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: If you ask ChatGPT what time it is, it can’t actually tell you — even though it feels like it should. There’s no built-in awareness of the current clock or moment. What’s Going On: - ChatGPT doesn’t have a live clock or real-time awareness by default. - It generates responses based on patterns, not the current moment. - Time only exists for the model if you explicitly provide it. - So asking “What time is it?” is a bit like asking a calculator what day it is. 🔧 What To Do If You See It: - Don’t assume AI knows “now” — give it the time when it matters. - Include the date, time, or timeframe directly in your prompt. - Try this prompt: “It’s currently 3:15 PM on Tuesday. Based on that, what should I do next?” Why This Matters: This quirk is a reminder that AI is context-driven, not situationally aware. The clearer the context you provide, the smarter it feels. This one genuinely surprised me. For some reason, I assumed “knowing the time” was basic. Turns out, it’s not. Does this surprise you too? Or is this something you already knew? Try asking ChatGPT the time, and see what response you get.

What happens when you dive one hour a day into RAG with absolute intensity? I’m about to find out. Starting today, I’m committing to a personal challenge:Every single day, I will spend at least one hour digging deep into Retrieval-Augmented Generation and I’ll share every step of my progress right here. Why?Because I want to grow. Deeply. Consistently. With purpose.And because RAG is becoming one of the most important building blocks of future AI systems. To make this challenge truly powerful, I need your support and I need everything this community has. 🔥 I genuinely need all of it: – your RAG automations – your RAG-enabled AI agents – your workflows– your best practices – your mistakes and lessons– your resources, tutorials, and websites – every piece of knowledge that exists here – every experience you’ve made To start this challenge in the right way, I need your help with a few key questions: 🔍 Which RAG automations have you already built? 🔍 What RAG-related information, examples, or materials already exist in this community? 🔍 What was absolutely essential for you to truly understand RAG? 🔍 Which websites, videos, or tutorials helped you the most? 🔍 Which RAG systems have you built — and would you be open to sharing them with me? I’m excited to dive deeper every single day and to build real RAG excellence together with all of you. Let’s go. Please put all informations in the comments, it will be helpful