It would be WONDERFUL to be able to drag and drop pages between classes. LOL I completely changed my site today... tucking the main class into a different sub-class (LOTS of new pages because no sub-folders options here yet) and then copy pasting a lot of pages over. WHAT FUN!!!! :D lol So.... is there?

Can somebody recommend me a good video about prompting for veo3. i am curently working on my small project, and i need specific stuff from ai, i mean i need just little details to improve my video quality, not some cinmeatic movie shot.(i know its harder for ai to understand some basic things, than making a cinematic shot)it would save me a lot of time. Thank you

Stop limiting yourself to one LLM. 🛑 Here's why switching could have saved me a month of frustration. I spent almost a month stuck on a website issue. Went back and forth with ChatGPT and Claude—smart models, but neither could crack it. Then I tried something different: I asked Claude to summarize our entire conversation into a markdown file, dumped it into Gemini, and picked up right where I left off. Gemini solved it in an hour. 🤯 It was like bringing a fresh perspective into a cross-functional team meeting. Sometimes you need the engineer's view, sometimes the designer's. Different LLMs have different strengths—and different blind spots. Here's the thing that hit me: I was treating my AI tools like I had one coworker I kept asking about everything. Would you keep asking your marketing person to solve your database architecture problem? Probably not. So why do we default to one LLM for everything? Now I'm rethinking my whole approach: - ChatGPT for creative brainstorming 🎨 - Claude for systems thinking and structured docs 📝 - Gemini for technical problem-solving when I'm stuck 🛠️ - The cross-platform handoff? Dead simple. Export conversation summary → import to new LLM → keep moving. 👇 Question for the group: Are you stuck in a single-LLM relationship? What's your strategy for knowing when to switch models? I'm curious if anyone else has had breakthrough moments by bouncing between LLMs. Drop your experience below.

We thought AI would change work by making us better writers. It may change work more by making us better communicators. When AI can listen, speak, see, and respond in real time, the interface becomes conversation, and that shifts everything. ------------- Context ------------- One of the strongest trends in AI right now is the move toward multimodal interaction. Voice, text, images, and video are converging into a single experience. This reduces friction between intention and action, and it changes how work feels. In many organizations, the biggest bottleneck is translation. Turning conversations into tasks. Turning ideas into documentation. Turning meetings into decisions. Typing becomes the tax we pay to make work real. Voice and multimodal AI reduce that tax. But speed alone does not guarantee clarity. Without discipline, we risk accelerating confusion instead of resolving it. ------------- The Shift from Prompting to Briefing ------------- Text-based AI rewards clever prompts. Conversational AI rewards clear thinking. Briefing is a different skill. It involves context, constraints, audience, and desired outcomes. It feels less like operating a tool and more like delegating to a colleague. That makes AI more accessible, especially for people who never enjoyed “prompt engineering.” At the same time, natural speech can hide ambiguity. Casual phrasing often skips assumptions. So the skill gap shifts from who can phrase prompts to who can frame work clearly. Teams that develop shared briefing habits will compound value faster than teams that rely on individual prompt tricks. ------------- The Attention Problem We Are About to Create ------------- Conversational AI feels always available. That can be empowering, or overwhelming. If voice agents interrupt constantly, triage poorly, or encourage reactive behavior, they fragment attention. If designed well, they protect focus by absorbing noise and surfacing only what matters. This is a human performance issue. Efficiency without boundaries leads to burnout. Speed without recovery erodes judgment.

After a lot of tinkering, I finally landed on a simple process that solves it. The best part? It's costing me less than $10/m. Here's the simple fix I'm using: 1. First, you create a simple voice memo on your phone right after you get a good AI result (literally just say: "For [project name], this prompt worked: [prompt]. Why it worked: [reason].") 2. Then, once a week, use Otter.ai (free tier) to transcribe all your voice memos into text. 3. Finally, paste the transcriptions into a Google Doc organized by project, so you can search it later when you need that same approach. It took me a while to figure this out, so I hope this saves some of you the headache. The problem I kept hitting: I'd get amazing AI results, then weeks later I'd completely forget what prompt I used or why it worked. Started from scratch every time, wasting hours re-testing things I'd already figured out. This tiny voice-memo system is helping me actually remember what works without adding extra screen time to my workflow. Let me know if any of that is unclear. Happy to help! 🙏