YHK - yet another hacker news clone

Yes, but still, what about users looking for NPU support. What happened to Microsoft's copilot+ PC campaign? (the NPU hardware, not the logo key and ad campaign) AMD's XDNA2 NPU is best supported by ONNX, I think Qualcomm Hexagon is too. The best consumer unified architectures are Apple, AMD ai+ and Qualcomm Snapdragon X Elite, even intel has an NPU offering.

As someone not in the loop, can you explain more of what you mean.

Anyone who has tried a smaller/quantized version locally on apple silicon? Would love to know your experience, also tk/s on what hardware specs?

This is what stood out to me. They did the actual work of testing and documenting deployments on several platforms. It's a bit of upping the standard for open weights instead of just posting the blob and letting the community figure it out.

It makes sense from a provider standpoint. If you are going to give it away for free what is the end game? Helping build the world around your model such that it is a pathway that leads to greater adoption of your ecosystem is smart. Dev relations in a way.

Yes you need big compute to deploy this locally but $20-30K in hardware for specialty markets is nothing.

This isn't an accident. It is revealing a strategy.

So what makes Muse "very sticky"?

Google, Apple, and Microsoft are each in a stronger position to win consumer AI because they're guardians over the foundations of the user's experience. Meta always has to wonder whether they're going to be edged out, like when Apple decided to limit permissions or set new rules on user data handling.

How has Meta responded? With a failed phone and speculative headset.

Sounds like cope. You can't claim the 'rent market on intelligence' never materialized when revenue is absolutely booming for Anthropic/etc.

> Muse is VERY sticky

How, exactly?

> now that we're all going to standardize on Meta's model

Who's this "we" exactly?

If you’re falling for “sticky” interfaces, you’re not going make it. Your competitor won’t have the stickiness and the ability to use any harness and that will overcome Muse in the long-run. Let’s not forget, Muse is a close to SOTA model right now, but so was Google, and now they’re irrelevant. Google is one of many funded models that were close to SOTA, but we realized it’s benchmaxxed.

People are going to say that it doesn’t matter if you’re the best. If Muse, Grokbot, and Dot are able to reach mass appeal, it doesn’t matter if a person using a custom harness is better.

I respectfully disagree. There is a big difference between someone that’s running a custom harness that’s almost like a personal OS versus someone using something out of the box. And if you’re for using this to run your business, think for yourself who’s going to win, the person customizing everything or the one using what the big corps give you.

> The rent market on intelligence never materialized

So where is the ~$100B combined revenue for OpenAI and Anthropic coming from? Open weights models have been close enough to the frontier for over a year now, probably longer, yet the $$ shows no sign of shifting.

last year was google winning AI, this year is meta winning AI. I think what is true is Meta, Google will all find their place in the AI world and continue to make huge amount of money. A consumer will use many different AI products, each with a different use case. Everyone will make enough money to live well.

There is no winner in a race to the bottom.

> Meta is going to win consumer AI.

That zucks.

I think the jury's still out on whether Muse will be sticky. My guess is that it won't be that good relative to the field.

isn't it a bit early to call Muse sticky?

>Muse is VERY sticky

its not. ppl downloaded it in hype arent using anymore

> Meta is going to win consumer AI.

Are you defining consumer AI as just AI as it relates to a narrow subset of social networking?

I'm looking forward to seeing Muse devices like Amazon's devices but with superintelligence: - Muse-equivalent of Alexa - Muse-equivalent of Ring security devices: doorbells, cameras, alarms, etc - Muse home automation devices (lights on/off, smart roller, smoke detector, water detecto, etc) - Muse ring ...

Very sticky?

Theyve had a few million downloads on what, DAU of billions?

Lol get a reality check.

> Meta is going to win consumer AI.

That's a bold statement lol.

But why are we all going to standardize on Meta's model, and how is it sticky? What does it have on the big Chinese models?

Also honestly, the models don't have to get much smarter to handle the consumer side of things. They could innovate a ton on the harness to farm out requests and manage the state of the agent. They have an incentive to do this to reduce the infra usage so they can go wider with the agent.

Paid bot or Meta employee, please disclose

Bold statement. I wouldn’t count anyone as a winner on consumer market at this point.

I think the weight of people who dislike Meta is going to be a problem - that and forcing to give a lot of information just to use it is problematic for adoption.

Is this Zucks personal AI bot?

> Meta is going to win consumer AI.

I disagree. Google is doing a phenomenal job at capturing use cases at the edge, right where "normie" consumers live. Google's models absolutely suck at coding but if you haven't tried Google Transcribe, you should.

Google has also, far larger distribution and stickiness than Meta.

okay that's gonna be interesting to try

Read it again. Two M5s.

Wait read that again. Two 512GB, unreleased model M5s.

This is gonna cost like 50k to run at home.

”Serve the model on either two Mac Studios, with M5 Ultra and 512GB of unified memory; or four Mac Studios with 256GB of unified memory, for a total of 1TB of unified memory, enough to hold a NVFP4 quantized version of Muse Spark Open”

Can not find any info on inference speed.

Then it's not open-weight. Open-weight means "weights available under an open-source license", and licenses prohibiting commercial use are not open-source.

The nearest analogue would be "weights available", I guess.

ya especially because it's huge, you aren't running this on your desktop

Meta did this from the very beginning with the original Llama's. Open weights, but you have to use an account with them and agree to an EULA.

There's obvs decreased price competition, but it also does help with ensuring model quality.

Quite a lot of providers on OpenRouter run degraded or broken models. I think some is deliberate, but I think a lot is just providers not setting up models properly; especially with the rush to day zero models.

Right. The US has famously never installed government-mandated backdoors in security-critical technology like Dual_EC_DRBG.

Link to papers?

Mistral just released Mistral Large 4?

Is Google not gonna make another Gemma or something?

Nemotron, Inkling, Gemma, Trinity Large, Snowflake Arctic were all released/updated in the last 6 months, and there are countless other smaller models out there. US open weight is very far from "dead", and certainly doesn't need Zuck as a savior.

Please give it a try and let us know ;) judging by the benchmark scores I would not expect too much of a difference.

Main difference seems to be that this one is cheaper/easier to host.

I used spark 1.3 extensively in prime-agent (a pi derivative) and I thought it was great.

Speaks nicely, great at driving linux. Not frontier-level coding, but comparable to luna, deepseek. It was amazing for the price.

Artifical Analysis says is this comparable to DeepSeek 4.1.

But weaker than Muse Spark 1.3: https://i.imgur.com/8ST1rRY.png

    Muse Spark 1.3 (Xhigh)     45
    Muse Spark Open (Xhigh)    39
    DeepSeek V4.1 Flash (Max)  39

1024GB of ram is bigger than 96GB of ram.

Also you are comparing apples to or— dang it, I don’t mean to do that in this thread. Well, it’s a very poor comparison, as they are completely different computing devices with completely different use cases, capabilities, and customer bases. Not to mention power requirements!

> Also, how did Meta get access to Apple's unreleased M5 Ultra w/512GB RAM Mac Studio? Or is that simply a marketing piece that they wrote purely on specs?

It should be well known that Apple provides early access to developers under strict conditions. Otherwise, how do you think they announce new hardware not for sale, and show demos of third party apps taking advantage of such hardware?

The 512GB is obviously geared towards local LLM crowds. I'm just speculating, but I would be surprised if there's not at least a few devices with the 'ecosystem players' like LM Studio, Unsloth, etc. Of course, they'd never confirm nor deny :)

how are the Macs connected to each other?

> Future is for everyone

So long as you can choose between running AI locally or a new Corolla

> Keep in mind that computers are appreciating assets recently.

You are correct but at my age my brain is never going to stop being surprised by this statement.

It's not open, like the Llama. There is a licensing agreement by which they allow you to use it for free on conditions.

You don't know what is hiding in the model ...

By finding another way to siphon user data?

You trust anything he says?

Not at an appreciable token rate. I wonder if these large open weight models are considered safer to release for that reason? I'm thinking no because any small to medium size criminal organization or state funded group will be able to cobble together the resources to achieve some kind of runtime. Even at 10 tok/sec, making bioweapons at 10 tok/sec is concerning.

> The 2-bit quant fits on devices with 256GB of RAM, VRAM or unified memory.

Ouch. I wonder how it compares to Qwen 3.8 running with 4-bit quant on 10 times less VRAM.

I do not know about you, but I will probably also run this model, even with a mini-PC like an ASUS NUC, albeit very slowly.

Even if for your main LLM you want a speed high enough for interactive use and fast iterations, it is still useful to also run a much bigger LLM from time to time, even if that requires many hours for completing a task, due to the slow inference speed when the weights are read from SSDs.

There are tasks where the quality of the result is more important than the time needed to produce it. For example you might run all day a great number of iterations of a fast small LLM, and then run overnight a big LLM to check the end results of that work.

Therefore we need both small open-weights LLMs that can be run from the GPU RAM or at most from the CPU RAM, but also the biggest open-weights LLMs, even if the latter can be run only from SSDs by most people.

How is this open weights? Open weights means anyone can use the weights. The license forbids certain categories.

I wonder if all the AI companies are making dossiers on their users.

I would say meta and google definitely are. xAI? Probaly. The others? Perhaps. Perhaps Anthropic less than the others?

How so?

Try Muse Wonder Full, it's comparable.

Weathered the storm

Probably too small, the comparisons are all larger models.

Best I can do is p(doom)

Is that combination of tokens still relevant or did it leave the news cycle by now?

They completely overhauled their team since then.

Unless the total (!) revenue of the provider is under 20 MM USD, eh? Not exactly fully open, but not _that_ harsh.

This is close to GLM terms

Also, IANAL, but under U.S. copyright law, the weights of machine learning models are excluded from copyright as they are the product of an automated optimization process (e.g., stochastic gradient descent, expectation maximization, genetic algorithms) rather than human authorship. Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway, similar to how one might treat a ToS agreement. Nonetheless, this was partially tested last year in Thaler v. Perlmutter which affirmed that copyright requires human authorship, reading the Copyright Act's provisions on ownership, duration, and transferability as presupposing a human author[1].

If you want to assess the position of the U.S. Copyright Office for yourself, the relevant text can be found in the Compendium of U.S. Copyright Office Practices § 313.2, "Works That Lack Human Authorship"[2], which states:

> […] the Copyright Act protects “original works of authorship.” 17 U.S.C. § 102(a) (emphasis added). To qualify as a work of “authorship” a work must be created by a human being. See Burrow-Giles Lithographic Co., 111 U.S. at 58. Works that do not satisfy this requirement are not copyrightable.

> […] the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author. The crucial question is “whether the ‘work’ is basically one of human authorship, with the computer [or other device] merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.” U.S. COPYRIGHT OFFICE, REPORT TO THE LIBRARIAN OF CONGRESS BY THE REGISTER OF COPYRIGHTS 5 (1965).

Oh, and there's also a bit in the following Section 313.3, "Works That Do Not Constitute Copyrightable Subject Matter"[2], which explicitly excludes mathematical principles, formulas, algorithms, and equations, along with DNA sequences and other genetic or chemical compounds, regardless of whether they are produced by humans or by nature. If one takes the perspective that machine learning models are algorithms, the conclusions on copyrightability are pretty clear.

[1] https://media.cadc.uscourts.gov/opinions/docs/2025/03/23-523...

[2] https://www.copyright.gov/comp3/chap300/ch300-copyrightable-...

Still not an open-source license. Means no restrictions on "fields of endeavor", a.k.a. the free software definition's "freedom 0": the freedom to run the software for any purpose.

I'm sick to death of these shameless dilutions (including the notion that open-weight is open-source; the OSI is totally captured at this point)

Crucially, it's about the same as DeepSeek v4.1 Flash, and about 10x more expensive to run...

No thanks...

Hmm.. i don't know.

Coding is the main application for AI in 2026, unless I'm wrong

It's not just openclaw, there's OpenAI's Dots and Gemini Spark.

Facebook doesn't have an real advantage to drive adoption other the being free. Which is not a moat because Google, certainly, and OpenAI, very probably, would offer this for free, in the event this capability turns out to be important to consumers.

Gmail, which half of the people in the US use their daily e-mail is certainly strong thing to build an agent on top of.

Just because meta can shove stuff in the faces of people doesn't mean they can succeed at any random thing. Most Facebook users distrust it, and dislike Zuck personally (at least that's what his lawyer said in court) and that's a rather large barrier to succeeding with agents, where trust is foundational.

But my assumption about the speed of product uptake may be skewed by not-valuing Meta's "consumer task running" pitch. But if people actually want this, for some reason, competitors at scale seem very likely to appear instantly.

This really seems more about meta AI team needs more users of their AI to do a good job building AI, and doing this for free is niche where they can get a few more users, rather than this being genius gambit.

It did feel very odd to read that.

The sad thing is I think it's like Elara, it's just a favorite LLM name for things and there's likely not a human behind it.

very true. when configuring custom agents for enterprise, we almost always default to none or low reasoning knobs. The added latency from medium or higher reasoning levels compared to the improvement in response quality is a diminished return.

I agree, however It's too easy to game arbitrary limits like "medium". There is no olympics for "just rolled out of bed and slightly hungover"

Why didnt thry include OpenAI or Anthropic models? I get they were comparing open weights.

There’s a reason they put the “US” in “the most capable US open weight model”.

> As an aside, it's really unfortunate that artificial analysis only provides comparison at the highest reasoning level, which is mostly unusable for high-volume production use cases. I would really like them to start providing comparisons at low and medium reasoning levels across these models because those tend to be the ones that actually get used in production.

The frustrating thing is they started doing this for the big labs—you can see their benchmarks for all reasoning levels of Sol 6.1 or Haiku 5.5. They just don't seem to do it for any of the open weight models.

Artificial analysis puts it next to Mistral's new model.

this is properly bad as you can run glm on 6000pro, at (in my eyes) pretty good quant, and a q2 on a 5090. im suprised at how good 5.3 flash is at some parts. i still think ds4.1 flash is better but it is more demanding. i hope meta pushes something in a range of 300-400b with a 15-20b active parameters. i know its a crowded space. but it would be fast and probably mostly good enough.

yeah for botsbench.com side where we do cybersecurity investigation evals of models + harnesses, we've been having to play with:

1) time budget model gets (5min / task) -- this became a real problem with opus/astra/fable at high+

2) tuning which effort level we report on (when not all)

Which is crazy bc they promised to open source muse 1.2 which is good. And they give us this bait and switch

Yep, just benchmarked it against GLM5.3 Flash on my own local bench and it's slower in TTFT, tok/s and concurrency. It also scored lower.

Tried different reasoning levels and didn't really see any difference but GLM 5.3 flash only really works high, max or none so I couldn't really compare the lower efforts which appear to work on Spark.

AA is a joke.

Been using GLM 5.3 Flash via Ollama Cloud basic subscription:

if this is the only model I had to use for the rest of my life, I would be cool with that. It's good at a wide variety of tasks including reverse engineering, design, working with ESP 32 etc.

I think Muse Spark 1.3 is better but GLM 5.3 Flash is an excellent daily driver.

My immediate reaction is that it’s weaker on coding than other open-weight models and that coding is a primary use case for running locally (not the only one, but surely a major one). That said, it takes a lot to get full Kimi and GLM running locally.

The most important graph for me on Artificial Analysis is this: https://artificialanalysis.ai/?agentic-speed=intelligence-vs...

It shows the time to complete a task versus intelligence index, which is much more important for my use-case as I do quick back and forths usually. Someone on here suggested it to me a while ago. It also captures speed and token efficiency nicely, since both of these decrease time per task.

it’s quite suspect to claim that a model is ‘underperforming’ so resoundingly, especially after so little time since release.

there’s this objectivist approach of reducing llm quality to a single dimension, furthermore one with measurability bias.

my thesis is that the chinese models beat the american models on benchmarks, on quantitative measures, but probably do worse in subjective measures. to put an example, compare a chinese phone with an iphone, the specs are great, but the OS? the materials? the privacy posture?

so will chinese llms have great specs and benchmarks, but will miss the mark when it comes to the actual quality in practice when your input stops looking like the benchmarks it was trained on.

America number one, it’s the chinese that play catch up with clones, not the other way around

Um a 512GB m5 max Mac Studio is what 25k? A B300 GPU and the computer to use it is what? Double? Triple that? Apples v oranges my friend. Apples v oranges.

> As an aside, it's really unfortunate that artificial analysis only provides comparison at the highest reasoning level

They do other reasoning levels for some models.

Here is Sonnet 5.5 across the full range of efforts from low to max: https://artificialanalysis.ai/?models=claude-sonnet-5-5-low%...

Opus 5.5 low to max: https://artificialanalysis.ai/?models=claude-opus-5-5%2Cclau...

Here is Muse Spark 1.3 Max and Xhigh: https://artificialanalysis.ai/?models=muse-spark-1-3%2Cmuse-...

I'm guessing it depends how much the vendor wants to contribute. Running these benchmarks isn't cheap!

I feel like reasoning is a useless metric, and we should instead focus on results, and time to achieved said results. That is a more useful metric overall.

Ahat's the cost of a b300 versus two mac minis?

> only provides comparison at the highest reasoning level, which is mostly unusable for high-volume production use cases

Similarly, AFAIK evals tend to be performed at native precision, while anyone who cares about speed is probably running quantized.

Qwen 3.8 flash next runs pretty damn fast on my 5090, no need for a b300.

Model X vs Y is the new Vim vs Emacs.

> Artificial analysis shows this: underperforming Qwen3.8 Flash Next and GLM 5.3 Flash, both of which you can run on a single B300

You don't need anywhere near a B300 to run Qwen 3.8-Flash-Next in its Q8 largest variant published as a GGUF by unsloth. It fits very comfortably in a 256GB system.

To try to prevent as many people as possible from using one model, that gets all the data, and winner takes all.

spoiling the leaders' profit margins

Pissing off Dario, probably?

Or rather capturing the people Anthropic keeps alienating.

The Zuck has (in a different context) said that a few billions wasted are a decent bet (or something along those lines), so I think they're playing extra long games here with no real focus on profit any time soon.

And disclaimer since this comes up with these - objective with this test is to be a good image->html test, not a good web design. Also it's meant to use 3js to render the planets, which if you don't have GPU acceleration enabled for your browser will lower the FPS significantly.

Besides including the sota could you include a link to the best previous open-weights (and if possible fully open-source) model?

Very big LLMs can still be useful even when you run them only at the speed of around 1 token per second that can be reached with almost any mini-PC or laptop with the weights streamed from SSDs.

While at that speed they will not be your main LLM, there are tasks that are still useful even when requiring many hours, e.g. an overnight run, for instance for checking what has been produced after many interactive iterations of a fast but small model.

I think it is safe to say that Muse models are not pelican-benchmaxxed.

A question I have about the pelican benchmark : why are the pelicans always cycling to the right?

I really would love a browser plugin that blocks having to read about fucking pelicans on every model release.

It's interesting the high reasoning pelican is animated. I don't think I've seen another model do that before.

In light of recent pieces like "The Emperor Pelican Has No Clothes" (https://arcfu.com/post/the-emperor-pelican-has-no-clothes/), I'd be more curious to see how 'PelicanBench' becomes more robust in the time to come -- I feel like all models are now benchmax'd on the standard pelican SVG rendering task and we need something more multi-dimensional. It would be great to have a script that prompts a model to render the pelicans in several different situations (like the linked article) that showcase some kind of broad generalizability or other sort of 'transfer learning' going on with these models that would really then be impressive and worth noting on - otherwise PelicanBench is losing its relevance.

Did you order one of the new studios?

Given the size of Macs (2 or 4 of them) required to run it, they're just paying lip service to that option. Other models, e.g. deepseek flash, are much more practical size. Even that takes a 96 or 128GB ram Mac.

If you can’t win everything, at least win a niche.

I partly agree with the sentiment, but this is also the first time I've seen an actual lab announce they optimized for consumer hardware (very expensive, but still consumer).

It's kind of absurd that no non-quantized MOE model exists that's sweet spotted for 128GB Macs, for example. I hope more open labs follow this lead but also offer lower tier models, perhaps specialized on certain types of tasks

Tbf that wasn’t such an outrageous proposition until about ~6 months ago.

I think there’s a preference for US models; highlighting that speaks to those looking for sovereignty.

I assume they’re doing it without distilling since they’re within reach of lawsuits, so they’re basically handicapped by ethics, which has to feel unusual for Meta.

This is kind of a standard at this point for many models (including prior meta models like llama)

You create a hugging face token, set an env variable (HF_TOKEN) and download it with the hugging face CLI or just automatically as needed with trandormers, vllm, etc…

   until curl --user "name" --oauth2-bearer "access_token" --location --continue-at - "HF_XET_link"; do sleep xx; done

The existence of such attacks changes nothing.

You should not trust any LLM, regardless if it was abliterated or it comes from its original creator.

You should check and control what the tools invoked by an LLM may do.