WebMCP is a new technology that allows AI agents to interact with web pages more directly. It works by turning web pages into MCP (Model Context Protocol) servers via a Chrome extension. This enables agents to understand and manipulate web content in a structured way, potentially improving efficiency and user experience.
The technology, backed by Google and Microsoft, is designed to work alongside human users, allowing them to ask agents questions about the page they are viewing. WebMCP uses a Declarative API for standard actions and an Imperative API for more complex tasks. Early experiments demonstrate the ability to query web pages and receive structured data back.
The technology, backed by Google and Microsoft, is designed to work alongside human users, allowing them to ask agents questions about the page they are viewing. WebMCP uses a Declarative API for standard actions and an Imperative API for more complex tasks. Early experiments demonstrate the ability to query web pages and receive structured data back.
Microsoft's Phi-4-Reasoning-Vision-15B model challenges the trend of ever-larger AI models by demonstrating strong reasoning capabilities with a comparatively compact size. Trained on curated reasoning data, it aims to achieve performance without the massive compute costs associated with frontier models. The model supports multimodal tasks, combining text and image understanding, and offers flexible reasoning modes for different workloads. This research highlights the importance of data quality and training strategy, suggesting that smarter training techniques can be as impactful as simply increasing model size, particularly for AI agents and practical deployments.
>"Google knows asking agents to navigate GUIs designed for humans is ridiculous. Microsoft might not."
The article argues that the command line interface (CLI) is experiencing a resurgence due to the limitations of graphical user interfaces (GUIs) for autonomous agents. GUIs, once lauded for reducing cognitive load, have become cluttered and inconsistent, hindering agent efficiency. Agents struggle with GUIs, requiring repetitive image analysis and complex actions. CLIs provide a universal and efficient interface for agents to interact with software. Google's release of gws, a CLI for Google Workspace, exemplifies this trend. The author predicts a "SaaSpocalypse" where software providers scramble to develop CLIs to remain competitive.
The article argues that the command line interface (CLI) is experiencing a resurgence due to the limitations of graphical user interfaces (GUIs) for autonomous agents. GUIs, once lauded for reducing cognitive load, have become cluttered and inconsistent, hindering agent efficiency. Agents struggle with GUIs, requiring repetitive image analysis and complex actions. CLIs provide a universal and efficient interface for agents to interact with software. Google's release of gws, a CLI for Google Workspace, exemplifies this trend. The author predicts a "SaaSpocalypse" where software providers scramble to develop CLIs to remain competitive.
This article discusses the impact of Anthropic's Claude Code, an AI agent that is significantly impacting software development and the broader information work economy. It analyzes Claude Code's capabilities, its potential to drive revenue growth for Anthropic, the challenges it poses for Microsoft, and the shift in competition within the AI landscape.
A 12-week, 26-lesson curriculum all about Machine Learning, using primarily Scikit-learn and avoiding deep learning.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
A course teaching everything you need to know to start building AI Agents. Includes 12 lessons, code samples, and multi-language support.
Microsoft has released version 1.1 of Microsoft BASIC for the 6502 under an MIT license. This version, from mid-1978, supports the Commodore PET, KIM-1, and early Apple models. It's a version that has circulated unofficially for some time, now with licensing clarified.
Agentic AI is beginning to reshape malware detection and broader security operations. These systems are being used not to replace humans, but to take on the lower value jobs that have historically tied up analysts — from triaging alerts to reverse-engineering suspicious files.
An overview of the top AI-powered coding tools available in 2025, including Cursor, Claude Code, Windsurf, VSCode, and others, highlighting their features and popularity among developers.
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
MarkItDown is an open-source Python utility that simplifies converting diverse file formats into Markdown, designed to prepare data for LLMs and RAG systems. It handles various file types, preserves document structure, and integrates with LLMs for tasks like image description.
