#shell#buildroot_external_tree#firmware#ingenic#ip_camera#ipc#ipcamera
Thingino is free, open-source firmware designed specifically for IP cameras using Ingenic SoC chips. It customizes the software to fit each supported camera model, making the camera easier to use and more efficient. You can build the firmware yourself using the provided instructions and tools, and there is a helpful web interface to control camera features like pan, tilt, night mode, and streaming. This gives you more control and flexibility over your camera without relying on proprietary software. It supports many camera models, and the community offers resources like a wiki, chat groups, and development guides to help you get started and customize your device. This benefits you by providing a customizable, transparent, and community-supported alternative to closed camera firmware.
https://github.com/themactep/thingino-firmware
#DL
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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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#dl
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
#dl
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/
#dl
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.
#dl
This repo is really nice.
yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models
https://github.com/yuanchenyang/smalldiffusion
#dl
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