email protected] / @brianokken.bsky.social

Show: @[email protected] / @pythonbytes.fm (bsky)
Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too.
Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

Michael #1: Command Book App

New app from Michael

Command Book App is a native macOS app for developers, data scientists, AI enthusiasts and more.

This is a tool I've been using lately to help build Talk Python, Python Bytes, Talk Python Training, and many more applications.

It's a bit like advanced terminal commands or complex shell aliases, but hosted outside of your terminal. This leaves the terminal there for interactive commands, exploration, short actions.

Command Book manages commands like "tail this log while I'm developing the app", "Run the dev web server with true auto-reload", and even "Run MongoDB in Docker with exactly the settings I need"

I'd love it if you gave it a look, shared it with your team, and send me feedback.

Has a free version and paid version.

Build with Swift and Swift UI

Check it out at https://commandbookapp.com

Brian #2: uvx.sh: Install Python tools without uv or Python

Tim Hopper

Michael #3: Ending 15 years of subprocess polling

by Giampaolo Rodola

The standard library's subprocess module has relied on a busy-loop polling approach since the timeout parameter was added to Popen.wait() in Python 3.3, around 15 years ago

The problem with busy-polling

CPU wake-ups: even with exponential backoff (starting at 0.1ms, capping at 40ms), the system constantly wakes up to check process status, wasting CPU cycles and draining batteries.

Latency: there's always a gap between when a process actually terminates and when you detect it.

Scalability: monitoring many processes simultaneously magnifies all of the above.

+ L1/L2 CPU cache invalidations

It’s interesting to note that waiting via poll() (or kqueue()) puts the process into the exact same sleeping state as a plain time.sleep() call. From the kernel's perspective, both are interruptible sleeps.

Here is the merged PR for this change.

Brian #4: monty: A minimal, secure Python interpreter written in Rust for use by AI

Samuel Colvin and others at Pydantic

Still experimental

“Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code. “

“Instead, it lets you safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.”

Extras

Brian:

Expertise is the art of ignoring - Kevin Renskers

You don’t need to master the language. You need to master your slice.

Learning everything up front is wasted effort.

Experience changes what you pay attention to.

I hate fish - Rands (Michael Lopp)

Really about productivity systems

And a nice process for dealing with email

Michael:

Talk Python now has a CLI

New essay: It's not vibe coding - Agentic engineering

GitHub is having a day

Python 3.14.3 and 3.13.12 are available

Wall Street just lost $285 billion because of 13 markdown files

Joke: Silence, current side project!

Topics covered in this episode:

django-bolt: Faster than FastAPI, but with Django ORM, Django Admin, and Django packages

pyleak

More Django (three articles)

Datastar

Extras

Joke

Watch on YouTube

About the show

Sponsored by us! Support our work through:

Our courses at Talk Python Training

The Complete pytest Course

Patreon Supporters

Connect with the hosts

Michael: @[email protected] / @mkennedy.codes (bsky)

Brian: @[email protected] / @brianokken.bsky.social

Show: @[email protected] / @pythonbytes.fm (bsky)

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too.

Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

Brian #1: django-bolt : Faster than FastAPI, but with Django ORM, Django Admin, and Django packages

Farhan Ali Raza

High-Performance Fully Typed API Framework for Django

Inspired by DRF, FastAPI, Litestar, and Robyn

Django-Bolt docs

Interview with Farhan on Django Chat Podcast

And a walkthrough video

Michael #2: pyleak

Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak.

Has patterns for

Context managers

decorators

Checks for

Unawaited asyncio tasks

Threads

Blocking of an asyncio loop

Includes a pytest plugin so you can do @pytest.mark.no_leaks

Brian #3: More Django (three articles)

Migrating From Celery to Django Tasks

Paul Taylor

Nice intro of how easy it is to get started with Django Tasks

Some notes on starting to use Django

Julia Evans

A handful of reasons why Django is a great choice for a web framework

less magic than Rails

a built-in admin

nice ORM

automatic migrations

nice docs

you can use sqlite in production

built in email

The definitive guide to using Django with SQLite in production

I’m gonna have to study this a bit.

The conclusion states one of the benefits is “reduced complexity”, but, it still seems like quite a bit to me.

Michael #4: Datastar

Sent to us by Forrest Lanier

Lots of work by Chris May

Out on Talk Python soon.

Official Datastar Python SDK

Datastar is a little like HTMX, but

The single source of truth is your server

Events can be sent from server automatically (using SSE)

e.g

yield SSE.patch_elements(
f"""{(#HTML#)}{datetime.now().isoformat()}"""
)

Why I switched from HTMX to Datastar article

Extras

Brian:

Django Chat: Inverting the Testing Pyramid - Brian Okken

Quite a fun interview

PEP 686 – Make UTF-8 mode default

Now with status “Final” and slated for Python 3.15

Michael:

Prayson Daniel’s Paper tracker

Ice Cubes (open source Mastodon client for macOS)

Rumdl for PyCharm, et. al

cURL Gets Rid of Its Bug Bounty Program Over AI Slop Overrun

Python Developers Survey 2026

Joke: Pushed to prod

Topics covered in this episode:

GreyNoise IP Check

tprof: a targeting profiler

TOAD is out

Extras

Joke

Watch on YouTube

About the show

Sponsored by us! Support our work through:

Our courses at Talk Python Training

The Complete pytest Course

Patreon Supporters

Connect with the hosts

Michael: @[email protected] / @mkennedy.codes (bsky)

Brian: @[email protected] / @brianokken.bsky.social

Show: @[email protected] / @pythonbytes.fm (bsky)

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too.

Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

Michael #1: GreyNoise IP Check

GreyNoise watches the internet's background radiation—the constant storm of scanners, bots, and probes hitting every IP address on Earth.

Is your computer sending out bot or other bad-actor traffic? What about the myriad of devices and IoT things on your local IP?

Heads up: If your IP has recently changed, it might not be you (false positive).

Brian #2: tprof: a targeting profiler

Adam Johnson

Intro blog post: Python: introducing tprof, a targeting profiler

Michael #3: TOAD is out

Toad is a unified experience for AI in the terminal

Front-end for AI tools such as OpenHands, Claude Code, Gemini CLI, and many more.

Better TUI experience (e.g. @ for file context uses fuzzy search and dropdowns)

Better prompt input (mouse, keyboard, even colored code and markdown blocks)

Terminal within terminals (for TUI support)

Brian #4: FastAPI adds Contribution Guidelines around AI usage

Docs commit: Add contribution instructions about LLM generated code and comments and automated tools for PRs

Docs section: Development - Contributing : Automated Code and AI

Great inspiration and example of how to deal with this for popular open source projects

“If the human effort put in a PR, e.g. writing LLM prompts, is less than the effort we would need to put to review it, please don't submit the PR.”

With sections on

Closing Automated and AI PRs

Human Effort Denial of Service

Use Tools Wisely

Extras

Brian:

Apparently Digg is back and there’s a Python Community there

Why light-weight websites may one day save your life - Marijke LuttekesHome

Michael:

Blog posts about Talk Python AI Integrations

Announcing Talk Python AI Integrations on Talk Python’s Blog

Blocking AI crawlers might be a bad idea on Michael’s Blog

Already using the compile flag for faster app startup on the containers:

RUN --mount=type=cache,target=/root/.cache uv pip install --compile-bytecode --python /venv/bin/python

I think it’s speeding startup by about 1s / container.

Biggest prompt yet? 72 pages, 11, 000

Joke: A date

via From Pat Decker

Topics covered in this episode:

Better Django management commands with django-click and django-typer

PSF Lands a $1.5 million sponsorship from Anthropic

How uv got so fast

PyView Web Framework

Extras

Joke

Watch on YouTube

About the show

Sponsored by us! Support our work through:

Our courses at Talk Python Training

The Complete pytest Course

Patreon Supporters

Connect with the hosts

Michael: @[email protected] / @mkennedy.codes (bsky)

Brian: @[email protected] / @brianokken.bsky.social

Show: @[email protected] / @pythonbytes.fm (bsky)

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too.

Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

Brian #1: Better Django management commands with django-click and django-typer

Lacy Henschel

Extend Django <code>manage.py</code> commands for your own project, for things like

data operations

API integrations

complex data transformations

development and debugging

Extending is built into Django, but it looks easier, less code, and more fun with either <code>django-click</code> or <code>django-typer</code>, two projects supported through Django Commons

Michael #2: PSF Lands a $1.5 million sponsorship from Anthropic

Anthropic is partnering with the Python Software Foundation in a landmark funding commitment to support both security initiatives and the PSF's core work.

The funds will enable new automated tools for proactively reviewing all packages uploaded to PyPI, moving beyond the current reactive-only review process.

The PSF plans to build a new dataset of known malware for capability analysis

The investment will sustain programs like the Developer in Residence initiative, community grants, and infrastructure like PyPI.

Brian #3: How uv got so fast

Andrew Nesbitt

It’s not just be cause “it’s written in Rust”.

Recent-ish standards, PEPs 518 (2016), 517 (2017), 621 (2020), and 658 (2022) made many uv design decisions possible

And uv drops many backwards compatible decisions kept by pip.

Dropping functionality speeds things up.

“Speed comes from elimination. Every code path you don’t have is a code path you don’t wait for.”

Some of what uv does could be implemented in pip. Some cannot.

Andrew discusses different speedups, why they could be done in Python also, or why they cannot.

I read this article out of interest. But it gives me lots of ideas for tools that could be written faster just with Python by making design and support decisions that eliminate whole workflows.

Michael #4: PyView Web Framework

PyView brings the Phoenix LiveView paradigm to Python

Recently interviewed Larry on Talk Python

Build dynamic, real-time web applications using server-rendered HTML

Check out the examples.

See the Maps demo for some real magic

How does this possibly work? See the LiveView Lifecycle.

Extras

Brian:

Upgrade Django, has a great discussion of how to upgrade version by version and why you might want to do that instead of just jumping ahead to the latest version. And also who might want to save time by leapfrogging

Also has all the versions and dates of release and end of support.

The Lean TDD book 1st draft is done.

Now available through both pythontest and LeanPub

I set it as 80% done because of future drafts planned.

I’m working through a few submitted suggestions. Not much feedback, so the 2nd pass might be fast and mostly my own modifications. It’s possible.

I’m re-reading it myself and already am disappointed with page 1 of the introduction. I gotta make it pop more. I’ll work on that.

Trying to decide how many suggestions around using AI I should include.

It’s not mentioned in the book yet, but I think I need to incorporate some discussion around it.

Michael:

Python: What’s Coming in 2026

Python Bytes rewritten in Quart + async (very similar to Talk Python’s journey)

Added a proper MCP server at Talk Python To Me (you don’t need a formal MCP framework btw)

Example one: latest-episodes-mcp.png

Example two: which-episodes-mcp.webp

Implmented /llms.txt for Talk Python To Me (see talkpython.fm/llms.txt )

Joke: Reverse Superman