Welcome to the Out of The Box Series — where I test how far curiosity and AI can take you in 30, 60, or 90 minutes, using today’s best no-code and low-code tools. No setup. No training. Just pure exploration — right out of the box. 🎬 This Episode: HeyGen 🕒 Time Limit: 60 Minutes 📂 Category: AI Avatar Video Creation What is HeyGen? HeyGen is an AI video generation platform that turns a photo or default avatar, and a text script into lifelike talking videos. 💡 What I Built in 60 Minutes: Using a series of five close up photos, a few short scripts and HeyGen’s My Avatar, Voice Mirror, and Create Video features I created My Avatar. Once the photos (shown below) were uploaded, I generated a full talking video by just entering a script. Real Avatar Videos: Side Photo (6 seconds): https://app.heygen.com/videos/4e059b101cbc4847bca60f9d8f8a326d Front Photo Holiday Message (24 seconds): https://app.heygen.com/videos/baee6d942e4342dea949c84b75464bcf Front Photo Holiday Message, Final (24 seconds) https://app.heygen.com/videos/0bf850f682694553bb5ae3bea54db5b1 This is what out of the box really looks like. Just a few photos, a few prompts, and 60 minutes. While it show and renders my teeth a bit off, over all it is rapidly improving. Interested in HeyGen? Click here https://www.heygen.com/invite/7FFDTETE Have a blessed and creative AI day! @Michael Wacht

Because there's 1 month left in 2025. And it COULD be the month that changes your life. Watch here: https://youtu.be/sneY4NEV9fs

A few weeks ago, I wrote a post saying I had zero idea what “chunking” even was. Now, a few weeks later I definitely understand more, but not enough.That’s why today is fully dedicated to Pillar 1: Chunking. Back then I got great examples: 🍞 “Slice a loaf of bread into pieces.” 🍕 “Cut a pizza into slices.”Perfect analogies — and still true. But now I understand why chunking is so important: 🔹 What Chunking Really Is Chunking is the most critical preprocessing step in any RAG system. IT means breaking large documents into smaller, meaningful segments (“chunks”), which are then embedded, indexed, and retrieved later. Chunks are the atomic information units your RAG system uses. If the chunks are bad, retrieval is bad — and the LLM can’t fix it. 🔹 The Core Dilemma Chunking is always a balance between: 1️⃣ Precision – smaller chunks give cleaner embeddings 2️⃣ Context – bigger chunks give more meaning to the LLM Too big → diluted meaningToo small → missing context→ And THAT is the hardest challenge in chunking. 🔹 Best Practices for Chunking Here are the key strategies I’m learning: 📌 Recursive Character ChunkingRespects natural text boundaries (paragraphs, sentences).Often the recommended default. 📌 Overlap (10–20%) Ensures context isn’t lost at the edges.Example: 500-token chunk → 50–100-token overlap. 📌 Optimal Sizes A strong starting point is 512–1024 tokens per chunk. 📌 Advanced Methods– Semantic Chunking: uses embeddings to detect topic changes– Agentic Chunking: LLM splits text into atomic, meaningful statements These methods help avoid context loss and improve retrieval quality. 🔹 Why This Matters Chunking literally determines what your RAG system can find.And if retrieval fails, the LLM fails — it can’t magically invent the missing context. All resources, diagrams, and notes as always:👉 Notebook: https://notebooklm.google.com/notebook/ea1c87b2-0eda-43f8-a389-ba1f57e758ce

Term: Prompt Level: Beginner Category: Core Concept 🪄 Simple Definition: A prompt is the text or question you type to tell AI what you want. 🌟 Expanded Definition: A prompt is your instruction to the AI. It could be a simple question (“What’s the capital of France?”) or a detailed request (“Write a 3-step marketing plan for a bakery that wants more local customers”). The quality of the AI’s answer depends heavily on how clear and specific your prompt is. ⚡ In Action:Prompt: “Explain photosynthesis like I’m 10 years old.”The AI adjusts its answer to be kid-friendly because the prompt sets the style. 💡 Pro Tip:Think of prompts like recipes: the clearer the ingredients and steps you give, the better the dish turns out.

Term: ChatGPT Level: Beginner Category: Core Concept 🪄 Simple Definition: ChatGPT is an AI chatbot built by OpenAI that can answer questions, explain ideas, and create text that sounds human. 🌟 Expanded Definition: ChatGPT runs on a GPT model and is designed for conversation. Instead of just generating text, it can follow instructions, hold context across turns, and adjust tone or style. People use it to brainstorm ideas, draft emails, explain concepts, or even role-play scenarios. Think of it as a “smart assistant” that communicates in natural language. ⚡ In Action: You ask: “Write a bedtime story about a robot who loves pizza.” ChatGPT replies with a fun, creative story in seconds. 💡 Pro Tip: The clearer your prompt, the better the result. Treat ChatGPT like a collaborator — the more context you give, the more useful the answer. For great prompt advice, refer to this post by @Roger Richards https://www.skool.com/ai-bits-and-pieces/get-around-prompting-8-core-chatgpt-skills-to-always-get-the-result-you-want?p=d6453dc8