judo VS hnrss

I maintain two projects that I use daily for both work and personal stuff, that have attracted a modest, but appreciable amount of contributions. In both cases, the codebases are relatively small (500-1k sloc), and laser-focused on doing exactly one thing well.

I'm very grateful for every contribution, no matter how small - people have found bugs, fixed real problems, done cleanups. The hardest part is telling someone that a feature/idea does not have a place in this project. I think the general emphasis on minimalism tends to help here - I've never had to deal with any drama.

In terms of workload, again - the minimalist design and extremely clear goals have helped so much. I got trapped by that once before - I volunteered to build an internal automation tool (that saved someone else from doing like 1h/d of work), but literally couldn't spare 1h/mo to maintain it; the cause of the maintenance burden was an influx of changes in the APIs of the external services it integrated. So now I'm much more careful about volunteering to maintain integrations with external tools; in case of these two projects, the targets are SSH and ZFS - both have extremely stable interfaces.

In both cases it was absolutely worth it to publish and (very lightly) promote the projects; since these are "devops" tools that theoretically have unlimited potential for causing great harm, having any response at all helped reassure me that the code I'm running against production infrastructure has fewer unknown bugs. https://i.pinimg.com/474x/2f/e0/87/2fe08785e8eb112cada6da789...

The projects: <https://github.com/rollcat/judo>; <https://github.com/rollcat/zfs-autosnap>.

"This simple tool solves X at my org" is probably the most underrated type of project. There's not enough room to overcomplicate something that isn't a core part of the business, it must be practical to maintain, simple&stupid enough so that onboarding is not a hurdle, etc.

I encourage everyone to share your "splunk in 1kloc of Python" projects! Some of my own:

- https://github.com/rollcat/judo is Ansible without Python or YAML

- https://github.com/rollcat/zfs-autosnap manages rolling ZFS snapshots

I've written a minimalist replacement for Ansible. It started as a weekend hack, and I'm still using it daily after 7 years. Perhaps it's not technically impressive, but so wasn't the original UNIX, which served as a direct inspiration: how much work can you do with the simplest design and the least amount of code?

https://github.com/rollcat/judo

Ansible and YAML were my primary (de)motivators to create Judo (https://github.com/rollcat/judo). This combo is extremely frustrating: for every line in a (hypothetical) shell script that would do one thing, I needed 3-5 (sometimes many more) lines of YAML. Most people on the team who were just getting started with Ansible, would often do half of their work just shelling out. I would usually push to do things "the Ansible way", but even I had to acknowledge the mental overhead of translating back & forth. I think what finally pushed me over the edge was when we started venturing into compose & k8s, and had to mix & juggle YAML+Jinja in two entirely different contexts, each with its own quirks, bugs, gotchas and brain damage.

I figured I just need a layer of glue to run shell scripts across a bunch of remote hosts (hence Judo), and otherwise resort to other tooling (like Terraform, AWS CLI, k8s CLI, etc) for problems that don't map to SSH.

You're right, once an adversary gains physical access (or even remote access as your main login account), all bets are off. This is the area where the traditional UNIX security model has failed to adapt at all: you need a password to install a random game from apt (a vetted and trusted source), but you don't need a password to install a cryptolocker, or exfiltrate personal data.

However I like having a password (or some other form of confirmation), just so that I can stop to think for a second, whether what I'm about to do is a good idea.

What's annoying is that I effectively need two different policies on workstations and on servers, since I still want to be able to escalate privileges from maintenance scripts[1].

[1]: https://github.com/rollcat/judo/issues/9

Hi! I'm the author of judo - it seems like our projects share a lot in common, all the way down to implementation language and license ;) feel free to borrow some inspiration or solutions (e.g. master mode for SSH connections might be useful).

I wrote judo[1] because I was frustrated with Ansible. I wanted a very basic tool that could do 80% of the work in 1% of the code. It has one or two bugs, but I've been using it for personal and work stuff since 2016 and I'm not looking back.

[1]: https://github.com/rollcat/judo

That’s interesting.

I have predictive models that can predict if a headline (w/o the rest of the article and not considering the URL) will (a) get more than 10 votes and (b) if it does get more than 10 votes will the votes/comments ratio be more than 2 (which is roughly average)

The first model gets a ROC-AUC (see https://scikit-learn.org/stable/modules/generated/sklearn.me...) in the low 60’s (not good, the second model gets in the low 70’s (actually pretty good though it is a heat seeking missile for clickbait headlines) and my latest content-based recommender for RSS items gets almost 80. (I saw a paper that one system at TikTok gets about 85)

To do all that you need about 10,000 headlines and don’t get a lot of benefit from having more than 100,000. The ceilings on performance have more to do with the nature of the problem rather than my models: the same article can get submitted twice and get 0 votes one time and 200 the other time so it can never be as accurate as “is this an article about galactic astronomy?”

I had it ingest the HN comments firehose and found the amount of articles was overwhelming, my YOShInOn RSS reader now ingests the “best comments” from

https://hnrss.github.io/

together with 110 other feeds and actually I like the comments it picks out a lot. Now that the system is adding about 3000 items per day it might be able to handle a big feed like the comments firehose since now those comments are diluted with so many quality articles. For a problem like that you might want a two-score system with: (i) is it relevant? (something I like) and (ii) is it popular? (like Google’s PageRank)

I think you could make a model that compares comments in the best comments feed with other comments. I have tried formulating the problems above as regression problems where I try to predict the actual score and it does not work well because of the uncertainty problem but formulated as a classification problem for a score over a threshold it is easy to make a well-calibrated model that tells you “this article has a 20% chance of frontpaging” which is about the best anyone can do.

Subscribe via rss, so you can scratch the curiosity itch and each the FOMO, without coming to the site all the time and looking over the same things 20 times?

https://hnrss.github.io/

https://news.ycombinator.com/item?id=9491978

and this https://hnrss.github.io/

ps i’m ok with some % of false positives, but hopefully a sprinkle of OpenAI could keep that magically low?

thanks

It sounds interesting but I use https://hnrss.github.io/

Unless it had most of the features of hnrss.org I would not be able to use it.

Perhaps you could pivot your approach and submit a PR to hnrss for the feature?

Looks like there's an unmerged PR on the third-party hnrss project that would add this: https://github.com/hnrss/hnrss/pull/84

Check out below link to get a more customized, topic wise rss feeds.

https://hnrss.github.io/