NVIDIA: Nemotron 3 Super 120B

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Josh’s total cost is the purchase price plus the repair costs: \[ \text{Total cost}= \$80{,}000 + \$50{,}000 = \$130{,}000. \] An increase of **150 %** means the house’s value grew by 150 % of its original value. The original value (the purchase price) is \$80,000, so the increase in value is: \[ \text{Increase}=1.5 \times \$80{,}000 = \$120{,}000. \] Thus the house’s value after repairs is: \[ \text{Final value}= \$80{,}000 + \$120{,}000 = \$200{,}000. \] Profit is the final sale price minus the total amount invested: \[ \text{Profit}= \$200{,}000 - \$130{,}000 = \$70{,}000. \] **Josh made a profit of \$70,000.**

The time complexity of binary search is **O(log n)** in the worst case, where **n** is the number of elements in the sorted array. ### Why? - Binary search works by repeatedly dividing the search interval in half. At each step, it compares the target value to the middle element of the current interval and eliminates half of the remaining elements from consideration. - Starting with **n** elements, after **k** steps, the search space is reduced to **n / 2ᵏ** elements. - The algorithm terminates when the search space is reduced to 1 element (or empty), so we solve for **k** in: **n / 2ᵏ ≤ 1** → **2ᵏ ≥ n** → **k ≥ log₂n**. - Thus, the maximum number of comparisons required is **⌊log₂n⌋ + 1**, which simplifies to **O(log n)** in Big O notation. ### Important Notes: - **Base of the logarithm**: While the exact count uses log₂ (base 2), Big O notation ignores constant factors and logarithmic bases (since log_b(n) = log_c(n) / log_c(b), and the denominator is a constant). Thus, **O(log n)** is sufficient regardless of the base. - **Best case**: O(1) (if the target is the middle element on the first comparison). - **Average case**: Also O(log n). - **Assumption**: This assumes random access to elements (e.g., in an array). For data structures without O(1) access (like linked lists), binary search is not efficient, and the complexity would differ. ### Example: For **n = 1,000,000** elements: - log₂(1,000,000) ≈ 20, so at most ~20 comparisons are needed. - A linear search (O(n)) could require up to 1,000,000 comparisons in the worst case. This logarithmic efficiency is why binary search is vastly superior to linear search for large, sorted datasets.

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A baby cat is called a **kitten** (pronounced "KIT-ten"). Kittens are typically born after a gestation period of about 63–65 days, and they’re incredibly dependent on their mother for the first few weeks of life. They’re known for their playful energy, tiny paws, and adorable meows! If you’re curious about kitten care, development stages, or fun facts—just ask! 😺

That's a really sharp observation—Black Mirror *does* have an uncanny way of feeling less like fiction and more like a distorted preview of where we're headed. When something in real life triggers that "wait, this is literally an episode" feeling, it usually means it's tapping into one of the show's core themes: **how technology amplifies human flaws, ethics lag behind innovation, or seemingly convenient tools slowly erode our autonomy, privacy, or humanity.** To engage with what you're noticing: - **What specific aspect** felt Black Mirror-esque? (e.g., surveillance, AI manipulation, social credit systems, addictive tech, reality distortion, etc.) - **Was it a news story, a personal experience, a trend you observed?** - **Which episode** did it remind you of most? (That often pinpoints the exact anxiety—like *Nosedive* for social scoring, *The Entire History of You* for memory tech, *White Bear* for justice-as-spectacle, etc.) Black Mirror’s power isn’t just in the dystopia—it’s in how it makes us question: *"Are we already sleepwalking into this? And if so, what choice do we actually have right now?"* If you want to unpack why this particular moment felt so familiar, I’m here to listen—and maybe even brainstorm how to push back against the creepier implications. What’s on your mind? 🔍

The SI unit of force is the **newton**, symbolized as **N**. One newton is defined as the force required to give a mass of one kilogram an acceleration of one metre per second squared (1 N = 1 kg·m/s²).

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