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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 • 3 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.

Mechanical Knowledge

60,511 次观看 • 4 个月前

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

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:

Elgi Equipments Limited

85,677 次观看 • 7 个月前

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!

Chuckling Charlie

26,441 次观看 • 3 个月前

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,950 次观看 • 1 年前

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How Older Adults Can Stop Muscle Loss | Rhonda Patrick, Ph.D. (Dr. Rhonda Patrick) This clip is from episode # 369 of The Drive which was released today (10/20/25). In the full episode, we cover: -How protein requirements evolve with age, activity, and goals, and why building muscle early is critical for long-term health -The connection between protein intake, mTOR, and longevity, and why fears around activation are largely misplaced -Why creatine’s cognitive effects may be as important as its role in physical performance -How regular heat exposure supports recovery, enhances mood, and may help protect against neurodegenerative disease -More Watch or listen (Ep. # 369) to the full episode on my website or your favorite podcast player.

Peter Attia

106,234 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.

Fetch.ai

38,282 次观看 • 1 年前

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

Augadh

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

🇺🇸🇮🇷 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

🇺🇸🇮🇷 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 • 7 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 • 22 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

126,507 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

BREAKING - EARLY EXCLUSIVE: XPENG Partners with Google Maps to Fuel Global Expansion The global autonomous driving landscape is becoming even more competitive! XPENG has announced a milestone global partnership with Google Maps, bringing a complete refresh of its in-car navigation and advanced driver assistance systems (ADAS). This major upgrade will come standard in the newly debuted XPENG L03. At the official launch event for the L03 in Munich, Germany, XPENG Chairman and CEO He Xiaopeng announced that the Next-Gen AI SUV Coupe will launch simultaneously in 64 countries and regions. XPENG is building its native in-car navigation system using Google Maps’ Auto SDK for vehicles outside China, which I’m sure MANY of you will be excited about. First in APAC to Adopt Google Maps Auto SDK XPENG is the first automaker from the APAC region to ship a vehicle with Google Maps Auto SDK integration. Instead of using your smartphone for basic screen mirroring, drivers get a native, custom in-car map application designed entirely by XPENG and powered directly by Google Maps. This integration brings: Real-time precision: Live traffic-aware guidance and highly accurate place search. Smart EV routing: Direct in-car EV energy estimation and trip planning. Native control: Full compatibility with voice control and multi-screen display transfer. Laying the Foundation for Global Autonomous Driving This partnership goes beyond finding directions. XPENG is officially utilizing Google Maps' in-vehicle Map Data Services to support its flagship full-scenario ADAS, Next Generation Pilot (NGP), as well as its foundational assist system, XPILOT ASSIST. To scale XPENG's advanced driving systems internationally, future applications of NGP (VLA 2.0) must rely on highly accurate map data and navigation maneuvers. Google Map Data Services will serve as the cornerstone for this phased global rollout, excluding markets in the Commonwealth of Independent States (CIS). Think of it as a fusion of XPENG’s technology with Google Maps Technology to get a more robust, intelligent, powerful, and safe autonomous driving experience. Overseas users of the XPENG L03 will be the very first to experience this upgraded, Google-powered native navigation system. My Thoughts I personally love and use Google Maps, so I’m glad to see this partnership, and I find it really neat how XPENG and Google Maps technology will work together. What are your thoughts and questions on XPENG integrating Google Maps natively to power its global self-driving tech? Let me know in the replies, and I will try to find the answers to your questions.

BREAKING - EARLY EXCLUSIVE: XPENG Partners with Google Maps to Fuel Global Expansion The global autonomous driving landscape is becoming even more competitive! XPENG has announced a milestone global partnership with Google Maps, bringing a complete refresh of its in-car navigation and advanced driver assistance systems (ADAS). This major upgrade will come standard in the newly debuted XPENG L03. At the official launch event for the L03 in Munich, Germany, XPENG Chairman and CEO He Xiaopeng announced that the Next-Gen AI SUV Coupe will launch simultaneously in 64 countries and regions. XPENG is building its native in-car navigation system using Google Maps’ Auto SDK for vehicles outside China, which I’m sure MANY of you will be excited about. First in APAC to Adopt Google Maps Auto SDK XPENG is the first automaker from the APAC region to ship a vehicle with Google Maps Auto SDK integration. Instead of using your smartphone for basic screen mirroring, drivers get a native, custom in-car map application designed entirely by XPENG and powered directly by Google Maps. This integration brings: Real-time precision: Live traffic-aware guidance and highly accurate place search. Smart EV routing: Direct in-car EV energy estimation and trip planning. Native control: Full compatibility with voice control and multi-screen display transfer. Laying the Foundation for Global Autonomous Driving This partnership goes beyond finding directions. XPENG is officially utilizing Google Maps' in-vehicle Map Data Services to support its flagship full-scenario ADAS, Next Generation Pilot (NGP), as well as its foundational assist system, XPILOT ASSIST. To scale XPENG's advanced driving systems internationally, future applications of NGP (VLA 2.0) must rely on highly accurate map data and navigation maneuvers. Google Map Data Services will serve as the cornerstone for this phased global rollout, excluding markets in the Commonwealth of Independent States (CIS). Think of it as a fusion of XPENG’s technology with Google Maps Technology to get a more robust, intelligent, powerful, and safe autonomous driving experience. Overseas users of the XPENG L03 will be the very first to experience this upgraded, Google-powered native navigation system. My Thoughts I personally love and use Google Maps, so I’m glad to see this partnership, and I find it really neat how XPENG and Google Maps technology will work together. What are your thoughts and questions on XPENG integrating Google Maps natively to power its global self-driving tech? Let me know in the replies, and I will try to find the answers to your questions.

M. Brandon Lee | THIS IS TECH TODAY

65,878 次观看 • 1 个月前

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 • 4 个月前

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

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