Anthropic just released Claude Opus 4.6 today, and I'm already running it. Here's what you need to know: What's New: 1M Token Context Window — First Opus model to handle this scale. That's 1,500 pages of text or 30,000 lines of code in a single session. A 5x jump from Opus 4.5. Agent Teams — Multiple AI agents now work in parallel on different parts of your task, coordinating together instead of working sequentially. Think of it as having a skilled team rather than a single assistant. Claude in PowerPoint — Direct integration (research preview). Create and edit slides without leaving the app. Cybersecurity Breakthrough — In testing, Opus 4.6 found 500+ zero-day vulnerabilities in open-source code using standard tools. No special instructions needed. Why This Matters for Business Anthropic's Scott White describes this as the shift to "vibe working" — where you describe what you want and the AI handles the execution end-to-end. For knowledge workers, analysts, and developers, this means: → Financial analyses that took days, done in minutes→ Agentic coding sessions that run 20-30 minutes autonomously→ Documents, spreadsheets, and presentations closer to production-ready on first attempt Rakuten already deployed it to autonomously manage tasks for a 50-person organization — closing 13 issues in a single day. The AI race continues. No single model dominates everything anymore — it's about choosing the right tool for the job. What's your first use case going to be?

January 25, 2026, agentic AI is rapidly reshaping the food and beverage industry by shifting from reactive, recommendation-based systems to autonomous agents capable of independently initiating and executing complex tasks. This newsletter explores three pivotal areas where agentic AI drives innovation: operational autonomy in foodservice, intelligent automation in food production, and predictive analytics for inventory and supply chain management. Together, these developments highlight how industry leaders can leverage AI to enhance efficiency, sustainability, and customer experience. Read the complete newsletter https://www.linkedin.com/pulse/transforming-foodservice-agentic-ai-efficiency-van-bokhorst-tezve/?trackingId=AreEBEvdSlGTokQntdUrqw%3D%3D

Claude code is a massive tool . Used it to build our own CRM system with all specific functionalities we need for our business. And 4 levels of accessibility. That being said times have changed and what do you do to keep up with the speed of changes ?

Ever feel like you're not getting the most out of Claude Code? Here's a methodology that's transforming how developers work with AI assistants. What is it? The Ralph Wiggum Loop is an iterative approach based on Geoffrey Huntley's technique, named after the Simpsons character known for unexpected brilliance. The core insight: single-shot prompting rarely produces optimal results, but intelligent iteration creates magic. Get Started with the Official Repo 🔗 github.com/snarktank/ralph This is the autonomous AI agent loop that runs repeatedly until all PRD items are complete. Each iteration spawns a fresh instance with clean context, while memory persists via git history, progress.txt, and prd.json. How It Works Write a PRD — Define what "done" looks like Run the loop — Ralph executes your task iteratively Persist progress — Git + artifacts maintain state between iterations Repeat until complete — Fresh context each cycle, building on previous work Quick Setup bash# From your project root mkdir -p scripts/ralph cp /path/to/ralph/ralph.sh scripts/ralph/ cp /path/to/ralph/prompt.md scripts/ralph/ chmod +x scripts/ralph/ralph.sh The Philosophy Don't aim for perfect on first try — Let the loop refine the work Failures are informative — "Deterministically bad" means failures are predictable Prompt engineering is key — Success depends on writing good prompts Fresh context is reliability — Each iteration clears context, re-reads specs, plans, and codes Key Tips Set --max-iterations as your safety net (start with 20-30) Include what to do if stuck in your prompt Keep PRD items small enough to complete in one context window Let Ralph update AGENTS.md with learnings for future iterations Why It Works You're not babysitting Claude step-by-step anymore. Define success criteria upfront, then walk away. Solutions that survive multiple iterations are stress-tested far more thoroughly than single-shot generations. Check out the repo and try it on your next project 👇 🔗 github.com/snarktank/ralph

Hello All, I have a script which needs to be put into a video. The video is between 2 and 3 minutes. Somebody can give me advice which system to use, or perhaps this person can help me to generate this video. With high quality Let me know, thank you very much for your support. Have a great day!