WORST Fails On The Gram on Instagram: "Follow @FAILLGRAM for daily fails 🤣 Live sports broadcasting leaves almost no room for error, which is why on-air slip-ups by commentators often go viral. Broadcasters work under constant pressure—juggling real-time statistics, producer cues, replays, and fast-changing game dynamics—so a poorly phrased line or accidental double meaning can slip out before they have time to filter it. When a commentator immediately realises what they’ve said, the instinctiv

Code Intellectus on Instagram: "Optical sorters scan 2,000 tomatoes per minute using computer vision. Each analyzed for ripeness, size, and defects in milliseconds. Ripe tomatoes pass. Dirt, rocks, damaged fruit get ejected by robotic arms instantly. Manual sorting: 2 per second. This system: 33 per second with zero errors. The machines don’t replace workers because they’re smarter. They replace them because they never need to be paid twice. California farms run these 24/7 processing millions of

Ganesh | AI Engineer on Instagram: "Most ML beginners stop at a Jupyter notebook. Real engineers know how to productionize a model end-to-end. This is how I shipped ML features at Microsoft (Azure-based, but same concepts apply to AWS/GCP). If you’re early in your career, practice this with free student credits on cloud. Here’s the path 👇 ⸻ 1️⃣ Two steps of an ML model Training pipeline → data → features → training → metrics → registry. Inference pipeline → real-time endpoint (REST) or batch jo