Running AI models on your own device starts with one important question: What can your hardware actually handle? This guide explores simple ways to check your device capabilities, understand which models are a good fit and get started with local AI without sacrificing performance. Have you tried running an AI model locally? What device are you using? 👇

You do not always need another monthly AI subscription. Many paid tools now have capable open-source alternatives for writing, research, automation, image generation and running AI locally. The bigger lesson is learning which AI capabilities you actually need and choosing tools that give you more control, flexibility and ownership without building an expensive stack of subscriptions. 💬 Which AI subscription would you replace first and what would you switch to?

Claude Opus 5 is being used for far more than writing code. Developers are building complete games, interactive simulations, developer tools, creative applications and AI-powered experiences that would have taken weeks or even months to create manually. These projects highlight how modern AI is evolving from a coding assistant into a collaborative development partner capable of accelerating both creativity and software engineering. Exploring real-world projects is one of the fastest ways to discover new workflows, design patterns and ideas you can apply to your own builds. 💬 Which Claude Opus 5 project has inspired you the most or what would you build with it if there were no limits? Share your ideas below.

Most people use Telegram for messaging but it can become much more powerful. By connecting an AI agent with MiMo Claw, you can turn Telegram into a workspace that writes code, searches the web, creates documents, generates images, edits files, connects with tools like GitHub and Notion and automates everyday tasks—all from a simple chat. The setup takes only a few minutes and is a great example of how AI agents are transforming everyday applications into powerful productivity tools. Have you tried using AI agents inside Telegram or another messaging app? Share your experience or your favorite AI workflow below. 👇

Most people still use AI like a chatbot. Write a prompt. Get an answer. Rewrite the prompt. Repeat. Loop engineering changes that. Instead of manually reviewing every response, you create a workflow where AI evaluates its own output, follows predefined rules and continuously improves until it meets your standards. This approach isn't about replacing human judgment—it's about reducing repetitive work and building more reliable AI systems. In this guide, you'll learn how to: - Build your first review loop with Codex - Structure projects using AGENTS.md - Create repeatable AI review workflows - Automate quality checks for code and content - Develop systems that improve through iteration The most effective AI systems are not built from a single perfect prompt. They are built from simple, reliable loops that consistently solve one problem at a time. How do you see loop engineering changing the way we build with AI over the next few years? I'd love to hear your thoughts.