While composing a reply to a comment throwing tons of shade on American AI, I took some time to check out the commenter’s HN profile. Their comment history was about 50% such comments. Their submission history started with an article about how Russia was unfairly blamed for some hacking campaign.

It’s entirely possible that this is not a foreign influence campaign. Perhaps there’s a group here that is simply anti-American as its primary interest, and passionately so to upvote each other.

On the other hand, one should not discount the value of HN as tastemaker and trendsetter. Also, it would be fairly easy to run bots here. I wouldn’t be surprised if HN were a field of combat for many parallel influence campaigns, foreign and domestic.

Good points, I personally believe that if/when China takes the lead, they will immediately stop releasing model weights. It only makes sense as a strategy to counterbalance (current) American labs' monopoly on frontier models.

Holding both those positions would be hypocritical all right, but are you sure it's the same people commenting/voting in both cases? I don't think there's a strong consensus on Hacker News. Even something like the time of day an article is posted might get different engagement depending on who is active in which time zones.

These sentiments aren’t formed in a vacuum. America is becoming increasingly oligarchic and corrupt with decades of experience of companies profiting off harming people and lying through their teeth and Meta is like one of the worst offenders. They are pissing on every ally they have and once again started a war and both have material impacts on other countries.

Americans seem to take the US’ great reputation for granted and don’t realize how it has slipped and what that means. They also take for granted that China BAD is truth when this sentiment basically just sprang out of nowhere when the west realized it was their geopolitical rival. But to the rest of the worlds citizens, China is not starting any wars and is the source of cheap goods and innovation to other countries. EV batteries recently.

Those factors are all rightly part of the sentiment.

Of the two competing models Meta compare Glimmer to in the post, one is Google's Gemma 4.

At this size open weight model, a Western company was already state of the art, Meta is joining that competition.

And my memory is that Gemma 4 got little criticism or doom/gloom. And no, it isn't Chinese.

The GP is claiming Meta is an awful company for what they have done and how they continue to treat their employees. That’s a perfectly ok opinion to hold, and many seem to agree.

Have DeepSeek, Moonshot, or the other Chinese AI companies done such things that attract moral outrage?

Nobody wants to live in a world where one party dominates due to access to superior AI and the others have to fear it (well, except for a few psycopaths who would gamble on being in control of that party). So the underdog will always be the good guy in this race. It has been framed as a race between countries, so Meta fails to be the underdog because they're in the wrong country. That's all.

> When an American company does anything? Doom. And. Gloom. The engineers? Taken to the slaughterhouse! America? Behind! The public? Bamboozeled!

I think I've always had a pretty healthy amount of cynicism towards China. In recent years my cynicism towards the US has increased significantly. I don't see all of my US peers with cynicism, but I think you're living in an age of grift, corporate capture, and unheard of corruption. I also think there's nuance to both. There are some US and Chinese companies and people that I do respect regardless of what's going on politically. (Meta / Zuck isn't one of them though...)

I live in the 51st state though, so maybe I'm just overreacting...

Apparently Americans haven’t got the memo yet that the world is moving closer to China.

Can you point to a lot of posts lauding the Chinese government based on the release of Chinese open models? Because that would be the equivalent to contrast with the OP.

There are comments like the one you're replying to on literally every Chinese model release. This is textbook goomba fallacy, btw.

I suspect it's just the generalized anti-West/anti-American sentiments extended into anything and everything. Anything that makes the US look anything close to good goes against their cause and therefore must be countered and talked down.

But you're not wrong about the bias here. You just don't see many comments talking about it because they get mass flagged/downvoted for obvious reasons.

There is a big astroturfing going on social media platforms by the chinese. Did you notice 'day in a life of unmarried 30 yr old lady in china' videos flooding usa social media.

Regular ppl in the west now hold mildly positive views of the ccp and how 'advanced' china is than usa.

Then there are europeans who now are looking for china to give them the technology handout now that relationship with usa has soured.

Deepseek never fucked us over. zuck has. A decades of harm creation run doesn’t get excused by the US flag. Zuck is not on your team and if you can’t see that by now, oh my.

> When an American company does anything? Doom. And. Gloom

Meta, "an American company". Being the main driver of an ethnic cleansing in Myanmar - and just sticking your head in the sand when told about it - is just another day's affairs at the average American Acme Inc.

These are comments on a release by easily the most societally damaging Western tech company there is. They so far easily beat Flock, Palantir, Anduril and so on, as a result of their incomparable scale. You're just ignoring that and pretending any negative comments are because it's an American company rather than Meta. That's much more FUD than any pro-China comments I've seen on HN.

Get off HN Mark, you have ten million pervert glasses to sell.

Sorry this comment is a bit snarky, but yours is indeed a sight to behold.

I don’t think Meta is bad for releasing open models, but are you really going to ignore all the terrible things they’ve done over the years just because of that?

As for DeepSeek or any other Chinese lab, I’m not aware of any practices that would make me consider them a bad actor. Can you say the same about OpenAI, Meta or Anthropic?

> FB at one point had a very badass engineering culture

Perpetually kneecapped by one of the worst management cultures I've ever seen

> It's better than the benchmark scores seem to indicate compared to Qwen 3.6 27B. I'm very excited for 3.8

Is it worth considering if it's only marginally better than Qwen 3.6 though? Qwen 3.8 27B is almost there, and will probably be better suited as drop-in replacement for 3.6. Not even considering there's probably going to be a 3.8-35B-A3B too - which will have even better performance.

Meta doesn't need to be "redeemed". They have two of the most popular social media apps in the world. And theyll prob survive without ever having you work there

What is the other company that you would never work for?

Mind telling me roughly what you had on your resume that had meta /fb hounding you for a job? ( Of course so I can avoid having this situation happen to me, naturally)

Thomas Bayes would say that the population of people who hate Facebook is really big and the population of people who are scrupulous about whether or not their comments are specific to the matter at hand is relatively small

First time here?

Unfortunately there are a few topics that short circuit some terminally only people. One of them being anything related to meta. Few others recently emerging is Flock or Musk. It's really exhausting since you can't have a discussion relating to anything that may be adjacent to said topics. It's like a black hole.

Certain topics bring out the hidden Reddit inside.

Heaven forbid people have a moral compass and communicate it

It's trendy to hate on Meta just like it's trendy to handwave on Apple.

One can do no right regardless, the other can do no wrong.

At least in HN.

virtue signalling?

This is par for the course, HN is far worse than reddit on balance, especially involving upvoting/downvoting decorum.

Go vibecode something to auto upvote all downvoted posts, call it "Antiechochamber.HN" or something, and if enough people used it this website might improve a bit.

The latter bankrolled and continues to bankroll the former. It is not incorrect to conflate them.

All models come with some bias. Given Meta's track record, I would not touch anything from them with a 10 feet pole.

Social media, often owned and perpetuated by Meta, has poisoned the world. It is not redeemable at this point.

I think it’s also worth pointing out that that there are numerous less evil options to choose from.

Perhaps none of the AI companies are shining examples of high ethics, but basically all of them have ethical high ground over Meta.

At least Anthropic isn’t sending private videos from pervert glasses to contract workers in Africa. It’s a low bar but it’s a bar nonetheless.

Do AI companies make release plans based on upcoming other models like this? I would think all the processes that go into the repository and weight infrastructure pre-training, checkpointing, knowledge distillation, model compression, post training pipeline, ecosystem integrations, inference API, benchmarking, human eval/safety/alignment, docs, etc... all that dictates the release schedule.

Qwen3.6 27B is the go-to medium sized model for coding, so beating it is not a small achievement

I am working on a project where we have to classify customer calls into more than 10 categories. As the client wants everything locally I tried a few local LLMs. Gemma turned out to be the best model for this task. The classification accuracy is impressive, and the client is happy that I am using an American model.

You can partially tell by the tokeniser; which gives you some hint into the training corpus mix.

</div> is four Gemma4 tokens, but one Qwen3.6 token.

I'd be skeptical w.r.t. "by all metrics".

Qwen3.6 is a definitive, significant downgrade from Qwen3.5 for creative writing and prose for example. Yes, it's better at agentic and coding, but it regresses in many non-coding areas compared to Qwen3.5.

Of course, I do expect the 3.8 ones to perform better for agentic coding.

I've been using Qwen3.6 35B A3B, and with reasoning turned on, I'd say 2/3 (give or take) of the tokens for a response are thinking tokens. Which at 70+ tps locally, that isn't that awful. I run an 80k context across 4-10 "agents" for my solo TTRPG, where Qwen is the GM, each NPC at a location, the director, and the narrator.

Each turn is about 45-60 seconds to generate all of the various responses. The GM and director have reasoning on, and the NPCs/Location/Narrator do not.

It's a fairly good "engine" for that. I'm not sure how a denser Qwen would do here regarding speed.

Just to play devil’s advocate: you can’t compare Qwen to a (proprietary/closed source) hosted model and deduce that Qwen is overthinking, as Qwen gives you the full reasoning/thinking trace while all the proprietary models now give you only a summary “to prevent distillation”, making it hard to properly compare apples to apples here.

Qwen thinking is really good in Mandarin; and probably natively trained the most there.

Try a system prompt requiring it to think in Mandarin, while still delivering the response in the user’s language.

How is 30B smaller than 27B?

And a breakthrough in hardware, specifically.

Not to mention all the other ways they can screw you:

- Middle of the day, servers busy? Swap to Sonnet while pretending it's still Opus. Many people won't notice, and nobody can prove anything if they suspect.

- Middle of the night, server load is light? Put it into extra thinky mode so it burns more tokens to ramp up the bills. Flip the switch where it gets really pedantic about writing lots of extra test cases and verifying against documentation.

- Demand increases, but don't feel like running more hardware? Switch to low bit quants, but have a monitor model swap back to quality if it can tell you're running a benchmark.

Assuming model capability plateaus (I think it will), token providers will be in a race to the bottom to maximize profits at the expense of quality that's very difficult to measure.

LLMs are becoming commoditized, which means the margins are trending to zero. It's a lot less exciting to spend another trillion on a new model if you can barely make any profit. Meta getting out of the game might be the smarter move.

Can I ask, do you feel the pain of the level of abstraction? I haven't tried local in a few months, but last time I tried, I felt like I was directing a coding exercise - whereas with a frontier model, it feels more like directing a product building. "I need this feature", vs "write code to do this in this file".

That’s like saying an apartment building is “profitable” because the rent covers utilities while ignoring the real cost which is the mortgage on the capital cost of the building.

It’s funky math and a good way to quickly go bankrupt.

They make money on each token when you look at the electricity and interconnect fees, but no, I don’t think they’ve turned a profit on their Capex, even a little bit

> The major AI labs are gross profitable when selling access to inference.

Do you have a good source for this?

There are lots of points in a spectrum of choices. DGX Sparks, Strix Halos, and the surviving Mac Studios can easily run these 30B class models, just not as fast. So maybe just the leg, but you can keep the arm and first born.

And super noteworthy is that a 27B model (Qwen 3.6 27B) from this year is a huge improvement over a 120B model (gpt-oss:120b) from last year. The goal posts are moving, but at some point "good enough" is good enough for the kind programming I like to do.

It runs very quickly on my RTX 5090 fwiw. Whole thing is loading entirely into vRAM with a ~130k context size (the max) fitting as well.

The biggest gain you'll get is faster memory, provided you have enough capacity to load all the weight into vram. The DGX sparks and Apple silicon memory bandwidth (and also memory access latency) drag down the decode speed quite a bit.

I have two GPU rigs both with 2x RTX Pro 6000, can get ~250 tk/s decode with deepseek-v4-flash in native mixed precision. For context, in antirez's dwarfstar project he only gets ~20-40 tk/s on the same model @ 2bpw.

The latter is for sure usable if it's your only option, but it's really hard for me to personally go back to speeds like that when I've experienced the former.

(Also worth noting dwarfstar only has experimental support for dspark spec dec, when that lands it will definitely give a big boost at higher acceptance rates)

Agreed, the trend in this consumer-accessible range is encouraging.

Native dflash support on day 1 helps a lot! High quality speculative decoding speeds up a lot of agentic work.

You personally? Just curious. Context window is also a factor and ram isn’t really cheap. Sparks are assembled units which I like.

I would love to see any good research projects about it but i have the feeling that Frontier with MoE is making too fast of a progress so that a customized model would always be worse and that the MoE part is actually going somehow in this direction.

On the other hand, at the GTC was a talk about coding in different lanugage (like spanish) and explaining that the quality between spanish and english is relevant different.

But i have not found a good article about the impact of learning data with practical experiments or even if the order of the learning data matters.

At least I think i remember that Meta mentioned having better and less data can be better than more data with lower quality.

As long as these models can explain to you facts about any other topics, its still overfitted for the task though.

I kick myself a couple times a week for not getting the 512GB Mac Studio in February. I was holding out for an M4 or M5 chip...

Pulled the trigger?

Bought an M1 64 GB for 2000 euro’s second hand a year ago. That was sweet

If you're dropping thousands on API tokens, you're going to be slowed down at least 10x trying to do everything on a single MBP.

The models people are spending thousands on require more on the range of 600-800gb memory.

128gb hardly runs deepseek v4 flash which is almost free via api pricing.

Don't forget about energy usage, you'll probably never break even vs same model on openrouter.

If you don't mind exfiltrating all your IP to the API provider

Problem is that might go away or get nerfed.

Haha wow. I’m trying to even imagine the AI landscape in 15 years and I can’t.

I'm waiting for the speed/quality per dollar metric to go down a little bit further and then I will def run it at home.

Its not just that you send a sentence to an API endpoint, you always send EVERYTHING to that agent as a context.

You want to analyse your spending history? You now send everything to someone.

Either no one cares but understands this implication on how easy it is to really capture you or no one really things about it.

But i'm a lot more diligent on what I send. I disabled the gemini activity feature for example because google started telling me that my stuff could be reviwed by humans.

"With dirt cheap models like deepseek-v4-flash that will run "forever" on $10, the answer for me is clearly: no."

When it's free, you are the product.

It's the size of a big vm. There's nothing wrong with reserving that much working space for one item.

I’m quite optimistic about the long-term future of local LLMs for privacy and cost control reasons. An LLM running on my own hardware, even if it’s not a laptop but a home server, is one where I don’t need to worry about token limits, token fees, privacy, and “rug-pulling” from the vendor.

In the short term, the big challenge is being able to afford hardware that can run a ~30B model. Last month I got to experiment with LLMs on a NVIDIA RTX 6000 Ada Generation as a visiting researcher during my summer break. I see the power of local LLMs for agentic coding; they’re no Claude, but they are quite useful. I wish I had gotten into local LLMs before hardware has gotten prohibitively expensive and in some cases unavailable; Apple discontinued certain Mac Minis and Mac Studios with high amounts of RAM due to the RAM shortage.

Hopefully high RAM prices don’t become a new normal, though the next year or two doesn’t look good.

> A model you can run on a loptop is simply not going to work as well as it's needed for programming

The models you can run on a high-spec laptop today are approximately where frontier models were 12-18mo ago (albeit at a lower tok/s rate). If you scan back through hn comments from that era, you’ll find plenty of people saying “this is powerful enough to massively increase my productivity”.

I've never done it but would be interested because it cuts out the burden of worrying about costs. Maybe I'm mistaken on energy cost here. There's a constant raincloud that follows me around regarding limits, and it would be nice to shake that.

I've been able to accomplish incredible feats (for myself) since GPT-4, so model intelligence is secondary.

> I don't understand the desire to run own AI models for programming locally.

Privacy. Security. Not bulk uploading your trade secrets and intellectual property to Sam and Dario’s servers.

For some companies there might be a need to run them locally. For instance, Apple decided to run LLMs on the phone locally. I guess it depends on how important latency and privacy are. Perhaps Meta is looking at how much interest for those local models is there.

Yet.

Small models are still great for lots of “simple intelligence” use cases, like annotating or summarising files and media; or even just basic chat when given web search tools.

My local NAS is private and I’m not going to send it off to APIs for captioning or metadata; but even Qwen3VL 8B does an excellent job at this, despite being quite old.

They are also really excellent for fine tuning. Unsloth and Tinker (from Mira’s TML) are great places to start.

If your use case is narrower than “coding agent for everything”, you can probably match frontier performances on that narrow domain with ~30b and exceed it with ~100b+.

Gemma4-E4B (4B params) works pretty well as a local wiki, or when you don't have connectivity.

Cant wait to kick the tires. I am running qwen3.5-coder:35b on my laptop, and while thats nice, such alternatives are welcome.

2a03:2880: by any chance?:

https://news.ycombinator.com/item?id=48137854

Have asked them to stop numerous times and they just keep hitting for about eight months now.

Dense models run at a very usable speed (Qwen 3.6 was running at ~50t/s last I looked) on my dual 7900 XTX desktop. (And before anyone brings it up, I did not buy them for this purpose, so the up-front cost is irrelevant in my case.)

The benchmark comparison is against the dense variants not MoE

3090/4090 probably would do 40 t/s, for 5090 75 t/s is shown in the blog.

Don’t sleep on NVIDIA and Nemotron.

It’s not completely open source, but they actually release their pretraining and post-training datasets with some redactions for (cough) pirated content.

They also have very good code and playbooks for actually doing a fine-tune, CPT, etc.

Even if you’re not tuning a Nemotron model, its mixes are very excellent for your replay data slice; or general experiments. Way better curation and quality than Dolma, etc; or other large huggingface data mixes I tested.

There was a good discussion yesterday on the DeepSeek Flash release thread about this.

There's a large market, very large, who want the best regardless of what it costs. Probably a large enough market to keep that domain of research afloat (as opposed to shifting research manpower to cost cutting).

The reasoning is just that the marginal cost of AI is very secondary to fixed costs of the businesses themselves; it's not an excuse to sacrifice performance.

GDM -- Ok, I'll bite. Why are you including them?

Any pointers to more info about that? Sounds interesting.

I am extremely well aware of how rescues evaluate dogs. And I'm also fully aware that they do not know the full history of the dog. They go through a limited set of testing and interrogation to evaluate the safety of the dog. That's it.