Big update for ChatGPT/Claude Desktop users!MCP servers in Claude/Cursor don't offer UI any experience yet, like charts. It's just text/JSON, like below:```{“symbol”: “AAPL”,“price”: 178.23,“change”: “+2.45%”}```Displaying this as a visual element isn’t impossible, but most MCP servers make you write the same boilerplate twice, once for the React component and again to register it as an MCP tool.So you end up with duplicate schemas, manual prop mapping, and a bunch of registration code.A simplified approach ...

RT Avi Chawla (@_avichawla)RAG vs. CAG, clearly explained!RAG is great, but it has a major problem:Every query hits the vector database. Even for static information that hasn't changed in months.This is expensive, slow, and unnecessary.Cache-Augmented Generation (CAG) addresses this issue by enabling the model to "remember" static information directly in its key-value (KV) memory.Even better? You can combine RAG and CAG for the best of both worlds.Here's how it works:RAG + CAG splits your knowledge into two ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/VFSWzmhNL9Avi Chawla (@_avichawla):RAG vs. CAG, clearly explained!RAG is great, but it has a major problem:Every query hits the vector database. Even for static information that hasn't changed in months.This is expensive, slow, and unnecessary.Cache-Augmented Generation (CAG) addresses this issue by https://t.co/VPImg6xzfo ...

OpenAI prompt caching guide: https://t.co/IHFcWRZQJN ...

RAG vs. CAG, clearly explained!RAG is great, but it has a major problem:Every query hits the vector database. Even for static information that hasn't changed in months.This is expensive, slow, and unnecessary.Cache-Augmented Generation (CAG) addresses this issue by enabling the model to "remember" static information directly in its key-value (KV) memory.Even better? You can combine RAG and CAG for the best of both worlds.Here's how it works:RAG + CAG splits your knowledge into two layers:↳ Static data (poli ...

AI Engineering Resources - Stanford 提供 6 份 AI 工程师必备的速查表 [1] - 速查表涵盖监督/非监督机器学习 [1] - 速查表涉及深度学习 [1] - 速查表包含机器学习技巧与窍门 [1] - 速查表包括概率与统计 [1] - 速查表覆盖代数与微积分 [1]

AI Resources - Stanford provides 6 must-read cheat sheets for AI Engineers [1] - The cheat sheets cover Supervised/Unsupervised ML, Deep Learning, ML Tips & Tricks, Probability & Statistics, Algebra & Calculus [1] Social Sharing - The author encourages readers to reshare the content [1] - The author shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

Resources - A free visual guidebook to learn Agents from scratch is available [1] - The guidebook includes 12 projects [1] External Link - A repository link is provided for further information: https://t.co/E6GJTTT50q [1]

AI Engineering Resources - Stanford provides 6 must-read cheat sheets for AI Engineers [1] - The cheat sheets cover Supervised/Unsupervised ML, Deep Learning, ML Tips & Tricks, Probability & Statistics, Algebra & Calculus [1]

RT Avi Chawla (@_avichawla)7 LLM generation parameters, explained visually: https://t.co/z4uPYyooc5 ...