Paperless VS river

Paperless-ngx is the successor to the original Paperless & Paperless-ng projects, both of which are now in public archive. The original projects are not dead, but rather, continued through the open source community!

There's this:

https://github.com/the-paperless-project/paperless/issues/20

I don't know if it made it's way into this fork.

Paperless: https://github.com/the-paperless-project/paperless

Agreed. It's difficult to beat paperless* for single-user systems. It does have some rudimentary user management from Django, but it's an admin party and there's no way to give users separate repositories. They've been looking into it for years, going back as far as the original paperless project. I imagine such a feature is difficult to add on as an afterthought because it touches everything, so it should be built in from the very beginning. And it seems they're looking for a perfect implementation, which may or may not exist. Thus, years later, paperless* remains effectively a single-user app.

If I understand this correctly, the original Paperless was archived (Archival notice)[https://github.com/the-paperless-project/paperless/commit/9b...], so Paperless-NG was created.

Now that Paperless-NG seems to be going unmaintained (last commit on 15th Sep 2021), Paperless-NGX has been created with a focus on an org, so that the continuity of the project can be maintained with a simple path for the original creators to join back if they want to.

I don't think the community could have handled this better!

As many of you know "Paperless-ng" was a very popular fork of the document management system "Paperless". The initial author of -ng, Jonas Winkler, created an amazing project that was eventually designated as the 'official' successor. He maintained a furious development pace for some time but as of this post hasn't been heard from in months. A group of folks dedicated to the software (myself included) decided to try and revive the project and hopefully set it up for a long future. Yes, a similar thing happened with the original Paperless, we are hoping to avoid some of the same mistakes. See jonaswinkler/paperless-ng#1599, jonaswinkler/paperless-ng#1632 and historically the-paperless-project/paperless#711 if you are curious for more about all of this.

yes, I know that project. Paperless-ng is actually a fork of paperless project. I borrowed many great ideas from both projects. Unfortunately both projects are now archived (paperless-ng is not officially archived, but in last 6 months there was no development, as it looks to me that main developer lost interest in the project).

I have a Brother ADS-1700W, works fine, a little fiddly to set up the profiles for one touch scanning but once it's done it's fine. I set up a workflow with https://github.com/the-paperless-project/paperless that lets me scan straight into OCR. https://github.com/jonaswinkler/paperless-ng is the fork that I'm going to upgrade to in my CFT.

River is a Python library for online machine learning. Online machine learning can dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction, content personalization.

Today, I'm going to take you on a thrilling coding adventure inspired by a LinkedIn code snippet, where I tangled with FastAPI, River, Watchdog, and Tenacity. Ready? Buckle up!

Complimentary: river and skorch

River - https://github.com/online-ml/river (Online machine learning, best used with Bytewax for Kafka integration)

Hey, great work. Do you think this algorithm would be amenable to be done online? I'm the author of River (https://riverml.xyz) where we're looking for good online clustering algorithms.

If you don't know Rust, but know Python, you can install Python libraries written in Rust with pip. Like, pip install polars or pip install robyn. In this case you follow the two bottom links. But then you don't write your own libraries and stuff so.. I guess that's not what you want.

But, if you want to learn Rust, you probably wouldn't start out with pyo3. You first install Rust with https://rustup.rs/ and then check out the official book, and the book rust by example, that you can find here https://www.rust-lang.org/learn - and maybe write some code on the Rust playground https://play.rust-lang.org/ - then, you use pyo3 to build Python libraries in Rust, and then use maturin https://www.maturin.rs/ to build and publish them to Pypi.

But if you still prefer to begin with Rust by writing Python libraries (it's a valid strategy if you are very comfortable with working with multiple stacks), the Maturin link has a tutorial that setups a program that is half written in python, half written in Rust, https://www.maturin.rs/tutorial.html (well the pyo3 link I sent also has one too. You should refer to the documentation of both, because you will use the two together)

After learning Rust, the next step is looking for libraries that you could leverage to make Python programs ultra fast. Here https://github.com/rayon-rs/rayon is an obvious choice, see some examples from the Rust cookbook https://rust-lang-nursery.github.io/rust-cookbook/concurrenc... - when you create a parallel iterator, it will distribute the processing to many threads (by default, one per core). The rust cookbook, by the way, is a nice reference to see the most used crates (Rust libraries) in the Rust ecosystem.

Anyway there are some posts about pyo3 on the web, like this blog post https://boring-guy.sh/posts/river-rust/ (note: it uses an outdated version of pyo3, and doesn't seem to use maturin which is a newer tool). This post was written by the developers of https://github.com/online-ml/river - another Python library written in Rust

Oh they definitely can be incrementally updated, there is just added complexity. Online learning has been used with more classical machine learning methods in real-time analytics for a while now. River is a library that handles that.

We have both bandits and FTRL implemented in River (https://riverml.xyz) if that helps.