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Together with ZINOVA's Tool Intelligence, TRON 2 takes on increasingly complex construction workflows. Zi Nova Labs builds on TRON 2 to explore an innovative robotic configuration for construction, demonstrating key tasks in a scaled-down tilt-up construction workflow, including formwork assembly, multi-layer rebar placement and tying. TRON 2 serves as...

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LimX Dynamics

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I missed this when it was released a few weeks ago... probs the most unhinged crab robot out there. Love to see it. What it is: A heavy-duty construction robot that can swap tools and arms automatically during operation. Think modular platform rather than a single-purpose machine. Key specs: - 100kg payload, 4m reach, Fits in a Euro-pallet for easy transport - Autonomous tool switching via standardised interfaces - Handles placement, fastening, finishing, and inspection What makes it different: Designed from first principles for construction work, not human mimicry. The modular architecture means one robot tackles multiple tasks across different build phases without redesign.
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I missed this when it was released a few weeks ago... probs the most unhinged crab robot out there. Love to see it. What it is: A heavy-duty construction robot that can swap tools and arms automatically during operation. Think modular platform rather than a single-purpose machine. Key specs: - 100kg payload, 4m reach, Fits in a Euro-pallet for easy transport - Autonomous tool switching via standardised interfaces - Handles placement, fastening, finishing, and inspection What makes it different: Designed from first principles for construction work, not human mimicry. The modular architecture means one robot tackles multiple tasks across different build phases without redesign.

Jack 🤖

29,454 Aufrufe • vor 1 Jahr

China’s construction tech is on another level! If you still picture construction sites with tones of workers swinging hammers, you’re officially stuck in the 90s. In China they’re out here building skyscrapers like it’s a speedrun challenge, with a tech stack that would make Silicon Valley jealous. AI-Powered Smart Sites Drones, IoT sensors, and AI are managing construction sites better than some human managers. Real-time safety alerts, automated progress tracking, and even predictive maintenance are standard tools, not fancy bonuses. Green Building Technologies Solar panels built into facades. Rainwater harvesting embedded into skyscrapers. Buildings that regulate their own temperature to save energy, not future dreams, but today’s standards. Robotics in Construction From brick-laying robots to rebar-tying machines, construction crews in China now have mechanical teammates with no lunch breaks, no grumbling. Welcome to Construction 2.0 (China Edition) Humans supervising, robots building! Gone are the days when a construction site meant a hundred+ guys with shovels. In China’s construction sites today, the machines are the real MVPs, humans mostly sip coffee and supervise. Bricklaying Human hands? Please. Robotic arms now lay bricks faster, straighter, and neater than your uncle’s weekend DIY projects. Some can lay 1,000+ bricks an hour and never ask for overtime. Excavation Remember digging trenches with shovels? That’s adorable. Now AI-guided autonomous excavators carve the earth with GPS-level precision while operators chill in air-conditioned control rooms. Concrete Pouring Giant robotic nozzles squeezing out entire building frames like they’re icing a wedding cake. Layers and layers, no sweat, no mess, no “oops, we need to redo that wall.” Welding You think shaky human hands are still melting steel together? Nah. Robot welders buzz and spark with perfect, tireless accuracy, even in places that are too risky for humans to reach. Inspection Sending humans to climb scaffolding 50 stories up? Very 2002. Now drones buzz around construction sites like paparazzi, scanning every beam, bolt, and brick, spotting defects before a human eye would even blink. Material Transport Why carry things manually when you can have self-driving forklifts and delivery bots moving steel, concrete, and pipes faster than an Amazon Prime truck? Site Monitoring Forget some guy in a neon vest wandering around with a clipboard. Today it’s AI-driven command centers tracking thousands of data points: worker safety, machinery health, material delivery schedules all from one giant screen. Project Planning Old: Paper blueprints and arguing architects. New: AI and BIM (Building Information Modeling) simulate the entire building in virtual reality before the first brick is even printed. This video shows how construction sites in China are now using robots to handle tasks like rebar placement, concrete pouring, and polishing, which has led to a 50% reduction in the workforce at some projects. The robots are proving to be more efficient, with one project's furnace robot achieving a 20% efficiency increase in just 5 days, saving 5% on labor costs. The construction company is developing even more advanced robots for future use as they work to improve smart construction technology for widespread real-time application. At China’s next-gen construction sites, humans don’t sweat, they strategize. Machines are not seen as luxury, they are seen as a necessity. Why does it matter? China is using 5G, AI, IoT, and robotics to build entire smart cities. And when the world comes looking for fast, sustainable, and scalable construction models… guess who’s already handing out the blueprints? Next time you hear: ‘Made in China,’ think bigger, it is: ‘Engineered for the future in China.’
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China’s construction tech is on another level! If you still picture construction sites with tones of workers swinging hammers, you’re officially stuck in the 90s. In China they’re out here building skyscrapers like it’s a speedrun challenge, with a tech stack that would make Silicon Valley jealous. AI-Powered Smart Sites Drones, IoT sensors, and AI are managing construction sites better than some human managers. Real-time safety alerts, automated progress tracking, and even predictive maintenance are standard tools, not fancy bonuses. Green Building Technologies Solar panels built into facades. Rainwater harvesting embedded into skyscrapers. Buildings that regulate their own temperature to save energy, not future dreams, but today’s standards. Robotics in Construction From brick-laying robots to rebar-tying machines, construction crews in China now have mechanical teammates with no lunch breaks, no grumbling. Welcome to Construction 2.0 (China Edition) Humans supervising, robots building! Gone are the days when a construction site meant a hundred+ guys with shovels. In China’s construction sites today, the machines are the real MVPs, humans mostly sip coffee and supervise. Bricklaying Human hands? Please. Robotic arms now lay bricks faster, straighter, and neater than your uncle’s weekend DIY projects. Some can lay 1,000+ bricks an hour and never ask for overtime. Excavation Remember digging trenches with shovels? That’s adorable. Now AI-guided autonomous excavators carve the earth with GPS-level precision while operators chill in air-conditioned control rooms. Concrete Pouring Giant robotic nozzles squeezing out entire building frames like they’re icing a wedding cake. Layers and layers, no sweat, no mess, no “oops, we need to redo that wall.” Welding You think shaky human hands are still melting steel together? Nah. Robot welders buzz and spark with perfect, tireless accuracy, even in places that are too risky for humans to reach. Inspection Sending humans to climb scaffolding 50 stories up? Very 2002. Now drones buzz around construction sites like paparazzi, scanning every beam, bolt, and brick, spotting defects before a human eye would even blink. Material Transport Why carry things manually when you can have self-driving forklifts and delivery bots moving steel, concrete, and pipes faster than an Amazon Prime truck? Site Monitoring Forget some guy in a neon vest wandering around with a clipboard. Today it’s AI-driven command centers tracking thousands of data points: worker safety, machinery health, material delivery schedules all from one giant screen. Project Planning Old: Paper blueprints and arguing architects. New: AI and BIM (Building Information Modeling) simulate the entire building in virtual reality before the first brick is even printed. This video shows how construction sites in China are now using robots to handle tasks like rebar placement, concrete pouring, and polishing, which has led to a 50% reduction in the workforce at some projects. The robots are proving to be more efficient, with one project's furnace robot achieving a 20% efficiency increase in just 5 days, saving 5% on labor costs. The construction company is developing even more advanced robots for future use as they work to improve smart construction technology for widespread real-time application. At China’s next-gen construction sites, humans don’t sweat, they strategize. Machines are not seen as luxury, they are seen as a necessity. Why does it matter? China is using 5G, AI, IoT, and robotics to build entire smart cities. And when the world comes looking for fast, sustainable, and scalable construction models… guess who’s already handing out the blueprints? Next time you hear: ‘Made in China,’ think bigger, it is: ‘Engineered for the future in China.’

Evrim Kanbur

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New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!
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New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

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🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy
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🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy

RAVI KUMAR SAHU

63,568 Aufrufe • vor 10 Tagen

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.
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Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

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Zebec is powering Velo Protocol's branded card Velo Official has announced Zebec Network as its exclusive infrastructure partner for the launch of a Velo-branded card experience. The integration bridges Velo’s PayFi settlement layer with the Zebec Card network, enabling users to convert on-chain assets into real-world purchasing power across global merchant terminals. The rollout supports native funding via $USDT and $USDC across BNB Chain, Ethereum, and TRON DAO. The dual-tier Silver and Carbon cards allow for multi-currency balances in $USD, $EUR, and $GBP, with full integration for Apple Pay and Google Pay.
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Zebec is powering Velo Protocol's branded card Velo Official has announced Zebec Network as its exclusive infrastructure partner for the launch of a Velo-branded card experience. The integration bridges Velo’s PayFi settlement layer with the Zebec Card network, enabling users to convert on-chain assets into real-world purchasing power across global merchant terminals. The rollout supports native funding via $USDT and $USDC across BNB Chain, Ethereum, and TRON DAO. The dual-tier Silver and Carbon cards allow for multi-currency balances in $USD, $EUR, and $GBP, with full integration for Apple Pay and Google Pay.

BSCN

47,674 Aufrufe • vor 1 Monat

Today, we’re announcing that we’ve raised $115 million in funding, including a $100M Series A led by Kleiner Perkins. America has lost the ability to build, and we’re here to restore it. My co-founder, Noah McGuinness, and I left our jobs at SpaceX , where we worked on programs including Starship, Starshield, and Starlink, to build a company that will solve construction’s greatest challenges. Infrastructure is the foundation of civilization, and construction is the precursor to innovation. If America wants to build a brighter future for the next generation, we have to make it faster, cheaper, and safer to build. That’s where TerraFirma comes in. We’re a new type of company, a robotic construction company that builds the full technology stack needed to deliver an order-of-magnitude improvement in one of the world’s oldest, largest, most important, but least efficient industries. We are building technology that expands what’s possible in construction on Earth, and then we'll use that same technology to build megastructures and colonies on the Moon and Mars. We’ve made tremendous progress over the past year, growing the company more than 10x in the last 12 months. We are performing projects across the world. By the end of October 2026, we are on track to operate 3 of the top 3 largest robotic construction fleets in the world, each on a different continent, bringing unprecedented speed, scale, and efficiency to some of the world’s most complex critical infrastructure projects. This funding will allow us to step on the gas and scale our manufacturing, software, operations, and construction deployments, including work on massive commercial and government contracts. We’re building the future of construction right here in Austin, Texas, and scaling it globally. If you want to be part of the team changing the world, now and on Mars, join us. Our Series A was led by Kleiner Perkins, with participation from Bain Capital Ventures, Glade Brook Capital Partners, BANNER VC, Saga Ventures, Trust Ventures, Definition, PEAK6, Magnetar Capital, and Ravelin Capital. Huge thanks to all of our angel investors, friends, and family who have helped and supported us throughout this journey. Apply here:
1:31

Today, we’re announcing that we’ve raised $115 million in funding, including a $100M Series A led by Kleiner Perkins. America has lost the ability to build, and we’re here to restore it. My co-founder, Noah McGuinness, and I left our jobs at SpaceX , where we worked on programs including Starship, Starshield, and Starlink, to build a company that will solve construction’s greatest challenges. Infrastructure is the foundation of civilization, and construction is the precursor to innovation. If America wants to build a brighter future for the next generation, we have to make it faster, cheaper, and safer to build. That’s where TerraFirma comes in. We’re a new type of company, a robotic construction company that builds the full technology stack needed to deliver an order-of-magnitude improvement in one of the world’s oldest, largest, most important, but least efficient industries. We are building technology that expands what’s possible in construction on Earth, and then we'll use that same technology to build megastructures and colonies on the Moon and Mars. We’ve made tremendous progress over the past year, growing the company more than 10x in the last 12 months. We are performing projects across the world. By the end of October 2026, we are on track to operate 3 of the top 3 largest robotic construction fleets in the world, each on a different continent, bringing unprecedented speed, scale, and efficiency to some of the world’s most complex critical infrastructure projects. This funding will allow us to step on the gas and scale our manufacturing, software, operations, and construction deployments, including work on massive commercial and government contracts. We’re building the future of construction right here in Austin, Texas, and scaling it globally. If you want to be part of the team changing the world, now and on Mars, join us. Our Series A was led by Kleiner Perkins, with participation from Bain Capital Ventures, Glade Brook Capital Partners, BANNER VC, Saga Ventures, Trust Ventures, Definition, PEAK6, Magnetar Capital, and Ravelin Capital. Huge thanks to all of our angel investors, friends, and family who have helped and supported us throughout this journey. Apply here:

Noah Schochet

2,289,988 Aufrufe • vor 1 Monat

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:
0:54

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

Lukas Ziegler

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We partnered with artists, designers, and builders to create new AI tools that solve real problems in their creative workflows. Here’s what’s new: — Introducing Google Pics in Google Workspace: A brand-new image creation & editing tool. Move and resize objects, add text, and translate just by hovering and clicking — Big updates to صافي النيه😉: 1) You can now create with Gemini Omni Flash in Google Flow 2) Google Flow Agent is a multi-step creative partner that reasons and plans complex tasks with you. 3) Google Flow tools are custom tools you can “vibe code” for animations, video effects, text layering & more — Design live with Stitch by Google: Now, you can use text or voice prompts to edit layouts in real time then export those designs straight to code — More creative control in صافي النيه😉Music: Edit songs section by section, remix the style of full songs, and create music videos with our new Gemini Omni Flash model
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We partnered with artists, designers, and builders to create new AI tools that solve real problems in their creative workflows. Here’s what’s new: — Introducing Google Pics in Google Workspace: A brand-new image creation & editing tool. Move and resize objects, add text, and translate just by hovering and clicking — Big updates to صافي النيه😉: 1) You can now create with Gemini Omni Flash in Google Flow 2) Google Flow Agent is a multi-step creative partner that reasons and plans complex tasks with you. 3) Google Flow tools are custom tools you can “vibe code” for animations, video effects, text layering & more — Design live with Stitch by Google: Now, you can use text or voice prompts to edit layouts in real time then export those designs straight to code — More creative control in صافي النيه😉Music: Edit songs section by section, remix the style of full songs, and create music videos with our new Gemini Omni Flash model

Google AI

13,954,152 Aufrufe • vor 3 Monaten

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.
1:16

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,548 Aufrufe • vor 2 Jahren

🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace
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🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace

OpenBMB

13,996 Aufrufe • vor 7 Monaten

🚨🚨Can agents earn money, run a business, or even self-organize a society in the physical social world? 🤖🤖 Can agents learn continually to survive and thrive in embodied environments, like how human babies grow? 👶 Super excited to introduce SimWorld, an open-ended simulator of LLM agents in infinite, realistic embodied worlds. SimWorld features 3 key designs: 1⃣Open-ended realistic world simulation - built on Unreal Engine 5, with accurate physical social dynamics - 100+ built-in environments (city, island, wilderness ...) - language-controllable procedural generation - text-to-3D asset generation 2⃣Native interface for LLM/VLM agents - Gym-like agent-environment interaction APIs - plug in any LLMs/VLMs (GPTs, Gemini, Qwen ...) - rich multi-modal perception - open-vocabulary natural-language action outputs 3⃣Diverse physical and social reasoning scenarios - long-horizon embodied reasoning - multi-agent collaboration / competition - easily customizable for any reasoning tasks SimWorld is fully open-sourced, with a hope to become a foundational infrastructure for real-world agent research across disciplines: robotics, economy, public health, education, etc. Project website + more details in the thread👇 ...1/
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🚨🚨Can agents earn money, run a business, or even self-organize a society in the physical social world? 🤖🤖 Can agents learn continually to survive and thrive in embodied environments, like how human babies grow? 👶 Super excited to introduce SimWorld, an open-ended simulator of LLM agents in infinite, realistic embodied worlds. SimWorld features 3 key designs: 1⃣Open-ended realistic world simulation - built on Unreal Engine 5, with accurate physical social dynamics - 100+ built-in environments (city, island, wilderness ...) - language-controllable procedural generation - text-to-3D asset generation 2⃣Native interface for LLM/VLM agents - Gym-like agent-environment interaction APIs - plug in any LLMs/VLMs (GPTs, Gemini, Qwen ...) - rich multi-modal perception - open-vocabulary natural-language action outputs 3⃣Diverse physical and social reasoning scenarios - long-horizon embodied reasoning - multi-agent collaboration / competition - easily customizable for any reasoning tasks SimWorld is fully open-sourced, with a hope to become a foundational infrastructure for real-world agent research across disciplines: robotics, economy, public health, education, etc. Project website + more details in the thread👇 ...1/

Lianhui Qin

65,156 Aufrufe • vor 9 Monaten

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️
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🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

LangChain

61,205 Aufrufe • vor 1 Jahr

CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.
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CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

Dami-Defi

10,127 Aufrufe • vor 2 Monaten

🚨 BREAKING: The first-ever agentic browser is here — and it's shockingly good. Just tried Fellou, an AI browser that doesn’t assist you with browsing, it does the browsing for me. It's like Chrome but with a brain—AI agents handle deep research and workflows solo. Handles several projects in parallel. A top-tier AI intern — takes care of all the dirty and tedious work, so you don’t have to and its 100% Free 1️⃣Fellou’s not just another browser—it's an Agentic assistant that acts for you. 2️⃣It handles real tasks autonomously: research, cross-platform flows, and full automation. 3️⃣ Past browsing. Into real action. Fellou can automatically plan tasks, invoke tools, and execute actions to coordinate operations across multiple web interfaces, enabling various in-browser tasks. These include shopping, scheduling meetings, sending emails, and posting tweets based on webpage content. It’s the first Agentic Browser — with deep research, tab-level collaboration, and seamless automation. Deep Search acts like a smart intern: spins up five shadow browsers, digs across web and private platforms, and compiles richer insights fast. Highlights gaps and surfaces info you missed. Runs in parallel, won’t slow anything down. Automated workflows: Replaces manual clicking with invisible ops across pages. Reduces drag, frees up hours. Automation-aware browsing: Ask the page questions, reuse content in your drafts. 🧵 1/n
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🚨 BREAKING: The first-ever agentic browser is here — and it's shockingly good. Just tried Fellou, an AI browser that doesn’t assist you with browsing, it does the browsing for me. It's like Chrome but with a brain—AI agents handle deep research and workflows solo. Handles several projects in parallel. A top-tier AI intern — takes care of all the dirty and tedious work, so you don’t have to and its 100% Free 1️⃣Fellou’s not just another browser—it's an Agentic assistant that acts for you. 2️⃣It handles real tasks autonomously: research, cross-platform flows, and full automation. 3️⃣ Past browsing. Into real action. Fellou can automatically plan tasks, invoke tools, and execute actions to coordinate operations across multiple web interfaces, enabling various in-browser tasks. These include shopping, scheduling meetings, sending emails, and posting tweets based on webpage content. It’s the first Agentic Browser — with deep research, tab-level collaboration, and seamless automation. Deep Search acts like a smart intern: spins up five shadow browsers, digs across web and private platforms, and compiles richer insights fast. Highlights gaps and surfaces info you missed. Runs in parallel, won’t slow anything down. Automated workflows: Replaces manual clicking with invisible ops across pages. Reduces drag, frees up hours. Automation-aware browsing: Ask the page questions, reuse content in your drafts. 🧵 1/n

Rohan Paul

55,351 Aufrufe • vor 1 Jahr

Just over a year after Dust was born, we’re excited to announce: - Our series A round led by Sequoia Capital. - A new generation of multi-tool assistants for all users. - We just went over the $1M ARR with over 5k active subscribers to Dust. We’ll accelerate investment to get work to work better, abiding by our “𝗼𝗻𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗮𝗹𝗹, 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹” vision. We believe in highly focused assistants that tackle specialized tasks and in the value of a unified platform to manage adoption, costs, and security. ✍️ Blog → 📰 More from: Les Echos - Bloomberg - As announced to users already, we’ve worked hard to make Dust assistants smarter. They can now handle complex workflows, thinking in multiple steps and using various tools to answer queries. ✍️ Blog → We’re proud to be partnering with some of the fastest-moving teams in the world, and excited to accelerate. PS — we’re hiring! Explore our careers page and reach out if you think you’re a fit for any of our open positions!
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Just over a year after Dust was born, we’re excited to announce: - Our series A round led by Sequoia Capital. - A new generation of multi-tool assistants for all users. - We just went over the $1M ARR with over 5k active subscribers to Dust. We’ll accelerate investment to get work to work better, abiding by our “𝗼𝗻𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗮𝗹𝗹, 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹” vision. We believe in highly focused assistants that tackle specialized tasks and in the value of a unified platform to manage adoption, costs, and security. ✍️ Blog → 📰 More from: Les Echos - Bloomberg - As announced to users already, we’ve worked hard to make Dust assistants smarter. They can now handle complex workflows, thinking in multiple steps and using various tools to answer queries. ✍️ Blog → We’re proud to be partnering with some of the fastest-moving teams in the world, and excited to accelerate. PS — we’re hiring! Explore our careers page and reach out if you think you’re a fit for any of our open positions!

Dust

39,056 Aufrufe • vor 2 Jahren

Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw🦞 and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw
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Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw🦞 and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw

BytePlus

96,329 Aufrufe • vor 5 Monaten

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.
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Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.

King

155,637 Aufrufe • vor 8 Monaten

Humanoid Marathon Champion "TienKung" Enters the Factory At IROS 2025, the Beijing Humanoid Robot Innovation Center (BHRIC) and UBTECH officially launched the Software Development Kit (SDK) for their general-purpose embodied intelligence platform, "HuiSiKaiWu." This move signals a significant step towards an open-source embodied AI ecosystem. The HuiSiKaiWu platform is designed for ease of use and low-threshold deployment, enabling multi-agent collaboration (one brain, multiple functions/robots). The core "brain" uses a dual-model architecture: the Pelican VLM and the WoW World Model, driving autonomous learning and decision-making. The "cerebellum" includes the cross-body XR-1 VLA Model, which has shown strong performance in rapid, few-shot skill transfer across tasks. BHRIC simultaneously announced its first industrial application: since September 2025, the "TienKung 2.0" and "Tianyi 2.0" humanoids have been deployed at the Foton Cummins engine factory. They autonomously handle material bin fetching and transportation on the "unmanned production line," adapting to various goods and shelf heights. Foton Cummins highlighted the robots' high stability and generalization potential in complex industrial processes. Beyond industrial work, BHRIC showcased other applications: the high-performance "TienKung Ultra" is being used at the Li-Ning Sports Science Lab for running shoe tests, precisely simulating human gaits. The platform is expanding applications across manufacturing, logistics, and sports science. The HuiSiKaiWu SDK is now available for collaborative trials by research institutions and development teams, offering a full toolchain for skill calling and scene deployment. BHRIC plans to gradually open-source its core algorithms, including VLM and VLA models, and hosted the "X-Humanoid Young Talent Meetup" and the "2026 Re-Action Humanoid Robot Challenge" to build a comprehensive developer ecosystem.
3:22

Humanoid Marathon Champion "TienKung" Enters the Factory At IROS 2025, the Beijing Humanoid Robot Innovation Center (BHRIC) and UBTECH officially launched the Software Development Kit (SDK) for their general-purpose embodied intelligence platform, "HuiSiKaiWu." This move signals a significant step towards an open-source embodied AI ecosystem. The HuiSiKaiWu platform is designed for ease of use and low-threshold deployment, enabling multi-agent collaboration (one brain, multiple functions/robots). The core "brain" uses a dual-model architecture: the Pelican VLM and the WoW World Model, driving autonomous learning and decision-making. The "cerebellum" includes the cross-body XR-1 VLA Model, which has shown strong performance in rapid, few-shot skill transfer across tasks. BHRIC simultaneously announced its first industrial application: since September 2025, the "TienKung 2.0" and "Tianyi 2.0" humanoids have been deployed at the Foton Cummins engine factory. They autonomously handle material bin fetching and transportation on the "unmanned production line," adapting to various goods and shelf heights. Foton Cummins highlighted the robots' high stability and generalization potential in complex industrial processes. Beyond industrial work, BHRIC showcased other applications: the high-performance "TienKung Ultra" is being used at the Li-Ning Sports Science Lab for running shoe tests, precisely simulating human gaits. The platform is expanding applications across manufacturing, logistics, and sports science. The HuiSiKaiWu SDK is now available for collaborative trials by research institutions and development teams, offering a full toolchain for skill calling and scene deployment. BHRIC plans to gradually open-source its core algorithms, including VLM and VLA models, and hosted the "X-Humanoid Young Talent Meetup" and the "2026 Re-Action Humanoid Robot Challenge" to build a comprehensive developer ecosystem.

RoboHub🤖

39,337 Aufrufe • vor 10 Monaten

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp
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Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp

Scale Network

33,325 Aufrufe • vor 20 Tagen