My goal: Improve my ability to build practical AI automations that solve real business problems. Why I chose it: I'm rebuilding my consulting business, Precision, around AI Automation and Strategic Advisory. I've been learning a lot about AI tools and agents, but I want to continue moving from understanding the concepts to actually implementing useful solutions. The MIT Monk focuses on practical AI implementation and business workflows, which aligns well with what I'm trying to accomplish. How I'll use it: I'll pick one tutorial relevant to a business process I'm considering automating, work through it, and evaluate whether the approach could be useful for Precision or its clients. What I hope to learn: How to turn AI concepts into reliable, repeatable business solutions without unnecessary complexity. I'm trying to be selective about what I learn and build, rather than chasing every new AI tool.

I’ll use it for: Website design inspiration for Precision, my technology consulting business focused on AI Automation and Strategic Advisory. What I like about it: Lapa Ninja has examples of real landing pages, which makes it useful for seeing how other businesses organize their messaging, services, and calls to action. How I'll use it: I've recently rebuilt the Precision website, and I'm looking for ways to refine it. I'll find three examples of professional consulting or technology websites and compare their layouts, navigation, and calls to action with my current site. I'll also use Mobbin for inspiration around forms and other interactive elements. How it will help: Rather than asking AI to make my website look more professional and hoping for the best, I can provide specific visual references and explain what I like about each. My goal is a clean, professional website that reflects Precision's services and personality, not another generic AI-generated design.

Before this lesson, I had heard of RAG (Retrieval-Augmented Generation) but didn't fully understand how it worked. In simple terms, it lets an AI retrieve relevant information from documents or other sources before generating an answer, rather than relying only on its training or the current conversation. I've come across RAG while learning about AI agents and building my AI Workspace and Second Brain. I want AI tools like Claude and ChatGPT to find and use the right project information without having to explain everything each time. The biggest thing that made it click: RAG doesn't retrain the AI model. It gives the model access to relevant information when it's needed. My current system uses structured Markdown files and instructions to help AI find the right context. It's not necessarily a full RAG implementation, but the underlying goal is similar. I'm interested in exploring RAG further, but first I want to see how far I can get with my existing approach before adding more complexity.

I've been building an AI Workspace and Second Brain using Claude Code, VS Code, Obsidian, and GitHub. As my projects grow, I'm organizing them into separate contexts rather than loading everything into one session. My setup: - AI-Workspace — My central Second Brain, learning system, and shared project instructions. - AI Employees — Rex and Piper, two AI agents that research potential customers and help prepare sales outreach. - RentalManager — Automates rental paperwork and lease preparation, with human approval before generating documents. What each context includes: - AI-Workspace: A root CLAUDE.md that directs Claude to shared instructions, learning notes, and individual projects. - AI-Employees: Agent responsibilities, prospecting criteria, research workflows, and safeguards. Piper drafts emails but cannot send them. - RentalManager: Lease templates, workflow instructions, validation rules, and approval checkpoints. I'm following three principles from the lesson: - One fact, one location: Each project has its own authoritative documentation. My Second Brain points to the appropriate sources instead of duplicating them. - Clean sessions: Claude loads only the context needed for the current task, keeping unrelated project information out of the conversation. - Team consistency: Shared Markdown instructions, project conventions, and GitHub version control make the workflows repeatable, whether I'm using Claude or another AI tool. What I'm testing next: I'm refining how Claude starts new sessions and picks up where I left off. My goal is to open a project and have Claude understand its structure, current status, and rules without having to explain everything again. My biggest takeaway: Scaling AI isn't about loading more information. It's about making the right information available when it's needed.

I created a CLAUDE.md file for my AI-Workspace, a project I've been building as part of my AI learning journey. It's a portable "Second Brain" that organizes what I'm learning, manages AI projects, and maintains reusable context across tools like Claude, ChatGPT, and Perplexity. CLAUDE.md Project Overview AI-Workspace is a Markdown-based knowledge and project management system. It stores learning materials, project documentation, and shared instructions so AI tools can understand my work without starting from scratch every session. Tech Stack - VS Code and Claude Code - Markdown and Obsidian - GitHub and GitHub Desktop Commands - Read the root CLAUDE.md and shared AI instructions. - Read the project's PROJECT.md and STATE.md before working on a project. - Follow relevant workflow documents and review checkpoints. - Use Git to review changes before committing. Conventions - Keep information in one authoritative location. - Use PROJECT.md, STATE.md, and DECISIONS.md for structured project management. - Keep instructions short and load only the context needed. - Separate source material from AI-generated interpretations. Avoid - Don't invent missing project information. - Don't duplicate instructions or create unnecessary files. - Don't make destructive changes without approval. - Don't commit or push changes without permission. One way I expect Claude to behave better: Instead of guessing how my projects are organized or requiring me to explain everything again, Claude can navigate to the right files, follow established workflows, and respect my approval boundaries. My biggest takeaway: The real value of CLAUDE.md isn't telling Claude everything. It's helping Claude find the right information when it needs it.