Gemma 3 27BvsMuse Spark 1.3 Contributor

HarborTech Services LBO - Underwriting Analysis Here's the simplified LBO analysis based on provided information. 1) Year-by-Year Projections (FY2026-FY2030) - $M Year Revenue EBITDA Cash Interest (TL) Cash Interest (Mezz) Cash Taxes Capex ΔNWC FCF After Debt Service Ending TL Ending Mezz 2026 $972 $138.6 $36.0 $18.9…

HarborTech Services - 5-Yr LBO - Base Case Entry - Close end FY2025, $m EV = 12.0x $120 = $1,440 Fees = 2.0% x 1,440 = $28.8 Uses = 1,440 + 28.8 = 1,468.8 Term Loan 4.0x = $480, 9.0% cash, 1.0% of original amort = $4.8/yr Mezz 1.5x = $180, 12.0% cash + 2.0% PIK to principal Total Debt 5.5x = $660 Equity In = 1,468.8…

The Interview: Steve Jobs, 2025 (Setting: A minimalist, sun-drenched room. Polished concrete floors, a single, perfectly placed bonsai tree. Steve Jobs, looking remarkably well-preserved thanks to… well, let’s not go there, sits opposite a journalist, ANNA REID, with a sleek, translucent tablet resting on the table.

Here's a fictional simulation — Steve Jobs passed away in 2011, so this is 100% imagined, in his voice and style, about what he might have said about AI in 2025: FAKE INTERVIEW: Steve Jobs in 2025 on the Future of AI Imagined in Cupertino, California Interviewer: Steve, welcome back. It's 2025.

(Stands center stage, adjusts mic) Alright, alright, how we doin’ tonight? Good? Good. You all look… awake. That’s a start. Seriously though, thanks for coming out. It’s a Tuesday. A Tuesday! Like, who chooses Tuesday? It’s the beige of days. Monday’s got drama, Friday’s got vibes, but Tuesday’s just…exists.

Here's a clean 3-minute set you can actually perform — all observational / storytelling, no puns. It's about 420 words, so at a normal pace it's right around 3 minutes. "Adulthood Is A Scam" Hey, thank you guys so much. I'm so excited to be here.

A Most Curious Gathering Setting: A surprisingly comfy tavern, somehow existing outside of time. Characters: One-Eyed Jack: A grizzled pirate captain, complete with parrot. Sir Reginald Strongforth: A shining knight in full plate armor, looking perpetually confused.

[In a tavern with mysteriously good Wi-Fi] Pirate: Arrr! So ye be tellin' me this "AI model" be a treasure chest o' words? I feed it a map — er, a prompt — and it spits back gold? Knight: Verily, good sir pirate. Methinks 'tis an oracle in a box. I asked it "How might I slay a dragon?"

Okay, let's estimate the FLOPs required to train GPT-3. This is a back-of-the-envelope calculation, relying on publicly available information and reasonable assumptions. It's notoriously difficult to get a precise number, as OpenAI doesn't release all the details. We'll break it down step-by-step. 1.

This is a classic Fermi estimate for a Transformer. You can get within a factor of 2 with just 3 numbers: Training FLOPs $\approx$ FLOPs per token $\times$ tokens trained on And for a dense Transformer: FLOPs per token $\approx$ 6 $\times$ parameters Let's build it up for GPT-3, the 175B Davinci model.

Explaining LLMs to Different Audiences To an Experienced Software Engineer You're used to building systems with clearly defined logic and state. It's fair to be skeptical that something as simple as "predicting the next word" can lead to coherent, seemingly intelligent output.

1. For the experienced software engineer Think of training not as writing business logic, but as building a lossy compression of the internet into a queryable API. Raw text is first serialized into tokens — roughly 3/4 of a word chunks, the way you'd chunk a byte stream for protobuf.