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.

Level: Foundational Category: AI System Categories This term introduces the major categories of AI systems and what they are designed to do in practical use. 🪄 Simple Definition: A Large Language Model (LLM) is an AI system trained to understand, interpret, and generate human language. 🌟 Expanded Definition: LLMs are built using deep learning and trained on massive collections of text. This enables them to recognize patterns, understand context, and produce writing that feels natural and human-like. Examples include ChatGPT, Claude, Gemini, and Grok.LLMs can summarize documents, answer questions, write content, support research, and assist in decision-making.They don’t “think” like people—they generate responses based on statistical patterns learned during training. ⚡ In Action: You type: “Draft a follow-up message for customers who missed their service appointment.” The LLM produces a polished, professional message in seconds. 💡 Pro Tip: Clear instructions produce stronger results. Define the role, purpose, tone, and audience to guide the model effectively. This term is part of the Classroom Course - AI Fundamentals

✨ 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.

If you’re new to the community — or you’re just starting to use (or trying to understand) ChatGPT and tools like it — this is the course for you. We’ve just added a new Classroom course: How LLMs Like ChatGPT Work This course is designed to help you understand what’s actually happening when you use ChatGPT, so you can stop guessing and start getting better results. The big idea is simple:👉 The more you understand how ChatGPT works, the better you can guide it with your prompts. What you’ll learn: - The building blocks behind ChatGPT and large language models (LLMs) - How prompts, responses, and conversation work together - Why ChatGPT answers the way it does — and why it sometimes sounds confident but gets things wrong - How this understanding helps you write clearer prompts and use AI more intentionally 📌 Quick Note: There are many LLMs similar to ChatGPT available today, like Gemini and Claude, which are covered in a separate course. In this course, we use ChatGPT illustratively to explain how LLMs work in practice. This course is: - Beginner-friendly - Plain English - Built for real-world use (not engineers) If you’ve ever wondered why ChatGPT responded the way it did — or how to steer it more effectively — this course will help.

Real quick question — what email platform do you mostly use? I’m shaping upcoming classroom content and want the examples to line up with how you really work, not just theory. Thanks for taking a second to vote 👍