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Source channel @CodeXBotz · Post #1457 · 10月8日

🚀 Exciting Project Announcement! 🚀 I'm thrilled to share that I’ve developed GitInvite – an open-source platform that makes collaborating on GitHub easier than ever! 🎉 💡 What is GitInvite? GitInvite allows users to generate secure GitHub repository invite links that can be shared with collaborators. No more manual collaborator additions! With just one link, you can grant access to your repos in a secure and efficient way. 🌟 Key Features: - Generate secure invite links to share repository access. - Cancel invite links anytime to prevent further use. - Revoke access from users who gained access via the link. - Easy collaboration for developers, teams, and open-source projects. 🎯 Beta Stage: GitInvite is currently in its beta stage, and I'm actively seeking feedback and suggestions for improvements. I would love to hear from the developer community to help shape the future of this tool! 💻 Want to try it out? You can access GitInvite here: https://gitinvite.vercel.app/ 🛠 Developers: The code is open-source, and I welcome contributions! Check out the GitHub repo here: https://github.com/rahulps1000/GitInvite Feel free to share your feedback, open issues, or contribute to the project! Let’s make GitHub collaboration even smoother together. 🙌 #GitHub#OpenSource#NextJS#GitInvite#Collaboration#Beta#WebDevelopment

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Machinelearning

@ai_machinelearning_big_data · Post #8519 · 2025/09/11 18:21

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🚀 Релиз:Qwen3-Next-80B-A3B - эффективная модель заточенная на работа работу с очень длинным контекстом! 🔹80B параметров, но активируется только 3B на токен → тренировка и инференс 10x дешевле и быстрее, чем у Qwen3-32B (особенно при 32K+ контексте). 🔹Гибридная архитектура: Gated DeltaNet + Gated Attention → сочетает скорость и точность. 🔹Ultra-sparse MoE: 512 экспертов, маршрутизируется 10 + 1 общий. 🔹Multi-Token Prediction → ускоренное speculative decoding. 🔹 По производительности обходит Qwen3-32B и приближается к Qwen3-235B в рассуждениях и long-context задачах. 🟢Qwen3-Next-80B-A3B-Instruct показатели почти на уровне 235B flagship. 🟢Qwen3-Next-80B-A3B-Thinking превосходит Gemini-2.5-Flash-Thinking. ▪Попробовать: https://chat.qwen.ai ▪Анонс: https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list ▪ HuggingFace: https://huggingface.co/collections/Qwen/qwen3-next-68c25fd6838e585db8eeea9d ▪ ModelScope: https://modelscope.cn/collections/Qwen3-Next-c314f23bd0264a ▪Kaggle: https://kaggle.com/models/qwen-lm/qwen3-next-80b ▪ Alibaba Cloud API: https://alibabacloud.com/help/en/model-studio/models#c5414da58bjgj @ai_machinelearning_big_data #AI#LLM#Qwen#DeepLearning#MoE#EfficientModels#LongContext#Reasonin

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