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!

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.