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The spy hoax flopped. So they pulled a new rabbit out of the hat. Right when people finally started seeing the witch hunt against Jensen Huang for what it is— right when the sympathy turned— the Ivy League spin doctors ran the same play again. Same quiet weekend. Same meaningless signature stunt. Same goal: kill the heat and let Anthropic (@AnthropicAI) walk away clean. Please. I could sign Putin and Kim Jong Un onto that standwithjensen list myself. And you know what hurts? It worked. Again. I'm nobody. No clout. No machine. No PR firm. But I have a conscience. And my money goes where my conscience goes. Zero shares. Zero derivatives. Zero exposure. Not me. Not anything I own. Not a dime for $ANT.

I am actually disgusted by the timing of this drop. And this will get a bit more technical. Let’s do a real technical teardown of this "1,385-sigma Blackwell weight fingerprint" report that OpenAI and Anthropic are breathlessly promoting today, because banking on nobody reading past the headline is how DC narratives get manufactured. ​First, look at the actual CUDA maths: when PyTorch initialises model weights (torch.nn.init) with a fixed PRNG seed, curand/Philox parallelises random number generation across the GPU’s Streaming Multiprocessors (SMs). Because Hopper has 132 SMs and Blackwell has 148 or 152 SMs, the exact same seed lays out initial weights in a different spatial pattern depending on the GPU’s SM count. In huge MoE models, certain low-gradient null-spaces or unrouted slices preserve faint directional traces of those step-0 weights even after training. ​So yes, brute-forcing the seed and SM grid can show that a model’s step_0 weight initialisation script ran on a 148/152-SM Blackwell GPU. Now read what the summary explicitly admits in the fine print: 1. Initialisation is not full training: The report literally states that these signatures show how the weights were created, and "support (but do not prove) the inference that Blackwell also participated in model training". Initialising a model checkpoint takes seconds on a single rented dev node before loading the safetensors onto a standard Hopper cluster. The "2,000 to 3,000 Blackwell GPUs" and "$500 million" cluster numbers do not come from the weight fingerprint and Depue admits in his own thread that he simply "estimated" cluster sizes from release schedules and model details. ​2. Zero geolocation and zero proof of smuggling: The final sentence of the executive summary explicitly admits that the findings "do not establish the physical location of the chips, how they were acquired, or whether export control regulations were violated". Depue himself rented a cloud GB200 instance just to run his verification test. Commercial labs that are not on the Entity List renting bare-metal Blackwell nodes in offshore clouds (Singapore, Japan, or the Middle East) is completely legal under BIS rules and leaves the exact same SM fingerprint. Now ask yourself why a former OpenAI researcher dropped this on November 12, the exact day Jensen Huang was pushed out, with OpenAI and Anthropic immediately stepping in to "independently verify" it. US closed-source labs are watching their paid API moats get eaten alive by free Chinese open-weights models. Conflating a step-0 PRNG initialisation seed with a "$500M smuggled Blackwell cluster" hands DC hawks the exact pretext OpenAI and Anthropic want to regulate global cloud compute and kneecap open-source AI.

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.

The NBA mourns the passing of Dan Hicks, whose distinguished broadcasting career included contributions to NBC’s NBA coverage. He was one of NBC Sports’ signature voices for golf, football and the Olympics. We extend our deepest condolences to his wife, Hannah Storm, their three daughters and his friends and colleagues.

Jensen Huang is effectively OUT of NVIDIA🚨 Man His wife is 65 years 🇺🇸 They have been married for 41 years. As early microchip designers, she reportedly earned more money She totally left engineering work to raise their two children while Jensen Huang built Nvidia She was one of only three women in a class of 250 electrical-engineering students at Oregon State University when she and Jensen met. Jensen was 17 years when he walked up to the 19 year old Lori (his wife) and asked "Do you want to see my homework?" He promised that if they studied together every Sunday she would get straight A's, turning homework sessions into weekly dates. He also told her he would be a CEO by age 30. A promise he kept when he co-founded Nvidia in 1993. Jensen has repeatedly called meeting her "the most important, single event of Oregon university, and of my life" They married in 1985, the same year she graduated with her electrical-engineering degree. In 2022 the couple gave $50 million to Oregon State University (the campus where they met) to create a major innovation complex named after both of them. Jensen has said he does "zero percent" of his own shopping and that Lori (with their daughter) chooses his clothes, including the signature black leather jacket. He has publicly credited her with being "part of Nvidia every step of the way" and with handling the personal and financial details of their life. It's quite sad what is happening currently and how shattered Lori Huang must be.

Our company has a rule: any purchase over $500 needs a director's signature. Directors take about six weeks. Our coffee machine broke in January. A new one costs $996. We asked. "Six weeks." By week three, someone was drinking instant coffee out of a soup bowl. So my manager had an idea. She placed two orders. Same day. Same supplier. "Coffee machine: top half. $498." "Coffee machine: bottom half. $498." Both approved automatically. It arrived on Thursday. We were drinking real coffee by 10am. On Friday, the finance director walked into our kitchen holding two invoices...

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.

New on Tera: stop-loss & take-profit Protect what you hold. Set a floor, set a target, and let Tera watch the price. How it works: • Open any token you hold and tap Protect • Set a stop-loss to cap your losses and a take-profit to lock in gains, or both • Tera suggests levels from your average cost and shows your P&L at each one • Set together, they're linked: whichever is reached first cancels the other • When a level is hit, Tera tells you. Tap Sell now, check it, sign What makes it different: • Take-profits are enforced on-chain. Your sale can't fill below your target. • Stop-losses sell at the live price, protected by your slippage limit. We tell you exactly how far the price is below your stop before you sign. No hidden surprises. • Nothing sells on its own. Tera never holds your keys, so every sale waits for your signature. • Your orders stay encrypted on your device and are never sent to our servers. Find it under Protect on any token, or More Profit & loss, in the Tera app. The agent can think. Tera checks. You approve.

NEW: President Trump dances his way off stage to "Y.M.C.A." after delivering remarks at the Bethlehem Sparrows Point Shipyard in Baltimore. The president brought his signature moves to the stage after highlighting the launch of construction on what he says will be the largest new U.S. shipyard since World War II.