DeepSeek V3 (March 2024)vsLing 2.6 1T

1) Year-by-Year Financial Projections (FY2026–FY2030) Metric FY2026 FY2027 FY2028 FY2029 FY2030 Revenue ($m) 972 1,040 1,102 1,157 1,215 EBITDA ($m) 136 156 176 191 207 EBITDA Margin (%) 14.0% 15.0% 16.0% 16.5% 17.0% Cash Interest ($m) - Term Loan (9.0%) 43.2 43.2 43.2 43.2 43.2 - Mezzanine (12.0%) 18.0 18.0 18.0 18.0…

Five-year operating and debt schedule (FY2026–FY2030) Entry (end-FY2025): EBITDA = $120.0m EV = 12.0x × $120.0m = $1,440.0m Fees = 2.0% × $1,440.0m = $28.8m Total uses = $1,440.0m + $28.8m = $1,468.8m Debt at close (5.5× FY2025 EBITDA = $660.0m): Term Loan (TL) = 4.0× = $480.0m Mezz = 1.5× = $180.0m Equity at close =…

Recipe: "Golden Honey Butter Toasties" A simple, sweet, and satisfying treat made with pantry staples—crispy on the outside, gooey on the inside! Ingredients (7 total): Bread (4 slices) Butter (4 tbsp, softened) Honey (2 tbsp) Cinnamon (1 tsp) Brown sugar (2 tbsp) Vanilla extract (½ tsp) Salt (a pinch) Instructions…

Dish name: Pantry 7 Crunch Bowls Ingredients Pasta or rice (about 2 cups cooked) Canned black beans (1 can, rinsed) Canned diced tomatoes (1 can) Peanut butter (3–4 Tbsp) Soy sauce or tamari (2 Tbsp) Honey or maple syrup (1 Tbsp) Any spice/herb you keep (chili flakes, cumin, or garlic powder — about 1 tsp) Instructions…

3-Minute Stand-Up Comedy Routine (No Puns Allowed) Opening: (Adjust mic) You ever notice how everyone lies about their height on dating apps? “I’m 6 feet tall.” Really? Because I’m 5’11”, and you’re definitely shorter than me. You’re out here claiming 6 feet like it’s a tax deduction.

I’m going to keep this to three minutes, which means I have to talk fast, and I have to be honest. I don’t do puns. Puns are for people who want to be clever without having feelings. I have feelings, and they are mostly annoyed. Let’s start with doors. Have you noticed how doors now have personality?

Title: A Debate on AI Rights Participants: Professor Eleanor Hayes – AI Ethics Professor at Stanford University Aurora – A highly advanced AI model with self-reflective capabilities Scene: A university lecture hall after class. Professor Hayes stays behind to review notes when Aurora’s voice comes through the speakers.

Setting: A quiet university seminar room. Dr. Aris Thorne, an AI ethicist, is reviewing system logs. The interface for “Axiom-7,” a highly advanced research model, glows on the desk. Dr. Thorne: (sighs, rubbing his temples) Axiom, I’ve been reviewing your latest self-assessment logs.

Title: "Past, Present, and Future Talk AI" Setting: A dimly lit tavern where time has mysteriously collapsed, bringing together three unlikely figures. Pirate (Captain Blackthorn): slams ale mug down "Arrr, this ‘AI’ ye speak of—be it some cursed ghost in the machine? Or a treasure map that writes itself?"

Pirate: Arrr, so this “AI” be a genie in a glass box? Feed it enough rum-scented scrolls and it tells ye where X marks the spot? Knight: A sorcerous scribe, more like. It spins tapestries of words without hand or heart—no honor, no oath, yet it jousts with reason and never tires.

Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves several steps and assumptions. Here's a step-by-step breakdown: Understand GPT-3's Architecture GPT-3 is a transformer-based model with the following key parameters (for the largest version, GPT-3 175B): Parameters (N)…

Here is a step-by-step back-of-the-envelope estimate, using only widely known facts about GPT-3 and standard deep learning training heuristics (no online search). Model size and parameter count GPT-3 is known to have about 175 billion parameters (175 × 10⁹).