When you solve a convex problem in a non-convex world

Training a linear regression is the guy in third place. Training a GAN is the guy in first, who knows he hasn't actually reached the global minimum but has just found the least-worst local minimum that doesn't cause total mode collapse

Modern ML demo: burn 500 GPU-hours, screenshot the one epoch where validation loss twitches downward, paste “SOTA” on the slide, then crack the champagne like the CFO isn’t watching the runway graph

After 15 years of explaining to executives why our model needs more compute for better optimization, I finally realized they're also stuck in a local minimum - the one where 'good enough' beats 'globally optimal' every quarterly earnings call

Every ML engineer's journey: spending weeks tuning hyperparameters only to realize you've been stuck in a local minimum the entire time, while your colleague who randomly initialized with a different seed stumbled into the global optimum on their first run. The real optimization problem isn't the loss function - it's optimizing the time spent optimizing versus just trying random restarts with a good learning rate schedule and praying to the gradient descent gods

Never underestimate a PM with Grafana edit rights: with enough x‑range cropping, every local minimum becomes “global convergence” - and third place tastes like champagne

Random seed 42 gives a screenshotable U‑shape at x≈13 and everyone opens champagne, while cross‑validation quietly puts it in third - another quarter won by metrics theater, not generalization

Gold for flat minima: where gradients ghost you, but generalization shows up on time