Pumpkin Escobar

Why did De Niro immediately pop into my head?

Oh, yeah…

I’ve got nipples, Greg, could you milk me?

But, I thought Trump was all about state’s rights? This is very confusing. /s

I’ve been tempted to ditch my current password manager and move to bitwarden. I think this is the final push I needed.

Bench warrant, let’s do this!

Similar to previous reply about MATE with font size changes, I do that with plasma. I hadn’t seen plasma big screen you linked, I’ll definitely try that one out. I’ve wondered about https://en.m.wikipedia.org/wiki/Plasma_Mobile? Like these sort of niche projects don’t always get a lot of attention, if the bigscreen project doesn’t work out, I’d bet the plasma mobile project is fairly active and given the way it scales for displays might work really well on a tv

Speaking of scaling since you mentioned it. I have noticed scaling in general feels a lot better in Wayland. If you’d only tried it in X11 before, might want to see if Wayland works better for you.

First a caveat/warning - you’ll need a beefy GPU to run larger models, there are some smaller models that perform pretty well.

Adding a medium amount of extra information for you or anyone else that might want to get into running models locally

Tools

• Ollama - great app for downloading/managing/running models locally
• OpenWebUI - A web app that provides a UI like the ChatGPT web app, but can use local models
• continue.dev - A VS Code extension that can use ollama to give a github copilot-like AI assistant running against a local model (can also connect to Anthropic Claude, etc…)

Models

If you look at https://ollama.com/library?sort=featured you can see models

Model size is measured by parameter count. Generally higher parameter models are better (more “smart”, more accurate) but it’s very challenging/slow to run anything over 25b parameters on consumer GPUs. I tend to find 8-13b parameter models are a sort of sweet spot, the 1-4b parameter models are meant more for really low power devices, they’ll give you OK results for simple requests and summarizing, but they’re not going to wow you.

If you look at the ‘tags’ for the models listed below, you’ll see things like 8b-instruct-q8_0 or 8b-instruct-q4_0. The q part refers to quantization, or shrinking/compressing a model and the number after that is roughly how aggressively it was compressed. Note the size of each tag and how the size reduces as the quantization gets more aggressive (smaller numbers). You can roughly think of this size number as “how much video ram do I need to run this model”. For me, I try to aim for q8 models, fp16 if they can run in my GPU. I wouldn’t try to use anything below q4 quantization, there seems to be a lot of quality loss below q4. Models can run partially or even fully on a CPU but that’s much slower. Ollama doesn’t yet support these new NPUs found in new laptops/processors, but work is happening there.

• Llama 3.1 - The 8b instruct model is pretty good, decent speed and good quality. This is a good “default” model to use
• Llama 3.2 - This model was just released yesterday. I’m only seeing the 1b and 3b models right now. They’ve changed the 8b model to 11b, I’m assuming the 11b model is going to be my new goto when it’s available.
• Deepseek Coder v2 - A great coding assistant model
• Command-r - This is a more niche model, mainly useful for RAG. It’s only available in a 35b parameter model, so not all that feasible to run locally
• Mistral small - A really good model, in the ballpark of Llama. I haven’t had quite as much luck with this as with Llama but it is good and I just saw that a new version was released 8 days ago, will need to check it out again

It’s a good thing that real open source models are getting good enough to compete with or exceed OpenAI.

I’ll preface by saying I think LLMs are useful and in the next couple years there will be some interesting new uses and existing ones getting streamlined…

But they’re just next word predictors. The best you could say about intelligence is that they have an impressive ability to encode knowledge in a pretty efficient way (the storage density, not the execution of the LLM), but there’s no logic or reasoning in their execution or interaction with them. It’s one of the reasons they’re so terrible at math.

I like the game, but agree with the over-tutorialed complaints. They have two difficulty modes, I wish only story mode got all the handholding. I think there’s enough obvious indicators to get you through all the game mechanics.

It has been on my list to figure out how to move to forgejo, need to do it soon before the migration process breaks or gets awful.

Coming from c# then typescript and nextjs, rye feels very intuitive and like a nice bridge / gateway drug into python.

surely he’ll be less of a twat then. right?

I guess it wasn’t bacon I hate for breakfast yesterday.

Why do you hate bacon, are you a windmill?

Lan-mouse looks great but keep in mind that there’s no network encryption right now. There is a GitHub ticket open and the developer seems eager to add encryption. It’s just worth understanding that all your keystrokes are going across the network unencrypted.

Things I will bet money on

• They will produce no evidence of any wrongdoing uncovered from any of these raids
• They will give some cryptic statement that tries to make it sound like they did find something
• Texas lawmakers will continue to not hold Paxton accountable for anything

Shoot your shot, player.

Don’t go crazy or over the top, don’t overdo it, but just say it. If they’re a good friend they won’t be scared away. If they’re like you that way you’ll both be happier.

Don’t overthink it, ask them if they’d ever like to hang out or do something more like a date.

Ballsy, direct, badass. That can be you.

Dating is awkward but life gets a lot better once you get more comfortable with it. Everyone is a dating idiot until they’re not, there’s a good chance your friend is still in the idiot stage and maybe hell be over the moon that you helped push through it.

More than distro hopping maybe try out a zen kernel or compiling kernel yourself and changing kernel config and scheduler, or a newer version of the stock kernel?

I’m not super current on what’s in each kernel but I’d expect latest mainline to handle newer processors better than some of the older stable kernels in some of the more mainstream slower releasing distros.

deleted by creator

Ran Asahi for several months, tried it out again recently. It’s good/fine, I just don’t love fedora.

There’s some funkiness with the more complicated install, the AI acceleration doesn’t work, no thunderbolt / docking station.

MacBooks are great hardware but I don’t think they’re the best option for Linux right now. If you’re never going to boot into macOS then I’d look for x13, new Qualcomm, isn’t there a framework arm64 option now or was that a RISC module?

I’m also assuming you’re not looking to do any gaming? Because gaming on ARM is not really a thing right now and doesn’t feel like it will be for a long while.