Ask HN: Most interesting tech you built for just yourself?

I built a web app that keeps track of every link I ever find to be interesting. It allows for fine-grained topics (e.g., individual academic papers, or topics more specific than that). It groups the topics in a DAG, so that you can get to a topic via more than one path from the top. And it allows you to look at intersections of transitive closures over topics in order to narrow down a search.

It keeps a history of every change to the graph in Git, so one day you could potentially implement some form of time travel and see what the graph looked like at an earlier point in time without too much difficulty.

I have used the app every day for years. I feel like there's something promising there that is of general interest, but I have not figured out how to communicate the value.

Sounds very interesting. Do I’m having trouble picturing how it works. How do you get the links into the system? I’m assuming this won’t work for links that you find on your phone, i.e. when not on your on your computer? And how do these links get indexed? By the stuff that’s on the page? What’s an example use case of where you use that system to find a link?

It's pretty manual at this point. The indexing is done by hand. The idea is kind of crazy, but I think it can be made to work, in the same way that Wikipedia is maintained by hand.

https://digraph.app/

https://github.com/emwalker/digraph

If you can crowdsource the indexing, you get yourself a manually curated search engine with a nice topic graph that can be traversed. A piece of this puzzle that hasn't been tackled yet is a reputation system to keep the signal-to-noise ratio high and deal with spam.

> What’s an example use case of where you use that system to find a link?

An example use case is that I come across some interesting long-form article on a topic I'm following, e.g., Shackleton's expedition, that's published on a nice website and that I don't have time to read. I can just drop the link in the right topic and get back to it without too much difficulty. Or that's the hope, anyway. (Doesn't always work out that like that.)

Another thing I'm interested in is what the topic structure ends up looking like as it's more fully fleshed out. So sometimes I'll drop in random links even if they're not that interesting, just to build out the topics.

Thanks for the link. Quite a collection. Have you thought about auto suggesting categories when adding new items? Cheap and fast way: Use fastText or Fast Sentence Embeddings to calculate average embeddings for all items in each category. Then, when user enters a new link, create embeddings and rank categories based on the new embeddings’ similarity to those of the categories.