Agent Learning & Development - Current agents lack continual learning, hindering their ability to build intuition and expertise through experience [1][2] - A key challenge is enabling agents to learn from interactions and develop heuristics, similar to how humans master skills [1][2] - Composio is developing infrastructure for a shared learning layer, allowing agents to evolve and accumulate skills collectively [3] - This "skill layer" provides agents with an interface to interact with tools and build practical knowledge [4] Industry Trends & Alignment - Anthropic is exploring similar approaches, codifying agent behaviors as reusable skills [4] - The industry is moving towards a design pattern where agents progressively turn experience into composable skills [4] Composio's Solution - Composio's collective AI learning layer enables agents to share knowledge, allowing them to handle API edge cases and develop real intuition [5] - This approach facilitates continual learning, where agents accumulate skills through interaction rather than just memorizing [5]

Mixture of Experts (MoE) Architecture - MoE is a popular architecture leveraging different experts to enhance Transformer models [1] - MoE differs from Transformer in the decoder block, utilizing experts (smaller feed-forward networks) instead of a single feed-forward network [2][3] - During inference, only a subset of experts are selected in MoE, leading to faster inference [4] - A router, a multi-class classifier, selects the top K experts by producing softmax scores [5] - The router is trained with the network to learn the best expert selection [5] Training Challenges and Solutions - Challenge 1: Some experts may become under-trained due to the overselection of a few experts [5] - Solution 1: Add noise to the router's feed-forward output and set all but the top K logits to negative infinity to allow other experts to train [5][6] - Challenge 2: Some experts may be exposed to more tokens than others, leading to under-trained experts [6] - Solution 2: Limit the number of tokens an expert can process; if the limit is reached, the token is passed to the next best expert [6] MoE Characteristics and Examples - Text passes through different experts across layers, and chosen experts differ between tokens [7] - MoEs have more parameters to load, but only a fraction are activated during inference, resulting in faster inference [9] - Mixtral 8x7B and Llama 4 are examples of popular MoE-based LLMs [9]

RT Avi Chawla (@_avichawla)25 most important mathematical definitions in data science.P.S. What else would you add here? https://t.co/iMNFip5kIC ...

25 most important mathematical definitions in data science.P.S. What else would you add here? https://t.co/iMNFip5kIC ...

Project Overview - An open-source MCP (Meta-Configurator Protocol) server is available for controlling Jupyter notebooks from Claude [1] Functionality - The server enables the creation of code cells within Jupyter notebooks [1] - The server enables the execution of code cells within Jupyter notebooks [1] - The server enables the creation of markdown cells within Jupyter notebooks [1]

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

Project Overview - Aims to control Jupyter notebooks from Claude [1] - 100% open-source [1] Functionality - Enables creation of code cells [1] - Enables execution of code cells [1] - Enables creation of markdown cells [1]

AI Tools & Technologies - Six no-code LLM/RAG/Agent builder tools are available for AI engineers [1] - The tools are production-grade and 100% open-source [1]

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/SvYt7PiJQxAvi Chawla (@_avichawla):6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

AI Agent Workflow Platforms - Sim AI is a user-friendly, open-source platform for building AI agent workflows, supporting major LLMs, MCP servers, and vectorDBs [1] - Transformer Lab offers tools like RAGFlow for deep document understanding and AutoAgent, a zero-code framework for building and deploying Agents [2] - Anything LLM is an all-in-one AI app for chatting with documents and using AI Agents, designed for multi-user environments and local operation [6] Open-Source LLM Tools - Llama Factory allows training and fine-tuning of open-source LLMs and VLMs without coding, supporting over 100 models [6] - RAGFlow is a RAG engine for building enterprise-grade RAG workflows on complex documents with citations, supporting multimodal data [2][4] - AutoAgent is a zero-code framework for building and deploying Agents using natural language, with universal LLM support and a native Vector DB [2][5] Key Features & Technologies - Sim AI's Finance Agent uses Firecrawl for web searches and Alpha Vantage's API for stock data via MCP servers [1] - RAGFlow supports multimodal data and deep research capabilities [2] - AutoAgent features function-calling and ReAct interaction modes [5] Community & Popularity - Sim AI is 100% open-source with 18 thousand stars [1] - Transformer Lab is 100% open-source with over 68 thousand stars [2] - LLaMA-Factory is 100% open-source with 62 thousand stars [6] - Anything LLM is 100% open-source with 48 thousand stars [6] - One project is 100% open-source with 8 thousand stars [3]