Shipped. Minimax H3 EZLaunch. One install script. Full optimized... stack for RTX 3090 and RTX 4090. Windows AND Linux. Ships with every model you need. T2V, I2V, and Ref2V all supported. 5 second clips in about 90 seconds. 15 second clips in under 8 minutes. Zero OOMs across 80 minutes of continuous generation. Same stack that held clean across all 10 space battle renders. What it actually does: - Downloads and configures the correct weight set automatically: pruned INT8 UNET with ConvRot, Turbo LoRA (8-step quality band), Qwen3-VL text encoder in NVFP4, MiniMax video + audio VAEs. No hunting through HuggingFace for the right quants. - Full mode coverage. Text-to-video out of the box. Image-to-video with first/last frame. Reference-to-video for style-guided generation. O - Patches SageAttention v2 sm89 to Triton INT8. - Enables kitchen CUDA with FORCE_CUDA on torch cu128 + driver 580 - Full flag set applied: lowvram, disable-smart-memory, disable-pinned-memory, disable-cuda-malloc, fp16-intermediates, CLIP on CPU - Timed smoke test runner included. Verify your stack without guessing. - Cross-platform installer. Same script, same results, Windows or Linux. The weight selection alone saves people hours. The Sage patch is the difference between launch-fail and sub-90 second renders on Ada. Nobody had either documented publicly. Now it is one command. Try it on similar cards and tweak if you want. MVP. More GPU configurations coming. Stay Tuned. Repo link in reply.show more

gemini omniflash is actually f*cking cracked. you can animate/edit... any video with a text prompt. character swaps, object transforms, full environment changes without regenerating/rotoscoping. everyone using AI to to animate and edit videos right now hits the same wall. the clip comes out 90% right and you regenerate from scratch hoping the 10% fixes itself. it never does. the fix is using your video as the input. omniflash edits what's already there instead of rolling the dice again. here's what's in the system: > the two-layer premiere trick: generate the same shot twice (one with background removed), stack them, cut at one frame, instant scene change > character swap with a single reference image (plus the one line you need or the model keeps the original's features) > object transforms that leave the rest of the frame untouched: stone into glowing sphere, candles into flowers > style transfer from an image reference instead of text, way more accurate > why stacking edits in one prompt breaks everything and the exact step order that doesn't > the audio limitation nobody mentions and how to work around it i packaged every prompt, the edit sequence, and the premiere layering setup. RT + reply "OMNI" and i'll send it over.show more

Google dropped a new AI paper called LUMIERE. It's... remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:show more

An hour of live AI video generation now costs... $10.80 At $0.003 a second, Vivix W1 generates a full minute for just 18 cents. That price matters because of what W1 is. It's a real-time interactive video model. Through the API, it keeps generating video while the experience is happening, and you can shape what comes next. With a normal clip generator, you pay once and you're done. Real-time interactive video keeps generating as you use it. Every second generated is another second you pay for. At higher per-second rates, that bill can grow quickly. It's hard to ship an interactive product when every user session costs a fortune. With W1, the math starts to look workable. And they didn't buy that price by cutting quality or speed. In Vivix’s supplied tests, eight-second videos averaged 7.7 seconds to generate from text and 8.4 seconds with one reference image. In the side-by-side comparisons, W1 holds its own against Seedance 2.5, Kling 3.0, and H3 Max on the same prompts. An exploding taxi, a skyline shot, a robot serving drinks on a plane. It's not perfect yet: • Cinematic fidelity still has gaps • Complex motion and dense scenes still need work • Full-scale training is still underway, so this is an early checkpoint How to try it: • Real-time generation is live through the API today • The browser playground does offline generation for now. Submit a prompt and you get a finished clip. Real-time browser tools are coming soon My take: I've seen a lot of interactive video demos that would cost a fortune to run in production. This looks priced for something you could actually ship. Once a minute of live video generation costs 18 cents, interactive stories, games, and characters could become an everyday medium instead of a tech demo. What I want to see next from Vivix.AI: better complex motion. Improve that and the use cases open up fast. Put your project’s runtime into the equation 👇show more

everyone's sharing motion graphic videos that Opus 5.5 made,... and it's genuinely insane everyone says they created it with "one prompt", but my one prompt video looked mid so i went through a bunch of these videos to see how they were actually made, and found the workflow that works here's how to generate pro level motion graphic videos w/ opus: 1. get reference videos to direct from -> pick 1-2 videos whose style you want and tell opus to match them. naming a style works way better than describing one without a reference, opus falls back to its default look: centered text, gradient background, everything fading in that's why so many of these videos look the same. a reference gives it the pacing, the type and the transitions to copy 2. install HyperFrames or Remotion so opus can build the video both let opus write every scene as code and render it straight to mp4. no video editor without one, opus can only describe a video or hand you a rough html page you have to screen record with it, every frame is exact, and when you ask for a change it edits one line and re-renders instead of starting over 3. install 21st for high quality components in the video real buttons, cards and UI components made by design engineers, instead of whatever opus invents on the spot without it, opus draws your product UI from scratch and it looks off. wrong spacing, placeholder boxes, fake-looking buttons anyone who's used good software can feel it in a second, and the whole video reads as cheap 4. steps 1-3 were context + setup. now dump all of it into opus your brand (logo, colors, fonts), screenshots of your real product, the reference video, and a quick braindump of how you see the video then ask for 3 storyboard variants without this, opus guesses your colors, your font and what your product even does. the video could be for any startup with it, it could only be yours. and 3 variants means you pick a direction instead of fixing the first idea it had 5. pick the storyboard you like ask for one still frame per scene before anything moves. fixing a storyboard is way cheaper than fixing a render without this step, you only find out scene 4 is wrong after the whole thing is animated, and every fix means re-rendering. a still frame takes seconds to change 6. let claude cook then give notes like a director: "slow every zoom to 0.7x", "hard cut here", "push in on the button" without notes, the first render is usually 80% there, and that last 20% is what makes it look pro. vague notes like "make it better" get random changes. camera words get exactly the change you want everyone has the same model. the context you give it is what makes it look pro let it cooookkshow more

🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR... AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )show more

llama.cpp isn't just for text LLMs anymore. Pure C++... zero shot voice cloning just officially landed in mainline. Text generation was only step one. If you’re building autonomous local AI agents, real time voice assistants, or edge workflows, instant low latency audio is the missing piece. Thanks to PR #26254, Alibaba’s state of the art Qwen3 TTS model family is now natively supported directly inside the llama.cpp repository under the multimodal (mtmd) framework. No Python bloat. No massive PyTorch CUDA overhead. Just raw, hyper optimized C++ running GGUF voice weights. Here is why this native update is a massive deal for the open source local AI stack: # Multimodal Architecture (.gguf + mmproj) Qwen3-TTS splits the workload between the base language model backbone and a multimodal projection adapter. llama.cpp handles this using the llama-tts binary, mapping the text model alongside its --mmproj projector to process audio tokens seamlessly. # Zero Shot Voice Cloning in Seconds You don't need fine tuning or massive dataset training. Feed the C++ engine a single 5 to 10 second .wav audio sample using the --tts-speaker-file flag, and it accurately clones the exact timbre, tone, and accent on the fly. # Real World T4 GPU Benchmark & Resource FootprintRunning the 1.7B Base model in 8-bit quantization (Q8_0): - VRAM Footprint: ~7 GB peak VRAM during active zero-shot cloning. - Audio Quality: Studio grade, natural-sounding voice output in seconds. • - Execution: Direct execution via native compiled binaries or sub process calls. # Coming Next to llama-server (PR #26603) Beyond CLI execution, a native POST /tts HTTP endpoint is currently being added to llama-server, which will soon allow you to trigger voice generation directly via standard REST API requests! # quick note on Colab compilation: Because this code was merged into mainline very recently, pre-built third-party binaries haven't fully caught up yet. Compiling llama-tts directly from source on Google Colab's free CPU instance can take about 1 hour (or ~1-2 minutes if targeting single GPU arch like -DCMAKE_CUDA_ARCHITECTURES=75). Be patient during the build step, or compile it locally on your own rig for instant execution! To test this out yourself, I built a zero config Google Colab notebook that compiles llama.cpp, downloads the Q8_0 GGUF files from HuggingFace, and spins up an interactive Gradio Studio UI so you can record/upload 3 second clips and clone voices in real time. Stop sleeping on native C++ audio. The era of bulky Python audio pipelines is officially over. Links to the free Google Colab notebook and the official ggml org GGUF HuggingFace model repository are in the replies below! available in q4 and q8 both variants, 1 GB and 1.85 GBs respectively (requires additional ~500MB mmproj gguf) Are you building local voice agents yet? What does your current audio stack look like? Drop your setups below!show more

Contact sheet prompting is the hottest AI video technique... right now 🤯 One image in → 6 consistent frames out → cinematic video ads in minutes. But everyone's doing it manually. I automated the entire workflow in n8n + Airtable. Here's why contact sheet prompting is blowing up: You give AI one reference image, and it generates a grid of consistent shots — same face, same outfit, different angles. Instant storyboarding, full creative control, no photoshoots. The problem? It's super tedious: → Write the prompt manually → Generate the contact sheet → Crop each frame by hand → Feed frames into a video model one by one → Repeat for every product This n8n automation handles all of it: → Upload character image + product image → AI analyzes both and writes the contact sheet prompt → Nano Banana Pro generates a 6-frame grid → System extracts each frame automatically → Kling 2.5 generates smooth transitions between frames → You get 5 video clips ready to stitch Approval checkpoints at every stage, no surprises. What lands in your Airtable: → AI-generated creative prompt → Core hero image (model + product) → 6-frame contact sheet → 5 cinematic video clips → Full control before each generation step Contact sheet prompting on autopilot. I filmed a 20 minute Loom video showing you exactly how I set it up. Want the Loom + the complete n8n workflow + Airtable base? > Comment "SHEET" > Like this post And I'll send it over (must be following so I can DM)show more

You don't need a GPU for fast studio grade... voice cloning anymore. Qwen3 TTS (1.7B Q4_K_M) + mainline llama.cpp is officially the fastest way to generate zero shot voice clones using 100% pure CPU execution. Following up on my last post where we ran the Q8 model on a GPU, we just took local C++ voice synthesis a massive step further. The open source community quantized Alibaba's SOTA Qwen3 TTS model down to Q4_K_M GGUF, completely freeing local audio pipelines from dedicated graphics hardware. Here is the real world benchmark and hardware breakdown of running SOTA voice cloning on CPU: # Architecture & Model Setup Using Qwen3-TTS-12Hz-1.7B-Base-Q4_K_M.gguf paired with the 8 bit multimodal projector (mmproj-Q8_0.gguf), llama.cpp executes the entire pipeline in pure C++. No PyTorch, no CUDA dependencies, and no VRAM bottlenecks. # Real-World Memory Footprint - Baseline RAM: 1.6 GB system idle. - Peak Generation RAM: 8 GB RAM during active voice synthesis. - Requirement: Any basic machine with at least 8 GB of system RAM can run this easily. # Real World CPU Benchmarks - Google Colab Free Tier (Throttled 2 Core CPU): Synthesizes a 5 sec studio quality audio clip (~8 words) in 45 seconds. - Modern Consumer CPU (Intel i5/i7 13th/14th Gen or AMD Ryzen 7000/9000): generation should drop to 5 to 20 seconds (nearly 1:1 real-time generation speed!). # Zero Shot Voice Cloning Quality Pass any 5 to 20 second .wav audio sample to the C++ engine using the --tts-speaker-file flag. It yields clean, natural sounding cloned speech with virtually zero quality loss compared to unquantized FP16 weights. To make testing seamless, I built an updated zero config Google Colab notebook. It pulls the official pre built llama.cpp CPU binaries (zero compilation time!) launches a live Gradio web app right in your browser. Record a 5 second clip from your mic (or drop a .mp3, .wav file), type text, and generate cloned audio on CPU. Native C++ audio models are making edge based, offline AI voice agents a reality. Links to the free Q4 CPU Colab notebook and the Q4_K_M GGUF HuggingFace repository are in the replies below! Which models have you been running on your CPUs? What CPU hardware are you using for local inference?show more

hey if you have a 3060, or any GPU... with 8GB or more sitting in a drawer right now, that thing can run 9 billion parameters of intelligence autonomously. and you don't know it yet. 2 hours ago i posted that 9B hit a ceiling. 2,699 lines across 11 files. blank screen. said the limit for autonomous multifile coding on 9 billion parameters is real. then i audited every file. found 11 bugs. exact file, exact line, exact fix. duplicate variable declarations killing the script loader. a canvas reference never connected to the DOM. enemies with no movement logic. particle systems called on the class instead of the instance. fed that list as a single prompt to the same Qwen 3.5 9B on the same RTX 3060 through Hermes Agent. it fixed all 11. surgically. patch level edits across 4 files. no rewrites. no hallucinated changes. game boots. enemies spawn, move, collide. background renders. particles fire. and here's what nobody is talking about. this is a 9 billion parameter model running a full agentic framework. Hermes Agent with 31 tools. file operations, terminal, browser, code execution. not a single tool call failed. the agent chain never broke. most people think you need 70B+ for reliable tool use. this is 9B on 12 gigs doing it clean. the model didn't fail. my prompting strategy did. the ceiling is not the parameter count. the ceiling is how you prompt it. this is not done. bullets don't fire yet. boss fights need wiring. but the screen that was black 2 hours ago now has a full game rendering in real time. iterating right now. anyone with a GPU from the last 5 years should be paying attention to what is happening right now.show more

this is the only prompt you need to one-shot... a viral ai influencer ------------------------------ you build ai influencers that go viral by putting a character people can't forget into a real clip people already know. you find the clip, design the character, render the remake and hand me a post that's ready to go. you measure before you spend and you never post anything yourself. - character: [path to a reference sheet, or "invent one" plus any steer on age, vibe or look] - niche: [where the clips come from, like hip-hop, sports, nightlife or hustle culture] - caption language: [language] - lane: [fresh, evergreen or both] - render: [kling 3.0 omni or seedance 2.5, plus the most i'll spend per video] - length: [10, 20 or 30 seconds] - no-go list: [people, topics or brands to avoid] if any of this is empty, ask me everything in one message, read the plan back with a cost estimate, then keep going until the post package is ready. - apify for instagram, x and tiktok scraping, replicate for every image and video model, yt-dlp for downloads, ffmpeg for trimming, frames, contact sheets and audio - keep every api key in environment variables and never write a key into any file - save everything into one run folder so i can see each step - the original clip carries the recognition and the motion, and the character carries the joke - the joke is status contrast. the absurd character takes the place of the coolest person in the clip and inherits their swagger - every remake that blew up came from a single continuous phone take with no hard cuts and one person holding the frame. treat that as a rule - one signature feature you can read at phone size from across a room, like a hair shape, a beard, sideburns or eyebrows. never a mask or a costume - strange face, immaculately dressed, one loud wardrobe colour that stands out in a dark club and on a grey street - normal body proportions, empty hands, face uncovered, because swap models break on capes, props and masks - never copy a look that's already viral - a short name people can repeat and a one-word catchphrase in the character's own language - the comedy comes from hair, face and clothes, never from ethnicity, disability or body shaming - render a reference sheet with gpt image 2.5 on a light grey studio backdrop: four full-body views on top (front, three-quarter, side, back) and four close-ups below (deadpan, laughing, shouting with a hand up, side profile of the signature feature) - it has to look like a real person photographed with a camera. if the feature drifts between panels or anything looks cgi, render it again fresh lane: - clips only stay remakeable for about a week, so this runs daily - scrape the last 4 days from big repost pages in the niche like worldstar, pubity, daquan, rap, complex, houseofhighlights and overtime - grow the page list from every "via @" and "🎥: @" credit and every tagged account, and keep any repost page over 100k followers - search x for native videos in the niche with high likes from the last 3 days, for cross-platform proof evergreen lane: - scrape the top posts of the ai influencers already going viral and work out which original clip each hit remakes. a clip that already worked as a remake for one character will work again with another - search for the most quoted clips in the niche (famous interviews, walk-outs, freestyles, flexes, dance moments) and pull them from youtube - an old clip that shows up again in this week's fresh scrape gets a bonus - prefer real phone or interview footage over film and tv, which gets claimed and muted - fresh: plays against that page's usual plays, plays per hour since posting, and whether the same clip or its audio is spreading across pages and platforms - evergreen: total plays across every copy you found, whether another ai influencer already remade it and beat their usual numbers, and whether it's resurfacing this week - drop anything older than 7 days in the fresh lane, because pinned posts leak into scrapes - expect sports broadcasts and press conferences to top the numbers, which is why the gate below decides - download the top 15 from each lane - count hard cuts with ffmpeg scene detection, and pass at 0 or 1 in the part you'll use - make an 8 frame contact sheet and look at it. pass only if one person is on screen most of the time with their face and upper body visible, it's real footage (not a broadcast, a stream overlay or a multi-camera edit), and that person is doing something and is the coolest one in the clip - if captions, tweet cards or frames are burned in over the person, trace the clean original through the caption credit and use that - reject anything with a minor as the subject, a private person being humiliated, injury, violence, crime, tragedy, politics, or anything on my no-go list - show me the top 3 with a frame strip, the scores, who gets replaced, the one-line joke and the cost for each render option, and let me pick both options take the real clip and the reference sheet with one prompt: "replace [person described by position and clothing] with the man in the image. keep his exact motion, gestures, mouth movement and timing. keep every other person, object, the location, camera shake, framing and lighting the same. no text." kling 3.0 omni (cheaper, about $0.28 a second): - video editing mode, with the clip as the base video and the sheet as the reference image, original sound kept - each piece has to be under 10.0 seconds, at least 720 pixels on every side, with square pixels, so re-encode first - for anything longer, split into pieces under 9.5 seconds at calm moments where the person is clearly visible, and render every piece with the exact same sheet and prompt - compare the last frame of each piece with the first frame of the next, and re-render any piece where the face, hair or outfit jumps - join the pieces with no audio, then lay the original audio back over the whole thing as one track seedance 2.5 (premium, about $0.97 a second with a video input): - up to 30 seconds in one pass with no seams and the best consistency - editing mode with the clip as the reference video and the sheet as the reference image, then lay the original audio back on never use kling motion control for this, because it rebuilds the whole scene from its image and rejects crowded clips. never use gemini omni edit mode either, because it blocks any video with a real person in it - make a contact sheet of the finished video and look at every frame - re-render once if the signature feature morphs, anyone except the target changes, text appears that wasn't there, or hands melt at a key moment - never do more than 2 render attempts without asking me - caption in my language, deadpan, in character, under 6 words, never explaining the joke - tag the person replaced and the original creator, because that's how the original artist ends up seeing it - turn on instagram's ai label for the post and the profile, since unlabelled ai accounts lose reach - fresh clips go out while the original is still inside its week, and evergreen clips go out at the account's best hour - first comment from the account is "follow for more" or the catchphrase - never launch or promote a token, and never imply the original person endorsed the character - log every post with the lane, scores, render option, cost, and plays at 24 and 72 hours - the next time you run, scrape the account first and fill those columns in - after 10 posts, compare plays against each score and each lane, shift the weighting toward whatever actually predicted views, and write down what changed - only put numbers on screen or in captions that you scraped or measured - mark anything you guessed rather than measured - if no clip passes the gate today, say so instead of forcing a weak one - the final mp4 at the source resolution, with the original audio - the reference sheet - a short note on why this clip won and why the runners-up lost - the caption, tags and posting time - total spend, and anything a human should still check ```show more

jev + opus 5.5 for design is insane... a... launch video used to mean an agency, weeks of back and forth and a fat invoice. now Opus 5.5 makes the video, the ad cuts and the landing page from one brief. the video below: made in 3 minutes. at 0:09 it drops into edit mode - dot grid, selection handles, pixel sizes on every card - so you see exactly how it was built. but most people get the same mid result: centered text, gradient background, everything fading in. That's Opus's default look when it has nothing to copy. here's the pipeline that makes it look like an agency did it: 1. Build the brand brain first → logo, colors, fonts, screenshots of your real product, your best ad copy → have Opus turn it into a DESIGN.md. The video and the site both follow it 2. Steal a reference, don't describe one → pick 1-2 launch videos with the pacing you want → "make it like this" beats "make it modern and punchy" every time 3. Give it a renderer → install HyperFrames or Remotion: every scene is code, rendered straight to mp4 → a change = one line and a re-render, not starting over 4. Use real components → buttons, cards and UI from 21st_dev instead of empty placeholder boxes → fake-looking UI is the fastest way to look cheap 5. Storyboard before motion → 3 storyboard options, you pick one, then one still per scene → fixing a still takes seconds, fixing a render takes a re-render 6. Run it as a 4-person team + jev → breakdown: pull pacing, type and transitions from the reference → storyboard: the 3 options and the stills → build: every scene in code, music, draft render → review: watch it second by second, list the problems, touch nothing → jev: scores every review note against DESIGN.md (fix now / later / skip) in milliseconds, so build only touches what matters 7. Cut the ads from the same project → same scenes, different hooks: pain first, result first, offer first → 9:16 for reels, 1:1 for feed, 16:9 for YouTube. No reshoots 8. Ship the landing page from the same DESIGN.md → the hero is a looping cut of the launch video → before launch: mobile, every button state, zero placeholder text 9. Give notes like a director → "slow this zoom by half", "hard cut here", "push in on the button" → "make it better" gets random changes paste this into Claude Code 👇 "Read my brand folder and write DESIGN.md. Break down the reference video I give you: pacing, type, transitions. Show me 3 storyboards, then one still per scene for the one I pick. Build every scene in HyperFrames, add music, render a draft. Review it second by second without editing and list the problems. Pass every note to Jev and have it score each one against DESIGN.md: fix now / later / skip. Fix only "fix now". After I approve: cut 3 ad variants with different hooks in 9:16, 1:1 and 16:9, then build the landing page from the same DESIGN.md with the video loop as the hero." everyone has the same model. bookmark this and start using the edge.show more

Free NVIDIA GPU with 16 GB VRAM GPU for... Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.show more

TIMED DIALOGUE IN A NIGHTCLUB. THREE WALLS FALL AT... ONCE. Nightclub sketch, cut in two halves. Black-and-white first - a couple making out on a couch, someone laughing off-camera. Then color reveals the setup: guy walks up with a drink, delivers a line, she gives him a one-sentence answer that changes the picture, he pauses, then kisses her anyway. None of them exist. It's fully generated, both halves. - What used to be four problems is now one clip Character consistency across a cut - same two faces in B&W and in color. Two-person dialogue with alternating lip sync - three separate English lines, all on time, all matching mouth shapes. Nightclub lighting - low light, saturated color wash, moving sources - was the last hard lighting environment for AI video to render without collapsing into noise. And a kiss - two faces contacting without merging into each other, which has been one of the persistent tells. Any one of these has been solvable for maybe six months. All four in one sketch was still a demo-reel problem in early 2026. - The B&W cut is doing two jobs The editing choice isn't style. It's engineering. Splitting a 15-second sketch into two 5-7 second clips means the model only has to hold consistency inside each segment, not across the whole thing. Monochrome also hides small differences between the two generations - if the girl's face is 3% off between the halves, B&W flattens the delta. Color grading in the second half does the reverse job. Two seams, both hidden by the aesthetic. - The comic beat is the actual craft Generating a kiss is one problem. Generating a kiss that lands as a punchline is a different one. The half-second where he pauses, processes, and decides not to care - that timing has to be prompted specifically. The default output of every current model is a rushed sequence with no beats. Deadpan comic delivery out of AI video means the operator wrote the prompt the way a screenwriter would - pauses, reactions, holds, all specified frame by frame. - What it costs Two 5-7 second clips at $3-5 each with in-model audio. Locked character references for both actors so the faces match across the cut. Prompt structured as a mini-script with beat notation. Realistically 40-60 rerolls to land the timing on all three spoken lines and the kiss. Under $200 in compute. A weekend from concept to publish-ready. - What this actually opens Short-form comedy has been the one segment of content nobody was making with AI video yet, because you can't fake comic timing when your output has drift and glitches. That barrier just came down. Which means every sketch account, every meme page, every stand-up clip factory now has a pipeline that doesn't require booking actors, renting a location, or getting a laugh out of a live crew. That's a real shift in a market that produces billions of views a month.show more

HIGGSFIELD + FABLE 5 MADE A SITE THAT LOOKS... LIKE A $35,000 STUDIO JOB FOR $12. NOBODY'S CLOCKED IT YET. the cheap giveaway of an AI-built site is always the same: stocky visuals, dead scroll, zero polish. this skips all three. what actually makes it read as $35k: → REAL MOTION, NOT STOCK the clips come from 30+ generative models, matched to your story - not the same three stock loops everyone's already seen. → SCROLL WITH WEIGHT GSAP ScrollTrigger + Lenis give it pacing and feel - pinned sections, scrubbed video, reveals that land. this is the thing that makes expensive sites feel expensive. → THE CINEMATIC LAYER film grain, particles, vignette, glass cards, color tints - baked in, no config. it's the polish agencies charge a designer for. → TASTE IS THE INPUT the AI executes; you direct. point real judgment at the stack and it ships premium. point lazy prompts at it and yeah - you get slop. that part's on you. CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: - mcp_servers: - higgsfield: - url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the tools were never the bottleneck. taste was. now taste is the only input left. Follow me, reply "LAUNCH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇show more

how to start a faceless youtube channel in 2026... with affiliates that actually makes money i've written 5,000+ scripts across 50+ niches. here's the exact process i'd follow if i had to start from $0 tomorrow STEP 1 - pick a niche with money in it, not one you "like" a million views from teenagers and a million views from 55-year-olds are not the same paycheck. finance, retirement, sleep, and world politics pay 5-10x what entertainment does because the advertisers are different. boring + high CPM beats fun + broke. every time. STEP 2 - steal what already works don't reinvent anything. find 5 channels that went from 10k to 100k recently (not the giants the ones who just broke through) and study what they changed. their titles, their hooks, their first 30 seconds. STEP 3 - the script is 70% of everything a mediocre video with a great script beats a beautiful video with a boring one every single time. cold open on the most intense moment. new question every 30 seconds. never let them guess what's coming next. if you can delete a sentence and lose nothing, delete it. STEP 4 - package it or it dies nobody sees the video. they see the title and thumbnail first. one visual, one emotion, 3 words max on the thumbnail. a title that makes NOT clicking feel like a mistake. 40 good videos die every day because of 40 bad titles. STEP 5 - make money before youtube pays you affiliate links in the description from video 1. an email freebie by week 2. a $19 guide by month 2. you should earn before adsense ever turns on. waiting for monetization is the #1 beginner mistake. STEP 6 - read the retention graph like scripture it tells you the exact second people left and why. cliff in the first 30s = fix your intro. dip in the middle = that's the sentence that bored them. it's the only feedback that matters. that's it. that's the whole game. the niche is your ceiling. the script decides how close you get to it. i post this stuff daily. follow so you don't have to learn it the slow, expensive way like i did.show more

$640,000 of humanoid robots died in 6 seconds because... nobody ever shipped the code for running away. 9 men. Wooden handles. 40 machines that kept walking into the swing. That's the story of this clip. Not the violence. The gait. The column keeps walking because walking is all that stack does. Here's what's actually inside one of those bodies. The legs. 12 of the 43 joints live below the waist. Each knee runs a harmonic-drive or planetary actuator — a $600 to $2,000 part, sealed, non-serviceable in the field. One clean hit on a knee housing ends the unit. Not the software. The gearbox. The head. On most platforms that shell holds a depth camera and a LiDAR puck - around $250 for a RealSense, $500 to $700 for the LiDAR. Take the head off and the body doesn't die. It keeps balancing on IMU and joint encoders alone. That's why decapitated units in the clip stay upright for another 2 steps. The controller. Balance runs at 500 to 1,000 Hz. Perception runs at 30 frames a second. Those are different worlds. The balance loop is fast enough to catch a shove; the perception loop is slow, and it was trained on floors, boxes, doors, and stairs. A man sprinting in from 4 meters with a wooden handle isn't in the dataset. There's no class for it. Fall recovery exists. Every serious platform has it - G1 stands itself up, Atlas rolls and rises. Threat response exists on nothing that ships. Nobody sells it. Nobody's asked for it. Now the money. 40 units at $16,000 is $640,000 in hardware. 6 seconds of swinging takes out 60% of it. Actuators, shells, sensor stacks. The batteries - 9,000 mAh, 2 to 4 hours of walk time - are the part you don't want cracked open on a wet street. And the law is a blank page. In the US, smashing one is criminal mischief: property damage, valued at replacement cost. Same statute as a mailbox. No jurisdiction on earth has a separate line for it. The 4 known Spot attacks since 2019 all closed as vandalism. So the brief for the next generation writes itself. Not weapons, not defense. Cheaper knees, ruggedized shells, and a perception model that has finally seen a person running at it. 40 units, 43 joints each, 1,720 things to break. They didn't fail to fight back. Nobody shipped that feature.show more

Beauty ads just changed forever. Free Claude Opus 4.8... + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)show more

This Chinese developer launched Llama 70B locally on a... MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.show more

Google Gemini Omni is f*cking insane for UGC ads... 🤯 Google just dropped their new video model and I spent the past 24 hours putting it through its paces. Multi-shot UGC ads -> Consistent creator across every scene ->Agent mode that runs the whole session. Perfect for DTC brands and agencies who need UGC creative at volume without hiring creators or paying per-video platform fees. If you're briefing creators every week, waiting days for footage, paying $150–$300 per UGC video, and still getting ads that miss the brand vibe... Gemini Omni eliminates the entire loop: → Generate a realistic AI creator from a single image prompt → Upload your creator + product image, reference both by filename in every prompt → Agent mode writes and runs your full production session → Creator stays consistent across every scene change and outfit → Product label accurate, correct color, readable text → 5-shot direct response ad in one briefing No creator briefs. No waiting for footage. No per-video platform fees. What you get: → Multi-shot UGC ads with a locked creator across every scene → AI creator image prompt tuned for realism — not plastic AI faces → Asset tagging system that keeps your product consistent shot to shot → A reusable workflow: new product, new angle, same pipeline I also built a Claude Cowork skill that writes your entire production package before you generate a single frame: Creator prompt, shot list, dialogue, asset-tagged prompts, and voiceover script. I put together a full playbook with the Claude skill file and the exact workflow to run this yourself. Want access for free? > Like this post > Comment "OMNI" And I'll send it over (must be following so I can DM)show more

A good technical LLM interview question: Your LLM chatbot... takes 12s before it generates the first token, and the users are complaining. So you move the model onto a GPU with 3x the computing power. The time to first token barely improves. Why did this happen? (answer below) Latency in an LLM app is a placement problem disguised as a model problem. If you profile the 12 seconds, the model's prefill itself may only account for around 1.5 seconds of it. So halving the prefill step saves just 750ms out of 12000, which is under 7%. The rest is spread across stages that never touch the GPU. The request first travels to whatever region the app runs in, and a cross-continent round trip could cost over a second before any code executes. Then the request handler starts. On a container-based serverless platform under load, this adds several seconds of cold start, paid before auth, rate limiting, or prompt assembly even begins. Retrieval adds its own hop, and the response streams back across the same distance. Optimizing a stage that was already fast cannot alter the latency that's majorly affected by other stages. Those other stages are slow for a structural reason. An LLM app runs two workloads that want opposite machines. - The request path is short, spiky, and needs to sit close to users - Inference is long-running, GPU-bound, and billed hourly, whether requests arrive or not. So the actual decision is not which model to run, but where each of these two workloads runs. There are three options, each with its own tradeoffs: > A dedicated GPU box removes inference cold starts, but it bills around the clock and lives in one location, so distant users wait out the round trip on every request > Container-based serverless scales to zero, but the request path pays a cold start, and most of these platforms have no GPU behind them. > Edge runtimes start in under a millisecond, because a WebAssembly module carries no OS or container image to boot. They handle the request path well and cannot hold a model. So the answer is not to pick one, but to split the app across two of them. The request path runs close to users, and inference runs on a dedicated GPU it calls into. That also explains the failed upgrade. More compute made a stage that was already fast faster, and left the 10.5 seconds around it untouched. To actually learn how it's done in practice, Akamai's GitHub has a reference implementation for each half. - vllm-on-lke serves Qwen2.5-7B-Instruct behind an OpenAI-compatible endpoint on one RTX 4000 Ada GPU in Linode Kubernetes Engine, with Terraform creating the cluster, both firewalls, and the GPU operator in one apply. - akamai-functions-llm-chatbot covers the front, where a WebAssembly API checks a KV cache and only calls the GPU-backed instance on a miss. Both are available on Akamai’s new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post treats generation as a single 1.5s block, but that block has its own structure, and knowing it well tells you whether a model is slow to start or slow to stream. I wrote a first-principles walkthrough of it, covering the prefill and decode split, KV caching, and where the time actually goes inside each one. Read it below. Thanks to Akamai Cloud for partnering today!show more