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Understanding the BitTorrent Swarm — A Broader Look With Real Data Dynamics BitTorrent isn’t just a file-sharing protocol; it’s one of the most efficient large-scale distribution systems ever designed. At its core lies a simple but powerful principle: when users contribute bandwidth, the entire network accelerates. This is the...

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Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

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ᴛʜᴇ ɴᴇxᴛ ᴇʀᴀ ᴏꜰ ᴅᴇᴄᴇɴᴛʀᴀʟɪᴢᴇᴅ ᴅᴀᴛᴀ ᴀɴᴅ ᴡɪɴᴋʟɪɴᴋ ᴏʀᴀᴄʟᴇ ɴᴇᴛᴡᴏʀᴋꜱ In today’s podcast breakdown, we explored how decentralized oracles and emerging OCR technology are transforming the way blockchains interact with the real world. ❇️ ᴡʜʏ ᴅᴇᴄᴇɴᴛʀᴀʟɪᴢᴇᴅ ᴅᴀᴛᴀ ɪꜱ ᴄʀᴜᴄɪᴀʟ One of blockchain’s biggest limitations has always been isolation smart contracts couldn’t access real-world information on their own. Decentralized Oracle like WINkLink solve this by delivering secure, verifiable data from off-chain sources directly to dApps. This unlocks: ➩ trustless financial automation ➩ real-world event settlements ➩ decentralized insurance, logistics & prediction markets ➩ verifiable randomness for gaming ➩ real-time data-driven smart contracts It’s the bridge that connects blockchain to reality. ❇️ ᴛʜᴇ ᴘᴏᴡᴇʀ ᴏꜰ ᴏꜰꜰ-ᴄʜᴀɪɴ ʀᴇᴘᴏʀᴛɪɴɢ (OCR) OCR represents a major leap forward for oracle networks. Instead of sending multiple on-chain transactions, participating nodes aggregate their data off-chain, reach consensus, and submit one verified report. This means: ➜ drastically lower fees ➜ faster, more scalable data delivery ➜ stronger decentralization ➜ reduced manipulation risks ➜ more frequent and higher-quality data feeds As OCR moves out of beta, it could become the new standard for decentralized data infrastructure. ❇️ ᴡʜᴀᴛ ᴄᴏᴜʟᴅ ᴛʜɪꜱ ᴜɴʟᴏᴄᴋ? With reliable, cryptographically verified external data, blockchains can evolve into autonomous systems capable of interacting with the real world. Potential new categories include: ✦ self-regulating DAOs reacting to real-time metrics ✦ autonomous “micro-economies” powered by data ✦ AI × blockchain hybrid systems ✦ on-chain reputation and identity primitives ✦ next-gen DeFi products across global markets We’re moving toward an internet where trust is based on verification, not authority. ❇️ ᴛʜᴇ ʙɪɢɢᴇʀ ɪᴍᴘᴀᴄᴛ ᴏɴ ᴛʀᴜꜱᴛ ᴏɴʟɪɴᴇ As decentralized oracle networks grow: ➜data becomes more transparent ➜manipulation becomes harder ➜applications become more autonomous ➜cross-chain ecosystems become more interoperable The result? A new digital world where truth is cryptographically proven not assumed. What do you think about the future of decentralized data? Will OCR and WinkLink Oracle networks redefine Web3 infrastructure? WINkLink H.E. Justin Sun 👨‍🚀 🌞 #Oracles #WINkLink #TRONEcoStar
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ᴛʜᴇ ɴᴇxᴛ ᴇʀᴀ ᴏꜰ ᴅᴇᴄᴇɴᴛʀᴀʟɪᴢᴇᴅ ᴅᴀᴛᴀ ᴀɴᴅ ᴡɪɴᴋʟɪɴᴋ ᴏʀᴀᴄʟᴇ ɴᴇᴛᴡᴏʀᴋꜱ In today’s podcast breakdown, we explored how decentralized oracles and emerging OCR technology are transforming the way blockchains interact with the real world. ❇️ ᴡʜʏ ᴅᴇᴄᴇɴᴛʀᴀʟɪᴢᴇᴅ ᴅᴀᴛᴀ ɪꜱ ᴄʀᴜᴄɪᴀʟ One of blockchain’s biggest limitations has always been isolation smart contracts couldn’t access real-world information on their own. Decentralized Oracle like WINkLink solve this by delivering secure, verifiable data from off-chain sources directly to dApps. This unlocks: ➩ trustless financial automation ➩ real-world event settlements ➩ decentralized insurance, logistics & prediction markets ➩ verifiable randomness for gaming ➩ real-time data-driven smart contracts It’s the bridge that connects blockchain to reality. ❇️ ᴛʜᴇ ᴘᴏᴡᴇʀ ᴏꜰ ᴏꜰꜰ-ᴄʜᴀɪɴ ʀᴇᴘᴏʀᴛɪɴɢ (OCR) OCR represents a major leap forward for oracle networks. Instead of sending multiple on-chain transactions, participating nodes aggregate their data off-chain, reach consensus, and submit one verified report. This means: ➜ drastically lower fees ➜ faster, more scalable data delivery ➜ stronger decentralization ➜ reduced manipulation risks ➜ more frequent and higher-quality data feeds As OCR moves out of beta, it could become the new standard for decentralized data infrastructure. ❇️ ᴡʜᴀᴛ ᴄᴏᴜʟᴅ ᴛʜɪꜱ ᴜɴʟᴏᴄᴋ? With reliable, cryptographically verified external data, blockchains can evolve into autonomous systems capable of interacting with the real world. Potential new categories include: ✦ self-regulating DAOs reacting to real-time metrics ✦ autonomous “micro-economies” powered by data ✦ AI × blockchain hybrid systems ✦ on-chain reputation and identity primitives ✦ next-gen DeFi products across global markets We’re moving toward an internet where trust is based on verification, not authority. ❇️ ᴛʜᴇ ʙɪɢɢᴇʀ ɪᴍᴘᴀᴄᴛ ᴏɴ ᴛʀᴜꜱᴛ ᴏɴʟɪɴᴇ As decentralized oracle networks grow: ➜data becomes more transparent ➜manipulation becomes harder ➜applications become more autonomous ➜cross-chain ecosystems become more interoperable The result? A new digital world where truth is cryptographically proven not assumed. What do you think about the future of decentralized data? Will OCR and WinkLink Oracle networks redefine Web3 infrastructure? WINkLink H.E. Justin Sun 👨‍🚀 🌞 #Oracles #WINkLink #TRONEcoStar

BEN GURIAN 🥇

11,243 Aufrufe • vor 7 Monaten

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Wow, since the last post blew up, here is another fascinating insight from my years working with car-on-demand companies and more traditional automakers. Most people still think the entire automotive business is about selling or leasing vehicles. But there is a new market emerging that is absolutely massive: data Modern cars are packed with sensors and constantly collect real-time information. And this data is quickly becoming one of the most valuable revenue streams in the industry. Companies like Tesla, with their autonomous, sensor-rich fleets, are positioned to benefit enormously, but the same applies to many newer connected vehicles across all brands. What makes this so powerful is how diverse the use cases are. Cities, for example, can tap into aggregated vehicle data to understand exactly where they need to intervene. If thousands of cars detect the same irregular bump on the road, you instantly know there is a pothole at a precise location. Scale that across a whole city and you have a live map of infrastructure issues before residents even complain. The same data can help optimize traffic flow, identify congestion patterns in real time, or highlight zones where drivers consistently brake or accelerate suddenly, revealing potential safety problems. Automakers can also use this information to better understand how people actually drive in the real world, which directly influences design, durability testing, and product evolution And then there is the insurance angle. As driving behavior becomes measurable at scale, dynamic insurance pricing emerge. Acceleration habits, braking patterns, cornering, speed consistency, environmental context all of this will feed into future scoring models What we are seeing right now is only the beginning. With cars full of electronics, sensors, and especially autonomous vision systems, data is becoming one of the largest and most predictable revenue lines for automakers and mobility companies. Even traditional vehicles are now connected and constantly transmitting information that can be analyzed or monetized in multiple ways We are still just scratching the surface, but the shift is already underway. The value is no longer just in the vehicle itself. It is also in the billions of data points it generates every single day

Aurelien

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The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.
0:54

The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.

Muriel Medard

30,518 Aufrufe • vor 10 Tagen

Google Ironwood TPU Memory Hierarchy in 9 levels by hand ✍️ 1. Bit – The most basic unit of information, the on–off decision from which every number, tensor, and model state is ultimately constructed. 2. FP8 (1×8 → 8 bits) – Eight bits are grouped to form a floating-point value, typically used for inference, where reduced precision is a deliberate trade-off to maximize throughput and efficiency. 3. BF16 (×2 → 16 bits) – Two FP8-scale chunks are combined to gain more dynamic range and stability, while still staying friendly to high-throughput hardware. 4. Tensor tile (×1024 → 1K) – Data moves through the chip in blocks of 1024 values at a time, defining the granularity at which tensors are fetched and manipulated. 5. Matrix Multiplication Unit (MXU) (×64 → 64K) – A systolic array where matrix multiplication is not abstract but physical, with tensor tiles flowing through fixed hardware to achieve the highest possible throughput. 6. Vector Memory (VMEM) (×2048 → 128M) – On-chip working memory that holds activations, partial results, and intermediates, sized specifically to keep the systolic array busy without stalling. 7. Common Memory (CMEM) (×8 → 1 GB) – A small but critical shared memory sitting between VMEM and HBM, used for staging, accumulation, synchronization, and cross-lane coordination. 8. HBM (×96 → 96 GB) – Off-chip high-bandwidth memory where model weights and large states live, implemented as HBM3e with 16 stacks at 6 GB each, for a total of 96 GB. 9. Dual-Die (x2 → 192GB) – Two tightly coupled compute dies operate as a single logical accelerator, each with its own local HBM, effectively doubling memory capacity and bandwidth while allowing tensors and activations to stream seamlessly across dies as if they lived on one chip. I created this drawing for this week's seminar. I’ll take you through these 9 levels in a beginner-friendly way by hand ✍️. RSVP 👉
0:27

Google Ironwood TPU Memory Hierarchy in 9 levels by hand ✍️ 1. Bit – The most basic unit of information, the on–off decision from which every number, tensor, and model state is ultimately constructed. 2. FP8 (1×8 → 8 bits) – Eight bits are grouped to form a floating-point value, typically used for inference, where reduced precision is a deliberate trade-off to maximize throughput and efficiency. 3. BF16 (×2 → 16 bits) – Two FP8-scale chunks are combined to gain more dynamic range and stability, while still staying friendly to high-throughput hardware. 4. Tensor tile (×1024 → 1K) – Data moves through the chip in blocks of 1024 values at a time, defining the granularity at which tensors are fetched and manipulated. 5. Matrix Multiplication Unit (MXU) (×64 → 64K) – A systolic array where matrix multiplication is not abstract but physical, with tensor tiles flowing through fixed hardware to achieve the highest possible throughput. 6. Vector Memory (VMEM) (×2048 → 128M) – On-chip working memory that holds activations, partial results, and intermediates, sized specifically to keep the systolic array busy without stalling. 7. Common Memory (CMEM) (×8 → 1 GB) – A small but critical shared memory sitting between VMEM and HBM, used for staging, accumulation, synchronization, and cross-lane coordination. 8. HBM (×96 → 96 GB) – Off-chip high-bandwidth memory where model weights and large states live, implemented as HBM3e with 16 stacks at 6 GB each, for a total of 96 GB. 9. Dual-Die (x2 → 192GB) – Two tightly coupled compute dies operate as a single logical accelerator, each with its own local HBM, effectively doubling memory capacity and bandwidth while allowing tensors and activations to stream seamlessly across dies as if they lived on one chip. I created this drawing for this week's seminar. I’ll take you through these 9 levels in a beginner-friendly way by hand ✍️. RSVP 👉

Tom Yeh

30,489 Aufrufe • vor 5 Monaten

3 days ago, Elon Musk sat in front of JP Morgan’s 3,500 wealthiest investors and explained why the AI economy is moving to space: 1. Starship is the first rocket in history designed to be fully reusable. Every other mode of transport... planes, cars, ships... you take reusability for granted. Rockets have always been thrown away after one use. That ends with Starship. Once you achieve full reusability, the only cost is fuel. Starship runs on liquid oxygen and methane. Both are cheaper than jet fuel. 2. Sending cargo to orbit will soon cost less than international air freight. This is not a distant projection. It is the direct mathematical outcome of reusable rockets plus cheap propellant. The economics of space change entirely. 3. Starlink V3 is 10 to 20 times more capable than what's currently in orbit. The satellite is so large it can only launch on Starship. It cannot fit on any other rocket on Earth. 100 times more bandwidth. Half the latency. It may become the highest bandwidth, lowest latency communication system that exists. 4. AI and robots will consume bandwidth at a scale humans cannot picture. Peak human bandwidth is a few hundred bits per second. A computer runs at a trillion. The appetite of AI for data infrastructure will be unlike anything built for human use. Starlink V3 is being built for that world... not this one. 5. Data centers are moving to space. Not as an experiment. As the primary way to scale AI compute going forward. It is increasingly hard to build power plants on the ground. Nobody wants one near their home. Space removes that constraint entirely. 6. From the moon, you can scale to 1,000 terawatts of compute per year. From Earth... maybe 1. The moon has no atmosphere and one-sixth Earth's gravity. You can manufacture solar panels from moon materials and launch data centers with a railgun. No rockets needed. The math on this is not close. 7. Current human civilization uses less than one trillionth of the sun's energy output. You could scale to a million times Earth's entire economy and still be using less than one millionth of what the sun produces. The ceiling on what's possible is so far above us it barely registers as a ceiling. 8. There is not a single high-volume computer memory fab in America right now. Zero. The chips needed to build the AI future do not exist in sufficient quantity anywhere in the Western world. That is why SpaceX is building one. Not to compete. Because there is no other option. 9. SpaceX has been cash flow positive since around 2014. The IPO is not a distress move. Past funding rounds were not even fundraising... they were liquidity events for employees. The company bought back its own stock. The IPO is happening now because the next phase requires capital private markets cannot absorb. 10. The senior team has barely changed in over a decade. The CFO has been there 15 years. Musk joined as the seventh employee in 2002. He says people who believe in the mission don't leave. And above technical skill, he now looks for one thing... whether someone is genuinely a good person.
17:54

3 days ago, Elon Musk sat in front of JP Morgan’s 3,500 wealthiest investors and explained why the AI economy is moving to space: 1. Starship is the first rocket in history designed to be fully reusable. Every other mode of transport... planes, cars, ships... you take reusability for granted. Rockets have always been thrown away after one use. That ends with Starship. Once you achieve full reusability, the only cost is fuel. Starship runs on liquid oxygen and methane. Both are cheaper than jet fuel. 2. Sending cargo to orbit will soon cost less than international air freight. This is not a distant projection. It is the direct mathematical outcome of reusable rockets plus cheap propellant. The economics of space change entirely. 3. Starlink V3 is 10 to 20 times more capable than what's currently in orbit. The satellite is so large it can only launch on Starship. It cannot fit on any other rocket on Earth. 100 times more bandwidth. Half the latency. It may become the highest bandwidth, lowest latency communication system that exists. 4. AI and robots will consume bandwidth at a scale humans cannot picture. Peak human bandwidth is a few hundred bits per second. A computer runs at a trillion. The appetite of AI for data infrastructure will be unlike anything built for human use. Starlink V3 is being built for that world... not this one. 5. Data centers are moving to space. Not as an experiment. As the primary way to scale AI compute going forward. It is increasingly hard to build power plants on the ground. Nobody wants one near their home. Space removes that constraint entirely. 6. From the moon, you can scale to 1,000 terawatts of compute per year. From Earth... maybe 1. The moon has no atmosphere and one-sixth Earth's gravity. You can manufacture solar panels from moon materials and launch data centers with a railgun. No rockets needed. The math on this is not close. 7. Current human civilization uses less than one trillionth of the sun's energy output. You could scale to a million times Earth's entire economy and still be using less than one millionth of what the sun produces. The ceiling on what's possible is so far above us it barely registers as a ceiling. 8. There is not a single high-volume computer memory fab in America right now. Zero. The chips needed to build the AI future do not exist in sufficient quantity anywhere in the Western world. That is why SpaceX is building one. Not to compete. Because there is no other option. 9. SpaceX has been cash flow positive since around 2014. The IPO is not a distress move. Past funding rounds were not even fundraising... they were liquidity events for employees. The company bought back its own stock. The IPO is happening now because the next phase requires capital private markets cannot absorb. 10. The senior team has barely changed in over a decade. The CFO has been there 15 years. Musk joined as the seventh employee in 2002. He says people who believe in the mission don't leave. And above technical skill, he now looks for one thing... whether someone is genuinely a good person.

Jaynit

180,512 Aufrufe • vor 1 Monat

Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.
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Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.

Avi Patel

124,728 Aufrufe • vor 5 Monaten

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
0:39

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,079 Aufrufe • vor 16 Tagen

$APP's pivot to e-commerce is a very intriguing strategy. The market perceives the entry of non-gaming advertisers as a potential friction point that could crowd out core gaming clients or inflate pricing, but this ignores the massive asymmetry between AppLovin’s reach of over one billion daily active users and its historically thin roster of active advertisers. This imbalance has meant that the vast amount of inventory were previously under-monetized, as the algorithm could only serve gaming ads to users who had no intent to install new games. By layering in e-commerce demand, the platform effectively monetizes this wasted inventory, driving up overall yield and floor prices without cannibalizing the high-intent impressions reserved for gaming clients. This creates a margin-accretive dynamic where the same unit of supply generates significantly higher revenue per user solely through better demand matching. This efficiency gain feeds directly into a data-driven moat that becomes increasingly difficult for competitors to replicate. The flywheel is the introduction of transactional e-commerce data that radically improves the Axon AI model's predictive capabilities for all participants. Unlike app install data, which is binary and relatively sparse, e-commerce purchase data provides immediate high-fidelity signals about user intent and purchasing power. As the model ingests this new layer of behavioral data, its ability to predict conversion improves universally, meaning that gaming advertisers actually benefit from the presence of e-commerce bids through sharper targeting and higher return on ad spend. The rapid 50% week over week growth in the self-service pilot is a great preliminary validation that this automated demand engine is functional. This signals that AppLovin can scale this new vertical with software operating leverage. The requirement for high-production video ads has left out the long tail of millions of small business advertisers who dominate platforms like $META. The launch of generative AI creative tools targets this specific bottleneck, commoditizing the production of high-performing video assets and allowing AppLovin to unlock global SMB demand instantly. If successful, this creates a self-reinforcing liquidity cycle where increased advertiser density leads to better data, which drives superior model performance, which in turn attracts more diverse advertisers. This helps decouple the company's growth trajectory from the cyclicality of the mobile gaming market. Really interesting biz and great CEO.
5:53

$APP's pivot to e-commerce is a very intriguing strategy. The market perceives the entry of non-gaming advertisers as a potential friction point that could crowd out core gaming clients or inflate pricing, but this ignores the massive asymmetry between AppLovin’s reach of over one billion daily active users and its historically thin roster of active advertisers. This imbalance has meant that the vast amount of inventory were previously under-monetized, as the algorithm could only serve gaming ads to users who had no intent to install new games. By layering in e-commerce demand, the platform effectively monetizes this wasted inventory, driving up overall yield and floor prices without cannibalizing the high-intent impressions reserved for gaming clients. This creates a margin-accretive dynamic where the same unit of supply generates significantly higher revenue per user solely through better demand matching. This efficiency gain feeds directly into a data-driven moat that becomes increasingly difficult for competitors to replicate. The flywheel is the introduction of transactional e-commerce data that radically improves the Axon AI model's predictive capabilities for all participants. Unlike app install data, which is binary and relatively sparse, e-commerce purchase data provides immediate high-fidelity signals about user intent and purchasing power. As the model ingests this new layer of behavioral data, its ability to predict conversion improves universally, meaning that gaming advertisers actually benefit from the presence of e-commerce bids through sharper targeting and higher return on ad spend. The rapid 50% week over week growth in the self-service pilot is a great preliminary validation that this automated demand engine is functional. This signals that AppLovin can scale this new vertical with software operating leverage. The requirement for high-production video ads has left out the long tail of millions of small business advertisers who dominate platforms like $META. The launch of generative AI creative tools targets this specific bottleneck, commoditizing the production of high-performing video assets and allowing AppLovin to unlock global SMB demand instantly. If successful, this creates a self-reinforcing liquidity cycle where increased advertiser density leads to better data, which drives superior model performance, which in turn attracts more diverse advertisers. This helps decouple the company's growth trajectory from the cyclicality of the mobile gaming market. Really interesting biz and great CEO.

CapexAndChill

29,897 Aufrufe • vor 5 Monaten

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.
1:36

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.

Ricardo

640,331 Aufrufe • vor 4 Monaten

🎙 Episode 1 Is Live: Visa x WeFi This is a major milestone for us. Our first official podcast with Visa is now live, and we’re opening with one of the most important conversations in modern finance: stablecoins and the convergence of traditional payments and crypto. The conversation features Alexandra Soroko, Growth Product & Partnerships at Visa, and Michael Batuev, Head of Global Payments at WeFi. 👉 This episode goes beyond surface level commentary. We explore how Visa views stablecoins not as a passing trend, but as infrastructure. While they still represent less than 1 percent of global money flows, their efficiency, speed, and programmability position them as a powerful new layer in how money moves globally. Visa has been observing and building in crypto since 2018. Innovation at that scale requires navigating regulation, technology, and multiple geographies. But when they integrate something new, it connects to a network of more than 150 million merchants worldwide. That scale turns innovation into real world access. A key theme in the discussion is convergence. Fintech companies like WeFi move fast, design around users, and ship quickly. Global payment networks bring distribution, resilience, and trust. The future is not about replacement. It is about combining strengths. Will stablecoins replace fiat? Unlikely. The strongest innovations coexist and enhance what already exists. Stablecoins can evolve into a powerful financial rail while remaining connected to established payment systems, making them usable in everyday life. At WeFi, we are at the forefront of this movement. By working within established infrastructure like Visa and building products designed around real user needs, we are helping bring stablecoins from theory into practical, global utility. Digital assets are no longer on the fringe. They are entering the core of global finance. Episode 1 is just the beginning. Watch now and be part of the conversation shaping the next chapter of payments 🌍
19:55

🎙 Episode 1 Is Live: Visa x WeFi This is a major milestone for us. Our first official podcast with Visa is now live, and we’re opening with one of the most important conversations in modern finance: stablecoins and the convergence of traditional payments and crypto. The conversation features Alexandra Soroko, Growth Product & Partnerships at Visa, and Michael Batuev, Head of Global Payments at WeFi. 👉 This episode goes beyond surface level commentary. We explore how Visa views stablecoins not as a passing trend, but as infrastructure. While they still represent less than 1 percent of global money flows, their efficiency, speed, and programmability position them as a powerful new layer in how money moves globally. Visa has been observing and building in crypto since 2018. Innovation at that scale requires navigating regulation, technology, and multiple geographies. But when they integrate something new, it connects to a network of more than 150 million merchants worldwide. That scale turns innovation into real world access. A key theme in the discussion is convergence. Fintech companies like WeFi move fast, design around users, and ship quickly. Global payment networks bring distribution, resilience, and trust. The future is not about replacement. It is about combining strengths. Will stablecoins replace fiat? Unlikely. The strongest innovations coexist and enhance what already exists. Stablecoins can evolve into a powerful financial rail while remaining connected to established payment systems, making them usable in everyday life. At WeFi, we are at the forefront of this movement. By working within established infrastructure like Visa and building products designed around real user needs, we are helping bring stablecoins from theory into practical, global utility. Digital assets are no longer on the fringe. They are entering the core of global finance. Episode 1 is just the beginning. Watch now and be part of the conversation shaping the next chapter of payments 🌍

WeFi

16,721 Aufrufe • vor 4 Monaten

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:
0:44

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:

Satya Nadella

907,531 Aufrufe • vor 8 Monaten

🚀 Pi Network Enters a New Era: $100M Boost and Thousands of Mainnet-Ready DApps on the Horizon The time has come. What started as a revolutionary idea is now becoming a full-blown movement. Pi Network, with its vibrant global community of over 50 million engaged pioneers, is entering a powerful new phase—an era defined by massive app adoption, AI integration, and a $100 million investment to accelerate the growth of its ecosystem. 🔥 The Explosion of Apps on Pi Network Over the past few years, developers around the world have been quietly and diligently building decentralized applications (DApps) on Pi’s testnet. What was once a trickle has become a wave. Now, with the Open Mainnet officially activated, these apps are shifting gears, preparing for mass deployment and real-world utility. From finance, e-commerce, education, and health, to gaming, social media, and digital identity, Pi Network is evolving into a truly diverse Web3 universe. Each DApp contributes to the broader goal: to bring decentralized, peer-to-peer value exchange to real people, in real situations, without complexity or high fees. 💰 $100 Million to Supercharge Ecosystem Growth In a bold move that reflects deep confidence in the ecosystem’s potential, the Pi Core Team has announced a $100 million fund to fuel innovation and utility creation. This capital injection is a game-changer. It means: •More grants and funding for developers. •Better tools and infrastructure to support scalability. •Enhanced user experience in existing apps. •Faster transition from testnet to Mainnet readiness. This isn’t just funding—it’s a signal. Pi Network is ready to lead the decentralized economy forward. 🤖 The Power of AI + Pi AI is already transforming industries, and Pi Network is embracing this transformation head-on. With intelligent systems now being integrated into Pi-based applications, developers can: •Automate user experiences. •Offer real-time language translation and smart support. •Deliver advanced data analytics. •Empower smart matching in social, dating, or job apps. •Create intelligent marketplaces and financial tools. AI will help scale the number of Mainnet-ready apps from hundreds to thousands—faster than ever before. 🌐 Let the Decentralized Revolution Begin Every movement has its moment—and this is Pi Network’s moment. The infrastructure is in place. The community is activated. The funding is secured. The tools, AI, and developer talent are aligned. With thousands of apps ready to go live, utility will drive real value for Pi (𝛑). Every transaction, service, and exchange within this ecosystem will show the world that Pi is not just another cryptocurrency—it’s the most accessible and human-centric digital currency ever created. So, to all pioneers, developers, and visionaries: Get ready. Build. Connect. Engage. The show has just begun. Let’s turn dreams into decentralized realities—one app, one transaction, and one Pi at a time. 💫 Pi Network Nicolas Kokkalis Chengdiao Fan
2:12

🚀 Pi Network Enters a New Era: $100M Boost and Thousands of Mainnet-Ready DApps on the Horizon The time has come. What started as a revolutionary idea is now becoming a full-blown movement. Pi Network, with its vibrant global community of over 50 million engaged pioneers, is entering a powerful new phase—an era defined by massive app adoption, AI integration, and a $100 million investment to accelerate the growth of its ecosystem. 🔥 The Explosion of Apps on Pi Network Over the past few years, developers around the world have been quietly and diligently building decentralized applications (DApps) on Pi’s testnet. What was once a trickle has become a wave. Now, with the Open Mainnet officially activated, these apps are shifting gears, preparing for mass deployment and real-world utility. From finance, e-commerce, education, and health, to gaming, social media, and digital identity, Pi Network is evolving into a truly diverse Web3 universe. Each DApp contributes to the broader goal: to bring decentralized, peer-to-peer value exchange to real people, in real situations, without complexity or high fees. 💰 $100 Million to Supercharge Ecosystem Growth In a bold move that reflects deep confidence in the ecosystem’s potential, the Pi Core Team has announced a $100 million fund to fuel innovation and utility creation. This capital injection is a game-changer. It means: •More grants and funding for developers. •Better tools and infrastructure to support scalability. •Enhanced user experience in existing apps. •Faster transition from testnet to Mainnet readiness. This isn’t just funding—it’s a signal. Pi Network is ready to lead the decentralized economy forward. 🤖 The Power of AI + Pi AI is already transforming industries, and Pi Network is embracing this transformation head-on. With intelligent systems now being integrated into Pi-based applications, developers can: •Automate user experiences. •Offer real-time language translation and smart support. •Deliver advanced data analytics. •Empower smart matching in social, dating, or job apps. •Create intelligent marketplaces and financial tools. AI will help scale the number of Mainnet-ready apps from hundreds to thousands—faster than ever before. 🌐 Let the Decentralized Revolution Begin Every movement has its moment—and this is Pi Network’s moment. The infrastructure is in place. The community is activated. The funding is secured. The tools, AI, and developer talent are aligned. With thousands of apps ready to go live, utility will drive real value for Pi (𝛑). Every transaction, service, and exchange within this ecosystem will show the world that Pi is not just another cryptocurrency—it’s the most accessible and human-centric digital currency ever created. So, to all pioneers, developers, and visionaries: Get ready. Build. Connect. Engage. The show has just begun. Let’s turn dreams into decentralized realities—one app, one transaction, and one Pi at a time. 💫 Pi Network Nicolas Kokkalis Chengdiao Fan

Mr Spock 𝛑

23,517 Aufrufe • vor 1 Jahr

Jim Chanos just made one of the most contrarian calls in the AI infrastructure trade right now (Save this). His argument is simple, the bull case for alternative energy stocks is built on a constraint that is temporary and when it resolves, the valuations collapse. Chanos's core claim is that the United States does not actually have a power shortage but rather has a permitting bottleneck and a turbine delivery backlog. His view is that if AI demand is as large as everyone says it is, those barriers will be cleared within two to three years because the economic pressure to resolve them will be overwhelming. The valuation math is what makes this compelling. Power costs represent only 5 to 7% of a data center's total revenues. Companies trading at 50, 60, or 70 times earnings and 30 to 40 times EBITDA to potentially supply a fraction of that cost line are priced as if they have won a permanent, irreplaceable monopoly on AI power. That is the bet Chanos thinks is wrong and it is hard to argue with the logic and the data partially supports his concern. Close to half of all planned U.S. data center builds in 2026 are projected to be delayed or canceled because the grid cannot deliver power fast enough. Eleven gigawatts of announced data center capacity is sitting frozen in 2026 with no construction underway, purely due to grid access limitations. That sounds like it validates the bull case for energy stocks but Chanos's rebuttal is that this is a bottleneck story, not a shortage story. A structural shortage means elevated prices for a decade, but a bottleneck means prices spike temporarily, smart capital floods in to fix the choke point, and valuations built on that scarcity eventually mean-revert. FERC has already voted unanimously to order grid operators to accelerate data center connections and the regulatory machine is beginning to move in exactly the direction Chanos predicted. Here is where the bear case gets complicated. Bloomberg NEF forecasts more than 106 gigawatts of new data center demand by 2035 and natural gas is the only fuel that can deliver round the-clock baseload power at the speed AI infrastructure requires. Small modular reactor technology is still years from commercial scale, and geothermal remains a niche contributor limited to favorable geographies. The companies trading at 60–70x earnings are not providing guaranteed power at scale but rather are providing optionality on technologies that may or may not arrive when the market needs them. Chanos is likely right on the valuation argument for the most speculative names. Companies at 50x+ earnings supplying a single-digit cost line to a data center are priced for perfection in a market that rarely delivers it. Whether that trade works in 2026, 2027, or 2028 is the harder question and the bottleneck is proving more stubborn than even the bears expected. Milk Road is tracking the power story in full, the bottlenecks, the valuations, and the companies we think are genuinely positioned versus the ones just riding the narrative. Come join Milk Road Pro and get our analysis every day for just a dollar, link below.
2:54

Jim Chanos just made one of the most contrarian calls in the AI infrastructure trade right now (Save this). His argument is simple, the bull case for alternative energy stocks is built on a constraint that is temporary and when it resolves, the valuations collapse. Chanos's core claim is that the United States does not actually have a power shortage but rather has a permitting bottleneck and a turbine delivery backlog. His view is that if AI demand is as large as everyone says it is, those barriers will be cleared within two to three years because the economic pressure to resolve them will be overwhelming. The valuation math is what makes this compelling. Power costs represent only 5 to 7% of a data center's total revenues. Companies trading at 50, 60, or 70 times earnings and 30 to 40 times EBITDA to potentially supply a fraction of that cost line are priced as if they have won a permanent, irreplaceable monopoly on AI power. That is the bet Chanos thinks is wrong and it is hard to argue with the logic and the data partially supports his concern. Close to half of all planned U.S. data center builds in 2026 are projected to be delayed or canceled because the grid cannot deliver power fast enough. Eleven gigawatts of announced data center capacity is sitting frozen in 2026 with no construction underway, purely due to grid access limitations. That sounds like it validates the bull case for energy stocks but Chanos's rebuttal is that this is a bottleneck story, not a shortage story. A structural shortage means elevated prices for a decade, but a bottleneck means prices spike temporarily, smart capital floods in to fix the choke point, and valuations built on that scarcity eventually mean-revert. FERC has already voted unanimously to order grid operators to accelerate data center connections and the regulatory machine is beginning to move in exactly the direction Chanos predicted. Here is where the bear case gets complicated. Bloomberg NEF forecasts more than 106 gigawatts of new data center demand by 2035 and natural gas is the only fuel that can deliver round the-clock baseload power at the speed AI infrastructure requires. Small modular reactor technology is still years from commercial scale, and geothermal remains a niche contributor limited to favorable geographies. The companies trading at 60–70x earnings are not providing guaranteed power at scale but rather are providing optionality on technologies that may or may not arrive when the market needs them. Chanos is likely right on the valuation argument for the most speculative names. Companies at 50x+ earnings supplying a single-digit cost line to a data center are priced for perfection in a market that rarely delivers it. Whether that trade works in 2026, 2027, or 2028 is the harder question and the bottleneck is proving more stubborn than even the bears expected. Milk Road is tracking the power story in full, the bottlenecks, the valuations, and the companies we think are genuinely positioned versus the ones just riding the narrative. Come join Milk Road Pro and get our analysis every day for just a dollar, link below.

Milk Road AI

71,335 Aufrufe • vor 24 Tagen

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

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

Milk Road AI

12,140 Aufrufe • vor 1 Monat

Here is a live demo of our AI solution I've been building non-stop over the past 8 months Binary Defense. How it works: Our own model trained on our analysts behavior. Our analysts submit tickets as false positives/true positives with context which enriches our LLM to be smarter over time. Key Highlights: If its a binary - will automatically spin up an agent for reverse engineering it and using EMBER ML to understand behavior and intent of the binary. File formats: Supports a vast array of pretty much any filetype, including email attachments like SVG, LNK, etc. Can handle DLLs, ELF, EXEs, PDF, XLS, DOC, etc. Interrogates the full chain of all events irrespective of log sources. Can handle any format of logs and integrates into APIs of customers for additional agentic data looping for confidence ranking when needed. This is an example of the back-end UI, this is transparent to analysts and enriches the alarms automatically in our SOAR. In these examples there's three different types: 1. Regsvr32 + sct downloader + scrobj.dll code execution - checks reputation of domain, pulls in threat intel, looks at entire picture of the chain - downloads the file itself and inspects for code analysis. Determines if malicious as well as historically looking back if seen in customer before in past. 2. Powershell Obfuscation - uses a universal decoder to un-obfuscate powershell and look at the raw code. Can handle pretty much any obfuscation thrown at it (thanks Justin Elze). 3. Email with malicious SVG - checks tonality of email, are they creating urgency to take action (increases confidence) - disassembles SVG to understand malicious content - checks URL to determine if harvesting credentials, payload delivery, etc. Creates an entire kill chain analysis with full response and dissecting of the attack to the analyst in seconds. Has greatly sped up our ability to respond to incidents and allowing analysts to focus on the most important alarms through prioritization. Once cool thing I've worked heavily on is a synthetic data normalizer which when an analyst says "Yes this is bad with context" or "No this is a false positive" - our local model generates training data to be smarter in the future without using the actual customer data to train it. The customers actual data is immediately destroyed once training data off of the original alarm is generated and contains no customer-centric data at all. We also have three model tiers. Opt-In (collective model, again no customer data but every organization contributes to training). Opt-Out - does not train on any customer data for customers who opt-out. Private LLM - LLM created specifically for individual customer and trains only off of their data. Uses shared model collective for better confidence rankings. It will generate automated playbooks to run based on confidence rankings to take action on behalf of the customer. Still human driven on execution - has to approve playbook actions. This thing is cooking and so cool to see this work live and shut down attackers much faster! If confidence ranking is low - will automatically attempt to enrich data through customer environments for better confidence rankings. Additionally if the model isn't trained well on a certain technology, I have created something we call "Nexus" that will research new protocols, devices, SDKs, etc and generate training data automatically. Works well for zero-days for example, point to a tweet, or a research paper, and automatically generates training data to recognize this attack much faster. Have over 8000+ yara rule integrations that help with confidence boosting as well that is automatically incorporated into the analysis. Creating some amazing stuff at Binary Defense that isn't marketing fluff - actionable things that are making a huge difference in this industry. #BinaryDefense
0:32

Here is a live demo of our AI solution I've been building non-stop over the past 8 months Binary Defense. How it works: Our own model trained on our analysts behavior. Our analysts submit tickets as false positives/true positives with context which enriches our LLM to be smarter over time. Key Highlights: If its a binary - will automatically spin up an agent for reverse engineering it and using EMBER ML to understand behavior and intent of the binary. File formats: Supports a vast array of pretty much any filetype, including email attachments like SVG, LNK, etc. Can handle DLLs, ELF, EXEs, PDF, XLS, DOC, etc. Interrogates the full chain of all events irrespective of log sources. Can handle any format of logs and integrates into APIs of customers for additional agentic data looping for confidence ranking when needed. This is an example of the back-end UI, this is transparent to analysts and enriches the alarms automatically in our SOAR. In these examples there's three different types: 1. Regsvr32 + sct downloader + scrobj.dll code execution - checks reputation of domain, pulls in threat intel, looks at entire picture of the chain - downloads the file itself and inspects for code analysis. Determines if malicious as well as historically looking back if seen in customer before in past. 2. Powershell Obfuscation - uses a universal decoder to un-obfuscate powershell and look at the raw code. Can handle pretty much any obfuscation thrown at it (thanks Justin Elze). 3. Email with malicious SVG - checks tonality of email, are they creating urgency to take action (increases confidence) - disassembles SVG to understand malicious content - checks URL to determine if harvesting credentials, payload delivery, etc. Creates an entire kill chain analysis with full response and dissecting of the attack to the analyst in seconds. Has greatly sped up our ability to respond to incidents and allowing analysts to focus on the most important alarms through prioritization. Once cool thing I've worked heavily on is a synthetic data normalizer which when an analyst says "Yes this is bad with context" or "No this is a false positive" - our local model generates training data to be smarter in the future without using the actual customer data to train it. The customers actual data is immediately destroyed once training data off of the original alarm is generated and contains no customer-centric data at all. We also have three model tiers. Opt-In (collective model, again no customer data but every organization contributes to training). Opt-Out - does not train on any customer data for customers who opt-out. Private LLM - LLM created specifically for individual customer and trains only off of their data. Uses shared model collective for better confidence rankings. It will generate automated playbooks to run based on confidence rankings to take action on behalf of the customer. Still human driven on execution - has to approve playbook actions. This thing is cooking and so cool to see this work live and shut down attackers much faster! If confidence ranking is low - will automatically attempt to enrich data through customer environments for better confidence rankings. Additionally if the model isn't trained well on a certain technology, I have created something we call "Nexus" that will research new protocols, devices, SDKs, etc and generate training data automatically. Works well for zero-days for example, point to a tweet, or a research paper, and automatically generates training data to recognize this attack much faster. Have over 8000+ yara rule integrations that help with confidence boosting as well that is automatically incorporated into the analysis. Creating some amazing stuff at Binary Defense that isn't marketing fluff - actionable things that are making a huge difference in this industry. #BinaryDefense

Dave Kennedy

29,036 Aufrufe • vor 4 Monaten

//The Wire//2300Z July 25, 2025// //ROUTINE// //BLUF: "DATING" APP DATA BREACH HIGHLIGHTS NATIONAL SECURITY CONCERNS.// -----BEGIN TEARLINE----- -HomeFront- USA: This morning a major PII leak was exploited on the Tea app, the infamous app that has gained notoriety around the United States. This data leak was not a hack by any means; the selfie ID feature and driver's license images used to register users were stored unencrypted on the app's servers for anyone on the internet to see. Furthermore, the location data was not scrubbed from the images, so the exact GPS coordinate of each user was also leaked, with tens of thousands of users' private location data being leaked online. -----END TEARLINE----- Analyst Comments: This app gained infamy as it's entire purpose is to serve as a "Yelp" for women to rate men, and to allow women to secretly share personal information regarding prospective dates, all without men being allowed to either face their accusers or even know that they are being gossiped about (thus the name of the app being a slang term that serves as a synonym for "gossip"). Most importantly, the app uses facial recognition to prevent biological males from obtaining an account. Beyond the unfortunate origins of the app and the equally unfortunate data leak, examination of the data that was leaked is likely to cause exceptionally grave risks to national security. The "gossipy" nature of this story doesn't matter, a bunch of unflattering selfies doesn't matter either; what does matter is that this may have inadvertently revealed significant national security concerns. For instance, preliminary analysis of the datasets indicates that many users of the Tea app downloaded the app, took a selfie, and registered for an account while at work. In some cases, at government facilities or on military bases...such as the rather unfortunate individual who decided it was a good idea to register for this app while stationed at Marine Corps Base Quantico. Or the person who felt that they needed to use this app while on a gunnery range at the Aberdeen Proving Grounds. So far, other interesting sites located via personnel taking a selfie to register for this app at work include the following locations: - An ammunition storage bunker at Naval Weapons Station Earle in New Jersey. - The legislative offices at the Connecticut State Capitol building. - One of the headquarters buildings at Minot Air Force Base. - A maintenance site on the airfield at Eglin Air Force Base. - Alumni Hall at the US Naval Academy in Annapolis. - And the off-base housing complexes at nearly every single military base in the United States. Of course, these data points only encompass the GPS coordinates that were embedded in the metadata of the selfies taken when users created an account on the app, so the data that was leaked is merely a snapshot of wherever a person was when they registered an account. Most of the GPS points presented in this data were very precise, pinpointing users within a diameter of 36ft or so on average. GPS errors are also likely to throw off this dataset, so it's probable that quite a few data points are inaccurate. However, most of the data (as leaked) is good enough for nationstate-level malign actors to have a field day when it comes to espionage. A person who is unhappy with the person they are in a relationship with, who is also willing to submit their full legal name and street address (or GPS location) makes for a prime espionage target when this data is cross-referenced with other data. It takes exactly two clicks to import the leaked data to a map, and overlay that map with known sensitive military sites around the nation...perhaps in the process finding a few new locations as well. It is also easy to cross-reference this data with property ownership documents to find out how many people took a selfie at a different address than listed on their driver's license...or on their spouse's voter registration records. In short, what seems to be at face value a rather superfluous data breach on a gossip app, may end up having serious national security concerns now that malign actors know the exact GPS location of tens of thousands of potential blackmail targets. Regardless of how this scandal started, the intelligence agencies of dozens of nations are probably crawling through this data set right now, looking for potential vulnerabilities. Furthermore, certain areas within the dataset appear to have been geofenced...there is not a single reported user's data in the entire district of Washington D.C. or all of lower Manhattan. While this is completely speculative at this time, if this app was designed as a honeypot trap from the ground up (for espionage purposes), it's possible that certain areas of the US would be geofenced, to prevent certain powerful people (or politicians) from being caught up in the operation. Once again, this is purely speculative, but it is interesting that some of the most densely populated cities in the US had absolutely zero reported users register. In the world of espionage, one might think that politicians would be a primary target for situations like this, but this is unlikely in this case. Politicians are often compromised by men in dark suits, not normally a cheap app that allows one to gossip about other people. Their spouses however, are a different story. The prime target for these types of operations (if this is indeed more of an espionage attempt) are exactly the people who were targeted...low-to-mid level employees (and their spouses) who would be more easily blackmailed with the type of information that they submitted to the app. What will come of this is anyone's guess, but beyond the obvious embarrassment angle of this situation (and this affair highlighting actual affairs), the national security risks via blackmail are probably extremely high. What makes this risk unique is that for now, most people are focusing on the socially embarrassing side of this scandal, and not the security risks that are present. As such, when this story is dropped like a hot potato from the news cycle in a few days, that moment is precisely when national-level assets are likely to attempt to exploit the tens of thousands of targets that have now been exposed. Everyone will forget about this in a few weeks, but the espionage potential of this data leak persists well into the future. Analyst: S2A1 Research: //END REPORT//
5:51

//The Wire//2300Z July 25, 2025// //ROUTINE// //BLUF: "DATING" APP DATA BREACH HIGHLIGHTS NATIONAL SECURITY CONCERNS.// -----BEGIN TEARLINE----- -HomeFront- USA: This morning a major PII leak was exploited on the Tea app, the infamous app that has gained notoriety around the United States. This data leak was not a hack by any means; the selfie ID feature and driver's license images used to register users were stored unencrypted on the app's servers for anyone on the internet to see. Furthermore, the location data was not scrubbed from the images, so the exact GPS coordinate of each user was also leaked, with tens of thousands of users' private location data being leaked online. -----END TEARLINE----- Analyst Comments: This app gained infamy as it's entire purpose is to serve as a "Yelp" for women to rate men, and to allow women to secretly share personal information regarding prospective dates, all without men being allowed to either face their accusers or even know that they are being gossiped about (thus the name of the app being a slang term that serves as a synonym for "gossip"). Most importantly, the app uses facial recognition to prevent biological males from obtaining an account. Beyond the unfortunate origins of the app and the equally unfortunate data leak, examination of the data that was leaked is likely to cause exceptionally grave risks to national security. The "gossipy" nature of this story doesn't matter, a bunch of unflattering selfies doesn't matter either; what does matter is that this may have inadvertently revealed significant national security concerns. For instance, preliminary analysis of the datasets indicates that many users of the Tea app downloaded the app, took a selfie, and registered for an account while at work. In some cases, at government facilities or on military bases...such as the rather unfortunate individual who decided it was a good idea to register for this app while stationed at Marine Corps Base Quantico. Or the person who felt that they needed to use this app while on a gunnery range at the Aberdeen Proving Grounds. So far, other interesting sites located via personnel taking a selfie to register for this app at work include the following locations: - An ammunition storage bunker at Naval Weapons Station Earle in New Jersey. - The legislative offices at the Connecticut State Capitol building. - One of the headquarters buildings at Minot Air Force Base. - A maintenance site on the airfield at Eglin Air Force Base. - Alumni Hall at the US Naval Academy in Annapolis. - And the off-base housing complexes at nearly every single military base in the United States. Of course, these data points only encompass the GPS coordinates that were embedded in the metadata of the selfies taken when users created an account on the app, so the data that was leaked is merely a snapshot of wherever a person was when they registered an account. Most of the GPS points presented in this data were very precise, pinpointing users within a diameter of 36ft or so on average. GPS errors are also likely to throw off this dataset, so it's probable that quite a few data points are inaccurate. However, most of the data (as leaked) is good enough for nationstate-level malign actors to have a field day when it comes to espionage. A person who is unhappy with the person they are in a relationship with, who is also willing to submit their full legal name and street address (or GPS location) makes for a prime espionage target when this data is cross-referenced with other data. It takes exactly two clicks to import the leaked data to a map, and overlay that map with known sensitive military sites around the nation...perhaps in the process finding a few new locations as well. It is also easy to cross-reference this data with property ownership documents to find out how many people took a selfie at a different address than listed on their driver's license...or on their spouse's voter registration records. In short, what seems to be at face value a rather superfluous data breach on a gossip app, may end up having serious national security concerns now that malign actors know the exact GPS location of tens of thousands of potential blackmail targets. Regardless of how this scandal started, the intelligence agencies of dozens of nations are probably crawling through this data set right now, looking for potential vulnerabilities. Furthermore, certain areas within the dataset appear to have been geofenced...there is not a single reported user's data in the entire district of Washington D.C. or all of lower Manhattan. While this is completely speculative at this time, if this app was designed as a honeypot trap from the ground up (for espionage purposes), it's possible that certain areas of the US would be geofenced, to prevent certain powerful people (or politicians) from being caught up in the operation. Once again, this is purely speculative, but it is interesting that some of the most densely populated cities in the US had absolutely zero reported users register. In the world of espionage, one might think that politicians would be a primary target for situations like this, but this is unlikely in this case. Politicians are often compromised by men in dark suits, not normally a cheap app that allows one to gossip about other people. Their spouses however, are a different story. The prime target for these types of operations (if this is indeed more of an espionage attempt) are exactly the people who were targeted...low-to-mid level employees (and their spouses) who would be more easily blackmailed with the type of information that they submitted to the app. What will come of this is anyone's guess, but beyond the obvious embarrassment angle of this situation (and this affair highlighting actual affairs), the national security risks via blackmail are probably extremely high. What makes this risk unique is that for now, most people are focusing on the socially embarrassing side of this scandal, and not the security risks that are present. As such, when this story is dropped like a hot potato from the news cycle in a few days, that moment is precisely when national-level assets are likely to attempt to exploit the tens of thousands of targets that have now been exposed. Everyone will forget about this in a few weeks, but the espionage potential of this data leak persists well into the future. Analyst: S2A1 Research: //END REPORT//

S2 Underground

40,028 Aufrufe • vor 11 Monaten

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 Aufrufe • vor 7 Monaten

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.
0:38

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

Hồng Ngọc | Ruby💎

12,837 Aufrufe • vor 5 Monaten

Cerebras just IPO’d and the stock already ran up over 100% (Save this). For the entire 70 year history of the semiconductor industry, every company on earth has followed the same process. You take a dinner plate sized silicon wafer, put hundreds of tiny chips onto it, and dice it up like a pizza. Nvidia does it this way, AMD does it this way, Intel has done it this way for six decades and everyone who tried to break that convention failed. Until Cerebras asked the most annoyingly obvious question in the industry’s history, what if you just didn’t cut it? The result is the Wafer Scale Engine, a single chip 56 times larger than Nvidia’s H100 and it fundamentally changes the physics of how AI inference works. The reason this matters is not the size, it’s the bandwidth. Every time an AI model generates a single word, it has to reach into memory, pull weights, multiply them together, and produce a prediction and when you’re running millions of concurrent sessions at once, the bottleneck is not raw processing power but how fast data moves between memory and compute. Nvidia’s H100 moves data at roughly 3 terabytes per second, while Cerebras’ WSE-3 moves data at 21 petabytes per second, roughly 7,000 times faster because memory and compute live on the same enormous piece of silicon and data barely has to travel at all. That gap is exactly why OpenAI went from 150 tokens per second on traditional GPUs to 2,000 tokens per second on Cerebras hardware, and why AWS integrated Cerebras into Bedrock to deliver roughly 5x more inference capacity in the same physical footprint. The macro setup is making the trade even more urgent. South Korea DRAM export prices recently jumped 35%, flash memory surged 47%, and SSD pricing spiked nearly 140% and every single one of those increases hits Nvidia-based infrastructure directly, because the H100 requires 80GB of the most expensive, most contested memory in the AI supply chain. Cerebras’ WSE-3 uses zero external HBM memory, baking 44GB of SRAM directly into the wafer itself which means as memory pricing goes parabolic, every CFO evaluating AI infrastructure is suddenly looking much more seriously at the architecture that sidesteps that cost entirely. The demand is already showing up in the backlog. Cerebras ended 2025 with $24.6 billion in remaining performance obligations for a company doing just over $500 million in annual revenue, that is a number that implies years of contracted growth already sitting on the books. The IPO was 20x oversubscribed, the price range was raised twice before listing, and shares opened 89% above their listing price on a $5.55 billion raise that made it the largest semiconductor IPO in history. The risks are real and worth naming. 86% of 2025 revenue came from two entities with UAE ties, U.S. revenue actually fell 34% to $187 million, and the $20 billion OpenAI contract is conditional, if Cerebras misses delivery milestones, OpenAI can terminate and trigger repayment demands on a $1 billion loan facility. And yet the market is valuing Cerebras at roughly 91x trailing revenue, richer than Nvidia, AMD, and Arm combined. What investors are betting on is not that Cerebras beats Nvidia, it is that the inference supercycle is large enough to support an entirely different architecture optimized for a different workload, and that $24.6 billion in contracted backlog converts to diversified revenue before the market starts asking harder questions. CEO Andrew Feldman said this took a decade of late nights to get right, everyone who tried to copy it failed and given that the entire inference economy is now running through exactly the bottleneck Cerebras was built to eliminate, the market is starting to believe him.
2:04

Cerebras just IPO’d and the stock already ran up over 100% (Save this). For the entire 70 year history of the semiconductor industry, every company on earth has followed the same process. You take a dinner plate sized silicon wafer, put hundreds of tiny chips onto it, and dice it up like a pizza. Nvidia does it this way, AMD does it this way, Intel has done it this way for six decades and everyone who tried to break that convention failed. Until Cerebras asked the most annoyingly obvious question in the industry’s history, what if you just didn’t cut it? The result is the Wafer Scale Engine, a single chip 56 times larger than Nvidia’s H100 and it fundamentally changes the physics of how AI inference works. The reason this matters is not the size, it’s the bandwidth. Every time an AI model generates a single word, it has to reach into memory, pull weights, multiply them together, and produce a prediction and when you’re running millions of concurrent sessions at once, the bottleneck is not raw processing power but how fast data moves between memory and compute. Nvidia’s H100 moves data at roughly 3 terabytes per second, while Cerebras’ WSE-3 moves data at 21 petabytes per second, roughly 7,000 times faster because memory and compute live on the same enormous piece of silicon and data barely has to travel at all. That gap is exactly why OpenAI went from 150 tokens per second on traditional GPUs to 2,000 tokens per second on Cerebras hardware, and why AWS integrated Cerebras into Bedrock to deliver roughly 5x more inference capacity in the same physical footprint. The macro setup is making the trade even more urgent. South Korea DRAM export prices recently jumped 35%, flash memory surged 47%, and SSD pricing spiked nearly 140% and every single one of those increases hits Nvidia-based infrastructure directly, because the H100 requires 80GB of the most expensive, most contested memory in the AI supply chain. Cerebras’ WSE-3 uses zero external HBM memory, baking 44GB of SRAM directly into the wafer itself which means as memory pricing goes parabolic, every CFO evaluating AI infrastructure is suddenly looking much more seriously at the architecture that sidesteps that cost entirely. The demand is already showing up in the backlog. Cerebras ended 2025 with $24.6 billion in remaining performance obligations for a company doing just over $500 million in annual revenue, that is a number that implies years of contracted growth already sitting on the books. The IPO was 20x oversubscribed, the price range was raised twice before listing, and shares opened 89% above their listing price on a $5.55 billion raise that made it the largest semiconductor IPO in history. The risks are real and worth naming. 86% of 2025 revenue came from two entities with UAE ties, U.S. revenue actually fell 34% to $187 million, and the $20 billion OpenAI contract is conditional, if Cerebras misses delivery milestones, OpenAI can terminate and trigger repayment demands on a $1 billion loan facility. And yet the market is valuing Cerebras at roughly 91x trailing revenue, richer than Nvidia, AMD, and Arm combined. What investors are betting on is not that Cerebras beats Nvidia, it is that the inference supercycle is large enough to support an entirely different architecture optimized for a different workload, and that $24.6 billion in contracted backlog converts to diversified revenue before the market starts asking harder questions. CEO Andrew Feldman said this took a decade of late nights to get right, everyone who tried to copy it failed and given that the entire inference economy is now running through exactly the bottleneck Cerebras was built to eliminate, the market is starting to believe him.

Milk Road AI

30,441 Aufrufe • vor 2 Monaten