harbormaster VS hnrss

I use an HP ProLiant Microserver with four drives in a ZFS RAIDZ array and an SSD for the OS. For software, I mostly run it in Docker using a very small container orchestration program I wrote:

https://gitlab.com/stavros/harbormaster

I needed something that would restart containers automatically when I pushed to a branch, so I wrote a few lines of code to do it:

https://gitlab.com/stavros/harbormaster

As far as PaaSes go, it's probably the simplest, and works really well.

I was in the same boat as you and built something simple that I really like:

https://gitlab.com/stavros/harbormaster

It'll just pull some repos, make sure the containers are up, and make your configuration simple and discoverable. It really works great at that.

I do this for our services, it works great and we can easily put SSO in front of them with CF Access. I publish a Docker container that you can use as a sidecar for your Compose deployments:

https://gitlab.com/stavros/docker-cloudflared

I use this with Harbormaster (https://gitlab.com/stavros/harbormaster) so I can expose containerized stuff without ever forwarding any ports outside of Docker.

I use Dokku for that (I can share my Bitwarden repo if you want, the entire thing is four lines or something). I also made https://gitlab.com/stavros/harbormaster for things that weren't so "web server -> app -> database" and love it.

I had the same problem and didn't want to manage things by hand, so I wrote Harbormaster:

https://gitlab.com/stavros/harbormaster

It basically pulls Compose apps from the git repositories you specify, builds the containers and makes sure they're running. Pretty simple and works really well for me.

(This post should read "Argo tunnel" instead of just "Argo")

I did the same to enable secure access to services via SSO at work. I used Harbormaster[1] to deploy Compose files, but it's otherwise the same setup.

One of the big advantages this has is that the services can't be accessed any other way (not even from the same host, as they only listen inside the Docker network). That makes it hard to forget some port exposed because you listened to 0.0.0.0 instead of localhost.

Cloudflare access is very easy to set up SSO with, as well. I'd recommend this setup if you need it, though for home usage I usually just set up Caddy as a reverse proxy with basic auth, as I'll be the only person using this and I don't want Cloudflare MITMing my personal stuff.

[1]: https://gitlab.com/stavros/harbormaster

Something like harbormaster? https://gitlab.com/stavros/harbormaster

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/