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跟大家分享下绝版的Claude Fable 5总结的AI生图焚决，+2个顶级美女人像提示词，这篇至少值3000块！ 昨晚睡前让Fable 5总结了AI生图之性感人像提示词最有效的写法： 1️⃣用“成人 + 气质 + 材质”来定人设，比如 25-year-old East Asian woman、old-money glamorous aura、editorial fashion portrait。 2️⃣用“服装剪裁 + 面料质感”替代直白身体描述，比如 fitted knit, silk satin, off-shoulder, tasteful neckline, fine jewelry。 3️⃣用“表情瞬间”制造吸引力，比如 soft knowing half-smile、caught mid-reaction、unaware she is on camera。 4️⃣用“镜头语言”强化质感，比如 telephoto compression、shallow depth of field、broadcast color grading、paused 1080i TV frame。 5️⃣用“光线”塑造皮肤和轮廓，比如 warm key light, luminous arena lighting, soft highlight on collarbone/cheekbone。 6️⃣用“背景虚化 + 前景留白”把主体抬出来，比如 soft bokeh, anonymous VIP guests softly out of focus。 7️⃣用“克制的性感”而不是夸张性感，比如 tasteful, classy, fully clothed, natural proportions, not exaggerated。 8️⃣用强负面词卡住跑偏方向，比如 no CGI, no plastic skin, no doll face, no exaggerated anatomy, no garbled text。 兄弟们，世界杯的狂野性感风， 和NBA总决赛的性感老钱风， 你们更喜欢哪一个？ 其实除了技法以外，还有一个很重要，那就是得有一个干净的IP， 要不总是会被风控和拒绝， 关于怎么有一个干净的住宅IP， 参考以下文章的保姆级方法⬇️

Earlier this week, the @DeptofWar — in full collaboration with Venezuelan security forces — conducted a kinetic strike on a Tren de Aragua (TdA) compound in Venezuela. TdA founder & leader Hector Rusthenford Guerrero Flores, aka “Niño Guerrero,” was confirmed killed during the strike. The operation underscores the shared U.S. and Venezuelan commitment to take the fight to narco-terrorists and deny them any safe haven in our hemisphere. We will continue to work closely with security partners, like Venezuela — and counties in the Americas Counter Cartel Coalition (A3C) partners — to take the fight to our enemies.

Everyone is talking about getting teams to use more AI. But how much of your AI spend is going toward work that's already been done? In PE diligence, the same VDR documents often get reprocessed across users, workstreams, and sessions. That means firms are paying for the same analysis again and again. As AI vendors move toward usage-based pricing, those inefficiencies start showing up fast. Our latest ToltIQ Insights article from Co-Founder and CIO @RikerTrek looks at why the Silicon Valley obsession with maximizing tokens misses the point in PE, and how the architecture underneath an AI platform affects cost, efficiency, and analytical depth throughout a deal. Read more:

There has been a lot of hand wringing on the appropriate valuation of SpaceX. Some large institutions believe SpaceX can only be valued at half what the market seems to be willing to pay for it. Others are claiming it has 15X appreciation ahead of it. Almost all of this difference of opinion comes down to how comfortable you are modeling beyond 2030 and what valuation method you use. 2030 valuation using a traditional Gordan DCF produces a very different result than a 2040 EV/EBITDA Multiple. Both have pros and cons. Most analysts don’t really discuss this and lead with a headline number. We are very comfortable modeling out to 2040, as large portions of what SpaceX is proposing is real world infrastructure, which provides modelable physics constraints to anchor against. The analysis we released today explores this in-depth, its open to the public all the way through IPO. I highly encourage you check it out prior to then. We’ve run 5,000 monte carlo runs across 500 variables (real number, even though it sounds fake) and three valuation methods. This video is of a 3D cloud chart showing every simulation outcome expected in valuation output across two of the most impactful variables to the model when using an EV/EBITDA multiple from 2026 to 2040. The horizontal axis is the steepness of the orbital data center demand S-curve. The vertical axis is the rate at which chip compute efficiency becomes cheaper. Each of the 5,000 dots is one simulated future; green dots are the ones where SpaceX's 2040 value clears the $1.77T IPO line, over time. Under EV/EBITDA valuation through 2040, 96% of our simulated futures clear the expected IPO price once the bell rings Friday. We aren’t publishing this publicly to tell investors what the stock is worth, we’re publishing this to help investors understand the world of outcomes, what the fundamentals suggest through 2040, and what frankly most analysis simply won’t share. SpaceX is a generational company working on long term infrastructure harnessing a domain no one has been able to tap in so far: space. It deserves doing the work as an investor. because this in not financial advice. The cleanest way to hold SpaceX is a bond stapled to a call option (AI-Compute); Starlink is the bond, the near term SatCom annuity that funds the next flywheel. Understand the world of outcomes and take your position accordingly. Comparables and P/E won't take you far enough.

🌍 GLOBE HUB We're allocating Genesis Hub access to the strongest signals inside the network. Study GLOBE. Understand the model. Synthesize the signal. How to participate: 1. Publish a Twitter thread explaining GLOBE and its network architecture. 2. Join Telegram: ➔ 3. Submit your thread together with your ETH wallet inside Telegram. ⏳ Submissions close in 48 hours. Selection is based on: • Depth of analysis • Original insight • Community engagement Write in any language. The network remembers signal.

Supercharge your Z-Image-Turbo workflows with the new Z-Image-Engineer-V6! Built specifically for Z-Image-Turbo, it turns simple prompts into cinematic descriptions and directly upgrades image depth, texture, and quality as a swappable text encoder.

Welcome Kittipong Wongkatanyoo, CEWA – Certified Elliott Wave Analyst, to the SkyLine Guide 2026 Thailand Judging Panel! With specialized technical analysis expertise and strong market interpretation skills, he brings valuable analytical depth to this year’s broker evaluation.

Introducing the Buy-Side Equity Research SOP on xBubble. State a ticker. Get a research memo built to the standard of an internal buy-side note. Thesis first, every number sourced to the original filing. One request in. A full equity deep dive out. What comes back isn't a news recap. It's the structure a real analyst writes: ✅ Where the company sits in its value chain ✅ Competitive map, edge, and key risks ✅ Filing-level financials + management read ✅ SOTP valuation, Bull / Base / Bear per share ✅ The catalysts to watch over the next 3 to 6 months Every claim is traceable. Each data point carries its original source and access date: SEC filings (10-K, 10-Q, 8-K), official IR decks, earnings call transcripts. You can check the work. That's the point. Here's what makes this more than one good report. The SOP isn't fixed. Bubble Engine builds it, tests it, and keeps generating stronger ones. Need a version tuned to a sector, a market, or your own thesis style? It generates that too. You don't get a template. You get a research engine. Buy-side research used to take 15 to 20 hours per company. Now it's one request. The edge was never about access. It was about depth. xBubble puts the depth on your side. Work mode → Buy-Side Equity Research SOP.

I have a hypothesis about the current 3D vs non-3D debate. Perhaps the endgame is that these two paths eventually merge. Non-3D approaches currently feel incredibly strong. Scale data, scale compute, scale parameters, and surprisingly quickly: 20 → 40 → 60 → 80. Realism improves. Motion improves. Benchmarks improve. But going from 80 → 100 feels fundamentally different. Physical correctness, multiview consistency, object permanence, contact dynamics, and long-horizon reasoning feel much harder. My intuition is that 0 → 80 is mostly a scaling problem, while 80 → 100 may be a world-state problem. Current large-scale data gives us enormous amounts of observations: pixels, videos, actions. But much less geometry, depth, pose, contact, physical constraints, or object state. As models become stronger, perhaps the bottleneck slowly shifts from “Can models fit the data?” to “Does the data contain enough world state?” This is why I think 3D matters—not necessarily as the final representation, but as data infrastructure. Multiview capture, simulation, synthetic interaction, counterfactual rollouts, state supervision. These systems don’t simply create more data; they create higher information density data. Which creates an interesting possibility: maybe non-3D systems win early because scaling observations is easy, while 3D systems catch up later because scaling world-state is harder. And eventually, perhaps the distinction disappears. Sufficiently strong non-3D systems may need to implicitly learn world structure, while sufficiently strong 3D systems must learn appearance, dynamics, and semantics. Perhaps the real question is not: 3D vs non-3D. But: How do we scale world states. Before the intelligence scale, the data engine needs to scale first.

The narrative that AI will wipe out enterprise SaaS overnight is one of the most misunderstood ideas circulating in markets right now, and the evidence does not support it (Save this). @DavidSacks made this case directly and the logic is worth working through carefully. Salesforce is a system of record debugged by millions of customer support tickets over twenty five years, stress tested across thousands of enterprise deployments and deeply embedded into revenue operations at the largest companies on earth. The idea that a CFO will replace that with probabilistically generated code from an AI assistant without compliance guarantees, integration depth, audit trails, and enterprise support infrastructure is not how these decisions actually get made. The market has been pricing in the existential version of this risk anyway and the results have been extreme. Over $1 trillion in SaaS market cap was erased in the first week of February 2026 alone. Global SaaS spending is still projected to grow from $318 billion in 2025 to $512 billion in 2028 which is not the trajectory of a category being killed. The operating reality is entirely disconnected from the stock price narrative. ServiceNow beat earnings nine consecutive quarters in a row and its stock crashed 11% on the same day. Salesforce raised its full year forecast to $41.5 billion on record results and the stock still fell. Sacks makes an important distinction between survivability risk and value capture risk. The survivability risk, enterprises ripping out Salesforce for AI generated software is largely overstated. The SaaS products genuinely at risk are narrow ones charging high prices for underused features with no proprietary data and low switching costs. The value capture risk is real and it is the more sophisticated threat. AI orchestration layers like Claude CoWork are being designed to sit above all of these tools pulling data from Salesforce, ServiceNow, and Snowflake simultaneously and owning the user's primary workspace in the process. If enterprise users move from living inside Salesforce to living inside an AI agent that calls into those systems on their behalf, the SaaS platforms do not disappear but rather become infrastructure. The expansion revenue, the premium pricing power and the next decade of value creation all migrate to whoever owns that orchestration layer.