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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...

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Yume_X

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13,279 次观看 • 1 个月前 •via X (Twitter)

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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.
0:19

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.

Sulfur

36,726 次观看 • 2 个月前

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:
0:13

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:

Bilawal Sidhu

44,822 次观看 • 2 年前

🚨 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 )
0:14

🚨 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 )

Kanika

14,769 次观看 • 5 个月前

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!
0:12

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!

Alok

47,881 次观看 • 1 个月前

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)
0:13

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)

Mike Futia

53,751 次观看 • 9 个月前

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?
0:16

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?

Alok

105,756 次观看 • 1 个月前

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.
0:12

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.

Sudo su

684,336 次观看 • 6 个月前

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.
0:11

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.

Alok

182,483 次观看 • 2 个月前

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.
0:16

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.

capONE 💎

82,533 次观看 • 1 个月前

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 👇
0:19

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 👇

ZEUS⚡️

80,782 次观看 • 2 个月前

$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.
0:14

$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.

HodlReaper

131,075 次观看 • 14 天前

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)
0:18

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)

Ahad Shams | AI Ads Guy

11,288 次观看 • 3 个月前

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.
0:10

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.

Blaze

1,843,280 次观看 • 4 个月前

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)
0:17

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)

Mike Futia

34,258 次观看 • 4 个月前

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!
0:18

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!

Avi Chawla

21,786 次观看 • 1 个月前

here's how you can scale to 10k/month on tiktok shop with slideshows so most affiliates are stuck doing the same thing every day. film, edit, post, make like $60-80 in commissions. and the problem isn't effort, it's that your time is the bottleneck. you can't film 15 videos a day. so your output caps and your money caps with it. meanwhile there's a guy in canada who made $18k in his first two weeks. never filmed anything. never showed his face. never even held a product. canada doesn't even support tiktok shop, he's running us tiktok from there. all slideshows. and when i say slideshows i mean 4-7 images posted like a normal tiktok with a product link attached. that's it. someone buys off it, you get paid. making one takes maybe 5% of the skill of making a video. now here's the catch. tiktok gated the feature. most accounts can't attach products to photo posts, you'll get a "product links not available in photo mode" error. some accounts randomly have it. quick way to check: open tiktok studio on desktop (has to be desktop, mobile won't work), hit upload. if you see "videos or photos" you have access. click photos, upload your images, attach the link. if you only see video upload, you're not in yet. uk and europe are getting it randomly right now, us is only top gmv creators for the moment. check every day because tiktok doesn't tell you when you get it, the option just shows up. worst case you build the system now and execute day one when your account unlocks. because the people getting random access with no clue what they're doing are posting random images and making nothing. the format is easy, that doesn't mean it's mindless. the format itself is one thing repeated over and over: pain point first, product second. slide one hits an insecurity. back acne from the gym. car turning into an oven all summer. makeup that never sits right. the person scrolling sees it and goes "wait that's literally me." middle slides twist the knife a bit more. then "so i tried this thing everyone's using," show it working, before and after, and the last slide is just the offer. sale, free shipping, link below. done. that structure sells cold traffic. people who've never seen the product buy off one slideshow because you sold the problem, not the product. and here's the part most people don't clock when they're scrolling past these: none of it is real. the guy holding the ceiling fan doesn't own a ceiling fan. the back acne was generated onto the model. the smoothies were never made. it's all ai images. which kills every excuse at once. no face, no product in hand, no waiting on shipping, no country restrictions. the workflow is dumb simple. screenshot a slideshow style you like, drop it in chatgpt, say "make me 3x4 images in this style." then describe your pain point scene. couple walking to a car that's been baking in the sun, whatever it is. then grab the product image off the tiktok listing, feed it in, "now show them using this." repeat per slide. no fancy prompts, the reference images do all the work. two small things that matter more than they should. keep everything 3:4 or the mixed sizes make the whole post look off. and don't bake text into the images, add it inside tiktok. native text looks like a person posted it. baked text looks like an ad. people can feel the difference even if they can't explain it. if you want it to look even more real, take a photo of your actual kitchen or desk and only generate the product into it. real room, ai product. nobody can tell. for ideas, don't invent anything. steal structure, swap one variable. the number one post in the uk right now is a simpsons style slideshow about linen trousers. take that exact skeleton and run it with a sports set or summer shorts instead. same format, different product, suddenly it's unsaturated again. or take viral videos and turn them into slides. one guy took a viral video about a sink drainage thing, rebuilt it as images, and beat the original with 1.7m views. first week on the platform. the biggest edge though is going backwards. pull products that went viral 2-3 months ago, take the exact hooks that already converted millions of views, and rerun them as slideshows. nobody's done them in this format because the format barely exists. you're not testing ideas, you're re-releasing proven hits. then it just comes down to volume. no filming, no editing, no product costs means each post is basically free. so post 10-15 a day. most will flop, who cares. one will do 500k views in two days and when it does you remake it 50 times and drain it. every gated feature on tiktok runs the same cycle. early access prints, wide rollout saturates, then it's just another format everyone does. slideshows are still in the first part of that cycle.
0:11

here's how you can scale to 10k/month on tiktok shop with slideshows so most affiliates are stuck doing the same thing every day. film, edit, post, make like $60-80 in commissions. and the problem isn't effort, it's that your time is the bottleneck. you can't film 15 videos a day. so your output caps and your money caps with it. meanwhile there's a guy in canada who made $18k in his first two weeks. never filmed anything. never showed his face. never even held a product. canada doesn't even support tiktok shop, he's running us tiktok from there. all slideshows. and when i say slideshows i mean 4-7 images posted like a normal tiktok with a product link attached. that's it. someone buys off it, you get paid. making one takes maybe 5% of the skill of making a video. now here's the catch. tiktok gated the feature. most accounts can't attach products to photo posts, you'll get a "product links not available in photo mode" error. some accounts randomly have it. quick way to check: open tiktok studio on desktop (has to be desktop, mobile won't work), hit upload. if you see "videos or photos" you have access. click photos, upload your images, attach the link. if you only see video upload, you're not in yet. uk and europe are getting it randomly right now, us is only top gmv creators for the moment. check every day because tiktok doesn't tell you when you get it, the option just shows up. worst case you build the system now and execute day one when your account unlocks. because the people getting random access with no clue what they're doing are posting random images and making nothing. the format is easy, that doesn't mean it's mindless. the format itself is one thing repeated over and over: pain point first, product second. slide one hits an insecurity. back acne from the gym. car turning into an oven all summer. makeup that never sits right. the person scrolling sees it and goes "wait that's literally me." middle slides twist the knife a bit more. then "so i tried this thing everyone's using," show it working, before and after, and the last slide is just the offer. sale, free shipping, link below. done. that structure sells cold traffic. people who've never seen the product buy off one slideshow because you sold the problem, not the product. and here's the part most people don't clock when they're scrolling past these: none of it is real. the guy holding the ceiling fan doesn't own a ceiling fan. the back acne was generated onto the model. the smoothies were never made. it's all ai images. which kills every excuse at once. no face, no product in hand, no waiting on shipping, no country restrictions. the workflow is dumb simple. screenshot a slideshow style you like, drop it in chatgpt, say "make me 3x4 images in this style." then describe your pain point scene. couple walking to a car that's been baking in the sun, whatever it is. then grab the product image off the tiktok listing, feed it in, "now show them using this." repeat per slide. no fancy prompts, the reference images do all the work. two small things that matter more than they should. keep everything 3:4 or the mixed sizes make the whole post look off. and don't bake text into the images, add it inside tiktok. native text looks like a person posted it. baked text looks like an ad. people can feel the difference even if they can't explain it. if you want it to look even more real, take a photo of your actual kitchen or desk and only generate the product into it. real room, ai product. nobody can tell. for ideas, don't invent anything. steal structure, swap one variable. the number one post in the uk right now is a simpsons style slideshow about linen trousers. take that exact skeleton and run it with a sports set or summer shorts instead. same format, different product, suddenly it's unsaturated again. or take viral videos and turn them into slides. one guy took a viral video about a sink drainage thing, rebuilt it as images, and beat the original with 1.7m views. first week on the platform. the biggest edge though is going backwards. pull products that went viral 2-3 months ago, take the exact hooks that already converted millions of views, and rerun them as slideshows. nobody's done them in this format because the format barely exists. you're not testing ideas, you're re-releasing proven hits. then it just comes down to volume. no filming, no editing, no product costs means each post is basically free. so post 10-15 a day. most will flop, who cares. one will do 500k views in two days and when it does you remake it 50 times and drain it. every gated feature on tiktok runs the same cycle. early access prints, wide rollout saturates, then it's just another format everyone does. slideshows are still in the first part of that cycle.

Mufasa

13,583 次观看 • 1 个月前

Watch this 17-second clip carefully. What you are about to see is not what it claims to be. For over 25 years I’ve worked as a specialist forensic analyst, both within the intelligence world as well as private security sector. So I’m going to show you how forensic analysis really works to uncover the truth about what we see put in front of us. TRT World, Turkish state media, released this video claiming it shows an Israeli airstrike on a residential building in Al Zaitoun, Gaza on January 5, 2025. The dramatic footage with emotional Arabic overlay text was designed to generate instant outrage. While obvious Pallywood often uses fake blood and staged injuries, this one is more sophisticated. It relies on perfect timing and internal preparation. Here is the second-by-second breakdown: Seconds 0 to 1: The camera is already perfectly framed on the building. White smoke begins venting from multiple windows simultaneously. At around the one-second mark, a bright red dot appears on an upper window for a fraction of a second right before the blast. This is the trigger light that lights up the instant before an explosive charge is detonated. It also acts as a marker for the cameraman. No incoming missile, no external impact flash. Seconds 1 to 2: A massive dark smoke plume erupts violently outward and upward. Debris is ejected. When a real missile strikes a building, you typically see large flames from the missile’s fuel and the force ripping apart major sections of concrete, producing large chunks of debris. Here, we see no significant flames and only small debris mixed with heavy dust, exactly what you would expect from a small internal explosive device designed mainly to blow out windows and create a dramatic smoke plume. The explosion originates from inside the building and expands symmetrically. Seconds 2 to 4: The smoke cloud balloons dramatically. The cameraman, already positioned and recording, smoothly tilts up to capture the most cinematic part of the plume. Seconds 4 to 6: The camera tilts down to street level. A girl in bright pink walks across the dusty area almost casually, not showing the expected panic. Seconds 6 to 9: Civilians appear relatively composed. Then the man in the red hoodie runs in, stops dramatically, and points upward while shouting in a theatrical manner. Seconds 9 to 17: People mill about with surprising calm as dust swirls. The camera work remains composed enough to capture the drama while TRT World adds the emotional propaganda text. This was not an airstrike. It was a carefully timed internal detonation, triggered from within (signaled by the red dot), filmed by someone who knew exactly when it would happen. The internal multi-window venting, perfect camera placement, and staged reactions all confirm it was manufactured for propaganda. If you value the truth and appreciate such detailed analyses, please share. Real tragedy does not need this level of staging. The information war continues.
0:17

Watch this 17-second clip carefully. What you are about to see is not what it claims to be. For over 25 years I’ve worked as a specialist forensic analyst, both within the intelligence world as well as private security sector. So I’m going to show you how forensic analysis really works to uncover the truth about what we see put in front of us. TRT World, Turkish state media, released this video claiming it shows an Israeli airstrike on a residential building in Al Zaitoun, Gaza on January 5, 2025. The dramatic footage with emotional Arabic overlay text was designed to generate instant outrage. While obvious Pallywood often uses fake blood and staged injuries, this one is more sophisticated. It relies on perfect timing and internal preparation. Here is the second-by-second breakdown: Seconds 0 to 1: The camera is already perfectly framed on the building. White smoke begins venting from multiple windows simultaneously. At around the one-second mark, a bright red dot appears on an upper window for a fraction of a second right before the blast. This is the trigger light that lights up the instant before an explosive charge is detonated. It also acts as a marker for the cameraman. No incoming missile, no external impact flash. Seconds 1 to 2: A massive dark smoke plume erupts violently outward and upward. Debris is ejected. When a real missile strikes a building, you typically see large flames from the missile’s fuel and the force ripping apart major sections of concrete, producing large chunks of debris. Here, we see no significant flames and only small debris mixed with heavy dust, exactly what you would expect from a small internal explosive device designed mainly to blow out windows and create a dramatic smoke plume. The explosion originates from inside the building and expands symmetrically. Seconds 2 to 4: The smoke cloud balloons dramatically. The cameraman, already positioned and recording, smoothly tilts up to capture the most cinematic part of the plume. Seconds 4 to 6: The camera tilts down to street level. A girl in bright pink walks across the dusty area almost casually, not showing the expected panic. Seconds 6 to 9: Civilians appear relatively composed. Then the man in the red hoodie runs in, stops dramatically, and points upward while shouting in a theatrical manner. Seconds 9 to 17: People mill about with surprising calm as dust swirls. The camera work remains composed enough to capture the drama while TRT World adds the emotional propaganda text. This was not an airstrike. It was a carefully timed internal detonation, triggered from within (signaled by the red dot), filmed by someone who knew exactly when it would happen. The internal multi-window venting, perfect camera placement, and staged reactions all confirm it was manufactured for propaganda. If you value the truth and appreciate such detailed analyses, please share. Real tragedy does not need this level of staging. The information war continues.

Mor Edge Insight

67,787 次观看 • 3 个月前

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.
0:17

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Dustin

22,458 次观看 • 2 个月前

Researchers made KMeans 200x faster. And the new technique also beats approaches like cuML and FAISS. Flash-KMeans is an IO-aware implementation of exact KMeans that redesigns the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 33x speedup over cuML - 200x speedup over FAISS This speedup comes from how it moves through GPU memory. Standard KMeans runs in two steps, and both are bottlenecked by reads and writes to GPU memory: 1) The first step matches every point to its nearest centroid. Standard KMeans computes the full point-to-centroid distance matrix, writes it out to GPU memory, then reads it back to find each nearest centroid. That write-then-read round trip is the bottleneck. Flash-KMeans combines the distance calculation with the nearest-centroid step, so the result is computed on-chip and the full matrix is never written out. 2) The second step recomputes each centroid by averaging the points assigned to it. Standard KMeans has thousands of threads writing into the same centroid slots at once, so they stall waiting for their turn. Flash-KMeans sorts points by cluster first, turning scattered writes into sequential reductions that read and write memory in one efficient pass. Using these two optimizations at the million-scale, Flash-KMeans completes a standard KMeans iteration in a few milliseconds. The video below depicts this in action. Several reasons why this is important: KMeans has always been an offline primitive. Something you run once to preprocess data and move on. These speedups make the approach viable in several runtime-critical systems. ↳ Vector indices like FAISS use KMeans to build search indices. Faster KMeans means you can re-index dynamically as data changes. ↳ LLM quantization methods need KMeans to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Flash-KMeans makes it viable to run this inside the inference loop, not just in preprocessing. I have shared the paper in the replies. That said, memory is the real constraint Flash-KMeans solves, and the problem is not just limited to clustering. The vectors a RAG system stores after indexing create similar bottlenecks. I wrote a detailed walkthrough recently on cutting this vector memory by 32x with binary quantization, querying 36M+ vectors in a few milliseconds. Read it below.
0:12

Researchers made KMeans 200x faster. And the new technique also beats approaches like cuML and FAISS. Flash-KMeans is an IO-aware implementation of exact KMeans that redesigns the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 33x speedup over cuML - 200x speedup over FAISS This speedup comes from how it moves through GPU memory. Standard KMeans runs in two steps, and both are bottlenecked by reads and writes to GPU memory: 1) The first step matches every point to its nearest centroid. Standard KMeans computes the full point-to-centroid distance matrix, writes it out to GPU memory, then reads it back to find each nearest centroid. That write-then-read round trip is the bottleneck. Flash-KMeans combines the distance calculation with the nearest-centroid step, so the result is computed on-chip and the full matrix is never written out. 2) The second step recomputes each centroid by averaging the points assigned to it. Standard KMeans has thousands of threads writing into the same centroid slots at once, so they stall waiting for their turn. Flash-KMeans sorts points by cluster first, turning scattered writes into sequential reductions that read and write memory in one efficient pass. Using these two optimizations at the million-scale, Flash-KMeans completes a standard KMeans iteration in a few milliseconds. The video below depicts this in action. Several reasons why this is important: KMeans has always been an offline primitive. Something you run once to preprocess data and move on. These speedups make the approach viable in several runtime-critical systems. ↳ Vector indices like FAISS use KMeans to build search indices. Faster KMeans means you can re-index dynamically as data changes. ↳ LLM quantization methods need KMeans to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Flash-KMeans makes it viable to run this inside the inference loop, not just in preprocessing. I have shared the paper in the replies. That said, memory is the real constraint Flash-KMeans solves, and the problem is not just limited to clustering. The vectors a RAG system stores after indexing create similar bottlenecks. I wrote a detailed walkthrough recently on cutting this vector memory by 32x with binary quantization, querying 36M+ vectors in a few milliseconds. Read it below.

Avi Chawla

89,234 次观看 • 3 个月前

I spend my days explaining to teams why a 770-billion-parameter open-weight model will never fit into their infrastructure. This week, I asked it to code a complete mobile game from a single prompt. The concept is one everyone has probably played before. A hole moving through an open-plan office, viewed from above, swallowing everything in its path. You start tiny, only able to swallow pens and cups. You grow, moving on to keyboards and plants, then chairs and printers, then desks and vending machines. Eventually, you swallow the entire meeting room. 60 seconds on the clock. One HTML file, zero external libraries, zero errors on launch. The video shows the generation and then the actual gameplay. What broke is more instructive than what worked. The structure came out right on the first try: fixed-timestep loop, spatial grid for collisions, tier system, spring camera. The balancing and rendering, not so much. The first version scored 70 points in 13 seconds with a tier threshold at 500, and drew colorful circles and triangles instead of furniture. I had to give it numbers and exact recipes. Speed: 640 pixels per second. Radii: 30, 58, 96, 150, 225. Tier thresholds: 150, 550, 1500, 3500. And for every object, a pixel-perfect drawing recipe. Once I gave it that, it followed the instructions exactly. A model that doesn't execute its own code won't tune itself. But it will execute, down to the exact numbers, what you tell it to build. The model is Hy4 preview, released by Tencent Hunyuan on August 28. 770 billion parameters in total, but 49 billion active per token. And that second number is what determines your serving bill. Native 1M context. Apache 2.0 license. vLLM and SGLang supported from day one, with an official FP8 checkpoint. Text-only preview. The part that matters for deployment is the compression. Tencent describes it as seven times smaller with almost no loss. GGUF builds use mixed per-layer quantization, where calibration data determines the bit width layer by layer. Some layers go as low as 1.31 bits, while others go up to 2.06 bits, averaging 2.38 bits per weight. The model drops from 1.5 TB in BF16 to 213.66 GiB while, according to their measurements, staying in the same performance range on real-world tasks. On this build, they report 204 tokens/s in prefill and 20 tokens/s in decoding, measured on an 8-GPU node. Their numbers, not mine. Their blind evaluation scores 2.99 out of 4 across 203 engineering tasks rated by 163 experts. Ahead of Kimi K3 at 2.94 and GLM-5.3 at 2.92. Their numbers too. My run went through the official hosted studio, not a local build, so I’m not claiming to have benchmarked the compressed GGUF myself. Two honest caveats. None of these builds run on standard llama.cpp. The hyv4 architecture isn't upstream yet, so patches are required. And 214 GiB of resident weights is still a server, not your laptop. This is a preview, and Tencent explicitly asks users to break it and report what fails. So here's my contribution.
0:19

I spend my days explaining to teams why a 770-billion-parameter open-weight model will never fit into their infrastructure. This week, I asked it to code a complete mobile game from a single prompt. The concept is one everyone has probably played before. A hole moving through an open-plan office, viewed from above, swallowing everything in its path. You start tiny, only able to swallow pens and cups. You grow, moving on to keyboards and plants, then chairs and printers, then desks and vending machines. Eventually, you swallow the entire meeting room. 60 seconds on the clock. One HTML file, zero external libraries, zero errors on launch. The video shows the generation and then the actual gameplay. What broke is more instructive than what worked. The structure came out right on the first try: fixed-timestep loop, spatial grid for collisions, tier system, spring camera. The balancing and rendering, not so much. The first version scored 70 points in 13 seconds with a tier threshold at 500, and drew colorful circles and triangles instead of furniture. I had to give it numbers and exact recipes. Speed: 640 pixels per second. Radii: 30, 58, 96, 150, 225. Tier thresholds: 150, 550, 1500, 3500. And for every object, a pixel-perfect drawing recipe. Once I gave it that, it followed the instructions exactly. A model that doesn't execute its own code won't tune itself. But it will execute, down to the exact numbers, what you tell it to build. The model is Hy4 preview, released by Tencent Hunyuan on August 28. 770 billion parameters in total, but 49 billion active per token. And that second number is what determines your serving bill. Native 1M context. Apache 2.0 license. vLLM and SGLang supported from day one, with an official FP8 checkpoint. Text-only preview. The part that matters for deployment is the compression. Tencent describes it as seven times smaller with almost no loss. GGUF builds use mixed per-layer quantization, where calibration data determines the bit width layer by layer. Some layers go as low as 1.31 bits, while others go up to 2.06 bits, averaging 2.38 bits per weight. The model drops from 1.5 TB in BF16 to 213.66 GiB while, according to their measurements, staying in the same performance range on real-world tasks. On this build, they report 204 tokens/s in prefill and 20 tokens/s in decoding, measured on an 8-GPU node. Their numbers, not mine. Their blind evaluation scores 2.99 out of 4 across 203 engineering tasks rated by 163 experts. Ahead of Kimi K3 at 2.94 and GLM-5.3 at 2.92. Their numbers too. My run went through the official hosted studio, not a local build, so I’m not claiming to have benchmarked the compressed GGUF myself. Two honest caveats. None of these builds run on standard llama.cpp. The hyv4 architecture isn't upstream yet, so patches are required. And 214 GiB of resident weights is still a server, not your laptop. This is a preview, and Tencent explicitly asks users to break it and report what fails. So here's my contribution.

Alexa Benchmark

16,315 次观看 • 17 天前