

Grok 4 early benchmarks in comparison to other models. Humanity last exam diff is Visualised by @marczierer

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Grok 4 early benchmarks in comparison to other models. Humanity last exam diff is Visualised by @marczierer
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GLM-4.1V-Thinking – a powerful new vision-language model for multimodal reasoning!
From STEM to GUI agents, it outperforms models 8x its size.
Open-source, scalable, and state-of-the-art.
Paper Link: https://
arxiv.org/abs/2507.01006 #AI #VLM #Multimodal #GLM4 #OpenSourceAI

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I have been advocating tensor methods for almost decade and a half. Take a look at our tensor methods in deep learning from a few years ago https://
arxiv.org/abs/2107.03436 Tensorly package allows defining tensor operations in Pytorch seamlessly http://
tensorly.org @JeanKossaifi
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Every $ invested in open-source generates $2,000 of value. Might be orders of magnitude more for open-source AI! https://
x.com/clementdelangu
e/status/1901751361320206554?s=46
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LangChain now supports Claude Sonnet 4's citable search results – bringing proper source attribution to your RAG apps. You can now: • Get natural source attribution with proper titles and sources automatically included in search results • Have citations seamlessly linked
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Keep notifications on because this will be released open source. Changed the world with Claude Engineer once, we'll do it again.

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In preparation for OpenAI's upcoming open-source model, I'm building the world's best local agent
— Pietro Schirano (@skirano) 3 juillet 2025
It seamlessly integrates with my OS, auto accesses my clipboard, is Finder-aware, creates/reads files, searches the web, and updates text in any app.
Local Jarvis. pic.twitter.com/DbH5snZqsf
In preparation for OpenAI's upcoming open-source model, I'm building the world's best local agent It seamlessly integrates with my OS, auto accesses my clipboard, is Finder-aware, creates/reads files, searches the web, and updates text in any app. Local Jarvis.
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here's where the code lives: https://
github.com/pytorch/pytorc
h/tree/main/torch/_numpy
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the numpy compatible torch.compile behavior is definitely a bug. the expected behavior is the numpy output should be the same as torch.compile output.
torch's advanced indexing differing from numpy's advanced indexing is a bit of an aside.
I've just filed a bug report,