Mckenzieking Designs

07/12/2026

🚀 I'm building Aurora Calm, an AI-powered wellness companion, and I'd love your input!

As I plan the next phase of development, I'm exploring new AI journaling features.

Which feature would you find most valuable?

📝 1. AI journal pattern insights
Identify recurring themes and habits across journal entries.

📊 2. Personal wellness insights
View trends and summaries over time.

🤝 3. Personalized AI follow-ups
Have Aurora check in on previous reflections and conversations.

💡 4. Personalized reflection prompts
Receive thoughtful questions that encourage deeper self-reflection.

Vote by commenting with 1, 2, 3, or 4—or share your own idea!

👉 Try Aurora Calm here: https://aurora-calm-1.onrender.com

I'd love your feedback as I continue building and improving the experience.

A serene space to manage stress and burnout with interactive breathing, mood analysis, and compassionate AI support.

07/11/2026

🚀 A project I've been quietly working on is finally live!

Over the past several weeks I've been teaching myself new technologies while expanding beyond my background in Product Design and UX.

Today I successfully deployed Aurora Calm—an AI-powered wellness companion that I designed and built using React, Node.js, the Google Gemini API, and cloud deployment with Render.

It was definitely a learning experience! There were plenty of debugging moments along the way (especially getting the frontend and backend talking to each other after deployment), but that's all part of building real software.

Aurora Calm includes:
✨ AI-powered conversations
📝 A reflective mood journal
🧘 Breathing exercises
🔒 Privacy-first design (journal entries are processed in memory and aren't stored)

If you'd like to take a look or give it a try, I'd love your feedback.

👉 https://aurora-calm-1.onrender.com

Note: It's hosted on Render's free tier, so the first time you open it, it may take up to a minute to wake up.

I'm excited to keep improving it and adding new features over the coming weeks.

06/08/2026

Last Tuesday I received my Certificate of Achievement from NPower Canada's Data Analyst Program.

Over the past four months, I've spent days and weekends learning data analytics, Python, SQL, Excel, dashboards, visualization, and AI while continuing my work in UX, Product Design.

It wasn't always easy. There were moments when the workload felt overwhelming, especially balancing multiple courses, assignments, team projects, and deadlines. But pushing through those challenges made this achievement even more meaningful.

One of the best parts of the experience was the people. I met an incredible group of teammates who supported each other, learned together, and became genuine friends along the way. I’m grateful for the connections I've made and hope to stay in touch long after this program.

This experience has strengthened my belief that great products are built at the intersection of customer insight, design, data, and AI. I'm excited to continue applying these skills to create more thoughtful, data-informed experiences.

Thank you to NPower Canada, the instructors, mentors, and my teammates for being part of the journey.

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04/27/2026

I’ve been working through a data analytics project exploring immigration trends to Canada from 1980 to 2013.

What stood out to me was how clearly the data reflects real-world events. For example, there’s a noticeable spike in immigration from Haiti around 2010, which aligns with the earthquake. Seeing that connection visually really brings the data to life.

It’s been a great way to practice not just analyzing data, but actually telling a story with it.

Github file here: https://github.com/DKTODesigns/immigration-trends-data-storytelling

End-to-end analysis of Canada immigration data (1980–2013) using Python. Includes data preprocessing, exploratory analysis, and time-series visualizations with Matplotlib to uncover trends and comp...

04/08/2026

I’ve been getting more hands-on with web scraping using Python, BeautifulSoup, and Pandas, and just completed my certification as part of my data analytics training.

In this project, I worked through:
• parsing HTML and navigating page structure
• extracting links and images
• scraping structured data from tables
• converting raw web data into clean, usable DataFrames

Honestly, seeing all that raw HTML turn into structured, usable data felt a bit like a magic trick.

What stood out to me is how quickly unstructured web content can be transformed into something usable for analysis, dashboards, or even AI-driven features.

As someone working at the intersection of UX, data, and AI, this kind of workflow opens up a lot of possibilities for building more intelligent, data-informed products.

I’ve shared the full notebook on GitHub here:
https://github.com/DKTODesigns/web-scraping-beautifulsoup-pandas/tree/main

Curious to keep building on this, especially around how scraped data can feed into user-facing experiences and insights.

04/06/2026

Added another small project to GitHub as part of my data analytics journey.
This one focuses on Python file handling and data cleanup — separating active and inactive records while preserving structure and formatting.

It’s a simple use case, but a good reminder that a lot of real-world data work starts with cleaning and organizing raw data before any analysis or AI comes into play.

Also validated the solution with a test to ensure data integrity.
Learning a lot through hands-on practice and building as I go.

It’s a simple use case, but a good reminder that a lot of real-world data work starts with cleaning and organizing raw data before any analysis or AI comes into play.

Here’s the project: https://github.com/DKTODesigns/member-data-cleanup

More to come as I continue building.

Python script for cleaning and organizing membership data by separating active and inactive users. - DKTODesigns/member-data-cleanup

04/06/2026

Starting to share more of what I’m building as I work through my data analytics + AI learning journey.

Just pushed a small project to GitHub, my first time posting code there, so definitely a learning experience on that front as well.

I built a simple text analyzer in Python that:
• cleans and normalizes text
• counts word frequency
• surfaces patterns in unstructured data

It’s a basic example using sample text, but it helped me understand how raw text can be structured and analyzed.

As a product designer, I’ve always worked with qualitative insights. Now I’m exploring how to approach that kind of data more systematically using code.

Here’s the repo:
https://github.com/DKTODesigns/text-analyzer-python

Simple Python text analyzer for word frequency and preprocessing - DKTODesigns/text-analyzer-python