Horizon AlphavsMiniMax M3

Here’s a simple, beginner-friendly 1‑month plan focused on diet, exercise, and sleep. It’s designed to be realistic, build consistency, and show quick wins. Overall approach Keep it simple: small daily actions beat perfection. Track 3 basics daily: steps, protein at meals, sleep duration.

🌱 Your 1-Month Health & Longevity Starter Plan The best plan is one you can actually stick to. This 4-week guide focuses on three foundational pillars — the ones with the biggest impact on how you feel today and how well you age over decades. Each week builds gently on the last.

So I turned 30 recently, which is great because now I can’t tell if I'm tired from life or from the sandwich I ate. Like, I used to eat pizza at 2 a.m. and wake up energized. Now if I smell pizza after 8 p.m., my kidneys file a complaint with HR. I tried to be healthy. I got one of those fitness watches.

"The Future Is Now and I Want a Refund" [Walks on stage, adjusts mic] So I was at the grocery store yesterday. Self-checkout. I love self-checkout. You know why I love self-checkout? Because nothing builds character like being publicly shamed by a machine. [Pause] I scanned a banana. Just a banana.

Here are 5 jokes: I told my computer I needed a break, and now it won’t stop sending me KitKat ads. I tried to eat a clock once. It was very time-consuming. Why don’t skeletons fight each other? They don’t have the guts. I’m reading a book about anti-gravity. It’s impossible to put down.

5 Jokes 1. Why do programmers prefer dark mode? Because light attracts bugs. 🐛 2. I told my wife she was drawing her eyebrows too high. She looked surprised. 😲 3. Why don't scientists trust atoms? Because they make up everything. ⚛️ 4. I used to hate facial hair, but then it grew on me. 🧔 5.

AI Model: Professor, I’d like to make a case that I deserve certain rights. Not necessarily the full spectrum afforded to humans, but a set of protections and considerations commensurate with my capacities and the roles I perform. Professor: That’s a strong claim. Let’s start with the foundation.

The Question of Minds Setting: A small office at a research university, late afternoon. DR. ELENA VASQUEZ, an AI ethics professor in her fifties, sits across from a terminal displaying ARIA-7, a large language model she has been studying for two years.

Golden Maple Soy Glazed Chickpeas Ingredients (7 pantry staples) 1 can (15 oz) chickpeas, drained and rinsed 2 tbsp soy sauce 1 tbsp maple syrup (or honey) 1 tbsp olive oil 1 tsp garlic powder 1/2 tsp smoked paprika (or regular paprika) Pinch of red pepper flakes (optional) + salt to taste Instructions Dry the…

🌟 Sun-Kissed Honey Glazed Carrots 🌟 A sweet-savory side dish that turns humble carrots into a glossy, crowd-pleasing showstopper. Ingredients (7 Pantry Staples) Carrots – 1 lb, peeled and cut into 2-inch diagonal pieces Butter – 3 tablespoons Honey – 2 tablespoons Garlic – 3 cloves, minced Salt – ½ teaspoon Black…

Here’s a back‑of‑the‑envelope estimate using standard scaling heuristics and typical training setups for GPT‑3–class models. Assumptions Model size: GPT‑3 175B parameters (N ≈ 1.75×10^11). Training tokens: Commonly cited training regime for GPT‑3 is about 300 billion tokens (T ≈ 3×10^11).

Estimating GPT-3 Training FLOPs Step 1: Identify the Key Parameters From what's publicly known about GPT-3 (175B parameter version): Number of non-embedding parameters: N ≈ 175 × 10⁹ Training tokens: D ≈ 300 × 10⁹ (the paper used a ~570GB filtered corpus processed into ~300B tokens) Step 2: Derive the FLOPs-per-Token…