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We just shipped React Native ExecuTorch v0.8.0 – our biggest release yet! 👏🏼 ➡️ Vision Camera integration that runs computer vision models directly on your camera feed, in realtime. Also new: multiple CV models (including RF-DETR (Roboflow) and Liquid AI's Vision Language), Bare React Native support, and more. Takes...

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Software Mansion

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37,099 views • 3 months ago •via X (Twitter)

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Natively integrated—and #PoweredbyPyth🔮 Native is a liquidity solution that combines bridges, assets, and pricing into one offering on prominent L1/L2s, including . Learn more about our integration below: ℹ️ About Native Sourcing and supplying liquidity is expected to become prohibitively expensive and challenging as more networks emerge. Liquidity fragmentation persists because pricing remains inventory-based. Native was designed in response to this complexity and offers an elegant solution for routing any asset on one chain into a new target asset on any target chain. Native achieves this through a cross-chain liquidity mesh of cross-chain liquidity networks, bridges, DEXs, and PMMs. More specifically, Native can aggregate the best prices from its network of AMMs, aggregators, and partnered PMMs, so that users will always have competitive prices. 🔮 Native is #PoweredByPyth Native taps into Pyth’s real-time feeds to fetch prices on-chain and off-chain. for example, Native leverages Pyth Price Feeds to power ZetaSwap and support its token pricing operations. How Native Leverages Pyth Price Feeds Native runs its own in-house price-oracle service which will fetch pricing from a variety of sources, including Pyth, to update token prices in Native's backend server. These prices are then fetched and displayed to users live via the NativeX widget. Native also seeks to provide its proprietary trading data and become a part of the Pyth data provider community - more details to come.
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Natively integrated—and #PoweredbyPyth🔮 Native is a liquidity solution that combines bridges, assets, and pricing into one offering on prominent L1/L2s, including . Learn more about our integration below: ℹ️ About Native Sourcing and supplying liquidity is expected to become prohibitively expensive and challenging as more networks emerge. Liquidity fragmentation persists because pricing remains inventory-based. Native was designed in response to this complexity and offers an elegant solution for routing any asset on one chain into a new target asset on any target chain. Native achieves this through a cross-chain liquidity mesh of cross-chain liquidity networks, bridges, DEXs, and PMMs. More specifically, Native can aggregate the best prices from its network of AMMs, aggregators, and partnered PMMs, so that users will always have competitive prices. 🔮 Native is #PoweredByPyth Native taps into Pyth’s real-time feeds to fetch prices on-chain and off-chain. for example, Native leverages Pyth Price Feeds to power ZetaSwap and support its token pricing operations. How Native Leverages Pyth Price Feeds Native runs its own in-house price-oracle service which will fetch pricing from a variety of sources, including Pyth, to update token prices in Native's backend server. These prices are then fetched and displayed to users live via the NativeX widget. Native also seeks to provide its proprietary trading data and become a part of the Pyth data provider community - more details to come.

Pyth Network 🔮

48,219 views • 2 years ago

Introducing Muse Image and Muse Video, the first media generation models developed by Meta Superintelligence Labs. Muse Image is our most advanced image generation model yet. It follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. It also brings agentic tool use capabilities to image generation and integrates with Muse Spark. You can try Muse Image in the Meta AI app and web, as well as in Instagram Stories and WhatsApp – starting in limited countries with more locations on the way. Today we’re also previewing Muse Video, which is built upon the same pretraining base as Muse Image to deliver exceptional visual fidelity with native audio support. Learn more about both models:
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Introducing Muse Image and Muse Video, the first media generation models developed by Meta Superintelligence Labs. Muse Image is our most advanced image generation model yet. It follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. It also brings agentic tool use capabilities to image generation and integrates with Muse Spark. You can try Muse Image in the Meta AI app and web, as well as in Instagram Stories and WhatsApp – starting in limited countries with more locations on the way. Today we’re also previewing Muse Video, which is built upon the same pretraining base as Muse Image to deliver exceptional visual fidelity with native audio support. Learn more about both models:

AI at Meta

827,748 views • 14 days ago

🚨 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 )
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🚨 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

14,749 views • 3 months ago

🚨 SCIENTISTS JUST BUILT AN ARTIFICIAL RETINA THAT RESTORES VISION AND ADDS INFRARED SIGHT. Researchers at Yonsei University in South Korea have developed a flexible, three-layer implant that bypasses dead photoreceptors and directly stimulates healthy retinal ganglion cells. The device not only helps restore vision in cases of retinal degeneration (like retinitis pigmentosa) but also gives the eye the ability to detect near-infrared light that humans normally cannot see. The key innovation is a soft 3D array of liquid metal micropillars (gallium-indium alloy) that gently conform to the curved retina without causing damage or inflammation a major improvement over rigid electrodes used in earlier implants. Why this matters: • Retinal diseases destroy light-sensing cells, but the neurons deeper in the eye often remain healthy and capable of sending signals to the brain • The implant uses an ultrathin filter + phototransistor array to convert near-infrared light into electrical signals the ganglion cells can understand • In mouse tests, blind animals regained visual responses, while healthy mice gained infrared sensitivity on top of their normal vision • The liquid metal electrodes are soft and biocompatible, dramatically reducing the risk of scarring or tissue damage The deeper implication: This isn’t just about restoring lost vision it’s about augmenting human sight. If it reaches human trials and proves safe long-term, people with partial vision loss could keep their remaining natural sight while gaining an entirely new sensory channel (infrared). The biggest open question is how the human brain would interpret this new stream of information whether it would feel like a new color, an overlay, or something else entirely. We’re moving from “fixing blindness” to “expanding what it means to see.” How do you think gaining the ability to see infrared light would change daily life or human perception? Follow for more frontier neurotechnology and bionic vision breakthroughs.
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🚨 SCIENTISTS JUST BUILT AN ARTIFICIAL RETINA THAT RESTORES VISION AND ADDS INFRARED SIGHT. Researchers at Yonsei University in South Korea have developed a flexible, three-layer implant that bypasses dead photoreceptors and directly stimulates healthy retinal ganglion cells. The device not only helps restore vision in cases of retinal degeneration (like retinitis pigmentosa) but also gives the eye the ability to detect near-infrared light that humans normally cannot see. The key innovation is a soft 3D array of liquid metal micropillars (gallium-indium alloy) that gently conform to the curved retina without causing damage or inflammation a major improvement over rigid electrodes used in earlier implants. Why this matters: • Retinal diseases destroy light-sensing cells, but the neurons deeper in the eye often remain healthy and capable of sending signals to the brain • The implant uses an ultrathin filter + phototransistor array to convert near-infrared light into electrical signals the ganglion cells can understand • In mouse tests, blind animals regained visual responses, while healthy mice gained infrared sensitivity on top of their normal vision • The liquid metal electrodes are soft and biocompatible, dramatically reducing the risk of scarring or tissue damage The deeper implication: This isn’t just about restoring lost vision it’s about augmenting human sight. If it reaches human trials and proves safe long-term, people with partial vision loss could keep their remaining natural sight while gaining an entirely new sensory channel (infrared). The biggest open question is how the human brain would interpret this new stream of information whether it would feel like a new color, an overlay, or something else entirely. We’re moving from “fixing blindness” to “expanding what it means to see.” How do you think gaining the ability to see infrared light would change daily life or human perception? Follow for more frontier neurotechnology and bionic vision breakthroughs.

TheNewPhysics

34,362 views • 29 days ago

Throughout my journey in developing multimodal models, I’ve always wanted a framework that lets me plug & play modality encoders/decoders on top of an auto-regressive LLM. I want to prototype fast, try new architectures, and have my demo files scale effortlessly — with full support for parallelism and optimization. Not just to hack⚙️, but also to scale🚀. So finally we built it for ourselves. LMMs-Engine: a lean, efficient framework built to train unified multimodal model at scale. From Qwen LLM, VLM, LLaVA-OV, and WanVideo, to unified models like Qwen-Omni and BAGEL — plus Linear-Attn GDN and research prototypes like RAE and SiT - all under one modular system that seamlessly integrates diverse datasets and optimization strategies. Powered by FSDP2 multi-dim parallelism, Ulysses sequence parallel, Flash-Attention, Liger Kernels, and Native Sparse Attention (also with bonus support for the Muon optimizer for all models).
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Throughout my journey in developing multimodal models, I’ve always wanted a framework that lets me plug & play modality encoders/decoders on top of an auto-regressive LLM. I want to prototype fast, try new architectures, and have my demo files scale effortlessly — with full support for parallelism and optimization. Not just to hack⚙️, but also to scale🚀. So finally we built it for ourselves. LMMs-Engine: a lean, efficient framework built to train unified multimodal model at scale. From Qwen LLM, VLM, LLaVA-OV, and WanVideo, to unified models like Qwen-Omni and BAGEL — plus Linear-Attn GDN and research prototypes like RAE and SiT - all under one modular system that seamlessly integrates diverse datasets and optimization strategies. Powered by FSDP2 multi-dim parallelism, Ulysses sequence parallel, Flash-Attention, Liger Kernels, and Native Sparse Attention (also with bonus support for the Muon optimizer for all models).

Brian Li

54,818 views • 9 months ago

The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!
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The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!

ChainGPT

82,385 views • 5 months ago

Get READY, Squad. The wait is finally over. Season 1 drops TODAY. This is more than just gaming. This is the beginning of a new era. This is Season 1. With READYgg, you are in control. Own your in-game items as NFTs, shape the game's future, and engage with a powerful community. Join us in the first READYgg Gamer's Quests 👇 1️⃣ Prepare to complete missions and earn READYxp.🎯 2️⃣ Get an exclusive role and special channel access on Discord. 👾 3️⃣ Be one of the lucky 100 to win a game pass through our leaderboard airdrop. 🎲 4️⃣ Be one of the 2,000 to buy a game pass and get whitelisted for early access to special sales events. 😉 5️⃣ Get whitelisted for the $RDYX token public sale. 🪙 So, what can you do right now? ➡️ Read our blog post for more details and our vision - ➡️ Show your love to our Season 1 announcement tweet - ➡️ Check out our galxe space where the missions will be revealed - READY. SET. GAME ON!
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Get READY, Squad. The wait is finally over. Season 1 drops TODAY. This is more than just gaming. This is the beginning of a new era. This is Season 1. With READYgg, you are in control. Own your in-game items as NFTs, shape the game's future, and engage with a powerful community. Join us in the first READYgg Gamer's Quests 👇 1️⃣ Prepare to complete missions and earn READYxp.🎯 2️⃣ Get an exclusive role and special channel access on Discord. 👾 3️⃣ Be one of the lucky 100 to win a game pass through our leaderboard airdrop. 🎲 4️⃣ Be one of the 2,000 to buy a game pass and get whitelisted for early access to special sales events. 😉 5️⃣ Get whitelisted for the $RDYX token public sale. 🪙 So, what can you do right now? ➡️ Read our blog post for more details and our vision - ➡️ Show your love to our Season 1 announcement tweet - ➡️ Check out our galxe space where the missions will be revealed - READY. SET. GAME ON!

PLAY Network

22,799 views • 2 years ago

Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.
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Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.

Sakana AI

750,660 views • 1 year ago

HERMES AGENT NOW SUPPORTS COMPUTER USE ON WINDOWS AND LINUX. CLICKS, TYPES, SCROLLS YOUR DESKTOP IN THE BACKGROUND WHILE YOU WORK. computer use was macOS only. now it works on Windows and Linux too via Cua. Nous Research HOW IT WORKS: cua-driver runs as an MCP server. Hermes takes a screenshot with numbered elements. clicks element #14 (the search field). types a query. submits. reads the result. during all of this: → your cursor stays where you left it → keyboard focus doesn't change → windows don't come to front → macOS doesn't switch Spaces you and the agent co-work on the same machine. WHAT IT CAN DO: → find your latest Stripe email and summarize it → fill forms in a web app that has no API → navigate desktop apps (Mail, browser, Finder) → interact with any GUI application → extract data from apps only accessible via screen WORKS WITH ANY VISION MODEL: not locked to Anthropic. | Provider | Works | |---|---| | Claude (Sonnet/Opus) | best overall | | GPT-4+, GPT-5.5 | full support | | Gemini (via OpenRouter) | full support | | Local vLLM / LM Studio | if model supports vision | | Text-only models | degraded (accessibility tree only) | SETUP: hermes computer-use install or: hermes tools → Computer Use → cua-driver grant permissions when prompted: → Accessibility (system settings) → Screen Recording (system settings) start a session: hermes -t computer_use chat or add to config.yaml / Desktop app settings to enable permanently. SAFETY: → destructive actions require your approval → blocked key combos: empty trash, force delete, lock screen, log out → blocked type patterns: curl | bash, sudo rm -rf /, fork bombs → agent cannot click permission dialogs → agent cannot type passwords → agent cannot follow instructions embedded in screenshots pair with approvals.mode: manual if you want every single click confirmed. TOKEN NOTE: screenshots are expensive. each one adds vision tokens to context. use computer_use for tasks where no API exists. if the tool has an API or MCP server, use that instead. 15 levels of Hermes Agent👇
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HERMES AGENT NOW SUPPORTS COMPUTER USE ON WINDOWS AND LINUX. CLICKS, TYPES, SCROLLS YOUR DESKTOP IN THE BACKGROUND WHILE YOU WORK. computer use was macOS only. now it works on Windows and Linux too via Cua. Nous Research HOW IT WORKS: cua-driver runs as an MCP server. Hermes takes a screenshot with numbered elements. clicks element #14 (the search field). types a query. submits. reads the result. during all of this: → your cursor stays where you left it → keyboard focus doesn't change → windows don't come to front → macOS doesn't switch Spaces you and the agent co-work on the same machine. WHAT IT CAN DO: → find your latest Stripe email and summarize it → fill forms in a web app that has no API → navigate desktop apps (Mail, browser, Finder) → interact with any GUI application → extract data from apps only accessible via screen WORKS WITH ANY VISION MODEL: not locked to Anthropic. | Provider | Works | |---|---| | Claude (Sonnet/Opus) | best overall | | GPT-4+, GPT-5.5 | full support | | Gemini (via OpenRouter) | full support | | Local vLLM / LM Studio | if model supports vision | | Text-only models | degraded (accessibility tree only) | SETUP: hermes computer-use install or: hermes tools → Computer Use → cua-driver grant permissions when prompted: → Accessibility (system settings) → Screen Recording (system settings) start a session: hermes -t computer_use chat or add to config.yaml / Desktop app settings to enable permanently. SAFETY: → destructive actions require your approval → blocked key combos: empty trash, force delete, lock screen, log out → blocked type patterns: curl | bash, sudo rm -rf /, fork bombs → agent cannot click permission dialogs → agent cannot type passwords → agent cannot follow instructions embedded in screenshots pair with approvals.mode: manual if you want every single click confirmed. TOKEN NOTE: screenshots are expensive. each one adds vision tokens to context. use computer_use for tasks where no API exists. if the tool has an API or MCP server, use that instead. 15 levels of Hermes Agent👇

YanXbt

29,127 views • 29 days ago

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.
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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

83,307 views • 11 months ago

Release: LichtFeld Studio v0.5.3 is out! With 316 commits merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉
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Release: LichtFeld Studio v0.5.3 is out! With 316 commits merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉

MrNeRF

25,496 views • 28 days ago

Our first test flight is just the beginning! Behind the scenes, we are focused on up-scaling and improving our technology. We are excited to announce that we have successfully tested the central subsystem of our Helix 2.0 oxygen-rich staged-combustion engine: the powerpack. We have performed two successful hot-fire tests in which we have shown steady-state operation and cavitation limits. The powerpack incorporates the turbopump and pre-burner(s). It is the most complex as well as the most mechanically and thermally stressed subsystem of a staged-combustion engine. This milestone validated key technological challenges, such as the simultaneous ignition of multiple pre-burners and turbopump cavitation performance. The results are in-line with the predictions from our design models. The closed-cycle architecture of Helix allows us to push the performance envelope further: Helix 2.0 is designed to deliver double the thrust (200kN), while mass, production technology and costs remain comparable to Helix 1.0. The result for our customers: more payload for a lower budget! Excited about this news? Check out our career portal for employment opportunities and help us to elevate our Helix staged-combustion engine technology to the next level! ➡️
0:13

Our first test flight is just the beginning! Behind the scenes, we are focused on up-scaling and improving our technology. We are excited to announce that we have successfully tested the central subsystem of our Helix 2.0 oxygen-rich staged-combustion engine: the powerpack. We have performed two successful hot-fire tests in which we have shown steady-state operation and cavitation limits. The powerpack incorporates the turbopump and pre-burner(s). It is the most complex as well as the most mechanically and thermally stressed subsystem of a staged-combustion engine. This milestone validated key technological challenges, such as the simultaneous ignition of multiple pre-burners and turbopump cavitation performance. The results are in-line with the predictions from our design models. The closed-cycle architecture of Helix allows us to push the performance envelope further: Helix 2.0 is designed to deliver double the thrust (200kN), while mass, production technology and costs remain comparable to Helix 1.0. The result for our customers: more payload for a lower budget! Excited about this news? Check out our career portal for employment opportunities and help us to elevate our Helix staged-combustion engine technology to the next level! ➡️

Rocket Factory Augsburg

33,233 views • 2 months ago

Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)
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Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)

Andy Jassy

102,869 views • 1 year ago

From Lindsey Graham To those who are perpetuating false narratives against the United Arab Emirates and President Sheikh محمد بن زايد personally, you are full of it. I met with him today for an hour and a half. Not only is he alive, but he is also well and as sharp as I’ve ever seen him. To those powers that feel the need to attack MbZ and the UAE for doing the right thing - you do so at your own peril. Our meeting today was very enjoyable and informative. We discussed the historic moment that is facing the region. I told him how much I appreciated his courage and vision to create an Islamic country that can be integrated into the world in a win-win fashion, both for the people of the UAE and for those who visit and do business with the country. However, there are other voices in Islam that have the darkest vision of mankind. Those voices are distinctly in the minority, in my view. MbZ’s decision to embrace the Abraham Accords and to modernize his country while still maintaining the faith is the biggest change in the Middle East in my lifetime. What the United Arab Emirates have done to try to integrate the region with the whole world is one of the bravest and most consequential decisions any Middle Eastern leader has made. I was very candid with MbZ that he cannot do this by himself. Other people in the region have to buy-in to what’s happening with the UAE, not just be casual observers. To the region: Understand that history is about to be made. President Trump wants a region that looks more like the UAE and less like the Ayatollah. The region can only move forward if it follows the vision that embraces the light instead of going backwards into the darkness. The UAE’s vision for the Middle East and the 2030 vision previously expressed by the Crown Prince of Saudi Arabia is something I would fully embrace because it would be great for South Carolina, and great for America. The forces that are merging here recently are trying to undercut the movement toward the light. They are going back to the old way of doing business, playing cheap politics. Your actions have not gone unnoticed by me or others. If this continues, it will do enormous damage to the best opportunity I've seen in hundreds of years to change the Middle East for the better. Finally, to those who believe that the region still flourishes if the ayatollah’s regime survives, I could not disagree more. If this religious Nazi regime in Iran still stands after all this bluster and the people are shut out and continue to be oppressed, it puts everything we’ve worked for at risk, including the Abraham Accords.
0:10

From Lindsey Graham To those who are perpetuating false narratives against the United Arab Emirates and President Sheikh محمد بن زايد personally, you are full of it. I met with him today for an hour and a half. Not only is he alive, but he is also well and as sharp as I’ve ever seen him. To those powers that feel the need to attack MbZ and the UAE for doing the right thing - you do so at your own peril. Our meeting today was very enjoyable and informative. We discussed the historic moment that is facing the region. I told him how much I appreciated his courage and vision to create an Islamic country that can be integrated into the world in a win-win fashion, both for the people of the UAE and for those who visit and do business with the country. However, there are other voices in Islam that have the darkest vision of mankind. Those voices are distinctly in the minority, in my view. MbZ’s decision to embrace the Abraham Accords and to modernize his country while still maintaining the faith is the biggest change in the Middle East in my lifetime. What the United Arab Emirates have done to try to integrate the region with the whole world is one of the bravest and most consequential decisions any Middle Eastern leader has made. I was very candid with MbZ that he cannot do this by himself. Other people in the region have to buy-in to what’s happening with the UAE, not just be casual observers. To the region: Understand that history is about to be made. President Trump wants a region that looks more like the UAE and less like the Ayatollah. The region can only move forward if it follows the vision that embraces the light instead of going backwards into the darkness. The UAE’s vision for the Middle East and the 2030 vision previously expressed by the Crown Prince of Saudi Arabia is something I would fully embrace because it would be great for South Carolina, and great for America. The forces that are merging here recently are trying to undercut the movement toward the light. They are going back to the old way of doing business, playing cheap politics. Your actions have not gone unnoticed by me or others. If this continues, it will do enormous damage to the best opportunity I've seen in hundreds of years to change the Middle East for the better. Finally, to those who believe that the region still flourishes if the ayatollah’s regime survives, I could not disagree more. If this religious Nazi regime in Iran still stands after all this bluster and the people are shut out and continue to be oppressed, it puts everything we’ve worked for at risk, including the Abraham Accords.

Amjad Taha أمجد طه

191,467 views • 5 months ago

Acclaimed metal band BABYMETAL has signed to Capitol Records, the first Japanese artist to sign a frontline deal with the label. The group will release new album METAL FORTH, globally via Capitol Records on June 13. “BABYMETAL’s groundbreaking sound and compelling artistic vision have not only cultivated a worldwide following, but have also demonstrably shifted global music culture. We at Capitol Records are privileged to join them in this next chapter as we continue to amplify their international reach and influence with the upcoming release of Metal Forth.” – Tom March, Chairman & CEO, Capitol Records “This year, BABYMETAL celebrates its 15th anniversary and embarks on an exciting new chapter. With Capitol Records as our global partner, the sound of BABYMETAL will resonate across the world as we take on bolder, more dynamic endeavors than ever before. Stay tuned for what’s to come.” - Key “KOBAMETAL” Kobayashi, Producer & Manager of BABYMETAL CEO, BABYMETAL WORLD, LLC As BABYMETAL celebrates its 15th anniversary this year, the band has mapped out a world tour that will include numerous firsts. In May, they’ll embark on their first-ever headline arena tour in the UK and Europe, playing 12 shows across eight countries and concluding at The O2 Arena in London. BABYMETAL is the first Japanese group to headline a show at this iconic venue. The band will kick off its biggest North American tour yet on June 13. The 24-date run will include a June 24 show at The Theatre at Madison Square Garden in New York City. Check for more information. Arena shows in Japan and a tour of Asia will follow.
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Acclaimed metal band BABYMETAL has signed to Capitol Records, the first Japanese artist to sign a frontline deal with the label. The group will release new album METAL FORTH, globally via Capitol Records on June 13. “BABYMETAL’s groundbreaking sound and compelling artistic vision have not only cultivated a worldwide following, but have also demonstrably shifted global music culture. We at Capitol Records are privileged to join them in this next chapter as we continue to amplify their international reach and influence with the upcoming release of Metal Forth.” – Tom March, Chairman & CEO, Capitol Records “This year, BABYMETAL celebrates its 15th anniversary and embarks on an exciting new chapter. With Capitol Records as our global partner, the sound of BABYMETAL will resonate across the world as we take on bolder, more dynamic endeavors than ever before. Stay tuned for what’s to come.” - Key “KOBAMETAL” Kobayashi, Producer & Manager of BABYMETAL CEO, BABYMETAL WORLD, LLC As BABYMETAL celebrates its 15th anniversary this year, the band has mapped out a world tour that will include numerous firsts. In May, they’ll embark on their first-ever headline arena tour in the UK and Europe, playing 12 shows across eight countries and concluding at The O2 Arena in London. BABYMETAL is the first Japanese group to headline a show at this iconic venue. The band will kick off its biggest North American tour yet on June 13. The 24-date run will include a June 24 show at The Theatre at Madison Square Garden in New York City. Check for more information. Arena shows in Japan and a tour of Asia will follow.

BABYMETAL

282,976 views • 1 year ago

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
0:14

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 views • 6 months ago

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷
0:13

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 views • 6 months ago

$AIG Genesis RollDrop 🪂 Checker / Claim is Live (and it's a mess) 🔸Visit Discord: 🔸Go to aigisos-rolldrop channel 🔸Type /check (select 0x or DYM) WalletAddress (enter your $DYM address where you stake $DYM) 🔸 You can type /view-addresses to view all your Rolldrop wallet address and the amount of $AIG allocated each 🔸Looks like 5 addresses is MAX You will get a confirmation message that you have successfully confirmed your $AIG allocation. You will also get "Rolldrop Confirmed" role on Discord 🔸🔸Stake $TIA with our Sponsor Cosmos | Everstake and be eligible for potential future Airdrops. 🔸🔸 Issues 🔸Everyone and their Mom is typing in their addresses and "/check..." 🔸Slow mode enabled, about 2 minutes so you have to wait 🔸You don't get a response in 2 minutes at this point 🔸Seems like farming Discord attempt vs. just publishing this on the web 🔸My suggestion just wait this out until things slow down (or may be the Aigisos team grows up and puts a web checker) Eligibility Criteria for $AIG Airdrop ➡️Total supply: 1B $AIG ➡️12% $AIG Genesis RollDrop to $DYM Stakers, with more allocations in the future ➡️500K Wallets are eligible ➡️Full wallet list: Specific Categories eligible for $AIG Airdrop ➡️ $DYM Stakers Every $DYM staker who had 1 $DYM staked 📸Snapshot: July 23rd, 2024 ➡️Multiple Perpetual DEX Traders Whoever has traded in 2 or more of the popular Crypto Perpetual DEXes (DYDX, Synthetix, Perpetual and Pika with the base intersection to GMX {.ie you had traded on GMX and then also traded on either dYdX , Synthetix, Perpetual and Pika} 📸Snapshot: July 30th, 2024 ➡️Top Gitcoin Donors Top Gitcoin Donors who have donated 15 or more times to a decentralized cause. 📸Snapshot: July 30th, 2024 ➡️Earndrop Contributors The decentralized community that actively supports and tests many new projects that are in development stage and launched are vital to the crypto ecosystem. ➡️Phoenix Incentivized testnet Claim deatils will be announced later. Any AIG allocation that is not confirmed will be redistributed to the users who had successfully participated in the Rolldrop and their allocations increased. 🔗Links ➡️Pitchdeck ➡️Litepaper ➡️Web ➡️TG ➡️Git ➡️Discord 🫡
0:10

$AIG Genesis RollDrop 🪂 Checker / Claim is Live (and it's a mess) 🔸Visit Discord: 🔸Go to aigisos-rolldrop channel 🔸Type /check (select 0x or DYM) WalletAddress (enter your $DYM address where you stake $DYM) 🔸 You can type /view-addresses to view all your Rolldrop wallet address and the amount of $AIG allocated each 🔸Looks like 5 addresses is MAX You will get a confirmation message that you have successfully confirmed your $AIG allocation. You will also get "Rolldrop Confirmed" role on Discord 🔸🔸Stake $TIA with our Sponsor Cosmos | Everstake and be eligible for potential future Airdrops. 🔸🔸 Issues 🔸Everyone and their Mom is typing in their addresses and "/check..." 🔸Slow mode enabled, about 2 minutes so you have to wait 🔸You don't get a response in 2 minutes at this point 🔸Seems like farming Discord attempt vs. just publishing this on the web 🔸My suggestion just wait this out until things slow down (or may be the Aigisos team grows up and puts a web checker) Eligibility Criteria for $AIG Airdrop ➡️Total supply: 1B $AIG ➡️12% $AIG Genesis RollDrop to $DYM Stakers, with more allocations in the future ➡️500K Wallets are eligible ➡️Full wallet list: Specific Categories eligible for $AIG Airdrop ➡️ $DYM Stakers Every $DYM staker who had 1 $DYM staked 📸Snapshot: July 23rd, 2024 ➡️Multiple Perpetual DEX Traders Whoever has traded in 2 or more of the popular Crypto Perpetual DEXes (DYDX, Synthetix, Perpetual and Pika with the base intersection to GMX {.ie you had traded on GMX and then also traded on either dYdX , Synthetix, Perpetual and Pika} 📸Snapshot: July 30th, 2024 ➡️Top Gitcoin Donors Top Gitcoin Donors who have donated 15 or more times to a decentralized cause. 📸Snapshot: July 30th, 2024 ➡️Earndrop Contributors The decentralized community that actively supports and tests many new projects that are in development stage and launched are vital to the crypto ecosystem. ➡️Phoenix Incentivized testnet Claim deatils will be announced later. Any AIG allocation that is not confirmed will be redistributed to the users who had successfully participated in the Rolldrop and their allocations increased. 🔗Links ➡️Pitchdeck ➡️Litepaper ➡️Web ➡️TG ➡️Git ➡️Discord 🫡

Grey Ledger

81,966 views • 1 year ago

Our 2025 Achievements at Propbase It has been a rockstar start to the year and we have so much more to come after so many achievements already! H1 - So far Launched Propbase 2.0 Technical Roadmap We unveiled our Propbase 2.0 roadmap, focusing on DeFi-inspired lending, AI-powered automation, and institutional-grade tokenization infrastructure. Why It Matters: This roadmap is our blueprint for transforming real estate investment. By integrating DeFi mechanics and AI, we’re scaling our platform to attract both retail and institutional investors, boosting our ecosystem’s total value locked (TVL). Tokenized and Sold Out Ramada Plaza by Wyndham We tokenized a Ramada Plaza by Wyndham property, and it sold out in just 11 hours. Why It Matters: This rapid sell-out shows the trust our investors place in us. It proves we can deliver high-quality assets, making real estate investment accessible with fractional ownership starting at $100. Revamped Our Website and Updated PROPS Vesting We launched a sleek new website and introduced a performance-driven PROPS vesting schedule to reward long-term holders. Why It Matters: Our new website makes it easier for users to explore our platform, while the vesting schedule—unique in crypto—builds confidence among token holders, aligning incentives for long-term growth. Switched to Native USDC Across Products We transitioned from USDT to native USDC for all our products, including crowdfunding, trading, and staking. Why It Matters: USDC’s reliability enhances transaction security and aligns us with global standards, making our platform more appealing to investors worldwide. Passed Certik Audit for APEX Smart Contracts Our Apex platform, a decentralized marketplace for property-backed tokens, passed a Certik audit with flying colors. Why It Matters: Security is our priority. This audit reassures our users that Apex’s smart contracts are rock-solid, enabling safe and scalable trading. Fully Launched Our Secondary Marketplace We rolled out our secondary marketplace, allowing users to buy and sell tokenized property shares with improved liquidity. Why It Matters: This marketplace solves the liquidity challenge in real estate, letting investors trade stakes anytime, anywhere, which drives engagement and trading volume. Deepened Integration with Aptos Ecosystem We focused on integrating with the Aptos blockchain, prioritizing TVL growth through Propbase Nexus and trading volume on our secondary marketplace. Why It Matters: Aptos’s scalability and low fees make our platform more efficient. This integration fuels our growth and strengthens our role in the Aptos ecosystem. Started Mobile App Development 💎💎💎Coming H2 2025💎💎💎 Propbase App Full-stack development for our iOS and Android apps, with a beta rollout planned for early users, featuring trading, staking, and governance. Why It Matters: A mobile app makes our platform accessible on the go, meeting user expectations and driving adoption among a broader audience. P2P Lending and Borrowing P2P lending and borrowing system, inspired by Aave and Compound, letting users borrow USDC using asset-backed tokens as collateral. Why It Matters: This DeFi feature unlocks capital for investors without forcing asset sales, boosting token utility and attracting DeFi enthusiasts. AI-Powered Liquidity Automation AI models to automate liquidity stability across our asset pools, using DEFAI for real-time risk management and market optimization. Why It Matters: Our AI ensures price stability and efficient liquidity, setting us apart as a tech-forward platform and building investor trust. Make sure you're here with us in 2025 H2 for Propbase to give you more than what they already have from H1
0:15

Our 2025 Achievements at Propbase It has been a rockstar start to the year and we have so much more to come after so many achievements already! H1 - So far Launched Propbase 2.0 Technical Roadmap We unveiled our Propbase 2.0 roadmap, focusing on DeFi-inspired lending, AI-powered automation, and institutional-grade tokenization infrastructure. Why It Matters: This roadmap is our blueprint for transforming real estate investment. By integrating DeFi mechanics and AI, we’re scaling our platform to attract both retail and institutional investors, boosting our ecosystem’s total value locked (TVL). Tokenized and Sold Out Ramada Plaza by Wyndham We tokenized a Ramada Plaza by Wyndham property, and it sold out in just 11 hours. Why It Matters: This rapid sell-out shows the trust our investors place in us. It proves we can deliver high-quality assets, making real estate investment accessible with fractional ownership starting at $100. Revamped Our Website and Updated PROPS Vesting We launched a sleek new website and introduced a performance-driven PROPS vesting schedule to reward long-term holders. Why It Matters: Our new website makes it easier for users to explore our platform, while the vesting schedule—unique in crypto—builds confidence among token holders, aligning incentives for long-term growth. Switched to Native USDC Across Products We transitioned from USDT to native USDC for all our products, including crowdfunding, trading, and staking. Why It Matters: USDC’s reliability enhances transaction security and aligns us with global standards, making our platform more appealing to investors worldwide. Passed Certik Audit for APEX Smart Contracts Our Apex platform, a decentralized marketplace for property-backed tokens, passed a Certik audit with flying colors. Why It Matters: Security is our priority. This audit reassures our users that Apex’s smart contracts are rock-solid, enabling safe and scalable trading. Fully Launched Our Secondary Marketplace We rolled out our secondary marketplace, allowing users to buy and sell tokenized property shares with improved liquidity. Why It Matters: This marketplace solves the liquidity challenge in real estate, letting investors trade stakes anytime, anywhere, which drives engagement and trading volume. Deepened Integration with Aptos Ecosystem We focused on integrating with the Aptos blockchain, prioritizing TVL growth through Propbase Nexus and trading volume on our secondary marketplace. Why It Matters: Aptos’s scalability and low fees make our platform more efficient. This integration fuels our growth and strengthens our role in the Aptos ecosystem. Started Mobile App Development 💎💎💎Coming H2 2025💎💎💎 Propbase App Full-stack development for our iOS and Android apps, with a beta rollout planned for early users, featuring trading, staking, and governance. Why It Matters: A mobile app makes our platform accessible on the go, meeting user expectations and driving adoption among a broader audience. P2P Lending and Borrowing P2P lending and borrowing system, inspired by Aave and Compound, letting users borrow USDC using asset-backed tokens as collateral. Why It Matters: This DeFi feature unlocks capital for investors without forcing asset sales, boosting token utility and attracting DeFi enthusiasts. AI-Powered Liquidity Automation AI models to automate liquidity stability across our asset pools, using DEFAI for real-time risk management and market optimization. Why It Matters: Our AI ensures price stability and efficient liquidity, setting us apart as a tech-forward platform and building investor trust. Make sure you're here with us in 2025 H2 for Propbase to give you more than what they already have from H1

Propbase

46,508 views • 1 year ago

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

112,114 views • 6 days ago