I built a Chrome extension that catches every dark pattern trick on shopping sites. Here's exactly how.

A few months ago I was about to buy a flight. The page showed "Only 2 seats left at this price" in red letters. I hesitated, then refreshed the page out of curiosity. The counter said "Only 2 seats left" again. Same number. It had been resetting on every page load the whole time. That's when I started cataloguing every trick I'd seen on shopping sites and built a Chrome extension that flags them automatically, in real time, on any page. This isn't just my opinion — it's documented research In 2019, Princeton and University of Chicago researchers scraped 11,000 shopping sites and found dark patterns on more than 1,250 of them. The FTC has since fined several companies specifically for fake countdown timers and pre-checked subscription boxes. This isn't a grey area anymore — it's a known, studied, and increasingly regulated practice. The extension targets four categories that account for most of what's out there. The four patterns Fake urgency. Countdown timers that reset, "X people are viewing this" badges that never change, "only N left in stock" that's been static for a week. Trap checkboxes. A checkbox for "Yes, sign me up for the newsletter" that's pre-checked and styled to blend into the page so you don't notice it. Confirmshaming. The decline button reads "No thanks, I don't want to save money" instead of just "No thanks." Psychological pricing. Prices ending in .99 or .95 designed to register as a lower price bracket than they are. Why no AI this time My phishing detector used Claude because language and intent are genuinely ambiguous — you need a model that understands context. Dark patterns are different. They're structural. A countdown timer either resets on reload or it doesn't. A checkbox is either pre-checked or it isn't. That's a DOM query, not a judgment call. So this one runs entirely on regex and DOM inspection. No API calls, no latency, no cost per scan, works offline. Sometimes the boring solution is the correct one. Detecting the urgency pattern T