Software Architect / Microsoft MVP (AI) and Technical Author

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Update: New Reporting and Artificial Intelligence Insights Added To SaaS Daily Tracker

In May I built a private journal and mood tracking prototype that ran on JSON files.

 

I decided to adapt this and publish this as an online micro-SaaS that anyone could use.

 

It’s called Daily Tracker and online at dailytracker.co.

Just signup with an email address, password and supply your timezone and you’re in.

 

I’ve added some new reporting features to the tool to help you really dive into the data that you add.  New charts, reporting periods and artificial intelligence insights are now available.

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New Reporting

In addition to being able to look back over mood data for the current year with column charts, new reporting capabilities have been added.

These feature different chart type such as heatmaps, tree maps, and word clouds are now available from the report menu:

 

Two clicks will let you access these. 30, 60, and 90 day reporting periods are available.

 

Selecting a reporting period will take you to detailed dashboard that contains insights from journal and mood data you have supplied.

 

From a single screen you can see

  • Activity heatmap
  • Mood map breakdown (smile/meh/frown)
  • People, Locations, Organizations, Events, Products, Skills, and Addresses you share the most
  • Word cloud of keyphrases you tend to mention in journal entries

 

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Activity Heatmap

The activity heatmap will show you when you tend to post your journal entries:

 

Use this to help you identify the days or times when you are / aren’t supplying entries.

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Moodmap

The mood map shows you the distribution of what made you smile, feel meh or frown for the selected reporting period:

 

Hovering over each cell will show you the total count of each.

 

A future update will let you click on each cell and take you to a detailed dashboard that shows you: the who, what and where that was responsible for each emotion.

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Leveraging Artificial Intelligence, Machine Learning and Text Analytics

Machine learning and text analytics powered by Azure AI are used to perform named entity recognition (NER) and entity extraction.

 

This helps you identify and classify entities for reporting.  At present, any content in journal entries you supply will be parsed with any identified entities being classified into following categories:

  1. Person
  2. Location
  3. Organization
  4. Event
  5. Product
  6. Skill
  7. Address

 

Very useful for letting you know the people, locations, companies, events, products, or skills that bring you joy, feelings of meh or making your frown.

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Things You Share the Most

Artificial intelligence insights are used to identify the things you share the most and rendered using a tree map in your reporting dashboard.

 

The top 5 items in each category are displayed for each category for the selected reporting period:

 

Use these insights to help you clarify your thoughts, inform your decision making, make better decisions and optimise your life.

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Keyword Cloud

The keyword cloud shows the main phrases you have used in your journal entries for the selected reporting period.

 

The more a phrase has been mentioned, the bigger the word.  Less frequently used phrases are shown using smaller text:

 

Use this to see the main phrases you tend to use the most.  Visualizing journal phrases like this quickly shows you the direction your thoughts tend to go in over a specific period.

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Future Updates – Mood Map and Distribution

A future update in the mood map will let you click on each cell.

 

Clicking on each cell will take you to a screen that contains information about the people, places, products, locations, skills, events, or organisations making you – smile, meh, or frown:

This will be like content in the Things You Share the Most chart but filtered and grouped by mood.

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Further Reading and Resources

The following resources show you how Daily Tracker originated from a single tweet, grew into a prototype, and then morphed into an online SaaS.  Presented in chronological order.

 

Tweet Thread: Building in Public Pt 1:

Jamie Maguire on Twitter: “Idea: A secure online journal that only lets you create one short entry per day. Single image upload optional. You can supply mood: sad, ok and happy. At the end of the year can look back over the last 365 days.” / Twitter

Tweet Thread: Building in Public Pt 2

Jamie Maguire on Twitter: “Some notes I took on my phone to keep me right when building the journal/mood tracker web app. The second half is what’s required to make it run online as a free service. https://t.co/Y9v6jhtmsY” / Twitter

Blog: Creating a Journal and Mood Tracking App Protype

How To: Creating a Private Journal and Mood Tracking App Using .NET 6, Chat GPT, C# and Visual Studio 2022 – Jamie Maguire

Blog: Journal and Mood Tracking Micro-SaaS Released

Announcement: Journal and Mood Tracking Micro-SaaS Released – Jamie Maguire

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Summary

These new features are available now at https://dailytracker.co.  The project has been built whenever I had an hour spare here and there.

I made an effort to write as little code as possible.  It didn’t take long to code and release.

The core solution is simple and uses out of the box .NET features, Azure Cloud Services, and free SDKs.

Doing this means I effectively have a cookie cutter SaaS toolkit and template that can handle:

  • authentication and authorisation
  • custom business logic
  • database access
  • web pages and reporting
  • back end long running system processes
  • text analytics
  • named entity recognition
  • key phrase extraction

 

These core features can quickly be leveraged in future SaaS ideas.  The architecture also makes it simple to extend existing capabilities.

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