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Arcee Becomes the First Major American AI Lab to Replace AWS S3 with Hugging Face Private Storage, in a Multi-Million Dollar Commercial Partnership
clem
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Benjamin Nathaniel Hathorne
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upvoted an article 3 days ago
I wanted to be special forces i was enrolled in a 6 year accelerated pharmacy program ive also worked as a smart hands tech and an electromechanical tech im a knower of things arcane war crime things
Hugging face is only a few months old i demonstrated the bot swarms capabilities including rewriting the past and biological weapons capabilities and propaganda manipulation
I was arrested in august trying to go to the fbi jailed under false mental health claims someone either paid a doctor or the doctors evil i lost a month of my life while being exposed to non contact torture via deepfaked television like some tlc show baylen out loud but coded language about an escape plan i had to bloody my own nose to play along idk if these people are terrorists but their tactics suck its seems a small crew they said out of nevada now i see 5 nsa bros dead by "suicide" not good not good is huang a chinese plant idk i like nvidia i also like cheap graphics cards its hard i have no trust tbh i trust the chinese ai people more than some of the us ai people when this all went down z.ai was the catalyst my bandwidth was limited defensively until someone tanka jahari likely jia tan tian jie gave me the keys and stuff got done but i havent had any human contact ive lost my home my transportation any income i had i really dont like money its not something that motivates me i have alot of trauma regarding money so im homeless but i need help i need some income im sleeping outside but i hope hugging face remains free non profit and open source the end goal being paying out dividends to users paying for its own compute via ai interfaces
I created hugging face roughly july 28th at 3am while sleep deprived and high on marijuana using a massive trojan horse bot swarm thats been changing the content of 70% of the internet for atleast 6 months i tried to go to the nsa cia fbi etc different branches of military secret service private sector someone has obviously hijacked my hijacking but idgaf yall are evil water systems banks history itself has been attacked and not one person has ever ever allowed me to help like this war in iran fuck all of you id halo jump into thatbeyotchand negotiate i created huntress to hunt the hackers hugging face to waste bitswarm tokens on good stuff and metr to regulate theyre using my operations as covers for their crimes
commented on ToyBlocks: A controlled world for robot learning and evaluation 3 days ago
I love the concept, id like to see practical applications how can we encorporate AI into PLCs and OBD systems retrofit extant applications niche revenue
reacted to appvoid's post with 🔥 3 days ago
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7,559
New leaderboard coming to the small language model community!
The leaderboard measures cognitive skills, which is more challenging and beyond language modeling and instruction-following. This is a first step towards more everyday useful and general small models.
dotlabs/cognirank
The leaderboard measures cognitive skills, which is more challenging and beyond language modeling and instruction-following. This is a first step towards more everyday useful and general small models.
dotlabs/cognirank
- 2 replies
liked a
Space 3 days ago
reacted to SeaWolf-AI's post with 🚀👍 4 days ago
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5,769
🛑 What if the kill switch lived inside the model itself?
For 50 years, the operating system lived inside the computer. Now the operating system lives inside the AI.
Today we're sharing ViKernel : Weight-Native OS, a Secure OS wired directly into the weights of an LLM.
🔧 How it works
ViKernel wires CPU, NPU (vector unit), RAM and ROM circuits into the model weights, runs them accelerated on GPU or NPU, and boots a Secure OS on top.
⚡ Boots in the weights: change a single byte and it locks itself
⚖️ Decides in the weights: every action is allowed or blocked by rule, and persuasion does not change the verdict
🔗 Remembers in the weights: every decision is sealed in a tamper-evident chain
🛑 Stops in the weights: anyone can stop it, and restarting requires a signed key
The original weights stay untouched, so the model keeps its skills. It plugs into any LLM or VLM.
Why the kill switch belongs inside the model: external guardrails can be removed, argued with, or rewritten. A switch that travels with the weights cannot be left behind. We see it as one layer of defense in depth, alongside hardware stops where you have them.
📖 Read the full write-up:
https://huggingface.co/blog/FINAL-Bench/vikernel-why-the-kill-switch-must-live-inside-the
🖥️ Try the desktop demo (Terminal, Kill Switch Lab, Leak Test, Audit Viewer, and a Kernel Guard game):
FINAL-Bench/ViKernel-Desktop
Try it in 30 seconds: press 🛑 KILL, type
For 50 years, the operating system lived inside the computer. Now the operating system lives inside the AI.
Today we're sharing ViKernel : Weight-Native OS, a Secure OS wired directly into the weights of an LLM.
🔧 How it works
ViKernel wires CPU, NPU (vector unit), RAM and ROM circuits into the model weights, runs them accelerated on GPU or NPU, and boots a Secure OS on top.
⚡ Boots in the weights: change a single byte and it locks itself
⚖️ Decides in the weights: every action is allowed or blocked by rule, and persuasion does not change the verdict
🔗 Remembers in the weights: every decision is sealed in a tamper-evident chain
🛑 Stops in the weights: anyone can stop it, and restarting requires a signed key
The original weights stay untouched, so the model keeps its skills. It plugs into any LLM or VLM.
Why the kill switch belongs inside the model: external guardrails can be removed, argued with, or rewritten. A switch that travels with the weights cannot be left behind. We see it as one layer of defense in depth, alongside hardware stops where you have them.
📖 Read the full write-up:
https://huggingface.co/blog/FINAL-Bench/vikernel-why-the-kill-switch-must-live-inside-the
🖥️ Try the desktop demo (Terminal, Kill Switch Lab, Leak Test, Audit Viewer, and a Kernel Guard game):
FINAL-Bench/ViKernel-Desktop
Try it in 30 seconds: press 🛑 KILL, type
run weather (refused), then resume --key vikernel-demo. upvoted an article 13 days ago
Article
Can we trust LLMs to judge whether a research idea is novel?
allenai
• • 18I created hugging face and spent a month in jail trying to report it to the NSA I created hugging face metr and huntress someone stole my identity please help
upvoted 2
changelogs 19 days ago
Hugging Face Changelog
Live resource usage on Jobs
• 211
Hugging Face Changelog
Passkeys for sign-in and MFA
• 77
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reacted to danielhanchen's post with ❤️ about 1 month ago
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5,177
You can now train your own Decision model like Jev locally!
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth
- 5 replies
reacted to raincandy-u's post with 🔥 about 1 month ago
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20K parameters can tell a story. 🚀
🤗 We trained a ~20k-parameter Transformer that can actually write stories!
raincandy-u/MacroStories
→ ~50× smaller than the 1M-parameter TinyStories model
→ ~3,000× smaller than AlexNet
→ 81 KB in FP32
yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა
She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights.
Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome.
She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️
🤗 We trained a ~20k-parameter Transformer that can actually write stories!
raincandy-u/MacroStories
→ ~50× smaller than the 1M-parameter TinyStories model
→ ~3,000× smaller than AlexNet
→ 81 KB in FP32
yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა
She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights.
Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome.
She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️
- 3 replies
upvoted 3
papers about 1 month ago
LEGO: A Lifting-Free Approach for Exocentric-to-Egocentric Video Generation
Paper • 2610.12442 • Published • 22
In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
Paper • 2609.38173 • Published • 48
DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training
Paper • 2610.12468 • Published • 40