distributed-transcode VS hnrss

Back when I still cared about saving disk space, I made a cluster of NVidia Jetson Nanos running in a docker swarm configuration [1], but honestly even when you have six computers working at once, H264 on a single computer is still often faster.

On the Jetson Nanos I was lucky to get maybe 1fps in ffmpeg using VP9. Multiply that by six boards and that's about 6fps in total; ffmpeg running x264 in software mode was getting around 11fps on a single board, not even counting using the onboard encoder chip, meaning that I was getting better performance from one board using x264 than all six using VP9.

Now obviously this is a single anecdote on specific hardware, so I'm not saying that this applies to every single case, but it's a big reason why I personally have not used VP9 for anything substantial yet.

[1] https://gitlab.com/tombert/distributed-transcode

I have a fairly large blu-ray collection (~300 movies, ~15 complete TV series). I rip them and serve them with Jellyfin, which works, but due to codec annoyances, I need to transcode them to run on web browsers, and the SBC I'm running Jellyfin + ZFS on is not really fast enough to transcode in real time.

Since I have a ton of little SBCs sitting around my house, I decided to write a clojure app the queues up and transcodes my movies to H264. It uses Docker Swarm to handle distribution of nodes, RabbitMQ to queue up the movies, and core.async to handle local queuing within the application, and uses the Java NIO filesystem stuff to handle any kind of atomicity.

It's hardly the "first" or the "best" at what it does, but the advantage of writing your own is of course that you can tailor it exactly to your setup, and of course it was fun to write.

https://gitlab.com/tombert/distributed-transcode

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/