GPT-5 MinivsKimi K2

Recommendation: SHORT 12‑month PT range: $17 – $42 (Bear → Bull; Base case PT $28) Thesis (2 sentences): LedgerLift is a high‑quality mid‑market spend-management / AP automation SaaS with strong unit economics (92% subscription, NRR 123%, FY25 operating margin 18%) that has built a sticky, expanding customer base.

LedgerLift (LLLT) – IC Memo Date: 10 Jun 2025 Author: [Redacted] RECOMMENDATION LONG – 12-month price target $56-65 (base-bull midpoint). Mid-market enterprises are finally swapping legacy AP workflows for cloud SaaS, and LedgerLift’s 123 % NRR and 18-month CAC payback give it a rare combination of durable growth and…

Why don't scientists trust atoms? Because they make up everything. Parallel lines have so much in common — it's a shame they'll never meet. Why did the scarecrow win an award? Because he was outstanding in his field. I told my wife she was drawing her eyebrows too high — she looked surprised.

Here are five freshly-minted jokes: I tried to start a procrastinators’ club, but nobody showed up… yet. My plants are the only living things I can keep alive—mainly because they can’t uninstall the watering-reminder app. I asked the cloud for my data back; it just rained SQL on my parade.

Short answer By 2035 AI will be deeply integrated into almost every phase of filmmaking. Expect routine use of generative AI for treatments, storyboards, previs, localized versions, and some drafts of scripts; widespread use of synthetic actors and AI-driven de‑aging/recreation in many productions; much faster, cheaper…

By 2035, the film industry will look less like a single “Hollywood” and more like a constellation of hyper-efficient, AI-native studios that produce content in days rather than months. Below are the key inflection points that will be considered normal workflow rather than headline news.

Sunfire Chickpea Skillet Yields: 3–4 servings Prep time: 8 minutes Cook time: 15–20 minutes Total time: ~25–28 minutes Ingredients (7 pantry staples) 2 tbsp olive oil 1 medium onion, diced 3 cloves garlic, minced 1 tsp smoked paprika (or regular paprika) 2 (15 oz) cans chickpeas, drained and rinsed 1 (15 oz) can diced…

Caramel-Cloud Banana Skillet Cake (aka “The One-Bowl Wonder that Tastes Like Sunday Morning”) Ingredients (all everyday staples) 2 very ripe bananas 1 cup all-purpose flour ½ cup granulated sugar 1 large egg ¼ cup milk (any kind) 3 Tbsp butter (or neutral oil) ½ tsp baking powder Pinch of salt is nice but…

Goal: estimate total floating-point operations (FLOPs) required to train GPT‑3 (the 175B‑parameter model). I’ll state assumptions, derive the FLOPs per token, and multiply by number of training tokens. Assumptions (reasonably standard for GPT‑3): Model size: N ≈ 175 billion parameters (175 × 10^9).

Here is one way to “Fermi-estimate” the training compute for GPT-3 without looking up any numbers. I will start from generally known facts (model size, data size, hardware specs) and work forward. Step 1 – Fix the model parameters A widely reported figure is that GPT-3 has 175 B parameters. N ≈ 1.75 × 10¹¹.

Pirate: "Arrr, so these 'AI models'—be they like a crew o' invisible sailors? Tell 'em to find treasure and sometimes they do, sometimes they bury the map wrong." Knight: "Methinks 'tis a trained squire. You teach it laws of reason and courtesy, and it performs tasks.

Pirate: Arrr, so ye be sayin' this metal-brain can spy a ship ten leagues off, even in fog? What sorcery be this? Knight: Nay, good pirate, 'tis no sorcery but artifice most cunning. The machine learns as a squire learns swordcraft—by seeing many battles and remembering every stroke.