Supported APIs are Wigle, Apple, Google and Milnikov

Le dernier jalon franchi de cette merdification date de juin de cette année, où le nombre de requêtes par minute des flux Atom a été sévèrement réduit à 1 (UNE) requête. PAR MINUTE. Une putain de requête par minute pour récupérer des flux Atom. Vous êtes abonnés à 15 sous-Reddit ? Cela fera QUINZE MINUTES pour tous les récupérer, merci au revoir.

Bref. J’ai donc créé un script Python simple qui vous permet de récupérer les flux désirés, en respectant leur limitation ridicule. Tout est expliqué dans le readme.

Peut-être qu'il serait pertinent que chaque utilisateur contribue à la limitation d'une requête par minute afin que la communauté recrée elle-même les flux RSS ? Ce n'est qu'une course perdue d'avance, tant que Reddit essaie de fermer de plus en plus la plateforme.

<script type="text/python"> lol

spotDL finds songs from Spotify playlists on YouTube and downloads them - along with album art, lyrics and metadata.

Un projet pour lire une carte d'identité française

The weekend PyPI wave showed how a compromised maintainer account could publish malicious wheels that abused Python startup behavior

It runs JS with Bun in a subprocess from a python program.

It includes a counter-measure to LLM analysis with a comment about biological und nuclear weapons.

Lazy Imports
Frozendict & sentinel (symbols ofJS) built-in type
UTF-8 encoding by default
...
and better performances

Créer ses propres cartes de villes en image, imprimables et toute belles.

RustPython is a Python interpreter written in Rust. RustPython can be embedded into Rust programs to use Python as a scripting language for your application, or it can be compiled to WebAssembly in order to run Python in the browser. RustPython is free and open-source under the MIT license.

It would be to go to to use Python in the browser because RustPython can compile in WebAssembly.

The playground: https://rustpython.github.io/demo/

Using text files. The expenses can be tracked with beancount. It tracks many in or out of many bank accounts with the "double-entry accounting".
Documents can be attached to transactions, etc...

The dataflow is the following:

1. Download transactions from the bank (csv, pdf or ofx files).
2. Convert these files to the beancount format. There are many plugins available.
3. Balance the transactions to get the "double-entry accounting"

In my experience so far, it has been the exact opposite. The entire process has never taken me more than 45 minutes to finish. Considering that I do this once a month, the time investment seems more than fair. And the added benefit is that by balancing these transactions by hand, I get a fairly good idea of what was happening in my accounts in the previous month.

Visualization can be made with SQL queries, or Fava as UI. See a demo

Optimizations that don't need Rust:

• HTTP range requests for metadata
• Parallel downloads
• Global cache with hardlinks
• Python-free resolution
• PubGrub resolver algorithm

Rust has benefits though:

• zero-copy deserialization
• Thread-level parallelism
• No interpreter startup
• compact version representation: uv packs version into u64 integers. The micro-optimization compounds across millions of comparisons

uv is possible because of many PEP that came since 2016 (so too soon for me): PEP 518, 517, 621, and 658. There are the low-handing fruits: static metadata, no code execution to discover dependencies, and the ability to resolve everything upfront before downloading

There are obvious for me, but they are good.

I see some are totally useless for Rust in comparison. Both have different targets though. It is moreover awesome to see 100x improvements.

It can be useful to rewrite git repositories.

Brute force URLs on a website with a built-in dictionary or one provided.

(via https://korben.info/2025-09-12-dirsearch-scanner-web-paths.html)

It bypasses Captchas and Cloudflare as far as I read.

(via https://korben.info/botasaurus-framework-python-rend-cloudflare-aussi.html)

Let's create a project and benchmark it :D

The naive Rust implémentation is 10 times faster than the python one.
It remains 6 times faster than the optimized one.

The Python has a collections.Counter class that is approximately as fast as the naive Rust version.