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Our CUDA-native genome generation stack can now generate a whole synthetic genome in just under 20 minutes on eight H200 GPUs from lium.io . That's roughly 440x faster than the traditional CPU-bound workflows. This opens the door to population-scale synthetic genome generation for benchmarking, variant calling evaluation, and genomic AI.

Minos

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16,787 görüntüleme • 1 ay önce •via X (Twitter)

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I just sequenced a human genome to 30× coverage entirely at home. As far as I know, this is the first time this has been done. I didn’t step foot in a lab once. Every step - from saliva collection, to running the sequencer - took place in a single room with a dining table + kitchenette. Six weeks ago, I had never done wet lab biology before. I used an Oxford Nanopore P2 Solo - the only commercially available sequencing device portable enough to do 30x human genome sequencing at home. Biggest takeaway - I could build something that combined software, hardware, and molecular biology far faster than I thought was possible. I can name >100 specific instances where AI helped me solve a technical problem that would previously have blocked me because I lacked access to a domain expert. For example: how do I save my sequencing run when my DNA extraction yield is 4x lower than I need it to be, and I have this limited set of reagents to hand? To make this work, I had to navigate multiple disciplines: - writing software to monitor sequencing runs and orchestrate remote GPU infra for basecalling - learning + executing 5 hour long molecular biology protocols - building a hardware device to quantify DNA concentration Apologies for the hyperbole, but I feel super lucky to be living in 2026. A few weeks ago I decided to sequence a human genome to 30x at home. Then I actually did it. And I did it really quickly.

I just sequenced a human genome to 30× coverage entirely at home. As far as I know, this is the first time this has been done. I didn’t step foot in a lab once. Every step - from saliva collection, to running the sequencer - took place in a single room with a dining table + kitchenette. Six weeks ago, I had never done wet lab biology before. I used an Oxford Nanopore P2 Solo - the only commercially available sequencing device portable enough to do 30x human genome sequencing at home. Biggest takeaway - I could build something that combined software, hardware, and molecular biology far faster than I thought was possible. I can name >100 specific instances where AI helped me solve a technical problem that would previously have blocked me because I lacked access to a domain expert. For example: how do I save my sequencing run when my DNA extraction yield is 4x lower than I need it to be, and I have this limited set of reagents to hand? To make this work, I had to navigate multiple disciplines: - writing software to monitor sequencing runs and orchestrate remote GPU infra for basecalling - learning + executing 5 hour long molecular biology protocols - building a hardware device to quantify DNA concentration Apologies for the hyperbole, but I feel super lucky to be living in 2026. A few weeks ago I decided to sequence a human genome to 30x at home. Then I actually did it. And I did it really quickly.

Seth Howes

410,148 görüntüleme • 2 ay önce

(1/n) 🚀 With FastVideo, you can now generate a 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licenses

(1/n) 🚀 With FastVideo, you can now generate a 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licenses

Hao AI Lab

78,660 görüntüleme • 1 yıl önce

The LimeWire Network is now LIVE in Public Beta 🚀 A decentralized object storage built for enterprise use - fully S3 compatible and easy to migrate from traditional providers like AWS or Google Cloud. Powered as a trustless system built on BNB Chain, LimeWire Network is already handling ~20M uploads every month. Web3 companies & enterprises can now tap into low-latency, cost-efficient storage at scale. Welcome to the next generation of storage 🔗

The LimeWire Network is now LIVE in Public Beta 🚀 A decentralized object storage built for enterprise use - fully S3 compatible and easy to migrate from traditional providers like AWS or Google Cloud. Powered as a trustless system built on BNB Chain, LimeWire Network is already handling ~20M uploads every month. Web3 companies & enterprises can now tap into low-latency, cost-efficient storage at scale. Welcome to the next generation of storage 🔗

LimeWire

77,768 görüntüleme • 4 ay önce

this video is 100% AI generated in under 2 minutes from a single prompt with seedance 2.0 prompt → full video these “yapping” style UGC ads are insanely fast to produce and even faster to scale one script → dozens of variations same product, different faces, tones, settings you can sell anything with this: - ecom - digital products - saas - supplements doesn’t matter speed + volume is the edge now rt + comment “yapper” and i’ll send the guide (follow for dm)

this video is 100% AI generated in under 2 minutes from a single prompt with seedance 2.0 prompt → full video these “yapping” style UGC ads are insanely fast to produce and even faster to scale one script → dozens of variations same product, different faces, tones, settings you can sell anything with this: - ecom - digital products - saas - supplements doesn’t matter speed + volume is the edge now rt + comment “yapper” and i’ll send the guide (follow for dm)

David

33,726 görüntüleme • 3 ay önce

Diffusion models are an amazing tool for cofolding, they allow us to predict a protein and the molecule bound to it at once. But they are not exactly fast and require a lot of denoising steps to get accurate predictions. So we distilled ours. Meet DeCAF-Pearl: the first flow map model for all-atom cofolding. Instead of inching along the denoising trajectory, a flow map learns to jump across it. DeCAF-Pearl runs structure generation ~5x faster than Pearl, our SOTA model, while still maintaining the performance of the teacher model. That speed up allows us to run larger experiments and generate more synthetic data to improve our models. Getting there meant reparameterizing into noise-level space to stabilize gradients, committing to clean-structure prediction to keep the rigid-alignment loss biomolecules needed, and building DeCAF-Search, one steering algorithm for every compute budget. For more technical details, read out blog post: And the paper:

Diffusion models are an amazing tool for cofolding, they allow us to predict a protein and the molecule bound to it at once. But they are not exactly fast and require a lot of denoising steps to get accurate predictions. So we distilled ours. Meet DeCAF-Pearl: the first flow map model for all-atom cofolding. Instead of inching along the denoising trajectory, a flow map learns to jump across it. DeCAF-Pearl runs structure generation ~5x faster than Pearl, our SOTA model, while still maintaining the performance of the teacher model. That speed up allows us to run larger experiments and generate more synthetic data to improve our models. Getting there meant reparameterizing into noise-level space to stabilize gradients, committing to clean-structure prediction to keep the rigid-alignment loss biomolecules needed, and building DeCAF-Search, one steering algorithm for every compute budget. For more technical details, read out blog post: And the paper:

Sergey Edunov

36,985 görüntüleme • 2 ay önce

SOUND ON. You’re hearing the first howl of a dire wolf in over 10,000 years. Meet Romulus and Remus—the world’s first de-extinct animals, born on October 1, 2024. The dire wolf has been extinct for over 10,000 years. These two wolves were brought back from extinction using genetic edits derived from a complete dire wolf genome, meticulously reconstructed by Colossal from ancient DNA found in fossils dating back 11,500 and 72,000 years. This moment marks not only a milestone for us as a company but also a leap forward for science, conservation, and humanity. From the beginning, our goal has been clear: “To revolutionize history and be the first company to use CRISPR technology successfully in the de-extinction of previously lost species.” By achieving this, we continue to push forward our broader mission on—accepting humanity’s duty to restore Earth to a healthier state. But this isn’t just our moment—it’s one for science, our planet, and humankind. All of which we love and are passionate about. Now, close your eyes and listen to that howl once more. Think about what this means for all of us.

SOUND ON. You’re hearing the first howl of a dire wolf in over 10,000 years. Meet Romulus and Remus—the world’s first de-extinct animals, born on October 1, 2024. The dire wolf has been extinct for over 10,000 years. These two wolves were brought back from extinction using genetic edits derived from a complete dire wolf genome, meticulously reconstructed by Colossal from ancient DNA found in fossils dating back 11,500 and 72,000 years. This moment marks not only a milestone for us as a company but also a leap forward for science, conservation, and humanity. From the beginning, our goal has been clear: “To revolutionize history and be the first company to use CRISPR technology successfully in the de-extinction of previously lost species.” By achieving this, we continue to push forward our broader mission on—accepting humanity’s duty to restore Earth to a healthier state. But this isn’t just our moment—it’s one for science, our planet, and humankind. All of which we love and are passionate about. Now, close your eyes and listen to that howl once more. Think about what this means for all of us.

Colossal Biosciences®

28,492,959 görüntüleme • 1 yıl önce

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?

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

57,362 görüntüleme • 6 gün önce

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 görüntüleme • 7 ay önce

Why spend hundreds on videography, creators, and production when AI UGC can deliver authentic, high-converting content in minutes? ✅ No actors needed ✅ No filming setup ✅ Faster turnaround ✅ Budget-friendly ✅ Easy creative testing at scale All you need is a product URL, and SmartUGC AI handles the heavy lifting—helping generate ad-ready UGC-style videos that would normally take days to produce. For brands, marketers, and creators looking to move faster without sacrificing creativity, this is definitely worth trying. Start creating AI UGC here

Why spend hundreds on videography, creators, and production when AI UGC can deliver authentic, high-converting content in minutes? ✅ No actors needed ✅ No filming setup ✅ Faster turnaround ✅ Budget-friendly ✅ Easy creative testing at scale All you need is a product URL, and SmartUGC AI handles the heavy lifting—helping generate ad-ready UGC-style videos that would normally take days to produce. For brands, marketers, and creators looking to move faster without sacrificing creativity, this is definitely worth trying. Start creating AI UGC here

FATHELA ESQ

130,887 görüntüleme • 1 ay önce

A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.

A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.

ALCHEMIST AI 🔮

48,744 görüntüleme • 1 yıl önce

Grok 2 mini is now 2x faster than it was yesterday. In the last three days Lianmin Zheng and Saeed Maleki rewrote our inference stack from scratch using SGLang ( This has also allowed us to serve the big Grok 2 model, which requires multi-host inference, at a reasonable speed. Both models didn’t just get faster, but also slightly more accurate. Stay tuned for further speed improvements!

Grok 2 mini is now 2x faster than it was yesterday. In the last three days Lianmin Zheng and Saeed Maleki rewrote our inference stack from scratch using SGLang ( This has also allowed us to serve the big Grok 2 model, which requires multi-host inference, at a reasonable speed. Both models didn’t just get faster, but also slightly more accurate. Stay tuned for further speed improvements!

Igor Babuschkin

1,803,113 görüntüleme • 2 yıl önce

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:

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 görüntüleme • 2 yıl önce

We've just wrapped our annual Delivering the Future event — showcasing our latest and greatest innovations in robotics, AI, and delivery technology. Here are the key updates: 🌟 We're using the latest in AI and robotics to get packages to you faster using tech pioneered for our next generation fulfillment center in Shreveport, LA — speeding up same-day deliveries by processing your orders 25% faster. 🌟 We're making deliveries for drivers easier with AI — using our Vision-Assisted Package Retrieval (VAPR). This in-van AI guidance system shows drivers exactly which packages need to be delivered at their current delivery spot — saving them ~30 mins per route and reducing their perceived physical and cognitive effort by 67% during their shifts. 🌟 We're enhancing our delivery services and shopping experience with AI — for example, you can now generate grocery lists directly from your meal plans. 🌟 We've removed all plastic air pillows from our delivery packaging, replacing them with paper filler made from recycled content. With this shift, we'll avoid nearly 15 BILLION plastic air pillows every year. 🌟 We've expanded our Amazon Pharmacy delivery services. We're more than doubling the number of Amazon Pharmacy locations at Same-Day delivery sites across the United States in 2025, which will mean more convenient access to medications, and upfront pricing.

We've just wrapped our annual Delivering the Future event — showcasing our latest and greatest innovations in robotics, AI, and delivery technology. Here are the key updates: 🌟 We're using the latest in AI and robotics to get packages to you faster using tech pioneered for our next generation fulfillment center in Shreveport, LA — speeding up same-day deliveries by processing your orders 25% faster. 🌟 We're making deliveries for drivers easier with AI — using our Vision-Assisted Package Retrieval (VAPR). This in-van AI guidance system shows drivers exactly which packages need to be delivered at their current delivery spot — saving them ~30 mins per route and reducing their perceived physical and cognitive effort by 67% during their shifts. 🌟 We're enhancing our delivery services and shopping experience with AI — for example, you can now generate grocery lists directly from your meal plans. 🌟 We've removed all plastic air pillows from our delivery packaging, replacing them with paper filler made from recycled content. With this shift, we'll avoid nearly 15 BILLION plastic air pillows every year. 🌟 We've expanded our Amazon Pharmacy delivery services. We're more than doubling the number of Amazon Pharmacy locations at Same-Day delivery sites across the United States in 2025, which will mean more convenient access to medications, and upfront pricing.

Amazon

52,491 görüntüleme • 1 yıl önce

Eleven years ago I founded my first startup. One of the first things I did was to get a desk at the startup coworking space at Google Campus in Old Street. Through that community, I met so many of the people who would shape my career and learned so much about what the startup ethos is truly about. As we have scaled Attio, we've partnered with Google Cloud to build on top of the mind blowing technologies they've created like Spanner, Colossus and Gemini to power a new generation of CRM for our customers. For all these reasons, Google is deeply woven into my startup journey and that makes today's announcement so special to me. I'm beyond excited to announce our $52 million Series B, led by Michael McBride and the GV, along with the continued support of our incredible partners from Redpoint , Point Nine , Balderton Capital and 01A. We're entering a software renaissance driven by AI and we're unbelievably excited about continuing the hard work of delivering the next generation of CRM. We're building a truly AI-native platform that can run code, integrate anywhere and bring unified context for the next generation of autonomous workflows. None of this would have been possible without the support of the more than 5,000 customers who have already chosen Attio as the backbone of their GTM stack. Your belief in our vision is what has made this possible and I can't wait to share more about some of the incredible functionality that's still to come. I'm incredibly grateful for the continued support of alex bard , Patrick Chase, Ricardo Sequerra Amram, Daniel Waterhouse and dick costolo whose belief in Attio from the earliest days has been so essential to our progress so far and to my co-founder Nicolas Sharp, for the many sleepless nights and challenging decisions that got us this far. We've got so much still to build, and we're always hiring, so if you are interested in being part of reinventing the core of the GTM stack, please take a look at For now though, it's back to building!

Eleven years ago I founded my first startup. One of the first things I did was to get a desk at the startup coworking space at Google Campus in Old Street. Through that community, I met so many of the people who would shape my career and learned so much about what the startup ethos is truly about. As we have scaled Attio, we've partnered with Google Cloud to build on top of the mind blowing technologies they've created like Spanner, Colossus and Gemini to power a new generation of CRM for our customers. For all these reasons, Google is deeply woven into my startup journey and that makes today's announcement so special to me. I'm beyond excited to announce our $52 million Series B, led by Michael McBride and the GV, along with the continued support of our incredible partners from Redpoint , Point Nine , Balderton Capital and 01A. We're entering a software renaissance driven by AI and we're unbelievably excited about continuing the hard work of delivering the next generation of CRM. We're building a truly AI-native platform that can run code, integrate anywhere and bring unified context for the next generation of autonomous workflows. None of this would have been possible without the support of the more than 5,000 customers who have already chosen Attio as the backbone of their GTM stack. Your belief in our vision is what has made this possible and I can't wait to share more about some of the incredible functionality that's still to come. I'm incredibly grateful for the continued support of alex bard , Patrick Chase, Ricardo Sequerra Amram, Daniel Waterhouse and dick costolo whose belief in Attio from the earliest days has been so essential to our progress so far and to my co-founder Nicolas Sharp, for the many sleepless nights and challenging decisions that got us this far. We've got so much still to build, and we're always hiring, so if you are interested in being part of reinventing the core of the GTM stack, please take a look at For now though, it's back to building!

Alexander Christie

16,506 görüntüleme • 11 ay önce

🚨 JUST IN: After saying ELON MUSK may be donating SpaceX stock to Trump Accounts, President Trump CONFIRMS he is opening them up to publicly traded stock This is HUGE for young Americans! 🇺🇸 "BIG NEWS! My Administration is delivering another fantastic America First Victory. The Department of Treasury will now accept philanthropic contributions of readily tradable Publicly Traded Stock to help fund TRUMP ACCOUNTS for eligible American children. This opens the door for GREAT American Businesses, Philanthropists, and Job Creators to invest directly in the future of our children, and help build a Generation of Savers, Investors, and Owners." "The response to TRUMP ACCOUNTS has been incredible — More than 6 MILLION TRUMP ACCOUNTS have already been requested before the Program has even launched. The American People LOVE this idea because it puts America’s children FIRST, rewards Success, and helps build Wealth for the next Generation. The Golden Age of America is just getting started! President DONALD J. TRUMP"

🚨 JUST IN: After saying ELON MUSK may be donating SpaceX stock to Trump Accounts, President Trump CONFIRMS he is opening them up to publicly traded stock This is HUGE for young Americans! 🇺🇸 "BIG NEWS! My Administration is delivering another fantastic America First Victory. The Department of Treasury will now accept philanthropic contributions of readily tradable Publicly Traded Stock to help fund TRUMP ACCOUNTS for eligible American children. This opens the door for GREAT American Businesses, Philanthropists, and Job Creators to invest directly in the future of our children, and help build a Generation of Savers, Investors, and Owners." "The response to TRUMP ACCOUNTS has been incredible — More than 6 MILLION TRUMP ACCOUNTS have already been requested before the Program has even launched. The American People LOVE this idea because it puts America’s children FIRST, rewards Success, and helps build Wealth for the next Generation. The Golden Age of America is just getting started! President DONALD J. TRUMP"

Eric Daugherty

276,821 görüntüleme • 1 ay önce

i apologize in advance for the shoddy footage of this once-in-a-generation event. this was the only copy we could license on our budget. tell you what: why don't you try going to another news provider to watch the video, and then just... come back here to read the story? okay, you're back now? alright, just pretend that you didn't hear about this yet. alright, here we go. well folks, you heard it here first: after millennia of stagnation, it seems that the mammal stacking scene has been brought back into global relevance... and it's all thanks to the talented antnards at the gigglegrub provincial zoo and bar. from what we've heard, the crew behind this feat is intending to take on the dreaded four-stack, once believed to be a TAS-exclusive stack for mammals. you can bet we'll be tuning into the event, sponsored by sloppco energy, when it airs live in early 2027.

i apologize in advance for the shoddy footage of this once-in-a-generation event. this was the only copy we could license on our budget. tell you what: why don't you try going to another news provider to watch the video, and then just... come back here to read the story? okay, you're back now? alright, just pretend that you didn't hear about this yet. alright, here we go. well folks, you heard it here first: after millennia of stagnation, it seems that the mammal stacking scene has been brought back into global relevance... and it's all thanks to the talented antnards at the gigglegrub provincial zoo and bar. from what we've heard, the crew behind this feat is intending to take on the dreaded four-stack, once believed to be a TAS-exclusive stack for mammals. you can bet we'll be tuning into the event, sponsored by sloppco energy, when it airs live in early 2027.

Myths and Stories

62,489 görüntüleme • 2 ay önce

𝗗𝗼𝗻𝗮𝗹𝗱 𝗝. 𝗧𝗿𝘂𝗺𝗽 𝗧𝗿𝘂𝘁𝗵 𝗦𝗼𝗰𝗶𝗮𝗹 𝗣𝗼𝘀𝘁 - 𝟬𝟴:𝟬𝟯 𝗣𝗠 𝗘𝗦𝗧 𝟬𝟳.𝟬𝟯.𝟮𝟲 BIG NEWS! My Administration is delivering another fantastic America First Victory. The Department of Treasury will now accept philanthropic contributions of readily tradable Publicly Traded Stock to help fund TRUMP ACCOUNTS for eligible American children. This opens the door for GREAT American Businesses, Philanthropists, and Job Creators to invest directly in the future of our children, and help build a Generation of Savers, Investors, and Owners. The response to TRUMP ACCOUNTS has been incredible — More than 6 MILLION TRUMP ACCOUNTS have already been requested before the Program has even launched. The American People LOVE this idea because it puts America’s children FIRST, rewards Success, and helps build Wealth for the next Generation. The Golden Age of America is just getting started! President DONALD J. TRUMP

𝗗𝗼𝗻𝗮𝗹𝗱 𝗝. 𝗧𝗿𝘂𝗺𝗽 𝗧𝗿𝘂𝘁𝗵 𝗦𝗼𝗰𝗶𝗮𝗹 𝗣𝗼𝘀𝘁 - 𝟬𝟴:𝟬𝟯 𝗣𝗠 𝗘𝗦𝗧 𝟬𝟳.𝟬𝟯.𝟮𝟲 BIG NEWS! My Administration is delivering another fantastic America First Victory. The Department of Treasury will now accept philanthropic contributions of readily tradable Publicly Traded Stock to help fund TRUMP ACCOUNTS for eligible American children. This opens the door for GREAT American Businesses, Philanthropists, and Job Creators to invest directly in the future of our children, and help build a Generation of Savers, Investors, and Owners. The response to TRUMP ACCOUNTS has been incredible — More than 6 MILLION TRUMP ACCOUNTS have already been requested before the Program has even launched. The American People LOVE this idea because it puts America’s children FIRST, rewards Success, and helps build Wealth for the next Generation. The Golden Age of America is just getting started! President DONALD J. TRUMP

Commentary Donald J. Trump Posts From Truth Social

153,322 görüntüleme • 1 ay önce

Imagine making 2D concept art for a game world –pressing a button – and suddenly you can walk around an interactive 3D world. That's what Google DeepMind's new paper Genie 2 can do – simulate virtual worlds, including the consequences of any action (e.g. unlock door, jump, swim etc). Right now Genie 2 can generate consistent worlds for up to a minute. And this world model seems to generate larger 3D worlds than what World Labs showcased yesterday. Plus they're dynamic vs. static worlds – the foliage moves in the wind, the water ripples etc. Not quite ready for prime time, but promising on two fronts: 1. For game developers: enabling rapid prototyping of interactive experiences straight from concept art 2. For AI research: providing unlimited, diverse 3D environments for training and testing AI agents The race for building the biggest, baddest world model is very much on. Meanwhile, all I can think is "if only Stadia was still around!"

Imagine making 2D concept art for a game world –pressing a button – and suddenly you can walk around an interactive 3D world. That's what Google DeepMind's new paper Genie 2 can do – simulate virtual worlds, including the consequences of any action (e.g. unlock door, jump, swim etc). Right now Genie 2 can generate consistent worlds for up to a minute. And this world model seems to generate larger 3D worlds than what World Labs showcased yesterday. Plus they're dynamic vs. static worlds – the foliage moves in the wind, the water ripples etc. Not quite ready for prime time, but promising on two fronts: 1. For game developers: enabling rapid prototyping of interactive experiences straight from concept art 2. For AI research: providing unlimited, diverse 3D environments for training and testing AI agents The race for building the biggest, baddest world model is very much on. Meanwhile, all I can think is "if only Stadia was still around!"

Bilawal Sidhu

71,326 görüntüleme • 1 yıl önce

Really excited to share that Attio has raised $52m in Series B funding, led by GV with participation from Redpoint, Balderton Capital, Point Nine, and 01A. Over the past two years, we’ve seen rapid growth. 5,000 customers now run their GTM on Attio, including , Granola, Modal, Replicate, Railway, and Public. We’re also on track to 4x ARR this year. CRM is entering its most exciting era. AI is reinventing the interface, killing manual work, and finally capturing customer context in full. But that’s only half the story. CRM isn’t just another tool. It should be the backbone of your GTM stack. If you want to scale, you need a CRM you can actually build on. This is our vision for Attio. We’re building a true AI-native CRM platform that can run code, integrate anywhere, and give every team the freedom to build their own applications, functionality, and GTM systems. Fully programmable, endlessly extensible and built for scale. If you can imagine it, you can build it. Grateful to my co-founder Alexander Christie, and to the support of our investors: Michael McBride (joining our board), alex bard, Daniel Waterhouse, Ricardo Sequerra Amram, and dick costolo. And most importantly, to our team, who’ve made all of this possible. The journey is just beginning. Back to building.

Really excited to share that Attio has raised $52m in Series B funding, led by GV with participation from Redpoint, Balderton Capital, Point Nine, and 01A. Over the past two years, we’ve seen rapid growth. 5,000 customers now run their GTM on Attio, including , Granola, Modal, Replicate, Railway, and Public. We’re also on track to 4x ARR this year. CRM is entering its most exciting era. AI is reinventing the interface, killing manual work, and finally capturing customer context in full. But that’s only half the story. CRM isn’t just another tool. It should be the backbone of your GTM stack. If you want to scale, you need a CRM you can actually build on. This is our vision for Attio. We’re building a true AI-native CRM platform that can run code, integrate anywhere, and give every team the freedom to build their own applications, functionality, and GTM systems. Fully programmable, endlessly extensible and built for scale. If you can imagine it, you can build it. Grateful to my co-founder Alexander Christie, and to the support of our investors: Michael McBride (joining our board), alex bard, Daniel Waterhouse, Ricardo Sequerra Amram, and dick costolo. And most importantly, to our team, who’ve made all of this possible. The journey is just beginning. Back to building.

Nicolas Sharp

83,447 görüntüleme • 11 ay önce

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!

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 görüntüleme • 9 gün önce