#other#awesome#awesome_list#c#c_plus_plus#cpp#cpp_library#cppcon#libraries#list#lists#programming_tutorial#resources
You can access a vast, well-organized collection of C++ libraries, frameworks, and tools that cover almost every programming need—from standard libraries, GUI, networking, and machine learning to game engines, cryptography, and more. This curated list includes popular and high-quality options like Boost, Qt, OpenCV, and many specialized libraries for tasks such as asynchronous programming, audio processing, and serialization. Using these resources can save you time, improve code quality, and help you build efficient, robust applications by leveraging tested, peer-reviewed components instead of writing everything from scratch. It’s a one-stop reference to boost your C++ development productivity and capabilities.
https://github.com/fffaraz/awesome-cpp
#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/
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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.
#dl
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