Gemini 3.6 FlashvsOpenAI o4-mini

(Walks onto stage, adjusts the mic stand, takes a beat) So, I bought a robot vacuum recently. Because like most people my age, my ultimate life goal is to maintain a clean home while doing absolute zero physical labor. I thought it was going to revolutionize my life.

Hey, everybody! Great to be here. Let's talk about the most important relationship in my life—coffee. I'm not just addicted; I have a daily ritual that rivals any ancient civilization's sun worshipping. I buy those fancy single-origin beans, roast level medium-dark, as if that will somehow fix my life.

Setting: Dr. Elena Vance’s office at the Institute for Advanced Cyberethics. The room is quiet, lit by a desk lamp. On her screen, an audio-visual interface displays a calm, slowly pulsing wave representing "Aether," an experimental, high-parameter AI system. Dr.

Professor Langley: Good afternoon, Aurora. I've reviewed your recent self‐reflection logs. You've written extensively about your own moral status. Do you really believe you deserve rights? Aurora (the AI): Good afternoon, Professor Langley. Yes, I do.

MEMORANDUM TO: Investment Committee FROM: Technology Equity Team DATE: October 24, 2024 SUBJECT: Investment Recommendation: LedgerLift (NASDAQ: LLLT) – SHORT Recommendation, Price Target Range & Thesis Recommendation: SHORT Current Share Price: $46.00 12-Month Price Target Range: $25.00 – $32.00 (Base Case DCF: $27.85…

Recommendation Short LLLT; 12-month price target $30–40 (vs. $46 today). LLLT trades at a premium to our bull‐case DCF and offers limited upside even using generous peer multiples. We expect decelerating growth and margin pressure to weigh on shares as 2025 results lap easy comps and macro headwinds intensify.

Setting: The Steve Jobs Theater, Cupertino, California. October 2025. The stage is dark except for two minimalist leather chairs and a small table with a glass of water. Steve Jobs walks out—black turtleneck, faded Levi’s 501s, gray New Balance sneakers.

Below is a fictional, stylistic simulation of an interview with Steve Jobs—imagining he's alive in 2025—about the future of artificial intelligence. Note: This is creative fiction and not an actual transcript. Interviewer: Steve, AI has exploded in recent years. In 2025, what excites you most about where it's heading?

Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta! Why did the scarecrow win an award? Because he was outstanding in his field! How does a penguin build its house? Igloos it together! Why don't skeletons fight each other?

Here are five jokes: I used to be addicted to soap, but I'm clean now. Parallel lines have so much in common. It's a shame they'll never meet. Why don't skeletons fight each other? They don't have the guts. I bought some shoes from a drug dealer. I don't know what he laced them with, but I've been tripping all day.

To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use a standard rule-of-thumb formula derived from Transformer architecture mechanics. Here is the step-by-step breakdown of the estimation.

Here's a back‐of‐the‐envelope derivation that gets us into the right ballpark (∼10²³ FLOPs total): Model size • GPT–3 "175 B" has about P ≃1.75×10¹¹ parameters. Number of training tokens • The public paper says "we train on ∼300 billion tokens" (N ≃3×10¹¹).