Oh, I forgot about Claude. Last time I tried it, it seemed on par or even better that ChatGPT-4o (but was missing features like browsing).

Regular users can use Gemini, Deepseek, Meta AI, and there will probably be many more services in the future.

NFS gives me the best performance. I’ve tried GlusterFS (not at home, for work), and it was kind of a pain to set up and maintain.

You can always create posts in appropriate communities to start conversations on topics you’re interested in. Be the change you wish to see in the world.

I don’t care much for most pop culture stuff and get enough by happenstance from other sources/people.

If it works, I don’t update unless I’m bored or something. I also spread things out on multiple machines, so there’s less chance of stuff happening like you describe with the charts feature going away. My NAS is pretty much just a NAS now.

You can probably backup your configs/data, upgrade, then deploy jellyfin again, restore, and reconfigure. You should probably backup your data on your ZFS pool. But, I recently updated to the latest TrueNas Scale from ~5 year old FreeBSD version of TrueNas and the pools still worked fine (none of the “apps” or jails worked, obviously). The upgrade process even ported my service configurations over. I didn’t care about much of the data in the pools, so only backed up the most important stuff.

I personally use a dual core pentium with 16GB of RAM. When I first installed TrueNas (FreeNas back then), I only had 8GB of RAM, but that proved to be not enough to run all the services I wanted, so I would suggest 12-16GB. Depending on the services you want to run any multi-core x86 CPU that allows 16GB of RAM to be used should be adequate. I believe TrueNas recommends ECC RAM, but I don’t think using consumer grade RAM and hardware has caused me any problems. I’m also using an old SSD for the system drive, which I is recommended now (I used to use 2 mirrored USB thumb drives, buy that’s not recommended anymore). Very importantly, make sure the HDD(s) you get are not shingled drives; made that mistake initially, and performance was ridiculously bad.

Yeah, I was disappointment when I bought a very expensive Galaxy S22 to replace my old Moto G whose charging port wore out,. The S22 had worse battery life, camera, and no noticeable performance improvements. Recently, my S22 stopped charging, and I just bought a “Mint”-grade used Pixel 6 and installed GrapheneOS on it. Happy so far, and it’s nice to be able to block network access to all apps, including Google’s.

Some of the “open” models seem to have augmented their training data with OpenAI and Anthropic requests (I. E. they sometimes say they’re ChatGPT or Claude). I guess that may be considered piracy. There are a lot of customer service bots that just hook into OpenAI APIs and don’t have a lot of guardrails, so you can do stuff like ask a car dealership’s customer service to write you Python code. Actual piracy would require someone leaking the model.

I’m curious if ByteDance could just create a new legal entity and call it TikTak or something.

Hmm. Looks like that was in Texas too. https://truthout.org/articles/a-city-in-texas-just-put-10000-bounties-on-trans-people-using-the-bathroom/, and they’re going to pass quite a few more bounty laws yhis year: https://prismreports.org/2025/01/08/bounty-laws-texas-trans-rights-abortion/

Dunno, they’d probably have a hard time suing European instances, but they can’t outright block, as that would be unconstitutional. U.S. states have recently been using lawsuits to get around constitutionality. I.e. Texas also has a “bounty” law, where if you know a woman went out of state to get an abortion, you can report it, and the state will sue them and give you $10,000. I think another state has a similar law for if you see a trans person using a restroom that doesn’t match the genitalia they were born with.

With the current laws on the books, Texas could probably sue Lemmy instances because they contain pornographic content and they don’t verify users’ identity.

If you have to verify children’s identity, you have to verify everyone’s identity. This is part of KOSA. https://www.eff.org/deeplinks/2024/12/kids-online-safety-act-continues-threaten-our-rights-online-year-review-2024

Oldest I got is limited to 16GB (excluding rPis). My main desktop is limited to 32GB which is annoying, because I sometimes need more. But, I have a home server with 128GB of RAM that I can use when it’s not doing other stuff. I once needed more than 128GB of RAM (to run optimizations on a large ONNX model, iirc), so had to spin up an EC2 instance with 512GB of RAM.

That’s really cool (not the auto opt-in thing). If I understand correctly, that system looks like it offers pretty strong theoretical privacy guarantees (assuming their closed-source client software works as they say, with sending fake queries and all that for differential privacy). If the backend doesn’t work like they say, they could infer what landmark is in an image when finding the approximate minimum distance to embeddings in their DB, but with the fake queries they can’t be sure which one is real. They can’t see the actual image either way as long as the “128-bit post-quantum” encryption algorithm doesn’t have any vulnerabilies (and the closed source software works as described).

I just use Joplin, encrypted, and synced through dropbox. Tried logseq, but never really figured out how to use its features effectively. The notebook/note model of Joplin seems more natural to me. My coding/scripting stuff mostly just goes into git repos.

The PC I’m using as a little NAS usually draws around 75 watt. My jellyfin and general home server draws about 50 watt while idle but can jump up to 150 watt. Most of the components are very old. I know I could get the power usage down significantly by using newer components, but not sure if the electricity use outweighs the cost of sending them to the landfill and creating demand for more newer components to be manufactured.

I’m loading up on vacuum tubes.

Last time I looked it up and calculated it, these large models are trained on something like only 7x the tokens as the number of parameters they have. If you thought of it like compression, a 1:7 ratio for lossless text compression is perfectly possible.

I think the models can still output a lot of stuff verbatim if you try to get them to, you just hit the guardrails they put in place. Seems to work fine for public domain stuff. E.g. “Give me the first 50 lines from Romeo and Juliette.” (albeit with a TOS warning, lol). “Give me the first few paragraphs of Dune.” seems to hit a guardrail, or maybe just forced through reinforcement learning.

A preprint paper was released recently that detailed how to get around RL by controlling the first few tokens of a model’s output, showing the “unsafe” data is still in there.