MiniMax H3 Fashion Lookbook | Anime Character Reveal + Editorial MV Prompt🔥 Made this high-saturation anime PV with 13 fast visual beats in 15 seconds. A few years ago, this would’ve been days of AE work. Now? Prompt → generate → refine. Prompt 👇 Create a **15s, 16:9, 24fps anime character reveal trailer** with **13 fast visual beats**. Style: **Japanese anime opening × premium AAA motion graphics × fashion campaign**. The video should feel **explosive, sexy, stylish, bold and high-impact**, with **80% motion graphics and 20% character action**. ## CHARACTER LOCK — HIGHEST PRIORITY AO is a **young adult East Asian anime woman** with a sexy, confident Japanese anime aesthetic. She has: * small refined face * sharp expressive crimson eyes * glossy lips * confident, teasing gaze * slim feminine curvy figure * long elegant legs * stylish, cool, alluring presence Keep her identical throughout: * chin-length vivid red bob with messy bangs * crimson-red eyes * black choker * fitted red cropped top * short white cropped jacket, worn open * black mini skirt or fitted shorts * red belt detail * black thigh strap * white-and-red platform sneakers * subtle silver accessories Preserve the same face, body proportions, hairstyle, outfit, materials and colors in every shot. **Never redesign AO. Never change her face or outfit. If a shot becomes too complex, simplify the action first.** ## VISUAL STYLE Palette: **vivid red, crimson, white, black, silver**. Use: giant kinetic typography, red circles, diagonal slashes, manga speed lines, split screens, halftone dots, barcode strips, UI ticks, freeze frames, RGB flashes, impact shakes, poster layouts and graphic wipes. Every beat should feel: **fast, sharp, sexy, explosive, graphic and iconic**. Keep typography bold and readable. Editing: hard cuts, aggressive snap zooms, whip pans, speed ramps, freeze frames, impact shakes and foreground wipes. ## 13 VISUAL BEATS **01 | 0.0–1.0s** White field. Massive red circle slams into frame. Black bars slash across. UI ticks flicker. Giant **A**, then **O**, hit with heavy impact shake. **02 | 1.0–2.0s** The O becomes a circular frame showing an extreme close-up of AO’s crimson eye and glossy lips. She gives a teasing side glance. RGB flash. Circle bursts into red-and-white fragments. **03 | 2.0–3.1s** Black background, huge white **AO**. AO enters fast, turns sharply and power-slides beneath the typography. Red speed streaks trail behind her. Whip-pan out. **04 | 3.1–4.0s** Three red/white/black vertical panels. AO appears in three poses: hip turn, hair touch, over-shoulder stare. Huge vertical **FULL SPEED** moves behind her. **05 | 4.0–5.1s** AO jumps through a rotating typography ring reading **NO BRAKES / ALL EYES ON ME**. One clean mid-air spin. Snap zoom into her confident face. **06 | 5.1–6.0s** White editorial frame. Huge black **HOT** with a red slash. AO crosses the frame with a runway-like step, one hand at her waist. Typography compresses and rebounds. **07 | 6.0–7.0s** Bright red field with black diagonal stripe. AO performs one smooth fast turn. Three ghosted freeze positions trace the movement. Giant outlined **TURN** rotates behind her. **08 | 7.0–8.0s** Words hit one per beat: **HOT / FAST / WILD / RED** AO changes pose with each word: direct stare, hair toss, hip shift, confident forward lean. **09 | 8.0–9.0s** Black frame with manga perspective lines and a graphic grid. AO steps forward and freezes in a strong hero pose. Red circular target graphics lock around her. **10 | 9.0–10.1s** AO moves toward camera through three red-and-white graphic panels. Each panel shatters as she passes. Large **A O** fragments appear behind her. Finish with a hair or leg foreground wipe. **11 | 10.1–11.1s** Rapid poster montage: four frames of the same AO — close-up stare, walking, side pose, hands at waist. Add **01–04**, barcodes, halftone dots and sharp Japanese poster graphics. **12 | 11.1–13.0s** Hero moment on a clean white background. AO lands in a powerful fashion pose: one leg forward, one hand at her waist, chin lifted, direct eye contact. Huge red shockwave rings explode behind her. Typography fragments and speed lines burst outward. Hold an iconic confident freeze. **13 | 13.0–15.0s** Final identity card. Huge black **AO** on a bright white field. AO stands relaxed and alluring in front of the letters. Red circles, halftone, technical arcs and sharp speed accents surround her. Final red pulse flashes through the frame and ends on a hard stinger. ## ANIME STYLE Premium modern Japanese anime rendering: * clean cel shading * sharp linework * polished highlights * cinematic close-ups * dynamic perspective * fashion-editorial full-body framing * smooth hair and fabric motion AO must look like a **stylish adult anime heroine**, not chibi and not childish. ## AUDIO Hard-hitting **electro / future bass / anime-opening style music**. Use: heavy drums, bass hits, risers, glitch fills, synth stabs, typography slams, whooshes and shutter impacts. Build continuously. Peak at Beat 12. End with a sharp electronic stinger. ## PRIORITIES 1. AO identity consistency 2. sexy adult anime character design 3. red-black-white outfit consistency 4. maximum visual impact 5. readable typography 6. premium anime rendering 7. fast beat-synced editing Avoid: childlike proportions, chibi style, blue clothing, face changes, outfit changes, extra characters, unreadable typography, weak motion, dull compositions or generic schoolgirl styling. Final result: **explosive, sexy, red-hot, premium, graphic-driven and visually unforgettable.** Try MiniMax H3 on Ima Studio 👉 #MiniMaxH3# #MotionDesign# #Anime# #AIVideo# #ImaStudio#

OECD Forecasts US 2026 Growth Of 2.2% (Vs 2.0% In June), 2.1% In 2027 (Vs 1.8%) - World 2026 Growth Of 2.9% (Vs 2.8% In June), 3.0% In 2027 (Vs 3.1%) - China 2026 Growth Of 4.5% (Unchanged), 4.2% In 2027 (Vs 4.3%) - Euro Area 2026 Growth Of 1.0% (Vs 0.8% In June), 1.0% In 2027 (Vs 1.2%) - Japan 2026 Growth Of 0.8% (Vs 0.6% In June), 0.7% In 2027 (Vs 0.8%) - UK 2026 Growth Of 1.1% (Vs 0.9% In June), 1.0% In 2027 (Vs 1.1%)

US Change in Nonfarm Payrolls Sep: 29K (est 90K; prev 162K; prev R 133K) - Unemployment Rate: 4.2% (est 4.1%; prev 4.1%) - Avg Hourly Earnings (M/M): 0.1% (est 0.3%; prev 0.3%) - Avg Hourly Earnings (Y/Y): 3.0% (est 3.1%; prev 3.1%)

US Change in Nonfarm Payrolls Aug: 162K (est 55K; prev -23K, prevR 21K) - Unemployment Rate: 4.1% (est 4.1%; prev 4.1%) - Avg Hourly Earnings (M/M): 0.3% (est 0.3%; prev 0.1%) - Avg Hourly Earnings (Y/Y): 3.1% (est 3.1%; prev 3.2%)

Germany’s comeback may be starting earlier than expected. Deutsche Bank has doubled its 2026 GDP forecast to 1.0% from 0.5%, after a surprisingly resilient H1: GDP grew 0.4% QoQ in Q1 and 0.3% in Q2. Growth is seen accelerating further to 1.3% in 2027, helped by the fiscal push and a rebound in investment.

World Currencies vs. the U.S. Dollar One-Year Change As of June 30, 2026: 🇨🇴 Colombian peso: +19.2% 🇮🇱 Israeli shekel: +13.2% 🇭🇺 Hungarian forint: +8.9% 🇿🇦 South African rand: +8.1% 🇲🇽 Mexican peso: +7.7% 🇨🇳 Chinese yuan: +5.6% 🇦🇺 Australian dollar: +5.2% 🇧🇷 Brazilian real: +5.2% 🇲🇾 Malaysian ringgit: +3.1% 🇳🇴 Norwegian krone: +1.8% 🇨🇱 Chilean peso: +1.0% 🇪🇬 Egyptian pound: +0.8% 🇭🇰 Hong Kong dollar: +0.1% 🇦🇪 UAE dirham: 0.0% 🇶🇦 Qatari riyal: 0.0% 🇸🇦 Saudi riyal: -0.2% 🇷🇺 Russian ruble: -0.4% 🇨🇿 Czech koruna: -1.1% 🇸🇬 Singapore dollar: -1.7% 🇨🇭 Swiss franc: -1.8% 🇹🇭 Thai baht: -2.3% 🇸🇪 Swedish krona: -2.4% 🇪🇺 Euro: -3.0% 🇩🇰 Danish krone: -3.1% 🇬🇧 Pound sterling: -3.4% 🇨🇦 Canadian dollar: -4.1% 🇵🇱 Polish złoty: -4.2% 🇳🇿 New Zealand dollar: -6.8% 🇹🇼 Taiwan dollar: -8.2% 🇵🇭 Philippine peso: -8.2% 🇮🇩 Indonesian rupiah: -9.3% 🇮🇳 Indian rupee: -9.4% 🇯🇵 Japanese yen: -11.3% 🇰🇷 South Korean won: -12.6% 🇹🇷 Turkish lira: -14.6% 🇦🇷 Argentine peso: -18.9% Source: Deutsche Bank, Bloomberg Finance LP.

CHOOSE YOUR FIGHTER Warren Buffett vs Stanley Druckenmiller Buffett stepped down as Charman of Berkshire Hathaway $BRK.B today. Druckenmiller has never had a losing year in more than 30 years. Here are their full portfolios as of Q2 2026. BERKSHIRE HATHAWAY - Apple $AAPL: 22% - American Express $AXP: 17% - Google $GOOGL: about 12.6% across both share classes - Coca-Cola $KO: 11% - Bank of America $BAC: 9.2% - Chevron $CVX: 4.7% - Occidental $OXY: 4.3% - Chubb $CB: 3.9% - Moody's $MCO: 3.7% - Kraft Heinz $KHC: 2.6% - DaVita $DVA: 2.1% - Delta $DAL: 1.8% - SiriusXM $SIRI: 1.2% - VeriSign $VRSN: 0.8% - Kroger $KR: 0.7% - Liberty Live $LLYVA: about 0.6% across both share classes - Ally $ALLY: 0.4% - Lennar $LEN: 0.4% - New York Times $NYT: 0.4% - Capital One $COF: 0.2% - Louisiana-Pacific $LPX: 0.1% - Nucor $NUE: 0.1% - Macy's $M: 0.1% - NVR $NVR - Jefferies $JEF - D.R. Horton $DHI DUQUESNE FAMILY OFFICE - Natera $NTRA: 17% - Insmed $INSM: about 5.7% in shares and calls - Taiwan Semi $TSM: 5.4% - Brazil ETF $EWZ: about 5.1% in shares and calls - Amazon $AMZN: about 4.6% in shares and calls - STMicro $STM: 4.5% - S&P 500 Equal Weight $RSP: about 3.7% in calls and shares - Fox $FOXA: about 2.8% across both share classes - YPF $REPYY: 2.7% - CDW $CDW: about 2.7% in shares and calls - BBB Foods $TBBB: 2.3% - Google $GOOGL: 2.3% - Seagate blockstack:native: 2.3% - United Airlines $UAL: 2.1% - Sea $SE: 2.0% - NewAmsterdam Pharma $NAMS: 2.0% - Russell 2000 ETF $IWM: 1.9% in calls - Sandisk $SNDK: 1.5% - Revolution Medicines $RVMD: 1.4% - S&P 500 ETF: 1.3% in calls - Bitdeer: 1.2% - CRH: 1.1% - Delta: 1.1% - Tesla $TSLA: 1.0% in calls - Fluor: 1.0% - D.R. Horton: 0.9% - Coupang: 0.9% - AMD: 0.8% - Palo Alto Networks: 0.8% - Cleveland-Cliffs: 0.8% - Hut 8: 0.7% - Caris Life Sciences: 0.6% - Argentina ETF: 0.6% - Woodward: 0.5% - Meta: 0.5% in calls - Nuvation Bio: 0.5% - Protagonist Therapeutics: 0.5% - Roku: 0.5% - Cavco: 0.5% - ADMA Biologics: 0.4% - Hyperliquid Strategies: 0.4% - Rambus: 0.4% - Rhythm Pharmaceuticals: 0.4% - Champion Homes: 0.4% - Daktronics: 0.4% - PureCycle: 0.4% - Southern Copper: 0.4% - Linde: 0.4% - Entegris: 0.4% - Teva: 0.4% - Unity: 0.4% - Aeva: 0.4% - Riot Platforms: 0.4% - Qnity Electronics: 0.4% - Equinix: 0.4% - Lam Research: 0.4% - Definium Therapeutics: 0.3% - Belite Bio: 0.3% - 10x Genomics: 0.3% - Wabtec: 0.3% - Eli Lilly: 0.3% - Xenon Pharmaceuticals: 0.3% - Olema Pharmaceuticals: 0.2% - Repligen: 0.2% - Rocket Companies: 0.2% - Baidu: 0.2% - Arm: 0.2% - Carvana: 0.2% - Reddit: 0.2% - Alcoa: 0.2% - Thermo Fisher: 0.2% - Danaher: 0.2% - F5: 0.2% - Vista Energy: 0.2% - Skeena Resources: 0.2% - JBS: 0.1% - Monte Rosa Therapeutics: 0.1% - Relay Therapeutics: 0.1% - DBV Technologies: 0.1% - CCC Intelligent Solutions: 0.1% - UWM Holdings: 0.1% - Navitas Semiconductor: 0.1% - Solstice Advanced Materials: 0.1% - FTAI Aviation: 0.1% - Beam Therapeutics: 0.1% - Aurora Innovation: 0.1% - IREN: 0.1% - Grupo Financiero Galicia: 0.1% - Wave Life Sciences: under 0.1% Both own Google, Delta and D.R. Horton. Neither owns Nvidia $NVDA.

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️