Level: Beginner Category: Working with AI OS Five terms that come up the moment you start building a home for your AI OS. Here’s each one, in plain English. 🪄 AI OS One organized place for your information that AI can read, so it already knows your work before you ask it for help. 🪄 Second Brain An organized collection of your notes, ideas, and files, kept outside your head so you can find and use them later. 🪄 Markdown A simple way to format plain text using a few symbols, so it’s easy for both people and AI to read. 🪄 Obsidian A free note-taking app that keeps your notes as plain files on your own computer. 🪄 Vault The folder on your computer where all your notes live. It’s the word the note app Obsidian uses for it. Want the full definition of each term, with real examples and a pro tips upgrade to Premium? ❤️ Learn with Your Whole Heart: Expanded lessons, special benefits and community recognition for $9 a month. And 30% of Premium revenue goes to our designated charities. 👉 Join AI Bits & Pieces Premium AI Bits & Pieces - helping people and businesses adopt AI with confidence.

You open a new AI conversation to work on something for your business. Before you can get to the work, you have to explain the business. Who you help. What you’re building. How you write. What you’ve already decided. Again and again and again... That repeated setup is the problem. This is what I wanted to address when I built my AI operating system, or AIOS. I wanted a place to keep the context that should carry forward. The rules I work by, the people I serve, the work underway, The work, the outcomes, and what I’ve learned. All in one convenient, highly functional single source of truth so I didn't have to hunt, peak, and constantly restart with base information each and every time. Then I could direct AI to the relevant information before asking it to help. The structure behind that system is TRACK: - Tenets: identity, business direction, and rules. - Reputation: positioning, voice, and what the audience tells us. - Actions: the work that moves things forward. - Commitments: agreed outcomes, who owns them, and what counts as complete. - Knowledge: lessons and reference material worth reusing. TRACK is a framework, a method that gives continuity a practical foundation to my AI OS. Now, important context has a place to live and a way to be found when the next piece of work starts. I still have to keep it current, give AI access to the right material, and check what it produces. Saving a note doesn’t guarantee an AI will read it or use it correctly. The aim is to spend less effort rebuilding the background so we can get to the work. In the next post, I’ll walk through TRACK and how its five parts work together.

McKinsey’s State of AI 2026 includes a statistic that should get our attention: 🛠️ 32% of respondents said their organizations decided against purchasing at least one software product or feature because they could build it internally using agentic coding tools. That is not a prediction. It is already happening. But here is an interesting question: Do you think these solutions are being built by formal IT departments—or by front-office professionals who understand the business problems? You may be surprised by the answer. Until recently, building software required a development team, significant capital, and months—or even years—of work. Agentic coding tools are changing that equation for both IT and the front office professional. For those of us in AI Bits & Pieces who are progressing beyond AI Enthusiast and are interested in becoming an AI Builder, learning this skill creates a significant career opportunity. Becoming a builder does not necessarily mean becoming a traditional software developer. It means learning how to use AI to: 🔹 Turn an idea into a working tool 🔹 Automate a business process 🔹 Connect systems and data 🔹 Build functionality a business might otherwise purchase 🔹 Solve a real problem and create measurable value These capabilities can help you advance your career, create new opportunities within your business, or begin building solutions for clients. The tools are lowering the technical barrier. What still matters is your ability to understand the problem, determine what should be built, and guide the solution toward a meaningful outcome. If you are interested in taking that next step, I invite you to be my special guest at the next AI for Life Weekly Live Session. 📣If you are interested in being one of my special guests at the next live session, comment “AI for Life” below or send me a direct message here in Skool: @Michael Wacht AI for Life is a community founded by @Matthew Sutherland on the Skool platform where I continue to hone my own low-code/no-code AI-building skills alongside others who are learning to build practical, real-world solutions to advance their career.

In Part 6, I shared the moment I realized that years of email, meetings, attachments, calendar entries, and conversations were more than just digital clutter. They were knowledge. And when Copilot helped me turn months of history across six AI initiatives into a quarterly executive update, I started looking at Microsoft 365 very differently. But having a lot of information does not automatically give you a Second Brain. A brain has to be able to do something with what it knows. For me, that comes down to five things: 🧠 Knowledge This is the foundation. - Emails, meetings, transcripts, documents, attachments, decisions, dates, conversations. Without the history, there is nothing to remember. 🔍 Access Having the information is not enough if you cannot find it. A Second Brain needs to let me ask: - What happened with this project over the last three months? I should not have to remember which email thread, meeting, or attachment contains the answer. 🧩 Context This may be the most important piece after knowledge. - A decision in one meeting may explain an email two weeks later. A budget change may explain why a project was delayed. A conversation with one person may affect three other projects. A useful Second Brain needs to understand those connections. 🗂️ Organization Hundreds of emails are not an executive update. - The information has to be organized into something useful: accomplishments, decisions, risks, open items, priorities, and next steps. That is where AI starts turning information into understanding. 🗣️ Articulation Finally, the Second Brain has to be able to communicate what it knows. - Turn several months of project history into an executive update. - Turn a meeting into a status report. - Turn scattered information into a presentation, email, briefing, or recommendation. That is where this starts getting really interesting. 🔁 Practice This Think about one project you have been working on for several months.

For the past several months, I have been working on six separate AI initiatives. The meetings, training, planning, specifications, decisions, changes, and sheer amount of information are astonishing. Every so often, I need to provide the executive sponsor with a clean status update. Recently, I was in a Teams meeting updating an internal customer on one of those projects. Since the meeting was transcribed, I already had most of the information I needed. Teams created a summary, and it was good, but some of the conversation was not relevant to the status report. No problem. I gave the transcript to Copilot. Asked it to pull out the important points. It did a surprisingly good job. Okay, useful. But still pretty straightforward. Then came the real test. 🧾The Quarterly Update🧾 After several months of work, I needed to create a quarterly executive update covering all six AI initiatives. My first thought was, OMG. This is going to take forever. I was going to have to go back through months of meeting notes, transcripts, emails, attachments, specifications, project changes, decisions, accomplishments, and probably a bunch of things I had forgotten about. This was not an hour of work. I could already see several evenings disappearing. And then it hit me. I almost never delete an email. Normally, that probably sounds like a bad habit. But suddenly I realized what was sitting inside Outlook: months of conversations, dates, decisions, attachments, questions, answers, changes in direction, accomplishments, and project history. In other words, the knowledge was already there. So on my brand-new Copilot license, I clicked the Copilot icon and asked it to create an executive summary of my AI initiatives - for the past 6 months. I expected a decent starting point. What I got back surprised me. The first pass was really good. Actually, better than good. Did it need refinement? Sure. Did I need to review it? Absolutely. But did it save me what could easily have been days and evenings of effort as I squeezed the report between projects and meetings?