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.

The integer used by the Django data model is an Int, but the database used BigInt. The discrepancy occured for the row IDs greater than the maximum value of the int.

Specifically, several tables—including this one—were specified as using an int for their primary key in Django, but used bigint in the real database.

Encoding the malware as an array of UUIDs

Petition to rename Python's init method to boa so we can call them Boa constructors