We’re glad you’re here. This community is all about exploring the human side of AI — through bite-sized insights, quips, quirks, and practical stories you can use right away. 📝 We Encourage You to Post Often - Share wins, ask questions, and share interesting AI news. - Keep posts short, practical and easy to digest (think 60–second reads). - Use our Post Protocol: catchy title, strong hook, main insight, and a takeaway or prompt. - If you like, add your Author Footer (name, one-line tagline, and a url to LinkedIn). 🎓 Start Learning In the Classroom New here? A great place to begin is our Classroom Training. It’s designed to help you build AI literacy and fluency in small, practical bites you can use in conversations, projects, and learning. 🚀 Your First Step Introduce yourself below! Share a bit about who you are, how you’re using AI, or where you’re curious to start. 📌 Before You Post Please take a moment to review our Community Rules. Keeping things respectful, helpful, and light-hearted ensures everyone gets the most out of being here. We’re building this community one small piece at a time — and we’re glad you’re part of it.

Welcome to the Out of The Box Series — where I test how far curiosity and AI can take me in 30, 60, or 90 minutes, using today’s best no-code and low-code tools. No setup. No training. Just pure exploration — right out of the box. 🎬 This Episode: ElevenLabs 🕒 Time Limit: 30 Minutes 📂 Category: AI Voice & Audio Creation What Is ElevenLabs? ElevenLabs is a tool that reads written text out loud using realistic-sounding AI voices. It’s often used to turn scripts, notes, or explanations into audio so people can hear information instead of just reading it. For this test, I had ChatGPT generate a short draft script for a brand-new video I am creating that is focused on an emerging shift many people are just starting to notice; the move from SEO (Search Engine Optimization) to AEO (Answer Engine Optimization). I started by testing two sort scripts, just to see how things work: - 🎙️ Audio Test #1: ElevenLabs default voice: Click to hear it - 🎧 Audio Test #2: ElevenLabs IVC (Instant Voice Cloning): Click to hear it Then I created a draft script for the video using ChatGPT: - 🎧 Audio Test #3: ElevenLabs IVC (Instant Voice Cloning): Click to hear it - Note: IVC required 10 seconds of my voice being recorded to provide the audio in a close representation of my voice. For this session there was no major editing tricks, no audio engineering - Just exploration. 🚀 Within 30 minutes, I created: - A complete video script written by ChatGPT - Three audio recordings using two different voice approaches (demo, and IVC) - The first draft of the audio for an upcoming video. Voice plays a big role, as most people realize. Thanks to ElevenLabs it is now accessible to the AI enthusiast and professional.

After learning how to prompt clearly and iterate effectively, a natural question emerges: Does it matter which LLM I use if I’m iterating well? In the short run, the honest answer is no. If you’re clear in your intent and willing to refine direction, most modern LLMs will get you where you need to go. Prompting and iteration do a lot of the heavy lifting early on. That’s why many people experience an initial breakthrough and think, “Okay, I’ve got this.” And they do. At first. 💎 Why Iteration Levels the Field Early When you’re iterating well, you’re doing a few important things: - Clarifying what you actually want - Responding to output instead of restarting - Adjusting direction in small, intentional steps Those behaviors transfer. They work across LLMs because the interaction pattern is the same: input → response → refinement. In that phase, differences between LLMs fade into the background. You’re building skill, not dependency. 💎 When Fit Begins to Show Up As AI becomes something you use regularly—not occasionally—another shift starts to happen. You’re no longer experimenting. You’re working. And that’s when fit begins to show up. Not in dramatic ways In small ones that compound over time. You notice how an LLM responds to follow-ups. How much structure it assumes. How easily you can steer it without over-explaining. Tone and writing style are often where this becomes most obvious. Some people gravitate toward Claude because it feels more measured, structured, and editorial. Others prefer ChatGPT because it feels more conversational, adaptive, and easy to steer through quick iteration. Neither is better. They simply feel different to work with. And once AI becomes part of your daily rhythm, those differences start to matter. To be clear, this isn’t about specialty capabilities like coding, image creation, or domain-specific features. It’s about how naturally an LLM mirrors: - Your tone - Your writing style - The way you think through ideas

Check this out! I was vibe coding a landing page I'm working on and I have a video that I will be playing when users come up on it. I just asked Claude to use a placeholder instead of my real link as the video is not completed yet. As you can see in the screenshot, it thought it was funny to implement this video instead of just a screenshot or a empty block 😂

I've been on Mac for 15 years, but after watching so many automation pros struggle with Windows, I put together a guide on what actually changes when you switch (especially for Claude Code, n8n, and MCP servers): The TL;DR: If you're running Claude Code with MCP servers, building n8n workflows, or doing serious automation work, Mac's Unix foundation saves you hours every week. No more WSL translation layers, no more path separator nightmares, no more "why does this work on the server but not my dev machine" debugging sessions. What actually got better: - 🖥️ MCP servers just work - filesystem server, database connectors, API tools - they all assume Unix paths and execute natively on Mac - 💻 Claude Code runs seamlessly - bash scripts, file operations, terminal workflows - zero translation needed - 🐳 Docker stopped being painful - no more WSL2 overhead, memory limit tweaking, or random performance crashes - 📦 Homebrew = game changer - brew install n8n, brew install docker, done. No installer archaeology - 🧠 16GB feels like 32GB - unified memory architecture handles Claude Code + n8n + browsers better than traditional RAM - ⚡ Dev = production - your Mac terminal IS Unix, just like your Linux servers Real talk on downsides: - ❌ Gaming performance tanks - ❌ Zero hardware upgrade path - ❌ Keyboard shortcuts break your muscle memory for 1-2 weeks - ❌ Higher upfront cost - I wrote a complete guide for the transition (mental models, technical differences, what to actually expect) - PDF attached. Worth considering if you're tired of debugging WSL and path translation issues instead of actually building automations. What's been your experience with automation tools across platforms?