#439 That Astral Episode

22 snips

Jul 7, 2025

Discover the launch of a new type checker that boosts performance for large Python projects. Learn about the UV build backend, making strides towards stability and efficiency. Dive into tips for refactoring complex Boolean expressions for better code readability. Explore the Fast API Machine Learning Skeleton, a game-changer for deploying models as APIs. The discussion wraps up with insights on automation in coding and a personal story from a local fair, blending tech talk with community vibes.

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INSIGHT

TY Type Checker and Web Playground

TY is a type checker by Astral that offers faster performance on large codebases, similar to Ruff for linting.
It runs in WebAssembly allowing type checks directly in the browser via an online playground.

INSIGHT

UV Build Backend Is Stable and Fast

The UV build backend from Astral is now stable and much faster than alternatives like hatchling or setuptools.
It improves build speeds by 10-35x, making local Python workflows faster and more efficient.

ADVICE

Refactoring Long Boolean Expressions

Refactor long boolean expressions by splitting them into multiple lines with operators at the beginning for readability.
Use named variables or functions for sub-expressions but beware of losing short-circuit evaluation for expensive checks.

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Topics covered in this episode:

* ty documentation site and uv migration guide*
* uv build backend is now stable + other Astral news*
* Refactoring long boolean expressions*
* fastapi-ml-skeleton*
Extras
Joke

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About the show

Sponsored by Sentry: pythonbytes.fm/sentry

Connect with the hosts

Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)
Brian: @brianokken@fosstodon.org / @brianokken.bsky.social
Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky)

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Michael #1: ty documentation site and uv migration guide

via Skyler Kasko
Astral created a documentation site for ty (PR #744 in release 0.0.1-alpha.13).
Astral added a page on migrating from pip to a uv project in the uv documentation. (PR #12382 in release 0.7.19).
Talk Python episode on ty.

Brian #2: uv build backend is now stable + other Astral news

The uv build backend is now stable
- Tim Hopper via Python Developer Tooling Handbook
From Charlie Marsh
- “The uv build backend is now stable, and considered ready for production use. An alternative to setuptools, hatchling, etc. for pure Python projects, with a focus on good defaults, user-friendly error messages, and performance. When used with uv, it's 10-35x faster.”
- “(In a future release, we'll make this the default.)”
```
[build-system]
requires = ["uv_build>=0.7.19,<0.8.0"]
build-backend = "uv_build"
```
- I believe it’s faster, but I agree with Brett Cannon in asking “What's being benchmarked? I'm not sure what a "backend sync" is referring to other than maybe installing the build back-end?”
See also: uv: Making Python Local Workflows FAST and BORING in 2025 - Hynek

Brian #3: Refactoring long boolean expressions

Trey Hunner
This is applied boolean logic, and even folks who learned this in a CS program probably did so early on, and may have forgotten it.
How can you improve the readability of long Boolean expressions in Python?
- Put parens around the whole expression and separate clauses onto different lines
- Where to put boolean operators between clauses? at the end of the line or the beginning?
  - PEP8 recommends the beginning
```
if (expression1
and expression2
and expression3):
...
```
- Naming sub-expressions with variables
  - Odd downside that wouldn’t occur to me. All expressions are evaluated, thus not taking advantage of expression short-circuiting.
- Naming operations with functions
  - Less readable, but takes advantage of short-circuiting
- Using De Morgan’s Law : replacing a compound expression with a similar (and hopefully easier to read) expression
```
# neither: we want both to be false
not (a or b) == (not a) and (not b)

# never_both: at least one false
not (a and b) == (not a) or (not b)
```

Michael #4: fastapi-ml-skeleton

FastAPI Skeleton App to serve machine learning models production-ready.
This repository contains a skeleton app which can be used to speed-up your next machine learning project.
The code is fully tested and provides a preconfigured tox to quickly expand this sample code.
A sample regression model for house price prediction is included in this project.
Short write up on "What does set -a do?"

Extras

Brian:

Michael:

via Wei Lee

Extra Airflow ruff rules:

Starting from Ruff version 0.11.13, most changes from Airflow 2 to Airflow 3 can be automated using AIR3. (It’s still in preview so a “—-preview” flag is needed)

e.g., if you have the following Airflow 2 code

import datetime

from airflow.models import DAG
from airflow.operators.empty import EmptyOperator

with DAG(
  dag_id="my_dag_name",
  start_date=datetime.datetime(2021, 1, 1),
  schedule_interval="@daily",
):
EmptyOperator(task_id="task")

it can be fixed with uvx ruff check --select AIR3 --fix --unsafe-fixes --preview

import datetime

from airflow.sdk import DAG
from airflow.providers.standard.operators.empty import EmptyOperator

with DAG(
  dag_id="my_dag_name",
  start_date=datetime.datetime(2021, 1, 1),
  schedule="@daily",
):
EmptyOperator(task_id="task")

which works with Airflow 3.

Joke:

Front Toward Enemy