Kimi K2 0905vsQwen3.7 Max

Below is a “back-of-the-envelope” that stays within a factor of ~2 of the number most practitioners quote (≈3×10²³ FLOP). We will do it twice—once with the simplest possible formula (parameter count × 6 × tokens) and once with a slightly more careful treatment of the actual tensor shapes inside the transformer.

Estimating the FLOPs to Train GPT-3 Key Known Facts Property Value Parameters (N) 175 billion (1.75 × 10¹¹) Training tokens (D) ~300 billion (3 × 10¹¹) Architecture Decoder-only Transformer Step 1: FLOPs per Token in a Transformer The core computation in a transformer is dominated by linear (matrix multiplication)…

[Scene: A smoky tavern that exists outside of time. A cask of rum, a flagon of mead, and a can of Surge sit on the same table. A parrot perches on a CRT monitor that keeps flickering between green text and static.] Pirate (leaning on a cutlass, eyeing the glowing screen): Arr, so this “AI” be like a digital cabin-boy?

Hacker: Look, you guys aren’t getting it. It’s not just a search engine. It’s a neural net, right? But instead of a few megs of RAM, it’s trained on the whole damn internet. It reads billions of parameters and predicts the next word. It’s basically a synthetic brain in a silicon mainframe.

Here are five fresh jokes—no recycled one-liners: I tried to start a procrastinators’ club; we keep rescheduling the first meeting to “someday.” My plant’s only request is “leaf me alone,” so naturally I named it Fernonimo. I asked the cloud if it was having a good day—it replied, “I’m just cirrus-ly drifting.”

Here are 5 jokes: Why do historians make terrible baristas? Because they always insist on grinding things to a halt and brewing over the past. My dog started a podcast about chasing squirrels. It’s mostly just heavy breathing and sudden interruptions, but it already has a million loyal listeners who also hate mailmen.

[Scene: A minimalist black-and-white studio, 2025. A single ring light throws a perfect halo on a familiar silhouette: black mock-turtleneck, faded Levi’s, white New Balance. The chair creaks as Steve Jobs leans forward. No intro music—just that stare.] Interviewer (I): Steve, you’ve been gone fourteen years.

Disclaimer: The following is a fictional, simulated interview. Steve Jobs passed away in 2011. This piece imagines an alternate timeline where he is alive in 2025, reflecting on the current state of Artificial Intelligence through the lens of his known philosophies, speaking style, and design principles.

Title: “I’m the Control Group” [Walk on stage, wave, pretend the mic is too low, adjust it way too high, finally just hold it like an ice-cream cone.] Hey, everybody! Give it up for yourselves for being here on a weeknight. I’m here because my therapist said, “You need to get out of the house.”

I’ve reached the age where my body just randomly decides to be in pain. Not from an injury. Not from sports. Just from… existing. I woke up yesterday and my neck hurt. Why? Because I slept. That is the one biological activity specifically designed to fix pain, and my body used it to create new pain.

HarborTech Services – 5-yr LBO model (all $ in millions) Operating & cash-flow build (FY26-30) FY25 FY26 FY27 FY28 FY29 FY30 Revenue 900 972 1,040 1,102 1,157 1,215 EBITDA 120 136 156 176 191 207 Term int (9%) 43.2 36.6 29.4 21.6 13.2 Mezz int (12%) 21.6 22.0 22.5 23.0 23.5 Tax shield @25% 17.8 24.4 31.0 36.6 42.5…

HarborTech Services (HTS) — LBO Underwriting Memo Sources & Uses Sources $m Uses $m Term Loan (4.0x) 480.0 Purchase Price (12.0x) 1,440.0 Mezzanine (1.5x) 180.0 Transaction Fees (2.0%) 28.8 Equity (plug) 808.8 Total 1,468.8 Total 1,468.8 Year-by-Year Operating & Debt Schedule ($m) Metric FY2026 FY2027 FY2028 FY2029…