We should promote more personal indexing, rather than algorithmic indexing

I like original primary sources for some topics and secondary sources for others. For programing topics, I'm ok reading the original papers.

But for other topics ... say "civil engineering" ... I prefer a "popularizer" like Grady Hillhouse's "Practical Engineering". His 15-minute presentations are the right amount of depth for exposing me to various city infrastructure topics. I'm not going to pretend I'd be interested in reading original scholarly journals from civil engineers. I deliberately outsource that to Grady. Hardcore engineers may complain that infotainment/edutainment is "shallow learning" but people have to strategically limit themselves to "shallow" explanations of some topics so they can spend more time to deep dive into other specialized areas of interest.

The "viral content makers" serve a useful purpose in the ecosystem to satisfy varying levels of interest. Therefore, a search engine that optimized for original academic papers instead of Grady blog posts when I ask "How does a city manage stormwater runoff?" -- would not be helpful to me in most cases. I dare say a "general" search engine that didn't put academic papers on page 1 of search results would be preferred by most people.

Comparing the atrocious search landscape of 2023 to personal indexing is to compare poorly.

Instead: those who lived through it should compare personal indexing to the golden years of altavista(.digital.com) and the extremely powerful and unpolluted results it produced and modified with boolean operators.

I never once thought that there was some utility left to be mined from the Yahoo approach to things once I switched to Altavista. I think we're only pining for personal indexing in comparison to the garbage that is Google in 2023.

I'm trying to downplay your comments, just its hard to start a social media company. there's a reason it's considered a tarpit idea.

Inventing a fake metric like "quality" and deprioritizing "engagement" is just messaging.

https://payperrun.com/%3E/search?displayParams={%22q%22:%22c...

(btw, I just launched this llm-embedding based search service that lets you check if a startup idea has already been tried/failed).

I don't know if this idea has a higher death rate than the baseline, but my guess is Google/PageRank is good enough for most use-cases, and then if you want quality sources, you can just follow them on YouTube, Twitter, Instagram, etc. Wait, maybe I shouldn't try to compete with Google?

It was not Google who destroyed the Search, it was the Internet that has changed

i have near 3 years worth of this at https://snafuhall.com/

doing my part.

Also maybe hackernews should do a monthly post your blog/website just like the monthly job posts.

But I think you have a good point.

There is the question of what my judgement is and the question of what your judgement is. The primary selection process (that shows me articles) is great right now, I am upvoting maybe 220 articles out of 300 in a cycle. I pick out links to post to HN and I am currently adding links to the queue faster than I am posting them which means I can definitely raise the quality of what I post but the definition of "quality" is where I get stuck. It has all sorts of factors such as a lack of annoyingness (I hate those cookie popups but there is a lot of good news behind them) but there are also articles that look really good to me at first (I like what they set out do) but then what I look at them again I realize they didn't accomplish what they set out do.

I do think votes and comments are worth something, but I also know that I could get more of both by posting clickbait articles. On one level I want to post things that are enlightening, boy I get frustrated that y'all just don't care about robotics or chemical recycling of polymers or Arduino projects. (Though my real secret ambition is to get a #1 post about sports...)

Somehow I want to pose the problem of posting to HN, Mastodon, etc. as a sequential recommendation problem which means I have to back and look at all the papers on the subject that YOShInOn has collected for me. Also I am likely to put some more work into "quality models", particularly a stacked model for predicting votes if not comments on HN articles, a broad topic model based on data from Tildes (is is sports? music? science?) and particularly sentiment models.

That last one is on my mind because I'm thinking about the emotional tone of what I post to Mastodon, some days I think I should just stick to posting flower photos because they get good engagement, but past the people who are calling everybody a "fascist" that get amplified there is a "silent majority" of people on Mastodon who try to avoid the news and other inflaming topics so I am torn between being unrelentingly positive or trying to balance out positive and negative articles to make a more appealing feed to the good people of Mastodon. It would probably be 2-3 days of labeling work to make a sentiment model but if I had to find and categorize 5000 angry toots it would kill me, but I am thinking now about grading my own posts (what the system is going to do inference on anyway) and also grading high-engagement and predicted high-engagement submissions to HN to make a model that finds high-engagement posts that aren't clickbait.