rekoros

Bulk of the series

And a good day to you, sir!

yes, exactly!

After using NixOS for a bit and eventually learning to rely on it, life without it seems inconceivably complex

When I was in college in 2001, I went to the library and checked out Kaare Christian's book called "The UNIX Operating System". One of the early chapters covered vi - I'd telnet into the school's Sun server with a pretty early version of vi (one-level undo) and follow the examples. Never looked back!

This should go into dictionary as definition for "fuck around and find out"

Oban is great!

A true derailer

One way is to become an apprentice with an experienced plumber - ideally skilled in hydronic heating.

I've been using daisyUI for a couple of years and really love it - it's a quick, reliable way to rig up a nice looking/functioning UI with minimal work, while making things appear reasonably uniform across pages/workflows.

The combination of Tailwind and daisyUI made it possible for me - a backend developer - to pretentd to be somewhat competent in frontendland, which has been incredibly handy, work-wise.

You're dealing with much larger datasets than I have, so far - mine is only a few million vectors. I have a hard constraint on resources, so had to get things working quickly in a relatively gutless environment.

pgvector - I wasn't able to get HNSW to build/rebuild quickly [enough] with a few million vectors. Very possibly I was doing something wrong, but fun research time ran out and I needed to get back to building features.

I ended up breaking up/sharding HNSW across multiple tables, but I'm dealing with many distinct datasets, each one just small enough to make HNSW effective in terms of index build/rebuild performance.

The article suggests IVF for larger datasets - this is the direction I'd certainly explore, but I've not personally had to deal with it. HNSW sharding/partitioning might actually work even for a very large - sharded/partitioned - dataset, where each query is a parallelized map/reduce operation.