Just started using Claude Cowork this evening, and I'm genuinely blown away. For those of us paying for Claude Max, Anthropic just handed us what might be the most significant AI productivity leap of 2025. What is Cowork? Think of it as Claude Code's power but for everyone. No terminal. No coding knowledge required. Just point Claude at a folder on your Mac, describe what you need, and watch it work. Here's what makes it remarkable: The AI doesn't just chat anymore. It operates. It reads your files, creates new ones, reorganizes your chaos, and builds actual outputs all autonomously. I've already tested it on: → Turning scattered meeting notes into structured reports → Organizing a messy downloads folder by content type → Converting receipt screenshots into expense spreadsheets And Claude did it like a capable colleague working in the background while I focused on other things. The meta moment nobody's talking about: Anthropic built Cowork in just 10 days. Using Claude Code. The AI built its own non-technical sibling product. We're watching recursive improvement happen in real-time. Currently available: ✅ Claude Max subscribers ($100-$200/month) ✅ macOS desktop app ⏳ Windows support coming soon ⏳ Waitlist for Pro/Team/Enterprise users This isn't a chatbot upgrade. This is AI becoming an operational partner. If you're running a business and doing repetitive file work, document prep, or data organization — this is the tool you didn't know you needed. The future of work isn't AI answering questions. It's AI doing the work. Welcome to the age of agentic AI. 🔥

DUNNO WHO NEEDS TO HEAR THIS, but If you could live anywhere in the world for one year, where would it be and why? 🌍✨ Would you go for the sunny beaches, the bustling cities, or somewhere completely off the grid? 🏖️🏙️🌲 And hey, bonus points if your reason is something totally unexpected! Maybe you’re chasing sunsets or hunting for the best street food – let’s hear it! 😎🍜

I've been exploring one of the most significant developments in AI assistance technology, and I need to share this with my network. Anthropic has introduced Skills in Claude a modular system that transforms a general-purpose AI into a specialized expert on demand. Think of it as giving your AI assistant the ability to load exactly the right expertise, workflows, and tools for any specific task. Why This Changes Everything: Traditional AI assistants are generalists. They know a bit about everything, but lack the procedural depth for specialized work. Skills solve this elegantly by providing Claude with: → Specialized workflows for multi-step domain-specific processes → Tool integrations for specific file formats, APIs, and systems → Domain expertise including company-specific knowledge and business logic → Bundled resources like scripts, templates, and reference materials What's Available Out of the Box: The current skill library is impressive. Professional document creation (Word, PowerPoint, Excel, PDF), production-grade frontend design that avoids generic "AI slop" aesthetics, algorithmic art generation, MCP server building, and sophisticated theme systems—all with best practices built in. The Real Game-Changer: Custom Skills Here's where it gets exciting for businesses. You can create your own skills that encode your company's brand guidelines, internal processes, database schemas, and proprietary workflows. I've implemented custom skills for Plexaris that automatically apply our brand identity—colors, typography, visual standards—to any deliverable Claude creates. Imagine asking Claude to create a pitch deck, and it automatically knows your exact brand colors, your preferred fonts , and your glass morphism card styling. That's not a template that's institutional knowledge embedded in your AI assistant. The Technical Elegance: What impressed me most is the progressive disclosure architecture. Skills use a three-level loading system: metadata always present (~100 words), the skill body loaded only when triggered (<5k words), and bundled resources pulled only as needed. This means efficiency without sacrificing capability.

In 2024, the customer service department at Klarna, the Swedish fintech company, completed a quiet revolution. The company announced that its AI assistant was now handling the work equivalent of 700 full-time customer service agents. The chatbot resolved two-thirds of customer inquiries in under two minutes, down from eleven minutes previously, with satisfaction ratings matching human agents. The company projected $40 million in annual profit improvement.¹ This wasn't automation in the traditional sense. This was something different. (An important postscript: In May 2025, Klarna reversed course, announcing plans to hire human customer service workers again. CEO Sebastian Siemiatkowski acknowledged that "quality of human support" had suffered and that the company's AI-focused path "wasn't the right one." The reversal is instructive, it reveals both the limitations of current AI and the relentless pressure to try again. We'll return to what Klarna's stumble tells us about the shape of this transition.)² --- The Familiar Script We've been through this before, or so the argument goes. Every technological revolution triggers the same cycle of panic and adaptation. In the early 1800s, the Luddites smashed textile machinery, fearing the machines would steal their livelihoods. By 1900, most Americans worked in agriculture; today, less than 2% do. Yet unemployment hasn't steadily climbed, it's fluctuated within a relatively narrow band for over a century. The economy adapted. New jobs emerged. Human ingenuity prevailed. The reassurances come from economists, technologists, and business leaders in predictable refrains: It's just a tool. It augments human capability. Yes, some jobs will change, but we'll create new ones. We always have. There's comfort in this narrative. It's backed by 200 years of evidence. It suggests that our economic anxieties, however real they feel, are ultimately misplaced, that we're once again mistaking transformation for catastrophe. But what if the pattern doesn't hold this time? What if the very thing that makes us confident, our long history of technological adaptation, is blinding us to a fundamentally different kind of disruption?

I’m currently working on a project called Smart Concert Experience Platform, which focuses on improving navigation, safety, and real-time assistance for attendees at large live events using QR-based access and intelligent routing. The idea is to help people easily find gates, washrooms, food stalls, and exits, while also supporting emergency guidance and offline usability in crowded venues. Core Features QR-Based Access: Allows users to instantly initialize event and venue context by scanning a QR code at the concert location. Intelligent Navigation: Provides shortest-path guidance within the venue using graph-based routing algorithms. Crowd-Aware Routing: Dynamically suggests routes that avoid simulated high-density areas to improve movement efficiency. Emergency Mode: Instantly guides users to the nearest safe exit during emergency situations using predefined evacuation paths. Offline Mode: Ensures core navigation and information access remain available without internet connectivity after QR initialization. Lost & Found Movement Tracking: Enables users to review their recent movement history to identify locations where they previously stopped or passed through. Value Add-Ons Micro-Notifications: Delivers location-based alerts and tips triggered by the user’s proximity to venue facilities. Multi-Language Assistant: Adapts the assistant’s responses to the user’s preferred language for improved accessibility. Future Scope Person-to-Person Sharing: Allows nearby users to share approximate locations with consent to help locate companions within the venue. Organizer Dashboard: A read-only interface that visualizes simulated attendee movement and crowd density across venue zones to support event monitoring and planning. I’d love to hear your thoughts, from a user, organizer, or tech perspective on how such a platform could be useful, what features you’d expect, or what challenges you see in real-world adoption. Open to feedback and ideas. Thanks in advance!