To illustrate what I mean more clearly, look at the top comments/replies for the NASA Artemis posts, as an example.

…It’s basically all conspiracy theorists, and government skeptics.

Twitter’s focusing the Artemis posts on them because it’s what they want to see, and most engaging for them.

In the EFF’s case, I’m not just talking about Musk’s influence. The algorithm will only show the EFF to users who would be highly engaged by it. E.g., angry skeptics who wouldn’t be swayed by the EFF anyway, or fans who already agree with the EFF. It’s literally not going to show the EFF to people who need to see it, as Twitter’s metrics would show it as unengaging.

This is the “false image” I keep trying to dispel. Twitter is less and less an “even spread” of exposure like people think it is, like it sort of used to be, more-and-more a hyper focused bubble of what you want to hear, and only what you want to hear. All the changes Musk is making are amplifying that. Maybe that’s fine for some orgs, but there’s no point in the EFF staying in that kind of environment, regardless of ethics.

I feel like the EFF’s messaging is just not going to get through to anyone still on Twitter.

Remember, it’s not a fair forum; it’s an algorithm. And it’s not going to show the EFF to users who need to see it.

Even not-fully-reproducible open-weights models are extremely important because they’re poison to OpenAI, and they know it. It makes what they’re trying to commodify and control effectively free and utilitarian.

But there are fully open models, too, with public training data.

It’s anticompetitiveness.

They want to squash open models, and anyone too small to comply with this.

I say this in every thread, but the real AI “battle” is open-weights ML vs OpenAI style tech bro AI. And OpenAI wants precisely no one to realize that.

Ughhh, I could go on forever, but to keep it short:

• Tech bro enshittification: https://old.reddit.com/r/LocalLLaMA/comments/1p0u8hd/ollamas_enshitification_has_begun_opensource_is/

• Hiding attribution to the actual open source project it’s based on: https://old.reddit.com/r/LocalLLaMA/comments/1jgh0kd/opinion_ollama_is_overhyped_and_its_unethical/

• A huge support drain on llama.cpp, without a single cent, nor a notable contribution, given back.

• Constant bugs and broken models from “quick and dirty” model support updates, just for hype.

• Breaking standard GGUFs.

• Deliberately misnaming models (like the Deepseek Qwen distills and “Deepseek”) for hype.

• Horrible defaults (like ancient default models, 4096 context, really bad/lazy quantizations).

• A bunch of spam, drama, and abuse on Linkedin, Twitter, Reddit and such.

Basically, the devs are Tech Bros. They’re scammer-adjacent. I’ve been in local inference for years, and wouldn’t touch ollama if you paid me to. I’d trust Gemini API over them any day.

I’d recommend base llama.cpp or ik_llama.cpp or kobold.cpp, but if you must use an “turnkey” and popular UI, LMStudio is way better.

But the problem is, if you want a performant local LLM, nothing about local inference is really turnkey. It’s just too hardware sensitive, and moves too fast.

Also, for any interested, desktop inference and quantization is my autistic interest. Ask my anything.

I don’t like Gemma 4 much so far, but if you want to try it anyway:

• On Nvidia with no CPU offloading, watch this PR and run it with TabbyAPI: https://github.com/turboderp-org/exllamav3/pull/185

• With CPU offloading, watch this PR and the mainline llama.cpp issues they link. Once Gemma4 inference isn’t busted, run it in IK or mainline llama.cpp: https://github.com/ikawrakow/ik_llama.cpp/issues/1572

• If you’re on an AMD APU, like a Mini PC server, look at: https://github.com/lemonade-sdk/lemonade

• On an AMD or Intel GPU, either use llama.cpp or kobold.cpp with the vulkan backend.

• Avoid ollama like it’s the plague.

• Learn chat templating and play with it in mikupad before you use a “easy” frontend, so you understand what its doing internally (and know when/how it goes wrong): https://github.com/lmg-anon/mikupad

But TBH I’d point most people to Qwen 3.5/3.6 or Step 3.5 instead. They seem big, but being sparse MoEs, they can run quite quickly on single-GPU desktops: https://huggingface.co/models?other=ik_llama.cpp&sort=modified

There’s a whole lot of interest in locally runnable ML. It was there even before ChatGPT 3.5 started the tech bro hype train, when tinkerers were messing with GPT-J 6B and GAN models.

In a nutshell, it’s basically Lemmy vs Reddit. Local and community-developed vs toxic and corporate.

They seem to have held back the “big” locally runnable model.

It’s also kinda conservative/old, architecture wise: 16-bit weights, sliding window attention interleaved with global attention. No MTP, no QAT (yet), no tightly integrated vision, no hybrid mamba like Qwen/Deepseek, nothing weird like that. It’s especially glaring since we know Google is using an exotic architecture for Gemini, and has basically infinite resources for experimentation.

It also feels kinda “deep fried” like GPT-OSS to me, see: https://github.com/ikawrakow/ik_llama.cpp/issues/1572

it is acting crazy. it can’t do anything without the proper chat template, or it goes crazy.

IMO it’s not very interesting, especially with so many other models that run really well on desktops.

it’s a form of private journalism, private opinion, and private art

But without any of the liability hazard.

This is my issue: the big platforms having their cake and eating it. In one breath, they claim to be little open-platform garage startups that can’t possibly be responsible for the content of their users; they’re just a utility. They need protection from Congress. In another breath, they’re the stewards of generations and children, the only ones responsible enough to tame the internet’s criminality. All while making trillions.

They want to be “private content” protected from the government? Fine. Treat them like it, legally.

It is when it warps the behavior of everyone else around you, and everything in charge of your life.

And I’m not just talking about the lost attention. The algorithms are not neutral.

I think you mean monitor their usage.

And to be fair, this is fairly technical. Many parents aren’t very technical. They’re unaware of parental controls they have access to, and I think that’s by design (as it would be unprofitable for social media).

Yeah. I prefer the idea of a bunch of 9-meters unless they can really perfect a cheap folding mirror to mass produce.

A small upper stage, an ion drive or something could get them to deep space. It’s not worth flying a whole Starship out there and burning more fuel to get it back; the return trip only makes sense for LEO.

I wonder how big you could get the mirror if you did it James Webb style in starship.

Presumably 7x ~8m hexagons folded up?

That is a good point though. And if one were to design a “budget” 9m space telescope, they could amortize the R&D dramatically by launching the same design many times, perhaps with different sensors for different purposes? Amortization is why the Falcon Heavy and such are so cheap, and why the Space Shuttle and JWST are obscenely expensive.

Okay, you’ve sold me. I hope this does happen.

Theoretically, even if we assume SpaceX is overshooting, that’s an interesting thought:

https://www.visualcapitalist.com/the-cost-of-space-flight/

In practice? I’m more concerned about interest in funding astronomy in the first place.

That, and big fat telescopes are fundamentally expensive. And (at least for the optical variety) “swarming” them with a bunch of cheaper units isn’t as effective as building a big one.

I’d love to be wrong though. There are some interesting papers on swarms of optical telescopes for a larger effective aperture, but I’m not qualified to assess them.

Yeah; 100%.

Go go China !

Bops the tankie.

Like, I have a Chinese LLM loaded right this second and follow them closely, but holy moly. Curb your enthusiasm.

Anyway, OpenAI has plenty of compute to train a Sora 2 if they want, but apparently they don’t. My guess is some combination of:

• They couldn’t figure out a more efficient architecture, like you speculated. I buy that. OpenAI’s development is way more conservative than you’d think, and video generation is inherently intense, especially if Sora 1 is the baseline.

• …Maybe they looked at metrics, saw Sora is mostly used for spam, scams, or worse, and pulled the plug for liability reasons?

• They’re focusing on short-term profitability, as other commenters mentioned.

And it can be used to verify how old you are.

How?

This is the part I’m hung up on. What actually physically happens to make me enter my real birthday in the systemd user field, and verify it’s actually my birthday?

January 1 1900 has been my official online birthday forever.

I just don’t see how it’s any different than my Sony PSP having an optional birthday field. Or oldschool forums having one. It can’t possibly affect me, or anyone who’s concerned about it.

If systemd starts talking about bundling face scanners or whatever they actually need to verify someone’s age, and then tons of linux systems start requiring it, then I will be gravely concerned.

So they’re introducing a system where a users age can be verified?

No. They are not.

It is an optional field that does no semblance of checking its veracity. Again, like basically every bit of electronics has had forever.

The source is the source: https://github.com/systemd/systemd/commit/acb6624fa19ddd68f9433fb0838db119fe18c3ed

Takes a birth date for the user in ISO 8601 calendar date format. The earliest representable year is 1900. If an empty string is passed the birth date is reset to unset.

That’s it. That’s all it does.

Whatever was discussed in the PR, the code does precisely nothing to implement any kind of verification. It’s just an optional birth date field, like tons of electronics have had forever.