Personametry V1.0 Worklog with Antigravity

Personametry - a decade's journey of time tracking now enhanced with AI

A summary of 2025 performance and intro to personametry.com

It is the time when I share my Performance metrics for the previous year, this card pretty much sums it up, logging 8700 hours, with 3006 time entries, tracking close to 24 hours per day:

Background

For the last ten years I've been running an experiment in logging my time spent activities like: Work, Family, Me Time, Sleep, Spirituality, etc. In 2015 I developed a model for personal development, called RAGE (Reality, Aspiration, Goals, Expectations). In 2016, I got more serious by inspecting my time across all areas of my life against my RAGE model, which triggered deeper reflection on my aspirations versus reality. For the first three years, I maintained a rhythm of personal monthly performance reviews (PMPRs) and then transitioned to quarterly, mid-year and final year reviews. At the start of each new year, I would dive deep into the previous year's data - build analytics and dashboards, which I shared on this blog.

Context about my workflow - the early days

In the early days, my process for insights was quite manual. Logging my time was easy, using Harvest App, which I'd been introduced to by a good friend, Farid - around the time I switched to professional consulting, servicing some contracts with Crossbolt that expected Harvest timesheets for billing. Incidentally, Farid was the source of inspiration for me to critically think about Reality V Aspirations that led to me creating my RAGE model. 

Generating reports initially started with exporting from Harvest, and importing to Excel and running pivot tables and charts, using content for my blog posts. I needed a way to transform the Harvest data to higher level constructs - so I transitioned to Amazon Quicksight (now Quick Suite), using an AWS Free Tier account. Quicksight was useful in acting as a yearly dataset, creating analysis that I would have done in Excel (so replaced excel) and created the dashboards, which I'd then copy and share in this blog. A downside of Quicksight is it's a closed system, had no way of publishing dashboards for public sites (like Google docs embedded pages mechanism). The free tier also prevented me from using its built-in insights features, and more recently Quicksight's AI analysis. I added Google slides to my workflow, sharing my deep dives as in this post. As AI tooling emerged, I transitioned to AI analysis as described here.

Introducing my latest workflow - finally, the Personametry Dashboard is born - ZERO Workflows

I spent just under 5 days building my Personametry app with Google's Antigravity as my coding partner. What a journey (look out for a future post). Since November 2025, I've been learning how to build apps with Antigravity, at first building my SMT app, then building tools for work - and I had enough insights to get Personametry app built.  What's my new workflow then? Everything is now automated, apart from my manual time logging. I've built a dashboard that syncs daily with Harvest data, through an automated GitHub actions workflow that pulls time entries via the Harvest API. Harvest is so cool that they allow even free users full access to their APIs. An automated data transformation job runs that cleans up the data and transforms it just the way I used to do the meta level transforms using Quicksight. So no more Quicksight. All the dashboards refresh automatically. I no longer need to create Google slides anymore. At the start of each year, I'd usually spend about a week's time analysing, reflecting and creating dashboards. Now my analysis can be anytime, with zero manual work. Giving a week's time back! Yes, anyone has access to my data and dashboard, I don't mind sharing because I believe other folks could benefit from my experiment, decide to start their own tracking journey or build an app for themselves. The codebase is on GitHub.

Personametry.com is more than just a dashboard - introducing Machine Learning

With my rich dataset, there's opportunities for applying machine learning forecasting techniques and instrumenting goals. Check out the Machine Learning page. I can now tune my personas and in real time see the effects, example: If I reduce my sleep hours, where would the gains go? If I reduce my work hours, subject to constraints, what can I do? If I invest in health and fitness, what's the impact on Family time, etc. For me, this is a game changer. The app will evolve and learn as the dataset is updated, without having to change code or do manual imports! I might have to tweak the code just a little to cater for special years like sabbatical breaks though.

What's next - where am I going with this?

Version 1.0.0 is now live! Depending on how much time I have in 2026, I will look at embedded AI data driven analysis into Personametry.com, leverage conversational analysis. Ultimately I'm still striving to build the perfect personal assistant that just "knows" me. I will look at bringing in additional data sources like Strava, Netflix, Youtube, even integrating Islamic and Gregorian calendars. And finally I'll hook in a RAGE scorecard to match my time against the RAGE model! I could also turn this into a paid platform service, creating a platform for anyone to sign up and build their own RAGE model personas and track with Personametry.com!

The SMT Chronicles - MVP Version 2 - NotebookLM story

In my previous post, I shared how NotebookLM synthesised the evolution of my experimental app, SMT (Software Management Tools) from my Gemini chat transcripts till June 2025. I then went further and added just the latest Readme file as an additional source for NotebookLM to see if it could connect the dots from June 2025 to December 2025. I also created a simple dashboard using Antigravity for my repo's storyline here. 

NotebookLM generated this infographic that was spot on! 

By June 2025, SMT's main feature was the Yearly Planning page - the inspiration behind the planning feature of SMT came from my Amazon AWS experience of their yearly planning mechanism called Operational Planning (OP1 & OP2) cycles. The lack of tooling within the company made the process quite time-consuming, error prone and not fun at all! We used spreadsheets in either Excel or Quip (Quip is a pain for spreadsheeting!). So SMT was going to make OP planning much more fun, and accurate as well - especially when calculating net engineering capacity. SMT is a proof-of-concept, but if anyone from AWS is reading this post, feel free to play with the app, get the codebase and have a go at using it for your team's planning. The app works fine IMHO but still has a lot more features to add.

Since the June 2025 MVP Version 1, I woke up again in November and added a ton of features. My productivity sky rocketed with the release new LLMs and coding assistants. I added a powerful AI feature which I think is a game changer for software management. I clobbered technical debt, rebuilt the app UX, added quality controls and overall rearchitected the codebase to something more respectable than a toy app coded by AI...

NotebookLM generated this audio overview, mostly correct!

NotebookLM generated this slide deck...

NotebookLM generated this video...

The SMT Chronicles - MVP Version 1 - Oct 24 to June 25, building with AI co-pilots

Over the last year, I've been building an experimental app, SMT (Software Management Tools) that started out initially out of curiosity to test LLM's ability of code generation, before the proliferation of AI coding agents and integrated development environments or vibe coding platforms. I worked on this project in my spare time over weekends and evenings, testing the waters - often getting quite frustrated in the early days, sometimes having streaks of productivity and other times, just pure going in circles and breaking things - often found myself cursing the AI :-) I didn't give up, despite the breaks with long periods of inactivity, I kept monitoring the latest AI news and releases - and continued to test the waters. Each time, learning something new, seeing the progress of LLMs, witnessing the pure potential this technology has to not only disrupt the software industry, but also the immense potential at empowering people to translate ideas and concepts into prototypes, without depending on outsourced developers. The journey of learning continues. I stuck with Gemini because it has, since the beginning been enormously generous with quotas and large context windows, unlike Claude and ChatGPT at the time. Even today, I prefer to stick with learning just one tool like Antigravity than to context switch with others - although my workflow includes Antigravity Agent Manager, and a separate VSCode with Codex to audit changes and pick up where Gemini or Claude Opus fails to complete their tasks.

Here's the activity story from GitHub:

I also created a simple dashboard using Antigravity for my repo's storyline here. 

In this post, I'm sharing some history of SMT. How did all begin? Interestingly enough, I went through a phase of saving major chat sessions with Gemini, that led to the first MVP of SMT. I saved all my prompts in google docs. I stopped tracking my prompts in June because it got quite tedious! With this chat record, I wanted to see what Google's NotebookLM would make of the doc's contents, here is what it produced - super fascinating the power of NotebookLM!

NotebookLM generated this infographic that was spot on! 

By June 2025, SMT's main feature was the Yearly Planning page - the inspiration behind the planning feature of SMT came from my Amazon AWS experience of their yearly planning mechanism called Operational Planning (OP1 & OP2) cycles. The lack of tooling within the company made the process quite time-consuming, error prone and not fun at all! We used spreadsheets in either Excel or Quip (Quip is a pain for spreadsheeting!). So SMT was going to make OP planning much more fun, and accurate as well - especially when calculating net engineering capacity. SMT is a proof-of-concept, but if anyone from AWS is reading this post, feel free to play with the app, get the codebase and have a go at using it for your team's planning. The app works fine IMHO but still has a lot more features to add.

NotebookLM generated this audio overview, mostly correct!

NotebookLM generated this slide deck...

NotebookLM generated this video...

My chat transcripts with Gemini from April 25 to June 25 - 50+ pages!

How I use visual annotations with Antigravity to build UI / UX

Sharing some of my workflow experience in building my SMT (Software Management Tools) application using AI as my coding assistant. With the launch of Google's Antigravity platform in November 2025, my curiosity got the better of me! I re-engaged with my codebase after about 5 months of dormancy. In under 2 months I was able to migrate the look and feel to a new UX paradigm, introduce a coding contract and constitutional framework, introduce dark/light mode theming, fundamentally refactoring the codebase to best practices software design patterns, integrate AI-features into the app, and clobber technical debt. Thanks to Antigravity... what this video shares is how powerful visual annotations can be for changing UX... As I experienced this journey, I became more aware the future of UX and Frontend Engineers is going to be disrupted quite radically!

Play with the SMT app here Codebase on Github