Once prompting feels natural and iteration becomes second nature, something important begins to happen. You stop thinking so much about how to work with AI. And you just work. 💎 Intuition Emerges Through Repetition Early on, every interaction is deliberate. You think about phrasing. You consider structure. You decide how much context to include. But as fluency builds, those decisions fade into the background. You don’t pause to plan each step. You know what to ask next. You sense when to refine, redirect, or move on. 🧭 This is where intuition takes over. Not instinct in the abstract—but familiarity earned through repetition. 💎 When Experience Starts Doing the Work At this stage, you’ve seen enough outputs to recognize what works. You’ve iterated enough times to trust your adjustments. You’ve internalized how the interaction responds. You’re not guessing. You’re drawing on experience. Because the mechanics no longer require attention, your focus shifts to what actually matters—the thinking, the creation, the decision at hand. 💎 Intuition Reduces Cognitive Load One of the most noticeable changes intuition brings is mental relief. You spend less energy: - Deciding where to start - Remembering what worked last time - Rebuilding context from scratch You’re not repeating effort. You’re reusing understanding. That reduction in friction is what allows AI to support your thinking instead of interrupting it. 💎 Intuition Forms Naturally Intuition isn’t something you configure, install, or copy from someone else. It forms through use. Through small decisions made repeatedly. Through noticing what feels right. Through learning when to intervene—and when not to. That’s why two people can use the same AI and develop entirely different instincts. Their intuition reflects how they work. 💎 The Quiet Advantage Once intuition is in place, work moves faster—but more importantly, it moves smoother. You’re not forcing structure. You’re not chasing outcomes.

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

Everybody experienced this once or twice 😅

There is a new addition to Daily Dose cycle of terms and posts. I'm intentionally stepping outside my comfort zone and learning Claude Code. Not with the goal of becoming an expert overnight, but as a practitioner in progress. Instead of waiting until I've "mastered" it, I'll be sharing the terms, concepts, and mental models I'm learning along the way — in real time. Why? Because for AI enthusiasts who are curious about becoming builders (or even just to explore making workflows, automations and more sophisticated websites), I think it's valuable to take those first steps together, as a shared experience. This is how most of us actually learn: • By trying • By getting things wrong • By refining our understanding one concept at a time • By relying on the team and community to help each other As I work through Claude Code, I'll be publishing beginner-to-intermediate terms that I'm learning along the way. This isn't about perfection on my part. If a term isn't quite right, that's okay — we have plenty of experienced builders and developers in this community, and I encourage you to jump in, correct, clarify, or guide us. That's part of the process. This series is about: • Taking the first step • Making progress while learning in public • And doing it together Let's see where this goes. 🚀 📚 NEW: Centralized Claude Code Learning Hub All AI Terms Daily Dose: Claude Code Addition terms and posts are now organized in one searchable location — no more scrolling through the feed to find what you need. 👉 Find it Here: AI Terms & Posts: Claude Code Edition Everything I am learning and sharing organized and ready to reference whenever you need it. This gives it its own section, highlights the value, and makes it feel like a significant resource.

We just crossed 400 members, and I want to take a moment to say thank you to everyone who’s joined AI Bits & Pieces and helped shape what this space is becoming.🎉 This community was built on a simple idea: ✨AI is now a life skill. The goal here isn’t to chase tools or trends. It’s to build real understanding and practical fluency. So AI can be applied thoughtfully in everyday life, work, and business. As the community continues to grow, you’ll see more relevant content as we learn more about member preferences. That said, content is only one part of what makes this community work. 🤝 Just as important are the contributors who consistently show up and share real work — including open build journeys like @Holger Peschke 30-day RAG build, and @Matthew Sutherland, who consistently adds deeper insight and context to our content. @Muskan Ahlawat and @Judith Vanegas also deserve recognition for their consistent encouragement and thoughtful engagement. 🏆 Community Leaderboard To recognize members who have made meaningful contributions through participation, learning, and engagement — thank you for showing up. 1. @Frank van Bokhorst 2. @Holger Peschke 3. @Muskan Ahlawat 4. @Matthew Sutherland 5. @Dena Dion 6. @Judith Vanegas 7. @Jason Hagen 8. @Dorota Mleczko 9. @Usman Mohammed 10. @Roger Richards And new contributors: @Glenn Marcus and @Reynoso Anubis for their in-depth posts and videos that inspire and encourage us pursue new AI goals and applications.