dream_weasel

Which also means there should be rigorous standards to continue; similar accountability to any other job.

You shouldn't be able to collect a hefty check and be like my college friend. He who failed out of our college 4 times because he was just there to go to bars do his own thing (which was not going to class or doing homework or really anything else).

Couldn't say, for me it was way way easier than ESXI which was my first break into the space. And also more complete / straightforward than bare metal which was what I had been doing before unraid.

I paid for the lifetime license. No regrets.

Because it's easy and does all the hard stuff out of the box? Also any sized drives!

Oh hi I picked up Linux for the CLI and shell and the UI for me has nothing to do with it.

There is no easy way to break into the scene and unraid is a one stop shop. So you want to set up a few little projects on your own? It's learning containerization, learning networking and NAT, figuring out filesystems (and shares and share locations) and backup strategies, how to integrate with VPN, deployment strategies and templates (think Ansible, docker compose, make scripts, etc). There's a shitload to know and not a "for dummies" place to learn it.

Considering the "easy" first project of ARR suite + jackett, integrate with transmission, and integrate with jellyfin or Plex: this is not a couple hours of work if you've never done it before. With unraid it's probably one video tutorial and less than an hour? Idk I haven't done that one yet. But it's a common request.

There are a lot of things that need to hang together for a good homelab to work, and unraid for me has made it so I don't have to spend all my time doing plumbing and background work to try a project and see if I even want to use it.

I would absolutely do a 101 on self hosting, but it seems everybody has different priorities on what to host and how so it's probably not cut and dry to implement.

Ridiculous negotiator to be sure.

His name is Jamie Raskin not Jeremy is the point.

Who TF is "Jeremy Raskin".

Can we proofread at least a little better rawstory?

I guess I'll have to read up. I have potentially had a long running misunderstanding.

I don't think you can argue that it's mathematically equivalent. Just because space and time become so spread that they are effectively meaningless is not the same as them having not meaningfully existed and then existing. Neither can you really say that since any baryons that have not decayed are so far apart none of them interact that they behave like the concentration of all matter in the known universe. At those scales of time I'm not even sure that there are any left.

It's like arguing that one tiny piece of something in one place is the same as all the matter and all of space and time being in one place: it's I guess analogous but not equivalent. I will of course caveat and say that my undergrad physics degree did not cover end of the universe timelines lol. Kurzgesagt does have a video though.

The cyclical universe approach as I understand it is predicated on an eventual big crunch which I don't think is being argued anymore.

Not sure about the "restart" bit.

My last project is using machine learning to sort data for a company and the flow kinda goes like this:

1. Use the advanced tab in the browser on the UI search pagr for the company to expose the API query for the data I want to have
2. Write some shell script to programmatically call the API for the data I want.
3. Realize that there's actually a limit to how much data the API can return BUT the metadata says how much there is beyond the limit.
4. Write some script to paginate (scrape over) all that data and save as a JSON file
5. Realize my JSON is badly formatted and some queries are empty, so do some error checking and massage to make it work right.
6. Create a python script to ingest the parts of the saved data I want and write it to a SQLite database. This takes ongoing refinement.
7. Do some sql to count how many entries I got in the time period I was getting data for, and compare to a tool the company has to aggregate said data. It was close enough.
8. Go learn about Jaccard similarity and locality sensitive hashing. Use this to write a script to deduplicate the database so I don't have a zillion repeated entries. Also python, but it starts with some cursor stuff and sql.
9. Do the actual project which is trying to get some reinforcement learning to label the data automagically. This means I also have to write some methods to get particular mixes of the data I've already collected. For example I want my mix to have 50 percent red balls, 20 percent green balls and 30 percent blue balls. How do you pick the right number of balls from the total set? It's not hard but it takes some thinking.
10. Wrap this thing up in a script that will do some load balancing on a machine with lots of gpus and CPUs so I don't hog the entire server rack. Submit it and let it cook.
11. Write it again so I can wrap options around it and optimize hyperparameters.
12. Start working up the results to show improvement overall. If it's better than what the company already has...
13. Time to fire up the old git repo and make sure the commits are clean. Add readme and make files (bc I prefer to deploy with make) and put it in the hands of another engineer who will roll it out for that company.

Idk it's not a super sophisticated problem but it has a lot of moving parts and you have to kind of tackle them in order. Mostly it's "hey here's an obstacle to the end goal, how do I fix it on my own in a smart way?"

Then do it.

Cooking and getting riled up by tankies I guess.