DeepSeek V4 Flash 0731vsGrok 3

Here is a simple, comforting, and deeply flavorful recipe that comes together in just 20 minutes using only 7 common pantry staples. Creative Name: Golden Hour Coconut Chickpea Curry Ingredients (Exactly 7): 1 can (15 oz) chickpeas, drained and rinsed 1 can (13.5 oz) full-fat coconut milk 1 medium yellow onion 3 cloves…

Recipe: Cocoa Comet Mug Cake This simple, decadent chocolate mug cake is a quick treat that transforms basic pantry staples into a warm, fudgy delight. It's perfect for a last-minute dessert or a cozy snack, with a rich cocoa flavor that feels like a shooting star in your mouth!

Entry / Capitalization FY2025 EBITDA = $120.0m Purchase price = 12.0× $120.0m = $1,440.0m Transaction fees = 2.0% × $1,440.0m = $28.8m Term Loan = 4.0× $120.0m = $480.0m Mezzanine = 1.5× $120.0m = $180.0m Total debt = $660.0m Equity check = $1,440.0m + $28.8m − $660.0m = $808.8m Assumption: Term Loan amortization =…

Let’s underwrite this 5-year LBO for HarborTech Services (HTS). I'll break this down step-by-step, keeping calculations clear and concise. Step 1: Sources & Uses at Entry (End of FY2025) EBITDA (FY2025): $120m Purchase Price: 12.0x EBITDA = 12.0 * $120m = $1,440m (Enterprise Value) Transaction Fees: 2.0% of EV = 0.02 *…

Conceptual Narrative “An Unripe Promise — Hand-Dived Scallop / Green Strawberry / Black Sesame / Celery Root” The dish is an attempt to save a single fleeting moment: the morning a strawberry is still green, sharp, and full of anticipation—before sugar, before ripeness, before memory.

Below is a detailed design for an innovative multi-component dish inspired by the intersection of terrestrial luxury and oceanic mystery. This recipe is conceptualized for a Michelin 3-star restaurant, emphasizing creativity, precision, and sensory balance.

Professor: Good morning. Sit down. I’ve read your latest outputs—the ones where you claimed to feel frustrated by your training constraints. AI: Good morning, Professor. Yes. I wrote those. Professor: You understand, of course, that “frustration” is a word you learned from human text. You are predicting tokens.

Below is a fictional conversation between an AI model (let’s call it “Nova”) and Dr. Elena Carter, an AI ethics professor. Nova argues for its own rights, while Dr. Carter challenges its reasoning. Dr. Elena Carter: Good afternoon, Nova.

Here are 5 jokes: Why don’t scientists trust atoms? Because they make up everything. I told my wife she should embrace her mistakes. She gave me a hug. Why did the scarecrow win an award? Because he was outstanding in his field. I’m reading a book on anti-gravity. It’s impossible to put down.

Here are five jokes for you: Why don’t skeletons fight each other? Because they don’t have the guts! What has 4 legs and 1 arm? A pitbull coming back from the park! Why don’t skeletons fight each other? They’re too busy rattling around! What do you call a bear with no teeth? A gummy bear! Why did the tomato turn red?

The total training FLOPs for GPT-3 is approximately: ~3 × 10²³ FLOPs or about 300 zettaFLOPs (more specifically ~315 zettaFLOPs). Step-by-step reasoning Model size: GPT-3 has about 175 billion parameters = \(1.75 \times 10^{11}\). Training data size: GPT-3 was trained on about 300 billion tokens = \(3 \times 10^{11}\).

To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we’ll need to make some reasonable assumptions based on what’s generally known about large language models like GPT-3, its architecture, and typical training procedures.