Posts

No Laptop November

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"Life is hard, but that's what makes it interesting" - Dad It's time to break the silence once more! Things did not go as I planned, but it makes for a good story.  So you're probably wondering... " Toni, what happened? I thought you were coming back? " and to that I would say, I thought the same. I thought that I would get back in my groove and post blog 109 weeks ago! My simple hardware failure quickly escalated into a complex and costly international shipping disaster. It all started on November 7th at about 5 AM while I was clacking away on my keyboard, the display for my newly purchased Zenbook S16 decided that it was time to say goodbye. I made every attempt to reilluminate it by doing the famous "turn-it-off-and turn-it-back-on" trick and confirmed that it was a hardware issue with Asus Helpdesk. I still held a warranty , which was a relief. However, I had to send the laptop to the States so the manufacturer could fix it and send it ba...

October Reflections

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The name October comes from the Latin “octo,” meaning eight . This morning, my dad asked about my blog. I winced — I’ve been dodging it since September! But that question was the nudge I needed. Because when I stopped to think, I realized:  even without posting, I’d been living. I hiked (and fell multiple times). Tried lobster for the first time. Met my coworkers in Barbados. And impulsively bought this... (I only had 1/4) All that living gave me plenty to reflect on — and two moments in particular stood out. 1. Women in Data Science (WiDS) TT Spotlight Talk Dr. Letetia Addison invited me to speak at the Women in Data Science TT Spotlight Talk — for the second time!  She actually found me through this very blog. I wasn’t publishing much here, but I was active on LinkedIn.  When it came time to prepare my WiDS talk, I had weeks, but I started only days before. Procrastination kept winning. Then came a call with my friend Carson White, we brainstormed ways in whic...

Wait! Before You Throw Away Your Laptop...

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“Our whole economy is based on planned obsolescence.” — Brooks Stevens   My Lenovo IdeaPad 350 carried me through my Computer Science degree and even hosted the beginnings of this blog series.  It witnessed late-night coding marathons, countless essays, and the very first drafts of ideas I’m still exploring today. I’ve since upgraded to an ASUS Zenbook S16 (which I’m very happy with) , but my old laptop still sits quietly on a shelf, waiting for its final chapter. If you didn’t know, the average lifespan of a laptop is about five years. When I asked a few friends what they do with their old machines, most admitted they just… keep them.  I guess I know a lot of hoarders... But honestly, that’s still better than those who throw them away like an old pair of shoes. Here’s something to think about: the world generates over 50 million metric tons of e-waste each year , and only about 20% is recycled properly . That means millions of tons of toxic metals—like lead, mercury, an...

Case Study: Bank of America’s ROI on AI

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  How does AI generate value? Today’s post springs from an episode of Bloomberg Technology that put a spotlight on Bank of America’s AI-powered virtual assistant, Erica. Billions are being poured into artificial intelligence. But here’s the hard truth: an MIT study found that 95% of corporate AI projects fail to deliver measurable financial returns.  Only 5% make it past the pilot stage with a real boost to the bottom line.  So when Bank of America’s Erica racked up billions of interactions and over $1 billion in value , it’s worth paying attention. What did they do differently—and what can Caribbean banks learn from it? Erica by the Numbers Since 2018, Erica has: Surpassed 3 billion client interactions with tens of millions of customers. Pushed millions of proactive money alerts —from subscription hikes to unusual spending. Cut employee IT help-desk calls in half , with >90% staff adoption. Why Erica Works When Others Fail Real pain points first: Customers ...

GPT-5, Gigawatts, and the Future of AI

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  The prefix “giga” comes from Greek and means billion (that's nine zeros) This week, I caught an insightful chat between Sam Altman, CEO of OpenAI, and Cleo Abram.  The topic? GPT-5—the smarter, sleeker sibling of GPT-4o. Redditors are already complaining that GPT-5 feels “dry” (some were apparently attached to GPT-4o—lol), but in the AI world models change is the only constant, and these models are evolving fast! The Three Pillars Holding AI Back Altman says AI isn’t just about clever code—it’s about Data, Algorithmic Design, and Compute. Here’s the breakdown: 1. Data: Knowing Everything (But Not Really) GPT-5 has practically ingested all the content you can find online. It’s a walking encyclopedia with a dash of personality. But don’t be fooled—AI still struggles with the “1000-hour problems” while breezing through the 1-minute stuff. Creativity? That’s still our turf! AI can summarize, predict, and analyze, but it hasn’t yet learned how to imagine something that never e...

Sentiment Analysis: What Makes a Good Hospital?

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 The Word “Stethoscope” Literally Means “Chest Watcher”   My dad always told me, "Toni, in life you need to have a doctor, a lawyer, and an accountant."  Today, I want to answer the question: How do people choose who takes care of them in Trinidad? Your health is something you shouldn’t take for granted — simply being able to walk is a blessing. (Gentle reminder to get your steps in!) We all find ourselves at a hospital at some point — whether it’s to welcome a new family member, say goodbye to one, or find out what’s causing that weird pain in your chest. In the past, choosing a doctor was simple: your mom would say, “We going by Dr. So-and-So,” and that was that. But now? We check Google. We scroll Reddit. We DM a friend on Instagram. Because when it comes to care, people want to know what others have lived through. What Are Patients Really Saying? To find out what patients really think, I analyzed over 120 reviews from hospitals across Trinidad. Here’s what...

What SQL Joins and Quilting Have in Common?

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  When I think of joins, I think of quilts. Let’s be real— JOINS are not always simple . Especially when your tables don’t have a primary key. Or when the data model is not documented anywhere and you’re just trying to make sense of it all. Picture this: You’re at your desk, staring at your screen (remember to blink). You’re writing a query to count the number of employees per department.  It should be simple Two tables: employees and departments . A quick JOIN, a simple GROUP BY , and done. Right? You hit "Execute" Boom— 5,732 rows . That can’t be right. Your company only has 200 employees. You frown. You try DISTINCT . Still too many rows. You tweak the join condition, change the table order, throw in a LIMIT just to see something normal—but nothing works. It’s still chaos. Welcome to the world of SQL joins—where your logic seems sound, but your results make no sense. Why This Happens Joins are powerful—but they’re picky. If you join on the wrong column, or if your...