#shell
OpenCode now supports Claude Max/Pro subscriptions through the `opencode-anthropic-auth` plugin, allowing you to use your Claude subscription with both Claude Code and OpenCode in your terminal. This integration works with Gentleman.Dots, a complete development environment configuration that includes Neovim with AI assistants, multiple shells (Fish, Zsh, Nushell), terminal multiplexers (Tmux, Zellij), and various terminal emulators. You can install it via Homebrew or direct download across macOS, Linux, and Android platforms. The setup includes an interactive TUI installer that automatically configures your preferred tools, plus a Vim Mastery Trainer for learning editor shortcuts through progressive lessons and boss fights. This gives you a fully integrated AI-powered coding environment optimized for terminal-based development workflows.
https://github.com/Gentleman-Programming/Gentleman.Dots
#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