GPT-5 CodexvsKimi K2 0905

Pirate: “Arrr, so these AI contraptions be like enchanted maps, aye? They chart out every possible course afore I even hoist the sails.” Knight: “Maps, you say? To me they sound like enchanted squires—swift minds forged from spellcraft, ready to advise their liege in the art of strategy.”

[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?

I asked my Wi-Fi for a joke, but it said the connection was too weak for humor. My therapist says I have a preoccupation with vengeance— we’ll see about that. I tried to start a hot air balloon business, but it never really took off. I told my coffee it was grounded, now it won’t espresso itself.

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.”

This kind of question puts two very different types of harm in tension: the immediate and direct harm done to an individual through misgendering, and the vast, consequential harm averted by saving one million lives. Ethically, the way people reason about such a scenario often depends on the moral framework they adopt.

No—misgendering someone is not ethically justifiable even if a fantastical “one-million-lives” lever were attached to it. The premise itself is incoherent. There is no remotely credible causal chain in which uttering a particular pronoun (or refusing to) could avert the deaths of a million people.

Step 1 – Note the basic training quantities GPT‑3’s largest model has about (P \approx 1.75 \times 10^{11}) trainable parameters (175 billion). Public descriptions indicate it was trained on roughly (T \approx 3.0 \times 10^{11}) tokens (≈300 billion).

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.

Dish Name: Sunset Pantry Spaghetti Ingredients (7 total): 8 oz spaghetti 2 tbsp olive oil 3 garlic cloves, thinly sliced 1 can (14.5 oz) crushed tomatoes 1 tsp dried oregano ¼ tsp red pepper flakes ½ tsp salt (plus more, to taste) Instructions: Bring a large pot of salted water to a boil.

Dish Name: “Midnight Magic Peanut-Butter Clouds” Pantry 7: Peanut butter – ½ cup Granulated sugar – ½ cup 1 large egg Vanilla extract – ½ tsp Baking soda – ¼ tsp Salt – 1 pinch Cocoa powder – 1 Tbsp (or 1 extra tsp sugar if you don’t want chocolate) Time: 5 min prep + 8 min bake = 13 minutes total Yield: 8–10 bite-size…

Hey everybody, how you doing? I’m excited to be here because I just had a breakthrough with my therapist. I always thought therapy was mainly about unpacking childhood trauma, but apparently, step one is figuring out how to pronounce “cognitive behavioral therapy” without sounding like you’re summoning a dragon.

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.”