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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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1,602,665 Aufrufe • vor 4 Monaten •via X (Twitter)

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Andrew Ng

85,012 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 1 Monat

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 Aufrufe • vor 5 Monaten