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Today PyTorch releases 2.9.1 GA which comes with fixes to cuDNN attention numerical issues. End users of cuDNN in PyTorch have been running into NaN & convergence issues with cuDNN as such cuDNN was disabled by default in PyTorch until 2.9.0. But unfortunately in 2.9.0, end users also reported...

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SemiAnalysis

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24,842 Aufrufe • vor 10 Monaten •via X (Twitter)

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I gave a talk at GPU MODE workshop last week on llm.c - the origin story of llm.c - being naked in the world without PyTorch and having to re-invent Array, Autograd, Device, Dtype, Compile, Distributed - how to port a PyTorch layer to 1) explicit PyTorch - and then to 2) write the backward pass - 3) port forward & backward pass to C - 4) string all the layers together - achieving one file of C with no dependencies that compiles and runs ~instantly, where all memory is pre-planned and allocated a single time, fully deterministic, portable code that can run on a potato or a von Neumann probe - how most of llm.c was built at 1am-7am in a water villa porch in Maldives and why this is the recommended way to develop software - convert all of it to run in CUDA on GPU in fp32 - port matmul to cuBLAS - port attention to cuDNN flash-attention - introduce bfloat16 mixed precision - introduce many more optimizations and features like kernel fusions, Packed128, stochastic rounding, full determinism - add multi-GPU training, NCCL, sharded optimizer - add multi-node with MPI or file system or socket - reproduce GPT-2 (1.6B) on one 8XH100 node in 24 hours for $672 in llm.c, achieving (at the time) 29% less memory, 19% faster training that PyTorch nightly, and much faster compile & run - how open source development attracts Avengers from the internet - port to training Llama 3 imminent (branch exists) - many other notable forks - last thought: how software abstractions like Python/PyTorch and everything else really exist only because humans are finite in knowledge, IQ and attention, and how with increasing AI capability LLMs may export custom binaries like llm.c for any application directly, tearing apart and refactoring all abstractions as needed. More links in reply
23:16

I gave a talk at GPU MODE workshop last week on llm.c - the origin story of llm.c - being naked in the world without PyTorch and having to re-invent Array, Autograd, Device, Dtype, Compile, Distributed - how to port a PyTorch layer to 1) explicit PyTorch - and then to 2) write the backward pass - 3) port forward & backward pass to C - 4) string all the layers together - achieving one file of C with no dependencies that compiles and runs ~instantly, where all memory is pre-planned and allocated a single time, fully deterministic, portable code that can run on a potato or a von Neumann probe - how most of llm.c was built at 1am-7am in a water villa porch in Maldives and why this is the recommended way to develop software - convert all of it to run in CUDA on GPU in fp32 - port matmul to cuBLAS - port attention to cuDNN flash-attention - introduce bfloat16 mixed precision - introduce many more optimizations and features like kernel fusions, Packed128, stochastic rounding, full determinism - add multi-GPU training, NCCL, sharded optimizer - add multi-node with MPI or file system or socket - reproduce GPT-2 (1.6B) on one 8XH100 node in 24 hours for $672 in llm.c, achieving (at the time) 29% less memory, 19% faster training that PyTorch nightly, and much faster compile & run - how open source development attracts Avengers from the internet - port to training Llama 3 imminent (branch exists) - many other notable forks - last thought: how software abstractions like Python/PyTorch and everything else really exist only because humans are finite in knowledge, IQ and attention, and how with increasing AI capability LLMs may export custom binaries like llm.c for any application directly, tearing apart and refactoring all abstractions as needed. More links in reply

Andrej Karpathy

337,695 Aufrufe • vor 1 Jahr

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:
2:41

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

Andrew Ng

132,400 Aufrufe • vor 1 Jahr

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.
1:10

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.

Ricardo

1,617,348 Aufrufe • vor 8 Monaten

We’ve worked with law enforcement and partners in the private sector to infiltrate a fraud platform used by thousands of criminals worldwide. LabHost provided users with sophisticated website templates of prominent banks and recognisable organisations. These were used to trick victims into providing data such as pin numbers and card details. Since its creation, LabHost made just under £1 million from criminal users. Some of these suspects have been arrested this week.
0:53

We’ve worked with law enforcement and partners in the private sector to infiltrate a fraud platform used by thousands of criminals worldwide. LabHost provided users with sophisticated website templates of prominent banks and recognisable organisations. These were used to trick victims into providing data such as pin numbers and card details. Since its creation, LabHost made just under £1 million from criminal users. Some of these suspects have been arrested this week.

Metropolitan Police

95,033 Aufrufe • vor 2 Jahren

This looks like a robot dog dancing to disco lights, but it's actually a very cool visualization of the neural network that controls the bot! Blue -> green -> red corresponds to stronger neural activation. The NN is a multilayer perceptron that maps proprioception (e.g. position, orientation, and acceleration of each body part) to motor control. It can be learned by reinforcement learning in physics simulations. I have trained a gazillion control NNs before, but never did I see it presented this way. Recently, PyTorch also open-sourced a tool that lets you step in matrix & attention computations immersively. Most people are intuitive learners - we should spend more efforts on visualization! Created by Brandon with NVIDIA Isaac Gym, three.js, and custom shaders.
0:37

This looks like a robot dog dancing to disco lights, but it's actually a very cool visualization of the neural network that controls the bot! Blue -> green -> red corresponds to stronger neural activation. The NN is a multilayer perceptron that maps proprioception (e.g. position, orientation, and acceleration of each body part) to motor control. It can be learned by reinforcement learning in physics simulations. I have trained a gazillion control NNs before, but never did I see it presented this way. Recently, PyTorch also open-sourced a tool that lets you step in matrix & attention computations immersively. Most people are intuitive learners - we should spend more efforts on visualization! Created by Brandon with NVIDIA Isaac Gym, three.js, and custom shaders.

Jim Fan

415,008 Aufrufe • vor 2 Jahren

Finally, Apple's ML-Sharp will run directly in the upcoming Python scripting module in LichtFeld Studio as a plugin. This is not another ML-Sharp UI! The LichtFeld release 0.5 will transform LichtFeld Studio from mere splat software into a comprehensive platform. I'm developing a plugin system that will require uploading Python plugin scripts to GitHub with all dependencies specified. In LFS, you'll simply need to type the GitHub repo URL, and it will import the extension. Dependencies are managed by the integrated UV packet management. You'll be able to run Colmap, SAM3, Depth Models, and more directly in LFS, and even create your own UIs. This change will be transformative for the 3DGS software ecosystem. The custom LichtFeld tensor library is already fully interoperable with PyTorch via dlpack, bringing all the ML power into LFS if desired. I'm extremely hyped and excited about these upcoming features.
0:28

Finally, Apple's ML-Sharp will run directly in the upcoming Python scripting module in LichtFeld Studio as a plugin. This is not another ML-Sharp UI! The LichtFeld release 0.5 will transform LichtFeld Studio from mere splat software into a comprehensive platform. I'm developing a plugin system that will require uploading Python plugin scripts to GitHub with all dependencies specified. In LFS, you'll simply need to type the GitHub repo URL, and it will import the extension. Dependencies are managed by the integrated UV packet management. You'll be able to run Colmap, SAM3, Depth Models, and more directly in LFS, and even create your own UIs. This change will be transformative for the 3DGS software ecosystem. The custom LichtFeld tensor library is already fully interoperable with PyTorch via dlpack, bringing all the ML power into LFS if desired. I'm extremely hyped and excited about these upcoming features.

MrNeRF

10,404 Aufrufe • vor 8 Monaten

Disabled Son Rushes to Defend His Mother in Heated Argument Over aHandicapped Parking Spot Backstory… Walmart— What should've been a simple moment-pulling into a handicapped parking space-turned into a full-blown confrontation. Witnesses say a woman in pink tights initially refused to let a woman with a disabled adult son park in a designated handicapped spot, even as the driver waited. Horns started blowing, tensions rose, and after a standoff, the space was finally cleared. But it didn't end there. Instead of letting it go, the woman in pink tights allegedly got out and approached the woman after she parked, continuing the confrontation face-to-face. That's when things took a sharp turn. The woman's son, described by witnesses as having a mental or physical disability, stepped in-and in a matter of seconds, the situation escalated into physical contact, with the woman being struck in the mouth by a mean right haymaker from the son. The husband who was arching patiently was about to step in but hesitated when he looked into the disabled man's eyes.
0:35

Disabled Son Rushes to Defend His Mother in Heated Argument Over aHandicapped Parking Spot Backstory… Walmart— What should've been a simple moment-pulling into a handicapped parking space-turned into a full-blown confrontation. Witnesses say a woman in pink tights initially refused to let a woman with a disabled adult son park in a designated handicapped spot, even as the driver waited. Horns started blowing, tensions rose, and after a standoff, the space was finally cleared. But it didn't end there. Instead of letting it go, the woman in pink tights allegedly got out and approached the woman after she parked, continuing the confrontation face-to-face. That's when things took a sharp turn. The woman's son, described by witnesses as having a mental or physical disability, stepped in-and in a matter of seconds, the situation escalated into physical contact, with the woman being struck in the mouth by a mean right haymaker from the son. The husband who was arching patiently was about to step in but hesitated when he looked into the disabled man's eyes.

LASHY BILLS

27,658 Aufrufe • vor 5 Monaten

Q: How do you decide which customers to listen to? As Superhuman founder & CEO Rahul Vohra puts it: “In a world where you’re drowning in feedback—and most startups are drowning in feedback—you have to filter it down to only the stuff that’s going to increase the number of people who fall in love with your product.” Most startups will listen to all feedback from on-the-fence customers, but this isn’t targeted enough and will often lead to a muddled, incoherent product. As Rahul argues in the clip below, you need to identify the main benefit of your product—for Superhuman this was speed. And then focus on the feedback of on-the-fence users who also view this as the main benefit—there’s often something small holding them back. Users for whom your main benefit does not resonate (e.g. Superhuman users who value offline capabilities rather than speed), are unlikely to ever fall in love with your product. When Superhuman ran this analysis in 2015, they found that the main thing holding back users who viewed speed as the main benefit was their lack of a mobile app. Probing further, they found some less obvious and more interesting requests, such as integrations, attachment handling, calendering, unified inbox and read receipts. With a clear understanding of their main benefit and missing features, they were able to move this cohort of users from on-the-fence into the territory of enthusiastic advocates. As Rahul puts it in his Product Market Fit Engine article: “To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back.” But make sure you’re focusing on users who love the main benefit of your product. Users who don’t are unlikely to ever fall in love with your product.
2:03

Q: How do you decide which customers to listen to? As Superhuman founder & CEO Rahul Vohra puts it: “In a world where you’re drowning in feedback—and most startups are drowning in feedback—you have to filter it down to only the stuff that’s going to increase the number of people who fall in love with your product.” Most startups will listen to all feedback from on-the-fence customers, but this isn’t targeted enough and will often lead to a muddled, incoherent product. As Rahul argues in the clip below, you need to identify the main benefit of your product—for Superhuman this was speed. And then focus on the feedback of on-the-fence users who also view this as the main benefit—there’s often something small holding them back. Users for whom your main benefit does not resonate (e.g. Superhuman users who value offline capabilities rather than speed), are unlikely to ever fall in love with your product. When Superhuman ran this analysis in 2015, they found that the main thing holding back users who viewed speed as the main benefit was their lack of a mobile app. Probing further, they found some less obvious and more interesting requests, such as integrations, attachment handling, calendering, unified inbox and read receipts. With a clear understanding of their main benefit and missing features, they were able to move this cohort of users from on-the-fence into the territory of enthusiastic advocates. As Rahul puts it in his Product Market Fit Engine article: “To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back.” But make sure you’re focusing on users who love the main benefit of your product. Users who don’t are unlikely to ever fall in love with your product.

Michael McGuiness

89,882 Aufrufe • vor 3 Jahren

AOC: We don’t have to talk about this as if it’s a hypothetical issue. Just a couple of months ago, Discord tried to roll out “age verification.” They contracted with a third party to conduct facial scanning — not just of kids, but also of adults. They decided to launch a teen default setting where they would lock users into a teen-appropriate experience. Which, mind you, if you are an adult who does not opt in, you then get cordoned off into the teen experience. Explain to me how that has anything to do with children’s safety. The only way to get out of that censored version is by scanning your face. What’s more shocking is that Discord made the decision to move forward with this after they had been hacked and 70,000 users had their data stolen.
2:53

AOC: We don’t have to talk about this as if it’s a hypothetical issue. Just a couple of months ago, Discord tried to roll out “age verification.” They contracted with a third party to conduct facial scanning — not just of kids, but also of adults. They decided to launch a teen default setting where they would lock users into a teen-appropriate experience. Which, mind you, if you are an adult who does not opt in, you then get cordoned off into the teen experience. Explain to me how that has anything to do with children’s safety. The only way to get out of that censored version is by scanning your face. What’s more shocking is that Discord made the decision to move forward with this after they had been hacked and 70,000 users had their data stolen.

Acyn

1,420,208 Aufrufe • vor 6 Monaten

The person of interest in the double homicides of Clinton and Cristen Brink MAY be in custody as a high speed chase ended in Redfield, AR with a man in taken into custody. As you can see in the video the disabled vehicle is a black Mazda and you can also see an officer carrying a spike strip which may have been used to disable the vehicle. I have not seen an official announcement from LE, but this looks promising.
0:21

The person of interest in the double homicides of Clinton and Cristen Brink MAY be in custody as a high speed chase ended in Redfield, AR with a man in taken into custody. As you can see in the video the disabled vehicle is a black Mazda and you can also see an officer carrying a spike strip which may have been used to disable the vehicle. I have not seen an official announcement from LE, but this looks promising.

Defense Diaries

55,964 Aufrufe • vor 1 Jahr

MASTER OF DECEPTION David Eby says Donald Trump is responsible for B.C.’s economic problems, workforce issues and global instability Then immediately blamed a government from 2004 for labour shortages. The NDP has been in power for NINE years. Apparently every problem in British Columbia was caused by: Donald Trump The Iraq War A government from 22 years ago And absolutely nothing to do with the people currently running B.C. into the ground. But don’t worry NOW they’re finally taking action. Thanks to Trump.
9:16

MASTER OF DECEPTION David Eby says Donald Trump is responsible for B.C.’s economic problems, workforce issues and global instability Then immediately blamed a government from 2004 for labour shortages. The NDP has been in power for NINE years. Apparently every problem in British Columbia was caused by: Donald Trump The Iraq War A government from 22 years ago And absolutely nothing to do with the people currently running B.C. into the ground. But don’t worry NOW they’re finally taking action. Thanks to Trump.

Marc Nixon

59,565 Aufrufe • vor 4 Monaten

IRAN: War continues and Drone losses seem to worry pentagon Trump knows what he is doing and it has nothing to do with Netanyahu’s good patience, but rather with logistical issues regarding equipment and ammunition. One of these issues refers to drones. Reaper drone losses in 2026 alone already exceed US$ 1 billion, with losses of more than 30 drones, which represents almost 20% of the entire U.S. pre-war Reaper inventory. But if we consider only the operational drones in inventory, this percentage certainly approaches ⅓ of the total pre-war fleet. These stocks were already suffering losses. At least 28 Reaper drones have been shot down in Yemen since the beginning of regional tensions in 2023. The loss of 1 MQ-4 drone was also confirmed, and there are strong suspicions regarding the loss of a second one that allegedly fell into the Gulf sea. Read more:
0:20

IRAN: War continues and Drone losses seem to worry pentagon Trump knows what he is doing and it has nothing to do with Netanyahu’s good patience, but rather with logistical issues regarding equipment and ammunition. One of these issues refers to drones. Reaper drone losses in 2026 alone already exceed US$ 1 billion, with losses of more than 30 drones, which represents almost 20% of the entire U.S. pre-war Reaper inventory. But if we consider only the operational drones in inventory, this percentage certainly approaches ⅓ of the total pre-war fleet. These stocks were already suffering losses. At least 28 Reaper drones have been shot down in Yemen since the beginning of regional tensions in 2023. The loss of 1 MQ-4 drone was also confirmed, and there are strong suspicions regarding the loss of a second one that allegedly fell into the Gulf sea. Read more:

Patricia Marins

165,985 Aufrufe • vor 3 Monaten

LMU seriously need to fix some stuff. Two weeks ago I tweeted how LMU has best netcode in all simracing games... then they released new update changing their online format so it will be "more stable" but with some netcode issues possible (at start they said) and its so broken right now. Also huge performance issues reported in same races by multiple drivers even DNFing... cmon...this is not early access this should not be happening. Literally end of 1.2 update was the most stable and best peforming game, and since 1.3 and 1.4 everything is getting worst in terms of stability and performance... PLEASE enough...
0:29

LMU seriously need to fix some stuff. Two weeks ago I tweeted how LMU has best netcode in all simracing games... then they released new update changing their online format so it will be "more stable" but with some netcode issues possible (at start they said) and its so broken right now. Also huge performance issues reported in same races by multiple drivers even DNFing... cmon...this is not early access this should not be happening. Literally end of 1.2 update was the most stable and best peforming game, and since 1.3 and 1.4 everything is getting worst in terms of stability and performance... PLEASE enough...

Jardier

43,169 Aufrufe • vor 29 Tagen

OpSec will support both normal and validator nodes for the U2U network Amigo BNB, allowing end users to stake their U2U holdings. By running validator nodes on cloudVerse users can participate in U2U staking which is the process of actively participating in transaction validation of the Unicorn Ultra Network Consensus. On the Unicorn Ultra network, anyone with a minimum required balance of *1* U2U can validate and earn staking rewards. This video outlines the process of deployment of U2U normal and validator nodes on the CloudVerse platform. OpSec users will be able to easily manage their balances and rewards as well as have full control over the mode their node operates in. This quick overview video shows what happens once a user clicks deploy and includes the installation steps, Docker container download, node start-up, and real-time validation & streaming. - End of post, beware of scams -
0:29

OpSec will support both normal and validator nodes for the U2U network Amigo BNB, allowing end users to stake their U2U holdings. By running validator nodes on cloudVerse users can participate in U2U staking which is the process of actively participating in transaction validation of the Unicorn Ultra Network Consensus. On the Unicorn Ultra network, anyone with a minimum required balance of *1* U2U can validate and earn staking rewards. This video outlines the process of deployment of U2U normal and validator nodes on the CloudVerse platform. OpSec users will be able to easily manage their balances and rewards as well as have full control over the mode their node operates in. This quick overview video shows what happens once a user clicks deploy and includes the installation steps, Docker container download, node start-up, and real-time validation & streaming. - End of post, beware of scams -

OpSec

16,775 Aufrufe • vor 2 Jahren

VATICAN CITY—After a long construction period to repair some significant issues, and modernization, Pope Leo XIV moved into the Papal Palace today. The apartments have not been occupied since the resignation of Pope Benedict XVI. Statement issued by Holy See Press Office to journalists accredited to the Holy See: “This afternoon, Pope Leo XIV will take up residence in the Apostolic Palace, moving in—along with his closest aides—to the quarters previously occupied by his predecessors.” The Papal Palace is incredibly beautiful (having been privileged to visit inside several times during COVID) and has been occupied by saints like John Paul II, Pius X. Pope Francis chose to move the residence to what was a hotel, during his pontificate—a move widely lauded as “a show of poverty”, but in reality causing the Vatican to expand security to both sites, and costing the city state (and thus the Church) additional millions. Pope Leo XIV’s return to the Palace has been widely applauded by the Faithful. 🎥: New Day, from the reopening of the apartments last year.
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VATICAN CITY—After a long construction period to repair some significant issues, and modernization, Pope Leo XIV moved into the Papal Palace today. The apartments have not been occupied since the resignation of Pope Benedict XVI. Statement issued by Holy See Press Office to journalists accredited to the Holy See: “This afternoon, Pope Leo XIV will take up residence in the Apostolic Palace, moving in—along with his closest aides—to the quarters previously occupied by his predecessors.” The Papal Palace is incredibly beautiful (having been privileged to visit inside several times during COVID) and has been occupied by saints like John Paul II, Pius X. Pope Francis chose to move the residence to what was a hotel, during his pontificate—a move widely lauded as “a show of poverty”, but in reality causing the Vatican to expand security to both sites, and costing the city state (and thus the Church) additional millions. Pope Leo XIV’s return to the Palace has been widely applauded by the Faithful. 🎥: New Day, from the reopening of the apartments last year.

Bree A Dail

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