Getting Started with AI

Over the past few years, AI has become one of the most talked-about topics at the college. Like everyone else, I was starting from scratch. In the beginning, "using AI" just meant typing questions into a chatbot. As new models appeared and the information around them grew, I kept trying new things. This page is a record of what I've tried along the way.

Understanding different levels of AI use

Google DeepMind's AlphaFold predicts the three-dimensional structures of proteins, work that earned a share of the 2024 Nobel Prize in Chemistry. Meanwhile, my own use of AI during my first years here amounted to typing questions into a chat window. The gap between the two bothered me. But I wasn't going to become an AI engineer overnight, and I wanted to understand what the steps in between looked like.

In April 2026, Michal Klacko of STRV published a framework that divides AI use into six levels: Skeptic, Tourist, Assisted Coder, Prompt-Driven Developer, AI-Native Engineer, and AI Architect. It was written with software developers in mind, and since I'm not one, the categories didn't mean much to me at first. As I got to know these tools better, though, I found I could draw my own lines based on what the work involves:

• Level 1 — Chatting. Asking questions in a chat window on an AI website.
• Level 2 — Organizing work into projects. Instead of separate chats that each start from scratch, the conversations and files for a larger task live together, so every new chat already has the task's context.
• Level 3 — Working on my own computer. AI runs as a desktop application with access to local files and programs, so it reads and edits the actual documents rather than me copying text in and out of a browser.
• Level 4 — Building a personal knowledge system. Material is stored and organized systematically, in a form that both I and AI agents can find and use.
• Level 5 — Connecting and automating. AI connects directly to websites and services, and I build tools that run on their own.

My first three years stayed within the first two levels. Asking a chatbot questions is convenient, but a chat window has no systematic way to handle work, and my tasks were growing bigger and running longer. Moving into the third level and beyond, at the end of my third year, was a natural next step.

Moving the work out of the chat window

In early June 2026 I watched a YouTube video demonstrating Codex, an AI agent shown printing shipping labels through a Telegram bot, and watching an inbox, summarizing new emails, and drafting replies. I asked Claude whether Claude Code could do the same. A search showed that the video overstated what Codex does, and the conversation turned into a different question: how would an ordinary user like me use this kind of AI? I felt uncomfortable pushing myself onto a new tool when I had no clear need for it and no clear understanding of it. Until then, I thought Claude Code was something developers used to write code, or to run several AI agents at once in separate windows. When I had asked about it in April, Claude had said much the same: it was meant for full-time developers. Still, I knew I would not learn it without trying it.

There was no one I could ask about this in person, so on June 6 I sat down with Claude to work it out myself, starting with the characteristics of my work and what I needed. The repetitive part of my work that bothered me most turned out to be keeping track of commitments, such as when I had told a student I would get back to them, or what I had said would be on the next quiz. Those commitments were scattered across my journal, notes I sent to myself in a messaging app, Outlook, and things I said out loud in lectures, which were recorded on Zoom and transcribed. I rarely forgot them outright, but I often had the feeling that I was about to. We narrowed the plan to the two things my existing tools could not do: pull commitments out of lecture transcripts, and search my own saved notes by meaning. Reminders I could leave to my calendar. For storage I chose plain files in a Google Drive folder with an index, instead of Notion, so the records would not depend on an outside service or sit in my school account.

To connect the pieces, Claude first suggested a workflow tool I had never heard of. When I asked whether that was what Claude Code was for, the plan shifted to Claude Code. I told Claude I did not really understand what Claude Code was, and installed it that day to try it.

The first thing I asked it to do was read one of my lecture transcripts and list the promises I had made to students in class, such as "this will be on the next exam" or "I'll get to this next week." In 26 seconds it found fifteen, sorted them into categories, and pointed out which ones to deal with first. Then I asked it to save the list as a file, and it did. The same job in a chat window would have taken six steps on my part: open the file, copy the text, paste it into the chat, copy the answer, create a new file, and save it. In Claude Code it took two short requests, one for each. The AI behind both is the same, but the chat window can only answer, while Claude Code can also act on the files on my computer, asking my permission before each step. From that point on, I moved the work out of the chat window.

That same day, the plan became the first version of the database described in the next section: a bot on the Mac mini that stays on, waits for messages from my phone, and files them. By the end of June, the research paper I was developing for the Math Readiness Initiative, with its analysis scripts and manuscript files, had moved to Claude Code as well.