I haven’t seen case studies proving it’s better or more efficient. You save on labour in one area but now need AI compliance, policy admin, hallucination checkers, prompters .etc. the total amount of jobs saved is close to nothing. Companies are hiring people back again because they see this.

Also there’s corporate slop where you summarise a report and outsource the cognition of reading and summarising it to someone else. It looks like you’re super productive but really you’re using AI to obfuscate the fact that you aren’t doing your job and are relying on the people you interact with to do your work (reading and understanding the slop summary. Verifying it’s authentic and wasn’t hallucinated)

The only use case I’ve seen that works is unit test generation and limited integration testing. That way you have a test to fail an AI with if it changes things and breaks shit. You get it to write its own tests so you confine it’s behaviour. I personally do this when programming (I also dont let the AI make the code. It writes the tests, I make them pass)

You set it up for them and make a bookmark? That’s what I do with my grandpa

I use tailscale when I’m in these situations. It even works behind the most cursed CGNAT like starlink where it’s impossible to even port forward.

As long as your tunnel is running you just use the private IP address for your jellyfin machine and your parents will access it like it’s local.