#python#agent#android#app#automation#copilot#gui#mllm#mobile#mobile_agents#multimodal#multimodal_agent#multimodal_large_language_models
Mobile-Agent-v3.5 is Alibaba's top GUI agent family using GUI-Owl 1.5 models (2B to 235B sizes) for automating desktop, mobile, and browser tasks like stock checks, bookings, or document creation with planning, reflection, and memory. Try free online demos on ModelScope or Bailian, or use limited-time APIs—no setup needed. It leads 20+ benchmarks for real-world use. You benefit by saving time on repetitive tasks, boosting productivity, and handling complex operations hands-free across devices.
https://github.com/X-PLUG/MobileAgent
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Zeus New Pytorch Ecosystem Tool
Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads.
🖥Github
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Main channel: @repo_science
Coupons: @freecoupons_reposcience
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Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12.
https://www.nature.com/articles/s41467-025-63790-8
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A few cool ideas in this model.
Introducing Gemma 3n: The developer guide - Google Developers Blog
https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
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There is this new lib called scale. One could compile CUDA code to use it on AMD GPU.
https://docs.scale-lang.com/manual/how-to-use/
I don't know who is more pissed off, NVidia or AMD.
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This repo is really nice.
yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models
https://github.com/yuanchenyang/smalldiffusion
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Google & USC benchmarked a prompt based forecasting method, and the results are amazing.
Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948