> iv. Your use of the SAM Materials will not involve or encourage others to reverse engineer, decompile or discover the underlying components of the SAM Materials.
> v. You are not the target of Trade Controls and your use of SAM Materials must comply with Trade Controls. You agree not to use, or permit others to use, SAM Materials for any activities subject to the International Traffic in Arms Regulations (ITAR) or end uses prohibited by Trade Controls, including those related to military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons.
> b. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the SAM Materials, outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the SAM Materials.
We've seen b before. React was controversially released with a similar clause and ultimately Facebook dropped it and used a standard open source license. Their lawyers really love this idea for some reason.
Do their lawyers love the idea? Hard to say. My guess is, Zuck really likes the idea, so he keeps directing them and their chief counsel to try stuff like this.
It's almost as if having a CEO of a company placed beyond the control of the corporate board is a bad thing.
I'm no fan of Facebook or its social effects but I can't deny the wonderful downstream effect of their open source.
Popular microscopy models like Cellpose[0] have leaned heavily on the cornucopia of open and SOTA power. I have no doubt thousands of biologists have benefitted from the capabilities these models bring. I think it was unthinkable just 5 years ago that a single biologist with just a laptop could do mass-segmentation at this kind of fidelity.
Then there's Napari and it's plugin ecosystem[1] that wouldn't exist without the Chan Zuckerberg Initiative. Again, I'm not trying to glaze them but as someone in the biotech/microscopy space I can't understate how often I use and benefit from their open source.
From an AI perspective, PyTorch is obviously a huge (huge undersells it) contribution that shouldn’t be forgotten.
More recently though they’ve spent their time corrupting the meaning of open source, using that term for their various restricted model releases, which shouldn’t be forgotten either. Especially people like Yan LeCunn who know better.
You either die a hero or live long enough to become a villain.
React is more of a plague than a wonderful downstream effect or mixed bag. It’s essentially cemented Js as the way to build a website even if you don’t need the complexity. It is the leader in brain dead Js evangelism.
Credit where credit is due, though: ReactJS became #1 on its merits.
...yes, Redux (not React) deserves to be #1, but Redux isn't a complete, all-under-one-roof framework the way React is; but regardless of that: The Redux/React approach is just fundamentally a better design than what we had before: stateful-controls/widgets and two-way data-binding.
If you'd like to relive how UI devs suffered throughout the 1990s, 2000s, and most of the 2010s I invite you to try making a native Windows 11 desktop UI using WinUI3 using the MVVM (anti-) pattern: nothing but mutable objects of indeterminable state getting caught in infinite-loops or unbound recursion due to INotifyPropertyChanged - and Microsoft is still pretending that's the "right" way to build a UI.
Sorry am ranting on about something I have very little control over; it's just frustating.
The scoop: X-ray imaging of various strictures for scientific purposes produces colossal reams of data, previously hard to analyze. Meta provides machine analysis, both segmentation and classification, using unsupervised learning models.
> a fully reconstructed, semantically labeled 3D volume delivered back to the scientist physically standing at the beamline [x-ray] instrument, ready for interpretation while the experiment is still running. Total turnaround: approximately 15 minutes.
This is my impression from a couple years ago (in particular llama 3 for meta and phi) but I always said:
Meta: awkward alien trying to be human (see llama cracking lame jokes before replying to feel more warm)
Anthropic: condescending, that McKinsey style voice that talks to you like they know they’re better than you, plus acting like they’re somehow “humane”
OpenAI: precocious eighth grader / nouveau riche, uses lots of big words they don’t fully understand, tries to hard
Microsoft (phi, see my date reference): games all the benchmarks, not great in practice
To Grok's credit I think it's fairly good as a creative writing tool because it can be very "spontaneous" and it naturally seems to use an informal style. It also lacks a lot of the words and phrasing Claude and OpenAI get hyper-fixated on.
IDK if this is emergent from being trained on an endless trough of Twitter shitposts but compared to how stiff the rest are, I consider it a feature. I wouldn't use it for anything important though, heh.
Even that is kind tbh. The company is so poorly run and the leader so controversial that it makes it irresponsible to build anything serious that relies on their products. Other than the rocket part of the business, everything else under the SpaceX umbrella is a nonstarter.
Starlink arguably is not the rocket part and its the most profitable piece, it held together the rocket side of SpaceX and he expanded research and development.
> And I think OpenAI has solved more open math/ stats/ CS problems.
Still surprises me that OpenAI seems to lead in this one weird niche, I wonder what causes GPT to be able to routinely pull this off, there was one instance where some random 18 year old broke some mathematical question without knowing more than high school math if I remember correctly, all because of GPT.
> Claude often makes better looking interfaces and designs
How do you even qualify this? Either by "Well, when you're not specifying anything about it in the prompt" and then it almost doesn't matter at all, or by what actually goes into the prompt, then again it doesn't matter at all what model you use, more about the person driving it.
AI model inspects hundreds of thousands of scientific images. A job that previously took an expert roughly a month can now be completed in around 15 minutes.
Gonna need to have a talk with LLNL. I'm sure they didn't choose the name, but seems a tad leaning in to use the name of tech from Star Trek meant to produce untold abundance that instead became an unintentional doomsday device.
I used to work with images representing scans of brain tissue - for a full brain visualization at one horizontal slice terabytes was a common measure and the resolution of those images wasn't even particularly detailed - all the full resolution stuff was taken of tiny sub-sections of interest. This was also two decades ago - so I'm sure they've upped their game.
My company does whole-brain scans of mice on a Zeiss Z.1. Lower resolutions are typically in the low hundreds of GB. Higher-resolutions and multichannel staining can get you in the TB range. When a typical client is doing 10's of brains it definitely adds up. But even for us (and I consider us a smaller operation) we aren't output PB's. So I'd consider the above claim still pretty impressive.
Yeah - our multi TB scan from two decades ago was a dolphin brain image which is a fair bit larger than mouse - and it was a stained sample that ended up being used as our demo image frequently because the contrast dyes set well and resulted in a very pretty visual overall.
I didn't meant to say that PBs of image data is common place - we had no image that approached that size - but people outside the domain of microscope scan results might be unfamiliar with just how chonky these image files would get traditionally.
I think a big thing that the enterprise comparison misses is that this is petabytes worth of dense data that has to be put through fairly heavy processing (some of which is currently custom for the specific experiment) and studied by a human.
It isn't just a giant database of small files and metadata blindly feeding a recommender system.
Several petabytes is definitely a staggering amount of data in that context of being analyzed by human eyes to extract some scientific value.
I'm not sure exactly which enterprises you have in mind, but sure: quantities which can be expressed as "a year's worth fits on my desk" should not be described as "staggering", and 10 PB of hard drives will (just about) fit on my desk.
The LHC, on the other hand, that generates a petabyte a second and has to throw most of it away for obvious reasons:
As a former proposal specialist (B2B, B2G, non DoD) I looked into the Genesis Mission procurement site and process.
Unless someone can correct me, the total amount of grant monies is $280,000,000 or so.
It became obvious that it’s not worth my time to engage in the “mission” as they call it, even if I could benefit some worthwhile causes.
That’s a pittance and pretty insulting to the purported benefit of funding scientific endeavors. I’m not even attempting to be political here. $280 Million versus $XX Billion for warfighting is a seriously gross misallocation of public monies, IMHO.
Total lackluster reporting on the scale and scope of the actual numbers, but not surprising.
This is a really weird comparison. The Chan Zuckerberg foundation is nowhere involved with any war. All they’re doing is making money available for research. In which universe is $280m not enough?
Really fascinating writeup — I particularly appreciated how they didn't hesitate to delve into the load bearing design choices — the implications are staggering.
Whenever Meta claims their models are open source, you have to double-check.
SAM License (https://github.com/facebookresearch/sam3/blob/main/LICENSE):
> iv. Your use of the SAM Materials will not involve or encourage others to reverse engineer, decompile or discover the underlying components of the SAM Materials.
> v. You are not the target of Trade Controls and your use of SAM Materials must comply with Trade Controls. You agree not to use, or permit others to use, SAM Materials for any activities subject to the International Traffic in Arms Regulations (ITAR) or end uses prohibited by Trade Controls, including those related to military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons.
> b. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the SAM Materials, outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the SAM Materials.
The DINOv3 License (https://github.com/facebookresearch/dinov3/blob/main/LICENSE...) is similar but with the model names swapped.
It's always nice when a model's weights are released, but Meta's models are not open source because their weights always come with weird restrictions.
We've seen b before. React was controversially released with a similar clause and ultimately Facebook dropped it and used a standard open source license. Their lawyers really love this idea for some reason.
Do their lawyers love the idea? Hard to say. My guess is, Zuck really likes the idea, so he keeps directing them and their chief counsel to try stuff like this.
It's almost as if having a CEO of a company placed beyond the control of the corporate board is a bad thing.
I'm no fan of Facebook or its social effects but I can't deny the wonderful downstream effect of their open source.
Popular microscopy models like Cellpose[0] have leaned heavily on the cornucopia of open and SOTA power. I have no doubt thousands of biologists have benefitted from the capabilities these models bring. I think it was unthinkable just 5 years ago that a single biologist with just a laptop could do mass-segmentation at this kind of fidelity.
Then there's Napari and it's plugin ecosystem[1] that wouldn't exist without the Chan Zuckerberg Initiative. Again, I'm not trying to glaze them but as someone in the biotech/microscopy space I can't understate how often I use and benefit from their open source.
[0] https://cellpose.readthedocs.io/en/latest/models.html [1] https://chanzuckerberg.com/rfa/napari-plugin-grants/
From an AI perspective, PyTorch is obviously a huge (huge undersells it) contribution that shouldn’t be forgotten.
More recently though they’ve spent their time corrupting the meaning of open source, using that term for their various restricted model releases, which shouldn’t be forgotten either. Especially people like Yan LeCunn who know better.
You either die a hero or live long enough to become a villain.
> but I can't deny the wonderful downstream effect of their open source.
ReactJS has been a very mixed-bag...
React is more of a plague than a wonderful downstream effect or mixed bag. It’s essentially cemented Js as the way to build a website even if you don’t need the complexity. It is the leader in brain dead Js evangelism.
Credit where credit is due, though: ReactJS became #1 on its merits.
...yes, Redux (not React) deserves to be #1, but Redux isn't a complete, all-under-one-roof framework the way React is; but regardless of that: The Redux/React approach is just fundamentally a better design than what we had before: stateful-controls/widgets and two-way data-binding.
If you'd like to relive how UI devs suffered throughout the 1990s, 2000s, and most of the 2010s I invite you to try making a native Windows 11 desktop UI using WinUI3 using the MVVM (anti-) pattern: nothing but mutable objects of indeterminable state getting caught in infinite-loops or unbound recursion due to INotifyPropertyChanged - and Microsoft is still pretending that's the "right" way to build a UI.
Sorry am ranting on about something I have very little control over; it's just frustating.
> nothing but mutable objects of indeterminable state getting caught in infinite-loops or unbound recursion due to INotifyPropertyChanged
Rant on brother/sister. I'm a year into building a large WPF (because reasons) app for my employer and some days its very difficult.
The scoop: X-ray imaging of various strictures for scientific purposes produces colossal reams of data, previously hard to analyze. Meta provides machine analysis, both segmentation and classification, using unsupervised learning models.
> a fully reconstructed, semantically labeled 3D volume delivered back to the scientist physically standing at the beamline [x-ray] instrument, ready for interpretation while the experiment is still running. Total turnaround: approximately 15 minutes.
This fits with my impression of the 'personality' of various models:
Meta: Perceptive (strong vision)
Gemini: Fastest
Claude: Smartest
OpenAI: Prettiest
This is my impression from a couple years ago (in particular llama 3 for meta and phi) but I always said:
Meta: awkward alien trying to be human (see llama cracking lame jokes before replying to feel more warm)
Anthropic: condescending, that McKinsey style voice that talks to you like they know they’re better than you, plus acting like they’re somehow “humane”
OpenAI: precocious eighth grader / nouveau riche, uses lots of big words they don’t fully understand, tries to hard
Microsoft (phi, see my date reference): games all the benchmarks, not great in practice
I love how consistently none of us even vaguely consider Grok an actual player
To Grok's credit I think it's fairly good as a creative writing tool because it can be very "spontaneous" and it naturally seems to use an informal style. It also lacks a lot of the words and phrasing Claude and OpenAI get hyper-fixated on.
IDK if this is emergent from being trained on an endless trough of Twitter shitposts but compared to how stiff the rest are, I consider it a feature. I wouldn't use it for anything important though, heh.
The models might be good but the product design, user story, and marketing is so terrible that it’s difficult to see it as more than an also-ran
Even that is kind tbh. The company is so poorly run and the leader so controversial that it makes it irresponsible to build anything serious that relies on their products. Other than the rocket part of the business, everything else under the SpaceX umbrella is a nonstarter.
Starlink arguably is not the rocket part and its the most profitable piece, it held together the rocket side of SpaceX and he expanded research and development.
I would replace Gemini with DeepSeek. I also find OpenAI smarter than Claude but Claude is better for API ergonomics and frontend
They aren't talking about vision LLMs though. SAM and DINO are vision models, no LLM involved.
Why do you think OpenAI is the prettiest?
Claude often makes better looking interfaces and designs. And I think OpenAI has solved more open math/ stats/ CS problems.
> And I think OpenAI has solved more open math/ stats/ CS problems.
Still surprises me that OpenAI seems to lead in this one weird niche, I wonder what causes GPT to be able to routinely pull this off, there was one instance where some random 18 year old broke some mathematical question without knowing more than high school math if I remember correctly, all because of GPT.
> Claude often makes better looking interfaces and designs
How do you even qualify this? Either by "Well, when you're not specifying anything about it in the prompt" and then it almost doesn't matter at all, or by what actually goes into the prompt, then again it doesn't matter at all what model you use, more about the person driving it.
Qwen: Zestiest
Nemo: Straightest
DeepSeek: Craftiest
And, of course, Grok: Sir-not-appearing-in-this-listest
Drunkleist?
Muskiest
this page hijacks your tab's back button history :\
Well Meta has hijacked my privacy. Now IRL Meta can track me from the people wearing their sunglasses.
ELI5.
SAM 3 (Segment Anything Model 3) and DINOv3, projects released by Facebook, were used to do science and research.
AI model inspects hundreds of thousands of scientific images. A job that previously took an expert roughly a month can now be completed in around 15 minutes.
Gonna need to have a talk with LLNL. I'm sure they didn't choose the name, but seems a tad leaning in to use the name of tech from Star Trek meant to produce untold abundance that instead became an unintentional doomsday device.
So, not LLM models, right?
Also on this:
> The numbers are staggering: The DOE's light and neutron source facilities now produce tens of petabytes of data annually
Come on, petabytes are not staggering for entreprise software.
I used to work with images representing scans of brain tissue - for a full brain visualization at one horizontal slice terabytes was a common measure and the resolution of those images wasn't even particularly detailed - all the full resolution stuff was taken of tiny sub-sections of interest. This was also two decades ago - so I'm sure they've upped their game.
My company does whole-brain scans of mice on a Zeiss Z.1. Lower resolutions are typically in the low hundreds of GB. Higher-resolutions and multichannel staining can get you in the TB range. When a typical client is doing 10's of brains it definitely adds up. But even for us (and I consider us a smaller operation) we aren't output PB's. So I'd consider the above claim still pretty impressive.
Yeah - our multi TB scan from two decades ago was a dolphin brain image which is a fair bit larger than mouse - and it was a stained sample that ended up being used as our demo image frequently because the contrast dyes set well and resulted in a very pretty visual overall.
I didn't meant to say that PBs of image data is common place - we had no image that approached that size - but people outside the domain of microscope scan results might be unfamiliar with just how chonky these image files would get traditionally.
I think a big thing that the enterprise comparison misses is that this is petabytes worth of dense data that has to be put through fairly heavy processing (some of which is currently custom for the specific experiment) and studied by a human.
It isn't just a giant database of small files and metadata blindly feeding a recommender system.
Several petabytes is definitely a staggering amount of data in that context of being analyzed by human eyes to extract some scientific value.
I'm not sure exactly which enterprises you have in mind, but sure: quantities which can be expressed as "a year's worth fits on my desk" should not be described as "staggering", and 10 PB of hard drives will (just about) fit on my desk.
The LHC, on the other hand, that generates a petabyte a second and has to throw most of it away for obvious reasons:
https://www.itnews.com.au/news/computing-for-the-large-hadro...
As a former proposal specialist (B2B, B2G, non DoD) I looked into the Genesis Mission procurement site and process.
Unless someone can correct me, the total amount of grant monies is $280,000,000 or so.
It became obvious that it’s not worth my time to engage in the “mission” as they call it, even if I could benefit some worthwhile causes.
That’s a pittance and pretty insulting to the purported benefit of funding scientific endeavors. I’m not even attempting to be political here. $280 Million versus $XX Billion for warfighting is a seriously gross misallocation of public monies, IMHO.
Total lackluster reporting on the scale and scope of the actual numbers, but not surprising.
This is a really weird comparison. The Chan Zuckerberg foundation is nowhere involved with any war. All they’re doing is making money available for research. In which universe is $280m not enough?
When the people running it have a net worth of over $150 billion.
It's a weird world where $280M is not considered a lot of money.
That is — incredible
Really fascinating writeup — I particularly appreciated how they didn't hesitate to delve into the load bearing design choices — the implications are staggering.
> Meta's open-source approach makes this possible
I'm sorry, was this article drafted in 2024 and never updated?
This sort of random out of date comment is a common LLM writing trope. Though it is true that the segment anything model is open source.
https://github.com/facebookresearch/sam3
They have another with calling A100s modern.
> A100 GPUs — the high-performance computing chips that power today's most advanced AI systems.
They might not have written it with AI, but the article has a lot of em dashs and colons and not this but that statements.
The models in question have source and weights available. AFAIK not fully FOSS because the weights require registration to access.
SAM3.1 was released and open sourced March 2026.
But what's the point of correcting you? People will continue to propagate their own lies.