Open Food Facts (OFF) is a crowd-sourced database of food products from around the world. Somewhere in the world, a shopper scans the barcode on a packet of biscuits and adds basic product details. Another person photographs a package label for the same product in Hyderabad. A volunteer in France fixes a typo. That's Open Food Facts, a volunteer-built, openly licensed database of food products that's continually changing and growing. Each product is identified by a barcode and carries ingredients, nutrition facts, brands, labels and more. The whole database has millions of products, and let's say you only want the products recorded as being sold or available in one country like India. There are multiple ways to extract a subset of the OFF database. I have already tried using the daily dump available on Hugging Face through Google Colab and GitHub Actions in the past but it blew my mind that using the OFF deployed Apache Superset , you can execute a SQL query in a brows...
Sunday newspapers once featured fun “spot the difference” puzzles with two similar images. That same careful eye is now essential in the AI era to avoid falling for manipulated images. Here’s a practical checklist based on a New York Times article to tell if a photo is an AI fake: Check the perspective - Look at lines that should run straight into the distance (like floor tiles or walls). Do they bend, warp, or point in different directions? Watch for prompt keywords - Scan for brand names, place names, or other words from the original prompt that appear in strange or overly prominent places. Zoom in on small text - Big letters may look fine, but smaller writing often turns into gibberish. Also look for odd soft shadows on the characters. Examine how objects connect - Does a strap actually attach to a badge? Do skin tones match between a person and the photo on their ID? AI often fails at real physical relationships. Read the text carefully - Even sharp, pixel-perfect text can be ...
Funny.. but why?
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