I’m just really tired of the parroted and reactionary “blockchain/AI bad” comments because they often have no nuance. It’s not the implementation that’s “bad”, it’s seemingly that they used the tech at all.

Agreed on that, too.

There was this one instance of a remastered” TV show, partially processed with GANs, a long time ago. I pointed this out (as apparently this little detail was forgotten with time), and I got chewed out. Reddit commenters either claimed I was lying (when I helped work on it), or declared it was now awful and intolerable just because it’s “AI”… something they had loved for years, way before the LLM bubble.

Of course it can be discussed…

I’m really into (open weights) genAI myself, have been for years, but at the same time I’m under no illusion the space is clean. The vast majority of services are scams, many open source AI projects are autogenerated slop from someone with AI psychosis (if not outright Tech Bro scams), and that’s not even touching on what Big Tech is pushing.

What I’m asserting is that a fat slab of skepticism is healthy in this kind of space. Be an enthusiast, not a believer. I know much less about blockchain, so perhaps I was a little zealous in judgement, but something about this project just raised a lot of red flags in my head like scam-adjacent AI projects do.

Another thing is that the blockchain scams haven’t gone away, and in ten years they probably will still stubbornly persist. GenAI is going to be the same.

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.

Hardly. Power costs are trivial to them at the moment, and a server hardware bottleneck would just consolidate power to the big few that can afford it (which is what they want).

To be fair, it’s a consistent red flag.

Blockchain is theoretically interesting. Very interesting, in certain niches. But 9 times out of 10, “blockchain-based” is code for “enshittified” or “a pyramid scheme scam from the start.” And in the cases where its implementation is altruistic, it’s still questionably sustainable or creates considerable overhead.

To add to what others said:

LPDDRX is used in some inference hardware. The same stuff you find in laptops and smartphones.

Also, the servers need a whole lot of regular CPU DIMMs since they’re still mostly EPYC/Xeon severs with 8 GPUs in each. And why are they “wasting” so much RAM on CPU RAM that isn’t really needed, you ask? Same reason as a lot of AI: it’s immediately accessible, already targeted by devs, and AI dev is way more conservative and wasteful than you’d think.

Same for SSDs. Regular old servers (including AI servers) need it too. In a perfect world they’d use centralized storage for images/weights with near-“diskless” inference/training servers. Some AI servers do this, but most don’t.

Basically, the waste is tremendous, for the same reason they use cheap gas generators on-site: it’s faster-to-market.

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.

HDR version for browsers that support it:

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.