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The OriginTrail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000...

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466,082 次观看 • 3 个月前 •via X (Twitter)

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The Cronos zkEVM public mainnet (alpha) launch date is targeted for August 15. Cronos' newest, future-proof blockchain network is almost ready to go live. On Aug 15, users will be able to connect their wallet, bridge funds, and use a large and growing number of dapps. What are the next steps for you? It's your last chance #CROFam to participate in #Cronos zkEVM Passage, as deposits will close on Aug 9 at 12pm UTC. 👉 Deposit now at to: 🪙 Be the first to get zkCRO deposits when mainnet goes live 🌟 Accumulate loyalty points for the Pioneer Program 🏆 Be eligible for the $30,000 giveaway, and the additional $1000 prize pool specifically for users who deposit CRO from the Ethereum network ⚠️ As soon as Cronos zkEVM goes live, the Pioneer Program continues with a new series of "Mainnet Missions". Stay tuned to grow your loyalty point balance, which you can redeem later for rewards.
0:15

The Cronos zkEVM public mainnet (alpha) launch date is targeted for August 15. Cronos' newest, future-proof blockchain network is almost ready to go live. On Aug 15, users will be able to connect their wallet, bridge funds, and use a large and growing number of dapps. What are the next steps for you? It's your last chance #CROFam to participate in #Cronos zkEVM Passage, as deposits will close on Aug 9 at 12pm UTC. 👉 Deposit now at to: 🪙 Be the first to get zkCRO deposits when mainnet goes live 🌟 Accumulate loyalty points for the Pioneer Program 🏆 Be eligible for the $30,000 giveaway, and the additional $1000 prize pool specifically for users who deposit CRO from the Ethereum network ⚠️ As soon as Cronos zkEVM goes live, the Pioneer Program continues with a new series of "Mainnet Missions". Stay tuned to grow your loyalty point balance, which you can redeem later for rewards.

Ult

80,653 次观看 • 2 年前

let me explain what Anthropic just did they built an AI model so good at finding security vulnerabilities that they have refused to release it meet Claude Mythos → it’s Anthropic’s newest frontier model and it’s not available to the public. not because it’s not ready. because it’s too dangerous → Mythos found tens of thousands of zero day vulnerabilities across every major operating system and web browser… many of them 1 to 2 decades old. for context… Opus 4.6 found about 500. Mythos found tens of thousands → it found vulnerabilities in the Linux kernel. a 27 year old vulnerability in OpenBSD. a 16 year old vulnerability in FFmpeg → it doesn’t just find bugs. it writes the exploits too. that’s the part that scared them → so instead of releasing it… Anthropic has created Project Glasswing. a cybersecurity initiative where they hand picked 40+ companies to use Mythos for defense only → the partner list reads like a who’s who of tech… Amazon, Apple, Microsoft, Google, Nvidia, Broadcom, Cisco, CrowdStrike, Palo Alto Networks, JPMorgan, the Linux Foundation → Anthropic is giving up to $100 million in usage credits to these partners and $4 million to open source security organizations → they’re briefing CISA and the Commerce Department on how to handle this → the benchmarks are truly insane… Mythos hit 77.8% on SWE-bench Pro where Opus 4.6 scored 53.4%. hit 93.9% on SWE-bench Verified where Opus 4.6 scored 80.8% → Anthropic’s head of frontier red team said this is “the first time a model is this good that we decided to approach release in a very different way” this is the first time an AI company has held back a model because it was too capable not too expensive. not too slow. too dangerous and instead of locking it in a vault they weaponized it for defense and gave it to the companies that run the internet that’s either the most responsible thing an AI company has ever done… or the scariest only time will tell
0:11

let me explain what Anthropic just did they built an AI model so good at finding security vulnerabilities that they have refused to release it meet Claude Mythos → it’s Anthropic’s newest frontier model and it’s not available to the public. not because it’s not ready. because it’s too dangerous → Mythos found tens of thousands of zero day vulnerabilities across every major operating system and web browser… many of them 1 to 2 decades old. for context… Opus 4.6 found about 500. Mythos found tens of thousands → it found vulnerabilities in the Linux kernel. a 27 year old vulnerability in OpenBSD. a 16 year old vulnerability in FFmpeg → it doesn’t just find bugs. it writes the exploits too. that’s the part that scared them → so instead of releasing it… Anthropic has created Project Glasswing. a cybersecurity initiative where they hand picked 40+ companies to use Mythos for defense only → the partner list reads like a who’s who of tech… Amazon, Apple, Microsoft, Google, Nvidia, Broadcom, Cisco, CrowdStrike, Palo Alto Networks, JPMorgan, the Linux Foundation → Anthropic is giving up to $100 million in usage credits to these partners and $4 million to open source security organizations → they’re briefing CISA and the Commerce Department on how to handle this → the benchmarks are truly insane… Mythos hit 77.8% on SWE-bench Pro where Opus 4.6 scored 53.4%. hit 93.9% on SWE-bench Verified where Opus 4.6 scored 80.8% → Anthropic’s head of frontier red team said this is “the first time a model is this good that we decided to approach release in a very different way” this is the first time an AI company has held back a model because it was too capable not too expensive. not too slow. too dangerous and instead of locking it in a vault they weaponized it for defense and gave it to the companies that run the internet that’s either the most responsible thing an AI company has ever done… or the scariest only time will tell

klöss

21,270 次观看 • 5 个月前

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.
0:10

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.

Shanaka Anslem Perera ⚡

54,516 次观看 • 3 个月前

If someone was the first to release a fake AI-generated video, had their watermark and name already embedded on it, and circulated it publicly at 7:33 AM before it spread elsewhere, then authorities have every reason to immediately invite and question that individual. He may possess critical information about: • who created the manipulated content, • where it originated from, • who coordinated its release, and • whether there was an intentional attempt to mislead the public or cause unrest. Digital misinformation is a serious issue. The first distributor of such content is often a key link in tracing the source and determining whether any laws were violated. Department of State Services (DSS) get to work now.
0:11

If someone was the first to release a fake AI-generated video, had their watermark and name already embedded on it, and circulated it publicly at 7:33 AM before it spread elsewhere, then authorities have every reason to immediately invite and question that individual. He may possess critical information about: • who created the manipulated content, • where it originated from, • who coordinated its release, and • whether there was an intentional attempt to mislead the public or cause unrest. Digital misinformation is a serious issue. The first distributor of such content is often a key link in tracing the source and determining whether any laws were violated. Department of State Services (DSS) get to work now.

EasYOfLagos ✨️

31,452 次观看 • 4 个月前

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.
0:14

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

John Turner

21,029 次观看 • 7 天前

If this contract had gone live, attackers would have drained every single ETH within hours. A crowdfunding platform. Clean-looking Solidity. Ready for mainnet. The dev team was confident. Then we ran it through V2's AI Smart Contract Auditor. 9 vulnerabilities detected. 1 critical. 2 high. Under 20 minutes. The critical finding? A textbook reentrancy bug in claimRefund(). The contract sent ETH before zeroing the user's balance. An attacker could re-enter the function during the transfer and drain the same refund repeatedly until the entire contract was empty. Every backer's funds across every campaign, gone in one transaction. It also caught an emergencyWithdraw() function that let the owner pull the entire contract balance with zero restrictions. Active campaigns, pending refunds, everything. One compromised key and it's a complete rug. Plus an uncapped platform fee that could be set to 100% (creators get nothing) or above 100% (permanent DoS on all fund claims). The AI generated a 24-page report with severity classifications, exact code locations, detailed attack scenarios, and specific fixes for each issue. Final score: 89/100. Not because it was "pretty good," but because the architecture was sound and the issues were fixable. Without catching them though? Catastrophic. A traditional audit runs $5K to $50K and takes weeks. This took under 20 minutes. That's the power of V2 ⚡️
0:10

If this contract had gone live, attackers would have drained every single ETH within hours. A crowdfunding platform. Clean-looking Solidity. Ready for mainnet. The dev team was confident. Then we ran it through V2's AI Smart Contract Auditor. 9 vulnerabilities detected. 1 critical. 2 high. Under 20 minutes. The critical finding? A textbook reentrancy bug in claimRefund(). The contract sent ETH before zeroing the user's balance. An attacker could re-enter the function during the transfer and drain the same refund repeatedly until the entire contract was empty. Every backer's funds across every campaign, gone in one transaction. It also caught an emergencyWithdraw() function that let the owner pull the entire contract balance with zero restrictions. Active campaigns, pending refunds, everything. One compromised key and it's a complete rug. Plus an uncapped platform fee that could be set to 100% (creators get nothing) or above 100% (permanent DoS on all fund claims). The AI generated a 24-page report with severity classifications, exact code locations, detailed attack scenarios, and specific fixes for each issue. Final score: 89/100. Not because it was "pretty good," but because the architecture was sound and the issues were fixable. Without catching them though? Catastrophic. A traditional audit runs $5K to $50K and takes weeks. This took under 20 minutes. That's the power of V2 ⚡️

ChainGPT

26,504 次观看 • 6 个月前

Most AI Projects In Crypto Are Liars. Sentient is different. It is the only project in Web3 pursuing true Artificial General Intelligence. A few facts that set it apart: 1. First-of-its-kind: Sentient is not just building AI tools, but an open, crypto-native path to AGI. 2. Community-owned intelligence: Instead of Big Tech locking up breakthroughs, progress is built openly and shared on-chain. 3. Value capture: Every step forward in intelligence accrues to the ecosystem itself, not to a closed corporation. 4. Product before hype: While dozens of AI projects launch at $300M–$2B FDV with little to show, Sentient is shipping real technology first. 5. Designed to evolve: Its architecture is built to learn, adapt, and scale like a human mind. 6. The singularity play in crypto: Many see Sentient as the Tesla-equivalent for AGI—but crypto-native. In time, people will understand that this isn’t just another AI project. It’s the beginning of decentralized intelligence.
0:13

Most AI Projects In Crypto Are Liars. Sentient is different. It is the only project in Web3 pursuing true Artificial General Intelligence. A few facts that set it apart: 1. First-of-its-kind: Sentient is not just building AI tools, but an open, crypto-native path to AGI. 2. Community-owned intelligence: Instead of Big Tech locking up breakthroughs, progress is built openly and shared on-chain. 3. Value capture: Every step forward in intelligence accrues to the ecosystem itself, not to a closed corporation. 4. Product before hype: While dozens of AI projects launch at $300M–$2B FDV with little to show, Sentient is shipping real technology first. 5. Designed to evolve: Its architecture is built to learn, adapt, and scale like a human mind. 6. The singularity play in crypto: Many see Sentient as the Tesla-equivalent for AGI—but crypto-native. In time, people will understand that this isn’t just another AI project. It’s the beginning of decentralized intelligence.

BillionAireSon 🛡️

16,141 次观看 • 1 年前

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓
0:15

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

Argona

32,475 次观看 • 1 个月前

The world just paid $2 trillion for a rocket company that lost $4.9 billion last year. And the rockets are not why it lost the money. They are the only part making any. SpaceX went public Friday, the largest IPO in history. Up 19%, a $2 trillion valuation, Elon Musk the first trillionaire. Then you open the filing. Three businesses sit inside it. Starlink, the satellites, brought in $11.4 billion, 61% of all revenue, and $4.4 billion in profit. It is the only piece that earns a dollar. The rockets that land themselves run a small loss reinvesting in Starship. And the AI arm, Grok plus the app once called Twitter, folded in this February, lost $6.4 billion in a single year on $12.7 billion of spending. Read that again. The satellites pay for everything. The AI loses more than the satellites make. And the AI is the part the market fell in love with. It gets bolder. The prospectus claims a total market of $28.5 trillion, the largest any company has ever put in a filing. Larger than the GDP of the United States. That is the number underwriting a $2 trillion price tag built on a division bleeding $6 billion a year. Now the structure. About 4% of the company trades. That sliver sets the price for all of it. Musk is locked up for 366 days and holds roughly 80% of the votes. The public bought a company they cannot steer, priced on the one segment losing the most. This is the whole year in one ticker. The profit is satellites. The story is AI. The market bought the story. The rockets were never the risk. The risk is a $2 trillion price resting on the one bet that has yet to make a cent.
0:10

The world just paid $2 trillion for a rocket company that lost $4.9 billion last year. And the rockets are not why it lost the money. They are the only part making any. SpaceX went public Friday, the largest IPO in history. Up 19%, a $2 trillion valuation, Elon Musk the first trillionaire. Then you open the filing. Three businesses sit inside it. Starlink, the satellites, brought in $11.4 billion, 61% of all revenue, and $4.4 billion in profit. It is the only piece that earns a dollar. The rockets that land themselves run a small loss reinvesting in Starship. And the AI arm, Grok plus the app once called Twitter, folded in this February, lost $6.4 billion in a single year on $12.7 billion of spending. Read that again. The satellites pay for everything. The AI loses more than the satellites make. And the AI is the part the market fell in love with. It gets bolder. The prospectus claims a total market of $28.5 trillion, the largest any company has ever put in a filing. Larger than the GDP of the United States. That is the number underwriting a $2 trillion price tag built on a division bleeding $6 billion a year. Now the structure. About 4% of the company trades. That sliver sets the price for all of it. Musk is locked up for 366 days and holds roughly 80% of the votes. The public bought a company they cannot steer, priced on the one segment losing the most. This is the whole year in one ticker. The profit is satellites. The story is AI. The market bought the story. The rockets were never the risk. The risk is a $2 trillion price resting on the one bet that has yet to make a cent.

Shanaka Anslem Perera ⚡

722,071 次观看 • 3 个月前

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.
0:17

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Dustin

22,458 次观看 • 2 个月前

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)
0:19

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

12,868 次观看 • 3 个月前

nobody in that room realised what he just said microsoft's ceo told a conference that the frontier model you rent for $200 a month is the commodity, and someone has now measured what that costs everyone who assumed otherwise teams carrying 90 to 100% evaluation coverage reach excellent reliability 70.3% of the time, against 32.4% for teams sitting under half, on the same rented models i read the survey behind it twice because the sample is 500 enterprise teams rather than a vendor anecdote, and the spread holds across all of them this is Eval Engineering, and it is the part of the stack that stops being rented: - stop classifying behaviours as low-risk before you have data on them, because the 19.3% of teams who do take 2.3 times the production incidents and the intuition fails hardest exactly where behaviour is emergent - make the incident the source of the test: only 51.7% of teams turn an outage into a permanent regression, so half of all incident response gets paid for and then thrown away - budget coverage like continuous integration rather than like paperwork, since the pattern separating the elite teams is 70% coverage held together with 40% of development time spent on testing - gate the deploy on the eval instead of reporting the eval, because a threshold that cannot block a release is a dashboard with extra steps - keep the examiner private, since it encodes your own definition of correct: the model gets replaced from scratch twice a year and the examiner is the only asset that survives the swap - expect the incident rather than hoping to prevent it, because 84.9% of organisations hit one inside six months and only 8.4% report none, so detection speed is the real variable - review the gaps on a schedule and make the team justify an uncovered behaviour rather than defend a test that already exists the catch is what coverage actually costs, and it is not the tooling bill: the elite pattern spends 40% of development time on testing, which is the share of every sprint that stops being feature work that is the trade nobody puts in the quickstart, and it is why most teams stay at the coverage level where reliability lands at 32.4% bookmark this, the whole build sits in the article ↓
0:15

nobody in that room realised what he just said microsoft's ceo told a conference that the frontier model you rent for $200 a month is the commodity, and someone has now measured what that costs everyone who assumed otherwise teams carrying 90 to 100% evaluation coverage reach excellent reliability 70.3% of the time, against 32.4% for teams sitting under half, on the same rented models i read the survey behind it twice because the sample is 500 enterprise teams rather than a vendor anecdote, and the spread holds across all of them this is Eval Engineering, and it is the part of the stack that stops being rented: - stop classifying behaviours as low-risk before you have data on them, because the 19.3% of teams who do take 2.3 times the production incidents and the intuition fails hardest exactly where behaviour is emergent - make the incident the source of the test: only 51.7% of teams turn an outage into a permanent regression, so half of all incident response gets paid for and then thrown away - budget coverage like continuous integration rather than like paperwork, since the pattern separating the elite teams is 70% coverage held together with 40% of development time spent on testing - gate the deploy on the eval instead of reporting the eval, because a threshold that cannot block a release is a dashboard with extra steps - keep the examiner private, since it encodes your own definition of correct: the model gets replaced from scratch twice a year and the examiner is the only asset that survives the swap - expect the incident rather than hoping to prevent it, because 84.9% of organisations hit one inside six months and only 8.4% report none, so detection speed is the real variable - review the gaps on a schedule and make the team justify an uncovered behaviour rather than defend a test that already exists the catch is what coverage actually costs, and it is not the tooling bill: the elite pattern spends 40% of development time on testing, which is the share of every sprint that stops being feature work that is the trade nobody puts in the quickstart, and it is why most teams stay at the coverage level where reliability lands at 32.4% bookmark this, the whole build sits in the article ↓

Argona

16,482 次观看 • 1 个月前

🚀🔮 InterPredict V2: From Beta Testing to Production Readiness 🌐⚡ InterPredict is entering a critical new stage: moving from an early beta product towards a more reliable, scalable, and production-ready community prediction marketplace. 🧠📊 This is not simply a version update. It is a structured transition from testing the concept to strengthening the infrastructure required for real-world use. 🛠️🌍 🧪 What the Beta Phase Achieved The beta phase provided an important testing environment for: • ✅ Validating the core prediction-market concept • 👥 Observing how users interact with the platform • 🐛 Identifying technical issues and improvement areas • 💬 Collecting community feedback • 📈 Understanding how prediction markets perform under real usage conditions Beta testing was about learning what works, discovering what needs improvement, and preparing the foundation for the next phase. 🔍🚧 ⚙️🚀 What InterPredict V2 Brings With V2, the focus is shifting towards a smoother, faster, and more dependable user experience. Key priorities include: ⚡ Improved performance 🛡️ Greater reliability 📈 Better scalability 🔗 Seamless system integration ⚙️ More efficient platform operations 🎨 A clearer and more accessible interface The objective is to connect the platform’s core components into one integrated system that can support broader participation and future growth. 🌱🌐 🛠️📋 Development Progress The current development status is as follows: ✅ Smart contract development: Completed ✅ Backend and server development: Completed 🔄 Frontend development: In progress 🔗 Full platform integration: In progress 🧪 Final testing: Upcoming 🌐🚀 Mainnet launch: Planned after successful testing and review This staged approach is important. Before a Mainnet release, the platform must be tested thoroughly across its technical systems, user flows, security processes, and overall reliability. 🔐✅ 📊🧠 More Than Just Making Predictions InterPredict’s wider purpose extends beyond individual predictions. 🔮 The platform is designed to help transform community knowledge, opinions, and expectations into structured prediction markets. When participation is organised effectively, collective views can produce useful market signals and offer insight into how communities assess future events. 💡📈 This may create value in several areas: • 🗣️ Encouraging informed participation • 📊 Making community sentiment easier to analyse • 🔍 Creating transparent prediction-based markets • 🧠 Supporting data-driven discussion • 🤝 Giving users a more interactive role in the ecosystem Prediction markets do not guarantee correct outcomes, but they can provide a structured way to compare expectations and observe changing sentiment. ⚖️📉📈 🧪🔐 The Importance of the Final Testing Phase The period before Mainnet will be especially significant. It should focus on: • End-to-end testing: checking that all platform components work together correctly • Early-user feedback: identifying practical issues from real user interactions • Performance review: assessing speed, stability, and scalability • Security checks: reviewing smart contracts and supporting infrastructure • User-experience refinement: making the platform easier to understand and use • Operational readiness: ensuring the system can support activity after launch A successful Mainnet launch depends not only on completing development, but also on the quality of testing and review that comes before it. ✅🚀 🔄🌍 From Vision to Real-World Adoption InterPredict’s progress can be viewed as a clear development path: 🔬 Experimentation → 🏗️ Infrastructure → 🔗 Integration → 🔍 Testing → 🌍 Adoption Beta tested the initial vision 🧪 V2 strengthens the technical foundation 🛠️ Integration connects the platform’s main components 🔗 Testing prepares the system for wider use 🔍 Mainnet will measure how effectively the ecosystem performs in practice 🚀 Each stage has a different purpose, and completing them carefully is essential for sustainable growth. 🌱📈 🌟 The Road Ahead InterPredict V2 represents an important step towards building a stronger community prediction marketplace. 🔮🌐 The next milestones will determine how effectively the platform can convert its technical progress into a dependable experience for users. 👥⚙️ The goal is clear: create a system where people can predict, participate, share perspectives, and contribute to meaningful market signals in a transparent and structured way. 💬📊🤝 🔮✨ Final Takeaway → Beta tested the vision. 🧪 → V2 is strengthening the infrastructure. 🛠️ → Mainnet will put the ecosystem to the test. 🌐�* The journey continues: 🎲 Predict. 🤝 Participate. 🌍 Shape Tomorrow. 💜⚡ 🔗 Follow InterPredict: InterPredict 📲 Telegram: InterLink Labs 👤 + 🌐 InterPredict InterLink Foundation KV Reina | InterLink Labs Mira #InterPredict #ITP #InterLink #ITLG #ITL #PredictionMarkets #ITL #ITLG #Web3 #Blockchain #dApp #Mainnet #Tokenomics
0:11

🚀🔮 InterPredict V2: From Beta Testing to Production Readiness 🌐⚡ InterPredict is entering a critical new stage: moving from an early beta product towards a more reliable, scalable, and production-ready community prediction marketplace. 🧠📊 This is not simply a version update. It is a structured transition from testing the concept to strengthening the infrastructure required for real-world use. 🛠️🌍 🧪 What the Beta Phase Achieved The beta phase provided an important testing environment for: • ✅ Validating the core prediction-market concept • 👥 Observing how users interact with the platform • 🐛 Identifying technical issues and improvement areas • 💬 Collecting community feedback • 📈 Understanding how prediction markets perform under real usage conditions Beta testing was about learning what works, discovering what needs improvement, and preparing the foundation for the next phase. 🔍🚧 ⚙️🚀 What InterPredict V2 Brings With V2, the focus is shifting towards a smoother, faster, and more dependable user experience. Key priorities include: ⚡ Improved performance 🛡️ Greater reliability 📈 Better scalability 🔗 Seamless system integration ⚙️ More efficient platform operations 🎨 A clearer and more accessible interface The objective is to connect the platform’s core components into one integrated system that can support broader participation and future growth. 🌱🌐 🛠️📋 Development Progress The current development status is as follows: ✅ Smart contract development: Completed ✅ Backend and server development: Completed 🔄 Frontend development: In progress 🔗 Full platform integration: In progress 🧪 Final testing: Upcoming 🌐🚀 Mainnet launch: Planned after successful testing and review This staged approach is important. Before a Mainnet release, the platform must be tested thoroughly across its technical systems, user flows, security processes, and overall reliability. 🔐✅ 📊🧠 More Than Just Making Predictions InterPredict’s wider purpose extends beyond individual predictions. 🔮 The platform is designed to help transform community knowledge, opinions, and expectations into structured prediction markets. When participation is organised effectively, collective views can produce useful market signals and offer insight into how communities assess future events. 💡📈 This may create value in several areas: • 🗣️ Encouraging informed participation • 📊 Making community sentiment easier to analyse • 🔍 Creating transparent prediction-based markets • 🧠 Supporting data-driven discussion • 🤝 Giving users a more interactive role in the ecosystem Prediction markets do not guarantee correct outcomes, but they can provide a structured way to compare expectations and observe changing sentiment. ⚖️📉📈 🧪🔐 The Importance of the Final Testing Phase The period before Mainnet will be especially significant. It should focus on: • End-to-end testing: checking that all platform components work together correctly • Early-user feedback: identifying practical issues from real user interactions • Performance review: assessing speed, stability, and scalability • Security checks: reviewing smart contracts and supporting infrastructure • User-experience refinement: making the platform easier to understand and use • Operational readiness: ensuring the system can support activity after launch A successful Mainnet launch depends not only on completing development, but also on the quality of testing and review that comes before it. ✅🚀 🔄🌍 From Vision to Real-World Adoption InterPredict’s progress can be viewed as a clear development path: 🔬 Experimentation → 🏗️ Infrastructure → 🔗 Integration → 🔍 Testing → 🌍 Adoption Beta tested the initial vision 🧪 V2 strengthens the technical foundation 🛠️ Integration connects the platform’s main components 🔗 Testing prepares the system for wider use 🔍 Mainnet will measure how effectively the ecosystem performs in practice 🚀 Each stage has a different purpose, and completing them carefully is essential for sustainable growth. 🌱📈 🌟 The Road Ahead InterPredict V2 represents an important step towards building a stronger community prediction marketplace. 🔮🌐 The next milestones will determine how effectively the platform can convert its technical progress into a dependable experience for users. 👥⚙️ The goal is clear: create a system where people can predict, participate, share perspectives, and contribute to meaningful market signals in a transparent and structured way. 💬📊🤝 🔮✨ Final Takeaway → Beta tested the vision. 🧪 → V2 is strengthening the infrastructure. 🛠️ → Mainnet will put the ecosystem to the test. 🌐�* The journey continues: 🎲 Predict. 🤝 Participate. 🌍 Shape Tomorrow. 💜⚡ 🔗 Follow InterPredict: InterPredict 📲 Telegram: InterLink Labs 👤 + 🌐 InterPredict InterLink Foundation KV Reina | InterLink Labs Mira #InterPredict #ITP #InterLink #ITLG #ITL #PredictionMarkets #ITL #ITLG #Web3 #Blockchain #dApp #Mainnet #Tokenomics

Tekkaus® | InterLink Global Leader • MOD • OG

146,322 次观看 • 16 天前

Exciting Milestone: Our First CEX Listing with MEXC! We’re thrilled to announce that VentureMind AI ($VNTR) is officially listed on mexc_listings ! Choosing our first centralized exchange partner was a big decision, and we’re excited to team up with MEXC to bring $VNTR to a wider audience. This is a historic moment for us as the first AI incubator project launched on the Seedify platform to achieve a CEX listing. We couldn’t have reached this point without Seedify’s guidance, helping us navigate the journey with confidence and clarity. This partnership is just the beginning! It gives easier access to $VNTR and introduces the first onramp to purchase our token using fiat stablecoins, making it more accessible to a global audience. And we’re just getting started! Additional exchange listings are already in the works, and we can’t wait to share more milestones as we expand the reach and utility of $VNTR. To our incredible community, thank you for your unwavering support. This is only the start of an exciting journey, and we’re so grateful to have you with us every step of the way!
0:16

Exciting Milestone: Our First CEX Listing with MEXC! We’re thrilled to announce that VentureMind AI ($VNTR) is officially listed on mexc_listings ! Choosing our first centralized exchange partner was a big decision, and we’re excited to team up with MEXC to bring $VNTR to a wider audience. This is a historic moment for us as the first AI incubator project launched on the Seedify platform to achieve a CEX listing. We couldn’t have reached this point without Seedify’s guidance, helping us navigate the journey with confidence and clarity. This partnership is just the beginning! It gives easier access to $VNTR and introduces the first onramp to purchase our token using fiat stablecoins, making it more accessible to a global audience. And we’re just getting started! Additional exchange listings are already in the works, and we can’t wait to share more milestones as we expand the reach and utility of $VNTR. To our incredible community, thank you for your unwavering support. This is only the start of an exciting journey, and we’re so grateful to have you with us every step of the way!

VentureMind AI

22,406 次观看 • 1 年前

It's here - Avail's Clash of Nodes incentivized testnet is officially up and running! 🌟 This is a major step towards Mainnet, with a chance for validators, light clients, and everyone to test our network, earn points, and more. Ready for a journey? Here's what's ahead... We’re calling all validators, light client enthusiasts, and blockchain buffs -- Clash of Nodes is your playground. Rack up points, top the leaderboard, and embrace the challenges for a mix of fun and potential perks to come. We’re looking for those who’ll step up to the challenge: • Validators who consistently validate the chain throughout the testing period • Full nodes and light clients who complete challenges to mimic a world full of rollups on Avail • Brave validators and participants who help us simulate disaster scenarios Dive into the Details: Here's a snapshot of the specific challenges that await in Clash of Nodes. 🛡️ Gladiator's Entry: Show your mettle by becoming a block authoring champion. Outlast and outperform, one block at a time. The more you author, the higher you score. ⚖️ Noble Warrior: Uphold the validator's code. Stay active, avoid penalties, and keep your record clean. Honor earns points, while lapses cost you. 🔍 Finding Yourself: Embrace the quest of self-discovery by adding and verifying your identity. Prove who you are and carve your name in the annals of Avail. Each challenge is your chance to shine and shape the future of Avail. Here's the game plan: 1️⃣ The first invites will go to our existing validators from the Kate Testnet. Their unwavering support earns them the vanguard spots. 2️⃣ To the newcomers: Every validator will get their turn as we're setting the stage for 300 in this testnet. 3️⃣ We’ll share more challenges on how anybody of any technical ability can participate. Remember, we're still in test mode. Expect the occasional hiccup during the testing phase. Your insights are essential to building a robust Mainnet - even if that means taking on some big bugs. 🚧 To every blockchain visionary, from validators to app rollup devs, you're the architects of this rollup revolution. We’re excited to see what you build, and how you compete. Read more: 🛠️🚀
0:11

It's here - Avail's Clash of Nodes incentivized testnet is officially up and running! 🌟 This is a major step towards Mainnet, with a chance for validators, light clients, and everyone to test our network, earn points, and more. Ready for a journey? Here's what's ahead... We’re calling all validators, light client enthusiasts, and blockchain buffs -- Clash of Nodes is your playground. Rack up points, top the leaderboard, and embrace the challenges for a mix of fun and potential perks to come. We’re looking for those who’ll step up to the challenge: • Validators who consistently validate the chain throughout the testing period • Full nodes and light clients who complete challenges to mimic a world full of rollups on Avail • Brave validators and participants who help us simulate disaster scenarios Dive into the Details: Here's a snapshot of the specific challenges that await in Clash of Nodes. 🛡️ Gladiator's Entry: Show your mettle by becoming a block authoring champion. Outlast and outperform, one block at a time. The more you author, the higher you score. ⚖️ Noble Warrior: Uphold the validator's code. Stay active, avoid penalties, and keep your record clean. Honor earns points, while lapses cost you. 🔍 Finding Yourself: Embrace the quest of self-discovery by adding and verifying your identity. Prove who you are and carve your name in the annals of Avail. Each challenge is your chance to shine and shape the future of Avail. Here's the game plan: 1️⃣ The first invites will go to our existing validators from the Kate Testnet. Their unwavering support earns them the vanguard spots. 2️⃣ To the newcomers: Every validator will get their turn as we're setting the stage for 300 in this testnet. 3️⃣ We’ll share more challenges on how anybody of any technical ability can participate. Remember, we're still in test mode. Expect the occasional hiccup during the testing phase. Your insights are essential to building a robust Mainnet - even if that means taking on some big bugs. 🚧 To every blockchain visionary, from validators to app rollup devs, you're the architects of this rollup revolution. We’re excited to see what you build, and how you compete. Read more: 🛠️🚀

Avail

351,997 次观看 • 2 年前

Someone is vibe coding a live 3D news globe with Claude Code that pins real world events on a spinning Earth as they happen. Instead of doomscrolling a feed, you open a tab with a 3D Earth and watch the world light up in real time. Breaking news, conflicts, disasters and humanitarian alerts get pinned to the exact spot on the map, alongside live flights and upcoming rocket launches. Click any event and it pulls up a short brief with its actual sources, so you can see where it came from. People are already saying it's the only way they want to read news now, the kind of thing you keep open in a tab and just glance at all day.
0:16

Someone is vibe coding a live 3D news globe with Claude Code that pins real world events on a spinning Earth as they happen. Instead of doomscrolling a feed, you open a tab with a 3D Earth and watch the world light up in real time. Breaking news, conflicts, disasters and humanitarian alerts get pinned to the exact spot on the map, alongside live flights and upcoming rocket launches. Click any event and it pulls up a short brief with its actual sources, so you can see where it came from. People are already saying it's the only way they want to read news now, the kind of thing you keep open in a tab and just glance at all day.

0xMarioNawfal

39,104 次观看 • 2 个月前

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 次观看 • 2 个月前

There is a room in Málaga that was built to be the closest thing on earth to standing inside heaven. It is called the camarín of the Virgin of Victory, and it is hidden at the top of a tower inside the Santuario de la Victoria. To reach it, you climb and the ascent is the entire point... The building you are climbing through was completed in 1700, and it was designed as a single argument made in stone. At the bottom lies a crypt: a black chamber crowded with white plaster skeletons, a meditation on death and the brevity of life. From there a staircase rises, and as you climb it the light grows stronger and the imagery changes from bones to saints. The architects of the time understood this ascent as the soul's own journey, the dark crypt as the stage of penitence, the staircase as the stage of spiritual progress, and the room at the very top as the final stage: the union of the soul with the divine. That room at the top is the camarín, and its dome is one of the most extraordinary interiors in Spain... Every surface is covered in white and gold plasterwork. There is no empty space anywhere. The Baroque called this horror vacui, the horror of the void: the conviction that a space meant to represent heaven should not contain a single bare patch of stone. Out of that plasterwork emerge angels, flowers, birds, and mirrors. The mirrors are not decoration alone. They catch the light pouring in through the windows of the drum and throw it around the chamber, so that the gold seems to move and the whole room appears to shimmer and breathe. This wonder was built by people who believed that if you wanted to show a human being what heaven might feel like, you did not describe it to them. You built a room, and you let them climb into it... -- -- -- If you enjoyed this, I write a weekly newsletter read by over 50,000 people who love rediscovering the beauty of the past. You can join us here: If you'd like to support my work, a paid subscription is what makes it possible.
0:13

There is a room in Málaga that was built to be the closest thing on earth to standing inside heaven. It is called the camarín of the Virgin of Victory, and it is hidden at the top of a tower inside the Santuario de la Victoria. To reach it, you climb and the ascent is the entire point... The building you are climbing through was completed in 1700, and it was designed as a single argument made in stone. At the bottom lies a crypt: a black chamber crowded with white plaster skeletons, a meditation on death and the brevity of life. From there a staircase rises, and as you climb it the light grows stronger and the imagery changes from bones to saints. The architects of the time understood this ascent as the soul's own journey, the dark crypt as the stage of penitence, the staircase as the stage of spiritual progress, and the room at the very top as the final stage: the union of the soul with the divine. That room at the top is the camarín, and its dome is one of the most extraordinary interiors in Spain... Every surface is covered in white and gold plasterwork. There is no empty space anywhere. The Baroque called this horror vacui, the horror of the void: the conviction that a space meant to represent heaven should not contain a single bare patch of stone. Out of that plasterwork emerge angels, flowers, birds, and mirrors. The mirrors are not decoration alone. They catch the light pouring in through the windows of the drum and throw it around the chamber, so that the gold seems to move and the whole room appears to shimmer and breathe. This wonder was built by people who believed that if you wanted to show a human being what heaven might feel like, you did not describe it to them. You built a room, and you let them climb into it... -- -- -- If you enjoyed this, I write a weekly newsletter read by over 50,000 people who love rediscovering the beauty of the past. You can join us here: If you'd like to support my work, a paid subscription is what makes it possible.

James Lucas

69,389 次观看 • 4 个月前