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Source channel @olddriverGDstudy · Post #58 · Mar 27

#风哥避孕套如何选择课堂笔记 都说了多少遍了,别TMD买冈本,冈本TMD容易破 油少,一样的价钱不会买旁边的相模啊,玻尿酸套子也有缺点虽然润但是时间久了干的快,沐浴乳我不挑但是有一个沐浴乳我拒绝 ,力士的薰衣草真的不好闻,冈本最大的问题就是他油放的少拿出来就干,要润就玻尿酸 然后赤尾有小储精囊跟无储精囊套 要感觉我都是用浮点的,浮点套女的感觉来得快,有些人就马眼有感觉的这么办 不过无储精囊适合做多了跟射精量不大的用要不然会破的,超市就买杜蕾斯 杰士邦 相模,淘宝你看中啥买啥,然后小科普 0.01都是聚氨酯套 其他的都是乳交套,名流的玻尿酸套还是不错的,套子我是不追求的薄的,套子主要是为了安全还有就是润,很多套子很润但是油少玻尿酸少了也不行,像玻尿酸套子虽然很润但是也干的快,捷古斯也算日本大牌了,蝴蝶套一个形容 牌子叫捷古斯 因为包装上印着蝴蝶,买啥套子真的是最啥太大追求就用JS的套子 干了就跟JS说换个套子 #知识#避孕套

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GitHub Trends

@githubtrending · Post #14706 · 05/14/2025, 02:30 PM

#python#agents#knowledge_graph#llm#llm_agent#rag#search#search_agent#vector_database Airweave is a tool that helps make information from apps and databases easily accessible to AI agents. It connects over 100 data sources with minimal coding, allowing for fast data synchronization and semantic search. This means you can quickly turn app data into useful knowledge for AI agents, making them smarter and more efficient. It's especially helpful for tasks like customer support or generating reports, as it ensures AI agents have the most accurate and up-to-date information. https://github.com/airweave-ai/airweave

GitHub Trends

@githubtrending · Post #15547 · 03/07/2026, 01:30 PM

#python#agent_memory#financial_forecasting#future_prediction#knowledge_graph#llms#multi_agent_simulation#public_opinion_analysis#python3#social_prediction#swarm_intelligence MiroFish is a simple AI tool that predicts anything by creating a digital world from your data like news, policies, or stories. Upload seed info and describe what you want to predict; it builds thousands of smart agents with personalities and memories to interact, simulate futures, and give you a detailed report plus chat access. You benefit by testing decisions risk-free—like policy impacts or story endings—making smart choices or fun ideas win through safe, accurate previews. https://github.com/666ghj/MiroFish

GitHub Trends

@githubtrending · Post #15168 · 09/25/2025, 12:30 PM

#python#ai#context#embedded#faiss#knowledge_base#knowledge_graph#llm#machine_learning#memory#nlp#offline_first#opencv#python#rag#retrieval_augmented_generation#semantic_search#vector_database#video_processing Memvid lets you store millions of text pieces inside a single MP4 video file using QR codes, making your data 50-100 times smaller than usual databases. You can search this video instantly in under 100 milliseconds without needing servers or internet after setup. It works offline, is easy to use with simple Python code, and supports PDFs and chat with your data. The upcoming version 2 will add features like continuous memory updates, shareable capsules, fast local caching, and better video compression, making your AI memory smarter, faster, and more flexible. This means you get a powerful, portable, and efficient way to manage and search huge knowledge bases quickly and easily. https://github.com/Olow304/memvid

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

GitHub Trends

@githubtrending · Post #14791 · 06/05/2025, 12:30 PM

#python#ai#ai_agents#ai_memory#cognitive_architecture#cognitive_memory#contributions_welcome#good_first_issue#good_first_pr#graph_database#graph_rag#graphrag#help_wanted#knowledge#knowledge_graph#neo4j#open_source#openai#rag#vector_database Cognee is an open-source AI memory engine that helps improve how AI systems understand and process data. It mimics human cognitive processes, creating "memories" from various data types like text and images. This enhances the accuracy of large language models (LLMs) and allows them to recall past interactions and documents. Cognee is scalable, cost-effective, and integrates easily with existing systems, making it a valuable tool for developers seeking to boost AI performance without relying on expensive APIs. https://github.com/topoteretes/cognee