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Automatic transmissions rely on multi-plate clutch assemblies to control power transfer between gears. These clutches engage and disengage in milliseconds, using hydraulic pressure to manage torque flow smoothly without driver input. Precision in assembly is critical, as tolerances, friction material quality, and alignment directly affect shifting performance, heat generation,... show more

Mechanical Knowledge

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24,770 次观看 • 2 个月前 •via X (Twitter)

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Cement production relies on large rotating kilns that operate at temperatures exceeding 1,400°C to transform raw materials like limestone and clay into clinker-the key ingredient in cement. The continuous rotation ensures even heat distribution and consistent chemical reactions throughout the process. This stage is critical, as precise temperature control and material composition directly affect strength, durability, and performance in construction applications. Even minor variations can impact the final product quality. The process highlights how large-scale thermal engineering enables the production of materials that form the foundation of modern infrastructure.

Cement production relies on large rotating kilns that operate at temperatures exceeding 1,400°C to transform raw materials like limestone and clay into clinker-the key ingredient in cement. The continuous rotation ensures even heat distribution and consistent chemical reactions throughout the process. This stage is critical, as precise temperature control and material composition directly affect strength, durability, and performance in construction applications. Even minor variations can impact the final product quality. The process highlights how large-scale thermal engineering enables the production of materials that form the foundation of modern infrastructure.

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$Installing a Main Landing Gear (MLG) wheel on an Airbus A320 is a task where precision is non-negotiable. The wheel and brake assembly together weigh several hundred kilograms, requiring controlled handling and exact alignment on the axle. Every step follows strict Aircraft Maintenance Manual (AMM) procedures, including applying the correct torque to the axle nut within tightly defined limits. Even minor deviations in torque or alignment can lead to uneven wear, vibration, or critical safety risks during landing. Special attention is given to securing mechanisms that prevent the nut from loosening under extreme loads, as well as ensuring proper seating of bearings and components. Tolerances are often within fractions of a millimeter, and each step is verified through inspection protocols. In aviation maintenance, there’s no margin for error - every detail must be executed with absolute accuracy to ensure the aircraft performs safely under the immense forces of takeoff and landing. With muc_maintenance$

Installing a Main Landing Gear (MLG) wheel on an Airbus A320 is a task where precision is non-negotiable. The wheel and brake assembly together weigh several hundred kilograms, requiring controlled handling and exact alignment on the axle. Every step follows strict Aircraft Maintenance Manual (AMM) procedures, including applying the correct torque to the axle nut within tightly defined limits. Even minor deviations in torque or alignment can lead to uneven wear, vibration, or critical safety risks during landing. Special attention is given to securing mechanisms that prevent the nut from loosening under extreme loads, as well as ensuring proper seating of bearings and components. Tolerances are often within fractions of a millimeter, and each step is verified through inspection protocols. In aviation maintenance, there’s no margin for error - every detail must be executed with absolute accuracy to ensure the aircraft performs safely under the immense forces of takeoff and landing. With muc_maintenance

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In continuous cable manufacturing, compressed air performance must keep pace with constantly changing production demands. In a leading cable manufacturing facility, compressed air plays a critical role across extrusion controls, air-wipe drying, cleaning, and material handling systems, making pressure stability and compressor reliability essential for consistent product quality and high uptime. At the plant, a 75 kW fixed-speed compressor reliably met base air requirements. However, variations in production loads, line speeds, and process cycles led to frequent demand fluctuations, resulting in repeated compressor cycling, higher energy consumption, and pressure instability across the system. With the deployment of ELGi’s DEMAND=MATCH System, performance was evaluated under actual operating conditions by comparing operations with and without DEMAND=MATCH. The results were clearly validated. • 11.1% reduction in average power consumption (from 69.9 kWh to 62.1 kWh per hour) • Approximately 1,489 units of energy saved over just eight days of actual running • Reduced load cycling, leading to improved pressure stability and lower mechanical stress on the compressor This deployment demonstrates how intelligent airflow control can deliver measurable gains in energy efficiency, system stability, and operational reliability, all critical in a continuous manufacturing environment. A strong example of how aligning compressed air delivery with real-time demand can drive sustainable performance improvements on the shopfloor. Learn more about how DEMAND=MATCH optimises compressed air systems:

In continuous cable manufacturing, compressed air performance must keep pace with constantly changing production demands. In a leading cable manufacturing facility, compressed air plays a critical role across extrusion controls, air-wipe drying, cleaning, and material handling systems, making pressure stability and compressor reliability essential for consistent product quality and high uptime. At the plant, a 75 kW fixed-speed compressor reliably met base air requirements. However, variations in production loads, line speeds, and process cycles led to frequent demand fluctuations, resulting in repeated compressor cycling, higher energy consumption, and pressure instability across the system. With the deployment of ELGi’s DEMAND=MATCH System, performance was evaluated under actual operating conditions by comparing operations with and without DEMAND=MATCH. The results were clearly validated. • 11.1% reduction in average power consumption (from 69.9 kWh to 62.1 kWh per hour) • Approximately 1,489 units of energy saved over just eight days of actual running • Reduced load cycling, leading to improved pressure stability and lower mechanical stress on the compressor This deployment demonstrates how intelligent airflow control can deliver measurable gains in energy efficiency, system stability, and operational reliability, all critical in a continuous manufacturing environment. A strong example of how aligning compressed air delivery with real-time demand can drive sustainable performance improvements on the shopfloor. Learn more about how DEMAND=MATCH optimises compressed air systems:

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Transmission full rebuild done to perfection. Whether you're a gearhead or just love the satisfying sounds of mechanical precision, this 2002 4L60E transmission rebuild for a Chevy Tahoe is pure automotive ASMR. The 4L60E is a legend in the world of GM trucks, and seeing it come back to life piece by piece is a masterclass in engineering. Watch as the internals are meticulously reset, from the clutch packs to the planetaries, ensuring this Tahoe hits the road with smooth shifts and reliable power for years to come. There's something incredibly rewarding about taking a complex piece of machinery and making it better than new. Rebuild Highlights: Deep Clean: Every component inspected and prepped. Fresh Clutches: Because crisp gear changes are non-negotiable. Precision Assembly: Aligning everything to factory specs (and then some). Is your transmission feeling a bit sluggish? It might be time for some TLC. Drop a "⚙️" if you're a fan of these DIY restoration vibes!

Transmission full rebuild done to perfection. Whether you're a gearhead or just love the satisfying sounds of mechanical precision, this 2002 4L60E transmission rebuild for a Chevy Tahoe is pure automotive ASMR. The 4L60E is a legend in the world of GM trucks, and seeing it come back to life piece by piece is a masterclass in engineering. Watch as the internals are meticulously reset, from the clutch packs to the planetaries, ensuring this Tahoe hits the road with smooth shifts and reliable power for years to come. There's something incredibly rewarding about taking a complex piece of machinery and making it better than new. Rebuild Highlights: Deep Clean: Every component inspected and prepped. Fresh Clutches: Because crisp gear changes are non-negotiable. Precision Assembly: Aligning everything to factory specs (and then some). Is your transmission feeling a bit sluggish? It might be time for some TLC. Drop a "⚙️" if you're a fan of these DIY restoration vibes!

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Peter Attia

106,207 次观看 • 9 个月前

🚀Introducing Flockx by Fetch.ai your No-Code Business & Personal Agents 👇 Starting today FlockX by enables businesses of all sizes to launch dedicated AI Agents—intelligent, autonomous representatives designed to drive measurable growth in revenue, customer retention, and operational efficiency. ✅Key Features: Zero-Code Agent Creation: Deploy your AI Agent in minutes using FlockX’s intuitive platform. No technical expertise required. Global Reach via Businesses gain instant access to a global audience, while users unlock seamless connections to enterprises worldwide—mutually empowering growth. Engage clients, resolve queries, and capture global opportunities as users interact seamlessly with your business through the platform. Proven Business Impact: Effortlessly automate critical workflows, from lead generation to loyalty management, with precision and scalability. Seamless community integration: Understand and engage your audience by connecting your AI Agent to Discord and Telegram Messenger, with tools like calendars, information dashboards, and embedded chat widgets streamlining communication. Built with the trusted uAgents framework: and hosted securely on these Agents integrate directly into your existing systems while adapting to evolving demands. 🔗Next Steps: Create your AI Agent: Claim pre-registered access: Over 1 million businesses have already been onboarded—verify if your Agent is ready for activation. The AI-driven economy demands agility. Equip your business with the tools to compete—and lead. Learn how to deploy your Agent in one minute or less.

🚀Introducing Flockx by Fetch.ai your No-Code Business & Personal Agents 👇 Starting today FlockX by enables businesses of all sizes to launch dedicated AI Agents—intelligent, autonomous representatives designed to drive measurable growth in revenue, customer retention, and operational efficiency. ✅Key Features: Zero-Code Agent Creation: Deploy your AI Agent in minutes using FlockX’s intuitive platform. No technical expertise required. Global Reach via Businesses gain instant access to a global audience, while users unlock seamless connections to enterprises worldwide—mutually empowering growth. Engage clients, resolve queries, and capture global opportunities as users interact seamlessly with your business through the platform. Proven Business Impact: Effortlessly automate critical workflows, from lead generation to loyalty management, with precision and scalability. Seamless community integration: Understand and engage your audience by connecting your AI Agent to Discord and Telegram Messenger, with tools like calendars, information dashboards, and embedded chat widgets streamlining communication. Built with the trusted uAgents framework: and hosted securely on these Agents integrate directly into your existing systems while adapting to evolving demands. 🔗Next Steps: Create your AI Agent: Claim pre-registered access: Over 1 million businesses have already been onboarded—verify if your Agent is ready for activation. The AI-driven economy demands agility. Equip your business with the tools to compete—and lead. Learn how to deploy your Agent in one minute or less.

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How India Secretly Paid for Iranian Oil in Yuan and Washington Never Noticed - Prof. Jiang Xueqin In this powerful and insightful geopolitical analysis, the hidden architecture behind global finance is exposed through India’s silent workaround of U.S. sanctions on Iran. This video reveals how a $9.2 billion oil trade bypassed the dollar system entirely using a sophisticated rupee-yuan settlement network. It uncovers the fragile reality behind “maximum pressure,” the rise of parallel financial systems, and the early signs of a multipolar currency world. What looks like a quiet transaction is actually a blueprint for global de-dollarisation and a warning signal reshaping economic power. 🟠 Prof. Xueqin reveals how nations bypass US sanctions without breaking laws 🟠 Explains real mechanics behind dollar dominance and control systems 🟠 Shows early blueprint of global de-dollarization in action 🟠 Connects geopolitics directly to inflation and currency value shifts 🟠 Exposes gap between US policy claims and real outcomes 🟠 Highlights rising financial power shift toward multipolar currency systems

How India Secretly Paid for Iranian Oil in Yuan and Washington Never Noticed - Prof. Jiang Xueqin In this powerful and insightful geopolitical analysis, the hidden architecture behind global finance is exposed through India’s silent workaround of U.S. sanctions on Iran. This video reveals how a $9.2 billion oil trade bypassed the dollar system entirely using a sophisticated rupee-yuan settlement network. It uncovers the fragile reality behind “maximum pressure,” the rise of parallel financial systems, and the early signs of a multipolar currency world. What looks like a quiet transaction is actually a blueprint for global de-dollarisation and a warning signal reshaping economic power. 🟠 Prof. Xueqin reveals how nations bypass US sanctions without breaking laws 🟠 Explains real mechanics behind dollar dominance and control systems 🟠 Shows early blueprint of global de-dollarization in action 🟠 Connects geopolitics directly to inflation and currency value shifts 🟠 Exposes gap between US policy claims and real outcomes 🟠 Highlights rising financial power shift toward multipolar currency systems

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28,012 次观看 • 3 个月前

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,656 次观看 • 1 年前

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🇺🇸🇮🇷 SHOULD THE U.S. STRIKE IRAN? "IF THE GOAL IS WEAKEN THE REGIME AND NEGOTIATE, I'M IN" Patrick Bet-David, an Iranian-American entrepreneur and journalist, shares his opinion on the complex decision of whether the U.S. should strike Iran. He’s critical of the Iranian regime but highlights the risks involved. "If the goal is to completely destroy the regime, I worry about a massive power vacuum and chaos like Libya or Iraq. But if the intention is to weaken the regime and pressure them into negotiation, I could get behind it. I’d be open to pressuring Iran for a peaceful resolution but definitely not a full-scale war. I trust Trump’s instincts on these matters, but the real question is, how do we handle this situation long-term?" Patrick Bet-David

Mario Nawfal

27,639 次观看 • 6 个月前

I’ve been fighting data centers and government overreach in Ohio, and I’ve got to speak out on this one. The Department of Health and Human Services is handing out $6 million to states so they can start using predictive AI tools in child welfare. That means algorithms deciding whether families stay together or kids get taken. We didn’t ask for this. As sovereign citizens, we never consented to letting machines make these life-altering calls about our children. This is an absolute abomination to privacy. These systems pull in all kinds of data on families — and we all know how flawed government data can be. One wrong score, one biased input, and you’ve got real injustice. Families ripped apart who shouldn’t be, while others slip through. I’ve seen what these massive data centers are doing to our state — sucking up water and power so these surveillance systems can run on our information. They’re building the infrastructure for this whether we want it or not. Protecting children is non-negotiable. But handing that sacred responsibility over to unaccountable tech and federal grants? That’s not protection — that’s control. Parents, grandparents, concerned citizens — we have to push back. Demand real transparency and real human oversight, not black-box decisions made by machines. Tag a parent. Share this. Let your representatives know: We did NOT consent to this.

I’ve been fighting data centers and government overreach in Ohio, and I’ve got to speak out on this one. The Department of Health and Human Services is handing out $6 million to states so they can start using predictive AI tools in child welfare. That means algorithms deciding whether families stay together or kids get taken. We didn’t ask for this. As sovereign citizens, we never consented to letting machines make these life-altering calls about our children. This is an absolute abomination to privacy. These systems pull in all kinds of data on families — and we all know how flawed government data can be. One wrong score, one biased input, and you’ve got real injustice. Families ripped apart who shouldn’t be, while others slip through. I’ve seen what these massive data centers are doing to our state — sucking up water and power so these surveillance systems can run on our information. They’re building the infrastructure for this whether we want it or not. Protecting children is non-negotiable. But handing that sacred responsibility over to unaccountable tech and federal grants? That’s not protection — that’s control. Parents, grandparents, concerned citizens — we have to push back. Demand real transparency and real human oversight, not black-box decisions made by machines. Tag a parent. Share this. Let your representatives know: We did NOT consent to this.

Kim Georgeton for Ohio

17,466 次观看 • 11 天前

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,462 次观看 • 1 年前

Video: World’s first humanoid robot labor that swaps its own batteries to work endlessly | Jijo Malayil, Interesting Engineering Walker S2 uses dual-battery balancing and standardized modules to boost efficiency and ensure uninterrupted, optimized performance. In a leap for robotics, China’s UBTech has unveiled the Walker S2, the world’s first humanoid robot capable of fully autonomous battery swapping. Designed for non-stop industrial operations, the Walker S2 can replace its own power pack in just three minutes—no human intervention required. Equipped with advanced anthropomorphic bipedal locomotion and a hot-swappable battery system, Walker S2 is built to operate 24/7 across dynamic industrial environments. According to UBTech, the next-generation humanoid robot marks a major milestone in automation, bringing continuous, hands-free performance to the factory floor. In May 2025, UBTech Robotics and Huawei Technologies inked a significant partnership to accelerate the adoption of humanoid robots across China’s factories and households. Uninterrupted robot operations A video posted by the robotics firm opens with the sleek UBTech Walker S2 humanoid robot working in an industrial setting. The highlight, however, is its autonomous battery swap. Walker S2 approaches the charging station, carefully detaches its depleted power pack, and seamlessly installs a fresh one—all within about three minutes—without any human assistance, according to CGTN. The camera captures close-ups of the robot’s articulated limbs and the intelligent battery-handling mechanism, conveying precision and reliability. As the swap completes, Walker S2 resumes its duties, reinforcing the promise of uninterrupted, 24/7 operations in dynamic factory environments. UBTech’s Walker S2 humanoid robot is equipped with advanced dual-battery power balancing technology and uses standardized battery modules to optimize performance, reports CNEVPOST. This dual-battery system allows the robot to automatically switch to a backup battery in case of a main battery failure, ensuring that critical tasks are carried out without interruption. In addition to battery swapping, the robot can intelligently choose between charging and swapping based on task urgency, allowing it to manage energy dynamically and adapt to real-time operational demands. UBTech highlights these features as a step forward in deploying humanoid robots for industrial and domestic applications, combining flexibility, reliability, and autonomy in one intelligent platform. Factory intelligence upgrade Earlier in the year, UBTech unveiled a major advancement in humanoid robot collaboration, claiming the world’s first deployment of multiple humanoids working together across varied industrial tasks. Demonstrated at Zeekr’s 5G-enabled smart factory, the breakthrough centers on UBTech’s “BrainNet” framework, which orchestrates cooperative behavior through a cloud-device intelligence system. BrainNet integrates a “super brain” for high-level decision-making with an “intelligent sub-brain” for distributed multi-robot control. The super brain, powered by a proprietary large-scale multimodal reasoning model, handles complex production-line scheduling and decision-making. Meanwhile, the sub-brain coordinates real-time tasks using cross-field perception and Transformer-based control for dynamic adaptability. Together, they enable the Walker S1 humanoid robots to move beyond isolated operations and perform coordinated tasks with high precision and speed. The system is built on DeepSeek-R1 reasoning technology and trained on real-world data from automotive factory settings. Leveraging Retrieval-Augmented Generation (RAG), the model adapts to specific job functions and improves scalability across workstations. At Zeekr’s facility, dozens of Walker S1s now collaborate on tasks like assembly, inspection, and part handling. Using semantic VSLAM and shared mapping, they coordinate seamlessly via vision-based navigation and agile manipulation. UBTech says this marks a transition to “Practical Training 2.0,” where humanoid robots operate as a swarm, maximizing efficiency and setting the stage for next-generation intelligent manufacturing.

Video: World’s first humanoid robot labor that swaps its own batteries to work endlessly | Jijo Malayil, Interesting Engineering Walker S2 uses dual-battery balancing and standardized modules to boost efficiency and ensure uninterrupted, optimized performance. In a leap for robotics, China’s UBTech has unveiled the Walker S2, the world’s first humanoid robot capable of fully autonomous battery swapping. Designed for non-stop industrial operations, the Walker S2 can replace its own power pack in just three minutes—no human intervention required. Equipped with advanced anthropomorphic bipedal locomotion and a hot-swappable battery system, Walker S2 is built to operate 24/7 across dynamic industrial environments. According to UBTech, the next-generation humanoid robot marks a major milestone in automation, bringing continuous, hands-free performance to the factory floor. In May 2025, UBTech Robotics and Huawei Technologies inked a significant partnership to accelerate the adoption of humanoid robots across China’s factories and households. Uninterrupted robot operations A video posted by the robotics firm opens with the sleek UBTech Walker S2 humanoid robot working in an industrial setting. The highlight, however, is its autonomous battery swap. Walker S2 approaches the charging station, carefully detaches its depleted power pack, and seamlessly installs a fresh one—all within about three minutes—without any human assistance, according to CGTN. The camera captures close-ups of the robot’s articulated limbs and the intelligent battery-handling mechanism, conveying precision and reliability. As the swap completes, Walker S2 resumes its duties, reinforcing the promise of uninterrupted, 24/7 operations in dynamic factory environments. UBTech’s Walker S2 humanoid robot is equipped with advanced dual-battery power balancing technology and uses standardized battery modules to optimize performance, reports CNEVPOST. This dual-battery system allows the robot to automatically switch to a backup battery in case of a main battery failure, ensuring that critical tasks are carried out without interruption. In addition to battery swapping, the robot can intelligently choose between charging and swapping based on task urgency, allowing it to manage energy dynamically and adapt to real-time operational demands. UBTech highlights these features as a step forward in deploying humanoid robots for industrial and domestic applications, combining flexibility, reliability, and autonomy in one intelligent platform. Factory intelligence upgrade Earlier in the year, UBTech unveiled a major advancement in humanoid robot collaboration, claiming the world’s first deployment of multiple humanoids working together across varied industrial tasks. Demonstrated at Zeekr’s 5G-enabled smart factory, the breakthrough centers on UBTech’s “BrainNet” framework, which orchestrates cooperative behavior through a cloud-device intelligence system. BrainNet integrates a “super brain” for high-level decision-making with an “intelligent sub-brain” for distributed multi-robot control. The super brain, powered by a proprietary large-scale multimodal reasoning model, handles complex production-line scheduling and decision-making. Meanwhile, the sub-brain coordinates real-time tasks using cross-field perception and Transformer-based control for dynamic adaptability. Together, they enable the Walker S1 humanoid robots to move beyond isolated operations and perform coordinated tasks with high precision and speed. The system is built on DeepSeek-R1 reasoning technology and trained on real-world data from automotive factory settings. Leveraging Retrieval-Augmented Generation (RAG), the model adapts to specific job functions and improves scalability across workstations. At Zeekr’s facility, dozens of Walker S1s now collaborate on tasks like assembly, inspection, and part handling. Using semantic VSLAM and shared mapping, they coordinate seamlessly via vision-based navigation and agile manipulation. UBTech says this marks a transition to “Practical Training 2.0,” where humanoid robots operate as a swarm, maximizing efficiency and setting the stage for next-generation intelligent manufacturing.

Owen Gregorian

35,637 次观看 • 1 年前

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

Markus J. Buehler

47,242 次观看 • 2 年前

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Owen Gregorian

70,672 次观看 • 1 年前

Demoing how an artificial intelligence finance application connects two users transacting in $USDC & $EURC. Following today’s announcement from Circle that EURC is now available on Base , I wanted to show how the agent creates a connection between two users with different currency preferences, ensuring neither has to compromise. As I have mentioned before, AiFi connects you financially with friends, family, colleagues, and strangers worldwide—effortlessly and securely, without compromise. Using these two stablecoins demonstrates the impact this will have for users globally. Whether you're in: Vietnam Spain France Philippines Ukraine India Portugal United States United Kingdom Pakistan Brazil Thailand Korea Germany China or any other country. And regardless of whether your default currency is USDC or EURC, the application will connect you. In the new financial system, where every individual's needs are personalised, joi, an AiFi app, enables seamless transactions on your terms, ensuring no one has to compromise. Multi-language. Multi-network. Multi-token. With a simple request from you, your AI agent handles all transactions and personalization.

Demoing how an artificial intelligence finance application connects two users transacting in $USDC & $EURC. Following today’s announcement from Circle that EURC is now available on Base , I wanted to show how the agent creates a connection between two users with different currency preferences, ensuring neither has to compromise. As I have mentioned before, AiFi connects you financially with friends, family, colleagues, and strangers worldwide—effortlessly and securely, without compromise. Using these two stablecoins demonstrates the impact this will have for users globally. Whether you're in: Vietnam Spain France Philippines Ukraine India Portugal United States United Kingdom Pakistan Brazil Thailand Korea Germany China or any other country. And regardless of whether your default currency is USDC or EURC, the application will connect you. In the new financial system, where every individual's needs are personalised, joi, an AiFi app, enables seamless transactions on your terms, ensuring no one has to compromise. Multi-language. Multi-network. Multi-token. With a simple request from you, your AI agent handles all transactions and personalization.

jamie

12,399 次观看 • 2 年前

Everyone sees #Songkran as water. But what matters isn’t the splash — it’s the flow behind it. This time, something interesting is happening: KUB Wallet now supports KTRX — connecting directly into the TRON ecosystem. At first glance, it looks simple: store, transfer, manage. But structurally, it’s much more than that. Because in this market, the real difference isn’t between tokens… It’s between movement and infrastructure. Most activity you see is reactive: • driven by attention • driven by short-term narratives • driven by liquidity rotation But sustainable systems are different. They’re built on: 👉 continuous flow 👉 accessible liquidity 👉 real usage across environments And this is where integrations like this start to matter. When liquidity becomes easier to access, and movement becomes more efficient… you don’t just improve UX — you strengthen the entire system. That’s the part most people miss. They focus on price. But price is just the surface. The real signal is: 👉 how capital moves 👉 how smoothly it flows 👉 and whether the system can handle scale Because in the end… the systems that last aren’t the ones that spike — they’re the ones that keep working. So next time you see movement… ask yourself: Is this momentum? Or is this structure? H.E. Justin Sun 👨‍🚀 🌞 TRON DAO TRON DAO THAILAND🇹🇭 #TRON #TRONGlobalFriends

Everyone sees #Songkran as water. But what matters isn’t the splash — it’s the flow behind it. This time, something interesting is happening: KUB Wallet now supports KTRX — connecting directly into the TRON ecosystem. At first glance, it looks simple: store, transfer, manage. But structurally, it’s much more than that. Because in this market, the real difference isn’t between tokens… It’s between movement and infrastructure. Most activity you see is reactive: • driven by attention • driven by short-term narratives • driven by liquidity rotation But sustainable systems are different. They’re built on: 👉 continuous flow 👉 accessible liquidity 👉 real usage across environments And this is where integrations like this start to matter. When liquidity becomes easier to access, and movement becomes more efficient… you don’t just improve UX — you strengthen the entire system. That’s the part most people miss. They focus on price. But price is just the surface. The real signal is: 👉 how capital moves 👉 how smoothly it flows 👉 and whether the system can handle scale Because in the end… the systems that last aren’t the ones that spike — they’re the ones that keep working. So next time you see movement… ask yourself: Is this momentum? Or is this structure? H.E. Justin Sun 👨‍🚀 🌞 TRON DAO TRON DAO THAILAND🇹🇭 #TRON #TRONGlobalFriends

BTC_Chopsticks

66,019 次观看 • 3 个月前

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇

Galileo

1,276,298 次观看 • 1 年前

What would happen if the USA invaded Greenland right now? A US invasion of Greenland could plausibly have escalated into a direct great power confrontation, because China, Russia, and Europe would all see core interests on the line. Europe would treat it as an attack on European territory through Denmark, triggering a NATO level rupture and a risk of military standoffs between allied forces. Russia could respond aggressively to protect its Arctic bastion and freedom of movement in the High North, using the crisis as cover for rapid deployments and coercive pressure. China, with significant strategic and economic stakes tied to access, future shipping routes, research presence, and long term positioning in the Arctic, could decide it must defend its interests by force or by creating a parallel crisis elsewhere, which is how limited conflicts jump into wider war. In short, it is one of those moves that turns a regional land grab into a multi front escalation spiral, fast. Stay connected, Follow Gandalv Gandalv

What would happen if the USA invaded Greenland right now? A US invasion of Greenland could plausibly have escalated into a direct great power confrontation, because China, Russia, and Europe would all see core interests on the line. Europe would treat it as an attack on European territory through Denmark, triggering a NATO level rupture and a risk of military standoffs between allied forces. Russia could respond aggressively to protect its Arctic bastion and freedom of movement in the High North, using the crisis as cover for rapid deployments and coercive pressure. China, with significant strategic and economic stakes tied to access, future shipping routes, research presence, and long term positioning in the Arctic, could decide it must defend its interests by force or by creating a parallel crisis elsewhere, which is how limited conflicts jump into wider war. In short, it is one of those moves that turns a regional land grab into a multi front escalation spiral, fast. Stay connected, Follow Gandalv Gandalv

Gandalv

1,143,545 次观看 • 7 个月前