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#MCP/工具调用

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Oct 17Sat
  1. Simon Willison44

    LaTeX renderer

    Inspired by Tiny Little Office, Simon Willison used Claude Code (Opus 5.5 Extra) to build a LaTeX renderer tool that renders LaTeX source as MathJax-formatted equations or faster-latex-formatted full documents, and stores content in a URL #fragment for shareable links.

  2. The Decoder71

    ChatGPT becomes a full Chrome browser assistant that reads and acts on your open tabs

    OpenAI's ChatGPT Chrome extension has been upgraded to directly access and act on open browser tabs. Users can target tabs with @chrome, Chat mode can summarize data across multiple tabs, and Work mode uses Computer Use to send messages, fill forms, download files, or group tabs; official examples include comparing products across tabs, summarizing articles, and comparing prices. The upgraded sidebar shows full chat history, and the extension is available on all ChatGPT plans.

Oct 16Fri
  1. The Verge · AI65

    ChatGPT can peek at your Chrome tabs now

    OpenAI upgraded ChatGPT's Google Chrome extension: typing @chrome in the ChatGPT web or desktop app lets the model view currently open tabs. New capabilities include filling spreadsheets or forms with content from multiple tabs and comparing products or services. ChatGPT Work can also control Chrome tabs to send messages, fill forms, download files, and organize tabs into groups, and ChatGPT in the sidebar can use full conversation history within a page.

Oct 15Thu
  1. AWS Machine Learning Blog56

    Trust in Depth: Giving agents more autonomy without losing control

    AWS released its Trust in Depth methodology, which lets agents operate safely without per-step human approval through four layers: Scope (limiting capabilities and access), Know (Agent Registry and independent identities), Bound (AgentCore Policy enforced at the Gateway, Guardrails, and the open-source sandbox Strands Box), and Observe (CloudWatch Omni tracking and evaluation).

  2. AWS Machine Learning Blog49

    Build self-improving document processing using Amazon Bedrock AgentCore

    A multi-agent intelligent document processing (IDP) system built on Amazon Bedrock AgentCore can automatically classify, extract, verify and learn from business documents. The solution uses Strands Agents to orchestrate 8 specialized agents, with Amazon Textract for text extraction and S3 Vectors for vector matching; new document types go through a training workflow that generates and saves extraction instructions for reuse, while known types go straight through the automated pipeline.

Oct 14Wed
Oct 13Tue
  1. GitHub Blog · AI & ML61

    GitHub Copilot app for Beginners: How to teach Copilot about your codebase

    GitHub released the Copilot app for Beginners tutorial series, covering four ways to give Copilot project context, including .github/copilot-instructions.md custom instructions.

    Why it matters: GitHub's official tutorial lays out four mechanisms for giving Copilot project context and when to use each, so readers can combine them as needed in their own repos.

Oct 10Sat
Oct 9Fri
Oct 8Thu
Oct 7Wed
Oct 2Fri
  1. AWS Machine Learning Blog44

    Building ambient agents with Amazon Bedrock AgentCore: From event-driven signals to human-in-the-loop workflows

    AWS provides an end-to-end reference implementation for ambient agents on Amazon Bedrock AgentCore: an event (such as an S3 upload) triggers an agent on AgentCore Runtime to run a workflow, pausing for human input via a single ask_human tool whenever approval or clarification is needed, then resuming from where it left off.

Sep 30Wed
  1. AMD Chips and Compute43

    AMD Brings the Power of Agentic AI to Embedded Design and Development Cycle

    AMD launched AMD Ross™, an agentic AI assistant covering the entire embedded development cycle, including architecture design, optimization, debugging, PCB design, schematic review, software and deployment. It connects to AMD Embedded tools via an MCP server, and bundles the AMD Knowledge Base, expert-written agent skills and runnable design examples, supporting natural-language document queries, command execution and workflow automation.

Sep 29Tue
Sep 26Sat
  1. GitHub Blog · AI & ML64

    GitHub Copilot app for Beginners: How to build custom workflows with canvases

    An official GitHub tutorial shows how to build custom workflows with canvases in the GitHub Copilot app: run the /create-canvas skill in an agent session, describe the workflow, user actions, and agent actions in natural language, and the agent generates boards, checklists, dashboards, and other interfaces that open in the side panel.

    Why it matters: The tutorial covers the three elements of /create-canvas and its two-way collaboration mechanism, so readers can turn their own workflows into reusable canvases.

Sep 25Fri
  1. GitHub Blog · AI & ML68

    AI-powered fuzzing with the GitHub Security Lab Taskflow Agent

    Antonio Morales of GitHub Security Lab has released Fuzzing Taskflow, an autonomous fuzzing pipeline for C/C++ projects built on the Taskflow Agent — you only specify a GitHub repository, and it automatically identifies entry points, analyzes the build system, writes harnesses, runs AFL++, and triages crashes.

    Why it matters: The author explains the design of the automated fuzzing pipeline, showing how LLM agents can take over manual steps like coverage analysis and crash triage.

Sep 18Fri
  1. GitHub Blog · AI & ML42

    Should you read the code, is RAG dead, and did Skills kill MCP?

    The GitHub Podcast breaks down five common AI hot takes one by one. Key conclusions: AI-generated code still needs review scaled to risk; Skills and MCP solve different problems, with MCP providing a standard for tool and data access while Skills package team workflows and best practices in Markdown, making them complementary; RAG is not dead—retrieval brings models closer to answers and can coexist with agents, MCP, and Skills in the same workflow.

Jul 9Thu
Jul 2Thu
  1. Mistral AI65

    Leanstral 1.5: Proof Abundance for All

    Mistral released the Apache-2.0 open-source model Leanstral 1.5, with 119B total parameters and 6B activated parameters, trained via mid-training, supervised fine-tuning and CISPO reinforcement learning.

    Why it matters: The official post discloses the full training pipeline, benchmark figures and real code verification cases, letting readers gauge how practical formal verification models are for mathematical proofs and open-source bug finding.

Jun 24Wed
Jun 22Mon
  1. Chrome Developers · AI and Browser68

    A developer toolkit to make your website agent-ready

    Chrome released an agent-ready toolkit for developers, and Lighthouse gains a new Agentic browsing audit category from M150, checking three metrics: accessibility tree, CLS visual stability, and WebMCP integration.

    Why it matters: The article details the specific checks in Lighthouse's new Agentic browsing audit and Chrome DevTools debugging methods, so developers can directly improve how agent-friendly their sites are.

Jun 18Thu
Jun 9Tue
  1. Chrome Developers · AI and Browser65

    Join the WebMCP origin trial

    Chrome announced the WebMCP origin trial in Chrome 149, letting developers build structured tools for web apps that declare the purpose of buttons, input fields, and other UI elements and manage page state, so agents can complete tasks without guessing.

    Why it matters: With the official WebMCP origin trial open, developers can let agents operate web forms and debugging flows via declared tools and submit API feedback.

May 28Thu
  1. Mistral AI64

    Introducing Search Toolkit

    Mistral AI released the public preview of Search Toolkit, an open-source framework for building production-grade search pipelines for AI applications, unifying ingestion, retrieval and evaluation in a single shared-interface framework.

    Why it matters: The original details modules, evaluation metrics and enterprise case studies, helping readers judge whether it can replace their existing retrieval infrastructure.

May 22Fri
  1. Mistral AI59

    Connect the dots: Build with built-in and custom MCPs in Studio

    Mistral released Connectors in public preview for Studio; both built-in connectors and custom MCPs can be used via API/SDK with all models and agent calls. New features include direct tool calls, human-in-the-loop approval flows, and programmatic management of connectors. Once centrally registered, connectors can be reused in LeChat and AI Studio, making it easy to connect to enterprise systems like CRMs and knowledge bases.

Mar 17Tue
Sep 2Tue
  1. Mistral AI59

    Le Chat. Custom MCP connectors. Memories.

    Mistral AI launched 20+ secure MCP-powered connectors (beta) for Le Chat, covering tools such as Databricks, Snowflake, GitHub, Atlassian, Box, Stripe, and Zapier, enabling search, summarization, and actions, with support for connecting any remote MCP server and deployment on-premises, in your own cloud, or on Mistral Cloud.