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Researchers at @wiz_io put Gemini 4 Argon to the test against complex cyber threats.
In real-world testing, it found more than two times more vulnerabilities than baseline models — catching a critical risk that previous frontier models missed.
See how they did it ↓
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think today’s php disclosure is a harbinger of things to come. these models will make us all safer by finding long lurking issues.
however the entire software supply chain will need the muscles to rapidly build and deploy fixes.
we might need a lot more public + private partnerships to secure infrastructure across the country.
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Let’s set the record straight: @lcfr_hax was the first to discover php2shell and develop a working exploit. See his August 5 tweet: xitter.cf/lcfr_hax/status/208503…
His work convinced the PHP Foundation to invest in fixing this entire bug class. He then worked around the clock to review, test, and retest the patches.
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🚨 BREAKING: @Wiz_io Research uncovers #PHP2SHELL, a decade-old PHP Core vulnerability reaching CVSS 9.2 severity impacting 70% of the Internet.
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Introducing SKY-001, the first Engineering World Model.
On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested:
-> 51-125x faster
- > 150-210x cheaper
-> Higher IOU than all frontier models
SKY-001 lets you dream a physical object before you build it.
18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch).
The first ‘AI General Engineer’ will be the backbone for Autonomous Manufacturing.
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llama serve -hf ggml-org/Muse-Spark-Open-816B-A42B-GGUF:Q2_K (on two mac studios).
Well done @finkd & @johnternus!
Wow yet another model
I personally am losing track of the various models
Introducing Atria Dawn🫡 — the official release!
Atria is a 700B agentic foundation model for long-horizon tasks and real-world productivity 🙌
Upgraded from Preview: 512K context, stronger tool use and ML coding for stable long-horizon execution🥳.
📈 Excited results over Preview:
• Automation Bench 54.7%
• WideSearch 84.8%
• Workspace Bench 66.1%
• MLE Bench 88.2%
From protein structure training & CAD modeling to knowledge viz, PPT/poster design & data analysis
🌐 Homepage: atria-asi.ai
💻 GitHub: github.com/atria-asi/Atria-D…
🤗 Hugging Face: huggingface.co/internlm/Atri…
🚀 Try it: discovery.intern-ai.org.cn/t…
Wow!!
Hope it's open weights.
But then this put @MicrosoftAI MAI in direct competition with @OpenAI @AnthropicAI @GoogleDeepMind @Alibaba_Qwen @deepseek_ai @NVIDIAAI and the rest of the AI ecosystem
We now have state-of-the-art pre-trained base models in the 3T-parameter weight class, built entirely in-house at MAI.
Post-training results coming soon -- stay tuned!
Lots of details in our technical blog post.
microsoft.ai/news/frontier-p…
Much needed, Good Work!!
@MistralAI
What can large-scale AI models bring to the growing complexity of particle physics?
@CERN and Mistral join forces to explore this question.
The ambition: a single Large Physics Model, trained on billions of simulated collisions.
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What can large-scale AI models bring to the growing complexity of particle physics?
@CERN and Mistral join forces to explore this question.
The ambition: a single Large Physics Model, trained on billions of simulated collisions.
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Fast is better than slow.
We've got two new PhoneLLM models optimized for conversational voice AI.
PhoneLLM Mini (30B) — When you need the lowest possible latency (sub-100ms TTFAT), smallest model size, and lowest cost. Good multi-turn conversation behavior and tool-calling accuracy in a model that can run >100 concurrent agents on a B200 and fits on an RTX 5090.
PhoneLLM Max (120B) — A "microthinking" model for complex conversational tasks. Sub-200ms TTFAT. SOTA task completion performance at 1/10th the latency and cost of models with similar capabilities.
Both of these models are full-weights fine-tunes of @NVIDIAAI Nemotron models. Mini is based on Nemotron 3 Nano. Max is based on the brand new Nemotron 3.5 Super VL.
Both of these models are available as open weights on HuggingFace, and from four inference partners: AWS, Baseten, Cloudflare, and Modal.
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🚀 Meet Comet 60B, in collaboration with @nvidia
Built from Nemotron 3.5 Super VL:
📉 50% fewer total parameters
🎯 Within 5% of original accuracy
🖼️ Text, image & video
Open-source multimodal AI for agentic workloads.
📰 Learn more here: multiversecomputing.com/reso…
👉 Explore the model: huggingface.co/MultiverseCom…
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NVIDIA releases 2 new models: Nemotron-3.5-Bolt and Nemotron-3.5-Super-VL.
Nemotron-3.5-Bolt-74B-A9B runs on 24GB RAM. It delivers top-tier performance for its size.
Super-VL-120B-A12B runs on 82GB RAM.
Bolt GGUF: huggingface.co/unsloth/NVIDI…
Guide: unsloth.ai/docs/models/nemot…
Super GGUF: huggingface.co/unsloth/NVIDI…
Yeah Openess against #Orwellian #1984
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muse spark open -- weights out today! best US open-source model, and runs on the new mac studio 🍎
Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.
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Congrats to the team! Amazing to run a model of Muse Spark Open's caliber and scale this efficiently, right from your desk. Mac Studio keeps pushing the boundaries of on-device AI!
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Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.
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Run it your way:
• Serve an OpenAI-compatible API with vLLM or SGLang
• Run offline inference with Transformers
• Adapted for multiple chip platforms and inference frameworks
Day-0 support from @NVIDIAAI:
try it on build.nvidia.com/stepfun-ai/…,
fine-tune with NeMo AutoModel github.com/NVIDIA-NeMo/Autom…,
deploy with model-free NIM catalog.ngc.nvidia.com/orgs/…,
and run on Blackwell.
Also available on Day 0 with @FireworksAI_HQ, @togethercompute, @modal, @DeepInfra and @FeatherlessAI.
