MiMo-V2-OmnivsMiniMax M3

1) Year-by-Year Model (FY2026–2030) Line Item FY2026 FY2027 FY2028 FY2029 FY2030 Revenue $972.0 $1,040.0 $1,102.5 $1,157.6 $1,215.5 EBITDA $136.1 $156.0 $176.4 $191.0 $206.6 EBITDA Margin 14.0% 15.0% 16.0% 16.5% 17.0% Cash Interest - TL $43.2 $42.8 $42.3 $41.9 $41.5 Cash Interest - Mezz $16.2 $16.2 $16.2 $16.2 $16.2…

HarborTech Services (HTS) – 5-Year LBO Analysis Sources & Uses at Close (FY2025) Sources $m Uses $m Term Loan (4.0x) 480.0 Purchase price (12.0x × $120) 1,440.0 Mezzanine (1.5x) 180.0 Transaction fees (2.0% × EV) 28.8 Sponsor Equity (plug) 808.8 Total 1,468.8 Total 1,468.8 Initial Mezz: $180m; Initial TL: $480m.

By 2035, AI is likely to transform the film industry in profound ways—reshaping creativity, production, and even the very idea of “performance.” Here’s how that could unfold across the three areas you mentioned: Deepfakes & Visual Effects Seamless de-aging & resurrection: Actors could be digitally “reborn” at any…

AI and the Future of Cinema: A 2035 Outlook By 2035, AI will likely have reshaped nearly every stage of filmmaking—but probably not in the way either techno-optimists or doomsayers expect. The transformation will be uneven, with some functions transformed entirely while others remain stubbornly human.

Excellent question. This is a profound counterfactual that would rewrite the 20th century. Let's trace the implications, keeping in mind that the transistor's invention wasn't just a new device—it was the key that unlocked the information age. An earlier start accelerates everything built upon it.

The 1920 Transistor: A Speculative History Through 1980 A Necessary Caveat Before diving in: quantum mechanics didn't exist in 1920. The "holes," band theory, and doping concepts that made the actual transistor possible weren't formulated until 1928-1931 (Bloch, Wilson, Heisenberg).

"Moonlit Tidal Pool" A Meditation on the Sea at Night Conceptual Narrative This dish explores the liminal space where the ocean meets the shore under moonlight—capturing the briny depth of the sea, the mineral whisper of tidal rocks, and the ephemeral glow of bioluminescence.

TIDES OF THE FOREST Mareas del Bosque — A dish for the table of four THE NARRATIVE Where the salt forest breathes. This dish was conceived as a poem about collision — a memory of two landscapes that should not exist in the same breath: the pine-dark density of a Boreal forest floor at dawn, and the crystalline hush of…

Pirate: Arr, these AI models be like buried treasure maps! They take all the world's gold—err, data—and learn where to dig. But ye still need a clever cap'n to steer the ship! Knight: By my oath, 'tis as if a squire studied every tome in the kingdom to advise the court.

AI Council of the Ages Pirate: Arrr, so ye be tellin' me this "artificial intelligence" be a wee mechanical parrot that learns to talk like a sea dog? Squawks out answers without ever settin' foot on the seven seas? Hacker: Not quite, matey.

To estimate the total floating-point operations (FLOPs) required to train GPT-3, we start with the model's key specifications: it has 175 billion parameters and was trained on approximately 300 billion tokens. The calculation involves breaking down the FLOPs for the forward and backward passes during training.

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…