If you’ve ever stood in a petting room — surrounded by wagging tails and hopeful eyes — you already understand this. Because selecting a dog or LLM as a companion isn’t really about specs, scores, or comparison charts. It’s more like that moment when a dog finally chooses you. ❤️ We spend so much time obsessing over characteristics. Speed. Size. Capabilities. But for most of us, that’s just noise. These companions have become so endlessly adaptable. Whether we’re talking about abilities or temperament, what actually drives our choice isn’t a technical spreadsheet. It’s a feeling. Style. Comfort. An intuitive sense that this one fits. 🐶 Pedigree vs. Personality On paper, pedigree matters. Performance matters. But in real life, you don’t fall in love with lineage — you fall in love with temperament. One companion might be “smarter” on a leaderboard, but if its tone feels robotic, or it misses your humor, the connection breaks down. We gravitate toward the personality that matches our rhythm, our thinking style, and the way we work. ❤️ The Instant Connection There’s a moment when a dog rests its head on your knee — and you just know. With an LLM companion, that moment often happens within the first few interactions. It anticipates your next thought. Formats things exactly the way your brain wants to see them. Responds in a way that makes you pause and think, oh… this works. That’s the difference between something you have to manage and a companion that simply gets the assignment. 🐕 Growing Together The best companions learn your routines without a word being said. They know the difference between work mood and walking mood. With a LLM, over time, it learns your shorthand. Your preferences. Your voice. Eventually, switching your LLM starts to feel less like a software upgrade and more like starting over with a brand-new puppy. Exciting, yes. But also — a little heartbreaking. 🐾 Care and Feeding In the end, it’s simple. My companion gets me. Both my dog and my LLM.

🌀 The Quirk: When a prompt sounds authoritative, AI often mirrors that confidence — even if the answer itself is a best guess. 🌀What’s Going On: - AI is trained to mirror tone as much as intent. - Confident prompts signal “this is established knowledge.” - The model fills in missing context with the most likely answer. - Fluency can hide uncertainty, especially with new tools or edge cases. 🌀 What To Do If You See It: - Ask the model to flag assumptions before answering. - Request uncertainty explicitly: “What might be wrong here?” - Reframe the prompt as exploratory, not declarative. 👉 Try these prompts: “Answer cautiously. If any part is a guess, say so.” “Answer cautiously. If you’re unsure about any part, say so.” “Answer cautiously. Identify any assumptions and note where certainty is low.” “Answer cautiously. Call out any guesses.” Why This Matters: AI confidence is a delivery style, not a truth signal. Knowing when to slow the model (LLM) down is part of real AI fluency. 🎯 AI Bits & Pieces — helping people and businesses adopt AI with confidence.

I’m excited to share something special with our AI Bits & Pieces community, especially the AI-curious members of our community. 🥁 🚀 Starting this week, my wife Michele will be contributing a new series called AI in Real Life — a warm, honest look at what it actually feels like to learn and use AI from the perspective of an everyday, real-world user. ✨ AI in Real Life is for anyone who’s ever thought: “I’m curious… but where do I begin?” This series will follow her personal journey with ChatGPT and other AI tools as she explores how they show up in everyday life — conversations with family, planning and organizing, trying new ideas, and even navigating the hesitation many of us felt in the beginning. Each week, Michele will share a short story, a small discovery, or a real-life moment that brought AI into her world in a simple, human way. My hope is that her voice helps make this community feel even more welcoming for those who are just getting started. ________ 🕰️ By way of background, Michele (@Michele Wacht ) spent twenty years as an executive selling services to the automotive OEM industry. She came from a corporate marketing and sales background, achieving top salesperson status at her company for many years before stepping away eight years ago to prioritize our family and be fully present for our daughter during her teen years. Now that Emma is off to college and recently turned 21, Michele felt ready to re-engage. And to my delight, she decided to join AI Bits and Pieces in helping people understand the benefits of AI — not from the perspective of an engineer or a strategist, but from the vantage point of someone discovering her own curiosity and how AI fits in as a life skill. If you’ve read Michele’s writing — as I and many of her friends have — you know she has a gift for turning simple moments into meaningful reflections. She approaches AI the same way — with curiosity, humor, and a down-to-earth honesty that reminds us that learning something new doesn’t always start with confidence. Sometimes it starts with dinner plans for friends, a college-age daughter on speed dial, and a willingness to try (a preview of her first post).

We were deep in conversation, driving to Florida. Talking at some length about the 2026 strategy for AI Bits & Pieces. What stays. What evolves. What new ideas might want a little room to grow. After a lot of back and forth, there was a pause. The kind that means someone is thinking, not finished. Then Michael started thinking out loud — as he does. I was listening. Or so I thought. Michael was reciting a finished thought — pulling together all the pieces of the conversation we’d just had about AI Bits & Pieces and its next chapter. Naturally, I answered. Quickly. Confidently. Like a spouse who’s been married a long time and knows the rhythm of these conversations. And then ChatGPT started talking. And then it just… stopped. Like, oh — sorry, go ahead. I remember thinking, "Why did it start talking?" Completely forgetting that Michael had ChatGPT set to voice mode to capture our thoughts and notes. So, I kept going. Added a little more context. And then, suddenly, ChatGPT jumped back in and essentially said, “Yes, I agree with Michele.” 😳 I looked at Michael with that "what just happened" face. That’s when it clicked. He wasn’t asking me. He was asking "TARS" (yes, from *Interstellar*) — as Michael calls ChatGPT. And somehow, without meaning to, I had jumped into a three-way conversation… and the AI wasn’t waiting at all — more like a cat behind the couch, ready to spring. I didn’t know whether to laugh or shake my head in bewilderment. Probably both 😂 And I thought to myself — "damn… it’s already here." Woven quietly into our conversations, our thinking, our planning. It made me wonder — where else is AI showing up that I’m not even consciously aware of? We’re just going on about our day — the kind of conversation I’ve had a thousand times with my partner of 27 years — and it’s already inserting itself into our lives. And maybe that’s how the biggest changes arrive — already settled in, before we realize we’ve adjusted. And that's AI in Real Life...

🌀 The Quip: “AI favors the curious, not the technical.” My largest customer has over 400 employees. One thing I’ve noticed over time is that the people getting the most value from AI aren’t the most technical ones in the room. They’re the curious ones. ✨ Why It Matters Curious people ask better questions. They try things without overthinking. They’re willing to explore, adjust, and keep going. The more curious you are, the more useful it becomes. ✨ What I’m Seeing With Clients The employees making real progress with AI aren’t doing anything fancy. They’re using it in small, practical ways — drafting an email, exploring an app, expanding an idea, or learning something new on the fly. They’re not worried about prompts being “right.” They’re focused on staying curious and thinking more clearly and efficiently. ✨ Takeaway AI doesn’t reward who knows the most — it rewards who explores the most. 👉 Who around you seems to benefit most from AI — and why?