laserboot VS hnrss

In this way I estimate I create about 32 new programs a day for only my own personal use. But in many cases I only use each one once. ^R makes it easy to use them again a few times, but in other cases I save them into a shell script to make them easier to distribute to other machines, parameterize, and maintain. The one I most often use is probably a user interface for YouTube via youtube-dl or yt-dlp that consists of a few such shell scripts.

My main editor is Emacs. If I want to do the same thing repeatedly (e.g., delete a line containing the string .LVL) I create a keyboard macro with F3 and F4 (or C-x ( and C-x )) when I do it once, then run it repeatedly with C-x e. I probably write about 4 programs a day in this way.

Emacs has a M-: command to evaluate Lisp expressions, which are programs. Recent programs I have written in this way include (/ 43.2 1.7), (* 9.3 1.2), and (+ 8 3 2.50 3.50 3 7 3.50 3 1.50 4.50 6 5.50 6.50 6 3 2.50 2.50). Probably I write about 1 program a day in this way but I only use each one once. Longer Lisp programs like this can be written in scratch and executed with C-j or in .emacs (or .emacs.d/init.el) and executed with C-x C-e. For example, (global-set-key [f5] 'recompile). I use my .emacs file constantly every day but probably only add something to it about once a month. An outdated version is at https://github.com/kragen/kragen-.emacs.d/blob/master/init.e....

Sometimes I write bigger programs for my own personal use too.

A few years ago I wrote https://github.com/kragen/pytebeat for a livecoding performance of bytebeat in a bar. I finished writing it in the train on the way to the bar.

In https://github.com/kragen/laserboot I wrote a simple parametric 2-D CAD system for laser cutters in PostScript.

For Dercuano http://canonical.org/~kragen/dercuano I wrote a kind of shitty HTML rendering engine that generates a PDF file, as well as a simple CMS for generating a tree of HTML files from a directory of Markdown.

The other night I wrote a bytecode interpreter with a graphical display in C as a sort of mockup for the operating system of a small computer I recently got the parts for; it's in http://canonical.org/~kragen/sw/erika.git.

I've also written compilers, interpreters, ray tracers, database engines, parser generators, graphics libraries, logic circuit optimizers, 2-D game engines, etc., for my own use. Some of the recent ones are in http://canonical.org/~kragen/sw/dev3.

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/