与「HITGS」相关的搜索结果

🎬Scene stealer #HITGS# #힛지스# #VV# #비비# #HYERIN# #혜린# 🔗 🔗 🔗 #BOYNEXTDOOR# #보이넥스트도어# #BND# #리우# #RIWOO# #The_Action# #Hollywood_Action# #Hollywood_Action_Challenge#

Unpaused MachiPool. Use $MachiPool coin for spins. About 8 usd per spin. Let’s go til someone hits a mutant or ape. Bubblegum Zombie Ape is swimming in the pool now.

You're right that cost efficiency is what actually decides usage at scale. Hydrogen hits near-frontier scores at the lowest price and highest speed of the group, so it leads for most practical work. Carbon still takes pure hard-task capability when the budget allows. The rest sit higher on the price curve for smaller gains.

The Binance Button Game is back, Anthropic Edition! Play for a chance to win $30,000 in Anthropic bStocks Token $ANTB or USDC . Be the last to click before the timer hits 00:00 and the prize is yours. 👉

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.

We’re rolling out Orchestrator in @Antigravity to Google AI Ultra subscribers, letting you run multiple models in a single session instead of routing the entire task to one model. Orchestrator pairs a frontier model with a fast execution model: Carbon sets the plan, hands off code edits and testing to Hydrogen, and steps back in to repair the plan if it hits friction — reaching near-Carbon task success rates on Terminal-Bench 4.0 while consuming 24% less quota and completing 19% faster on average.

We’re on track to hit $2B in annualized run rate in the next 3 months. We crossed $1B and got faster. In October we hit $1.35B. Higgsfield is the third-largest independent, private AI-native company by annualized revenue, behind only Anthropic and OpenAI. When they go public, Higgsfield becomes the largest AI-native company still private. AI video is going to be a trillion-dollar market. This is only the beginning.

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.

In May, @alexmashrabov CEO @higgsfield_ai and I did a deep dive at SaaStr AI on how Higgsfield rocketed to $600m ARR in record time. Just a few months later, I had to update the thumbnail to $1B ARR Now, they're on track for $2B by December Proud to be one very first customers & investors @saastrfund

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?