Lux – Drop-in Redis replacement in Rust. 5.6x faster, ~1MB Docker image
1MB Docker image beats Redis 30MB while hitting 10M ops/sec.

Rust-powered BeautifulSoup with 10x speed and full API compatibility.
Python developers doing large-scale web scraping
BeautifulSoup · lxml · selectolax
I’ve been using BeautifulSoup for sometime. It’s the standard for ease-of-use in Python scraping, but it almost always becomes the performance bottleneck when processing large-scale datasets.
Parsing complex or massive HTML trees in Python typically suffers from high memory allocation costs and the overhead of the Python object model during tree traversal. In my production scraping workloads, the parser was consuming more CPU cycles than the network I/O. Lxml is fast but again uses up a lot of memory when processing large documents and has can cause trouble with malformed HTML.
The Solution
I wanted to keep the API compatibility that makes BS4 great, but eliminates the overhead that slows down high-volume pipelines. It also uses html5ever which That’s why I built WhiskeySour. And yes… I *vibe coded the whole thing*.
WhiskeySour is a drop-in replacement. You should be able to swap from "bs4 import BeautifulSoup" with "from whiskeysour import WhiskeySour" and see immediate speedups. Your workflows that used to take more than 30 mins might take less than 5 mins now.
I have shared the detailed architecture of the library here: https://the-pro.github.io/whiskeySour/architecture/
Here is the benchmark report against bs4 with html.parser: https://the-pro.github.io/whiskeySour/bench-report/
Here is the link to the repo: https://github.com/the-pro/WhiskeySour
Why I’m sharing this
I’m looking for feedback from the community on two fronts:
1. Edge cases: If you have particularly messy or malformed HTML that BS4 handles well, I’d love to know if WhiskeySour encounters any regressions.
2. Benchmarks: If you are running high-volume parsers, I’d appreciate it if you could run a test on your own datasets and share the results.
1MB Docker image beats Redis 30MB while hitting 10M ops/sec.
jscodeshift drop-in replacement, 8x faster on real monorepos—API compatibility is the moat.
Reverse-engineered undocumented MOBI format — builds dictionaries in 6 seconds vs 12 hours.
77x faster data loading but only helps if you're already using Axolotl specifically.
Half the cores as Tailscale's derper using io_uring and kernel TLS offload.
22x faster startup than jc with full parser compatibility and zero Python dependencies.