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!
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.
I've been using Azure's "Document Intelligence" thingy (prebuilt "read" model) to extract text from PDFs with pretty good results [1]. Their terminology is so bad, it's easy to dismiss the whole thing for another Microsoft pile, but it actually, like, for real, works.
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.
I've had browser and Linux VM logging to HyperDX with great success, but have been struggling to get OTel logging working with Azure Functions. Turns out, new (currently in Preview) "Flex Consumption" functions [0] natively support OTel and work with HyperDX.
We've been using Nix flakes and direnv (https://direnv.net/) for developer environments and NixOS with https://github.com/serokell/deploy-rs for prod/deploys - takes serious digging and time to set up, but excellent experience with it so far.