This week taught me one important lesson: progress doesn’t come from feeling ready; it comes from showing up anyway. 🚀 I’ve been developing a tool that I keep referred to in the video as an “automation” tool. However, I know better it’s a custom quote generator. 🧾 This tool features reusable templates, allowing companies to avoid the tedious process of rebuilding quotes from scratch. They won’t have to jump into Canva, place images, format text, and repeat that entire process each time. Or constantly looking for files to copy paste The contents to not have to write it down every time The real value lies not in buzzwords, but in the time saved. ⏱️ I’m new to this, and things break; some features only work halfway. My laptop moves as if it's on vacation! 💻🐢 However, I still committed. 🔁 Here’s what I learned: - Consistency beats talent when talent gets tired. 💪 - Confidence comes after taking action. 💼 - Broken things are feedback, not failure. 🧠 Nothing is perfect yet. The user interface still needs improvement, and the templates aren’t finished. But I’m not the same person I was two weeks ago Heck! I’m not even the same person I was a year ago. 🌟 This journey isn’t just about building one app; it’s about developing discipline. 📈 If I occasionally use the wrong words, understand that my intentions are right. I might call it “automation,” but what I really mean is saving people time and reducing stress. 😊 And if my Accent sounds different, that’s just the Caribbean in me! 🌴 I’m focused on fixing code, not my accent. So for those that might have an accent remember what matters most is your hard work and dedication 😂🫡👊🏾 Week two is complete, and week three is on the way. I’m not stopping! 🚀

See "check my GitHub repo" everywhere but have no clue what people are talking about? Same here. So here's a simple explanation. What is GitHub? Think Google Drive, but for code. The big difference: it tracks a detailed history of every change. Every save-point. Every edit. By who, when, why. Why do people use it? → Version control - Want to go back to yesterday's code? No problem. → Collaboration - Multiple people on the same code without chaos → Backup - Your work is safe online → Portfolio - For developers, their GitHub is their resume What is a repo (repository)? Just a project. Building a website? That's one repo. All code, files, and history in one place. The basic flow: Create a repo (new project) Write/change code on your computer "Commit" your changes (save-point) "Push" to GitHub (upload) Others can "pull" (download) and work with it Private vs Public This one tripped me up: → Private repo = only you (and who you invite) can see it → Public repo = everyone can see and use your code For client work: ALWAYS private. Their business logic, API keys, custom flows - that shouldn't be public. Best practices you need to know: → Use branches (parallel worlds for your code - test new features separately) → Write clear commit messages ("Fixed login bug" not "fix stuff") → Create a README.md (explains what your project does) → Use .gitignore (keeps passwords and API keys out of your repo) Practical example for AI automation: You're building an AI chatbot for a client: Create repo "client-x-chatbot" (private) Claude Code writes the code Push to GitHub after each feature Client gets access (transparency) Bug? See exactly what changed New client? Copy the repo and adjust Deployment story GitHub is often the middle step: Write code → Push to GitHub → Automatic deployment to production Platforms like Vercel or Netlify deploy automatically every time you push to GitHub. This is called CI/CD (Continuous Integration/Continuous Deployment).

I've been exploring one of the most significant developments in AI assistance technology, and I need to share this with my network. Anthropic has introduced Skills in Claude a modular system that transforms a general-purpose AI into a specialized expert on demand. Think of it as giving your AI assistant the ability to load exactly the right expertise, workflows, and tools for any specific task. Why This Changes Everything: Traditional AI assistants are generalists. They know a bit about everything, but lack the procedural depth for specialized work. Skills solve this elegantly by providing Claude with: → Specialized workflows for multi-step domain-specific processes → Tool integrations for specific file formats, APIs, and systems → Domain expertise including company-specific knowledge and business logic → Bundled resources like scripts, templates, and reference materials What's Available Out of the Box: The current skill library is impressive. Professional document creation (Word, PowerPoint, Excel, PDF), production-grade frontend design that avoids generic "AI slop" aesthetics, algorithmic art generation, MCP server building, and sophisticated theme systems—all with best practices built in. The Real Game-Changer: Custom Skills Here's where it gets exciting for businesses. You can create your own skills that encode your company's brand guidelines, internal processes, database schemas, and proprietary workflows. I've implemented custom skills for Plexaris that automatically apply our brand identity—colors, typography, visual standards—to any deliverable Claude creates. Imagine asking Claude to create a pitch deck, and it automatically knows your exact brand colors, your preferred fonts , and your glass morphism card styling. That's not a template that's institutional knowledge embedded in your AI assistant. The Technical Elegance: What impressed me most is the progressive disclosure architecture. Skills use a three-level loading system: metadata always present (~100 words), the skill body loaded only when triggered (<5k words), and bundled resources pulled only as needed. This means efficiency without sacrificing capability.

I recently heard that, Matthew McConaughey has secured trademarks (not patents) for his famous catchphrases and key elements of his voice and likeness to protect himself against unauthorized AI-generated deepfakes. So yes we need validate our AI experiences.

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