Category: Open Source

  • Anthropic’s Claude Now Controls Your Mac: The Dawn of True AI Agents

    In a move that fundamentally redefines what an AI assistant can do, Anthropic has launched a major update that gives its Claude chatbot the ability to directly control a user’s Mac — clicking buttons, opening applications, typing into fields, and navigating software on the user’s behalf while they step away from their desk.

    The update, available immediately as a research preview for paying subscribers, transforms Claude from a conversational assistant into something closer to a remote digital operator. It arrives inside both Claude Cowork, the company’s agentic productivity tool, and Claude Code, its developer-focused command-line agent.

    Anthropic is also extending Dispatch — a feature introduced last week that lets users assign Claude tasks from a mobile phone — into Claude Code for the first time, creating an end-to-end pipeline where a user can issue instructions from anywhere and return to a finished deliverable.

    How Computer Use Works

    The computer use feature works through a layered priority system. When a user assigns Claude a task, it first checks whether a direct connector exists — integrations with services like Gmail, Google Drive, Slack, or Google Calendar. These connectors are the fastest and most reliable path. If no connector is available, Claude falls back to navigating the Chrome browser. Only as a last resort does Claude interact directly with the user’s screen — clicking, typing, scrolling, and opening applications the way a human operator would.

    This hierarchy matters. As Anthropic’s documentation explains, “pulling messages through your Slack connection takes seconds, but navigating Slack through your screen takes much longer and is more error-prone.”

    Dispatch: Your iPhone as a Remote Control

    The real strategic play may not be computer use itself but how Anthropic is pairing it with Dispatch. Dispatch creates a persistent, continuous conversation between Claude on your phone and Claude on your desktop. A user pairs their mobile device with their Mac by scanning a QR code, and from that point forward, they can text Claude instructions from anywhere.

    The use cases Anthropic envisions range from mundane to ambitious: having Claude check your email every morning, pull weekly metrics into a report template, organize a cluttered Downloads folder, or even compile a competitive analysis from local files into a formatted document.

    The Competitive Landscape

    Anthropic’s timing is not accidental. The company is shipping computer use capabilities into a market that has been rapidly reshaped by the viral rise of OpenClaw, the open-source framework that enables AI models to autonomously control computers. OpenClaw exploded earlier this year and proved that users wanted AI agents capable of taking real actions on their computers.

    Anthropic is now entering a market that the open-source community essentially created, betting that its advantages — tighter integration, a consumer-friendly interface, and an existing subscriber base — can compete with free.

    Security Considerations

    The announcement has naturally raised security concerns. When Claude interacts with the screen, it takes screenshots of the user’s desktop to understand what it’s looking at. That means Claude can see anything visible on the screen, including personal data, sensitive documents, or private information.

    Anthropic has built several layers of defense. Claude requests permission before accessing each application. Some sensitive apps — investment platforms, cryptocurrency tools — are blocked by default. Users can maintain a blocklist of applications Claude is never allowed to touch. The system scans for signs of prompt injection during computer use sessions. And users can stop Claude at any point.

    But the company is remarkably forthright about the limits of these protections. “Computer use is still early compared to Claude’s ability to code or interact with text,” Anthropic’s blog post states. “Claude can make mistakes, and while we continue to improve our safeguards, threats are constantly evolving.”

    The Bottom Line

    The new features are available to Claude Pro subscribers (starting at $17 per month) and Max subscribers ($100 or $200 per month), but only on macOS for now. Early hands-on testing suggests the feature works well for information retrieval and summarization but struggles with more complex, multi-step workflows.

    As one early user on social media captured the broader ambition: “combine this with /schedule and you’ve basically got a background worker that can interact with any app on a cron job. that’s not an AI assistant anymore, that’s infrastructure.”

    Whether consumers are ready to hand their computers over to an AI remains to be seen. But with this launch, Anthropic has made it clear: the era of AI agents that can actually do work is no longer a distant promise. It’s a present reality, available for download today.

  • ruflo: The Open-Source Agent Orchestration Platform That’s Turning Claude Into Infrastructure

    ruflo: The Open-Source Agent Orchestration Platform That’s Turning Claude Into Infrastructure

    While the big AI labs fight over foundation models, a new category of tooling is emerging to coordinate what those models actually do once they’re deployed. ruflo — currently trending on GitHub — is positioning itself as the leading agent orchestration platform for Claude, and it’s attracting serious attention from developers tired of stitching together fragile chains of prompts.

    The project, from ruvnet, describes itself as an enterprise-grade platform for deploying intelligent multi-agent swarms, coordinating autonomous workflows, and building conversational AI systems. It’s built with TypeScript and integrates deeply with both Claude Code and Codex.

    What Agent Orchestration Actually Means

    If you’ve been following the AI agent space, you’ve probably encountered a frustrating pattern: take an LLM, give it a set of tools, and hope it figures out when to use which one. That approach works for demos. It falls apart in production.

    Agent orchestration platforms like ruflo take a different approach. Instead of relying on a single model to decide everything, they decompose complex tasks across multiple specialized agents, each with defined roles, tools, and escalation paths. A research task might involve one agent for web search, another for document synthesis, another for fact-checking. A coding task might have agents specialized in reading, writing, testing, and deployment.

    The orchestration layer — what ruflo is building — coordinates these agents, manages their communication, handles failures, and ensures that the right information flows to the right agent at the right time.

    Key Features of ruflo

    ruflo’s GitHub description highlights several capabilities that distinguish it from simpler agent frameworks. Distributed swarm intelligence suggests the platform can coordinate agents across multiple machines or processes, enabling parallelism and fault tolerance. RAG integration means agents can retrieve relevant context from large document stores before responding — critical for enterprise use cases where the model’s training data doesn’t include proprietary information.

    The native Claude Code and Codex integration is particularly interesting. Rather than building its own model interaction layer from scratch, ruflo leverages the tooling that Anthropic and OpenAI have already built for their coding agents. That suggests a platform designed to extend existing investments rather than replace them.

    The Enterprise Bet

    The timing of ruflo’s traction is notable. We’re entering a phase where enterprises are moving past the question of whether to use AI agents and onto the harder question of how to deploy them reliably. Simple prompt chains don’t scale. Human-in-the-loop approval processes don’t work when you’re running thousands of tasks. What the enterprise market is starting to demand is infrastructure — the kind of reliable, observable, controllable systems that have characterized enterprise software for decades.

    ruflo appears to be one of several bets that the answer lies in sophisticated orchestration layers that treat individual model calls as commodities and competitive differentiation as something that lives above the model layer. Whether ruflo specifically becomes the standard or simply informs what a future standard might look like, it’s worth watching.

    The project is open source and available on GitHub, where it currently has over 24,000 stars and continues to attract contributors. For developers building serious agentic workflows, it’s worth a look.