#c_lang
ESP-IDF is Espressif's official software framework for developing applications on ESP32 and related chips, supporting Windows, Linux, and macOS. It offers a complete set of tools, libraries, and drivers for Wi-Fi, Bluetooth, and IoT features, enabling you to build connected devices efficiently using C or C++. ESP-IDF supports multiple chip versions with stable releases and ongoing updates, ensuring reliability and production readiness. It includes easy commands for building, flashing, and monitoring your projects, plus example templates to start quickly. Using ESP-IDF helps you create robust, feature-rich IoT applications with strong community and official support. This saves time and effort in development and deployment.
https://github.com/espressif/esp-idf
#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