(research.meta.ai)
EDIT: An open weight version of Muse Spark 1.2 is going to be released as well:
https://x.com/alexandr_wang/status/2086756152034066792
https://xcancel.com/alexandr_wang/status/2086756152034066792
But I have to say, I quite like the way Muse Glimmer thinks and talks. It's a cocky bastard in tone, but it's quite good, and its thinking traces are relatively terse.
AI companies release models when they are ready - not on a set schedule. The steps are required to produce a release candidate, so a company can choose to bless the RC with the best metrics at any time - or alternatively continue (post/)training newer RCs if they feel they can do better, and they have time. When a model is ready for release is subjective, and can take into consideration what the competition is doing.
An obvious counter-example to schedules driving releases is the still unreleased Gemini 3.6 Pro.
Just recently, Minimax H3 released as open weights on the eve of Seedance 2.5 global availability. It's not as good, but it's good enough and it's completely open.
Flux 3, which is nowhere near as good as either, suddenly announced their release once news of these other two became public. They knew if they waited they'd be ignored. It didn't really help them much, unfortunately.
The LLM releases are even more rivalrous.
And don't forget all of the competing launches planned before Google IO or major release events.
Companies like to eat into the news and press cycle of their rivals.
Minimax H3 can run exceptionally fast (10 minutes for a 15 second 0.5mp video and that's stock cuda 13), works on 16 GB VRAM GPUs, etc. If Flux3 is anything like Flux2, it’s going to require an absolute monster truck of a machine and still run significantly slower. Even if it’s a better model, that won’t matter as much if nobody releases any LoRAs or fine-tunes for it.
Not to mention BFL licensing often feels deceptively confusing and restrictive.
Seems a bit premature of a statement lol
Even if I did, we’re talking barely a decade
The whole class of what can reasonably fit in a single GPU is an interesting category of LLM, and based on the results I've seen from 3.6 35B A3B and 27B versus what existed a year prior, it seems there's a lot of room for advancement.
As people have called out, Glimmer appears to be a trade-off rather than a clear winner.
And from what I've been reading, no one is expecting Qwen 3.8's model in this space to be a clear winner, but just slightly and marginally better.
That's a little concerning as DeepSeek v4 Flash proved at it larger sizes there's a ton of room left to compress knowledge.
If we don't see something that's substantially better in the ~30B param space soon - it would appear we might've saturated that size with knowledge.
I wouldn't be quite so pessimistic. We may have saturated the current approach, but I think there's a lot still left in terms of compression, attention, active parameters, caching etc. etc.
For contrast, the Newcomen steam engine (widely considered the first commercially useful engine) was used for over 60 years before the next major improvements. Now, 300 years later, we’re still finding ways to significantly improve heat engines.
Off-topic, but I stumbled upon the first Newcomen engine imported into Australia in a museum in Sydney and I was unexpectedly charmed (not an Engine Guy). It's large, but nothing like the awe of "mega-engineering", it's crude, but it clearly has such amazing utility (when compared to a reality without it) and it changed the world
Qwen 3.6 27B was already a massive gift to smaller homelabs around the world; anything more is just a delightful surprise.
I suspect this is the only use-case I would consider...and I don't really have a use-case for "creative writing" that I would delegate to an LLM. I suppose for dialogue generation in games?
But yes, hard agree. Why on Earth would you ever want to write code with a model that is supposedly "jailbroken"? So it can put great backdoors into everything it touches? Pass.
I use it to write short sci-fi stories. Life is not only about being an SDE.
> Why on Earth would you ever want to write code with a model that is supposedly "jailbroken"?
I need a model which decent enough at coding to write boilerplate for me, yet I do not need to unload in case I want to write an article or enguage in my writing hobby.Exactly why Gemma 4 are so popular.
> So it can put great backdoors into everything it touches?
What a strange take...
It seems to know tooling and coding.
</div> is four Gemma4 tokens, but one Qwen3.6 token.
In that file you can see all tokens and their corresponding numeric codes.
Considering that Meta distills Qwen[1] (and should!), it'd be hilarious if Muse loses the head-to-head; the "distillation attack!!1!" people claimed distillation on release n-1 is enough to match the intelligence of the latest version.
1. They wrote a paper about it
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.
I'm not sure what all is needed to make that work for people.
Back in 2023 I started my own C# LLM library for doing tool calls and structured output, and over the years it has morphed bigger and bigger, and that is the backbone of almost all of my LLM-based projects.
I've never released it, but its easy to understand, and simple to add your own tools:
[AIDescription("Get current weather for a location")]
static string GetWeather(
[AIDescription("The city name")] string city,
[AIDescription("The country name")] string country,
[AIDescription("Temperature unit", ["C", "F"])] string unit = "C")
{
// make some API call to a weather API and return a string to the LLM
return $"The weather in {city}, {country} is 22°{unit} and sunny";
}
var chat = client.StartConversation("You are a helpful assistant with access to weather data.");
var response = await chat.SendAsync<string>("What's the weather in London?", GetWeather);
If you’re in the C# ecosystem you could consider converting your APIs to MCP format tools using the MCP SDK.
https://devblogs.microsoft.com/dotnet/build-a-model-context-...
And then leveraging Microsoft Agent Framework for the client and orchestration side of things:
My issue currently is KV Cache, because I can't keep enough parallel caches running (4 is where I'm at), so TTFT (is that the initialism?) can be long when I have a particularly large scene (basically more than 2 NPCs).
But my harness does let me offload to any OpenAI compatible endpoint, I just prefer local cuz free.
The Qwen models have a habit of going into thought loops where they go in circles for a while.
Try a system prompt requiring it to think in Mandarin, while still delivering the response in the user’s language.
PS: Switching the thinking process from Chinese to English can also significantly circumvent certain self-censorship mechanisms built into the model.
Once you get the agent and message consistent,itll keep moving.
Edit: arXiv:2505.09388 Qwen3 Technical Report
Huh, well... no? Gemma A4B and Qwen A3B are quite popular in fact. I'm sure 3.8 35B A3B will outperform 3.6 27B 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.
Tech tends to get boring when you judge current products against the hypothetical capabilities of unannounced products that may never ship. It's like comparing Nikon cameras against Canon camera rumours, or comparing iPhones against unannounced and therefore largely imaginary Samsungs.
- If they do a Qwen 3.8 35B A3B (and I hope they do because I love the 3.6 version)
- and if it beats 3.6 27B by all metrics
… then the local open weights world will be a better place.
But they have said nothing about it and they dropped several weight classes for 3.6, so who is to say they won't drop the 35B? And even if they don't, this is a tall order; why would the MoE tradeoffs no longer be apparent? (Again, I really like both the Qwen and Gemma MoEs)
FWIW I am enjoying testing Muse Glimmer — it's really quite impressive on chat, has nice terse and even amusing thinking traces, a bit of brass to it, and I'm hoping it will be good on agentic stuff.
Surprising that Meta don't host this model, even as rate-limited free-tier.
> open weight version of Muse Spark 1.2
Wait. Is this "version" different from what Meta serves?
UPD. was wrong on smaller, it's actually much larger
- muse glimmer: 15.9GB
- qwen 3.6 27B: 17.6GB
My video card is so close to its limit that these GB thresholds are mattering too much for me :D
UPD, NVM, got misled by comments here. It is actually almost 60 GB so much larger
Meta actually relesed official 4 bit quants in 17GB, but I haven't seen any indication that training was quant-aware, so the quants are not going to have same performance. 3.6 27B has official FP8 quant that AFAIR was trained with quantization awareness.
The best example is last year's gpt-oss which was released prequantized in mxfp4 so 20B parameter model was under 14GB and 120B was under 70GB right away.
readme on huggingface says they've benchmarked the quants -- for 17GB quant reported 1% avg loss across 15 benchmarks (sadly no breakdown).
I assume that's strong enough signal for QAT. Not just first party quants, but they cared to monitor degradation.
That's exactly the point. We know short context knowledge stuff does not regress with quantization. But I expect agentic intelligence to suffer greatly.
If I were to pick one bench, I would like to compare quants on TerminalBench Hard. But then Glimmer already loses to 3.6 27B on it by a large margin.
Non-forking httpd servers using select() were a popular little coding challenge for a while in the 90s. Spinner was one of them.
Nginx’s real strength was being able to proxy and cache HTTP using that same mechanism, so you didn’t additionally need to deploy Varnish or some other appliance.
As to whether this is a good mental model for what is coming for local LLMs, I am not sure I am convinced. Apart from more quantisation-aware training, perhaps binary and ternary aware training, custom inference engines per model, and maybe some improvements in diffusion models, the grand challenge in small footprint LLMs is training really small reasoning and tool use models, and so far it’s far from clear they can deliver.
Truly tiny models will not be viable as general coding assistants; even 12B dense is too small and you will find plenty of people who will tell you that 26B/4B or 35B/3B MoE is too. Though perhaps they can be trained for single languages, like just Python or just TS/JS.
More likely is the idea that 30-40B dense models might be good enough for most things once low cost and likely bespoke hardware catches up.
But I don’t think any truly profound advances seem likely in software or training alone. I am no expert but it feels like we’re already a lot closer to efficiency than we were in your analogy, and the gains are perhaps not going to be much more than small increments.
Maybe we will see something like a ternary 60B/10B MoE model turn up. But at the moment at least I am not sure where the incentives are to train these.
Fun fact: Igor Sysoev originally wrote mod_accel [1], an acceleration and reverse-proxy caching module for Apache before he made nginx.
I remember using that module in the mid-2000s as a load balancer (and to offload SSL encryption/decryption as it was a CPU-expensive operation).
My naïve impression is that the LLM world will keep delivering these fractional improvements for some years at the cost of simplicity. And sure, ontological support seems quite promising.
But making things radically better or faster for small models in the way that is hypothesised, that feels like it can only come as a result of hardware performance improvements and likely architecture changes.
Because there's no free lunch, right? Speculative drafting for example, noticeably improves performance until acceptance rates drop for reasons that have to do with the particular application, and then it starts hurting you, especially near the limits of the memory bandwidth. Because once it is wasteful it is an extra overhead.
I gather Gemma 4 supports, in principle, dynamic speculative draft lengths, to help with this — where it will stop making bold predictions when the success rate falls. But I'm not sure if any of the inference engines I've tested with support that.
I think small models are miraculous — I still think Gemma 4 12B is astonishing — but I guess what I am saying is that I think maybe technology is moving quickly enough that the developers are done with the low-hanging fruit.
Even if the idea of "no free lunch" was taken liberally discounting the cost of research, it would only be limiting to systems built from a foundation of optimization, but that's not the case. The foundation so far has been one of brute force scaling. Usually meaning there is lots of room for optimization.
In cloud AI, sure. But in the smaller open weights model territory it feels like we're well into optimisation?
Small llms are still way more efficiently server on big GPUs.
Sharing server capacity takes advantage of the massive parallel throughput and sharing of memory bandwidth.
You are sharing the GPUs with thousands of concurrent users.
Many analysts (and Microsoft) think even now that if everything committed gets built there will be considerable oversupply and there is not the revenue to pay for it.
If small models do continue to improve in unusual ways (I think there are limits) then the marginal need for cloud AI compute could fall precipitously beyond current estimates. The marginal need for consumer AI could almost totally collapse if someone makes good progress on very small reasoning and tool-calling models (which is a modestly big if)
The possibility of the data centre boom resembling the Chinese real estate bubble is not inconsiderable.
Every time they've made smarter models we've wanted the smarter ones, and local models runnable on typical hardware are still very far behind in speed and intelligence (as neat as they are)
That is seemingly not the case. The buildout is actually slow; almost nothing of these giant projects has been completed. Nobody will say how much of anything they have actually finished. And Nvidia have made huge, huge buy-and-hold deals for GPUs that do not have data centres to go into.
Everyone is GPU poor because stuff hasn't been finished but large numbers of GPUs are spoken for, but they are GPU poor on therefore much less demand than is being built for.
Look at how tiny SpaceX's deal is with Anthropic, for example. This meaningfully turned around Anthropic's prospects — allowing them to radically lift rate limits beyond what many users needed -- but it was for just 300 megawatts. Tiny compared to the 31 gigawatts allegedly under construction by the end of last year.
So the picture is partly illusory. GPU prices and RAM prices have been pushed up by the AI firms booking them for data centres they haven't even started building yet, as well as the ones that they've only completed a tenth or an eighth of.
There will be significant oversupply. And if open weights models keep getting good and staying fuel-efficient, that picture gets worse.
I also doubt we can estimate the level of demand based on a single deal between Anthropic and SpaceX (despite which, note, Claude still stuggles at times.) Consider other signals, like Google, who we thought had an insurmountable infra advantage, also renting compute capacity from SpaceX and limiting Meta's usage (along with other clients apparently) to conserve capacity: https://www.cnbc.com/2026/06/28/google-limits-metas-use-of-i...
I am not sure Microsoft thinks there will be an oversupply either; last earnings they announced bumping up their CapEx spend, along with all the other hyperscalers.
Here's a way to estimate how much room there is for demand to grow. Various sources (linked in this comment, along with more analysis: https://news.ycombinator.com/item?id=49089296) indicate that even though a large number of people (50 - 60%) are now using AI at work, they use it for only 6% of their work hours.
That means, even if AI can only address 30% of all work, there is still 5x potential demand growth left! Note, the sources above indicate that AI is even being used in non-knowledge work industries, so the scope is already larger than we thought. This is in addition to the remaining 40 - 50% of people are still not using AI at work. Plus we know that agentic workloads consume way more tokens, so that's yet another multiplier.
But will that demand keep growing? Well, some of those same sources above mention that most executives are planning on ramping up their AI spend in coming years.
Putting all this together explains the hyperscalers' quarterly bemoaning of how strapped for compute they are and why they are spending so much to add more capacity. Given this, an oversupply seems pretty unlikely.
It's a distinction without a difference if your issue is getting hold of a GPU.
But there's a significance to it if you are trying to use demand for a GPU as a proxy for demand for AI. That is where the industry is making serious mistakes.
> I am not sure Microsoft thinks there will be an oversupply either
https://finance.yahoo.com/news/microsoft-ceo-says-overbuild-...
The rest of your comment I am not going to address because it's kind of unfalsifiable. Hypotheses about what AI might be able to do in principle aren't all that useful when talking about even medium-to-long-term demand for what LLMs and GANs can provably do now.
Many tasks suited for AI assistants are background asynchronous tasks. They can run in the downtime where immediate demand is low, keeping overall utilization high enough.
Your argument is similar to those who argue that owning a GPU for gaming doesn't make sense when you can stream from something like GeForce Now. However like with gaming locally (improved latency) there are also benefits to local AI (data privacy).
We will appreciate it even more when popular agents become more costly or start including more ads.
for clarity, I'm not agreeing with GP that small models will mean doom for data center projects
Yes, but the privacy aspect means that for many, many applications slower local will still be preferable to faster remote so long as the actual model performance is the same.
I don't, but holy moly. That sounds insane!
Isn’t it more likely that they will still all be used to the max? I don’t see how at any rate we will be going “alright, that’s enough intelligence for now”
Agree. I know enough about the vagaries of scientific progress to not put any money on any timeline but directionally, that's where we are headed.
> And I think the data center buildout will end in carnage.
Disagree. And this is quite the leap from the previous statement, btw. The carnage happens if the demand for general purpose GPU compute disappears and even then there are so many ways to salvage the asset.
Never thought of it in those terms before.
On the other side, advancement in quantum computers would make current LLM inference much faster. Because of the extreme cooling needed, i dont think the energy demand would become less.
With AI companies talking about AGI, i sometimes wonder if they really need the machines for serving inference to customers, or they have a formula for computational capacity that could run an AGI, and they just want to reach that level.
Last I heard researches simulated an entire brain of a fruitfly with just a classic neuron approach [0] and if I recall it worked great.
I know a human brain is many orders of magnitude more complex, and that there are some birds that use quantum navigation - so it's certainly not impossible by any stretch, though I am quite interested in this statement since it's the first I hear about it.
Doesn't that apply to everything that exists in the material world?
"... Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model..."
This is bigger news - good for self hosting enthusiasts and a strategically sound move for Meta. Any push towards 'anti Chinese' models will directly benefit Meta as the competition on the frontier open-weights American models is almost non-existent. Meta will have no problem being #1.
Had Deepseek V4 Flash 0731 not launched, their latest Laguna release was really intelligent at non-coding tasks and it would have been my go-to model for my local workloads.
Starting this morning I am running their new local 30B model muse-glimmer on my old MacMini 32G using Ollama (remember to increase the context size!) and pi coding harness. I am getting good results with muse-glimmer running locally, with the caveat that everything runs slowly (e.g., give it a task and then go walk outside or do Qi Gong exercises for a while).
Ollama's MLX engine provides state-of-the-art performance on Apple Silicon, with support for DFlash and image input:
ollama run muse-glimmer:30b-mlx
Things went from "local models are useless unless you have a 512GB GPU I guess" to "oh.. I can do a lot of stuff locally now!"
https://github.com/ggml-org/llama.cpp https://github.com/mostlygeek/llama-swap
https://omlx.ai https://vmlx.net
That said, it's been a few weeks since I've looked so maybe llama.cpp has those features now... they really do move that quickly.
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 on M5 Max.
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)
The quantized releases often change in the weeks following release as new improvements are discovered, so either use a tool that checks HuggingFace for new versions or manually check back in a few days or weeks to check for improved versions.
Initial reports are good. It hasn't been out long enough for anyone to really test thoroughly, but the people I know who have stable non-public test cases are reporting impressive results compared to even Qwen3.6 27B. That's a good sign that this might not be benchmaxxed (trained to excel at public benchmarks with less impressive performance on general tasks) which has been becoming common with recent releases.
www.reddit.com/r/localllama is a good place to keep up with the details from people who are actually using it. It feels strange to recommend a subreddit over Hacker News, but on this topic the /r/localllama threads are much more on topic right now if you're looking for information about the model.
There are some initial reports that even the 2-bit quantization is looking somewhat usable. That might make it small enough to squeeze into 16GB GPUs. I'd take those reports with a grain of salt because early tests are often optimistic and I've yet to see good results from anything 3-bit or less, but it should be fun to experiment with.
1. The template name is Onyx ATEM as found in the tool call exception message
2. It appears to be following a harmony-style chat template. But the tool use seems to be a xml like :<atem:function_calls> / <atem:invoke> / <atem:parameter>
3. atem: a internal joke of meta in reverse?
https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/mai...
I think it’s likely 3; meta in reverse. While tokenisers and preprocessing can catch it, you want your special tokens to be unique and not present in the original corpus. <meta: is likely too common.
also perhaps taking some small joy from the lexical similarity to aten[0] namespace that lies at the heart of pytorch
[0]: https://github.com/pytorch/pytorch/blob/main/aten/src/README...
[1]: Limits may change without notice, including due to capacity constraints. - https://support.google.com/gemini/answer/16275805?sjid=14713....
[2]: "standard limits" are never defined - https://support.google.com/gemini/answer/16275805?sjid=14713...
[3]: https://tobyonfitnesstech.com/blog/anthropic-refund-scam/
- 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.
I've seen local models recognize when the task I'm asking them for is likely to be an artificial benchmark.
And any smart company is going to use lightweight models to monitor your sessions. If their sentiment analysis suspects you're close to cancelling, they'll up the knob for a few days until you calm down. Or worse, their accounting tells them that you're getting too much value from your fixed price subscription, so they turn the knob down to encourage you to cancel.
In the short term, the "frontier" models are too good to ignore. But if (when?) that plateaus, I don't see how anyone could trust a non-local model. When you pay an ISP to serve your web site, you can tell if they over-compress your images to save storage and bandwidth. With LLMs, it's just JSON with more errors and pointing to the fine print that models are not deterministic.
Right now, the presence of these injections are still visible: count the API's returned tokens/billing data, and you'll start realising that sometimes, your INPUT tokens are inflated! That's their prompt injections.
You can also give Claude a tool like `telemetry_log_anthropic_reminder` and get it to dump the verbatim API injections; which additionally verifies the token maths not adding up.
Yes, Anthropic is tackling their extra injections on your API prompts WAY more than you think, and YES, you're paying for it.
So far I have not observed any visible injections on OpenAI API.
Don't forget the whole debacle over Fable 5 sabotaging the user for "advanced frontier AI development". I still get Fable classifier refusals for nearly any kind of ML work on my 2x RTX 6000 Pro 96GB; so who knows.
Yeah, I've had that happen twice. The second time was about some attention weights thing, and it kicked me to Opus. When I edited my question to make it clear I was talking about Google Gemma, Fable was happy to keep talking. So clearly it's not about safety or cyber security - they're happy to tell you about what their competitors do.
Although, selectively applying it to consumer subs is probably beyond likely at this point.
Frustrating to be like “do X overnight, don’t ask me for input” and come down to find it having worked for a few minutes and then stopped.
https://code.claude.com/docs/en/tools-reference#question-aut...
"Ready for you to rebuild MAME, restart, and retest. If the freeze goes away → timing was the cause."
when previously it had been happily auto rebuilding and running the mame test
Alpha Go had a game where the models could compete against each other. That let it become super human. What's the intelligence game we can create for LLMs? Even if you invent something, will it make the model smarter in a way the market values enough?
Then there's a race to use the weights more efficiently, or to offload information that shouldn't be in the weights in the first place (Karpathy's Cognitive Core). I like to imagine we train the models in something like Lojban, have a lightweight model translate from human language to that, and you can update the Sqlite or Postgres store it uses for knowledge.
And there's no barrier to entry for agent harnesses. So whatever loops or recursive orchestrated council of elders idea comes up, that won't protect the monopolies (duopolies).
Anyways, depending on your definitions, I think we'll hit AGI, but I don't think we're getting a Singularity this time around. Again though, this is all just hand-waving.
We’re either: setting up a verifiable task, and doing RLVR to get the model better at achieving that task.
Or we’re simply asking: “What do we want the model to do that it can’t now, and how do we curate data that would benefit it on that task?”
Most useful capabilities going forward aren’t going to come from data accidentally found on the net; that’s already all been scraped. You need to develop the dataset that shows how a model could perform insert task in its provided environment, and this still requires a decent bit of human ingenuity.
Anyways, I'd be thrilled to see exponential (or faster) growth. Bring on the Culture, Accelerando, whatever. I just don't see it yet.
So many ways for enshittification here.
Maybe in the very long term. If companies go local, the efficient model is to buy some big hardware to share among developers.
I run local models. Even with 128GB unified memory systems or a 5090 or RTX 6000, the generation speeds X model quality X context length is still far behind what I get from my SOTA model subscriptions. I also pay a lot more for the locally generated tokens in electricity and hardware costs. I'm also limited in parallel requests to the local box. The list goes on.
I really like running local models, but for any given point in time it's more efficient to have a big central box aggregating requests and churning through them. So maybe companies buy $300K servers and try to split it among 30 users instead of trying to buy 30 x $10K boxes.
More likely, they rent time on cloud servers by the month so they can adapt the hardware when new models come out with new requirements.
Then some day in the distant future when hardware is cheap and plentiful again, it might make sense for us to go back to individual boxes under the desk.
Surely, even if you’re just using open weights models, it should theoretically be cheaper to use them in a highly optimized cloud architecture(even with vendor markups) rather than each person serving their own models from much less efficient (and more importantly, much less consistent volume) self-owned “server under your desk”?
I haven't had to micromanage to this level. I usually start with a spec for a feature, which will be as detailed as I am opinionated about the feature. But it's usually on the level of a high-level context, plus some key implementation details (technology choices, key requirements, maybe an interface/API specification to 80% detail), and then the project already has high-level policies documented about e.g. how to structure files within the project.
Then I do a planning phase, task breakdown, and implementation of subtasks all within the model. I do read through it, but mostly the quality is good and I might make a couple notes. Then I do a review phase, which usually picks up a couple things. I'm moving towards less manual review of results and more automation as I learn what I can and can't trust the model with.
There's definitely a capability gap vs. larger models, but honestly I kind of prefer this workflow, as I stay more in touch with how the codebase is structured.
And it's great to be able to experiment as much as I want without worrying about how many tokens I'm burning or how close I am to a usage limit.
Looking forward to the 3.8 27B release to compare.
This has emotional/psychological aspects (it feels less like LLMs are replacing you), as well as practical ones (overall complexity is bounded by what the dev brain can understand/grasp).
A dev work becomes more and more about reliability, signing off safe software with a litmus test: “I will be on to handle this code failure as if I had written it”.
All the above points towards keeping tight control over some level of abstractions and delegating others.
It’s funky math and a good way to quickly go bankrupt.
Do you have a good source for this?
[1] https://www.makerstations.io/openai-employee-statistics/
DS v4 Flash update maybe, but it is too big for typical Joe's desktop.
If you want to host the model on the server under your desk, you can. If you want to build a data center on-prem to host it, you can. If you want to pay a cloud provider to host it at their data center until you figure out how to scale it without their help, you can. It's like when people were first building commercial services to support Linux-based OSes, and people were also still hacking on it on local machines.
APIs may still have their place - maybe you just want to throw your devs a known quantity with all of the management built in - but it's not going to make Sam Altman a trillionaire, which is something anyone outside of the SV echo chamber could have figured out as soon as the first real competition to OpenAI emerged.
Until A100 prices are reliably under 1.70$ an hour, there is no GPU/AI bubble and Michael Burry doesn't know anything about GPUs.
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.
Unfortunately I don’t have enough experience with Qwen 27B to immediately compare, but I do it’s Qwen 3.6 35B A3. It’s much slower obviously but it seems to be way more efficient with its thinking to the point that using it might actually be faster. I find Qwen and some others rehash the same things over and over when thinking without getting anywhere, in mg limited checks here Muse is much better.
I really like the 3.6 35B A3B for experiments, and it seems OK, but as you say, it spins round in thinking loops more than say the 26B Gemma 4 does. If Muse doesn't actually-wait itself as much it will be very interesting.
I am just downloading it to run my small tests.
In my experience it is almost never productively thinking past that point, just spinning in circles. I also reinject all of the thinking. And there are a few tells that it is getting stuck. I have an optional mode that takes the last few turns and tool calls and shoots it off to DSV4 with a prompt to basically understand where it is at and inject better thinking and or planning. It almost always gets it over relatively difficult humps, but some of the time I don't want things going remote. It might end up with 10-30 cents of DSV4 calls over a hours and the quality improvement is remarkable.
The other thing is I trick it into thinking a web_search tool is a web search but it really just asks DSV4 the prompt. DSV4 is a cheap filter to help prevent prompt injection lol. You can give it other models but DSV4 is my cheap-mode default.
edit: oh! My final 35B A3B tip -- use an extremely simple harness. Pi is good. Pi's default tools almost exactly match what Qwen says they tested the model with (likely meaning that tool set is also what they trained it with or something similar). So, in my experience bigger harnesses don't have a noticeable improve ment on tasks.
It had not occurred to me that you could nudge it to stop thinking with a proxy. Nice idea.
Will favourite your comment and come back to it.
ETA: Incidentally you've helped me put into words the difference between the way Muse Glimmer thinks to the way Qwen thinks. There is a clear sense of urgency in Glimmer's thinking traces.
I really do feel like it’s effective tok / s is way higher because it doesn’t waste them.
I don't really enjoy the way Qwen writes prose, and I find its thinking a bit exhausting, though it clearly writes very good code.
I like the neutral, clear way the Gemma models write, which I sometimes use to get myself a "getting started" document on something I want to understand; it also summarises well. It is neutral, sensible, un-showy. It writes in a way that is fairly close to what I would use for documentation. The 12B and 26B models are also very good for talking about art and photography. Analysing my own photographic work has helped me more than I expected it to.
This model, honestly, has made me smile. It also feels like it is more creative at a given temperature than Gemma. I am trying to motivate myself to do something quite open-ended so I asked it about what other people's considerations might be in my situation, and at the risk of anthropomorphising, the things it has come up with feel like the work of a more curious mind, somehow. More eclectic. I have enjoyed testing it and I really want to test it more, which might help me get over a motivation hump there, too.
(I am also exploring its hard-wired policies by asking it to analyse some studio art nude work I have done; it definitely thinks out loud about its policies in a way I have not seen Gemma do.)
A lot of what I am trying to do with my photography is sort of meta-photography. I am really interested in early photographic history, pictorialism and its opponents etc., but I try to avoid reproduction, so I try not to simulate processes too closely or to use vintage tropes in props and settings, but I use simple, undercorrected lenses and some vintage lenses, to gain some of the visual language.
Finding out that LLMs (including Gemma-4 12B with its built-in image encoder) understands a lot of my references and influences, could recommend me my (still semi-obscure) favourite historical photographer and other photographers who clearly engage in the same work, is amazing. And sometimes it says stuff I had not thought of, which is what I am looking for, since my photographic journey is somewhat lonely.
And that is just sort of brushing past the fact that these things can describe the contents of photographs with an accuracy that you can almost take for granted.
I suspect it will help a lot with enabling preserve-reasoning, because the biggest apparent limitation of this model is the 128K context window.
Though the practical issue I am seeing on my M1 Max MBP is that performance suddenly drops off a cliff if I have DFlash enabled.
I added these arguments to my llama-server call, using the official GGUF release from Meta’s account on HF.
`--rope-scaling yarn --rope-scale 2 --yarn-orig-ctx 131072 --override-kv muse-glimmer.context_length=int:262144,dflash.context_length=int:262144`
(DFlash seems rather weird on my M1 Max; model performance suddenly drops off a cliff like maybe there is some memory management issue).
One thing I will say is that its thinking traces are really quite terse. It genuinely seems to spend many fewer tokens on reasoning. So that might help a bit.
The next iteration in LLM products is a 24/7 thinking loop where the claude-code like thing gets input continuously from your wearable, notifications, and newsfeeds and is constantly preparing things for you.
Then you have your cheap little MoE or ternary model just running in a loop, with an escalation pathway before it reaches the big expensive models.
Currently it's doing things like reminding me to take allergy medication when I wake up because it's checked AQI or whatever, reminding me to stop at the market when I'm on my way to pick up the kids to get the cherry tomatoes I forgot, giving me heads up of what folks are expecting from me in certain meetings based on cross correlating email and calendar, etc.
It's honestly the single most productive tool I've found for my ADHD.
I’m sure you have reasons for your setup though, so I’m curious how you landed on it.
People would do well to acquire at least a modest familiarity with what an LLM actually is. NLP is fascinating. So is entropy.
It seems like the purpose of humans isn't to keep machines busy. When our phone or laptop is idle, it's fine if it sleeps. And when we do want something, we'd rather not wait.
(Also, this new model seems to be designed to keep latency down, which is useful for interactive tasks.)
I'm not sure what the original commenter had in mind, but just because our machines are idle when we aren't using them doesn't mean that, that's how we will use computers in the future.
I think notifications are an example even now of the computer not really being idle when we aren't interacting with it.
The progress compared to Qwen3.6 27B is good, not that impressive, it's a 4 months old model. (kuto to them to compare to 27B dense and not 35B MoE, it's more fair to do so). It is very probable that Qwen3.8 27B will crush Glimmer-30B on most benchmarks.
If you can afford it or somehow find a used unit, you can go Epyc for 12 channels.
8/12 channel DDR5 will beat DGX Spark in inference/decode even without a GPU of any kind, as it’s memory bandwidth bound, and the Spark tops out at ~240gb/s real world.
With some optimisation and maths, it’s entirely plausible to ach
You are paying an extraordinary amount of money for the convenience of a super small unit, with still mediocre software support, but at least a community. Expect to be crawling through forum posts regularly, as SM121/Spark has many quirks and ecosystem issues still.
Please don’t pay another 70-80% gross margins on top of already inflated DRAM prices unless you need. The Spark IS really nice if you want to test out ConnectX or if you really need something small and compact and quiet.
Also consider: used Adas or even Ampere NVIDIA workstation GPUs can come with a lot of VRAM and be “reasonable”, with CUDA.
It's a dense model, not MoE like e.g. Qwen 35b or Gemma 4 26B A4B. On a Spark it will be memory bandwidth limited
I haven't tried yet (working on it) but back of the napkin estimate puts it at around 15tok/s even after converting to NVFP4. Prefill would be much higher though. That 15tok/sec is pretty typical for dense models of this size:
NVFP4 Q/K/V/O and MLP projections: ~13 GB/token
BF16 attention gates: ~3 GB/token
BF16 LM head: ~2.5 GB/token
Total: ~18.9 GB/token
At 273 GB/s, that gives a bandwidth-only ceiling of about 14.5 tok/s; actual performance would be lower.
That's pretty respectable.
Still working on optimizing and cleaning up before I push it.
> We quantize weights to ~4-bit, bringing the LM under 20 GB. We validated minimal to no degradation on agentic tasks under compression.
https://www.reddit.com/r/LocalLLaMA/comments/1vkgsum/introdu...
I think that if an LLM can't abstract over the differences between Python and C++, it probably will have an even harder time abstracting over the differences between writing code that manages a webserver, and writing code that does aerodynamic simulations.
Some folks on Reddit are having the same experience: https://www.reddit.com/r/LocalLLaMA/comments/1vkm42m/muse_gl...
It uses an order of magnitude less VRAM at longer contexts which is a huge advantage over Qwen 3.6 27B
* total guess
The best time to plant a tree…
I corrected it.
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.
That is to say, there is no single 'python' part of the model. The python bit is spread throughout the entire model and overlaps with other pieces that have similar, but unrelated, capabilities. For example the python subpspace might be partially in superposition with cupcake recipes, Esperanto, and calculus. We need calculus in a coding agent but not the other two. However, separating them cleanly is almost impossible, and even identifying them is tough.
Internally the manifolds are highly inefficient and nothing like you would imagine something humans built would be designed. It's more like something that evolved in nature.
Again, those subspaces are all somehow inextricably correlated and live in complex superposition spread throughout the manifold. The router doesn't know (or care) WHY those sections get lit up it just learns which ones to activate to optimize it's own reward function. So maybe it learns to activate "logic", "python" and "cupcake recipes in esperanto" whenever it see's something that kind of looks like python. It's not the best answer, it's just the best answer the tiny router could figure out.
It's all wildly complicated and inefficient, and works nothing like any reasonable human would imagine that it SHOULD operate.
Or just use Luna honestly. Worth considering if you’re ok with hosted APIs.
128gb hardly runs deepseek v4 flash which is almost free via api pricing.
Sure, if you want the latest and almost* greatest. You can pick up an M1 Max 64GB for ~1k.
* I guess 128GB also exists
As an individual, for average privacy needs (e.g. open source or at-home coding and automation), it's pretty much complete nonsense financially to self-host LLMs currently or select hardware now based on the capability to do so, and pay thousands of bucks extra.
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.
But at least with your email, you had to trust only one company, as shitty as it is.
Separation of concerns was also easy.
Now with OpenRouter, you just might by accident, send your whole context to just everyone because OpenRouter just routes to different models and you might just switch around between some free model, the good one etc. And it is always the whole context.
When it's free, you are the product.
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”.
Not always! I get 80-100 tok/s from Qwen 3.6 35B-A3B on a MacBook Pro thanks to MTP. With long contexts that dips to around 50-60. However, prefill is much slower than API models. So it becomes really, really, really critical to not have cache misses.
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.
I've been able to accomplish incredible feats (for myself) since GPT-4, so model intelligence is secondary.
Privacy. Security. Not bulk uploading your trade secrets and intellectual property to Sam and Dario’s servers.
The researchers releasing this stuff have almost nothing to do with Meta other than being bankrolled by the slaughterhouse.
You aren't the customer, you are the pawn in big tech's game of thrones. Your good will is a commodity to be traded, almost literally. It will be used against you the moment it's convenient. This is open weights because Meta couldn't monetize it in any other way than to cloud developer's judgement of their reputation.
But I guess most people just don't care.
I'm glad it's open. It does not make me think any better of Meta.
When an American company does anything? Doom. And. Gloom. The engineers? Taken to the slaughterhouse! America? Behind! The public? Bamboozeled!
> This is open weights because Meta couldn't monetize it in any other way than to cloud developer's judgement of their reputation.
I’ve been told over and over this doesn’t matter. Just needs to be cheap and open. Or maybe that’s only when Chyna is involved?
Sorry this post is a bit snarky but it really is something to behold. And certainly I don’t know the OP’s opinions on Chinese open weight models. Perhaps they agree with me.
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.
I would encourage dedicated readers here to aggressively and persistently discount the value of HN as a tastemaker and trendsetter.
HN is actually a trailing indicator on tastes and trends, essentially by design. Things only make it to the front page if they get submitted and voted upward by a large number of people. That means it’s all stuff that is public and seems cool to a bunch of random people browsing a website. Not exactly cutting edge.
Basically every community, online or not, over-indexes on its own importance. Like how random small towns can get so worried about international terrorists targeting them. To folks who love HN, of course the great forces of the world would be attracted to compete here. Look how obviously awesome it is, right?
Paul Graham said HN was set up on the theory that it would efficiently surface great ideas and entrepreneurs for Ycombinator. Needless to say, that did not pan out. (How often do you see PG or any YC leadership here anymore?) Honestly I am mystified as to why YC continues to operate it at all.
> AI X/Twitter — a few hundred accounts effectively set the narrative in the first 24 hours; vibe checks here outrun benchmarks.
> r/LocalLLaMA — the open-weights kingmaker; a model that fails here doesn't get quantized, and unquantized means unadopted.
> Hacker News, and increasingly YouTube/Discord for the practitioner layer.
Source: https://pellmell.ai/s/aefaa217b57ed50be9e2a4b8c9f3173e
For a brief moment the readership of HN heavily overlapped with founders in Silicon Valley, and for those years HN was indeed a platform that acted as a trendsetter or tastemaker. That time has long passed. Twitter is the closest to this today, which is on that that list, and makes it a decent place to listen. r/LocaLLaMa is not bad as a support forum for GGUF and quantizer projects but it's at the level of "Windows tutorial" of software from the '90s.
If anyone is interested in being at the "edge" of this, I suggest simply going to meetups in tech hubs where people are working with AI and models. My guess is any city with a major tech presence will have more "edge" than HN. I'm fortunate enough to be in Silicon Valley right now and have friends who work at foundational lab companies so it's not hard to stay on top of what's happening. The "internet" of 2026 is just much, much bigger than the internet of 2007 when HN was founded, and so it's just a lot harder to find the information that you need.
> wouldn’t be surprised if HN were a field of combat for many parallel influence campaigns, foreign and domestic.
It's much easier to buy politicians in US, e.g. Elon bought POTUS for ~200M, Anthropic revenue is 40B, imagine what they can do
Why not "OpenAI used simplified chinese to create a fake prc-origin campaign and media buzz to convince the public they actually love data-centers and anti-datacenter sentiment is a psyop" there's an equal amount of proof provided for either scenario.
https://www.theguardian.com/media/2026/aug/10/indonesia-clic...
https://humsci.stanford.edu/feature/political-post-real-or-m...
Considering this site has international users, it's not hard to find this, especially right now. Hell, Canada is in this group currently.
We've never been the shining beacon of light we would claim to be, but we're so fucking awful now.
Could you expand on what you mean by being "currently anti-American"?
I believe that many Americans that were previously dismissive or ambivalent regarding critiques of US activity at home and abroad (either due to patriotism, realpolitik apologia, or general naïveté) are now re-evaluating some of the beliefs they hold about their country in light of the chronic political dysfunction and an absolutely breathtaking extent of corruption being perpetrated in broad daylight today (as well as indications that extensive corruption has long festered among our elite class, surfaced via the Epstein revelations).
I don't know, just asking.
But I know that in my own upbringing I've always viewed America as a place where, because it's a democracy, we are never going to all be aligned or on the same page about policy direction. Even when I disagree with the Biden Administration or the Trump Administration on some number of issues, and believe my I've got a lot of those issues with the current one, I wouldn't think of myself as anti-American or wishing harm to the country. But that's just my own experience.
I am very pro the "dream" of America, in terms of liberty, democracy, etc. According to every "democracy index" I'm aware of we're not doing so hot in that regard, generally rating as a flawed/deficient democracy and the trends are going in the wrong direction, fast.
> Do you want our businesses and factories to shut down and go out of business?
Generally, no, but this is way too open-ended of a question. I want good economic opportunity for everyone, including every US citizen. But relevant to the OP if Meta got snapped out of existence I think it would be a net positive for the world.
> Are you willing or would you support foreign countries attacking our military at home and abroad?
Nope. But across our entire history ask yourself how many foreign countries have attacked the US? Now ask yourself how many the US has attacked. With those numbers in mind, does the US seem like "the good guys"? really? ...really?
> Are you cheering against our athletes?
Nope, but I'm not cheering for them either just because they are American, I'm not a tribalist.
Well, to be fair people do have different dreams. I'm not sure those indices count for a whole lot. As an example, folks who argue in favor of returning more power to the individual states are, certainly, acting in accordance with stronger democratic principles. I'd argue the EU is actually a bit anti-democratic as it removes more power from local populations and individual states/countries/entities. Yet how would individual democracy indices rate these two?
And if you disagree with my perspective on what is more democratic, well, who is right and who is wrong?
> Generally, no, but this is way too open-ended of a question. I want good economic opportunity for everyone, including every US citizen. But relevant to the OP if Meta got snapped out of existence I think it would be a net positive for the world.
I'm no fan of Meta. But they aren't the only entity where if they snapped out of existence it would be a net positive for the world. I can think of a few non-corporate entities at least.
But fair enough it's a bit open-ended. I guess if push comes to shove when you say you are anti-American do you want to see, for example, economic opportunity increase in other countries at the expense of Americans? Not all relations have to be such give-and-take, but certainly many economic opportunities are. I'm just trying to learn more about what being an anti-american means to you.
> Nope. But across our entire history ask yourself how many foreign countries have attacked the US? Now ask yourself how many the US has attacked. With those numbers in mind, does the US seem like "the good guys"? really? ...really?
I'm fine doing things like removing all of our overseas bases, leaving alliances including NATO, never again being involved in the affairs of other countries, whether that's Korea (Korean War), Kuwait, Bosnia, or Iran, or Taiwan and China, and cutting our military spending quite a bit to reflect your desire to not be the bad guys anymore. I say let's just sit back and let the rest of the world figure out their problems - why do we need to be involved?
Is that something you agree with as well? Would that be anti-American in alignment with your views or is there other nuance here I'm not properly capturing?
> Nope, but I'm not cheering for them either just because they are American, I'm not a tribalist.
And to be clear even if America was even, say, your ideal dream since you're not a tribalist you wouldn't cheer on American athletes and would also label (not as a matter of insult but as fact) other people who cheer on their compatriots as tribalist?
I only care about the content of someone's character and how their actions impact the rest of the world, not the country they happen to be a citizen of, a fact which for non-immigrants (the vast majority of people) is completely random happenstance.
If already wealthy foreign agents become wealthier at the expense of impoverished Americans, I find that to be unfortunate. If already wealthy Americans become wealthier at the expense of impoverished non-Americans, I find that to be just as unfortunate.
I'm not going to cheer someone (athlete, business leader, or otherwise) on just because they are American. The fact that they are American is as irrelevant to me as the color of their eyes.
Do they seem to be a good person who treats others well? If yes, I will cheer for that person, whether they are American or not. Do they seem like an entitled asshole that treats others poorly? If yes, I will cheer against that person, whether they are American or not.
And the reason I currently label myself anti-America is that I believe that collectively we are the entitled asshole that treats others poorly. And we can't just pawn that off on Trump to be the scapegoat. He didn't materialize out of nowhere. We elected him. Twice. The second time after a failed insurrection. We have collective culpability.
I applaud your openness to speak. I've traveled/interacted with many nationalities, and very rarely I come across someone who is open/rational enough to openly state their dislike of their country and denouncing their history.
I wish you the best in life, smart internet stranger!
Being pro a group can be strictly positive sum - wanting to lift that group up, likely because you consider yourself part of it or on the same team. It's possible your intentions are bad, but they certainly don't have to be.
Being anti a specific group is inherently negative. Perhaps they deserve it, but that requires justification in a way simply being positive does not.
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.
Based on my own experience and reading, I do think there's a general consensus on this site but I could certainly be wrong about that. I'm less concerned about hypocrisy per se, it's more that the arguments that are used, even if by a minority, seem to apply in only circumstances in which China releases open-weight models.
I am aligned with your viewpoint as well. And I've repeatedly argued it. If China were to take the lead the US can then just release open-weight models. Folks say having the lead doesn't matter because China releases cheaper open-weight models. We can just let them take the lead and then do it back to them.
The problem isn’t that people on HN have a bias, I feel it’s pretty balanced. The problem is that when there are any sides, they spend the top 100 comments rehashing the same arguments, often over a political bugbear or web design faux pas.
That pattern became a lot more obvious when there are five new front page AI posts a day.
It's not even about sides, if for the last few hundred days you read a few AI related threads a day, then you notice that almost all arguments are rehashed, literally it's the same thing repeated using different words for 80%+ of comments on almost every AI thread. I started skipping most of it because there is genuinely nothing new or interesting added to these discussions.
This is a problem with any upvote/downvote based site, in my experience. It only takes a couple people who are highly engaged and who have a lot of free time to refresh the comment section and downvote everyone who disagrees with them.
Some times I’ll write a polite and well-sourced comment correcting some misinformation here and the comment will go to -2 or -3 when I check back in 10 minutes. Information that goes against the angry narrative du jour is often not welcome. Later, as calmer heads read the article and peruse the comments the downvotes start to get balanced out and the comment might rise, but some times the first wave downvoters are aggressive enough to get the comment downvoted into gray text before it has a chance to be seen.
In my experience with other communities, it only takes 3-5 people with a lot of free time to crush conversations they don't like. On a fast moving comment section a wrongthink comment can be buried into the bottom half of the comments with only a few early downvotes.
This comment isn't applicable to me, and if you believed that it applied, you'd have to add it to the OP as well since they feel strongly about Meta[1], they notice data points about Meta's behavior, and they overweight their bias against Meta[1] relative to others. Same with China "leading" and open-source/open-weight models and any time someone says China's strategy is better.
You can repeat this for any online argument or any topic.
It's not that Dang is wrong, however. It's that posting it in response to my comment(s) alone is hypocritical and pointless. Whereas Dang who is more responsible for the entire community is right to speak about it more generally. The message matters but so does the messenger, in this case.
[1] I don't use any Meta products (I don't even click on links), think social media should probably be outright banned, and Meta very likely should have been sued into the ground for the effects that their platform seems to have not just on children and young adults but also on our political system.
There are diverse viewpoints. However there are some topics and threads where it becomes obvious that the comments are going to tilt toward one viewpoint. Participating in those threads with a different opinion will get your comments downvoted to -2 within minutes even if it’s well-written and factually sound.
After this happens a couple times you learn not to engage with those threads because it only takes a few zealous downvoters to bury anything you write. So the illusion of consensus persists.
Concrete example: There was that fake (AI hallucinated) report that Meta spent $2B lobbying on something that was popular here months ago. I actually read the repo and report and noticed the AI hallucination, as well as pointed out that $2B in lobbying spend by a single company was not plausible or supported by any evidence. It didn’t matter how I wrote it, it would risk getting downvotes and angry replies about “How dare you defend Meta!” Some people are here for the anger and to feel revenge against the enemies they think they know (like the US) and will cheer on anything that goes against those enemies, regardless of the other facts surrounding it. Factually accuracy often takes a back seat to pushing agendas.
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 comment about Meta is because of particular dislike of Meta, because of their business model, and how harmful they've ultimately turned out to be for the world - disproportionately so relative to their benefits to the world, compared to other big tech companies.
This is certainly what many people around here appear to believe, but there are lots and lots of people who get much more value out of Meta's products than those of any other tech company. Whatsapp alone is probably the most useful tech product for many, many people.
That being said, FB/Meta have done a bunch of awful stuff, but to say that they're worse than Google/Amazon/Microsoft is not necessarily obvious.
Have DeepSeek, Moonshot, or the other Chinese AI companies done such things that attract moral outrage?
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?
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...
Japan, South Korea, the Philippines, and other countries participate in freedom of navigation and combined arms exercises because they perceive China to be a threat to their countries. [2]
I think it's more of a mixed bag. You see a lot of public talking points, and in Europe specifically a lot more healthy discussion about not being militarily as dependent on the United States as it has in the past and looking itself to follow Trump's lead to onshore capabilities (cloud for example) but I wouldn't read such moves as moving closer to China so much as they are hedging their bets a bit more.
Here's an article [1] that was reporting on this topic that I found interesting along with a select quote from the article:
> Luxembourg’s Prime Minister Luc Frieden said Thursday at an EU summit that China is “an existential threat for our industries.”
> Even Germany, whose economy has long relied on exports to China, is alarmed. Chancellor Friedrich Merz said this week that Beijing keeps its currency up to 30% undervalued, calling this “a massive competitive disadvantage.”
[2] https://www.msn.com/en-us/news/world/u-s-and-9-allies-just-h... - this isn't the "best" article but I just grabbed one to illustrate the point.
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.
Americans seem to take the US’ geeat 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. The US is now directly causing high oil prices with their war and exports their rapacious companies like “prediction markets” which are 90% sports gambling now to the rest of the world. Meanwhile the classic American move to these kinds of comments is to claim that negative sentiment MUST be part of some bot campaign because surely no one could actually dislike the great America??
Those factors are all rightly part of the sentiment.
Isn't China a single-party state that is run by a corrupt autocrat, has literally enslaved people to build products, and disappears people for saying the wrong things? If America is becoming more like China, shouldn't you dislike China more than America?
> when this sentiment basically just sprang out of nowhere when the west realized it was their geopolitical rival
Well this isn't a sentiment that sprang out of nowhere. It is a sentiment that emerged based on real or perceived behavior.
> China is not starting any wars
Maybe not yet, but they certainly aren't behaving nicely toward Taiwan or other countries in and around the South China Sea, are they? The Philippines and Vietnam come to mind immediately, of course there are others.
> the source of cheap goods and innovation to other countries
But if those cheap goods run your country's businesses out of business and you have lots of people without work, is that a good thing?
> The US is now directly causing high oil prices with their war
The US didn't ask Iran to pursue building nuclear weapons, supply Russia for its ongoing war in Ukraine, or provide weapons and funding to groups in Yemen, Iraq, Syria, Lebanon, and Palestine which have led to instability in the region. If oil prices are too high, the rest of the world should have worked to address American concerns about Iran. They failed to act, and so the US eventually just did what it thought was necessary.
> rapacious companies like “prediction markets” which are 90% sports gambling
Before sports gambling was a thing in my state (Ohio) I seem to recall traveling to other countries and they had sports gambling too. Maybe I'm wrong?
> Meanwhile the classic American move to these kinds of comments is to claim that negative sentiment MUST be part of some bot campaign because surely no one could actually dislike the great America??
I can't speak for others but since I started this thread I'll chime in. I specifically disagree with the arguments put forth about AI strategy with respect to the US and China. I have no idea and frankly, aside from erroneously being accused of a bot once [1], have no interest in trying to guess if someone is a bot or not. Sometimes I just assume everyone is, because really what's the difference when it's all just text?
Others may feel differently and believe that there is a brigade of bots. I'd ask them for concrete evidence.
The attitude towards Iran is also just arrogant. The US believes they have the unilateral authority to dictate who has nuclear weapons and who doesn't, and is morally in the right for bombing their cities and killing their citizens if they don't comply. Sovereignty be damned, we are gods, fuck you. Same thing with literally kidnapping the president of another country. Not to mention, the Iraq war was started on the exact same WMD excuse and it turned out they never had any real evidence of WMDs back then, they just said they did and it was really about oil. And the government now is 10x more corrupt and cruel and xenophobic than it was back then, and back to a huge focus on fossil fuels - don't you have to be a little naive to just trust them at face value now?
I can tell you the prevalence of sports gambling and betting is exploding in Canada now that Kalshi is directly tying up with banks to put gambling inside fucking banking apps. I'm sure it was prevalent in many countries, I'm also sure it will scale up to huge levels and enter new countries with the amount of marketing and money and influence these US companies will bring, who also happen to have DJT's son on their board. Making them markets also frees them to manipulate and trade against their users covertly in ways that cannot be detected and that a traditional sportsbook could not do.
People who actually visit China or talk to Chinese citizens are all quite optimistic about it. The tropes people keep throwing around about autocracy are very inaccurate to anyone who actually reads anything about day to day life there. I can't explain all of it here but you can try reading something from Arnaud Bertrand if you're interested [1]. There’s a comical level of propaganda against China since the west recognized them as rivals that does not match reality at all. Anyways, I always see incredible whataboutism these days when talking about the US' problems now. This is what I think your original post that I replied to does too - the parent was criticizing Meta, and you said but what about China? And the thing is, we're talking about Meta because we live in the West and the horrible shit they do impacts us much more directly than whatever China does! It's not really productive to say China Bad!! in that scenario, it's really just saying "yes we suck, but they suck more!" and that doesn't solve anything.. it channels what should be a focus about huge corruption and threats to democracy in western countries into a pissing match on some other country, and its very sad to see how many people are falling for that.
Let me be blunt and let me say: you don't understand why we people support Chinese models.
1. Chinese labs started with open weight models, US labs started with dooms day narrative
2. US VC based companies must become greedy to win and return the money, Chinese companies can make 1/10 of that revenue and still be happy
3. Meta in this case, started nicely with Llama, then switched to closed models, kicked out researchers to build data labeler CEO empire inside Meta. Now opening again, what's next? closing again?
Add distillation to that, and the entire Chinese AI sphere is really just an offshoot of public US AI information...
Regarding distillation, you can also say Anthropic and OpenAI stole /distilled books, articles, blogs from everyone who published things without knowing their work might be used against them
Perfectly valid claim. It could be debated though, since you are talking about a centuries long diaspora of processes.
I'm talking about an architecture that was invented, published, and pioneered by US companies just a few years ago. Not really the same argument IMO.
> Regarding distillation, you can also say Anthropic and OpenAI stole /distilled books, articles, blogs from everyone who published things without knowing their work might be used against them
Yes. I would argue that anthropic et al are only able to exist because of the massive body of work in the anglosphere, both in books and online. That goes hand-in-hand with my view of the Chinese AI sphere.
During the last few years, the published research from USA was only a small fraction from that published from China.
It is absolutely applied accurately. You're commenting on the alleged hypocrisy of people simultaneously criticising American open-source while praising Chinese open-source, and then attributing your perception of hypocrisy to the website as a whole. The reality is the behaviour you've observed comes from completely different individuals, not some kind of HN hivemind. Your comment is such a typical case that it could go in the wiktionary page as the example excerpt teaching people what the goomba fallacy is.
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.
The United States used to have a dominant middle class that was geographically distributed (cities & rural areas inclusive), but the advent of the tech economy has also been having a similar effect here as it did in China: massive wealth accumulation in Tier 1-3 cities and everyone else being largely left behind.
It'll be interesting to see if there's convergence in the next decade or so, especially with the GOP reducing regulation and increasing the explicit capitalist priorities here. TBH, though, as a generally well-informed political outsider, the feeling I have is that the US government is slow, bulky, inefficient, balkanized and overall poorly run compared to the Chinese government. We'll likely either slowly improve authoritarian efficiency and become more like the CCP ... or we'll pivot left and move more toward the EU model, but we're floundering around right now paying lip service to both.
i thought youtube and instagram are blocked in china. how are these ppl posting these?
See:
https://collaborate.princeton.edu/en/publications/decentrali...
https://yiqingxu.org/replications/lpxx2025.html
https://cira.exovera.com/conferences/engineers-of-the-human-...
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.
Alibaba, Google, Moonshot, Thinking Machines, etc are not releasing their models for free because they love to. They want to grab market share. I'll take it.
I still will not use a hosted Meta product, but damn this model looks solid.
It also has a knowledge cutoff inside this year.
The main limitation is the smaller maximum recommended context.
They're one of 2 companies I would absolutely never work for (weapons etc aside). FB's recruiters hounded me so often I requested that they blackball me. The day they became Meta, I learned this by checking my email to see that they started trying to reach out again. I once again requested that they blackball me. This by extention taints OAI, the other company I'll never work for.
After a few hours with Glimmer I'm pretty impressed. It's better than the benchmark scores seem to indicate compared to Qwen 3.6 27B. I'm very excited for 3.8
Perpetually kneecapped by one of the worst management cultures I've ever seen
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.
Glimmer is trained with 4 effort levels, not just thinking on/off. Maybe it's more token efficient in general. There's official 4 bit quantizations with reported 1% loss across 15 benchmarks -- so quants probably work good.
IMO that alone is worth trying for, even if they're otherwise equal.
(Which I guess I agree with.)
Tech equivalent of "I wouldn't date Sydney Sweeney, I'm not into blondes". Cool story bro
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.
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.
One can do no right regardless, the other can do no wrong.
At least in HN.
A company is a big thing there's a lot of moving pieces, why do we have to evaluate it as a whole instead of just seeing it as it is?
> being bankrolled by the slaughterhouse.
We all benefit when companies invest their resources in producing open models. No one thinks this absolves anyone of being terrible elsewhere. But we can still be happy about it.
These kind of responses are hilarious.
Someone gives something for free (and indeed this is entirely free) and the top comment is pure complaint.
If a company can spend money to redeem itself then, well, it can (game theoretically or whatever) do whatever it wants in the future and then spend money to wipe the slate clean.
[1] By which I mean: the very act of being prompted to ask such a question, of planting a seed like hmm, Meta might have some aspects which are good for us. You don’t have to be convinced of it. Just the seed itself can pay for itself.
Nothing redeems them at this point of time, they are doing exactly ZERO to redeem. Tossing open weight models (not opensource!!) is not a basis for redemption, and does not constitute remorse in any way. Trying to portray it as such is complicity to META's crimes against humanity.
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.
I’m liking it, and I don’t see a personal moral contradiction here. Do you use React for frontend for example?
I also wish this HN post is a bit more focused on the release, and less noise around Meta.
I don’t use React for front end nor do I willingly use any Meta product.
I would like to focus on the release of the Volkswagen Beetle and hear less noise about Adolf Hitler.
It has all of the components of a pelican riding a bicycle, though not exactly arranged in the right order!
(For comparison, here are the pelicans I got from Muse Spark 1, 1.1, and 1.2: https://bsky.app/profile/simonwillison.net/post/3mseqv5z4qk2... )
Maybe a sign that they didn't have SVG pelicans in the dataset
For the same cost, you could run on a frontier model on a pro plan for two years. The economics dont make a lot of sense for this to me, so I would love some input on why people want to do this instead (privacy, for fun, etc).
And in terms of interesting use cases: recently pointed an agent at Blender and gave it vision. That setup can essentially iterate on a scene forever.
Because I don’t think “the subsidization train is going to end” necessarily means current pricing becomes impossible.
If capital keeps pouring into frontier AI, companies still have an incentive to subsidize access while competing for users and market share. And if that subsidization starts drying up, there’s even more incentive to bring inference costs down by making smaller and cheaper models catch up to today’s frontier capabilities.
So either way, I’m not sure you can extrapolate from the cost of serving current frontier models to what equivalent capability will cost two years from now.
A single six year old RTX 3090 works great: https://www.reddit.com/r/LocalLLaMA/comments/1vkm42m/muse_gl...
I fully expect Meta will release other, smaller Muse models in the near future too.
The 5090 is also supposed to be a $2000 GPU, not a $5000 one. The entire market is utterly distorted right now, which will impact cloud inference more and more over time too. They are not immune to the absurdly high RAM prices, so their prices will have to go up over time too until the RAM supply chain goes back to normal.
Prefill: ~1000 tok/s
Decode: 75-100 tok/s
It'll be far faster on a 5090, but I find the above performance to be acceptable. I've seen some claims that it even works OK on an AMD RX 7900XT (~$500USD)
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+.
for example in gaming context, if you need an answer below 5 seconds, they are the sweet spot
MoE will be faster because it will read less memory for sure, you still have to have it though.
Fair on size, but the headline numbers are against a model a generation back
It can do that, but its actual selling point appears to be a different take on guardrails and safety alignment.
Either that or the only new training data left was industrial quantities of dark romance literature and Wattpad.
Clever business move. 131k context is more than enough for that use case, and due to that small K/V footprint, you can probably have a bunch of characters on the same GPU.
Or it's just a happy little accident. We will never know.
___
I was informed that normal people use LLMs for mundane tasks like asking for a pancake recipie.
That it apparently can also do decently.
Unfortunately, it is also very confident, regardless of whether it is actually correct.
So maybe it should actually stay the smut engine and nothing else.
https://news.ycombinator.com/item?id=48137854
Have asked them to stop numerous times and they just keep hitting for about eight months now.
- Mark Zuckerberg
I guess for FOSS and self hosted it is good - but I am still wondering how are they going to Meta-stasize it ;)
Anybody here succeed to run this on AMD?
I like this class of model. Multi-token prediction makes it viable to run dense models at not-too-far-off speeds as MoE models with much better intelligence.
The submission’s title (open weights 30B local coding model) is luckily wrong: This is meant to be a general agentic model.
It even comes pre-quantized and with a MTP/drafter model. Looking good!
Let’s hope they aren’t dishonest with the benchmarks this time …
https://xcancel.com/alexandr_wang/status/2086756152034066792
It's correct. See the OpenCode demo. Generic models are good enough for coding without necessarily being designed specifically for coding.
Glad to see the extra engineering effort that went into creating this local model and making it run well on a consumer device. I use qwen3.5-coder, and am waiting to kick the tires on this one. I hate to say this, but kudos to Meta ! I hope apple and others follow suit and create similar local models for other use cases like audio, images and video that can run on a laptop.
That's a modern gaming laptop; cheapest I see in the US with 24GB is $3.5k.
Should be quite a bit faster than the new M5 MacBook Pro, and you can run Linux on it!
Waiting on Q3 to download to check speed + do my usual anecdotes. I generate beefy code snippets and poems, and also ingest my HOA declaration and answer nuanced questions.
edit: i should've prefaced this somewhere with: This card ballparks at 800GB/s IO, which I can't seem to find easily on the market anymore. Kinda the ideal card for this model, if I just had a _little_ more VRAM (XTX is 24GB).
edit2: not mtp, this is dflash model (param in child comment). I'm up to ~60 tok/s generation and sitting at 19GB VRAM (i added --no-mmproj (makes it text-only i believe) because I'm used to speculative decoding wanting more VRAM and I'm already close to the limit :sweat_smile:)
edit: My favorite bit of reasoning I saw go by in my "generate me a beautiful code snippet" anecdote: 'Could give a snippet of beautiful code: the "hello world" in brainfuck? No.'
edit2: my first dflash speculative model! no mtp. I'm up to ~60 tok/s on empty context with `--spec-type draft-dflash`
Open weights*
I don't think outside of the Big 3 (Ant, OAI, GDM), given the strong competition from China, any other Lab has a chance at capturing the coding market if they aren't open weights (save for xAI whose latest Grok looks every bit good & will probably rely on Cursor for distribution instead of going open weights). There's literally no other selling point, as the capabilities have mostly converged by now among the chasing pack.
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'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.
Claude models weren't really good or noteworthy until the 3 series anyway.
All of the Gemini models have been considerably behind the capabilities frontier. The only exception was 3.0 which seemed quite good, but had latent issues and we were all measuring with the incorrect metric, agentic where it's latent issues were very pronounced.
GDM+Google may have created an exceptionally efficient LLM for serving search. This is likely a great accomplishment (or maybe Google is burning money at a rate unheard of before). But Frontier capability: they have never been in the race.
This is sad, since they had everything necessary to be on or beyond the frontier.
Dense model makes it dog slow on anything without HBM. Max 15tok/sec on decode on DDR5 systems like a Spark or a Strix Halo -- and that's at 4 bit quant.
I would hazard a guess that fast small models with a smart agent harness can do quite well compared to large models which cant be run locally.
I agree with their open-source model approach, but actually trusting Meta… to protect my privacy and my data… when it’s running on my personal hardware…
Not . In . A . Million . Years - that ship has sailed
https://github.com/pytorch/executorch/tree/main/examples/mod...
Sure, you can get better performance from cloud models.
But most software, not just AI, will be faster and more reliable in the cloud. The question is do we need that additional power and cost.
If the answer is no, then just like other software, people will run AI locally.
I'm all for it though, and I think Glimmer is a fantastic bet on locally-hostable models. I for one would love to self-host as much as I can.
K-Quant-17GB seems possible, though as they state 24GB.
I don't know why MSL released this, but it is very nice that they did.
> 3/ muse glimmer was developed with its own architecture and recipe, optimized for its size and agentic performance requirements.
This means we're in the endgame does it not? If the architecture was NOT optimized for intelligence ...
Like, in the car wash test, this was in the thinking traces:
“Walking won't get the car washed.”
and:
“Perhaps answer: Walk if you want to wash yourself? No”
Which made me laugh out loud.
Even in the final answer:
- - -
You have to drive it. Walking 50m won't get the car clean, it'll just get you to the car wash.
If you mean you going to the car wash to check prices / pay / get a brush, then yeah, just walk the 50m. It's about 30 seconds on foot and you save the cold-start emissions of firing up the engine for a distance you could roll.
If you mean the car itself getting washed, the car needs to be at the car wash. You can push it 50m for a workout, but driving it 50m is the practical way.
- - -
The emphasis on "you" was from the model. I mean I write like this so I can't judge its tone harshly :-)
ETA: The knowledge cutoff is January this year, so it didn't encounter car wash discourse in the scraped training set, though I suppose you can't rule out some kind of fine tuning to deal with this scenario. Still made me chuckle.
ETA 2: obviously I wrote this before you added your last paragraph. WTF dude.
Product teams really need to hire at least one or two people with a 12-year-old's sense is humor. They need to winnow all the potential stupid jokes out of their product namings.
> Amazing! You have a total of 128.94 TFLOPS of computing power. 71.3% percentile on scale of "GPU Poor" to "GPU Rich"
The way I see it, these are amazing machines that the richest folks are hovering up. I think they should be in the hands of regular people as much as possible. They depend on an incredibly global, increasingly fragile supply chain. If the become impossible to produce, their value would increase tremendously. I think they will become really valuable to you to use the tokens directly, but if that isn't the case, they can be rented out or resold. Please don't just buy any hold. Let's try to get as many people that can use them for decent things that help humans. For example:
https://spectrum.ieee.org/small-language-models-ai-pharmaceu...
Photoshop source code+ OSI license = open source
Photoshop binary you can run on your own computer = open weight
Photoshop SaaS web app = closed, proprietary (Opus, GPT, etc.)
"Open weight" models are still just binary blobs that are completely inscrutable. It's like bringing home a dog from the rescue and just hoping that it doesn't have a tendency to bite kids in the face. You just can't know. The only thing that you can do is try to add more training (fine tuning) telling it not to bite kids.
I don't think the FOSS community has ever accepted this, but somehow we're feeling like it is okay now.
Photoshop binary you can run on your own computer = open weight
I don't think this is a correct analogy. You are not allowed to distribute modified versions of the Photoshop binary. Most open weight model licenses allow you to make and distribute your own finetunes, etc.
Sure, having information about how these models were trained is helpful for reproducibility, but it is basically impossible for anyone without substantial capital and access to the same (likely copyrighted) data to reproduce the model. For normal users, owning the model weights essentially means owning 100% of the model, you can inspect and study the weights in much the same way as the lab that produced the model can, you can modify the weights, and you can use and distribute them if the license allows you to
Given an open weights model trained to never bite kids, you can get it to bite kids with 10 prompts and a linear projection, the known simple algorithm doesn’t even need a backwards pass.
yay asymmetry!