Technology & Development - Open-source tools enable building production-grade LLM web apps rapidly [1] - Interactive apps are more suitable for users focused on results rather than code [1] Data Science & Machine Learning - Data scientists and machine learning engineers commonly use Jupyter for data exploration and model building [1] - Tutorials and insights on DS, ML, LLMs, and RAGs are shared regularly [1]

Resources - A GitHub repository is available [1] - A free visual guidebook for learning MCPs from scratch, including 11 projects, is offered [1]

Overview - Taipy is an open-source Python AI & data web application builder [1] - Taipy can build prototypes and robust production-ready data apps [1] Technology & Features - Taipy eliminates the need to learn JavaScript, CSS, or HTML [1] - Taipy's VS Code extension provides no-code functionalities to build data apps [2] - Taipy is presented as a more robust version of Streamlit [1] - Taipy has a noticeable latency difference compared to other apps [1] Community & Adoption - Taipy is fully open-source with over 18 thousand stars [2]

LLM技术解析 - KV caching in LLMs：LLM 中的 KV 缓存机制被清晰地解释，并附有可视化图表 [1]

Key Takeaways - The author encourages readers to reshare the content if they found it insightful [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1] Focus Area - The content clearly explains KV caching in LLMs with visuals [1] Author Information - Avi Chawla's Twitter handle is @_avichawla [1]

That said, KV cache also takes a lot of memory.Llama3-70B has:- total layers = 80- hidden size = 8k- max output size = 4kHere:- Every token takes up ~2.5 MB in KV cache.- 4k tokens will take up 10.5 GB.More users → more memory.I'll cover KV optimization soon. https://t.co/VjnyLa6aLa ...

Technology Overview - KV caching is utilized in Large Language Models (LLMs) to enhance performance [1] - The document provides a clear explanation of KV caching in LLMs with visuals [1]

Agentic AI Systems Levels - The report explains 5 levels of Agentic AI systems with visuals [1]

General Overview - The document is a wrap-up and encourages sharing with the network [1] - It directs readers to Avi Chawla's profile for tutorials and insights on DS, ML, LLMs, and RAGs (Data Science, Machine Learning, Large Language Models, and Retrieval-Augmented Generation) [1] Focus Area - Avi Chawla's content includes explanations of Agentic AI systems [1]

AI Agent Patterns - Basic responder agents primarily generate text responses [1] - Router pattern agents determine the appropriate path to take based on input [2] - Tool calling agents select and execute specific tools to accomplish tasks [3] - Multi-Agent pattern manages and coordinates multiple agents to achieve a common goal [4] - Autonomous pattern agents operate fully independently without direct human intervention [5]