#python#copilot#csharp#dotnet#github#github_copilot#github_copilot_chat#github_copilot_for_azure#github_copilot_free#github_copilot_training#javascript#lab#labs#microsoft#python#sql#tutorial#tutorial_code#tutorial_exercises#visual_studio_code#vscode
GitHub Copilot’s new Agent Mode is a powerful AI coding partner that goes beyond just suggesting code—it can independently write, debug, and improve your code, handle complex workflows, and even fix its own mistakes automatically. It works with multiple programming languages and integrates with popular development tools, helping you save time on repetitive tasks like testing, deployment, and refactoring. By using natural language prompts, you can guide it to complete multi-step projects, making coding faster and easier whether you’re a beginner or an expert. This course teaches you how to fully use these features, boosting your productivity and coding skills.
https://github.com/microsoft/Mastering-GitHub-Copilot-for-Paired-Programming
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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
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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