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Source channel @olddriverGDstudy · Post #13 · Mar 17

#秀哥语录 2020.12.27【撩妹模板】#撩妹#语录 告诉你们一个小秘密 没事多去逛逛有年轻漂亮老板娘的美甲店 不要问我为什么 小姐姐 我买几瓶指甲油送给喜欢的人 买好付完钱送给老板娘 你就是我喜欢的人 你可以直白的告诉老板娘 其实我已经关注你好久了 第一次见到你 就有种心跳的感觉 我已经好多次想进来了 就是不知道怎么和你搭讪 可是 你的身影实在挥之不去 我今天忍不住了 豁出去了 就想告诉你 我真的好喜欢你 能不能加个好友

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djangoproject

@djangoproject · Post #228 · 01/16/2017, 01:11 PM

http://www.aparat.com/v/miNUS pycon 2016- Andrew Godwin - Reinventing Django for the #Real_Time Web Django has long been tied to the #request_response pattern, but the upcoming "#channels" project changes this and allows #Django to natively support #WebSockets, running tasks after responses, easily handle #long_polling and more. Come and learn about the design, how we're trying to keep things as Django-like as possible, and how you can use it in your projects.

GitHub Trends

@githubtrending · Post #14989 · 07/23/2025, 11:30 AM

#c_lang#bluetooth#bluetooth_le#embedded#embedded_c#iot#mcu#microcontroller#real_time#rtos#zephyr#zephyr_rtos#zephyros Zephyr is a free, open-source real-time operating system (RTOS) designed for small, resource-limited devices like sensors, wearables, and IoT gateways. It supports many hardware types such as ARM, Intel x86, and RISC-V, making it flexible for different projects. Zephyr is modular, so you can include only what you need, saving memory and power. It focuses on security with features like memory protection and secure boot. It also offers built-in networking and tools for easy development and testing. This helps you build reliable, fast, and secure embedded systems efficiently, especially for IoT and real-time applications[1][2][3]. https://github.com/zephyrproject-rtos/zephyr

GitHub Trends

@githubtrending · Post #15350 · 12/21/2025, 11:30 AM

#rust#ai#change_data_capture#context_engineering#data#data_engineering#data_indexing#data_infrastructure#data_processing#etl#hacktoberfest#help_wanted#indexing#knowledge_graph#llm#pipeline#python#rag#real_time#rust#semantic_search **CocoIndex** is a fast, open-source Python tool (Rust core) for transforming data into AI formats like vector indexes or knowledge graphs. Define simple data flows in ~100 lines of code using plug-and-play blocks for sources, embeddings, and targets—install via `pip install cocoindex`, add Postgres, and run. It auto-syncs fresh data with minimal recompute on changes, tracking lineage. **You save time building scalable RAG/semantic search pipelines effortlessly, avoiding complex ETL and stale data issues for production-ready AI apps.** https://github.com/cocoindex-io/cocoindex