Today, we would like to recognize @Matthew Sutherland for his contributions inside the AI Bits & Pieces community. Matthew has been a great sounding board, and has also spent time reviewing Claude Code–related content and providing specific, actionable suggestions. His feedback has focused on structure, clarity, and how ideas translate into practical use. His comments are thoughtful and grounded in hands-on experience. They tend to clarify intent, tighten explanations, and make the material easier to apply for others working through the same topics. Outside the community, Matthew is the founder of Byteflow AI, where he builds and runs AI systems that automate real operational work. His focus is on workflows, agents, and integrations that run in production and support day-to-day business execution. His work follows a clear framework — Scope. Shoot. Solve. — emphasizing problem definition, working deliverables, documentation, and clean handoff. Engagements range from operational assessments and system builds to incident response and targeted briefings. With more than 25 years of experience across technology, operations, and business development, Matthew brings a practical, execution-first perspective to applied AI and automation. We appreciate the time and care Matthew puts into strengthening shared work and contributing to the quality of the conversation. Thank you, Matthew, for the role you play in helping this community learn and improve together. Follow Matt on LinkedIn: https://www.linkedin.com/in/matthewsutherland/ For a highlight of Matt's Post: https://www.skool.com/ai-bits-and-pieces/classroom/5bebee2e?md=13b025f0574742bca30bc136b78d0d7e

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

Hi Everyone, I would like to invite you to a free webinar I am hosting today. It's a comparison of the 4 models: ChatGPT, Claude, Gemini and Copilot. Today, January 15th, 19:00 CET / 1:00 PM EST https://www.linkedin.com/events/4mainaichatbotscompared7414659699100344321/ It would be great to see some of you there. There will be gifts :-)

We gave AI Bits & Pieces logo a little visual refresh. It stands out a bit more. And honestly… we think it’s fun. Total marketing effort? We asked our daughter which one she liked better. She picked this one. Bingo — new logo. You’ll start seeing it everywhere.

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.