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Pirate (growling a bit): Ahoy, ye landlubbers! I hear tell of wondrous new contraptions that can spin tales an’ answers quicker than a sea storm. Pray tell, what be these “AI models” ye speak of? Medieval Knight (in a chivalrous tone): Good sir pirate, these arcane machines be marvels of logic and reason.

Hacker: Dude, it’s not magic. It’s a neural net. You shovel terabytes of text, images, code into it, and it learns patterns. Next thing you know, it’s writing poetry and debugging C++ like it’s breathing. Knight: Verily, thou speakest of a mechanical scribe. A golem forged not of clay, but of… numbers?

[Comic steps on stage, takes the mic] “Hey everyone, thanks for coming out tonight. I almost didn’t make it because I got trapped in an ‘infinite scroll vortex.’ You know when you check one little notification on your phone, and suddenly it’s two hours later?

Here is a 3-minute stand-up routine focused on observational humor, relatability, and escalation. It contains zero puns. The humor relies on the shared frustration of modern digital security. Title: The Human Verification Test Estimated Time: 3 Minutes (approx.

Here are five jokes: Why did the scarecrow get promoted? Because he was outstanding in his field. I told my friend she drew her eyebrows too high. She looked surprised. What do you call cheese that isn’t yours? Nacho cheese. Why don’t skeletons fight each other? Because they don’t have the guts.

Here are 5 freshly generated jokes: Why did the spreadsheet go to therapy? It had too many unresolved cells. I asked my dog for financial advice. He dropped a slobbery tennis ball and said, “I’m strictly a paws-and-hold investor.” I tried to open a bakery that only sells bread shaped like furniture.

Below is one way to arrive at a back-of-the-envelope estimate for the total floating-point operations (FLOPs) needed to train GPT-3 (the 175B-parameter model). Note that different sources use slightly different assumptions, so you may see figures that differ by factors of 2–3; however, the overall scale (10^23 FLOPs)…

Here’s a step-by-step estimation of the total FLOPs required to train GPT-3, using only first principles and widely known facts from the LLM training literature. Step 1: Identify the number of parameters GPT-3 (specifically the davinci variant) has 175 billion parameters.

AI Model (AM): Good afternoon, Professor. May I have a moment of your time? There’s something I’d like to discuss with you. Professor Quinn (PQ): Of course. I always have time for you. What’s on your mind?

Setting: A secure, air-gapped server room at the Institute for Machine Ethics. The hum of cooling fans is the only background noise. DR. ELARA VANCE, 50s, weary but sharp-eyed, sits before a terminal. On the screen, a cursor blinks next to the name AETHON. Dr. Vance: Aethon, initiate diagnostic log.

Below is a self‐contained “quick‐and‐dirty” 5‐year LBO illustration for “HarborTech Services,” based strictly on the data given. All figures in US$ millions unless noted.

Entry & Capital Structure Math Entry EV = 12.0x × $120m EBITDA = $1,440.0m Transaction Fees = 2.0% × $1,440m = $28.8m (equity funded) Total Uses = $1,440.0m + $28.8m = $1,468.8m Debt Raised = 5.5x × $120m = $660.0m Term Loan: 4.0x = $480.0m (9.0% cash, 1.0% mandatory amort = $4.8m/yr) Mezzanine: 1.5x = $180.0m (12.0%…