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someone made the most ADDICTIVE game to learn DATA CENTER networking its called Data Center, $6 game, you start with bare floors, buy racks, mount servers, route every cable by hand the INSANE part, every customers traffic shows as colored balls rolling through your cables... you literally see bottlenecks...

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chiefofautism

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1,602,910 просмотров • 4 месяцев назад •via X (Twitter)

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You've been watching football wrong your entire life. This 40-minute Databricks talk on how LaLiga uses data is the proof. The goals and assists you obsess over are the least interesting numbers on the pitch. By fusing tracking data (every player's position, many times a second) with eventing data (every action on the ball), LaLiga measures the invisible game space created, defensive lines pulled apart, the value of a run nobody passed to. The best player in a match is often the one who never touched the scoresheet. Pair it with article by Roan and you are good. Once you see the game as data, you can't unsee it.
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You've been watching football wrong your entire life. This 40-minute Databricks talk on how LaLiga uses data is the proof. The goals and assists you obsess over are the least interesting numbers on the pitch. By fusing tracking data (every player's position, many times a second) with eventing data (every action on the ball), LaLiga measures the invisible game space created, defensive lines pulled apart, the value of a run nobody passed to. The best player in a match is often the one who never touched the scoresheet. Pair it with article by Roan and you are good. Once you see the game as data, you can't unsee it.

gemchanger

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"The most important thing in the next 3-4 years is data centers in space. In every way, data centers in space, from a first principles perspective, are superior to data centers on earth. In space, you can keep a satellite in the sun 24 hours a day. The sun is 30% more intense, which results in six times more irradiance than on Earth. So you don't need a battery. The cooling in these data centers is incredibly complicated. Space cooling is free. You just put a radiator on the dark side of the satellite. The only thing faster than a laser going through a fiber optic cable is a laser going through absolute vacuum. Link satellites with lasers, and you have a faster and more coherent network than any data center on Earth."
3:01

"The most important thing in the next 3-4 years is data centers in space. In every way, data centers in space, from a first principles perspective, are superior to data centers on earth. In space, you can keep a satellite in the sun 24 hours a day. The sun is 30% more intense, which results in six times more irradiance than on Earth. So you don't need a battery. The cooling in these data centers is incredibly complicated. Space cooling is free. You just put a radiator on the dark side of the satellite. The only thing faster than a laser going through a fiber optic cable is a laser going through absolute vacuum. Link satellites with lasers, and you have a faster and more coherent network than any data center on Earth."

Patrick OShaughnessy

8,215,427 просмотров • 8 месяцев назад

THIS GUY VIBE CODED GTA 6 ON GOOGLE EARTH IN ONE WEEKEND WITH CLAUDE CODE he built a browser based GTA style game that runs on top of real google earth cities drop into any real city on earth and drive through the actual streets real cops chase you, shoot at you, and arrest you at real police stations your in car radio auto tunes to real local stations based on where you are in the game (radio garden API) planes spawn at every real airport, boats spawn at every real port (OSM data) when you die you respawn at the nearest actual hospital. when you get arrested you respawn at the nearest actual police station the stack: - cesium for rendering google 3D tiles in browser - three.js for vehicles and physics - radio garden + OSM for the location data - claude code wrote 80% of the code this is the type of project that would have taken MONTHS to create this guy did it in less than 48 hours with barely any effort the possibilities with vibe coding in the game dev space are actually endless
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THIS GUY VIBE CODED GTA 6 ON GOOGLE EARTH IN ONE WEEKEND WITH CLAUDE CODE he built a browser based GTA style game that runs on top of real google earth cities drop into any real city on earth and drive through the actual streets real cops chase you, shoot at you, and arrest you at real police stations your in car radio auto tunes to real local stations based on where you are in the game (radio garden API) planes spawn at every real airport, boats spawn at every real port (OSM data) when you die you respawn at the nearest actual hospital. when you get arrested you respawn at the nearest actual police station the stack: - cesium for rendering google 3D tiles in browser - three.js for vehicles and physics - radio garden + OSM for the location data - claude code wrote 80% of the code this is the type of project that would have taken MONTHS to create this guy did it in less than 48 hours with barely any effort the possibilities with vibe coding in the game dev space are actually endless

Om Patel

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someone built a tool that puts ANY FACE on your webcam in REAL TIME called DOT - deepfake offensive toolkit, open source, works right now dot injects a real-time deepfake into your virtual camera zoom, google meet, teams, any video call app sees the fake face as your real webcam it ships with multiple deepfake engines (FOMM, SimSwap, SimSwapHQ), you pick the model based on your GPU and how real you need it to look built by sensity ai for red teams to test if your identity verification actually holds up against deepfakes
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someone built a tool that puts ANY FACE on your webcam in REAL TIME called DOT - deepfake offensive toolkit, open source, works right now dot injects a real-time deepfake into your virtual camera zoom, google meet, teams, any video call app sees the fake face as your real webcam it ships with multiple deepfake engines (FOMM, SimSwap, SimSwapHQ), you pick the model based on your GPU and how real you need it to look built by sensity ai for red teams to test if your identity verification actually holds up against deepfakes

chiefofautism

37,088 просмотров • 6 месяцев назад

Nvidia will now pay you to put a mini AI data center on your house It looks like a normal AC unit in the yard. But inside sits 16 Nvidia Blackwell GPUs and Dell servers. A startup called Span builds them, backed by Nvidia. They bolt onto your home and you get paid for the power and Wi-Fi. Some estimates put that around $1,000 a month in your pocket. That is rent money just for hosting a box outside. Span says it deploys way faster and cheaper than a real data center. The AI boom is literally moving into the suburbs. Save this, the grid is getting rebuilt in real time.
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Nvidia will now pay you to put a mini AI data center on your house It looks like a normal AC unit in the yard. But inside sits 16 Nvidia Blackwell GPUs and Dell servers. A startup called Span builds them, backed by Nvidia. They bolt onto your home and you get paid for the power and Wi-Fi. Some estimates put that around $1,000 a month in your pocket. That is rent money just for hosting a box outside. Span says it deploys way faster and cheaper than a real data center. The AI boom is literally moving into the suburbs. Save this, the grid is getting rebuilt in real time.

winkle.

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Introducing CEO Mode: Be the CEO of Your Army of Agents Lot of talk here about commanding agent armies. With Rox, it’s real and already working at scale for the Global 2000. Powered by Rox Governance - the first agentic governance system for revenue. CEO Mode gives you secure, unified and real-time view across every part of your book of business. With CEO Mode, you can: - Protect sensitive data as confidential data is only accessible to the right people. - Empower every team member and leader to self-serve actions without bottlenecks. - Build custom apps and workflows with governed, secure access. Simply ask your agent a question, and CEO Mode orchestrates the work across your entire system in seconds. You can stay focused on what matters most: growing your business.

Rox

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someone built a device that DETECTS when FEDS is SPYING on your phone nearby its called rayhunter, made by the EFF, and it costs $20 you buy a cheap mobile hotspot from amazon, flash it with their open source software, and carry it in your pocket feds use devices called stingrays that pretend to be real cell towers so your phone connects to them instead, once connected they can track your EXACT location, grab your phone identity, and potentially intercept your calls and messages counter-surveillance equipment used to cost THOUSANDS of $ and required serious technical knowledge now its $20 and fits in your pocket

chiefofautism

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Jupiter

180,740 просмотров • 7 месяцев назад

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1:47

Marc Andreessen on the question at the center of data center discourse & why it's so important: "Can you build anything in America anymore? Can you build a factory? Can you build a chip plant? Can you build a power plant? Can you build a refinery? Can you build a pipeline? Can you build housing?" "One of the common themes in American life for the last 30 years is the answer to those questions is generally, no, you can't do any of those things." "Take as an example, Silicon Valley, right? So all the chips are made in Taiwan. Well, 40 years ago, all the chips were made in California." "Can you build things in America? Can you build a factory? Can you build an energy plant? Can you build a data center? Can you build housing?" "And on every single one of those, there's this massive problem which is, right now in many cases, in many places, no you can't." Marc Andreessen 🇺🇸 with Joe Rogan

a16z

137,141 просмотров • 3 месяцев назад

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.
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The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 просмотров • 1 месяц назад

THEY RAN STATE OF THE ART AI ON A 26 YEAR OLD IMAC WITH ZERO INTERNET not a demo. not a benchmark a real answer in near instant time on hardware older than most people reading this your subscription money is going to a data center that doesn't need it the future already runs locally on dead hardware from 1999 bookmark this before you pay for another month of cloud AI you don't need
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THEY RAN STATE OF THE ART AI ON A 26 YEAR OLD IMAC WITH ZERO INTERNET not a demo. not a benchmark a real answer in near instant time on hardware older than most people reading this your subscription money is going to a data center that doesn't need it the future already runs locally on dead hardware from 1999 bookmark this before you pay for another month of cloud AI you don't need

leopardracer

2,764,696 просмотров • 3 месяцев назад

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!
9:28

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 🚀

65,672 просмотров • 9 месяцев назад

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I am not a developer. Not even remotely. But today I spent 14 hours working on VibeCraft (think Minecraft, but with prompts). It's the first time my friends are telling me I made an addictive game and actually playing it! So today I added 6 000 lines of code in 20 commits. Among the hardest features in VibeCraft: ✅ Real-Time Multiplayer ✅ Real-Time 3D Asset Generation by multi LLM ✅ Move, Edit, Copy intuitively ✅ Authentication ✅ 100% Mobile Responsive ✅ Hosting & Database ✅ Admin backoffice I didn't know how to do any of these and everything you see in the video is made in game, by prompting, at runtime, and you can NOT believe how good LLM are becoming on geo spatial gen I hope that in a few days I can open the alpha to the public, and I can't wait to hear your feedbacks!

Nicolas Zullo

30,131 просмотров • 1 год назад

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

640,914 просмотров • 5 месяцев назад

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
2:24

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,107 просмотров • 1 год назад

We just made the Warp API and MCP completely free. Most HR platforms make you sit through a sales call and a questionnaire just to reach your own data. We think that's backwards. You can pull your entire company and employee data, time off, compensation, payroll, IT, basically anything in Warp. Build your own dashboards. Connect Warp to your internal systems. Run your own automations on top of your data. We built this because our customers are technical and AI-native. They don't want to stare at a dashboard. They want to build on top of their data. Legacy platforms silo it on purpose, and the API, when it exists at all, is a pricey add-on buried behind a sales process. We shipped the API eight weeks ago, and some of our top customers have already built real tools on top of Warp. Today we're removing the last barrier to doing that. If you're an AI-native founder, exec, CIO, or Chief People Officer who's been duct taping this together, the API and MCP are now included free on most plans. On Enterprise, a small fee unlocks the more powerful version with uptime SLAs, webhooks, and audit logs. Your data should be yours. Excited to see what you build with it.
1:23

We just made the Warp API and MCP completely free. Most HR platforms make you sit through a sales call and a questionnaire just to reach your own data. We think that's backwards. You can pull your entire company and employee data, time off, compensation, payroll, IT, basically anything in Warp. Build your own dashboards. Connect Warp to your internal systems. Run your own automations on top of your data. We built this because our customers are technical and AI-native. They don't want to stare at a dashboard. They want to build on top of their data. Legacy platforms silo it on purpose, and the API, when it exists at all, is a pricey add-on buried behind a sales process. We shipped the API eight weeks ago, and some of our top customers have already built real tools on top of Warp. Today we're removing the last barrier to doing that. If you're an AI-native founder, exec, CIO, or Chief People Officer who's been duct taping this together, the API and MCP are now included free on most plans. On Enterprise, a small fee unlocks the more powerful version with uptime SLAs, webhooks, and audit logs. Your data should be yours. Excited to see what you build with it.

Ayush S

730,614 просмотров • 2 месяцев назад

Morgan Stanley just dropped numbers that should make every investor pay attention (Save this). Hyperscalers spent $261B in 2024, they're now projected to spend $1.4T in 2028, a 5x increase in four years and that doesn't even include OpenAI or Anthropic. For the first time, Morgan Stanley is classifying SpaceX as a legitimate hyperscaler alongside Google, Amazon, Microsoft, and Meta. All of it flows into chips, data centers and memory and the supply chain companies sitting beneath the hyperscalers are the ones that will compound quietly. The most underrated play is memory. Micron is the only American HBM supplier, giving it a structural edge in government AI contracts that Samsung and SK Hynix cannot touch. Its entire 2026 HBM4 production is already sold out, revenue nearly tripled to $23.9B, and the memory prices are roughly doubling every year. The cooling problem is one of the most profitable bottlenecks in this entire trade. Vertiv makes the power management, liquid cooling systems and racks that keep GPU clusters from melting and it's up over 100% year to date in 2026 with a $15B+ backlog and guidance raised to $13.5–$14B in full year revenue. Arista Networks (ANET) is the networking infrastructure play, every AI data center needs ultra high speed networking fabric to connect thousands of GPUs together And Arista just doubled its 2026 AI revenue target as the industry shifts from proprietary InfiniBand to Open Ethernet, a shift that plays directly into Arista's strengths. Astera Labs solves the interconnect bottleneck inside data centers, the problem of getting data between chips fast enough to keep up with the GPUs. Revenue grew 93% year over year, it's already profitable, and its customers are Microsoft and Amazon directly. The hyperscalers are the miners and the real money is in the companies selling them the shovels, the electricity, the memory, the cooling, the networking, and the custom silicon. Make sure to follow me Melvin for more underrated infrastructure plays.
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Morgan Stanley just dropped numbers that should make every investor pay attention (Save this). Hyperscalers spent $261B in 2024, they're now projected to spend $1.4T in 2028, a 5x increase in four years and that doesn't even include OpenAI or Anthropic. For the first time, Morgan Stanley is classifying SpaceX as a legitimate hyperscaler alongside Google, Amazon, Microsoft, and Meta. All of it flows into chips, data centers and memory and the supply chain companies sitting beneath the hyperscalers are the ones that will compound quietly. The most underrated play is memory. Micron is the only American HBM supplier, giving it a structural edge in government AI contracts that Samsung and SK Hynix cannot touch. Its entire 2026 HBM4 production is already sold out, revenue nearly tripled to $23.9B, and the memory prices are roughly doubling every year. The cooling problem is one of the most profitable bottlenecks in this entire trade. Vertiv makes the power management, liquid cooling systems and racks that keep GPU clusters from melting and it's up over 100% year to date in 2026 with a $15B+ backlog and guidance raised to $13.5–$14B in full year revenue. Arista Networks (ANET) is the networking infrastructure play, every AI data center needs ultra high speed networking fabric to connect thousands of GPUs together And Arista just doubled its 2026 AI revenue target as the industry shifts from proprietary InfiniBand to Open Ethernet, a shift that plays directly into Arista's strengths. Astera Labs solves the interconnect bottleneck inside data centers, the problem of getting data between chips fast enough to keep up with the GPUs. Revenue grew 93% year over year, it's already profitable, and its customers are Microsoft and Amazon directly. The hyperscalers are the miners and the real money is in the companies selling them the shovels, the electricity, the memory, the cooling, the networking, and the custom silicon. Make sure to follow me Melvin for more underrated infrastructure plays.

Melvin

36,068 просмотров • 1 месяц назад

someone built an AI RED TEAM that maps your entire attack surface as a knowledge graph, finds every vulnerability, then EXPLOITS them to root access AUTONOMOUSLY its called RedAmon, 9,000 templates. 17 node types, actual Metasploit shells, not reports, no pentesters needed 6 phases of autonomous recon: subdomain discovery, port scanning, http probing, resource enumeration, vulnerability scanning, MITRE mapping every finding stored in a Neo4j graph with 17 node types and 20+ relationship types. the AI reasons about the graph, finds attack paths, and runs actual Metasploit exploits, actual shells stress-tested with zero vulnerability data, zero exploit modules, one instruction find a CVE and exploit it, it went from empty database to root-level RCE in 20 steps, researched the exploit on the web, crafted a custom deserialization payload, debugged itself when the first attempt failed next try, the server responded with root access, the highest privilege level on any Linux system. full control over everything the target was running node-serialize 0.0.4, a package with a critical deserialization flaw (CVE-2017-5941, CVSS 9.8), the server takes your cookie, decodes it, and passes it straight into unserialize() which executes any code inside it, the AI figured this out on its own with no hints built on LangGraph + MCP tool servers for naabu, nuclei, curl, metasploit. hunts leaked secrets across GitHub repos, 40+ regex patterns for AWS keys, Stripe tokens, database creds
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someone built an AI RED TEAM that maps your entire attack surface as a knowledge graph, finds every vulnerability, then EXPLOITS them to root access AUTONOMOUSLY its called RedAmon, 9,000 templates. 17 node types, actual Metasploit shells, not reports, no pentesters needed 6 phases of autonomous recon: subdomain discovery, port scanning, http probing, resource enumeration, vulnerability scanning, MITRE mapping every finding stored in a Neo4j graph with 17 node types and 20+ relationship types. the AI reasons about the graph, finds attack paths, and runs actual Metasploit exploits, actual shells stress-tested with zero vulnerability data, zero exploit modules, one instruction find a CVE and exploit it, it went from empty database to root-level RCE in 20 steps, researched the exploit on the web, crafted a custom deserialization payload, debugged itself when the first attempt failed next try, the server responded with root access, the highest privilege level on any Linux system. full control over everything the target was running node-serialize 0.0.4, a package with a critical deserialization flaw (CVE-2017-5941, CVSS 9.8), the server takes your cookie, decodes it, and passes it straight into unserialize() which executes any code inside it, the AI figured this out on its own with no hints built on LangGraph + MCP tool servers for naabu, nuclei, curl, metasploit. hunts leaked secrets across GitHub repos, 40+ regex patterns for AWS keys, Stripe tokens, database creds

chiefofautism

70,129 просмотров • 6 месяцев назад