Picture this: you’re scrolling through your phone, you snap a photo of a broken kitchen faucet, type “why is this leaking,” and then record a ten-second video of the drip pattern. A few seconds later, you get back a coherent answer that references what it saw in the video, what it read in your question, […]
Ever watched ChatGPT or Claude spit out a sentence and wondered how it actually landed on that specific word instead of a dozen other options that would’ve made just as much sense? It’s not magic, and it’s definitely not the model “thinking” the way we do. Underneath the hood, there’s a surprisingly elegant bit of
Ask a chatbot how many times the letter “r” appears in “strawberry” and there’s a decent chance it will stumble. The people who built it aren’t fools, and the model isn’t stupid. The trouble starts earlier, at a stage most users never see. Before a language model reads a single word, a small piece of
I still remember the first time an AI chatbot confidently gave me a completely made-up statistic. It didn’t stutter, didn’t hedge, didn’t say “I’m not sure.” It just stated a fake number like it was reading it off a government report. I actually used that number in a client deck before a colleague caught it.
A product designer at a fintech startup in San Francisco once described her Monday mornings like this: a stakeholder walks in with an idea scrawled on a napkin, wants to see “something” by end of day, and she has exactly four hours between two other project deadlines to turn a vague sentence into a wireframe
I once pasted an entire ninety-page contract into a chatbot and asked it to find every clause related to termination fees. A couple of years ago, that request would have gotten me a polite apology and a truncated response, because the model simply couldn’t hold that much text in its head at once. This time,
A few months ago, I asked an AI assistant on my phone to summarize a long email while I was standing in line at a coffee shop with terrible signal. No internet, no cloud server, no delay. It just worked, instantly, right there on the device in my pocket. That moment stuck with me, because
Customer Support Chatbots We’ve all had the experience. You’re stuck on a billing issue at 11 p.m., you open the little chat bubble in the corner of a website hoping for a human, and instead you get a cheerful bot asking if you’d like to “learn more about our products.” You type your actual question.
Machine Learning I still remember the exact moment machine learning stopped feeling like magic to me. I was three weeks into my first “real” ML project, feeding data into a model, watching accuracy numbers climb, and nodding along like I understood what was happening under the hood. Then a colleague asked me a simple question:
Machines Learned to See A few years ago, I watched my phone unlock itself just by me glancing at it, and for a split second, I genuinely paused. Not because it was slow or glitchy it was the opposite. It was instant, almost unnervingly so. Somewhere inside that little rectangle of glass and metal, software
