A Year in Review: Turning Calendars, Notes, and AI into Clarity

As the year ends, many people are recapping what their past year looked like. I am no different, but I’m using a twist on how I’ve done it before.

This year, I am using Notebook LM to try to make more sense of what the heck happened last year.

What this post is really about: how I used calendars, notes, and AI to reconstruct a full year, spot patterns, and identify where deeper reflection was needed—without relying on memory alone.

What This Process Looked Like

Gathering source material (calendars, bullet journal, audio)
Turning everything into a single yearly document
Using AI to summarize, map, and reflect
Identifying gaps and areas for deeper reflection

Gathering the Source Material

Over the course of a day, I opened my personal and work calendars, opened my bullet journal starting with the first month, turned on an audio recorder, and basically started recapping what happened each month.

I would compare the two calendars—some things were on one but not the other. Also, I would review my bullet journal for additional insights for each day. I talked through each event and note.

I took my audio notes and ran them through Otter AI, which is my transcription program. I exported the results from Otter AI and did some basic cleanup with ChatGPT to handle grammar and punctuation.

Building the Google Document

I created a Google document for the entire year. I used headings to delineate each quarter and each month.

Once the transcript was cleaned up in ChatGPT, I would paste the results into the appropriate area of the Google Doc.

I handled December differently because I had already captured daily results with my morning pages. Instead, I went to my morning pages in NotebookLM and requested a daily recap for December. I copied and pasted the results into the annual recap document I was creating.

Using the Document as a Source

Next, I added the Google Doc as a source to NotebookLM. One document covers a year. NotebookLM will now support my recaps for the next 50 years.

Exploring the Results

Once the source document was complete, I did some interesting and fun things. One, I created a mind map. The mind map outlined all the major areas I was focusing on. Each mind map node allows me to drill down and find specific notes.

I created an infographic that visually outlined the whole year. The resulting image was quite fascinating. It helped me identify everything I was working on.

I also created an audio note. It was fun to listen to a year in my life as artificial intelligence interpreted it.

Bias and Perspective

Now, naturally, as I’m doing the recap, it’s from my perspective.

So there is some bias.

Deeper Reflection with AI

After the initial review using the studio tools, I asked NotebookLM to identify areas where I could reflect further. I wanted it to identify areas where I was superficial with my initial input. Its insight would give me an opportunity to expand more.

Further reflection is the next step in my process. For example, there are some meetings I need to delve into a bit more.

So far, this has been an absolutely fascinating exercise.

Going Forward and Looking Ahead

Now, going forward, as I prepare the annual recap document, I will be using the results from my morning pages. The morning pages exercise has been quite enjoyable and quite enlightening, and I have no reason to discontinue it.

Then next year, I can just go to NotebookLM for my morning pages, ask it to do a monthly summary, and add that to my annual recaps. It’ll only take me a couple of minutes to do each month.

I find that I’m much more thorough in my recaps when I’m doing my morning pages, because I’m looking at what happened just yesterday, while it’s still fresh. I explain what I actually did versus my well-intentioned plan for the day. I then outline events and my plan for the upcoming and next day. As a result, there’s a lot of overlap and many gaps get filled in, which makes the process quite useful.