GitHub repo: https://t.co/BkdYa590RrGet a free visual guidebook to learn MCPs from scratch (with 11 projects): https://t.co/v3cQWlQtR4 ...

Technology & Software Development - MCP servers are now capable of delivering UI-rich experiences [1] - mcp-ui enables the addition of interactive web components to its output, which can be rendered by the MCP client [1] - The solution is 100% open-source [1] Limitations - Current MCP servers in Claude/Cursor do not yet offer UI experiences like charts, only text/JSON [1]

AI Agent Evolution - AI agents have evolved from simple LLMs to sophisticated systems with reasoning, memory, and tool use [1] - Early transformer-based chatbots processed small chunks of input, exemplified by ChatGPT's initial 4k token context window [1] - LLMs expanded to handle thousands of tokens, enabling parsing of larger documents and longer conversations [1] - Retrieval-Augmented Generation (RAG) provided access to fresh and external data, enhancing LLM outputs with tools like search APIs and calculators [1] - Multimodal LLMs process text, images, and audio, incorporating memory for persistence across interactions [1] Key Components of Advanced AI Agents - Current AI agents are equipped with short-term, long-term, and episodic memory [1] - Tool calling capabilities, including search, APIs, and actions, are integral to advanced AI agents [1] - Reasoning and ReAct-based decision-making are crucial components of modern AI agents [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.Avi Chawla (@_avichawla):5 levels of evolution of AI Agents.Over the last few years, we’ve gone from simple LLMs → to fully-fledged Agentic systems with reasoning, memory, and tool use.Here’s a step-by-step breakdown.1) Small context window LLMs- Input: Text → LLM → Output: Text- Early https://t.co/DvNTsnXpYT ...

Learning Resources - Offers a free visual guidebook for learning Agents, including 12 projects [1]

AI Agent Evolution - The industry has progressed from simple LLMs to sophisticated Agentic systems with reasoning, memory, and tool use [1] - Early transformer-based chatbots were limited by small context windows, exemplified by ChatGPT's initial 4k token limit [1] - The industry has seen upgrades to handle thousands of tokens, enabling parsing of larger documents and longer conversations [1] - Retrieval-Augmented Generation (RAG) provided access to fresh and external data, enhancing LLM outputs [1] - Multimodal LLMs can process multiple data types (text, images, audio), with memory introducing persistence across interactions [1] Key Components of Advanced AI Agents - Advanced AI Agents are equipped with short-term, long-term, and episodic memory [1] - Tool calling (search, APIs, actions) is a crucial feature of modern AI Agents [1] - Reasoning and ReAct-based decision-making are integral to the current AI Agent era [1]

RT Avi Chawla (@_avichawla)Temperature in LLMs, clearly explained (with code): ...

LLM Insights - Tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) are shared daily [1] - Temperature in LLMs is clearly explained with code [1] Engagement - The author encourages readers to reshare the content if they found it insightful [1]

Temperature Setting Best Practices - Low temperature values lead to predictable responses [1] - High temperature values result in more random and creative responses [1] - Extremely high temperature values rarely have practical use [1]

Temperature in LLMs, clearly explained (with code): ...