#go#external_secrets#hacktoberfest#kubernetes#kubernetes_secrets#secrets_manager
External Secrets Operator (ESO) is a Kubernetes tool that connects external secret managers like AWS Secrets Manager, HashiCorp Vault, and others to Kubernetes, automatically injecting secret values into Kubernetes Secrets. However, official releases are paused because the current maintainer team is too small to support ongoing development and community help. You can still use the latest code from the main branch, but no new official versions or support will be provided until more maintainers join. If your team relies on ESO, contributing helps keep the project healthy and ensures future updates. This pause highlights the importance of community support for open-source tools you depend on. Using ESO benefits you by simplifying secure secret management in Kubernetes across multiple cloud providers.
https://github.com/external-secrets/external-secrets
#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
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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.
#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