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Smart heating! 🫡 Beneath Finnish cities are massive underground data centers running 24 hours a day. They process emails, cloud storage, AI workloads & more, which generates an enormous amount of heat. It's captured & pumped directly into the city’s district heating network.

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165,370 次观看 • 6 个月前 •via X (Twitter)

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Most people think AI data centers are giant buildings in the desert. One guy installed four mini Nvidia AI data centers right behind his work desk and now they pay him every month. Each unit is about the size of a small fridge. Inside: Nvidia GPUs running AI workloads 24/7. He hooked them up next to his AC system and that was basically it. Now the company pays him a flat monthly fee for the electricity and Wi-Fi they use. According to him, it brings in around $10,000/month straight into his account. The crazy part: the units also cool part of the house, cutting his AC bill by roughly $600. That’s more than $120,000 a year from four AI boxes sitting inside his home office. His mortgage is basically being paid by AI hardware behind his chair. Quietly, regular homes are starting to become AI infrastructure. Save this post. You’re watching the next gold rush move into people’s homes.

Most people think AI data centers are giant buildings in the desert. One guy installed four mini Nvidia AI data centers right behind his work desk and now they pay him every month. Each unit is about the size of a small fridge. Inside: Nvidia GPUs running AI workloads 24/7. He hooked them up next to his AC system and that was basically it. Now the company pays him a flat monthly fee for the electricity and Wi-Fi they use. According to him, it brings in around $10,000/month straight into his account. The crazy part: the units also cool part of the house, cutting his AC bill by roughly $600. That’s more than $120,000 a year from four AI boxes sitting inside his home office. His mortgage is basically being paid by AI hardware behind his chair. Quietly, regular homes are starting to become AI infrastructure. Save this post. You’re watching the next gold rush move into people’s homes.

Shelpid.WI3M

975,436 次观看 • 2 个月前

🚨 BREAKING: Starcloud just turned Starlink’s laser network into the backbone for orbital AI data centers. A company called Starcloud has ordered 50+ Starlink Mini Laser terminals to equip 25+ future satellites. Not ground stations. Not fiber cables. Direct laser-linked computing nodes in orbit plugged straight into SpaceX’s space-based optical mesh. This is the sci-fi future arriving now: Orbital cloud computing AI servers floating in space Powered by 24/7 sunlight Connected globally at light speed via Starlink lasers The insane part: Starcloud says its satellites will eventually handle full AI inference and training workloads directly in orbit. Data won’t always need to come back to Earth to be processed. The advantages are massive: • Unlimited solar energy (no grid limits) • Zero land or water constraints • Passive radiative cooling in vacuum • Instant global relay with zero terrestrial bottlenecks • Near real-time Earth observation analysis Their first major spacecraft (Starcloud-3) is designed for 200 kilowatts in orbit a full-on space-based data center node, not just a satellite. And here’s the bigger picture: SpaceX has filed plans for up to ONE MILLION orbital data centers of its own. Read that again. We may be watching the birth of the first true space-based computing infrastructure layer for civilization. The internet already left the ground. Now AI might be next. What happens when the cloud literally moves into space? Follow for more frontier physics and future technology.

🚨 BREAKING: Starcloud just turned Starlink’s laser network into the backbone for orbital AI data centers. A company called Starcloud has ordered 50+ Starlink Mini Laser terminals to equip 25+ future satellites. Not ground stations. Not fiber cables. Direct laser-linked computing nodes in orbit plugged straight into SpaceX’s space-based optical mesh. This is the sci-fi future arriving now: Orbital cloud computing AI servers floating in space Powered by 24/7 sunlight Connected globally at light speed via Starlink lasers The insane part: Starcloud says its satellites will eventually handle full AI inference and training workloads directly in orbit. Data won’t always need to come back to Earth to be processed. The advantages are massive: • Unlimited solar energy (no grid limits) • Zero land or water constraints • Passive radiative cooling in vacuum • Instant global relay with zero terrestrial bottlenecks • Near real-time Earth observation analysis Their first major spacecraft (Starcloud-3) is designed for 200 kilowatts in orbit a full-on space-based data center node, not just a satellite. And here’s the bigger picture: SpaceX has filed plans for up to ONE MILLION orbital data centers of its own. Read that again. We may be watching the birth of the first true space-based computing infrastructure layer for civilization. The internet already left the ground. Now AI might be next. What happens when the cloud literally moves into space? Follow for more frontier physics and future technology.

TheNewPhysics

152,463 次观看 • 2 个月前

🚨THE SHOCKING TRUTH ABOUT DATA CENTERS THEY’RE HIDING FROM US Across America, massive AI data centers are being built in total secrecy. At night. Behind high fences. Without real public hearings or announcements. But here’s what’s truly disturbing. These facilities are deliberately placed exactly on the intersections of ley lines - the planet’s ancient energetic arteries. Right on top of powerful energy vortices and underground water flows fed by Primary Water (the “source water” that Earth generates deep inside itself). Coincidence? Or intentional interference? These data centers consume insane amounts of electricity. They literally stab their cables and cooling systems into the most sensitive points of the Earth’s energetic grid, suppressing natural vibrations, disrupting the planetary current, polluting and “drying out” sacred underground waters. Local residents are already sounding the alarm: constant low-frequency hum that makes walls shake headaches, insomnia, anxiety strange electromagnetic anomalies wells mysteriously drying up Lawsuits are flying. And this is just the beginning. Why exactly on ley lines? Why in silence? Who is making these decisions - and why do they need to “jam” the planet’s energy grid right now, when AI is exploding? Is this just business... or something much darker? An attempt to sever humanity’s connection to the living Earth?

🚨THE SHOCKING TRUTH ABOUT DATA CENTERS THEY’RE HIDING FROM US Across America, massive AI data centers are being built in total secrecy. At night. Behind high fences. Without real public hearings or announcements. But here’s what’s truly disturbing. These facilities are deliberately placed exactly on the intersections of ley lines - the planet’s ancient energetic arteries. Right on top of powerful energy vortices and underground water flows fed by Primary Water (the “source water” that Earth generates deep inside itself). Coincidence? Or intentional interference? These data centers consume insane amounts of electricity. They literally stab their cables and cooling systems into the most sensitive points of the Earth’s energetic grid, suppressing natural vibrations, disrupting the planetary current, polluting and “drying out” sacred underground waters. Local residents are already sounding the alarm: constant low-frequency hum that makes walls shake headaches, insomnia, anxiety strange electromagnetic anomalies wells mysteriously drying up Lawsuits are flying. And this is just the beginning. Why exactly on ley lines? Why in silence? Who is making these decisions - and why do they need to “jam” the planet’s energy grid right now, when AI is exploding? Is this just business... or something much darker? An attempt to sever humanity’s connection to the living Earth?

XSpirit

36,342 次观看 • 1 个月前

So I’ve lived in Hillsboro, Oregon for 10 years. Drove around today and tonight and shot this myself. This is what the “Data Center Plains” looks like.👇 My town sits at the end of 6 transpacific sea cables connecting the US to Asia. That’s why 30+ data centers landed here. They’re everywhere. Spread across the entire north and west end of the city. Road after road. Building after building. Miles of it. And they keep building. Pushing further west every year into farmland that’s been here for generations. Buying up land, Giving mass amounts of money to home owners to move, Tearing down homes. Tearing down historic sites. $7.2 billion in exempted property taxes. Some of these finished buildings are literally sitting completely dark… PGE told them no power for 3-5 years. They still built them anyway. A power plant is now going up right next to the data centers because they maxed the local grid. There are families still living next to construction zones. Old farmhouses directly across the street from data center walls. Nobody asked the people who already lived here. These are the families who refused to leave, so they said we are just gonna put them up next to your houses anyway. A pioneer homestead from 1865, 190 years of continuous farming is about to be gone, NTT Global Data Centers got that land tax-free until 2051. Signed in a single day at City Hall. Intel, the employer that actually brought thousands of real jobs here is laying off locals at the same time. This sound runs 24 hours a day. 7 days a week. My electricity bill went from $80 to $150. Water rates are set to increase 105% over 5 years, critics say to fund data center infrastructure, not residents. Data centers aren’t a joke. Just wanted to share my first hand experience with them. #datacenters

So I’ve lived in Hillsboro, Oregon for 10 years. Drove around today and tonight and shot this myself. This is what the “Data Center Plains” looks like.👇 My town sits at the end of 6 transpacific sea cables connecting the US to Asia. That’s why 30+ data centers landed here. They’re everywhere. Spread across the entire north and west end of the city. Road after road. Building after building. Miles of it. And they keep building. Pushing further west every year into farmland that’s been here for generations. Buying up land, Giving mass amounts of money to home owners to move, Tearing down homes. Tearing down historic sites. $7.2 billion in exempted property taxes. Some of these finished buildings are literally sitting completely dark… PGE told them no power for 3-5 years. They still built them anyway. A power plant is now going up right next to the data centers because they maxed the local grid. There are families still living next to construction zones. Old farmhouses directly across the street from data center walls. Nobody asked the people who already lived here. These are the families who refused to leave, so they said we are just gonna put them up next to your houses anyway. A pioneer homestead from 1865, 190 years of continuous farming is about to be gone, NTT Global Data Centers got that land tax-free until 2051. Signed in a single day at City Hall. Intel, the employer that actually brought thousands of real jobs here is laying off locals at the same time. This sound runs 24 hours a day. 7 days a week. My electricity bill went from $80 to $150. Water rates are set to increase 105% over 5 years, critics say to fund data center infrastructure, not residents. Data centers aren’t a joke. Just wanted to share my first hand experience with them. #datacenters

The Darkpulse Files  𝕏

421,375 次观看 • 2 个月前

Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻

Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻

Shadō Network | シャドウネットワーク

23,546 次观看 • 1 年前

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 次观看 • 1 年前

‼️🚨🇨🇳🇹🇼 The tension between China and Taiwan is heating up again, this time on a new front. Beijing just launched a coast guard patrol east of Taiwan, calling it a routine "law enforcement" operation in what it claims are its own waters. Taiwan didn't sit back, though—they spotted two Chinese vessels about 54 nautical miles off Hualien and immediately sent two of their own ships to keep a close eye on them. This move is a big deal geographically. Usually, China flexes its muscles in the Taiwan Strait or to the southwest. Pushing to the east coast shows Beijing is trying to encircle the island, essentially cutting off the one side Taiwan always felt was a bit safer and more secure. It’s also a classic example of "gray-zone" tactics. By sending the coast guard instead of the actual navy and calling it "law enforcement," China is trying to treat the area like its own backyard. It lets Beijing squeeze Taiwan and test its boundaries without actually crossing the line into open warfare, which would trigger a massive global backlash. Taiwan’s response was smart and measured. By sending exactly two ships to match the Chinese presence, they showed they won't be bullied out of their own economic zone, but they also avoided escalating the situation into a full-blown crisis. Still, it's clear China is trying to make these patrols the new normal, making the region's future harder and harder to predict. Video is made Grok AI

‼️🚨🇨🇳🇹🇼 The tension between China and Taiwan is heating up again, this time on a new front. Beijing just launched a coast guard patrol east of Taiwan, calling it a routine "law enforcement" operation in what it claims are its own waters. Taiwan didn't sit back, though—they spotted two Chinese vessels about 54 nautical miles off Hualien and immediately sent two of their own ships to keep a close eye on them. This move is a big deal geographically. Usually, China flexes its muscles in the Taiwan Strait or to the southwest. Pushing to the east coast shows Beijing is trying to encircle the island, essentially cutting off the one side Taiwan always felt was a bit safer and more secure. It’s also a classic example of "gray-zone" tactics. By sending the coast guard instead of the actual navy and calling it "law enforcement," China is trying to treat the area like its own backyard. It lets Beijing squeeze Taiwan and test its boundaries without actually crossing the line into open warfare, which would trigger a massive global backlash. Taiwan’s response was smart and measured. By sending exactly two ships to match the Chinese presence, they showed they won't be bullied out of their own economic zone, but they also avoided escalating the situation into a full-blown crisis. Still, it's clear China is trying to make these patrols the new normal, making the region's future harder and harder to predict. Video is made Grok AI

Visioner

34,918 次观看 • 25 天前

Introducing my new OSS framework: OhSnap provides really simple way to record and reproduce the data your users saw when encountering an issue (bug/crash), integration in your project should take a few minutes at most. Majority of bugs are related to data you have to deal with and often times we have to work with frequently changing data via network API's. Even if you have access to multiple environments (prod/staging/dev) it's still going to be PITA to reproduce a lot of bugs your user saw, since we often get to them a long time after the bug occured... OhSnap allows you to easily record any data your app downloads, pack it and put it on server so that you can replay it on your device later on, while connected to debugger and save hours of development time trying to figure out what exactly they experienced! Here's a demo of 2 app instances running, and me manipulating what server reply I'll be getting, there is 1 line of code needed to record and reply this data (outside of just setting up your framework). I built this so that I can show dev tool building process for the members of which I encourage you to join if you want to put your engineering efficiency at a different level😉

Introducing my new OSS framework: OhSnap provides really simple way to record and reproduce the data your users saw when encountering an issue (bug/crash), integration in your project should take a few minutes at most. Majority of bugs are related to data you have to deal with and often times we have to work with frequently changing data via network API's. Even if you have access to multiple environments (prod/staging/dev) it's still going to be PITA to reproduce a lot of bugs your user saw, since we often get to them a long time after the bug occured... OhSnap allows you to easily record any data your app downloads, pack it and put it on server so that you can replay it on your device later on, while connected to debugger and save hours of development time trying to figure out what exactly they experienced! Here's a demo of 2 app instances running, and me manipulating what server reply I'll be getting, there is 1 line of code needed to record and reply this data (outside of just setting up your framework). I built this so that I can show dev tool building process for the members of which I encourage you to join if you want to put your engineering efficiency at a different level😉

Krzysztof Zabłocki

30,484 次观看 • 2 年前

🚨 SOUTH KOREAN SCIENTISTS JUST CREATED HOLLOW SILICON NANOTUBES THAT TRAP HEAT AND TURN WASTE ENERGY INTO ELECTRICITY. Researchers at POSTECH have developed a new hollow silicon nanotube structure that dramatically reduces thermal conductivity. By turning solid nanowires into microscopic pipes, they trapped heat-carrying particles (phonons) inside the tubes, cutting thermal conductivity by 70% compared to solid wires. Even when both structures had the same surface area, the hollow nanotubes still ran 33% cooler. This phonon localization effect previously thought to require extreme cold or exotic materials was achieved at near-room temperature using simple silicon nanotubes. Why this matters: • Waste heat from data centers, EV batteries, factories, and electronics is currently lost this could capture and convert it into usable electricity • The technology uses abundant, cheap silicon instead of rare and expensive materials like bismuth and tellurium • It’s highly compatible with existing semiconductor manufacturing, making large-scale production more realistic • It solves a long-standing problem: silicon is great for chips but terrible for thermoelectric energy conversion The deeper implication: This breakthrough shows that clever nanoscale engineering can unlock new capabilities from ordinary materials. By controlling how heat moves at the atomic level, researchers are opening a path to more efficient energy recovery systems without relying on scarce resources. As AI and computing power keep growing, finding ways to recycle the massive amounts of waste heat they generate will become increasingly important. How significant do you think waste-heat recovery technologies like this could become in the next decade? Follow for more frontier materials science and energy innovation.

🚨 SOUTH KOREAN SCIENTISTS JUST CREATED HOLLOW SILICON NANOTUBES THAT TRAP HEAT AND TURN WASTE ENERGY INTO ELECTRICITY. Researchers at POSTECH have developed a new hollow silicon nanotube structure that dramatically reduces thermal conductivity. By turning solid nanowires into microscopic pipes, they trapped heat-carrying particles (phonons) inside the tubes, cutting thermal conductivity by 70% compared to solid wires. Even when both structures had the same surface area, the hollow nanotubes still ran 33% cooler. This phonon localization effect previously thought to require extreme cold or exotic materials was achieved at near-room temperature using simple silicon nanotubes. Why this matters: • Waste heat from data centers, EV batteries, factories, and electronics is currently lost this could capture and convert it into usable electricity • The technology uses abundant, cheap silicon instead of rare and expensive materials like bismuth and tellurium • It’s highly compatible with existing semiconductor manufacturing, making large-scale production more realistic • It solves a long-standing problem: silicon is great for chips but terrible for thermoelectric energy conversion The deeper implication: This breakthrough shows that clever nanoscale engineering can unlock new capabilities from ordinary materials. By controlling how heat moves at the atomic level, researchers are opening a path to more efficient energy recovery systems without relying on scarce resources. As AI and computing power keep growing, finding ways to recycle the massive amounts of waste heat they generate will become increasingly important. How significant do you think waste-heat recovery technologies like this could become in the next decade? Follow for more frontier materials science and energy innovation.

TheNewPhysics

25,739 次观看 • 1 个月前

The Environment Agency crisis comms swings into action ahead of the first episode of ‘Dirty Business’ tonight with the usual mix of gaslighting, cover up and half truths. They say they “act on breaches of environmental law”, but on my river, the Roding, there are dozens of illegal outfalls, spewing over a billion litres of raw sewage into the river every year and the Environment Agency has not brought a single prosecution this century. Not one! The evidence is not hard to find- many of these outfalls have been reported by the public for decades and run through parks & major population centres. There seems to be actual policy in place to just ignore rampant criminality, although we cannot say if this policy springs from inertia, incompetence, corruption or a mixture of all three. So that the EA cannot say I’m making this up, I’m going to post pictures, locations and data measurements from illegal outfalls on the Roding. First up is the Cran Brook. Running 24 hours a day, 365 days a year at least 25 litres a second and ammonia concentrations of 5-7 PPM, this river is arguably the most polluted river in London, spewing 750 million litres of sewage & landfill leachate directly into the Roding, here:

The Environment Agency crisis comms swings into action ahead of the first episode of ‘Dirty Business’ tonight with the usual mix of gaslighting, cover up and half truths. They say they “act on breaches of environmental law”, but on my river, the Roding, there are dozens of illegal outfalls, spewing over a billion litres of raw sewage into the river every year and the Environment Agency has not brought a single prosecution this century. Not one! The evidence is not hard to find- many of these outfalls have been reported by the public for decades and run through parks & major population centres. There seems to be actual policy in place to just ignore rampant criminality, although we cannot say if this policy springs from inertia, incompetence, corruption or a mixture of all three. So that the EA cannot say I’m making this up, I’m going to post pictures, locations and data measurements from illegal outfalls on the Roding. First up is the Cran Brook. Running 24 hours a day, 365 days a year at least 25 litres a second and ammonia concentrations of 5-7 PPM, this river is arguably the most polluted river in London, spewing 750 million litres of sewage & landfill leachate directly into the Roding, here:

Paul Powlesland

30,642 次观看 • 5 个月前

🚨Little Devils Gang Terrorizes Midtown, Threatening NYC’s Tax Revenue and Local Businesses. This morning, around 10 AM, they robbed an innocent New Yorker in broad daylight. Four suspects were arrested and taken to Downtown Manhattan courthouse A few hours later, the same crew hit Five Below. What started as shoplifting quickly escalated when they pulled a knife on employees, turning it into a full-blown robbery, according to sources. This isn’t a one-off. Every single day, this crew is tied to multiple incidents—robberies, shoplifting, and violent confrontations. They run through the city with complete impunity, fully aware that because of their age, the law rarely holds them accountable, according to sources. Most of these younger gang members live in taxpayer-funded shelters. They get arrested, they rob people, they stab people, and then they return to their city-funded accommodations. Parents of these suspects do nothing. Why are New Yorkers paying for these gang members to hurt people—and then paying for their safe return? Business owners have been telling me this gang is costing them tons of money, not only through theft but by scaring away customers. With the holiday season around the corner, tourists may avoid spending money in Midtown or even in New York. Some may even tell friends they got robbed while visiting the city, which could have a lasting impact on tourism and local businesses. Midtown alone generates roughly 20% of New York City’s taxpayer revenue. Yet gangs like the Little Devils, coming from the Bronx or Queens, are terrorizing the area, hurting local businesses, scaring residents and visitors, and costing the city millions. If nothing changes, sooner or later they’re going to kill a cop or an innocent person. The situation is spiraling out of control, and Midtown is on edge. Every day there’s another crime, and every day they get bolder. This gang is a ticking time bomb, and the city’s residents, tourists, and businesses are watching in fear. By Leeroy Johnson For licensing email viralnewsnyc@gmail.com

🚨Little Devils Gang Terrorizes Midtown, Threatening NYC’s Tax Revenue and Local Businesses. This morning, around 10 AM, they robbed an innocent New Yorker in broad daylight. Four suspects were arrested and taken to Downtown Manhattan courthouse A few hours later, the same crew hit Five Below. What started as shoplifting quickly escalated when they pulled a knife on employees, turning it into a full-blown robbery, according to sources. This isn’t a one-off. Every single day, this crew is tied to multiple incidents—robberies, shoplifting, and violent confrontations. They run through the city with complete impunity, fully aware that because of their age, the law rarely holds them accountable, according to sources. Most of these younger gang members live in taxpayer-funded shelters. They get arrested, they rob people, they stab people, and then they return to their city-funded accommodations. Parents of these suspects do nothing. Why are New Yorkers paying for these gang members to hurt people—and then paying for their safe return? Business owners have been telling me this gang is costing them tons of money, not only through theft but by scaring away customers. With the holiday season around the corner, tourists may avoid spending money in Midtown or even in New York. Some may even tell friends they got robbed while visiting the city, which could have a lasting impact on tourism and local businesses. Midtown alone generates roughly 20% of New York City’s taxpayer revenue. Yet gangs like the Little Devils, coming from the Bronx or Queens, are terrorizing the area, hurting local businesses, scaring residents and visitors, and costing the city millions. If nothing changes, sooner or later they’re going to kill a cop or an innocent person. The situation is spiraling out of control, and Midtown is on edge. Every day there’s another crime, and every day they get bolder. This gang is a ticking time bomb, and the city’s residents, tourists, and businesses are watching in fear. By Leeroy Johnson For licensing email [email protected]

Viral News NYC

71,752 次观看 • 8 个月前

Here is the CEO & co-founder of Palantir, Alex Karp at WEF in 2023 saying "Our primary goal is to set a global standard for the world for behavior." 🚨🚨🚨⚠️⚠️⚠️ This is ALARMING for several reasons… Trump just made a deal with him and allowed his company to weave intricately into the fabric of the whole Federal Government, creating a merged massive database of ALL of our personal, sensitive data for the first time in history. This is significant because yes, the government has always had our data by way of the NSA, but it’s never been merged together across all agencies with the help of a third party corporation, like Palantir, which acts more like a intel agency than a company. They did this using a backdoor through DOGE, and Elon Musk facilitated the process. That was his true role. Palantir and Elon go way back & recently xAI has partnered with Palantir along with Blackrock. DEEP. STATE. In fact, Palantir is quite LITERALLY funded by the CIA through In-Q-Tel, which is the CIA’s venture capital arm. In a nutshell, Trump just employed a proxy of the CIA to create the LARGEST surveillance police state in American history. Did you know that Alex Karp also bragged about stopping the “rise of the far right” in Europe? WHY would Trump, who is supposed to be a “right wing populist” allow such intentional subversion by a self-professed “progressive” like Alex Karp to infiltrate our government? They want to set a standard for behavior, because that is the standard upon which the coming Social Credit system will be measured & that will tie directly into our data profile (financial, medical, personal) including our usage of “carbon”, which will be TAXED. EVERYTHING we do will be monitored by smart devices and that information will factor into our Social Credit score, which will dictate the privileges we are allowed to have, or not. That is the NIGHTMARE vision that these PSYCHOPATHS have for our future & it is being ENABLED. Through the Internet of Bodies (IoB), they will even know our thoughts, and any dissent or opposition will be swiftly punished. They are even able to create “pre-crime” (thought crime) scenarios to set us up for crimes we never committed using advanced AI. Sound familiar? This all plays into the DEPOPULATION agenda as well, because WE are seen as the “carbon”. WE ARE THE CARBON THEY WANT TO REDUCE. Imagine if the movies Terminator and Minority Report became a reality, and that is what we are up against. If you can’t already SEE IT, and are still having a hard time understanding… Palantir is LITERALLY THE DEEPEST PART of the “deep state” & Trump is rolling out the red carpet for them, to usher in what will certainly be the Beast system. A cashless society in which you will be tracked and monitored every second of every day, and your “behavior” will dictate how you get to live in society. This is absolutely “The Great Reset” & “6uild 6ack 6etter” REPACKAGED MAGA style. THE BIGGEST RUG PULL IN HISTORY.

The Patriot Voice

329,913 次观看 • 1 年前

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

Brian Roemmele

37,827 次观看 • 6 个月前

$ASI watch out! We've got more projects popping up on Bittensor and $TAO and just starting, Akash $AKT, Shell $SHELL, Einstein-AIT $AIT, Sturdy $STRDY, Stratos $STOS and Comtensor $COMAI and more. Each pushing what's possible with AI and crypto. MyShell: $SHELL First up, MyShell is doing something amazing work. They're all about making AI chat like humans. They're using this TTS Subnet on Bittensor, powered by thier tokens, to make it happen. AI doesn't have to be complicated thing only some can use. They want everyone in on the action, making AI smarter in the process. Einstein-AIT: $AIT Then there's Einstein-AIT. Imagine this as the network's brain but on a turbocharge. It's all about math, logic, and crunching numbers. This subnet makes the whole Bittensor network sharper. They've got NumPAL, and it's like giving the AI a smarter way to think about time and dates automatically. Sturdy: $STRDY Jumping into DeFi, we've got Sturdy. These guys are on a mission to make lending and borrowing way less of a headache. They've got isolated lending pools, you can pick and choose how to manage your risks and money. And they're using some serious tech to keep your investments growing without you needing to babysit them. It's like having a smart financial assistant. Comtensor: $COMAI Comtensor is where it gets interesting. Think of it as a bridge between CommuneAI and Bittensor, kind of like the best of both worlds. It's about making sure all these AI modules and subnets can talk to each other smoothly. The goal? To boost decentralized AI by making everything more connected and smart. Stratos: $STOS Teaming up with τensorage to supercharge the $TAO ecosystem on Bittensor. Stratos is all about solid, decentralized storage - think of it as the bedrock for making sure data's not just stored but also used right . Then there’s τensorage, making sure everything AI needs is stored safe. Together, they’re making sure Bittensor's AI brainpower gets a boost, making everything faster, safer, and smoother. Akash: $AKT Compute Subnet 27 linked up with Akash. All about keeping things open-source. With Akash as a decentralized GPU provider into the mix. Giving access to top-notch AI processing power on top of Neural Internets compute-composable subnet, integrating various cloud platforms. This partnership is all about breaking away from those giants and opening the doors wide for smaller teams and startups who need this tech to innovate. Whats this all about? Making decentralized AI a something that everyone can get behind and into. Whether it's chatting with AI, boosting the network's IQ, DeFi, compute or making sure all these projects work together, it's about pushing forward. Each project has its own way of making things better, faster, and smarter for all of us. Inviting everyone to join in, contribute, and be a part of community effort to make AI not just for the few but for everyone. Credit to Mr.Franc Q for the awesome video production!

$ASI watch out! We've got more projects popping up on Bittensor and $TAO and just starting, Akash $AKT, Shell $SHELL, Einstein-AIT $AIT, Sturdy $STRDY, Stratos $STOS and Comtensor $COMAI and more. Each pushing what's possible with AI and crypto. MyShell: $SHELL First up, MyShell is doing something amazing work. They're all about making AI chat like humans. They're using this TTS Subnet on Bittensor, powered by thier tokens, to make it happen. AI doesn't have to be complicated thing only some can use. They want everyone in on the action, making AI smarter in the process. Einstein-AIT: $AIT Then there's Einstein-AIT. Imagine this as the network's brain but on a turbocharge. It's all about math, logic, and crunching numbers. This subnet makes the whole Bittensor network sharper. They've got NumPAL, and it's like giving the AI a smarter way to think about time and dates automatically. Sturdy: $STRDY Jumping into DeFi, we've got Sturdy. These guys are on a mission to make lending and borrowing way less of a headache. They've got isolated lending pools, you can pick and choose how to manage your risks and money. And they're using some serious tech to keep your investments growing without you needing to babysit them. It's like having a smart financial assistant. Comtensor: $COMAI Comtensor is where it gets interesting. Think of it as a bridge between CommuneAI and Bittensor, kind of like the best of both worlds. It's about making sure all these AI modules and subnets can talk to each other smoothly. The goal? To boost decentralized AI by making everything more connected and smart. Stratos: $STOS Teaming up with τensorage to supercharge the $TAO ecosystem on Bittensor. Stratos is all about solid, decentralized storage - think of it as the bedrock for making sure data's not just stored but also used right . Then there’s τensorage, making sure everything AI needs is stored safe. Together, they’re making sure Bittensor's AI brainpower gets a boost, making everything faster, safer, and smoother. Akash: $AKT Compute Subnet 27 linked up with Akash. All about keeping things open-source. With Akash as a decentralized GPU provider into the mix. Giving access to top-notch AI processing power on top of Neural Internets compute-composable subnet, integrating various cloud platforms. This partnership is all about breaking away from those giants and opening the doors wide for smaller teams and startups who need this tech to innovate. Whats this all about? Making decentralized AI a something that everyone can get behind and into. Whether it's chatting with AI, boosting the network's IQ, DeFi, compute or making sure all these projects work together, it's about pushing forward. Each project has its own way of making things better, faster, and smarter for all of us. Inviting everyone to join in, contribute, and be a part of community effort to make AI not just for the few but for everyone. Credit to Mr.Franc Q for the awesome video production!

Andy ττ

18,193 次观看 • 2 年前

🚨 SCIENTISTS JUST TURNED WET COFFEE GROUNDS INTO COAL-LIKE FUEL IN 90 SECONDS WITHOUT DRYING IT FIRST. Researchers in South Korea have developed a plasma-based system that converts moisture-rich coffee waste directly into high-energy biochar. The process uses flame plasma (reaching 1,470–1,650°F) to trigger rapid carbonization through a “popcorn effect,” where steam bursts inside the grounds break apart the structure and accelerate the reaction. In under two minutes, the system produces a carbon-rich material with an energy content of 29.0 MJ/kg comparable to anthracite coal while tripling the fixed carbon content and completely removing sulfur compounds. Why this matters: • Most biomass conversion methods require energy-intensive pre-drying, which this process eliminates • The resulting biochar has a much higher heating value and surface area, making it useful as fuel or for activated carbon applications • It generates minimal smoke and tar compared to traditional methods • The technology could work on other high-moisture wastes like food waste, sewage sludge, and agricultural residues The deeper implication: This represents a fast, potentially decentralized way to turn problematic organic waste into valuable resources. Instead of spending energy and money drying biomass before processing, the moisture itself becomes part of the solution. If scaled, technologies like this could help close the loop on organic waste streams while producing renewable solid fuels or advanced carbon materials with far less processing time and cost than current methods. It’s a clever example of working with the properties of waste rather than fighting them. How useful do you think rapid, drying-free waste-to-fuel systems like this could be for industries or cities dealing with large volumes of organic waste? Follow for more frontier energy and materials recycling breakthroughs.

🚨 SCIENTISTS JUST TURNED WET COFFEE GROUNDS INTO COAL-LIKE FUEL IN 90 SECONDS WITHOUT DRYING IT FIRST. Researchers in South Korea have developed a plasma-based system that converts moisture-rich coffee waste directly into high-energy biochar. The process uses flame plasma (reaching 1,470–1,650°F) to trigger rapid carbonization through a “popcorn effect,” where steam bursts inside the grounds break apart the structure and accelerate the reaction. In under two minutes, the system produces a carbon-rich material with an energy content of 29.0 MJ/kg comparable to anthracite coal while tripling the fixed carbon content and completely removing sulfur compounds. Why this matters: • Most biomass conversion methods require energy-intensive pre-drying, which this process eliminates • The resulting biochar has a much higher heating value and surface area, making it useful as fuel or for activated carbon applications • It generates minimal smoke and tar compared to traditional methods • The technology could work on other high-moisture wastes like food waste, sewage sludge, and agricultural residues The deeper implication: This represents a fast, potentially decentralized way to turn problematic organic waste into valuable resources. Instead of spending energy and money drying biomass before processing, the moisture itself becomes part of the solution. If scaled, technologies like this could help close the loop on organic waste streams while producing renewable solid fuels or advanced carbon materials with far less processing time and cost than current methods. It’s a clever example of working with the properties of waste rather than fighting them. How useful do you think rapid, drying-free waste-to-fuel systems like this could be for industries or cities dealing with large volumes of organic waste? Follow for more frontier energy and materials recycling breakthroughs.

TheNewPhysics

84,583 次观看 • 1 个月前

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

Nesa

17,038 次观看 • 4 个月前

I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :) I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p. But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them. But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?

I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :) I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p. But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them. But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?

Andrej Karpathy

1,644,897 次观看 • 5 个月前

⚡️🇵🇸JUST IN: Government Media Office in Gaza: The number of deaths due to extreme cold in forced displacement camps has risen to 21 martyrs since the start of the genocide, and we warn of catastrophic repercussions of the upcoming low-pressure systems. We warn of the catastrophic humanitarian repercussions resulting from the waves of extreme cold hitting the Gaza Strip, in light of the continued genocide and the suffocating siege, and the widespread destruction of homes and infrastructure it has left behind, as well as the forced displacement of more than one and a half million Palestinians to displacement camps that lack the minimum requirements for human life. According to documented field data, the number of deaths as a result of extreme cold since the beginning of the genocide until today, Sunday, January 11, 2026, has risen to 21 martyrs, all of whom were displaced persons from forced shelter camps, including 18 children, in a dangerous indicator of the scale of the humanitarian disaster threatening the lives of the most vulnerable groups. We also record that the number of deaths due to extreme cold since the beginning of the current winter season has reached 4 deaths, in light of the absence of heating means, the lack of safe shelter, the shortage of blankets and winter clothing, and the continued prevention of the entry of sufficient humanitarian aid. We strongly warn of the repercussions of the upcoming low-pressure system and subsequent weather systems, and the accompanying frost waves and extreme cold during the coming days, which portend an increase in the number of victims, especially among children, the sick, and the elderly, if this catastrophic humanitarian reality continues without urgent intervention. We hold the "israeli" occupation fully and directly responsible for these crimes and deadly outcomes, as they are an extension of the policies of slow killing, starvation, and displacement. We demand the international community, the United Nations, and humanitarian and human rights organizations to take immediate and urgent action to provide safe shelter centers, allow the entry of heating and relief supplies without restrictions, and save the remaining lives before it is too late. Government Media Office Gaza Strip - Palestine Sunday, January 11, 2026

⚡️🇵🇸JUST IN: Government Media Office in Gaza: The number of deaths due to extreme cold in forced displacement camps has risen to 21 martyrs since the start of the genocide, and we warn of catastrophic repercussions of the upcoming low-pressure systems. We warn of the catastrophic humanitarian repercussions resulting from the waves of extreme cold hitting the Gaza Strip, in light of the continued genocide and the suffocating siege, and the widespread destruction of homes and infrastructure it has left behind, as well as the forced displacement of more than one and a half million Palestinians to displacement camps that lack the minimum requirements for human life. According to documented field data, the number of deaths as a result of extreme cold since the beginning of the genocide until today, Sunday, January 11, 2026, has risen to 21 martyrs, all of whom were displaced persons from forced shelter camps, including 18 children, in a dangerous indicator of the scale of the humanitarian disaster threatening the lives of the most vulnerable groups. We also record that the number of deaths due to extreme cold since the beginning of the current winter season has reached 4 deaths, in light of the absence of heating means, the lack of safe shelter, the shortage of blankets and winter clothing, and the continued prevention of the entry of sufficient humanitarian aid. We strongly warn of the repercussions of the upcoming low-pressure system and subsequent weather systems, and the accompanying frost waves and extreme cold during the coming days, which portend an increase in the number of victims, especially among children, the sick, and the elderly, if this catastrophic humanitarian reality continues without urgent intervention. We hold the "israeli" occupation fully and directly responsible for these crimes and deadly outcomes, as they are an extension of the policies of slow killing, starvation, and displacement. We demand the international community, the United Nations, and humanitarian and human rights organizations to take immediate and urgent action to provide safe shelter centers, allow the entry of heating and relief supplies without restrictions, and save the remaining lives before it is too late. Government Media Office Gaza Strip - Palestine Sunday, January 11, 2026

Suppressed News.

37,464 次观看 • 6 个月前

Cancelled ChatGPT -> Built JARVIS -> Pays $0 -> it works offline + it's smarter than the $20/month version. No WiFi needed, no cloud, no API keys, no rate limits, no queues, no $20/month just to ask a server in Virginia for the weather. Just a local model running directly on the laptop hardware, voice activated, system integrated, controlling apps, answering questions, doing the work. Iron Man had JARVIS embedded in his suit, this guy has it embedded in his MacBook and it works on a plane, in a basement, on a remote cabin with zero signal. OpenAI is burning $700,000 a day on infrastructure to deliver something this guy runs for free. Anthropic charges $200/month for unlimited Claude access, microsoft built Copilot into every product they sell. This guy skipped all of it, downloaded a model and made his laptop the smartest device in the room. No subscription. No login. No internet. No data sent anywhere ever. The most powerful AI assistant on earth is now the one running locally on hardware you already own. ChatGPT charges you to think slower, he pays nothing and thinks alone, he made it himself.

Cancelled ChatGPT -> Built JARVIS -> Pays $0 -> it works offline + it's smarter than the $20/month version. No WiFi needed, no cloud, no API keys, no rate limits, no queues, no $20/month just to ask a server in Virginia for the weather. Just a local model running directly on the laptop hardware, voice activated, system integrated, controlling apps, answering questions, doing the work. Iron Man had JARVIS embedded in his suit, this guy has it embedded in his MacBook and it works on a plane, in a basement, on a remote cabin with zero signal. OpenAI is burning $700,000 a day on infrastructure to deliver something this guy runs for free. Anthropic charges $200/month for unlimited Claude access, microsoft built Copilot into every product they sell. This guy skipped all of it, downloaded a model and made his laptop the smartest device in the room. No subscription. No login. No internet. No data sent anywhere ever. The most powerful AI assistant on earth is now the one running locally on hardware you already own. ChatGPT charges you to think slower, he pays nothing and thinks alone, he made it himself.

Defileo🔮

154,009 次观看 • 3 个月前

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

Argona

22,099 次观看 • 1 个月前