Tech Talk with Toni

“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...

  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 ...

  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...

 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...

  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...

Boston Dynamics created a robot dog named Spot that can dance Is it okay to kick a robot dog? It’s a funny question on the surface. It’s not alive . It doesn’t feel pain. So… who cares? Lately I’ve been reading a book on AI Ethics by philosopher Mark Coeckelberg, the book is filled with tricky questions that make you stop and rethink our relationship with machines—especially the smart ones. Let me walk you through a few of them, with a few everyday examples to help make sense of it. Q1: Should We Hold Robots Responsible for Their Actions? Imagine this: a self-driving car runs a red light and causes an accident. Who do we blame? The car? (It was the one driving!) The engineers who built it? The company that sold it? Mark brings up a similar question in the book: “We don’t hold very young children responsible for what they do because they don’t know better—should the same be done for AI?”  Q2: What are the things that should only be done by humans? Let’s say AI gets really, really...