Comparing Switch Immersion vs Aeropress?

That’s a good question. Been a while since I’ve gone actually immersion brewed with it (usually just pour over and use the stopper for pre heating with less water). I’ll make a couple of cups and get back to you.

Edit:

Cup making done! The immersion brew is super clean and easy. I do think some type of lid would help for longer brew times to keep the heat in on the Switch though.

I think both the Aeropress and Switch Immersion lend themselves well to darker (chocolatier/nuttier) extractions. After this little test I will probably switch over to immersion brewing for my afternoon decaf for the foreseeable future!

It’s mostly Mastodon. (Shoutout to @RmDebArc_5@sh.itjust.works for posting the link to FediDB)

asking the real questions

Can’t say enough good things about mine.

If I would start all over, the only two coffee makers I would own are:

• Aeropress
• Size 03 Hario Switch (V60 + a stopper for immersion brew)

A firejail doesn’t hurt either ;)

I prefer Syncthing-fork for some more straightforward configuration. Mainly the three button options equating to “follow the run conditions, damnit”, “run damnit”, and “stop damnit”

Coarse Salt. Add just enough water to move it around + a little dish soap and shake. Works like a charm.

“Chuff” in the context of rock climbing = bad, made an effort but didn’t get very far / fell a lot

Even in the US, state-level representation hardly counts as local. Neighborhoods, towns, counties, etc. all have people representing them.

It’s cool to care about and build up your community.

Even faster – tailscale. For a cheeky way to play with your friends make a burner account with a shared login to get on the same tailnet for free. On the endpoints, turn off tailscale-ssh and any of their other “features” you don’t need.

I immediately thought this was salt. Maybe I’m the monster.

Second this ^

I have one and it’s fine, but not directly supported by OpenWRT. Looks like Beryl and Slate are though

Excellent notes. If I could add anything it would be on number 4 – just. add. imagery. For the love of your chosen deity, learn the shortcut for a screenshot on your OS. Use it like it’s astro glide and you’re trying to get a Cadillac into a dog house.

The little red circles or arrows you add in your chosen editing software will do more to convey a point than writing a paragraph on how to get to the right menu.

Believe what you will. I’m not an authority on the topic, but as a researcher in an adjacent field I have a pretty good idea. I also self host Ollama and SearXNG (a metasearch engine, to be clear, not a first party search engine) so I have some anecdotal inclinations.

Training even a teeny tiny LLM or ML model can run a typical gaming desktop at 100% for days. Sending a query to a pretrained model hardly even shows up on HTop unless it’s gigantic. Even the gigantic models only spike the CPU for a few seconds (until the query is complete). SearXNG, again anecdotally, spikes my PC about the same as Mistral in Ollama.

I would encourage you to look at more explanations like the one below. I’m not just blowing smoke, and I’m not dismissing the very real problem of massive training costs (in money, energy, and water) that you’re pointing out.

https://www.baeldung.com/cs/chatgpt-large-language-models-power-consumption

I don’t disagree, but it is useful to point out there are two truths in what you wrote.

The energy use of one person running an already trained model on their own hardware is trivial.

Even the energy use of many many people using already trained models (ChatGPT, etc) is still not the problem at hand (probably on the order of the energy usage from a typical search engine).

The energy use in training these models (the appendage measuring contest between tech giants pretending they’re on the cusp of AGI) is where the cost really ramps up.

Amazing! I’ve used that before but just to look for packages offline. I’ll definitely check that out.

Love the example here!

I’m still learning about available references (ex config.services.navidrome.settings.Port). What resources did you find to be the best for learning that kind of thing?

I’ll accept RTFM if that’s applicable :)

Based

Or dust if you want it fastest with a pretty graph

Is there a reason you’re not considering running this in a VM?

I could see a case where you go for a native install on a virtual machine, attach a virtual disk to isolate your library from the rest of the filesystem, and then move that around (or just straight up mount that directory in the container) as needed.

That way you can back up your library separately from your JF server implementation and go hog wild.