#java#bedrock#bedrock_edition#bedrock_to_java#bungee#fabric#geyser#geysermc#hacktoberfest#java#java_edition#minecraft#minecraft_bedrock_edition#packet#pe#protocol#proxy#spigot#translator#velocity
Geyser is a free tool that lets you play Minecraft across different versions by connecting Minecraft Java Edition servers. It works by translating data between the two game versions, enabling cross-platform play on devices like Windows, iOS, Android, and consoles. You can install it as a plugin or standalone, and it supports recent Minecraft versions. This means you can join Java servers even if you only have Bedrock Edition, expanding your multiplayer options without needing a separate Java account if you use the Floodgate plugin. It’s great for seamless crossplay but may have some minor limitations due to game differences[1][2][5].
https://github.com/GeyserMC/Geyser
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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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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.
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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