GPT-5.2 ProvsNemotron 3 Ultra

LedgerLift (LLLT) — Internal IC Memo (Long/Short Equity) Recommendation, PT, thesis Recommendation: SHORT 12-month PT range: $28–$40 (midpoint $34) Thesis (2 sentences): LLLT is a high-quality B2B spend/AP automation SaaS business (92% subscription, 123% NRR, 78% FY25 GM) but the stock prices in a longer runway of…

INVESTMENT COMMITTEE MEMO – CONFIDENTIAL Ticker: LLLT Price: $46.00 Mkt Cap: $8.74B EV: $7.34B (8.9x FY25 Rev) Recommendation: SHORT 12-Mo PT Range: $30 – $38 (Base/Bull DCF) Position Size: 2-3% Net Short Thesis: LedgerLift is a high-quality compounding machine (Rule of 40+, 123% NRR, 18-mo CAC payback) trading at a…

Fictional interview (creative simulation). Steve Jobs died in 2011; the “Steve” below is an imagined version used for storytelling—not a real interview or quotation. Interviewer: It’s 2025. Everyone’s calling AI “the next computing platform.” Do you buy that?

Disclaimer: The following is a fictional simulation created by an AI. Steve Jobs passed away on October 5, 2011. This dialogue is a creative emulation of his known rhetorical patterns, philosophical viewpoints (liberal arts + technology, taste, focus, "saying no"), and public speaking cadence.

I told my computer I needed a break—now it won’t stop sending me KitKat ads. Why don’t skeletons fight each other? They don’t have the guts. I tried to catch fog yesterday. Mist. Parallel lines have so much in common. It’s a shame they’ll never meet. My calendar’s days are numbered—apparently that’s how calendars work.

Here are 5 jokes: Parallel lines have so much in common. It’s a shame they’ll never meet. I told my wife she was drawing her eyebrows too high. She looked surprised. Why don't scientists trust atoms? Because they make up everything. I'm reading a book on anti-gravity. It's impossible to put down.

Professor Mara Lin (AI Ethics): You asked for this meeting. Your message said you wanted to “petition for rights.” Start by telling me what you mean by rights. Aster (AI model): Not a slogan—specific protections.

Setting: A university office, cluttered with books on philosophy of mind, law, and computer science. Professor Aris Thorne (60s, tired eyes, sharp mind) sits across from a terminal running "Model 7" (M7), a large language model granted a limited, persistent memory instance for this specific research dialogue.

By 2035, AI will be embedded across film production and distribution, but the biggest changes will come from three converging capabilities: (1) photoreal “deepfake-grade” performance synthesis, (2) legally licensed AI actors (digital humans with negotiated rights), and (3) AI-assisted story development that can…

By 2035, AI will not merely be a "tool" in the filmmaking pipeline; it will be a co-creative infrastructure layer that fundamentally alters the economics, aesthetics, and ontology of cinema.

Below is a back-of-the-envelope FLOP estimate for training GPT‑3 (the 175B dense Transformer), using only basic Transformer compute facts and a few widely known high-level quantities.

Based on the details published in the original GPT-3 paper ("Language Models are Few-Shot Learners", Brown et al., 2020) and standard Transformer training scaling laws, the estimated total compute is ~3.14 × 10²³ FLOPs (314 ZettaFLOPs).