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🚨🇺🇸GOOGLE'S QUANTUM LEAP: 47-YEAR TASK INSTANTLY DONE Google's new quantum computer, Sycamore, can complete tasks in moments that would take supercomputers 47 years. With 70 qubits, it’s 241 million times more robust than its predecessor. This advancement pushes the boundaries of computational power, potentially revolutionizing fields like climate science...

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Mario Nawfal

3,618,452 subscribers

748,920 次观看 • 2 年前 •via X (Twitter)

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10 条评论

Alpha-Bravo 的头像
Alpha-Bravo2 年前

Someone has to stop that Google is the last company that should ever possess a super quantum computer

Vincent Van Code 的头像
Vincent Van Code2 年前

Once quantum computers become mainstream, human life as we know it will change forever. Cure for diseases, super materials, super batteries, new rocket systems, and dare I say it interstellar travel. Instead people freaking out that their crypto wallet containing $1000 of crypto is gonna get hacked.

AllYourTech 的头像
AllYourTech2 年前

watch out once they get to 128 or 256 qubits. That's when you can start to crack all major encryption methods.

G. Irvin O'Sullivan 的头像
G. Irvin O'Sullivan2 年前

I kinda want to see what happens when they load up an A.I. on one of them there quantum computational machine thingys. An absolutely dystopian hell scape?

Ivan Kircanski 的头像
Ivan Kircanski2 年前

Can we run Solitaire on it?

Soak 的头像
Soak2 年前

Excited for the debate tonight 🔥 CA: #MAGAONSOL 🇺🇸 @magamemesol $MAGA $SOL sfYDFZJguyF4YLZjje7qwwh41NRymFfZ3QXZbVm7Eyg

DESpaceX 的头像
DESpaceX2 年前

Quantum computing is not a general data processing computer. It can’t even run Lotus 1,2,3. It only solves for convergence of reduction equations. The magic is in asking the right questions solving for the right narrowly-defined problems.

Yogini 的头像
Yogini2 年前

We just pulled this out of some warehouse… or maybe it’s been there all along🤔 Shut-down globe Centralize Kill off old order/people Centralize Devolution-Bankruptcy Centralize Digital Centralize Quantum (4) years 🤔 It’s all so random.

Arman 的头像
Arman2 年前

So if google wanted too, soon they could hack the Bitcoin blockchain and render it useless.

Chris Hackney 的头像
Chris Hackney2 年前

The technology they show the public is probably 5 to 10 yrs behind where we’re at now.

相关视频

D-Wave announced a scientific breakthrough published in the esteemed journal Science Magazine, confirming that its annealing quantum computer outperformed one of the world’s most powerful classical supercomputers in solving a complex magnetic materials simulation problem with relevance to materials discovery. The new landmark peer-reviewed paper, “Beyond-Classical Computation in Quantum Simulation,” validates this achievement as the world’s first and only demonstration of quantum computational supremacy on a useful problem. An international collaboration of scientists led by D-Wave performed simulations of quantum dynamics in programmable spin glasses—a computationally hard magnetic materials simulation problem with known applications to business and science—on both D-Wave’s Advantage2™ prototype annealing quantum computer and the Frontier supercomputer at the Department of Energy’s Oak Ridge Lab. D-Wave’s quantum computer performed a complex simulation in minutes and with a level of accuracy that would take nearly a million years using the supercomputer. In addition, it would require more than the world’s annual electricity consumption to solve this problem using the supercomputer, which is built with graphics processing unit (GPU) clusters. For decades, scientists have aspired to build a quantum computer capable of solving complex materials simulation problems beyond the reach of classical computers. D-Wave's advancements in quantum hardware have made it possible for its annealing quantum computers to process these types of problems for the first time. Magnetic materials simulations, like those conducted in this work, use computer models to study how tiny particles not visible to the human eye react to external factors. Magnetic materials are widely used in medical imaging, electronics, superconductors, electrical networks, sensors, and motors. This is an incredibly important achievement. Please join us in congratulating the D-Wave team and our global collaborators on this remarkable milestone. It’s a significant moment for the quantum computing industry. Learn more about this monumental achievement: Read the press release here: #QuantumSupremacy #QuantumRealized #QuantumComputing #DWave #Technology #Innovation #Optimization #MaterialsDiscovery #ScientificBreakthrough $QBTS
3:26

D-Wave announced a scientific breakthrough published in the esteemed journal Science Magazine, confirming that its annealing quantum computer outperformed one of the world’s most powerful classical supercomputers in solving a complex magnetic materials simulation problem with relevance to materials discovery. The new landmark peer-reviewed paper, “Beyond-Classical Computation in Quantum Simulation,” validates this achievement as the world’s first and only demonstration of quantum computational supremacy on a useful problem. An international collaboration of scientists led by D-Wave performed simulations of quantum dynamics in programmable spin glasses—a computationally hard magnetic materials simulation problem with known applications to business and science—on both D-Wave’s Advantage2™ prototype annealing quantum computer and the Frontier supercomputer at the Department of Energy’s Oak Ridge Lab. D-Wave’s quantum computer performed a complex simulation in minutes and with a level of accuracy that would take nearly a million years using the supercomputer. In addition, it would require more than the world’s annual electricity consumption to solve this problem using the supercomputer, which is built with graphics processing unit (GPU) clusters. For decades, scientists have aspired to build a quantum computer capable of solving complex materials simulation problems beyond the reach of classical computers. D-Wave's advancements in quantum hardware have made it possible for its annealing quantum computers to process these types of problems for the first time. Magnetic materials simulations, like those conducted in this work, use computer models to study how tiny particles not visible to the human eye react to external factors. Magnetic materials are widely used in medical imaging, electronics, superconductors, electrical networks, sensors, and motors. This is an incredibly important achievement. Please join us in congratulating the D-Wave team and our global collaborators on this remarkable milestone. It’s a significant moment for the quantum computing industry. Learn more about this monumental achievement: Read the press release here: #QuantumSupremacy #QuantumRealized #QuantumComputing #DWave #Technology #Innovation #Optimization #MaterialsDiscovery #ScientificBreakthrough $QBTS

D-Wave

65,039 次观看 • 1 年前

How IIT Madras is Changing Global Security The End of Hacking? IIT Madras is spearheading India's push into quantum-secure communications through the IITM-C-DOT Samgnya Technologies Foundation, launched as the National Hub for Quantum Communication under the National Quantum Mission. This initiative aims to make communications "unhackable" by shifting from math-based encryption to physics-based security, countering future quantum computer threats. ​ The hub, inaugurated in December 2025 at IIT Madras Research Park, partners with C-DOT and is funded by the Department of Science and Technology (DST). It focuses on developing indigenous quantum hardware, QKD networks, quantum repeaters, and satellite-based systems to protect critical infrastructure like AI data centers and defense networks. Traditional encryption relies on complex math that quantum computers will crack in years. QKD uses quantum physics: any eavesdropping alters the quantum state, alerting the system instantly and burning the key. IIT Madras claims an edge in secure transmission over computing leaders like the US and China. ​ The hub builds real-world testbeds, subsidizes hardware for startups, trains experts, and links globally via IITM Global outposts. It positions India to lead in quantum-secure networks for sovereign AI and government use within five years. ​ IIT Madras also runs CyStar, a cybersecurity center advancing quantum security, AI model protection, and IoT defenses since 2024. This aligns with national goals for unhackable comms amid rising cyber threats. Credit : AIM Network.
6:11

How IIT Madras is Changing Global Security The End of Hacking? IIT Madras is spearheading India's push into quantum-secure communications through the IITM-C-DOT Samgnya Technologies Foundation, launched as the National Hub for Quantum Communication under the National Quantum Mission. This initiative aims to make communications "unhackable" by shifting from math-based encryption to physics-based security, countering future quantum computer threats. ​ The hub, inaugurated in December 2025 at IIT Madras Research Park, partners with C-DOT and is funded by the Department of Science and Technology (DST). It focuses on developing indigenous quantum hardware, QKD networks, quantum repeaters, and satellite-based systems to protect critical infrastructure like AI data centers and defense networks. Traditional encryption relies on complex math that quantum computers will crack in years. QKD uses quantum physics: any eavesdropping alters the quantum state, alerting the system instantly and burning the key. IIT Madras claims an edge in secure transmission over computing leaders like the US and China. ​ The hub builds real-world testbeds, subsidizes hardware for startups, trains experts, and links globally via IITM Global outposts. It positions India to lead in quantum-secure networks for sovereign AI and government use within five years. ​ IIT Madras also runs CyStar, a cybersecurity center advancing quantum security, AI model protection, and IoT defenses since 2024. This aligns with national goals for unhackable comms amid rising cyber threats. Credit : AIM Network.

Augadh

11,978 次观看 • 5 个月前

Quantum computing is coming. Google’s recent breakthrough in quantum computing serves as a great reminder that, while we don’t know exactly when, quantum computing is on its way—and it’s closer than we might have thought. The implications for the current blockchain space could be far more severe than we can imagine. Why? Because when quantum computers become operational, security methods that currently take hundreds of years to hack could be broken in minutes. Like the turkey oblivious to the 1001st day, failing to anticipate the risks posed by quantum computing could leave us vulnerable to devastating consequences when the time comes. At StarkWare, we’ve anticipated this risk and developed STARK, a proving technology for blockchains that is quantum-resistant. This cutting-edge innovation currently powers Starknet (BTCFi arc). If you want to stay ahead of the curve and future-proof your systems, now is the best time to migrate your stack to a STARK-powered one. As the inventors of this technology, we believe our team is uniquely positioned to assist you with this migration. Moreover, we are more than open to collaborating with the Ethereum Foundation and other relevant parties to build Ethereum’s quantum resistance as soon as the community is ready.
1:25

Quantum computing is coming. Google’s recent breakthrough in quantum computing serves as a great reminder that, while we don’t know exactly when, quantum computing is on its way—and it’s closer than we might have thought. The implications for the current blockchain space could be far more severe than we can imagine. Why? Because when quantum computers become operational, security methods that currently take hundreds of years to hack could be broken in minutes. Like the turkey oblivious to the 1001st day, failing to anticipate the risks posed by quantum computing could leave us vulnerable to devastating consequences when the time comes. At StarkWare, we’ve anticipated this risk and developed STARK, a proving technology for blockchains that is quantum-resistant. This cutting-edge innovation currently powers Starknet (BTCFi arc). If you want to stay ahead of the curve and future-proof your systems, now is the best time to migrate your stack to a STARK-powered one. As the inventors of this technology, we believe our team is uniquely positioned to assist you with this migration. Moreover, we are more than open to collaborating with the Ethereum Foundation and other relevant parties to build Ethereum’s quantum resistance as soon as the community is ready.

StarkWare (BTCFi arc)

62,316 次观看 • 1 年前

6,100-Qubit Processor Shatters Quantum Computing Record | David Nield, ScienceAlert Another major quantum computing record has been broken, and by a considerable margin: physicists have now built an array containing 6,100 qubits, the largest of its type and way above the thousand or so qubits previous systems contained. It's the work of scientists from the California Institute of Technology, who used cesium atoms as their qubits, trapping them in place with a complex system of lasers that acted as tweezers to keep the atoms as stable as possible. Qubits differ from the classical bits of traditional computers by exploiting what's known as a superposition: not just binary states of 1 or 0, but a spread of probabilities that allows for algorithms that can solve problems considered out of reach of conventional computing methods. Related: Quantum Advantage: A Physicist Explains The Future of Computers A lot of qubits will be needed to make quantum algorithms practical, however. One reason for these large arrays is error correction, which helps overcome the inherent fragility of the qubit by providing a surplus to double-check the machine's operation. "This is an exciting moment for neutral-atom quantum computing," says physicist Manuel Endres. "We can now see a pathway to large error-corrected quantum computers. The building blocks are in place." There was no single breakthrough that enabled this jump in qubit numbers, but rather a series of engineering advancements in many key areas – from the laser tweezers to the ultra-high (very low pressure) vacuum chamber. Stability has also been a problem for quantum computing systems. The innovations in this latest array kept qubits in a superposition state for almost 13 seconds – almost ten times longer than previous configurations had managed. What's more, individual qubits could be manipulated with 99.98 percent accuracy, establishing a significant benchmark in the programmability of quantum technology. "Large scale, with more atoms, is often thought to come at the expense of accuracy, but our results show that we can do both," says physicist Gyohei Nomura. "Qubits aren't useful without quality. Now we have quantity and quality." To make quantum computers a practical alternative to modern supercomputers, more qubits and even greater levels of stability will be required. Experts are tackling the problem from several different angles, which is why records for some types of quantum computer don't necessarily apply to others. Next, the researchers need to work on exploiting entanglement, which will enable the system to make the leap from storing information to actually processing it. Not too far in the future, we could be using these computers to discover new materials, matter, and fundamental laws of physics. "It's exciting that we are creating machines to help us learn about the Universe in ways that only quantum mechanics can teach us," says physicist Hannah Manetsch. Read more:
1:54

6,100-Qubit Processor Shatters Quantum Computing Record | David Nield, ScienceAlert Another major quantum computing record has been broken, and by a considerable margin: physicists have now built an array containing 6,100 qubits, the largest of its type and way above the thousand or so qubits previous systems contained. It's the work of scientists from the California Institute of Technology, who used cesium atoms as their qubits, trapping them in place with a complex system of lasers that acted as tweezers to keep the atoms as stable as possible. Qubits differ from the classical bits of traditional computers by exploiting what's known as a superposition: not just binary states of 1 or 0, but a spread of probabilities that allows for algorithms that can solve problems considered out of reach of conventional computing methods. Related: Quantum Advantage: A Physicist Explains The Future of Computers A lot of qubits will be needed to make quantum algorithms practical, however. One reason for these large arrays is error correction, which helps overcome the inherent fragility of the qubit by providing a surplus to double-check the machine's operation. "This is an exciting moment for neutral-atom quantum computing," says physicist Manuel Endres. "We can now see a pathway to large error-corrected quantum computers. The building blocks are in place." There was no single breakthrough that enabled this jump in qubit numbers, but rather a series of engineering advancements in many key areas – from the laser tweezers to the ultra-high (very low pressure) vacuum chamber. Stability has also been a problem for quantum computing systems. The innovations in this latest array kept qubits in a superposition state for almost 13 seconds – almost ten times longer than previous configurations had managed. What's more, individual qubits could be manipulated with 99.98 percent accuracy, establishing a significant benchmark in the programmability of quantum technology. "Large scale, with more atoms, is often thought to come at the expense of accuracy, but our results show that we can do both," says physicist Gyohei Nomura. "Qubits aren't useful without quality. Now we have quantity and quality." To make quantum computers a practical alternative to modern supercomputers, more qubits and even greater levels of stability will be required. Experts are tackling the problem from several different angles, which is why records for some types of quantum computer don't necessarily apply to others. Next, the researchers need to work on exploiting entanglement, which will enable the system to make the leap from storing information to actually processing it. Not too far in the future, we could be using these computers to discover new materials, matter, and fundamental laws of physics. "It's exciting that we are creating machines to help us learn about the Universe in ways that only quantum mechanics can teach us," says physicist Hannah Manetsch. Read more:

Owen Gregorian

43,078 次观看 • 10 个月前

🚨 QUANTUM COMPUTING: THE NEXT TECH GOLD RUSH Quantum computing (QC) is about to blow up the tech world. Think of it as AI on steroids—but here’s the kicker: the entire U.S. quantum sector is worth less than Dogecoin. Imagine buying Bitcoin at $500. That’s where QC is today. Regular computers process in 1s and 0s. QCs use qubits, which can be 1, 0, or both at the same time (superposition). Translation? They solve ridiculously complex problems insanely fast. Google recently ran a calculation on its quantum chip in 5 minutes that would take the world’s fastest supercomputer 10 septillion years to complete. Yes, that’s older than the universe. Why it matters: QC isn’t just a cool science project—it’s a game-changer: Medicine: Design drugs in weeks, not years. Finance: Smarter investments, faster decisions. AI: Supercharges AI, making today’s breakthroughs look tiny. McKinsey predicts QC will add trillions to the economy in the next decade. Entire industries will be rewritten. We are closer than most people think. Quantum startups are already hitting 10 qubits, aiming for 100 in 2 years and thousands in 5 years. QC is already solving real problems in medicine, defense, and finance, and platforms like AWS and Google let you rent quantum power right now. Here’s the wild part: the entire U.S. quantum sector is trading for less than a meme coin. Meanwhile, China is outspending the U.S. 5 to 1 in quantum development. Legislation like the Quantum Leadership Act is injecting billions into R&D, but QC’s “ChatGPT moment”—where its potential becomes undeniable—is just 2-3 years away. The upside here is massive. QC isn’t just going to change industries—it’s going to flip them on their heads. When its moment comes, the repricing will be fast and brutal. The clock is ticking. Source: Charles Edwards
0:59

🚨 QUANTUM COMPUTING: THE NEXT TECH GOLD RUSH Quantum computing (QC) is about to blow up the tech world. Think of it as AI on steroids—but here’s the kicker: the entire U.S. quantum sector is worth less than Dogecoin. Imagine buying Bitcoin at $500. That’s where QC is today. Regular computers process in 1s and 0s. QCs use qubits, which can be 1, 0, or both at the same time (superposition). Translation? They solve ridiculously complex problems insanely fast. Google recently ran a calculation on its quantum chip in 5 minutes that would take the world’s fastest supercomputer 10 septillion years to complete. Yes, that’s older than the universe. Why it matters: QC isn’t just a cool science project—it’s a game-changer: Medicine: Design drugs in weeks, not years. Finance: Smarter investments, faster decisions. AI: Supercharges AI, making today’s breakthroughs look tiny. McKinsey predicts QC will add trillions to the economy in the next decade. Entire industries will be rewritten. We are closer than most people think. Quantum startups are already hitting 10 qubits, aiming for 100 in 2 years and thousands in 5 years. QC is already solving real problems in medicine, defense, and finance, and platforms like AWS and Google let you rent quantum power right now. Here’s the wild part: the entire U.S. quantum sector is trading for less than a meme coin. Meanwhile, China is outspending the U.S. 5 to 1 in quantum development. Legislation like the Quantum Leadership Act is injecting billions into R&D, but QC’s “ChatGPT moment”—where its potential becomes undeniable—is just 2-3 years away. The upside here is massive. QC isn’t just going to change industries—it’s going to flip them on their heads. When its moment comes, the repricing will be fast and brutal. The clock is ticking. Source: Charles Edwards

Mario Nawfal

1,870,630 次观看 • 1 年前

BITCOIN RAILS EPISODE #18: MAKE BITCOIN QUANTUM RESISTANT | with BIP360 author Hunter Beast Hunter Beast 🕯️ Quantum computing is a complicated topic—one that incites equal amounts of fear and skepticism depending on who you talk to… especially in Bitcoin. In this episode, BIP360 author Hunter Beast wisely shares why the “truth is likely somewhere in the middle,” citing incremental advancements in quantum computing that may eventually pose a legitimate threat to some Bitcoin addresses—as well as steps we can take to protect ourselves in the short, medium and long term. The correct posture is to “be prepared, not scared,” says Hunter Beast 🕯️ Ultimately, the introduction of quantum resistant cryptography—via proposals like BIP360—will be needed for higher degrees of security. That said, individuals can mitigate personal risk substantially through proper address-use hygiene. This episode breaks down the specific challenges Bitcoin will face in the event of a quantum attack, the likelihood of an attack over time, and the steps we’ll need to take at the individual and communal level to ensure Bitcoin’s safety. This episode includes detailed discussion of: 1) How quantum computing could potentially affect Bitcoin public/private key cryptography—and technologies built on vulnerable addresses (e.g. Taproot) 2) Best practices for protecting yourself against quantum in the short and long term 3) Implications of vulnerable address types—e.g. what about Satoshi’s coins? 4) Deep Dive into BIP360 + proposed long-term solutions 5) Industry roadmaps for quantum computing + how banks and governments are preparing for “Q Day” As always, this episode can be viewed on Spotify or YouTuve—full episode in the comments or linktree in my bio. This episode is powered by Best In Slot—the leading API for Ordinals and BRC20 data aggregation and indexing. TIMESTAMPS: 00:00 Intro 02:05 What is quantum computing? 04:30 How could quantum threaten your Bitcoin wallet? 06:50 Addresses that are safe from quantum 09:13 Satoshi’s coins are in danger! 11:25 What happens if Satoshi’s coins are touched? 14:45 Do we softfork to shield Satoshi’s coins? 16:38 “Transitory inflation” for bitcoin after quantum 21:05 Why Taproot addresses are vulnerable 23:50 Do NOT reuse your Bitcoin addresses! 26:03 When will Quantum become a threat? 28:34 The long/short exposure attack; explained 31:45 Protection using private mempools 33:20 Why all the new Bitcoin L2s are in danger 37:45 Quantum is 5 to 10 years away, governments fear 40:34 Non-Bitcoin systems threatened by quantum 42:26 Centralized systems can adapt to quantum 43:50 Hunter’s BIP: Post quantum cryptography in Bitcoin 47:40 Hunter’s three new signature algorithms 53:48 Is new cryptography on Bitcoin risky? 56:33 Why not just stick to hash-based cryptography? 58:49 A 16X discount for quantum resistant addresses? 01:02:30 Creating quantum resistant multisig addresses 01:04:00 What is Frost? 01:06:50 The long process of approving a BIP 01:08:30 What developers think of Hunter’s BIP 01:10:00 Matt Corallo’s concerns with Hunter’s approach 01:11:00 Steps to implementing the BIP 360 01:17:00 Where to learn more about BIP 360 01:17:50 Who can push the button to change Bitcoin?
1:23:12

BITCOIN RAILS EPISODE #18: MAKE BITCOIN QUANTUM RESISTANT | with BIP360 author Hunter Beast Hunter Beast 🕯️ Quantum computing is a complicated topic—one that incites equal amounts of fear and skepticism depending on who you talk to… especially in Bitcoin. In this episode, BIP360 author Hunter Beast wisely shares why the “truth is likely somewhere in the middle,” citing incremental advancements in quantum computing that may eventually pose a legitimate threat to some Bitcoin addresses—as well as steps we can take to protect ourselves in the short, medium and long term. The correct posture is to “be prepared, not scared,” says Hunter Beast 🕯️ Ultimately, the introduction of quantum resistant cryptography—via proposals like BIP360—will be needed for higher degrees of security. That said, individuals can mitigate personal risk substantially through proper address-use hygiene. This episode breaks down the specific challenges Bitcoin will face in the event of a quantum attack, the likelihood of an attack over time, and the steps we’ll need to take at the individual and communal level to ensure Bitcoin’s safety. This episode includes detailed discussion of: 1) How quantum computing could potentially affect Bitcoin public/private key cryptography—and technologies built on vulnerable addresses (e.g. Taproot) 2) Best practices for protecting yourself against quantum in the short and long term 3) Implications of vulnerable address types—e.g. what about Satoshi’s coins? 4) Deep Dive into BIP360 + proposed long-term solutions 5) Industry roadmaps for quantum computing + how banks and governments are preparing for “Q Day” As always, this episode can be viewed on Spotify or YouTuve—full episode in the comments or linktree in my bio. This episode is powered by Best In Slot—the leading API for Ordinals and BRC20 data aggregation and indexing. TIMESTAMPS: 00:00 Intro 02:05 What is quantum computing? 04:30 How could quantum threaten your Bitcoin wallet? 06:50 Addresses that are safe from quantum 09:13 Satoshi’s coins are in danger! 11:25 What happens if Satoshi’s coins are touched? 14:45 Do we softfork to shield Satoshi’s coins? 16:38 “Transitory inflation” for bitcoin after quantum 21:05 Why Taproot addresses are vulnerable 23:50 Do NOT reuse your Bitcoin addresses! 26:03 When will Quantum become a threat? 28:34 The long/short exposure attack; explained 31:45 Protection using private mempools 33:20 Why all the new Bitcoin L2s are in danger 37:45 Quantum is 5 to 10 years away, governments fear 40:34 Non-Bitcoin systems threatened by quantum 42:26 Centralized systems can adapt to quantum 43:50 Hunter’s BIP: Post quantum cryptography in Bitcoin 47:40 Hunter’s three new signature algorithms 53:48 Is new cryptography on Bitcoin risky? 56:33 Why not just stick to hash-based cryptography? 58:49 A 16X discount for quantum resistant addresses? 01:02:30 Creating quantum resistant multisig addresses 01:04:00 What is Frost? 01:06:50 The long process of approving a BIP 01:08:30 What developers think of Hunter’s BIP 01:10:00 Matt Corallo’s concerns with Hunter’s approach 01:11:00 Steps to implementing the BIP 360 01:17:00 Where to learn more about BIP 360 01:17:50 Who can push the button to change Bitcoin?

Isabel Foxen Duke⚡️

31,148 次观看 • 1 年前

BITCOIN RAILS #38: Two Forces That Could Break Bitcoin: AI vs Quantum I with Martin Shkreli 🔗 YOUTUBE: 🌿 SPOTIFY: A couple months ago, I co-hosted an X space with LayerTwo Labs re: “Should Bitcoiners care about quantum computing?” You can imagine our surprise when (in?)famous tech investor Martin Shkreli arrived to share that he’s been researching this very question for years… and dropped that he’s been personally considering raising funds to hire a team of mathematicians to hack Satoshi’s Coins. In this episode, Martin and I explore the limits of Bitcoin’s security model and the two forces he believes could potentially challenge it: a computational path driven by advances in quantum hardware, and/or a mathematical path fueled by AI-assisted discovery. This interview additionally shares takes on: - Why hacking Bitcoin would be the "ultimate" mathematical achievement—and why hacking Satoshi’s coins should be considered a “bug bounty” for Bitcoin - Why quantum may be more problematic for Bitcoin than for the traditional tech world (e.g. why quantum doesn’t likely threaten NVIDIA) - The little known history of Bitcoin’s “overflow bug” (yup, Bitcoin *has* been hacked before… an exploit corrected by hard fork). - And of course, why mathematicians do their deepest work in prison 😉 As always, this episode of Bitcoin Rails can be viewed on YouTube or Spotify via the link available in my bio—and is brought to you with the help of my incredible partners: - Best In Slot (Best in Slot | BRC2.0 🧑‍🍳) – the leading API for Ordinals and BRC20 data aggregation and indexing - Spark (Lightspark) – a statechains implementation leading the path towards institutional adoption of Bitcoin-powered payments - Citrea (Citrea) – the leading Bitcoin rollup technology and contributor to the BitVM alliance 📷 Timestamps 00:00 Intro 02:57 Quantum Supremacy and Google’s Breakthroughs 05:02 Bitcoin’s Cryptographic Vulnerabilities 08:24 Studying Math and Cryptography Behind Bars 20:04 Governance and the Culture of Bitcoin Development 26:29 The Future of Quantum and AI in Cryptography 37:42 Hardware Challenges and Fidelity in Quantum 47:57 Game Theory and the Quantum Race 01:04:08 Bitcoin Recovery and the Quantum Security Question 01:08:38 Mathematical Challenges in Breaking Cryptography 01:15:08 The Role of AI in Future Mathematical Breakthroughs
1:28:17

BITCOIN RAILS #38: Two Forces That Could Break Bitcoin: AI vs Quantum I with Martin Shkreli 🔗 YOUTUBE: 🌿 SPOTIFY: A couple months ago, I co-hosted an X space with LayerTwo Labs re: “Should Bitcoiners care about quantum computing?” You can imagine our surprise when (in?)famous tech investor Martin Shkreli arrived to share that he’s been researching this very question for years… and dropped that he’s been personally considering raising funds to hire a team of mathematicians to hack Satoshi’s Coins. In this episode, Martin and I explore the limits of Bitcoin’s security model and the two forces he believes could potentially challenge it: a computational path driven by advances in quantum hardware, and/or a mathematical path fueled by AI-assisted discovery. This interview additionally shares takes on: - Why hacking Bitcoin would be the "ultimate" mathematical achievement—and why hacking Satoshi’s coins should be considered a “bug bounty” for Bitcoin - Why quantum may be more problematic for Bitcoin than for the traditional tech world (e.g. why quantum doesn’t likely threaten NVIDIA) - The little known history of Bitcoin’s “overflow bug” (yup, Bitcoin *has* been hacked before… an exploit corrected by hard fork). - And of course, why mathematicians do their deepest work in prison 😉 As always, this episode of Bitcoin Rails can be viewed on YouTube or Spotify via the link available in my bio—and is brought to you with the help of my incredible partners: - Best In Slot (Best in Slot | BRC2.0 🧑‍🍳) – the leading API for Ordinals and BRC20 data aggregation and indexing - Spark (Lightspark) – a statechains implementation leading the path towards institutional adoption of Bitcoin-powered payments - Citrea (Citrea) – the leading Bitcoin rollup technology and contributor to the BitVM alliance 📷 Timestamps 00:00 Intro 02:57 Quantum Supremacy and Google’s Breakthroughs 05:02 Bitcoin’s Cryptographic Vulnerabilities 08:24 Studying Math and Cryptography Behind Bars 20:04 Governance and the Culture of Bitcoin Development 26:29 The Future of Quantum and AI in Cryptography 37:42 Hardware Challenges and Fidelity in Quantum 47:57 Game Theory and the Quantum Race 01:04:08 Bitcoin Recovery and the Quantum Security Question 01:08:38 Mathematical Challenges in Breaking Cryptography 01:15:08 The Role of AI in Future Mathematical Breakthroughs

Isabel Foxen Duke⚡️

62,881 次观看 • 9 个月前

Joe Rogan just said what nobody in finance wants to hear about quantum computing. Rogan: “It’s over. Like there’s no privacy.” He’s not being hyperbolic. He’s being early. Every dollar in every bank account on Earth is protected by one thing. Math. Not vaults. Not guards. Not governments. Math problems that current computers can’t solve fast enough to break. That’s it. RSA encryption. Elliptic curve cryptography. The same math guarding your savings account guards sovereign wealth funds. Defense networks. Nuclear launch protocols. Quantum computing doesn’t pick the lock. It removes the door. A quantum system at sufficient scale breaks RSA encryption in hours. The same problem would take a classical computer longer than the age of the universe. This isn’t theoretical. Google, IBM, and China are racing toward fault-tolerant quantum systems. The timeline isn’t a century. It’s a decade. Maybe less. Rogan: “I think the real problem is the financial market. It’s all numbers, right? It’s all just ones and zeros.” He’s right. And it’s worse than he thinks. The first entity to reach that threshold doesn’t gain an advantage. They gain access. Every encrypted financial transaction ever recorded. Every classified document. Every crypto wallet. Every private key. Every medical record. Every secret ever sent over a wire becomes a postcard. China holds more quantum computing patents than any nation on Earth. Their research runs without public oversight or shareholder pressure. They’ve been harvesting encrypted Western data for years. Intelligence agencies have a name for the strategy. Harvest now, decrypt later. Rogan: “If somebody controls that before we do, if somebody breaks through with this type of technology and then just shuts all the other ones off.” If a foreign power achieves this before post-quantum encryption is deployed, there is no countermove. You can’t un-read stolen data. You can’t restore trust in a system whose entire security model became fiction overnight. People talk about AI as the defining technology of the century. They might be wrong. AI needs data and compute and infrastructure to be dangerous. Quantum computing just needs to exist. One breakthrough. One machine. One moment where the math that protects everything stops being hard enough. Most people heard Rogan and thought exaggeration. The ones paying attention heard a countdown. We built the entire modern world on one assumption. That certain math problems would stay unsolvable forever. Nobody promised us that.
0:22

Joe Rogan just said what nobody in finance wants to hear about quantum computing. Rogan: “It’s over. Like there’s no privacy.” He’s not being hyperbolic. He’s being early. Every dollar in every bank account on Earth is protected by one thing. Math. Not vaults. Not guards. Not governments. Math problems that current computers can’t solve fast enough to break. That’s it. RSA encryption. Elliptic curve cryptography. The same math guarding your savings account guards sovereign wealth funds. Defense networks. Nuclear launch protocols. Quantum computing doesn’t pick the lock. It removes the door. A quantum system at sufficient scale breaks RSA encryption in hours. The same problem would take a classical computer longer than the age of the universe. This isn’t theoretical. Google, IBM, and China are racing toward fault-tolerant quantum systems. The timeline isn’t a century. It’s a decade. Maybe less. Rogan: “I think the real problem is the financial market. It’s all numbers, right? It’s all just ones and zeros.” He’s right. And it’s worse than he thinks. The first entity to reach that threshold doesn’t gain an advantage. They gain access. Every encrypted financial transaction ever recorded. Every classified document. Every crypto wallet. Every private key. Every medical record. Every secret ever sent over a wire becomes a postcard. China holds more quantum computing patents than any nation on Earth. Their research runs without public oversight or shareholder pressure. They’ve been harvesting encrypted Western data for years. Intelligence agencies have a name for the strategy. Harvest now, decrypt later. Rogan: “If somebody controls that before we do, if somebody breaks through with this type of technology and then just shuts all the other ones off.” If a foreign power achieves this before post-quantum encryption is deployed, there is no countermove. You can’t un-read stolen data. You can’t restore trust in a system whose entire security model became fiction overnight. People talk about AI as the defining technology of the century. They might be wrong. AI needs data and compute and infrastructure to be dangerous. Quantum computing just needs to exist. One breakthrough. One machine. One moment where the math that protects everything stops being hard enough. Most people heard Rogan and thought exaggeration. The ones paying attention heard a countdown. We built the entire modern world on one assumption. That certain math problems would stay unsolvable forever. Nobody promised us that.

Dustin

10,857 次观看 • 2 个月前

BITCOIN RAILS #61: QUANTUM CRYPTOGRAPHY FOR BITCOIN | with Dan Boneh Dan Boneh 🔗 YOUTUBE: 🌿 SPOTIFY: One of the most prolific and influential cryptographers in the world, it’s difficult to fully quantify the impact that Dan Boneh has had on Bitcoin and digital assets more broadly. Through both his own research and his mentorship of some of the space’s most important contributors — e.g. Andrew Poelstra, Benedikt Bünz ☕️, and Robin Linus — few people have done more to shape the cryptographic foundations underlying modern blockchains and digital finance. More recently, Dan co-authored Google's widely discussed paper, “Securing Elliptic Curve Cryptocurrencies against Quantum Vulnerabilities,” which reduced prior estimates of the resources required to run Shor’s algorithm against the elliptic-curve cryptography used by Bitcoin. The paper reignited debate around quantum computing timelines and the long-term security assumptions behind modern cryptocurrencies. In this episode of Bitcoin Rails, Dan and I discuss the current state of quantum computing, its potential implications for Bitcoin, and how he believes the Bitcoin community should think about preparing for a post-quantum future over the coming decade and beyond. And yes, Dan shares his take on the “when quantum” question in the interview, among other key perspectives. This episode of Bitcoin Rails is brought to you by my NEW sponsors: LayerTwo Labs LayerTwo Labs — developing research, software, and technologies for scaling Bitcoin via the integration of Drivechains (BIP 300/301) Hashi on Sui — a primitive for executing Bitcoin Defi transactions, without having to trust a federated bridge or other centralized entity BitBox BitBox — an open-source Bitcoin-only hardware wallet, with smooth UX and no compromises on security. Check out Bitbox [dot] swiss and use code BITCOINRAILS to get a discount TIMESTAMPS: 00:00 — Intro and Dan’s history with cryptography and Bitcoin 11:44 — Shor's algorithm: how a 1994 paper became cryptography's most important threat 16:39 — Building a quantum computer: superconducting qubits vs neutral atoms 25:37 — When should we start worrying about quantum computers? The timeline debate 31:51 — Have we already reached quantum computing's “ahá” moment? 39:09 — Inside the Google paper: how Shor's algorithm was optimized 49:57 — The Bitcoin mempool attack and the 10-minute window 59:21 — Mitigation: what should Bitcoin do to prepare for quantum? 1:11:54 — Hash-based vs lattice-based signatures: Dan's case for lattice 1:23:15 — ZK proofs, BIP361, and what to do with Satoshi's coins 1:31:52 — Encrypted mempools and MEV 1:38:29 — Why Bitcoin will survive quantum and Dan's message to Bitcoin builders
1:44:02

BITCOIN RAILS #61: QUANTUM CRYPTOGRAPHY FOR BITCOIN | with Dan Boneh Dan Boneh 🔗 YOUTUBE: 🌿 SPOTIFY: One of the most prolific and influential cryptographers in the world, it’s difficult to fully quantify the impact that Dan Boneh has had on Bitcoin and digital assets more broadly. Through both his own research and his mentorship of some of the space’s most important contributors — e.g. Andrew Poelstra, Benedikt Bünz ☕️, and Robin Linus — few people have done more to shape the cryptographic foundations underlying modern blockchains and digital finance. More recently, Dan co-authored Google's widely discussed paper, “Securing Elliptic Curve Cryptocurrencies against Quantum Vulnerabilities,” which reduced prior estimates of the resources required to run Shor’s algorithm against the elliptic-curve cryptography used by Bitcoin. The paper reignited debate around quantum computing timelines and the long-term security assumptions behind modern cryptocurrencies. In this episode of Bitcoin Rails, Dan and I discuss the current state of quantum computing, its potential implications for Bitcoin, and how he believes the Bitcoin community should think about preparing for a post-quantum future over the coming decade and beyond. And yes, Dan shares his take on the “when quantum” question in the interview, among other key perspectives. This episode of Bitcoin Rails is brought to you by my NEW sponsors: LayerTwo Labs LayerTwo Labs — developing research, software, and technologies for scaling Bitcoin via the integration of Drivechains (BIP 300/301) Hashi on Sui — a primitive for executing Bitcoin Defi transactions, without having to trust a federated bridge or other centralized entity BitBox BitBox — an open-source Bitcoin-only hardware wallet, with smooth UX and no compromises on security. Check out Bitbox [dot] swiss and use code BITCOINRAILS to get a discount TIMESTAMPS: 00:00 — Intro and Dan’s history with cryptography and Bitcoin 11:44 — Shor's algorithm: how a 1994 paper became cryptography's most important threat 16:39 — Building a quantum computer: superconducting qubits vs neutral atoms 25:37 — When should we start worrying about quantum computers? The timeline debate 31:51 — Have we already reached quantum computing's “ahá” moment? 39:09 — Inside the Google paper: how Shor's algorithm was optimized 49:57 — The Bitcoin mempool attack and the 10-minute window 59:21 — Mitigation: what should Bitcoin do to prepare for quantum? 1:11:54 — Hash-based vs lattice-based signatures: Dan's case for lattice 1:23:15 — ZK proofs, BIP361, and what to do with Satoshi's coins 1:31:52 — Encrypted mempools and MEV 1:38:29 — Why Bitcoin will survive quantum and Dan's message to Bitcoin builders

Isabel Foxen Duke⚡️

113,570 次观看 • 2 个月前

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.
18:01

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 次观看 • 8 个月前

🚨12 HOUR NEWS RECAP 1. Trump paused all military aid to Ukraine, escalating tensions days after his heated Oval Office meeting with Zelensky. A senior Defense Department official says the halt will remain until Ukraine shows a "good-faith commitment to peace." 2. Marine Le Pen said that France can't promise NATO membership for Ukraine "when we know that this option was a justification for Russian aggression and is now clearly rejected by the United States." 3. Trump signed an executive order doubling tariffs on Chinese imports from 10% to 20%, citing China's failure to "take adequate steps to alleviate the illicit drug crisis." 4. Toymakers are scrambling after Trump's 20% tariff hike on Chinese imports, with price hikes now inevitable. The Toy Association is pushing for an exemption, and with U.S manufacturing practically nonexistent, there's nowhere left to turn. 5. China hit back at Trump's tariff hike by slapping fresh tariffs on U.S goods, hitting American farmers where it hurts - with up to 15% hikes on wheat, corn, soybeans, meat, and more. 6. Romanian MEP Georgiana Teodorescu said that her country was no longer a true democracy: "In Romania, they canceled the elections last December; last week, we had also the arrest of the independent candidate, Mr. Georgescu. So now we are trying to have new presidential elections, but we are not sure if, this time, the Constitutional Court will allow free elections to happen." 7. China's Zuchongzhi-3 quantum chip left classical computing in the dust, running tasks a quadrillion times faster than today's top supercomputers. According to researchers from the University of Science and Technology of China, it crushes Google's latest quantum benchmark by 6 orders of magnitude. 8. A nonprofit was raking in $18 million a month to run a migrant facility in Texas - one that's been sitting empty. Elon's DOGE called it out, and now the contract is dead. 9. Belgium plans to accelerate its defense spending, reaching 2% of GDP this year - the NATO minimum. Previously set for 2029, it marks a historic first for Belgium, which currently spends only 1.3% of GDP on defense. 10. Serbian opposition deputies set off smoke grenades inside parliament, disrupting a session in protest against government policies. The dramatic stunt was also a show of support for student-led demonstrations sweeping the country.
1:38

🚨12 HOUR NEWS RECAP 1. Trump paused all military aid to Ukraine, escalating tensions days after his heated Oval Office meeting with Zelensky. A senior Defense Department official says the halt will remain until Ukraine shows a "good-faith commitment to peace." 2. Marine Le Pen said that France can't promise NATO membership for Ukraine "when we know that this option was a justification for Russian aggression and is now clearly rejected by the United States." 3. Trump signed an executive order doubling tariffs on Chinese imports from 10% to 20%, citing China's failure to "take adequate steps to alleviate the illicit drug crisis." 4. Toymakers are scrambling after Trump's 20% tariff hike on Chinese imports, with price hikes now inevitable. The Toy Association is pushing for an exemption, and with U.S manufacturing practically nonexistent, there's nowhere left to turn. 5. China hit back at Trump's tariff hike by slapping fresh tariffs on U.S goods, hitting American farmers where it hurts - with up to 15% hikes on wheat, corn, soybeans, meat, and more. 6. Romanian MEP Georgiana Teodorescu said that her country was no longer a true democracy: "In Romania, they canceled the elections last December; last week, we had also the arrest of the independent candidate, Mr. Georgescu. So now we are trying to have new presidential elections, but we are not sure if, this time, the Constitutional Court will allow free elections to happen." 7. China's Zuchongzhi-3 quantum chip left classical computing in the dust, running tasks a quadrillion times faster than today's top supercomputers. According to researchers from the University of Science and Technology of China, it crushes Google's latest quantum benchmark by 6 orders of magnitude. 8. A nonprofit was raking in $18 million a month to run a migrant facility in Texas - one that's been sitting empty. Elon's DOGE called it out, and now the contract is dead. 9. Belgium plans to accelerate its defense spending, reaching 2% of GDP this year - the NATO minimum. Previously set for 2029, it marks a historic first for Belgium, which currently spends only 1.3% of GDP on defense. 10. Serbian opposition deputies set off smoke grenades inside parliament, disrupting a session in protest against government policies. The dramatic stunt was also a show of support for student-led demonstrations sweeping the country.

Mario Nawfal

2,237,444 次观看 • 1 年前

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.
1:12

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.

Mike

182,193 次观看 • 8 个月前

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:
21:31

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

Owen Gregorian

70,672 次观看 • 1 年前

Why I'm More Convinced Than Ever About Longevity Escape Velocity by 2030 Friends, many of you have been asking if I still believe we'll reach longevity escape velocity (LEV) by 2030, especially with all the recent AI breakthroughs dominating the headlines. My answer? Not only do I still believe it – I'm more convinced than ever. Let me share why this isn't wishful thinking but a rational conclusion based on converging evidence. We don't need to achieve immortality right away – we just need to add more than one year of lifespan per calendar year to buy time for even better solutions. The animal models already prove this is possible. We've extended the lifespans of mice (complex mammals!) by 30-50% using various therapies. The telomerase gene therapy alone extended mouse lifespan by 24% – equivalent to adding 30 human years. Imagine going from a max lifespan of 120 to 150 from a single treatment! Even more impressive, partial reprogramming using Yamanaka factors doubled the remaining lifespan of older mice. Applied to humans, this could dramatically extend our lives even starting in middle age. I'm 39 now – if something similar worked for me, it could potentially push my life expectancy well beyond 120 years. But here's where it gets really exciting: AI and quantum computing. These technologies are completely transforming how we approach longevity research. The computational barriers that previously made it impossible to simulate complete biological systems, metabolomes, or proteomes are rapidly falling. AI is accelerating drug discovery, deepening our genetic understanding, and enabling personalized medicine approaches that were purely science fiction just years ago. Tools like AlphaFold, AlphaProteo, and Deep Research aren't just making research 20% faster – they're making it 2,000% faster! World-leading experts are now saying that these technologies will likely cure essentially all diseases within the next 10-15 years. Cancer, heart disease, Alzheimer's – they're all computational problems at their core, and our computational power is exploding. What does this mean for you personally? In the immediate term, you can already improve your healthspan with existing compounds (I've started NAD+ precursors myself and noticed improvements). Within a few years, simple but effective anti-aging compounds like rapamycin (used carefully) will become more accessible. Looking further ahead, I expect we'll see comprehensive rejuvenation therapies becoming commonplace as we approach 2045. Imagine a sequence of treatments – perhaps once every 5-10 years – that dials your biological age back by 10-20 years. And each time you "kick the can down the road," the technology will have advanced even further. The biggest obstacle isn't scientific or technical – it's human. Regulatory systems like the FDA haven't even classified aging as a disease yet (though they happily medicalize other natural processes like pregnancy). There's also a strange cultural acceptance of death that resembles a death wish when you really examine it. I'd love to hear your thoughts on this topic in our community. Are you optimistic about LEV by 2030? What aspects of longevity research excite you most? And what steps are you taking today to extend your own healthspan while we wait for these breakthrough technologies?
28:57

Why I'm More Convinced Than Ever About Longevity Escape Velocity by 2030 Friends, many of you have been asking if I still believe we'll reach longevity escape velocity (LEV) by 2030, especially with all the recent AI breakthroughs dominating the headlines. My answer? Not only do I still believe it – I'm more convinced than ever. Let me share why this isn't wishful thinking but a rational conclusion based on converging evidence. We don't need to achieve immortality right away – we just need to add more than one year of lifespan per calendar year to buy time for even better solutions. The animal models already prove this is possible. We've extended the lifespans of mice (complex mammals!) by 30-50% using various therapies. The telomerase gene therapy alone extended mouse lifespan by 24% – equivalent to adding 30 human years. Imagine going from a max lifespan of 120 to 150 from a single treatment! Even more impressive, partial reprogramming using Yamanaka factors doubled the remaining lifespan of older mice. Applied to humans, this could dramatically extend our lives even starting in middle age. I'm 39 now – if something similar worked for me, it could potentially push my life expectancy well beyond 120 years. But here's where it gets really exciting: AI and quantum computing. These technologies are completely transforming how we approach longevity research. The computational barriers that previously made it impossible to simulate complete biological systems, metabolomes, or proteomes are rapidly falling. AI is accelerating drug discovery, deepening our genetic understanding, and enabling personalized medicine approaches that were purely science fiction just years ago. Tools like AlphaFold, AlphaProteo, and Deep Research aren't just making research 20% faster – they're making it 2,000% faster! World-leading experts are now saying that these technologies will likely cure essentially all diseases within the next 10-15 years. Cancer, heart disease, Alzheimer's – they're all computational problems at their core, and our computational power is exploding. What does this mean for you personally? In the immediate term, you can already improve your healthspan with existing compounds (I've started NAD+ precursors myself and noticed improvements). Within a few years, simple but effective anti-aging compounds like rapamycin (used carefully) will become more accessible. Looking further ahead, I expect we'll see comprehensive rejuvenation therapies becoming commonplace as we approach 2045. Imagine a sequence of treatments – perhaps once every 5-10 years – that dials your biological age back by 10-20 years. And each time you "kick the can down the road," the technology will have advanced even further. The biggest obstacle isn't scientific or technical – it's human. Regulatory systems like the FDA haven't even classified aging as a disease yet (though they happily medicalize other natural processes like pregnancy). There's also a strange cultural acceptance of death that resembles a death wish when you really examine it. I'd love to hear your thoughts on this topic in our community. Are you optimistic about LEV by 2030? What aspects of longevity research excite you most? And what steps are you taking today to extend your own healthspan while we wait for these breakthrough technologies?

David Shapiro (L/0)

35,264 次观看 • 1 年前

Will the “second” of the future run faster or slower than it does today? Maybe the very definition of this unit of time is about to shift, thanks to a more precise clock: a cutting-edge optical clock developed by a team at the University of Science and Technology of China, boasting an accuracy where the error stays under 1 second over roughly 30 billion years. This optical clock marks a roughly 30-fold improvement in precision over current global standards. Until now, leading-edge technology in this field has been dominated by a handful of elite institutions in the U.S. and Germany. The breakthrough is seen as a milestone: the International Bureau of Weights and Measures plans to redefine the “second” sometime around 2030, paving the way for a dramatic boost in the accuracy of nearly all physical measurements. This could enable ultra-precise monitoring of phenomena like earthquakes, volcanoes, and groundwater levels, while also providing new tools for testing general relativity, detecting gravitational waves, and probing dark matter. For any nation, the ability to independently calibrate its own time standards carries huge strategic weight—especially in scenarios like wartime disruptions. Last year, Chinese security agencies revealed that the country’s National Time Service Center had endured nearly two years of sustained cyberattacks from the U.S. It’s essentially a battle for control over time itself: a mere one-nanosecond (0.000000001s) discrepancy can throw satellite positioning off by 30 centimeters, potentially triggering widespread chaos on the ground. Accurate timekeeping is like air or water—we barely notice it until something goes wrong, at which point entire societal systems could grind to a halt.
0:37

Will the “second” of the future run faster or slower than it does today? Maybe the very definition of this unit of time is about to shift, thanks to a more precise clock: a cutting-edge optical clock developed by a team at the University of Science and Technology of China, boasting an accuracy where the error stays under 1 second over roughly 30 billion years. This optical clock marks a roughly 30-fold improvement in precision over current global standards. Until now, leading-edge technology in this field has been dominated by a handful of elite institutions in the U.S. and Germany. The breakthrough is seen as a milestone: the International Bureau of Weights and Measures plans to redefine the “second” sometime around 2030, paving the way for a dramatic boost in the accuracy of nearly all physical measurements. This could enable ultra-precise monitoring of phenomena like earthquakes, volcanoes, and groundwater levels, while also providing new tools for testing general relativity, detecting gravitational waves, and probing dark matter. For any nation, the ability to independently calibrate its own time standards carries huge strategic weight—especially in scenarios like wartime disruptions. Last year, Chinese security agencies revealed that the country’s National Time Service Center had endured nearly two years of sustained cyberattacks from the U.S. It’s essentially a battle for control over time itself: a mere one-nanosecond (0.000000001s) discrepancy can throw satellite positioning off by 30 centimeters, potentially triggering widespread chaos on the ground. Accurate timekeeping is like air or water—we barely notice it until something goes wrong, at which point entire societal systems could grind to a halt.

Sinical

428,469 次观看 • 4 个月前

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.
1:01

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.

Milk Road AI

12,140 次观看 • 1 个月前

🚨 EXCLUSIVE INTERVIEW: “ETH TO $60K, BITCOIN TO $1M, AND STABLECOINS WILL FUND THE U.S. GOVERNMENT” He called Bitcoin at $25K in 2017 – and was laughed at. Fundstrat’s Tom Lee is one of the most followed macro minds in crypto, with deep ties to Wall Street, CNBC, and the largest players in finance. He says the new financial system is already being built – and crypto will anchor it. Thomas (Tom) Lee (not drummer) FundstratDirect.com isn’t guessing. He was JPMorgan’s Chief Equity Strategist. He’s advised global funds, predicted market cycles, and published one of the earliest institutional theses on Bitcoin in 2017. In this interview, we discuss Crypto’s future, explain why billions are flowing into Digital Asset Treasuries’, and how AI, sovereigns, and Wall Street will drive the next bull run The herd isn’t coming. The herd is here. 01:09 – Why Tom left JPMorgan to publish the Bitcoin thesis 03:45 – Bitcoin to $25K: clients canceled, Wall Street mocked 05:22 – The 4-year cycle: broken or reflexive? 08:48 – Will sovereign buyers put a floor under BTC? 12:11 – The new story arc: AI, institutions, and crypto convergence 14:00 – “Crypto is already UBI.” The forgotten benefit of early adoption 20:01 – How stablecoins will fund U.S. treasuries – permanently 23:47 – ETH’s underperformance and what changed in 2024 25:36 – Ethereum, AI agents & the rise of authenticated instructions 27:43 – “ETH has been dead for 5 years, but it’s not dead tech” 28:46 – ETH vs TON vs Tether: who wins the stablecoin war? 32:53 – Gaming, Pudgy Penguins, and crypto’s IP revolution 34:29 – What are Digital Asset Treasuries? Why they matter 37:10 – BitMNR: how ETH/share went from $4 to $23 in weeks 42:00 – Why BitMNR avoided convertibles – and outperformed 46:29 – ETH down, but ETH/share up – thanks to treasury velocity 53:48 – Price targets: BTC $200K, ETH $60K, and the path to $1M 55:15 – “ETH has a 50% chance of flipping Bitcoin” 56:24 – RWAs and tokenizing the real world – how we hit $100T 58:35 – “Crypto is democratized wealth. Anyone can join.” 59:02 – One chain to rule them all? Or multichain future? 01:00:45 – “Hard work is 2%. Luck is 98%.” 01:01:40 – Why crypto treasuries may become Wall Street’s new hedge This episode is sponsored by BTQ. BTQ is driving the future of post-quantum solutions, delivering a neutral-atom quantum computing platform and quantum-safe security. Backed by a broad patent portfolio and the first commercially significant quantum advantage, BTQ serves finance, telecom, logistics, and defense. (Cboe CA: BTQ | FSE: NG3 | OTCQX: BTQQF) Disclaimer: This content was produced in collaboration with the client and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any financial decisions, especially in highly volatile markets like crypto.
1:01:44

🚨 EXCLUSIVE INTERVIEW: “ETH TO $60K, BITCOIN TO $1M, AND STABLECOINS WILL FUND THE U.S. GOVERNMENT” He called Bitcoin at $25K in 2017 – and was laughed at. Fundstrat’s Tom Lee is one of the most followed macro minds in crypto, with deep ties to Wall Street, CNBC, and the largest players in finance. He says the new financial system is already being built – and crypto will anchor it. Thomas (Tom) Lee (not drummer) FundstratDirect.com isn’t guessing. He was JPMorgan’s Chief Equity Strategist. He’s advised global funds, predicted market cycles, and published one of the earliest institutional theses on Bitcoin in 2017. In this interview, we discuss Crypto’s future, explain why billions are flowing into Digital Asset Treasuries’, and how AI, sovereigns, and Wall Street will drive the next bull run The herd isn’t coming. The herd is here. 01:09 – Why Tom left JPMorgan to publish the Bitcoin thesis 03:45 – Bitcoin to $25K: clients canceled, Wall Street mocked 05:22 – The 4-year cycle: broken or reflexive? 08:48 – Will sovereign buyers put a floor under BTC? 12:11 – The new story arc: AI, institutions, and crypto convergence 14:00 – “Crypto is already UBI.” The forgotten benefit of early adoption 20:01 – How stablecoins will fund U.S. treasuries – permanently 23:47 – ETH’s underperformance and what changed in 2024 25:36 – Ethereum, AI agents & the rise of authenticated instructions 27:43 – “ETH has been dead for 5 years, but it’s not dead tech” 28:46 – ETH vs TON vs Tether: who wins the stablecoin war? 32:53 – Gaming, Pudgy Penguins, and crypto’s IP revolution 34:29 – What are Digital Asset Treasuries? Why they matter 37:10 – BitMNR: how ETH/share went from $4 to $23 in weeks 42:00 – Why BitMNR avoided convertibles – and outperformed 46:29 – ETH down, but ETH/share up – thanks to treasury velocity 53:48 – Price targets: BTC $200K, ETH $60K, and the path to $1M 55:15 – “ETH has a 50% chance of flipping Bitcoin” 56:24 – RWAs and tokenizing the real world – how we hit $100T 58:35 – “Crypto is democratized wealth. Anyone can join.” 59:02 – One chain to rule them all? Or multichain future? 01:00:45 – “Hard work is 2%. Luck is 98%.” 01:01:40 – Why crypto treasuries may become Wall Street’s new hedge This episode is sponsored by BTQ. BTQ is driving the future of post-quantum solutions, delivering a neutral-atom quantum computing platform and quantum-safe security. Backed by a broad patent portfolio and the first commercially significant quantum advantage, BTQ serves finance, telecom, logistics, and defense. (Cboe CA: BTQ | FSE: NG3 | OTCQX: BTQQF) Disclaimer: This content was produced in collaboration with the client and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any financial decisions, especially in highly volatile markets like crypto.

Mario Nawfal

1,944,207 次观看 • 11 个月前

🟠 Venezuela’s Hydrocarbons Law reform, currently advancing in the National Assembly, could enable a much-needed near-term recovery in the country’s oil production, Venezuela-focused research outlet Bitácora Económica reports, echoing U.S. government projections. 📌 Key Details: ➤ Former Energy Vice Minister Dolores Dobarro told Bitacora that mature oil fields could be reactivated within 18–24 months, adding 200,000 to 600,000 barrels per day with relatively limited capital. ➤ Larger gains would require long-term commitments, and more than $100 billion in investment, and multiple decades to materialize, she said. ➤ Elías Ferrer, founder of Orinoco Research, similarly estimates output could rise by up to 500,000 bpd within two years and potentially double within three to four years, though growth beyond 2 million bpd would demand much heavier investment. ➤ Both experts point to the Production Participation Contracts (CPPs) as central to attracting private capital as it will allow private operators to function with more autonomy, and open the sector to new investment. ➤ Still, both experts and the New York Times caution that U.S. sanctions and ongoing political uncertainty remain significant risks to any sustained recovery. In a new report, the Times notes that Venezuela is subject to more than 400 U.S. sanctions, some of which bar companies from working with the state oil company, creating such broad legal risk that even requesting technical data to assess oil and gas projects can raise concerns about violating sanctions. ➤ U.S. Energy Secretary Chris Wright told oil executives on Wednesday that Venezuela’s oil output could rise about 30 % above its current ~900,000 bpd level in the short to medium term, according to three attendees, as President Trump pushes U.S. companies to invest $100 billion in expanding Venezuela’s oil production. 🎥 VIDEO: Jorge Rodríguez, president of Venezuela’s National Assembly and a senior Chavista leader, shares a video on his Telegram today showing the country’s lawmakers holding a public consultation on the Organic Hydrocarbons Law reform with workers at the Puerto La Cruz refinery. The refinery is the center of Venezuela’s eastern oil system and processes crude from the Orinoco Belt. Rodríguez told them the legislation is aimed at increasing oil production to fund hospitals, roads, and improved wages for workers. (Translations by Drop Site) Additional details on the reform in the QT’d post and posts in reply.
2:36

🟠 Venezuela’s Hydrocarbons Law reform, currently advancing in the National Assembly, could enable a much-needed near-term recovery in the country’s oil production, Venezuela-focused research outlet Bitácora Económica reports, echoing U.S. government projections. 📌 Key Details: ➤ Former Energy Vice Minister Dolores Dobarro told Bitacora that mature oil fields could be reactivated within 18–24 months, adding 200,000 to 600,000 barrels per day with relatively limited capital. ➤ Larger gains would require long-term commitments, and more than $100 billion in investment, and multiple decades to materialize, she said. ➤ Elías Ferrer, founder of Orinoco Research, similarly estimates output could rise by up to 500,000 bpd within two years and potentially double within three to four years, though growth beyond 2 million bpd would demand much heavier investment. ➤ Both experts point to the Production Participation Contracts (CPPs) as central to attracting private capital as it will allow private operators to function with more autonomy, and open the sector to new investment. ➤ Still, both experts and the New York Times caution that U.S. sanctions and ongoing political uncertainty remain significant risks to any sustained recovery. In a new report, the Times notes that Venezuela is subject to more than 400 U.S. sanctions, some of which bar companies from working with the state oil company, creating such broad legal risk that even requesting technical data to assess oil and gas projects can raise concerns about violating sanctions. ➤ U.S. Energy Secretary Chris Wright told oil executives on Wednesday that Venezuela’s oil output could rise about 30 % above its current ~900,000 bpd level in the short to medium term, according to three attendees, as President Trump pushes U.S. companies to invest $100 billion in expanding Venezuela’s oil production. 🎥 VIDEO: Jorge Rodríguez, president of Venezuela’s National Assembly and a senior Chavista leader, shares a video on his Telegram today showing the country’s lawmakers holding a public consultation on the Organic Hydrocarbons Law reform with workers at the Puerto La Cruz refinery. The refinery is the center of Venezuela’s eastern oil system and processes crude from the Orinoco Belt. Rodríguez told them the legislation is aimed at increasing oil production to fund hospitals, roads, and improved wages for workers. (Translations by Drop Site) Additional details on the reform in the QT’d post and posts in reply.

Drop Site

53,478 次观看 • 6 个月前

Ethiopia Must Prepare for War... Sisi Revives Military Options Once Again Egyptian President Abdel Fattah el-Sisi stated today, Sunday, that "Egypt will not stand idly by in the face of Ethiopia's irresponsible approach and will take all necessary measures to protect its interests and water security." In a striking escalation of Egypt's stance toward Ethiopia regarding the Renaissance Dam, amounting to a declaration of the end of Egypt's traditional patience, Sisi clarified during the opening of the Eighth Cairo Water Week (under the slogan "Innovative Solutions for Resilience Against Climate Change") that this approach has led to an "artificially induced industrial flood" that harmed the two downstream countries (Egypt and Sudan) in the past few days, due to undisciplined management of the dam's waters. Sisi reiterated his description of water security as an "existential issue" affecting the lives of more than 100 million Egyptians, calling on the international community and the African Union to confront "Ethiopia's recklessness" and impose a binding legal agreement that preserves the rights of downstream countries without harming upstream ones.Sisi's recorded speech reflects a shift in Egypt's strategy after 14 years of "wisdom and prudence" in ongoing fruitless negotiations since 2011. Here, Sisi revives potential military options with phrases like "will not stand idly by" and "all measures," which are not new (they were repeated in 2020-2021), but their current context—with an explicit reference to "actual harm" from the floods—suggests possible preparation for limited military operations, such as precision airstrikes on parts of the dam or closing the Nile waters at the border.Sisi's statements, which implicitly threaten Ethiopia, coincide with reports of recent Egyptian military exercises in Sinai involving "water security" scenarios. However, Sisi's insistence on "dialogue as the optimal path" leaves the door open for final negotiations before any radical step, emphasizing "cooperation" to avoid war.
1:13

Ethiopia Must Prepare for War... Sisi Revives Military Options Once Again Egyptian President Abdel Fattah el-Sisi stated today, Sunday, that "Egypt will not stand idly by in the face of Ethiopia's irresponsible approach and will take all necessary measures to protect its interests and water security." In a striking escalation of Egypt's stance toward Ethiopia regarding the Renaissance Dam, amounting to a declaration of the end of Egypt's traditional patience, Sisi clarified during the opening of the Eighth Cairo Water Week (under the slogan "Innovative Solutions for Resilience Against Climate Change") that this approach has led to an "artificially induced industrial flood" that harmed the two downstream countries (Egypt and Sudan) in the past few days, due to undisciplined management of the dam's waters. Sisi reiterated his description of water security as an "existential issue" affecting the lives of more than 100 million Egyptians, calling on the international community and the African Union to confront "Ethiopia's recklessness" and impose a binding legal agreement that preserves the rights of downstream countries without harming upstream ones.Sisi's recorded speech reflects a shift in Egypt's strategy after 14 years of "wisdom and prudence" in ongoing fruitless negotiations since 2011. Here, Sisi revives potential military options with phrases like "will not stand idly by" and "all measures," which are not new (they were repeated in 2020-2021), but their current context—with an explicit reference to "actual harm" from the floods—suggests possible preparation for limited military operations, such as precision airstrikes on parts of the dam or closing the Nile waters at the border.Sisi's statements, which implicitly threaten Ethiopia, coincide with reports of recent Egyptian military exercises in Sinai involving "water security" scenarios. However, Sisi's insistence on "dialogue as the optimal path" leaves the door open for final negotiations before any radical step, emphasizing "cooperation" to avoid war.

khaled mahmoued

132,951 次观看 • 9 个月前