After MCP, A2A, & AG-UI, there's another Agent protocol.It's fully open-source and launched by IBM Research.Here's a complete breakdown (with code): https://t.co/uxYPfFjmPX ...

RT Avi Chawla (@_avichawla)7 patterns to build multi-agent systems: https://t.co/VHpTFcjp4y ...

7 patterns to build multi-agent systems: https://t.co/VHpTFcjp4y ...

RT Avi Chawla (@_avichawla)voyage-3-large embedding model just topped the RTEB leaderboard!It's a big deal because it:- ranks first across 33 eval datasets- outperforms OpenAI and cohere models- supports quantization to reduce storage costsHere's another reason that makes this model truly superior:Most retrieval benchmarks test models on academic datasets that don’t reflect real-world data.RTEB, on the other hand, is a newly-released leaderboard on HuggingFace that evaluates retrieval models across enterpri ...

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/0M4PxqA4b5Avi Chawla (@_avichawla):voyage-3-large embedding model just topped the RTEB leaderboard!It's a big deal because it:- ranks first across 33 eval datasets- outperforms OpenAI and cohere models- supports quantization to reduce storage costsHere's another reason that makes this model truly superior: https://t.co/4dSdKx4s5n ...

General Information - Hugging Face 上有一个排行榜 [1]

voyage-3-large embedding model just topped the RTEB leaderboard!It's a big deal because it:- ranks first across 33 eval datasets- outperforms OpenAI and cohere models- supports quantization to reduce storage costsHere's another reason that makes this model truly superior:Most retrieval benchmarks test models on academic datasets that don’t reflect real-world data.RTEB, on the other hand, is a newly-released leaderboard on HuggingFace that evaluates retrieval models across enterprise domains like finance, la ...

RT Avi Chawla (@_avichawla)MCP & A2A (Agent2Agent) protocol, clearly explained!Agentic applications require both A2A and MCP.- MCP provides agents with access to tools.- A2A allows agents to connect with other agents and collaborate in teams.Let's understand what A2A is and how it can work with MCP:> What is A2A?A2A (Agent2Agent) enables multiple AI agents to work together on tasks without directly sharing their internal memory, thoughts, or tools.Instead, they communicate by exchanging context, task update ...

Core Concepts - A2A (Agent2Agent) enables AI agents to collaborate without sharing internal data, thoughts, or tools [1][2] - MCP provides agents with access to tools, while A2A facilitates agent-to-agent communication and teamwork [1] - A2A agents can be modeled as MCP resources using AgentCards [2] Functionality and Benefits - A2A supports secure collaboration, task and state management, UX negotiation, and capability discovery [3] - A2A allows agents from different frameworks to work together [3] - Remote Agents supporting A2A must publish a JSON Agent Card detailing their capabilities and authentication [2] Industry Implications - Standardizing Agent-to-Agent collaboration is beneficial, similar to MCP's role in Agent-to-tool interaction [3] - Clients can use Agent Cards to find the best agent for a task [3]

AI and API Development - 89% of developers use AI daily, but only 24% design APIs with AI agents in mind [2] - Every website and API must be "Agent-ready" to cater to AI agents [1][2] - APIs must be well-documented for AI agents to easily learn and use them, including endpoint discovery, parameter passing, data expectations, and error recovery [3] - Friction, inconsistency, and potential errors are introduced when agents have to infer how an API works [5] Postman's Solution - Postman's Agent Mode automatically generates detailed, contextual, and machine-readable documentation for APIs based on request/response examples and usage patterns [4] - Postman's AI-powered platform allows exploring, debugging, and building APIs using natural language [4] - Postman facilitates the design, documentation, publishing, and monitoring of APIs that can be reliably used by both humans and agents [8] Future Implications - Agents will increasingly handle tasks such as making purchases, finding the best options, and filling out job applications [6] - APIs should have predictable structures, machine-readable metadata, and standardized behavior to be effectively used by agents [7]