Technology & Innovation - Airweave builds live, bi-temporal knowledge bases for agents to reason on the freshest facts [1] - Supports fully agentic retrieval with semantic and keyword search, query expansion, and more across 30+ sources [1] - Airweave is 100% open-source [1] Real-time Data Challenge - RAG (Retrieval-Augmented Generation) struggles with real-time data [1]

GitHub repo: https://t.co/iU6P0KoaRf(Don't forget to star 🌟) ...

Technology & Innovation - Airweave addresses the limitations of RAG (Retrieval-Augmented Generation) in handling real-time data [1] - Airweave constructs live, bi-temporal knowledge bases to ensure agents utilize the most up-to-date information [1] - The system supports comprehensive agentic retrieval, incorporating semantic and keyword search, query expansion, and integration across 30+ sources [1] - Airweave is 100% open-source [1]

RT Avi Chawla (@_avichawla)Here's a common misconception about RAG!When we talk about RAG, it's usually thought: index the doc → retrieve the same doc.But indexing ≠ retrievalSo the data you index doesn't have to be the data you feed the LLM during generation.Here are 4 smart ways to index data:1) Chunk Indexing- The most common approach.- Split the doc into chunks, embed, and store them in a vector DB.- At query time, the closest chunks are retrieved directly.This is simple and effective, but large or nois ...

Here's a common misconception about RAG!When we talk about RAG, it's usually thought: index the doc → retrieve the same doc.But indexing ≠ retrievalSo the data you index doesn't have to be the data you feed the LLM during generation.Here are 4 smart ways to index data:1) Chunk Indexing- The most common approach.- Split the doc into chunks, embed, and store them in a vector DB.- At query time, the closest chunks are retrieved directly.This is simple and effective, but large or noisy chunks can reduce precisi ...

Product Overview - Sim is a 100% open-source alternative to n8n, designed as a drag-and-drop platform for building and deploying Agentic workflows [1] - Sim allows easy feature additions without disrupting existing functionality [3] Functionality and Integration - Sim can be used to build a finance assistance app and connect it to Telegram [2] - Sim agents support integration with MCP to connect with APIs like Alpha Vantage for stock data [2][3] - Sim works with any local LLM and runs 100% locally [3] Workflow and Components - The workflow involves an Intent Classifier to determine if a question is finance-related [3] - A Finance Agent uses Firecrawl for web searches and accesses stock data [2] - A Response Agent compiles the information and delivers it [2]

Open-Source Platform - Sim is presented as a 100% open-source alternative to n8n for building and deploying Agentic workflows [1] - Sim is a drag-and-drop platform [1] Application and Functionality - The platform was used to build a finance assistance app and connected to Telegram [2] - The workflow is simple and runs 100% locally [2] - It works with any local LLM [2]

Link to @simdotai GitHub repo: https://t.co/mnBXIe28JX(don't forget to star 🌟) ...

Core Functionality & Architecture - Sim is a 100% open-source alternative to n8n, designed as a drag-and-drop platform for building and deploying Agentic workflows [1] - The platform allows users to connect tools or agents as blocks on a canvas [2] - Sim agents support integration with MCP, facilitating connections with APIs like Alpha Vantage [3] - The architecture includes an Intent Classifier to determine if a user question is finance-related, redirecting if not [3] Finance Application & Use Case - A finance assistance app was built and connected to Telegram using Sim [2] - The Finance Agent utilizes Firecrawl for web searches and Alpha Vantage's API via MCP servers to access stock data [2] - A Response Agent compiles the information and delivers it to the user [2] - The platform supports easy feature additions, such as tracking crypto or portfolio analysis, without disrupting existing functionality [3] Deployment & Customization - Sim runs 100% locally [3] - It works with any local LLM (Large Language Model) [3] - The platform is easily extendable, allowing users to add agents for specific needs [3]

RT Avi Chawla (@_avichawla)You're in a Research Scientist interview at OpenAI.The interviewer asks:"Our investors want us to contribute to open-source.o3 crushed benchmarks.But we can lose a competitive edge by open-sourcing it.What do we do?"You: "Release the research paper."Interview over.You forgot that LLMs don't just learn from raw text; they also learn from each other.For example:- Llama 4 Scout & Maverick were trained using Llama 4 Behemoth.- Gemma 2 and 3 were trained using Gemini.Distillation helps ...