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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,...

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Mechanical Knowledge

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24,770 views • 5 months ago •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.
0:34

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 views • 6 months ago

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
1:33

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

aircraftmaintenancengineer

14,162 views • 5 months ago

Crude Oil Tankers in Extreme Summer Heat When a crude oil tanker is filled close to its maximum capacity, high summer temperatures can create a significant process safety concern. As the temperature increases, crude oil expands and the lighter hydrocarbon components can generate more vapor. This can increase vapor pressure inside the tank. The critical factor is ullage, which is the empty space above the liquid. Adequate ullage provides room for thermal expansion and vapor accumulation. When the tank is excessively full, there is less space available to accommodate these changes. This can lead to increased tank pressure and operation of pressure relief or venting systems. For safe crude oil transportation and storage, operators should carefully control loading limits, ullage, temperature, tank pressure, ventilation and the condition of pressure and vacuum relief systems. Any unexpected vapor or liquid release from a crude oil tanker should be treated seriously because hydrocarbon vapors can present fire, explosion and exposure hazards. Extreme summer temperatures are not only an environmental challenge. In the oil and gas industry, they can directly affect fluid behavior, tank pressure and overall process safety.
0:34

Crude Oil Tankers in Extreme Summer Heat When a crude oil tanker is filled close to its maximum capacity, high summer temperatures can create a significant process safety concern. As the temperature increases, crude oil expands and the lighter hydrocarbon components can generate more vapor. This can increase vapor pressure inside the tank. The critical factor is ullage, which is the empty space above the liquid. Adequate ullage provides room for thermal expansion and vapor accumulation. When the tank is excessively full, there is less space available to accommodate these changes. This can lead to increased tank pressure and operation of pressure relief or venting systems. For safe crude oil transportation and storage, operators should carefully control loading limits, ullage, temperature, tank pressure, ventilation and the condition of pressure and vacuum relief systems. Any unexpected vapor or liquid release from a crude oil tanker should be treated seriously because hydrocarbon vapors can present fire, explosion and exposure hazards. Extreme summer temperatures are not only an environmental challenge. In the oil and gas industry, they can directly affect fluid behavior, tank pressure and overall process safety.

Mechanical Knowledge

20,452 views • 1 month ago

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:
2:19

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 views • 8 months ago

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!
1:39

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 views • 5 months ago

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!
2:57

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

201,127 views • 1 year ago

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.
1:22

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

106,292 views • 11 months ago

There is a reason enormous steam and gas turbines still play an important role in modern power grids, even as renewable power generation grows. A 1,000+ MW class steam turbine generator spinning at 3,000 RPM isn't just generating electricity. Its rotating shaft line can store roughly 4-5 giga joules of kinetic energy. That matters because electricity generation and consumption have to remain continuously balanced. If demand suddenly rises, power plants cannot instantly produce more steam, burn more fuel or increase water flow to ramp up power generation. Instead, the electrical load on synchronised generators increases immediately, and hundreds of enormous rotating machines across the grid collectively surrender a fraction of their stored kinetic energy. Their rotors slow almost imperceptibly as grid frequency begins falling below 50 Hz. This is synchronous inertia. It buys the critical first seconds for governors, hydro, batteries, gas turbines and operating reserves to increase output and restore the balance. Without sufficient response, frequency can continue falling until protection systems begin disconnecting loads and generators leading to a power grid failure or widespread blackout. And this balancing act can span thousands of kilometres, connecting millions of consumers and generators. Yet electrically, the entire grid still has to behave like one enormous synchronised machine.
0:41

There is a reason enormous steam and gas turbines still play an important role in modern power grids, even as renewable power generation grows. A 1,000+ MW class steam turbine generator spinning at 3,000 RPM isn't just generating electricity. Its rotating shaft line can store roughly 4-5 giga joules of kinetic energy. That matters because electricity generation and consumption have to remain continuously balanced. If demand suddenly rises, power plants cannot instantly produce more steam, burn more fuel or increase water flow to ramp up power generation. Instead, the electrical load on synchronised generators increases immediately, and hundreds of enormous rotating machines across the grid collectively surrender a fraction of their stored kinetic energy. Their rotors slow almost imperceptibly as grid frequency begins falling below 50 Hz. This is synchronous inertia. It buys the critical first seconds for governors, hydro, batteries, gas turbines and operating reserves to increase output and restore the balance. Without sufficient response, frequency can continue falling until protection systems begin disconnecting loads and generators leading to a power grid failure or widespread blackout. And this balancing act can span thousands of kilometres, connecting millions of consumers and generators. Yet electrically, the entire grid still has to behave like one enormous synchronised machine.

Ammanichanda

257,605 views • 1 month ago

🚀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.
1:24

🚀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

38,316 views • 1 year ago

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
10:10

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 views • 5 months ago

A science fiction level breakthrough. MIT’s electrohydraulic fiber muscles bring robotic actuation closer to a biological level. A strand measuring only 2 millimeters in diameter has the same power density as human skeletal muscle. A 22-gram fiber bundle can lift 4 kilograms roughly 200 times its own weight. It responds in 0.3 seconds and operates silently without the humming of an electric motor or a bulky external hydraulic pump. The entire power system is embedded inside the fiber. Like yarn it can be woven bundled and wrapped around a robotic arm. When the robot shakes someone’s hand the fibers yield smoothly to reduce the force. This is more than a material upgrade. It represents a fundamental change in how robots are actuated. As robots move away from rigid geared motors and gain muscle structures closer to biological ones truly biomimetic humanoids move one major step closer. China has also had mature AI vision systems for some time. Ruigan Robotics uses self developed AI algorithms combined with dual-spectrum imaging fusion. The system responds within milliseconds and continues tracking a target even when it suddenly changes direction.
0:42

A science fiction level breakthrough. MIT’s electrohydraulic fiber muscles bring robotic actuation closer to a biological level. A strand measuring only 2 millimeters in diameter has the same power density as human skeletal muscle. A 22-gram fiber bundle can lift 4 kilograms roughly 200 times its own weight. It responds in 0.3 seconds and operates silently without the humming of an electric motor or a bulky external hydraulic pump. The entire power system is embedded inside the fiber. Like yarn it can be woven bundled and wrapped around a robotic arm. When the robot shakes someone’s hand the fibers yield smoothly to reduce the force. This is more than a material upgrade. It represents a fundamental change in how robots are actuated. As robots move away from rigid geared motors and gain muscle structures closer to biological ones truly biomimetic humanoids move one major step closer. China has also had mature AI vision systems for some time. Ruigan Robotics uses self developed AI algorithms combined with dual-spectrum imaging fusion. The system responds within milliseconds and continues tracking a target even when it suddenly changes direction.

Techniahqrobot | humanoid robots

50,254 views • 2 months ago

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:
1:39

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 views • 1 year ago

Hazet 7281-2 sTAC Digital Torque Wrench Did you know that, right after mechanical cell damage, one of the biggest risks in high-voltage battery systems is incorrect bolt torque? Over the years, we’ve found these mistakes not only on vehicles from Ferrari, Mercedes-Benz, Tesla, BMW, and numerous Chinese manufacturers, but also after repairs performed by third-party workshops. The consequences are far from harmless: melted copper busbars, increased contact resistance, thermal throttling during charging or driving, damage to junction boxes, battery systems, eDrive components, and many other issues that are often blamed on “bad technology” when they are actually caused by human error. While I was working in manufacturing at TPCA Kolín and the Port of Koper, every single bolt had to be certified using a digital torque wrench, with the measurement becoming an integral part of the production record. There was no “close enough.” There was no “by feel.” On high-voltage conductors and orange HV cables, that approach is not just incorrect, it can be dangerous. If a failure or safety incident occurs later, a digital torque certificate is the only real protection for both the workshop and the manufacturer. And yes, even manufacturers make mistakes. That’s why EV Clinic has decided to digitalize this part of the process as well, replacing conventional mechanical torque wrenches with digital verification. We dedicate nearly two working hours to inspecting and documenting every critical connection, and every customer receives a digital record of every torque-critical fastener as part of the service documentation. The implementation of this level of digitalization has already begun across all EV Clinic locations. Because quality isn’t what you promise. Quality is what you can prove. Life without Hazet does not exist.
1:44

Hazet 7281-2 sTAC Digital Torque Wrench Did you know that, right after mechanical cell damage, one of the biggest risks in high-voltage battery systems is incorrect bolt torque? Over the years, we’ve found these mistakes not only on vehicles from Ferrari, Mercedes-Benz, Tesla, BMW, and numerous Chinese manufacturers, but also after repairs performed by third-party workshops. The consequences are far from harmless: melted copper busbars, increased contact resistance, thermal throttling during charging or driving, damage to junction boxes, battery systems, eDrive components, and many other issues that are often blamed on “bad technology” when they are actually caused by human error. While I was working in manufacturing at TPCA Kolín and the Port of Koper, every single bolt had to be certified using a digital torque wrench, with the measurement becoming an integral part of the production record. There was no “close enough.” There was no “by feel.” On high-voltage conductors and orange HV cables, that approach is not just incorrect, it can be dangerous. If a failure or safety incident occurs later, a digital torque certificate is the only real protection for both the workshop and the manufacturer. And yes, even manufacturers make mistakes. That’s why EV Clinic has decided to digitalize this part of the process as well, replacing conventional mechanical torque wrenches with digital verification. We dedicate nearly two working hours to inspecting and documenting every critical connection, and every customer receives a digital record of every torque-critical fastener as part of the service documentation. The implementation of this level of digitalization has already begun across all EV Clinic locations. Because quality isn’t what you promise. Quality is what you can prove. Life without Hazet does not exist.

EV Clinic

33,699 views • 2 months ago

🇺🇸🇮🇷 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
5:40

🇺🇸🇮🇷 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 views • 8 months ago

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.
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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

18,078 views • 2 months ago

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:
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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

126,717 views • 1 year ago

1️⃣ How Hypersurface Actually Works There has been increasing interest in how on-chain options liquidity is created and how Hypersurface operates under the hood. 👇 This post breaks down the mechanics. - 2️⃣ How options liquidity works in traditional markets In OTC options markets, liquidity is not passive. When a user sells an option, a desk takes the other side and replicates the payoff through delta hedging. This process requires collateral to secure the position and capital to hedge directional exposure. If the user does not provide collateral, the desk must source capital externally. That cost of capital is embedded into pricing. The result is wider spreads and lower premiums for the user. - 3️⃣ What Hypersurface changes Hypersurface removes the dependency on external desks. Liquidity is created directly within the protocol. Instead of routing flow to third parties, the system prices the option, takes the position, and hedges exposure programmatically. - 4️⃣ Execution and hedging When a position enters the system, the protocol computes the delta of the position and executes a corresponding hedge directly on Hyperliquid. This is not manual execution. It is deterministic and contract-driven. - 5️⃣ Why Hyperliquid matters This architecture is enabled by Hyperliquid’s core EVM integration. It allows smart contracts to execute trades directly on the exchange without transferring funds to externally controlled accounts. This removes a critical trust assumption present in most systems. - 6️⃣ Fund custody and control. User funds remain within smart contracts at all times. There is no transfer to team-controlled wallets, no manual custody layer, and no off-chain execution dependency. All actions, including hedging and settlement, are executed at the contract level. - 7️⃣ What this enables. By internalizing liquidity and automating hedging, the system achieves tighter pricing, improved capital efficiency, and scalability without solely relying on external market makers. Market makers can participate to improve the quote, but they have to compete with the protocol, which results in better, more reliable prices for users. - 8️⃣ Context. In traditional and CeFi systems, similar strategies exist. However, they rely on centralized exchanges, custodial execution, and off-chain coordination. Hypersurface replicates these mechanics on-chain with reduced trust assumptions and full transparency. - 9️⃣ Conclusion. Liquidity is created within the protocol. Exposure is hedged programmatically. Funds remain in smart contracts. No intermediaries. No manual execution. This is how on-chain options infrastructure scales. - Join the Hypersurface community on Telegram or Discord! 📎 📎
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1️⃣ How Hypersurface Actually Works There has been increasing interest in how on-chain options liquidity is created and how Hypersurface operates under the hood. 👇 This post breaks down the mechanics. - 2️⃣ How options liquidity works in traditional markets In OTC options markets, liquidity is not passive. When a user sells an option, a desk takes the other side and replicates the payoff through delta hedging. This process requires collateral to secure the position and capital to hedge directional exposure. If the user does not provide collateral, the desk must source capital externally. That cost of capital is embedded into pricing. The result is wider spreads and lower premiums for the user. - 3️⃣ What Hypersurface changes Hypersurface removes the dependency on external desks. Liquidity is created directly within the protocol. Instead of routing flow to third parties, the system prices the option, takes the position, and hedges exposure programmatically. - 4️⃣ Execution and hedging When a position enters the system, the protocol computes the delta of the position and executes a corresponding hedge directly on Hyperliquid. This is not manual execution. It is deterministic and contract-driven. - 5️⃣ Why Hyperliquid matters This architecture is enabled by Hyperliquid’s core EVM integration. It allows smart contracts to execute trades directly on the exchange without transferring funds to externally controlled accounts. This removes a critical trust assumption present in most systems. - 6️⃣ Fund custody and control. User funds remain within smart contracts at all times. There is no transfer to team-controlled wallets, no manual custody layer, and no off-chain execution dependency. All actions, including hedging and settlement, are executed at the contract level. - 7️⃣ What this enables. By internalizing liquidity and automating hedging, the system achieves tighter pricing, improved capital efficiency, and scalability without solely relying on external market makers. Market makers can participate to improve the quote, but they have to compete with the protocol, which results in better, more reliable prices for users. - 8️⃣ Context. In traditional and CeFi systems, similar strategies exist. However, they rely on centralized exchanges, custodial execution, and off-chain coordination. Hypersurface replicates these mechanics on-chain with reduced trust assumptions and full transparency. - 9️⃣ Conclusion. Liquidity is created within the protocol. Exposure is hedged programmatically. Funds remain in smart contracts. No intermediaries. No manual execution. This is how on-chain options infrastructure scales. - Join the Hypersurface community on Telegram or Discord! 📎 📎

Hypersurface

15,206 views • 6 months ago

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
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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

47,269 views • 2 years ago