#python#ai_prompts#ai_skill#bluesky#claude#claude_code#clawhub#deep_research#hackernews#instagram#openclaw#polymarket#recency#reddit#research#social_media#tiktok#trends#twitter#web_search#youtube
/last30days is a Claude Code skill that scans Reddit, X, Bluesky, YouTube, TikTok, Instagram, Hacker News, Polymarket, and web for your topic's top discussions, upvotes, bets, and videos from the last 30 days, then synthesizes a cited briefing with ready-to-use prompts. New v2.9.5 adds Bluesky, "X vs Y" comparisons, and auto-saves to build your research library. Install easily via `/plugin install last30days@last30days-skill`. You stay ahead on AI trends, tools, and techniques with real community insights in minutes, skipping hours of manual searching.
https://github.com/mvanhorn/last30days-skill
#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