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lucille is thirsty, she is a vampire bat 🩸 DP: @CinebotFilms an ode to @JDMorgan on being the most iconic character in TV history @WalkingDead_AMC #twd# #thewalkingdead# #negan# #amcthewalkingdead#

GPT Image 2 Prompt：水墨风格 Slides/PPT 可以把下面的提示词模板发给 Agent，让 Agent 帮你生成每一页 Slides 的画图 Prompt，Codex 这样有画图能力的直接出图。 --- 提示词模板 --- Title: [在此输入幻灯片标题] Key Points: - [要点 1：简洁的描述] - [要点 2：核心数据或事实] - [要点 3：关键结论] Visual Elements: [描述视觉元素，例如：纹理宣纸背景 (Textured rice paper background)、水墨山水 (Ink-wash motifs)、 简约的圆圈 (Enso circle)、红色印章 (Red seal mark)、雾气效果 (Mist-grey effects)]。 整体风格应保持 [Quiet / Restrained / Wabi-Sabi / Contemporary East-Asian Luxury]。 Layout Preference: [布局说明，例如：左右分割 (Split layout)、居中对齐 (Centered layout)、 文字居左且右侧留白 (Left-aligned text with negative space)]。 Text Hierarchy: [文字层级，例如：标题使用大号衬线字体 (Large Display Serif)，正文使用易读的衬线字体 (Body Serif)， 确保视觉平衡和清晰的阅读顺序]。 Continuity Note: [延续性说明，例如：保持与前一页相同的背景纹理和色调 (#F5F0E8#, #2C3E2D#)， 使用相似的印章位置以维持视觉一致性]。 ----- 示例 ------ Title: Agent Loop 深度解析: 揭秘 AI 智能体的心脏. Key Points: 核心定义、主要职责、设计目标。 Visual Elements: 大号优雅标题，背景为宣纸纹理，带有淡淡的水墨山水和圆圈笔触，角落处有红色小印章。 Layout: 干净的布局，大量留白（Open Sky）。 Text Hierarchy: 标题最为突出，下方是较小的正文。

Here's the #1# thing most people don't know about Warren Buffett: There is nothing special about Buffett’s stock picking. That doesn’t mean that Buffett wasn’t a great investor. He was! Buffett was, by far, the greatest investor in history, by a huge margin. Over 486 months between October 1976 and March 2017 –— 41 years –— Berkshire Hathaway’s Class A stock earned an average excess return of 18.6% per year above U.S. Tbills. Annualized volatility was 23.5%. Sharpe ratio: 0.79. Berkshire’s Sharpe ratio of (0.79) is roughly 1.6x times the broad U.S. stock market’s Sharpe ratio of 0.49 over the same period. Among all large-cap U.S. stocks and mutual funds with 30-plus-year continuous track records, those are unmatched numbers. A dollar invested in Berkshire on October 31, 1976, was worth more than $3,685 by March 31, 2017. A dollar invested in the S&P 500 with dividends reinvested over the same period was worth approximately $76. Buffett beat a passive index by a multiple of 48. But he didn’t do it with stock picking! Three researchers at AQR Capital Management –— Andrea Frazzini, David Kabiller, and Lasse Heje Pedersen –— dissected Berkshire’s 50 years of investments through 2013. They expanded and republished their findings in 2018 in the Financial Analysts Journal, which is the most highly respected industry financial journal. Their work won the Graham and Dodd Award for the best published paper of the year. The paper is called Buffett’s Alpha. They found, after accounting for cheap leverage (from the insurance float) and exposure to a handful of publicly documented factor premiums, Buffett’s investment skill –— the portion of his returns that cannot be explained by any mechanical strategy –— is 0.3% per year. That's statistically indistinguishable from zero. In other words, the alpha that Berkshire enjoyed for 50 years (as it compounded capital at 24% a year!) wasn’t due to Buffett’s stock picking. So, how did he do it? He did it by gaining access to a huge amount of investment capital that he did not own, for free. Buffett’s track record was built on leverage. That’s a dirty word for most investors, but it's the secret behind Berkshire. The AQR researchers had access to something most Buffett commentators do not: 40 years of Berkshire’s audited financial statements and the full quarterly history of the public 13F stock portfolio. The researchers asked a specific question: If I take Berkshire’s monthly stock returns from October 1976 through March 2017, and I run a linear regression against a set of well-documented risk factors –— market beta, size, value, momentum, and two newer factors called Betting-Against-Beta and Quality-Minus-Junk (detailed below) –— how much of Buffett’s performance can the factors explain? And after the factors have been stripped out, how much excess return remains? The data show clearly there are a few qualities that drove Berkshire’s results. First, Buffett has always preferred large-cap stocks, contrary to the popular image of him as a small-cap value investor. He buys elephants. Second, no surprise, Buffett buys cheap. Berkshire is almost six standard deviations away from neutral on the value axis. So far the picture is ordinary. Every large- cap value manager in America loads positively on size and on value. Buffett’s genius lies in the last two factors. These last two factors are a little complicated, but please stick with me. There’s a new factor, that, like value and size, characterizes Buffett’s strategy. It’s called Betting-Against-Beta (“BAB”). What it means is intentionally investing in stocks with very low volatility. The BAB factor captures the excess return that accrues to investors who own low-beta stocks. Low-beta stocks have historically earned higher risk-adjusted returns than high-beta stocks. Financial theory teaches that higher beta (higher risk) should mean higher return. But it doesn’t. The opposite occurs, in fact. And Buffett was one of the very first people to figure this out. Why does this factor persist? In an efficient market, once that factor is known to investors, then they should bid the price up on low- beta stocks until it no longer provides an edge. The explanation, per the theory of AQR’s Frazzini and Pedersen’s theory, is that because ordinary investors do not use leverage and seek high returns, they create persistent excess demand for more volatile stocks. (Having worked with retail investors for 30 years, I can assure you that is true.) But, an investor with access to cheap leverage –— Warren Buffett, for instance –— can exploit the mispricing by owning the low-beta names and levering them up to produce market-beating returns. And the last factor that matters to Buffett is quality. Buffett buys companies with high returns on invested capital. Quality-Minus-Junk (“QMJ”) is a factor described by Cliff Asness, also at AQR with Frazzini, and Pedersen, in a 2019 paper in Review of Accounting Studies. The QMJ factor captures the return to owning stocks of high-quality companies –— profitable, growing, safe, with high payout ratios –— against stocks lacking those characteristics. QMJ has been positive and statistically significant in every major developed equity market for which it has been measured. Berkshire’s loading is 0.37, with a t-statistic of 4.6. –– meaning it is highly significant to Berkshire’s results. In plain English: Buffett only buys large, high- quality, low-volatility stocks of the highest quality. But, Berkshire’s results were not, in any way, unusual. Any investor buying these same kinds of stocks would have earned those same returns –– about 16% a year over time. So how did Berkshire compound at 23% a year? To figure that out, AQR’s researchers built a Berkshire replica. They constructed a simple, rules-based, publicly investable portfolio that mechanically tilts toward large-cap, cheap, low-beta, high-quality stocks, and levers it 1.6- to- 1 to match Berkshire’s insurance float leverage. The correlation between their replica’s returns and Berkshire’s were virtually identical. The authors’ conclusion is unambiguous. “In summary, we find that Buffett has developed a unique access to leverage that he has invested in safe, high-quality, cheap stocks and that these key characteristics can largely explain his impressive performance.” Berkshire’s cost of insurance float has averaged almost three percentage points below the Treasury bill rate across 50fifty years of data. In roughly two-thirds of all years, Berkshire has been paid to hold other people’s money. That is not an investment strategy. That is a financing miracle. It is also the living, breathing heart of Berkshire Hathaway. It’s what Buffett built, starting in 1967 when he paid $8.6 million for National Indemnity’s $19.4 million of float. And it is the factor every retail investor admiring Berkshire’s returns has never paid any attention to. The 1.6-to-1 leverage that AQR measured over the full period, financed at this negative cost, explains the dollar magnitude of Berkshire’s returns. How do we know? An unleveraged version of the same stock portfolio –— which you can approximate by looking at the 13F holdings alone –— has earned an average excess return of 12% percent per year. It’s Berkshire’s leverage that magnifies this excess return to 18.6 %percent. How does this square with Berkshire’s reported gains? Berkshire’s 18.6% excess return, plus the T-bill rate that averaged roughly 4.7% over 1976–2017, gives you a total nominal return of roughly 23% per year, which is the figure you usually see quoted for Berkshire’s historical performance. The 23% tells you what Berkshire returned. The 18.6% tells you how much of that return was compensation for taking investment risk, as opposed to the baseline yield every lender to the U.S. government was earning anyway. With both of Berkshire’s “edges” –— systematic factor exposures to cheap, high-quality, low-volatility stocks and roughly 1.6-to-1 leverage delivered with insurance float –— you get Berkshire Hathaway’s 23% annual gains over 60 years. It’s the structure that’s genius, not the stock picking. And that's very important because it means the original Berkshire formula can work for any investor. I show you exactly how, in my new book.

Minimal Line + Color Accent Illustrations PROMPT: “Minimal illustration of [SUBJECT/OBJECT], clean thin lines with selective vibrant color accents, lots of negative space, refined composition, modern editorial style, elegant and simple.”

Made with GPT Image 2 + Seedance 2.0 on @SocialSight Generate a 16-move choreography sheet in GPT Image ↓↓↓↓↓↓ Upload it to Seedance 2.0 as the "brain" of your dance ↓↓↓↓↓↓ Your character performs the full routine instantly ↓↓↓↓↓↓ A complete K-pop dance video. Done in minutes. GPT Image 2 prompt: A colored pencil sketch style choreography sheet infographic for a K-pop solo dance. Layout: 16 steps arranged in a clean 4x4 grid, each panel showing a different dance move. Subject: A teenage Asian girl with short blunt-cut hair and bold bangs, wearing a monochrome outfit — fitted black turtleneck, wide-leg tailored trousers, chunky white platform boots, with silver chain accessories and fingerless gloves. Style: Hand-drawn colored pencil illustration, bold outlines with sharp shading, slightly angular and graphic, monochrome base with pops of electric red and silver as accent tones. Confident, editorial energy. Movement: Each frame captures sharp, punchy dance motions — arm isolations, sharp head snaps, shoulder locks, chest contractions, low lunges, toe-point poses, dramatic wrist flicks, and a signature ending freeze — with bold red arrows showing force direction and snap timing. Design: High-contrast K-pop girl crush aesthetic, grid with thin black borders, step numbers (1–16) in bold sans-serif, short captions under each frame describing the motion. Text: Title at the top — "K-POP GIRL CRUSH – 16 COUNTS – 10 SECONDS – SHARP LOCK & POP" Environment: White studio background, dramatic downlight suggestion, minimal clean shadows. Quality: High detail, crisp composition, balanced layout, editorial dance tutorial poster. Negative prompt: blurry, low quality, extra limbs, distorted anatomy, bad proportions, messy layout, overcrowded design, text errors, watermark, soft or pastel tones.

GPT Image 2 Prompt: Soft poetic children's book illustration with watercolor and gouache textures.Clear gentle daylight with slightly brighter highlights.Muted pastel colors with soft blue and warm tones.Visible brush strokes and paper grain.Minimalist composition with large negative space.Calm, thoughtful, slightly open-ended atmosphere. Child character (around 12 years old).Subtle visual metaphors like light, shadow, perspective, reflection.Hand-painted picture book style, not cartoon, not anime, not 3D. Two children in calm conversation,soft connection forming.