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🌩️ Edge AI Network on Solana Captures 10 Million Vehicles Daily 375ai processes real-time highway traffic across 40,000 US billboard locations with 250,000+ mobile contributors, turning the physical world into structured data. NVIDIA edge devices compress 1TB of video into 35MB of insights daily while preserving privacy through local... show more

Generation Infinity

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181,518 views • 7 months ago •via X (Twitter)

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The 375ai Mainnet is live! 375ai indexes the real world, transforming video, audio and environmental data into real time intelligence using edge AI. A new data layer for the physical world, now on @Solana

The 375ai Mainnet is live! 375ai indexes the real world, transforming video, audio and environmental data into real time intelligence using edge AI. A new data layer for the physical world, now on @Solana

375ai

227,848 views • 11 months ago

Edge AI shifts intelligence directly into devices, allowing them to interpret and respond in real time—a direction that favors agility, reduces dependency on the cloud, and aligns with natural human-machine interaction. More: T-Mobile Business #TFBPartner Edge AI brings machine learning to the source of data generation, enabling devices to process and act on information in real-time. In logistics, this means staying ahead of slowdowns through real-time tracking and predictive routing. At clinics, medical devices can analyze patient vitals locally, supporting faster diagnostics and better care. In retail stores, Edge AI delivers personalized experiences on the spot, adapting promotions or layouts based on shopper behavior. By avoiding the delay of cloud communication and leveraging 5G speed, Edge AI enables smarter decisions where and when they’re most needed. #EdgeComputing #EdgeAI #5G #AI

Edge AI shifts intelligence directly into devices, allowing them to interpret and respond in real time—a direction that favors agility, reduces dependency on the cloud, and aligns with natural human-machine interaction. More: T-Mobile Business #TFBPartner Edge AI brings machine learning to the source of data generation, enabling devices to process and act on information in real-time. In logistics, this means staying ahead of slowdowns through real-time tracking and predictive routing. At clinics, medical devices can analyze patient vitals locally, supporting faster diagnostics and better care. In retail stores, Edge AI delivers personalized experiences on the spot, adapting promotions or layouts based on shopper behavior. By avoiding the delay of cloud communication and leveraging 5G speed, Edge AI enables smarter decisions where and when they’re most needed. #EdgeComputing #EdgeAI #5G #AI

Antonio Grasso

20,325 views • 1 year ago

one of my favorite aspects of this new work is that it is generative, not based on an arbitrary random number, but based on the chaos of real life. the physical sculpture is constantly pulling in news and real-time data from all over the world and the NFT is updated daily as well with this hectic collage of information overload... i believe in the future more work will be dynamic like this allowing us to reimagine real-time data in new ways based on different events happening in the world...

one of my favorite aspects of this new work is that it is generative, not based on an arbitrary random number, but based on the chaos of real life. the physical sculpture is constantly pulling in news and real-time data from all over the world and the NFT is updated daily as well with this hectic collage of information overload... i believe in the future more work will be dynamic like this allowing us to reimagine real-time data in new ways based on different events happening in the world...

beeple

155,812 views • 2 years ago

Explore where physical AI is headed next with the people shaping it. On Feb. 10 at 9 a.m. PT, join Ming‑Yu, Vice President of Research at NVIDIA, and Chubby♨️ from Superintelligence, for a conversation on how NVIDIA Cosmos reframes AI for a world of robots, AVs, and vision-driven systems. We'll dig into: ✅ Cosmos' origins and designing models for the physical world ✅ The scientific ideas and architectures behind Cosmos and its open models ✅ How open, reasoning-capable physical AI is changing how teams prototype, deploy, and iterate ✅ What a future of AI systems that understand space, time, and causality could unlock If you’re thinking into real‑world AI, this is the conversation to catch ➡️

Explore where physical AI is headed next with the people shaping it. On Feb. 10 at 9 a.m. PT, join Ming‑Yu, Vice President of Research at NVIDIA, and Chubby♨️ from Superintelligence, for a conversation on how NVIDIA Cosmos reframes AI for a world of robots, AVs, and vision-driven systems. We'll dig into: ✅ Cosmos' origins and designing models for the physical world ✅ The scientific ideas and architectures behind Cosmos and its open models ✅ How open, reasoning-capable physical AI is changing how teams prototype, deploy, and iterate ✅ What a future of AI systems that understand space, time, and causality could unlock If you’re thinking into real‑world AI, this is the conversation to catch ➡️

NVIDIA AI Developer

26,468 views • 7 months ago

“In a couple of years, you’ll see humanoid robots walking around. But they need a lot of infrastructure. They need a lot of data to understand what’s happening and where. They need exact positioning, for example. They need real-time streaming and edge computing. And there are a lot of DePINs that are basically creating the infrastructure physical AI needs to navigate the world.” – peaq co-founder Leo

“In a couple of years, you’ll see humanoid robots walking around. But they need a lot of infrastructure. They need a lot of data to understand what’s happening and where. They need exact positioning, for example. They need real-time streaming and edge computing. And there are a lot of DePINs that are basically creating the infrastructure physical AI needs to navigate the world.” – peaq co-founder Leo

peaq

68,650 views • 1 year ago

AMR Network: Portraits with CoreWeave. What happens when 40 live radio channels meet AI that never stops listening? “One of the things we're doing with the team is working to bring all of the data points into one place, processing them in real time and then being able to send out information which will enable us to drive real-time decisions.” Ben Richardson, VP Strategy, CoreWeave. Watch the full video:

AMR Network: Portraits with CoreWeave. What happens when 40 live radio channels meet AI that never stops listening? “One of the things we're doing with the team is working to bring all of the data points into one place, processing them in real time and then being able to send out information which will enable us to drive real-time decisions.” Ben Richardson, VP Strategy, CoreWeave. Watch the full video:

Aston Martin Aramco F1 Team

35,291 views • 1 month ago

Robotics has a massive, silent bottleneck. It isn’t just data collection—it’s the brutal 1x speed of the physical world. Genesis AI Genesis AI just unveiled Genesis World 1.0, and they are attempting to turn the notorious Sim2Real gap into a pure compute problem. Evaluating a robotics foundation model across edge cases usually means hundreds of hours of physical lab testing. With Genesis World 1.0, what traditionally takes nearly a week of continuous, real-world operation is being compressed into 30 minutes in simulation. What makes this different from just dropping a robot model into an off-the-shelf game engine? 1️⃣ Nyx Renderer: A custom, real-time path-traced engine rendering noise-free 1080p frames in under 4ms. Game engines use rasterization tricks that confuse AI; Nyx uses physically accurate multi-bounce lighting so the model's "eyes" see exactly what real sensors see. 2️⃣ Quadrants Compiler: A custom Python-to-GPU compiler to run heavily parallelized multi-physics simulations (rigid bodies, fluids, deformables) natively across architectures. 3️⃣ Evaluation First: They aren't rushing to train on synthetic data. They are using this purely for closed-loop evaluation to perfect the physics first, currently claiming an impressive 89% correlation with real-world hardware tests. If the industry can accurately evaluate models in simulation without the physical world bottleneck, humanoid development stops moving at wall-clock time and starts scaling with compute.

Robotics has a massive, silent bottleneck. It isn’t just data collection—it’s the brutal 1x speed of the physical world. Genesis AI Genesis AI just unveiled Genesis World 1.0, and they are attempting to turn the notorious Sim2Real gap into a pure compute problem. Evaluating a robotics foundation model across edge cases usually means hundreds of hours of physical lab testing. With Genesis World 1.0, what traditionally takes nearly a week of continuous, real-world operation is being compressed into 30 minutes in simulation. What makes this different from just dropping a robot model into an off-the-shelf game engine? 1️⃣ Nyx Renderer: A custom, real-time path-traced engine rendering noise-free 1080p frames in under 4ms. Game engines use rasterization tricks that confuse AI; Nyx uses physically accurate multi-bounce lighting so the model's "eyes" see exactly what real sensors see. 2️⃣ Quadrants Compiler: A custom Python-to-GPU compiler to run heavily parallelized multi-physics simulations (rigid bodies, fluids, deformables) natively across architectures. 3️⃣ Evaluation First: They aren't rushing to train on synthetic data. They are using this purely for closed-loop evaluation to perfect the physics first, currently claiming an impressive 89% correlation with real-world hardware tests. If the industry can accurately evaluate models in simulation without the physical world bottleneck, humanoid development stops moving at wall-clock time and starts scaling with compute.

Humanoids daily

17,302 views • 4 months ago

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

ard

225,265 views • 2 months ago

🚀 Roam Network x Coverage Critic🚀 We’re excited to partner with Coverage Critic to deliver transparent, real-time insights into mobile coverage and performance. This is a major real-world use case for Roam Network and a big step for DePIN solutions. 🔗 Powered by peaq blockchain peaq ensures data is secure, transparent, and decentralized, enabling trusted, community-driven connectivity data. 🌍 Together, we’re empowering users to make smarter decisions about mobile networks while driving real impact in the telecom industry. The future of connectivity is here. Watch the full video! 📲 #RoamNetwork #CoverageCritic #DePIN #Web3 #IoT #peaq

🚀 Roam Network x Coverage Critic🚀 We’re excited to partner with Coverage Critic to deliver transparent, real-time insights into mobile coverage and performance. This is a major real-world use case for Roam Network and a big step for DePIN solutions. 🔗 Powered by peaq blockchain peaq ensures data is secure, transparent, and decentralized, enabling trusted, community-driven connectivity data. 🌍 Together, we’re empowering users to make smarter decisions about mobile networks while driving real impact in the telecom industry. The future of connectivity is here. Watch the full video! 📲 #RoamNetwork #CoverageCritic #DePIN #Web3 #IoT #peaq

Roam Network 🌐

113,690 views • 1 year ago

Ever wondered how AI could understand your company’s private data the way Google understands the web? Elastic is doing just that by being the foundation for context engineering: ▶️ Linking an organisation’s proprietary data to AI, making its data searchable and actionable. ▶️ Enabling multimodal, multilingual AI experiences in India’s Tier II and III cities. ▶️ Driving global business impact for enterprises and governments alike. In this exclusive conversation with Ash Kulkarni, CEO of Elastic, learn how the company is helping enterprises turn data into clarity and real-world outcomes. See the full article and video here: Shradha Sharma

Ever wondered how AI could understand your company’s private data the way Google understands the web? Elastic is doing just that by being the foundation for context engineering: ▶️ Linking an organisation’s proprietary data to AI, making its data searchable and actionable. ▶️ Enabling multimodal, multilingual AI experiences in India’s Tier II and III cities. ▶️ Driving global business impact for enterprises and governments alike. In this exclusive conversation with Ash Kulkarni, CEO of Elastic, learn how the company is helping enterprises turn data into clarity and real-world outcomes. See the full article and video here: Shradha Sharma

YourStory

16,476 views • 9 months ago

WHERE THE ENTERPRISE AI STACK IS BEING BUILT 1. $PLTR building the orchestration layer of the AI economy through the control systems enterprises need before models can take real-world actions backed by a Rule of 40 score of 127 2. $CRWD, $PANW, $ZS & $RBRK building the security layer of the AI economy across endpoint, identity, Zero Trust & cyber recovery as every new agent, machine identity & AI-generated workflow expands the attack surface 3. $NET building the connectivity layer of the AI economy through the internet control plane that agents, APIs & inference traffic must move across in real time, with Cloudflare sitting behind over 20% of the web 4. $SNOW & $MDB building the data layer of the AI economy through the clean, structured, production-grade enterprise data systems that make AI usable inside real applications and workflows

WHERE THE ENTERPRISE AI STACK IS BEING BUILT 1. $PLTR building the orchestration layer of the AI economy through the control systems enterprises need before models can take real-world actions backed by a Rule of 40 score of 127 2. $CRWD, $PANW, $ZS & $RBRK building the security layer of the AI economy across endpoint, identity, Zero Trust & cyber recovery as every new agent, machine identity & AI-generated workflow expands the attack surface 3. $NET building the connectivity layer of the AI economy through the internet control plane that agents, APIs & inference traffic must move across in real time, with Cloudflare sitting behind over 20% of the web 4. $SNOW & $MDB building the data layer of the AI economy through the clean, structured, production-grade enterprise data systems that make AI usable inside real applications and workflows

Shay Boloor

399,438 views • 5 months ago

I usually skip benchmark comparisons. They're almost always picked to flatter. But EgoSchema is different. It measures first-person, egocentric video understanding. The AI equivalent of “look through the robot's eyes and tell me what happened.” I spent some time testing Perceptron Mk1 (“Mark One”) on the robotics workflows Perceptron walked me through, and the EgoSchema result suddenly made a lot more sense. Perceptron Mk1 scored 80.60% on it. Gemini 3.1 Flash Lite scored 62.4%. That's a completely different level of understanding. Most frontier models were trained on the internet through text, images, and static content. The physical world works differently. Context lives across time. The critical moment rarely exists in a single frame. And that's exactly where production deployments start mattering. Manufacturing QC. Robotics data pipelines. Security footage. Everything is temporal. Native video reasoning. Temporal grounding. Structured output. Perceptron calls it “Mk1” because it marks the beginning of their closed-model series for physical-world intelligence, and honestly, the name fits. It feels engineered for deployment, not just demos. Feels like one of the first video AI systems being built around how the physical world actually behaves instead of adapting static-image intelligence into video later.

I usually skip benchmark comparisons. They're almost always picked to flatter. But EgoSchema is different. It measures first-person, egocentric video understanding. The AI equivalent of “look through the robot's eyes and tell me what happened.” I spent some time testing Perceptron Mk1 (“Mark One”) on the robotics workflows Perceptron walked me through, and the EgoSchema result suddenly made a lot more sense. Perceptron Mk1 scored 80.60% on it. Gemini 3.1 Flash Lite scored 62.4%. That's a completely different level of understanding. Most frontier models were trained on the internet through text, images, and static content. The physical world works differently. Context lives across time. The critical moment rarely exists in a single frame. And that's exactly where production deployments start mattering. Manufacturing QC. Robotics data pipelines. Security footage. Everything is temporal. Native video reasoning. Temporal grounding. Structured output. Perceptron calls it “Mk1” because it marks the beginning of their closed-model series for physical-world intelligence, and honestly, the name fits. It feels engineered for deployment, not just demos. Feels like one of the first video AI systems being built around how the physical world actually behaves instead of adapting static-image intelligence into video later.

Jaymin Shah

116,573 views • 4 months ago

US government to deploy Grok across defense operations. • Pentagon partners with xAI to expand GenAI .mil across the Department of Defense • Military and civilian personnel to use Grok at Impact Level 5 • Real time global insights sourced from 𝕏 • Enables secure handling of Controlled Unclassified Information in daily workflows • Grok embedded directly into operational and planning systems • Social and open source data treated as live intelligence signals • Supports decision making, intelligence analysis, and military planning • Designed to scale across roughly 3 million users • Initial rollout next month

US government to deploy Grok across defense operations. • Pentagon partners with xAI to expand GenAI .mil across the Department of Defense • Military and civilian personnel to use Grok at Impact Level 5 • Real time global insights sourced from 𝕏 • Enables secure handling of Controlled Unclassified Information in daily workflows • Grok embedded directly into operational and planning systems • Social and open source data treated as live intelligence signals • Supports decision making, intelligence analysis, and military planning • Designed to scale across roughly 3 million users • Initial rollout next month

DogeDesigner

129,867 views • 8 months ago

Smarter machines won’t come from piling on compute. They come from smarter system design. Texas Instruments' Mark Ng and Olivier Blanchard 💬 break down why software-defined systems depend on consolidation, not sprawl. Zone architectures replace dozens of legacy edge nodes with a handful of intelligent boards—cutting wiring, weight, and cost while enabling real-time intelligence across vehicles, robots, and physical systems. Watch the full discussion:

Smarter machines won’t come from piling on compute. They come from smarter system design. Texas Instruments' Mark Ng and Olivier Blanchard 💬 break down why software-defined systems depend on consolidation, not sprawl. Zone architectures replace dozens of legacy edge nodes with a handful of intelligent boards—cutting wiring, weight, and cost while enabling real-time intelligence across vehicles, robots, and physical systems. Watch the full discussion:

Six Five Media

66,646 views • 8 months ago

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

_Victorad

19,657 views • 1 month ago

A 10-minute walk. That's all it takes to transform a real-world neighborhood into a machine-readable digital twin. Captured with an Insta360 X5 and reconstructed by Over the Reality, this environment is no longer just a place on a map. It's spatial data that can power Visual AI, Robotics localization, VPS, XR experiences, and next-generation world models. Today, the OVER community has already created more than 270,000 machine-readable locations like this, forming one of the world's largest 3D datasets for Physical AI, an increasingly valuable asset as demand for high quality real-world data continues to grow.

A 10-minute walk. That's all it takes to transform a real-world neighborhood into a machine-readable digital twin. Captured with an Insta360 X5 and reconstructed by Over the Reality, this environment is no longer just a place on a map. It's spatial data that can power Visual AI, Robotics localization, VPS, XR experiences, and next-generation world models. Today, the OVER community has already created more than 270,000 machine-readable locations like this, forming one of the world's largest 3D datasets for Physical AI, an increasingly valuable asset as demand for high quality real-world data continues to grow.

Over the Reality 🌐

16,816 views • 2 months ago

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

Sean Henry

1,275,331 views • 4 months ago

MWEB is one of the core reasons Litecoin stands out as the most practical chain for real-world use. This video breaks down exactly how it works, why it matters, and why you should care. Optional privacy. Real freedom. Special thanks to NFINITY AI for turning this vision into reality, one frame at a time.

MWEB is one of the core reasons Litecoin stands out as the most practical chain for real-world use. This video breaks down exactly how it works, why it matters, and why you should care. Optional privacy. Real freedom. Special thanks to NFINITY AI for turning this vision into reality, one frame at a time.

Litecoin

55,236 views • 1 year ago