Time tracking has always been one of those things that I’ve been meaning to get around to, but never end up doing. Even with dedicated time tracking apps like Toggl Track, I’d open the app, play around with the features, get distracted, then forget about it. The result is a messy trail of half-complete logs that don’t actually help me understand where my time is going. I knew the potential with this app was there, but I just wasn’t doing much with it.

Enter NotebookLM; my go-to tool for getting the most out of my apps and software. It helped me learn Blender, streamlined my use of Figma, and also helped me get more out of Google Keep. So, naturally, I paired it with Toggl Track to see if the app could be of more use to me. I started feeding my Toggl logs into a notebook and asking it to analyze the patterns, which gave my time logs more context. It basically turns time tracking from a boring logbook into a reflection tool.

What is Toggl Track?

The app that helps you keep track of where the time goes

Toggl Track, at its core, is a stopwatch for your work. You hit start when you begin a task, and stop once you’re done, and everything between gets logged. This is the main function of the app, but it comes with extras to help you navigate your logs. Every entry can be tracked with a project, client, or tag, so your data is structured. And the app runs on desktop, mobile, and web, so you can keep your timer going no matter where you’re working.

What I like about it is how accessible it feels; I don’t have to pre-plan or set up a rigid system before tracking. If I suddenly start “video editing” or “UX course assignment”, I just type it into the timer and it’s recorded. Later, I can go back and clean things up, add tags, sort them into bigger projects, and so on. Over time, you end up with surprisingly detailed reports of your habits; how long tasks really take compared to what you thought, what you procrastinate on, and how often you context-switch. I also like the calendar view, it’s like a visual time-blocked overview of the past.

Toggle Track is great for anyone who wants to stay on top of their productivity, but it’s especially useful for small businesses who need to track their billable hours.

Setting up Toggl Track with NotebookLM

I had to sort out my data and set up my notebook first

Although Toggl Track supports various integrations with other productivity apps like Asana and Slack, there isn’t any integration with NotebookLM (yet). So to get my Toggl information into NotebookLM took some manual work. I started by creating a dedicated notebook for my Toggl Track logs. But then I had to get my logged information out of Toggl…

You can export your data directly from Toggl Track as a CSV file or send it to your email as a downloadable JSON file. NotebookLM doesn’t support either .csv or .json files, so I had to find a way to convert my data into something readable and compatible. I used Jsontotable to convert my JSON file into readable tables, which I then downloaded as PDF files. NotebookLM reads PDF files, so it was just a matter of uploading them to my notebook.

However, I still wasn’t happy with how stiff the data felt even after being converted – NotebookLM read it like more of a technical log than something I could reflect on. So I decided to go old-school and copy over my logs in plain text using my plain-text stack. I created a text file with a record of my Toggl logs for the week, and added the text as sources to NotebookLM. Although tedious, this would prove to be much more usable and accessible, to me personally, than the JSON data.

How I’ve been using my Toggl-NotebookLM pairing

Contextual overviews of my time

The more I beefed up the sources of my Toggl notebook and asked the AI some questions, the more it started acting like an interpreter of my work habits. Instead of staring at a wall of time logs, I could ask questions in plain language to get a better sense of how I’m spending my time, where I’m lacking, what I’m overdoing, and so on. NotebookLM will skim through my selected sources and give me a contextual answer, often with a level of nuance I wouldn’t have noticed myself just by looking at my Toggl reports.

Here are some of the prompts I’ve been using to make better sense of my Toggl logs:

What kind of tasks took up most of my mornings on these days?

Did I spend more time on writing or design over the selected days?

I also love the follow-up prompt suggestions, which often yield further insights. But something I love most about NotebookLM is that it has a visual element – and I don’t mean Mind Maps. Within the prompting window, you can ask it to generate a table that restructures your information. Here, I asked it to sort my tasks from most to least time spent in a table format, which instantly gave me a digestible overview of where my time goes and what needs more attention.

An overlooked power combo

Toggl Track is already powerful in its own right, but I get more use out of these kinds of apps when pairing them with NotebookLM – kind of like using the AI as a second brain. This pairing reshaped how I understand my workdays thanks to NotebookLM’s context-aware answers. It helped me spot patterns, surface blind spots, and keeps me accountable in a way raw numbers don’t.