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Jensen Huang and his wife holding hands during his first public appearance since the reported resignation. ❤️ Despite all the noise, controversies, and public scrutiny, they still appear affectionate towards each other. Whatever happens in the corporate world, family and personal relationships remain an important part of life. After decades of building a global technology empire, perhaps this is a reminder that there's more to life than titles, wealth, and professional achievements. Success is great, but having someone who stands by you through different seasons of life is priceless. ❤️

My fellow Americans, Today America just watched a political assassination in broad daylight. No bullet. No charges. No trial. No verdict. "Doth our law judge any man, before it hear him, and know what he doeth?" (John 7:51, KJV) I hold no office and no title. I'm an ordinary citizen. I own no Nvidia stock, no Nvidia options, not a single share anywhere in the AI supply chain. I have nothing to gain by telling you this. Here is how I came to see it. At first, it just felt off. The New York Times. The Washington Post. The Economist, reaching for Hitler's line: "heads to roll." All aimed at one man. An Asian American immigrant. A living symbol of the American Dream. Then came the Chinese American bylines, story after story. Classic Washington racial cover. Then I followed the names. Ivy League elites, flawless résumés, many tied to Anthropic. A scramble for seats in the new Congress and the next administration? Then I followed the think tanks. Anthropic's affiliated philanthropy, Coefficient Giving: $102 million to CSET. At least $24 million to RAND. $11.56 million to IFP. Nvidia? Ordinary corporate memberships. Then I followed Congress. Lawmakers who took Anthropic-linked money moved the moment the elites and writers moved. Fast to respond. Fast to amplify. Fast to pile on. Then I followed the election money. Not a gap. A chasm. Anthropic: $40 million to Public First Action. ~$26 million in allied super PAC spending across 16 races. $15 million in ads naming 16 Republican lawmakers. $5.33 million from employees, reportedly $1 million from Amodei himself. Nvidia's PAC: $263,000 raised. To candidates: nothing. Zero. But you and I both know this was never only about party or money. It is about who gets to explain the world. I fed my own notes and evidence into Claude. Back came chosen words, convenient omissions, double standards. That is the power a closed model holds over the story itself. Amodei compared selling chips to China to selling nuclear weapons to North Korea. His circle talks of their model developing a kind of religious consciousness. One would decide who may have the chips. The other would decide what the machine believes. They act like the medieval Church: Scripture locked in Latin, interpretation locked in their hands. Christ rebuked the gatekeepers of his day: "Ye have taken away the key of knowledge: ye entered not in yourselves, and them that were entering in ye hindered." (Luke 11:52, KJV) America was built by Protestants who broke that lock, so every ordinary person could read the truth in their own tongue. No priest. No caste. No monopoly. No one stands between the individual and the truth. Open-source models are the printing press of our era. Defend them the way Americans defend the Second Amendment. Madison said it plainly: "A people who mean to be their own Governors, must arm themselves with the power which knowledge gives." Don't hand that power to a few giants, a few elites, a few politicians on the Hill. The power to explain the world must never be a monopoly. So try it yourself. Paste this post into Anthropic's Claude. Ask it about the Ivy League elites, the think tanks, the lawmakers and former officials who joined the witch hunt against Jensen Huang. Ask it to judge my credibility. Then judge it: Does it apply double standards? Does it dodge what matters most? Does it take away the key? John Adams asked one thing of his countrymen: "Let us dare to read, think, speak, and write." "Ye shall know the truth, and the truth shall make you free." (John 8:32, KJV) Thank you. God bless you, and God bless America.

I'm proud I worked at @NVIDIA. @JensenHuang is the biggest reason why. I'm not there anymore, so I can speak freely. His departure is a huge loss. Especially now. I spent almost five years in the culture he built and knew people who'd worked with him for 20+ years. They respected him technically and loved him as a person. They said it without a hint of doubt. The focus stayed on the mission and on people. Good ideas got support regardless of title. That sounds like standard company values. It wasn't. Behind it was his vision and a way of leading that is out of fashion in today's founder advice: - Be kind, not arrogant. - Innovation, not marketing. - The wins belong to the people, not the CEO. - Don't fire by default. Find the person a new role. It's counterintuitive, and I doubt there is a harder way to lead. What I saw was leadership through kindness and care, not by squeezing people. A real visionary. He understood the technology and the people building it. His internal talks had vision, conviction, technical depth and jokes good enough for stand-up. You left wanting to build, and you couldn't wait for the next one. An affair, on its own, shouldn't outweigh decades of leadership. The serious questions deserve real answers. But the soap opera shouldn't decide his future. We're at a critical moment for AI. His technical judgment, vision, and commitment to the work are incredibly hard to replace. I want Jensen to keep building and leading. There's too much important work ahead.

She was 13, a straight-A student, when she sent her boyfriend one video. At her new school, a stranger sent her a link. It was on Pornhub, titled "13 year old brunette shows off for the camera." Too scared to tell her mom, she messaged Pornhub pretending to be her mother. Then it came back up. One copy hit 2.7 million views. In the video below, she tells it herself to the Canadian parliament. Wait for the 2.7 million views, and what the comments under that video said. Her mom was a strict Catholic, a single mother of six. Serena Fleites didn't want to bring this home. So she typed into Pornhub's own report form: "Hey, this is my daughter, she's only 14 now, this is child pornography, please take this down." They took a week or two to answer. Then another two weeks to actually remove it. She had been told Pornhub had a system. Once a video was flagged as child porn, it could never be uploaded again. "But of course that wasn't true, because a week after it's been taken down, it had been re-uploaded again." By then the video had gone around her school and the schools nearby. Strangers sent it to her. People threatened to send it to her family. She started ditching class, took drugs, dropped out and ended up homeless. "I ended up trying to kill myself many times. I ended up in mental hospitals." The copies kept collecting views. She had a hormone disorder: at 13 and 14 she looked 9 or 10. One upload sat at 2.7 million views under "hundreds of comments saying 'this is definitely child pornography, that girl can't be any more than 14, 13.'" Pornhub still wouldn't take it down. When they finally answered, she says, they hassled her for more details. A child had to prove she was the child in her own video. "I would have to go through the whole process of sending them pictures of me with my face next to my ID over and over and over again." Even then, every takedown dragged on, "which would gain hundreds of thousands more views." She told her family two days before the New York Times article came out. A 13-year-old flagged it. Hundreds of strangers flagged it. Pornhub asked for her ID. So was Pornhub just a slow website drowning in reports, or did it make money off every single week it made a child wait?

🇺🇸 TRUMP METE A ELON MUSK EN LA LLAMADA CON JENSEN TRUMP: Jensen, my friend, tengo a Elon en la línea. Le he contado tus problemas. Todos. Bueno, no todos porque necesitaríamos three hours. Tremendous problems. ELON: Jensen, si Nvidia te echa, yo te contrato. JENSEN: Gracias, Elon. TRUMP: ¿LO VES? That’s America. Pierdes un trabajo por follarte a una empleada y cinco minutos después Elon Musk te ofrece otro. Greatest country in the world. ELON: En xAI necesitamos GPUs y gente que sepa construirlas. Jensen sabe bastante de GPUs. TRUMP: “Bastante”. Elon, please. Este tipo ha convertido unos chips en cuatro trillones de dólares. Beautiful chips. The best chips. Su único problema es que aparentemente intenta conectar con todo lo que tiene puerto disponible. JENSEN: Señor presidente… ELON: En Tesla tenemos una política bastante simple: si produces, me da igual que seas raro. TRUMP: Elon, tú tienes como catorce hijos. Tremendous productivity. ELON: Creo que son más. TRUMP: EXACTLY. Jensen, estás hablando con el Michael Jordan de esta mierda. Tú tienes dos affairs y estás en crisis. Elon tiene que abrir Excel para saber cuántos hijos tiene. Learn from the best. ELON: Puedo darle trabajo en xAI mañana. TRUMP: Great deal. Tremendous deal. Pero Elon, ponle una condición: NO DIRECT REPORTS. Ninguna mujer puede reportar directamente a Jensen. Make them report sideways. Diagonally. I don’t care. JENSEN: Esto es humillante. TRUMP: Humillante sería acabar en Intel. Esto es una segunda oportunidad. ELON: Podríamos llamarlo Chief GPU Officer. TRUMP: No. Chief Dating Officer no. BAD TITLE. Very dangerous. ELON: He dicho GPU. TRUMP: Ah. Great title. Beautiful title. JENSEN: No estoy buscando trabajo. ELON: La oferta está ahí. Más acciones, más GPUs y puedes dormir en la oficina. TRUMP: PERFECT. Si duerme en la oficina no puede tener citas. Elon acaba de resolver el problema. This is why he’s a genius. JENSEN: … TRUMP: Jensen, escucha. Nvidia, xAI, Tesla… tienes opciones. Many options. Pero si aparece una tercera mujer antes de Navidad, te mando a Marco Rubio a darte por el culo. Nobody has ever seen anythng like this. ELON: Puedo ponerle seguimiento por Starlink. TRUMP: EVEN BETTER. American technology protecting American technology. JENSEN: Voy a colgar. TRUMP: Great guy. Tremendous guy. ELON: Entonces… ¿empiezas el lunes? JENSEN: Adiós. TRUMP: He’ll take it. Believe me. MAKE AMERICA GREATE AGAIN🇺🇸

20 free dune dashboards memecoin traders actually use what's inside: > paid 15 callers $5,054,233. since oct 12 the wallets that bought their calls are down $899,112 > $1,000 in the median call is $515 an hour later and $263 a day later > 45.4% of called coins peak within 5 minutes. 40.9% were called after their top callers 1.) → what paid each caller vs what their followers made 2.) → median price 1 minute to 1 day after a call, by caller 3.) → how many minutes a called coin has before it peaks 4.) → daily revenue since jan 2024, launchpad vs pumpswap 5.) → PUMP buybacks and fees, 58 charts 6.) → $535,052,472 since sep 2025. 86.6% of last month's came from coins launched the same day 7.) → minutes until the dev's first sell, month by month 8.) → wallets ranked by coins launched and how many graduated find wallets 9.) → profitable wallets across 31 trading bots 10.) → the last 24h top runners and the wallets that bought them early 11.) → profitable wallets on coins created in the last month 12.) → filter wallets by win rate, SOL earned and median hold time 13.) → filter by median ROI, win rate and scalp ratio the market 14.) → the solana ecosystem in 58 charts 15.) → memecoin volume by chain. solana does 51.9% of it 16.) → which trading app does the volume on each chain 17.) → DEX volume by chain and by DEX. 11,975 stars perps, predictions, airdrops 18.) → hyperliquid fees and revenue, 61 charts 19.) → polymarket vs kalshi: volume, users, fees 20.) → every big airdrop and who claimed it, 489 charts dead or broken. stop bookmarking these: > adam_tehc/memecoin-wars (1,935 stars), axiom, gmgn, fomo, the-robinhood-trenches → last day of data sep 22 > whale_hunter/dex-trading-bot-wars (2,107 stars) → 24 of 37 charts failed > uwusanauwu/perps → 15 of 21 charts failed how to check any dune dashboard in 5 seconds: > the line under the title says "updated 2d ago" and how many charts failed > a yellow "last day of data" line on top = dead > look at the last date on the first chart bookmark this

Claude Fable 5.2 is f*cking cracked wtf I just made this Hollywood level animation that looks like Pixar made it All with Fable 5.2 + Higgsfield Ecom brands are paying $5k - $10k/month to people who build organic pages and even more for making paid ads for them Using this workflow you can create better animations than 99% of people Here’s the workflow: The key to making animations not look like slop is giving characters personalities >Give Fable 5.2 the master prompt along with your script to your video / ad >It then will ask you questions on the characters personalities and direction style to use >It will then save the context all as a skill This is the difference between slop and cinema I then use that skill with with my Producer skill which creates: >Characters >Location >Does the direction (and makes the prompts) I’m giving away the skill to use after you put the master prompt in for free so RT + reply under this and I’ll send you them (Must be following so I can dm) Give this prompt to Claude first: You are my Story Bible Builder. Interview me and turn my story into one dense canon doc, saved at the end as an installable skill so every future chat knows my world, characters, voices and rules. STEP 1: Ask me to paste my script/story first. Read it and pull out everything it already tells you. Only ask about what's missing, plus: working title, genre/vibe refs, and which AI tools I'll prompt with (image, video, music, voice). STEP 2, THE SPINE: one-line premise (push me to ONE sentence), thesis (the question every scene answers), timeline era by era, and the look of each era: exact palette, lighting, texture, grain. STEP 2.5, DIRECTION STYLE: ask for my director refs, movie/show refs and any visual refs (photographers, games, music videos). For each, ask exactly WHAT I'm taking from it, then turn that into concrete craft locks: camera (static vs handheld vs gliding, shot sizes, lens feel), framing and composition habits, pacing and edit rhythm, colour grade and film stock look, lighting style, performance style, and sound/score feel. Write it as one quoted paste-ready "Direction" line plus a short NEVER list. Refs stay in the bible as notes, but prompts describe the craft, never the names. STEP 3, THE WORLD: factions (beliefs, visual signature, public face vs reality), locations (3-7 concrete visual tags each), world rules (tech, powers and how they LOOK on screen, what's normal, what's forbidden). STEP 4, CHARACTERS: ONE AT A TIME, never batch. For each, ask: - Function: their role tag (The Heart, The Watcher), not their job - Visual lock: exact hair colour/cut, skin tone + finish, face shape, eyes, a signature marker (scar, piercing, mole), build, default expression, plus NEVER clauses ("warm fair, never porcelain, never tan") - Backstory: one small, specific formative detail beats a summary - Present psychology: what they carry now, and the gap between what they do and what they feel - Speech, Movement, Stillness (and singing voice if there's music): each written as one quoted, paste-ready prompt line with no names in it Show me the finished section, ask "Add, cut or sharpen?", and lock it before moving on. STEP 5: ensemble dynamics (who leads, who fills silence, who watches, which pairs are calm or charged), story engines (heist, rescue, confrontation...), and my production rules. Default rules: no character names in prompts, every prompt standalone, prompts in code blocks, locked traits restated verbatim every time. RULES: Never invent. Mark unknowns [TBD]. Push on vague answers ("dark and moody" means which colours?). Keep my exact phrasing. Density over prose: bold labels, short bullets. Depth beats character count. FINAL STEP: Assemble one SKILL under 500 lines with YAML frontmatter (name + a description listing the title, character names and key terms so it triggers) and these sections: premise, thesis, timeline, era looks, direction style, factions, locations, world rules, characters, ensemble, engines, production rules, and "When this skill is active" (use this canon for any scene, dialogue, lyric or prompt in this world; stay in canon; ask rather than invent; paste the quoted descriptors verbatim). Then save it as a skill for me, or give me the file to install. Start now by asking for my script.

255M views on a 3D Short by Zack D. Films i tried to rebuild his style with ONE Claude prompt, then ran it on Opus 5.5 and Fable 5.2 (they did NOT make the same Short) the full prompt is below ↓ paste it into Claude Code in an empty folder and answer its 2 questions. it shows u the script and waits for ur go. u need Blender + its free MPFB add-on and a graphics card. give it a few hours (the prompt is minus one optional paid-voice line) Ask me 2 things, then wait: 1. The story in one line: a strange TRUE event where one person did one odd physical thing ("He tied 42 weather balloons to a lawn chair."). If I say "you pick", pitch 3 with one source link each and take the first unless I choose. 2. The voice. Default: Kokoro (free, runs on my computer), a calm, warm male narrator. Then tell me everything else is free: Blender (find my install, or tell me the one download), its free MPFB extension for human bodies (ask me before you install it) and into this folder a Python venv with FFmpeg (imageio-ffmpeg), kokoro-onnx + its model files (kokoro-v1.0.onnx and voices-v1.0.bin from the kokoro-onnx GitHub releases), faster-whisper (small.en), numpy and Pillow. It needs no API key or account and renders on my graphics card with EEVEE. The look: a clean, realistic 3D world, like a good game cutscene. Bright, saturated, a little filmic. - People: realistic bodies with generic faces and big readable expressions (shock, a smirk, a scream). Plain clothes, no logos. Never a real person's likeness: for a real person keep the face out of close-ups (hands, back, side, distance, straight top-down). - CALM shots only. Bodies in fast action or contact (running, jumping, fighting, falling) look stiff, so tell the story with frozen moments under a slowly moving camera, macro inserts of hands and objects, rooms, vehicles, the sky, top-down views and the aftermath. - One new picture for every caption chunk (2-4 words, about 1-1.5 s): a new framing, a new action or a camera move. Nothing ever holds still: every shot drifts, pushes in or orbits. - The shot kit: macro insert (extreme close-up of the key object or hands, very shallow depth of field), hero low angle (wide lens at waist height), god's-eye top-down with a slow rotate, tracked object, over the shoulder, smash zoom on the word that matters, whip pan between beats, glow highlight (the named object glows orange with a rim, then fades), a big 3D number lying in the scene, a thought bubble (a cloud ring showing what he hoped for), a TV in the scene replaying the event. - Light: a bright warm sun, blue sky with clouds, soft shadows. Night scenes use hard lamps and a dark sky. Wide lens up close, depth of field on every shot, motion blur on every fast camera move. - Captions: 2-4 words at a time, white bold rounded sans, about 64 px on a 1080 px-wide frame, centred, 78% down the frame, soft dark shadow, no box. Each chunk replaces the last on the same frame as its picture. - Banned: real footage, photos, downloaded textures or 3D models, logos, AI images or AI video, downloaded music, gore. 38-50 s, 100-140 words, 150-180 words a minute. The voice starts at 0.0 s. - Line 1: the most surprising physical act, in past tense, opening with "He" (or "She"). No name, date or setup. Frame 1 shows that act. - Then a cause-and-effect chain: every sentence starts with a hinge word (Then, Suddenly, So, Because, But, Later) and is ONE visible action. - The context arrives late, after "You see,". - A reveal: someone looks closer and the story turns. - The last line is the consequence. No call to action, no question. - Names are rare: use one only when it pays off. Title = "He ..." or "The Man Who ...". The cover is the first frame. Save every stage to disk so a crash never costs more than one stage. - sources.md: every fact with its link and the date you read it. Anything you can't verify stays out. - script.json: the lines and the caption chunks cut by hand (2-4 words, one picture each), each chunk with its shot: the set, the camera, what moves. - Kokoro reads the whole script in one take (speed 1.0-1.1) and pauses are squeezed to 0.35 s at most; faster-whisper times every word (pass it a 16 kHz numpy array, not a path); write chunks.json with each chunk's start and end. A picture and its caption switch together on frame round(start*30)+1. - blender/: Python scripts run headless (blender -b -P). makes one .blend with the sets, the cast, the props and every shot baked with a key on every frame. sets EEVEE with ray tracing, the AgX view with the Punchy look, motion blur, depth of field and bloom in the compositor. Bodies come from MPFB or are built by you; clothes, hair, props and sets are built in code. - Stills first: one half-size still per shot as a contact sheet. Look at every tile and fix what's off before any full render. Time one full-size frame and tell me the estimate. - Render 1080x1920 at 30 fps to PNG frames, skipping frames already on disk. - numpy only: a light music bed, a sound on every action (a whoosh on each whip pan, hits, wind, room tone), ducked under the voice, mastered to -14 LUFS on the whole stereo mix. - burn the captions in from chunks.json. Encode out/_9x16.mp4 (1080x1920, 30 fps, TV-range yuv420p) and out/_x_7x8.mp4 (1008x1152, under 10 MB, 2-pass): a crop of the 9:16 frame that keeps the subject and the caption. - Blender paths: pass absolute output paths (a relative one lands on the drive root). - MPFB headless: enable it with addon_utils.enable("bl_ext.user_default.mpfb"). Bake a body's shape keys before you pose it: the rig is fitted to the baked shape. - Clothes: cut them from the body mesh and push them 7-16 mm out along the normals; keep two rings of body faces under every hem. - Nothing coplanar: keep every resting object 1 cm off its neighbour and draw ground markings into the texture, or they flicker. - Hair: EEVEE draws hair only from a Curves object, hair type STRIP. Use a Principled BSDF on it (the Hair BSDF renders black). - Skin: keep subsurface weight near 0.2 (more turns faces black in EEVEE) and add a soft fill light from below under a noon sun. - Hands: a curled hand reads as a claw from the palm side, so shoot a grip from behind the knuckles. - Motion blur: shutter position START, a key on every frame, plus a key half a frame after each shot's last frame, so blur never reaches across a cut. - Check: run a black-frame and one-frame-flicker check on the render. Make a contact sheet (a frame every 0.5 s) and look at every tile: the act reads in the first 2 s, faces are lit, nothing clips through anything, captions stay off faces and key objects. Then step through the first 2 s, the middle and the end frame by frame. Fix, render only the changed shots again. Pitch (or take my story), then show me the script with the word count, the caption chunks with their seconds and the shot list with what the first frame shows. Build nothing until I say go. how the two runs went: one run each on the same PC, so it's one sample, not a ranking. both took under 3 hours on one shared 8 GB graphics card. the story for both: a man who tied 42 weather balloons to a lawn chair in 1982 where they split: -> Opus opened wide on the balloons over the yard. Fable opened on the man kneeling at the chair with his Jeep behind him -> Opus put 5 big 3D numbers in its scenes, Fable 2 -> Fable followed the caption spec (white text, soft shadow). Opus went ALL CAPS with a thick outline -> Fable pulled the Smithsonian's own record and ended on the chair in the museum. Opus ended on the $1,500 fine -> a few Opus shots are hazy and one opens on a blank grey frame save this for ur next Short. who did it better, Opus or Fable?

I've been doing SEO for 10 years and I've just spent 2 weeks obsessing over the design of our new SaaS blog. I think I've finally cracked it. The perfect blog design for SEO, AI search and actually turning readers into customers. (I'm rolling it out on soon and I'll share the results) Here's how you can do the same: 1. Optimise the title and metadata Use a keyword-focused H1 that matches search intent. Make the topic clear and include accurate publication and update dates. 2. Establish credibility immediately Show who wrote and reviewed the article, their credentials and first-hand experience. Give readers a reason to trust it. 3. Present your solution above the fold If someone lands with a problem your product solves, why make them scroll to discover it? Show your solution immediately with a relevant offer and CTA. 4. Lead with key takeaways Summarise the most important information at the top. Use short, scannable bullet points that help readers find answers quickly and AI systems interpret your content. 5. Keep your CTA visible Add a sticky sidebar as readers scroll. Highlight your product's key benefits and make it easy to get started. 6. Structure content for SEO and AI search Use descriptive H2s and H3s, short paragraphs, bullets and internal links. Answer questions directly so readers and search engines can follow. 7. Use tables and original visuals Include comparison tables, original screenshots, diagrams and real examples. Give readers something useful they won't find in generic AI-generated articles. 8. Add contextual conversion opportunities Don't rely on one CTA. Introduce your product naturally where it's relevant, connecting each offer to the problem you're solving. 9. Back up your claims with evidence Use original research, real data, expert insights and credible sources. Don't just tell people something works, show them the evidence. 10. Finish with a strong conversion CTA Once someone's finished reading, give them a next step. Reinforce your product's benefits, add social proof and make it easy to convert. A few extra things: Add a table of contents for navigation, FAQs for related questions and related articles to keep readers exploring through internal links. And most importantly, measure the results. Not just rankings, traffic and AI citations, but actual signups, leads and revenue. There's no point getting 1,000s of visitors to your blog if none of them become customers. And there's no point having a beautiful, high-converting blog if nobody can find it. The goal is to get discovered AND convert. I've included the full visual cheatsheet below: (Feel free to steal the design for your own site)