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Stop letting a non-tech background hold you back. 🛑 Ana completely shifted her life from Swiss medical device manufacturing to becoming an AI DevOps Engineer using nothing but structured self-study. Her exact career shift framework: → Python self-study fundamentals → Data Engineering specialization → Cloud DevOps training (Kubernetes and... show more

TechWorld with Nana | DevOps 🚀

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12,157 views • 2 months ago •via X (Twitter)

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Natalia Quintero (Natalia) runs Every 🧱’s seven-figure AI consulting practice, and she just automated her job with Claude Code. She built an AI project manager, Claudie, that cuts her weekly project management workload from 15 hours to just one. It manages the fleet of spreadsheets she uses to manage new clients, track what has been delivered and what hasn’t, and aggregate feedback to improve our services over time. A month ago she would’ve described herself as non-technical, but now she’s a ‘bonafide vibe code addict’—waking up at 6 a.m. everyday to work on Claudie before her meetings start. She’s not just personally neck-deep in AI—she’s also seen it work, and not, in the smartest enterprises in the world. As our head of consulting, she’s worked with top hedge funds, PE, firms, tech companies, and Fortune 500s to help drive AI adoption and automations. I had her on AI&I to talk about: - How to transition a big company into a truly AI-native org - Why enterprise AI adoption only goes as far as CEO adoption - How to empower AI early adopters inside your org - Why you have to carve out space to “play” with AI if you want real transformation If you’re trying to figure out how to actually put AI to work inside your company, this episode is for you. Watch below! Timestamps: Introduction: 00:00:00 Why successful AI adoption requires coordinated, top-down effort: 00:01:30 How a private equity firm reduced investment memo creation from weeks to 30 minutes: 00:07:05 The benefits of connecting AI to proprietary context: 00:13:30 The plan-delegate-assess-compound framework for engineering teams: 00:15:20 How non-technical team members are becoming vibe coding addicts: 00:17:55 Building Claudie: an AI project manager from scratch: 00:20:50 Why creative exploration time outside the 9-to-5 is essential: 00:23:00 Live demo: How Claudie automates client onboarding and tracking: 00:27:50 The human side of AI: spending less time in spreadsheets, more time with people: 00:38:40

Natalia Quintero (Natalia) runs Every 🧱’s seven-figure AI consulting practice, and she just automated her job with Claude Code. She built an AI project manager, Claudie, that cuts her weekly project management workload from 15 hours to just one. It manages the fleet of spreadsheets she uses to manage new clients, track what has been delivered and what hasn’t, and aggregate feedback to improve our services over time. A month ago she would’ve described herself as non-technical, but now she’s a ‘bonafide vibe code addict’—waking up at 6 a.m. everyday to work on Claudie before her meetings start. She’s not just personally neck-deep in AI—she’s also seen it work, and not, in the smartest enterprises in the world. As our head of consulting, she’s worked with top hedge funds, PE, firms, tech companies, and Fortune 500s to help drive AI adoption and automations. I had her on AI&I to talk about: - How to transition a big company into a truly AI-native org - Why enterprise AI adoption only goes as far as CEO adoption - How to empower AI early adopters inside your org - Why you have to carve out space to “play” with AI if you want real transformation If you’re trying to figure out how to actually put AI to work inside your company, this episode is for you. Watch below! Timestamps: Introduction: 00:00:00 Why successful AI adoption requires coordinated, top-down effort: 00:01:30 How a private equity firm reduced investment memo creation from weeks to 30 minutes: 00:07:05 The benefits of connecting AI to proprietary context: 00:13:30 The plan-delegate-assess-compound framework for engineering teams: 00:15:20 How non-technical team members are becoming vibe coding addicts: 00:17:55 Building Claudie: an AI project manager from scratch: 00:20:50 Why creative exploration time outside the 9-to-5 is essential: 00:23:00 Live demo: How Claudie automates client onboarding and tracking: 00:27:50 The human side of AI: spending less time in spreadsheets, more time with people: 00:38:40

Dan Shipper 📧

31,828 views • 6 months ago

today we're announcing Zo Computer. when we came up with the idea – giving everyone a personal server, powered by AI – it sounded crazy. but now, even my mom has a server of her own. and it's making her life better. she thinks of Zo as her personal assistant. she texts it to manage her busy schedule, using all the context from her notes and files. she no longer needs me for tech support. she also uses Zo as her intelligent workspace – she asks it to organize her files, edit documents, and do deep research. with Zo's help, she can run code from her graduate students and explore the data herself. (my mom's a biologist and runs a research lab. hi mom) Zo has given my mom a real feeling of agency – she can do so much more with her computer. we want everyone to have that same feeling. we want people to fall in love with making stuff for themselves. in the future we're building, we'll own our data, craft our own tools, and create personal APIs. owning an intelligent cloud computer will be just like owning a smartphone. and the internet will feel much more alive. THIS ONE'S FOR YOU MOM ❤️ special thank you to Modal, Pydantic AI, and Steel for being great partners leading up to this launch. and thank you Cursor for being my sword 🗡️ and thank you to everyone who believed in us. a small handful: , Aditya Agarwal, Chris Best, Guillermo Rauch, immad, Shreyas Doshi, Matt Hartman, sam lessin 🏴‍☠️, Gokul Rajaram, Sabrina Hahn, iqram 🪼, Nnamdi Iregbulem, Guru Chahal🇺🇸, Mike Marg, Will Gaybrick, Stephan Cizmar, 👍🏻, Anne Lee Skates, joyce, jam, , Aaron Mak Hoffman, Sunfield

today we're announcing Zo Computer. when we came up with the idea – giving everyone a personal server, powered by AI – it sounded crazy. but now, even my mom has a server of her own. and it's making her life better. she thinks of Zo as her personal assistant. she texts it to manage her busy schedule, using all the context from her notes and files. she no longer needs me for tech support. she also uses Zo as her intelligent workspace – she asks it to organize her files, edit documents, and do deep research. with Zo's help, she can run code from her graduate students and explore the data herself. (my mom's a biologist and runs a research lab. hi mom) Zo has given my mom a real feeling of agency – she can do so much more with her computer. we want everyone to have that same feeling. we want people to fall in love with making stuff for themselves. in the future we're building, we'll own our data, craft our own tools, and create personal APIs. owning an intelligent cloud computer will be just like owning a smartphone. and the internet will feel much more alive. THIS ONE'S FOR YOU MOM ❤️ special thank you to Modal, Pydantic AI, and Steel for being great partners leading up to this launch. and thank you Cursor for being my sword 🗡️ and thank you to everyone who believed in us. a small handful: , Aditya Agarwal, Chris Best, Guillermo Rauch, immad, Shreyas Doshi, Matt Hartman, sam lessin 🏴‍☠️, Gokul Rajaram, Sabrina Hahn, iqram 🪼, Nnamdi Iregbulem, Guru Chahal🇺🇸, Mike Marg, Will Gaybrick, Stephan Cizmar, 👍🏻, Anne Lee Skates, joyce, jam, , Aaron Mak Hoffman, Sunfield

hraness 🎣

589,542 views • 9 months ago

This is Sandy. She went from sleeping on a beach in Hawaii to making over $50,000 a month in nine months. Zero technical background. Still works her 9-to-5 at a funeral home. She also has kids under 8. She spends one to two hours a day on this. That's it. Her first client? She applied to seven jobs on Upwork, had no testimonials, no portfolio, and landed a $3,000/month retainer... ...ok you have my attention A couple months later she increased it to $5,500. What is this awesomeness?! AI automation for businesses. - SEO workflows - blog automation - content systems But here's the thing. She didn't know anything about SEO when she applied. She didn't know how to code. She just used ChatGPT and Claude to build everything. She recorded a 2-3 minute Loom video walking through a workflow ChatGPT built for her, sent it to the client, and closed the deal. In this episode Sandy: - Breaks down how she went from $2 a day in college to multiple income streams - Shows exactly how she landed her first client with zero experience - Tells me why recording a Loom video is the secret to winning on Upwork - Gives me the "Ikigai" framework she used to pick the right niche - Walks through how she built an app in six hours while recording a YouTube video This one was INCREDIBLE. Check it out!

This is Sandy. She went from sleeping on a beach in Hawaii to making over $50,000 a month in nine months. Zero technical background. Still works her 9-to-5 at a funeral home. She also has kids under 8. She spends one to two hours a day on this. That's it. Her first client? She applied to seven jobs on Upwork, had no testimonials, no portfolio, and landed a $3,000/month retainer... ...ok you have my attention A couple months later she increased it to $5,500. What is this awesomeness?! AI automation for businesses. - SEO workflows - blog automation - content systems But here's the thing. She didn't know anything about SEO when she applied. She didn't know how to code. She just used ChatGPT and Claude to build everything. She recorded a 2-3 minute Loom video walking through a workflow ChatGPT built for her, sent it to the client, and closed the deal. In this episode Sandy: - Breaks down how she went from $2 a day in college to multiple income streams - Shows exactly how she landed her first client with zero experience - Tells me why recording a Loom video is the secret to winning on Upwork - Gives me the "Ikigai" framework she used to pick the right niche - Walks through how she built an app in six hours while recording a YouTube video This one was INCREDIBLE. Check it out!

Chris Koerner

62,147 views • 19 days ago

‼️EXPOSED: The RECEIPTS Are In That Prove Erika Kirk FAKED Her PREGNANCY—Utrasound Tech Of 16 Years CONFIRMS IT! 😱 After my last deep-dive into Erika Kirk's PREGANCY, an ultrasound tech with over 16 years of clinical experience reached out to me—and the details they pointed out are absolutely damning. If you’ve ever had an ultrasound, you know the drill. It requires a liberal amount of conductive gel for the wand to actually work, and your shirt gets tucked way up under your bra to keep it out of the way. In Erika’s video? There is no visible gel on her stomach, and she is just casually holding her shirt up. But here is the absolute smoking gun: The name formatting. The clinic she allegedly used—Camelback Women’s Health Center—prints their patient imaging strictly as [LAST NAME, FIRST NAME]. I went a step further and tracked down another ultrasound from that exact same clinic to verify it, and it matches the tech's claim perfectly: Last Name, First Name. The image Erika shows the camera? Printed as "Erika, Frantzve." First name, last name. The formatting for that medical system is completely backwards. Add in the fact that she strategically overlaps the two printed photos to completely cover up the dates, and then crops the date out of the second image entirely. I can't say it's definitive proof, but the preponderance of the evidence points to one very obvious conclusion. The official narrative keeps collapsing. Watch the breakdown and judge the tape for yourself. 👇 ⚠️They want this investigation shut down. Right now, my channel is facing targeted attacks to bankrupt me and silence the truth surrounding the Charlie Kirk assassination. If you believe in independent journalism and want to ensure this investigation stays alive, your support makes all the difference 💪 🔗

‼️EXPOSED: The RECEIPTS Are In That Prove Erika Kirk FAKED Her PREGNANCY—Utrasound Tech Of 16 Years CONFIRMS IT! 😱 After my last deep-dive into Erika Kirk's PREGANCY, an ultrasound tech with over 16 years of clinical experience reached out to me—and the details they pointed out are absolutely damning. If you’ve ever had an ultrasound, you know the drill. It requires a liberal amount of conductive gel for the wand to actually work, and your shirt gets tucked way up under your bra to keep it out of the way. In Erika’s video? There is no visible gel on her stomach, and she is just casually holding her shirt up. But here is the absolute smoking gun: The name formatting. The clinic she allegedly used—Camelback Women’s Health Center—prints their patient imaging strictly as [LAST NAME, FIRST NAME]. I went a step further and tracked down another ultrasound from that exact same clinic to verify it, and it matches the tech's claim perfectly: Last Name, First Name. The image Erika shows the camera? Printed as "Erika, Frantzve." First name, last name. The formatting for that medical system is completely backwards. Add in the fact that she strategically overlaps the two printed photos to completely cover up the dates, and then crops the date out of the second image entirely. I can't say it's definitive proof, but the preponderance of the evidence points to one very obvious conclusion. The official narrative keeps collapsing. Watch the breakdown and judge the tape for yourself. 👇 ⚠️They want this investigation shut down. Right now, my channel is facing targeted attacks to bankrupt me and silence the truth surrounding the Charlie Kirk assassination. If you believe in independent journalism and want to ensure this investigation stays alive, your support makes all the difference 💪 🔗

Project Constitution

351,316 views • 3 months ago

Kelsey Hightower has one of the most inspiring stories in tech: he went from a technician installing DSL modems, through self-directed study and very hard work, to one of the very few Distinguished Engineer at Google whom Satya Nadella personally persuaded to join Microsoft. Timestamps: 00:00 Intro 03:34 Kelsey’s first job at McDonald’s 05:04 His non-traditional path into tech 11:45 Landing his first tech job with an A+ certification 15:33 His entrepreneurial years 19:45 Joining Google as a data center technician 27:48 Learning automation at a Rackspace spinoff 33:26 Moving into financial services 50:00 Building a reputation through open source 53:55 From configuration management to containers 1:08:20 The rise of Kubernetes 1:25:05 Why he almost joined NASA instead of Google 1:29:20 Defining DevRel at Google 1:38:20 Demonstrating impact at Google 1:41:20 Microsoft's offer 1:55:20 Learning how to slow down 2:06:39 Advising and investing 2:15:03 A people-first view of GenAI 2:24:27 Using AI with guardrails 2:28:26 Matching AI to the task 2:36:06 Staying relevant in the AI era Brought to you by outstanding teams building products I love: • Antithesis: verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry: application monitoring software considered “not bad” by millions of developers • Buildkite: CI software built to absorb whatever your coding agents throw at the build queue. OpenAI, Anthropic, Uber and others are customers: Three interesting learnings from Kelsey: 1. Side hustles and doing your own thing teach you business like no IC job can. Before becoming a software engineer at Google, Kelsey was a manager for his comedian friend, operated a computer store, and did IT contracting. These gigs taught him logistics, planning, and about money. All this helped him be far more effective at talking with executives and acting as an executive sponsor inside Google. 2. Can you explain what your startup does without mentioning AI? When Kelsey researches startups seeking his advice, he challenges founders to not say “AI” once. This means that they must explain the actual value their company creates. One unexpected benefit of this is that it often reveals there are easier, cheaper ways to achieve a goal than with AI. 3. It’s very rare to get an extra zero put on your compensation figure – but it happened. Kelsey was a successful, well-paid Google engineer when Microsoft made him an offer that 10x’d his salary (!!). When Kelsey told Google he was planning to take the offer, it matched the offer, proving that his market value had massively increased. It shows that being well paid doesn’t necessarily mean you’re being paid at the correct market rate.

Kelsey Hightower has one of the most inspiring stories in tech: he went from a technician installing DSL modems, through self-directed study and very hard work, to one of the very few Distinguished Engineer at Google whom Satya Nadella personally persuaded to join Microsoft. Timestamps: 00:00 Intro 03:34 Kelsey’s first job at McDonald’s 05:04 His non-traditional path into tech 11:45 Landing his first tech job with an A+ certification 15:33 His entrepreneurial years 19:45 Joining Google as a data center technician 27:48 Learning automation at a Rackspace spinoff 33:26 Moving into financial services 50:00 Building a reputation through open source 53:55 From configuration management to containers 1:08:20 The rise of Kubernetes 1:25:05 Why he almost joined NASA instead of Google 1:29:20 Defining DevRel at Google 1:38:20 Demonstrating impact at Google 1:41:20 Microsoft's offer 1:55:20 Learning how to slow down 2:06:39 Advising and investing 2:15:03 A people-first view of GenAI 2:24:27 Using AI with guardrails 2:28:26 Matching AI to the task 2:36:06 Staying relevant in the AI era Brought to you by outstanding teams building products I love: • Antithesis: verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry: application monitoring software considered “not bad” by millions of developers • Buildkite: CI software built to absorb whatever your coding agents throw at the build queue. OpenAI, Anthropic, Uber and others are customers: Three interesting learnings from Kelsey: 1. Side hustles and doing your own thing teach you business like no IC job can. Before becoming a software engineer at Google, Kelsey was a manager for his comedian friend, operated a computer store, and did IT contracting. These gigs taught him logistics, planning, and about money. All this helped him be far more effective at talking with executives and acting as an executive sponsor inside Google. 2. Can you explain what your startup does without mentioning AI? When Kelsey researches startups seeking his advice, he challenges founders to not say “AI” once. This means that they must explain the actual value their company creates. One unexpected benefit of this is that it often reveals there are easier, cheaper ways to achieve a goal than with AI. 3. It’s very rare to get an extra zero put on your compensation figure – but it happened. Kelsey was a successful, well-paid Google engineer when Microsoft made him an offer that 10x’d his salary (!!). When Kelsey told Google he was planning to take the offer, it matched the offer, proving that his market value had massively increased. It shows that being well paid doesn’t necessarily mean you’re being paid at the correct market rate.

Gergely Orosz

61,384 views • 2 months ago

Meet Taapsee Pannu: A Software Engineer Who Hacked the Bollywood System > Born: August 1, 1987, in New Delhi to Dilmohan Singh Pannu and Nirmaljeet Pannu. Raised in a strict, middle-class household where education was the only currency that mattered. > Age 21: The corporate detour. Armed with a Computer Science Engineering degree, she entered the IT industry. But beneath the surface of code, she was fighting a secret war the battle against mediocrity in a world that expected her to settle for a desk job. > Age 22: The pivot. She traded her keyboard for the ramp, participating in beauty pageants. The struggle wasn't just physical; it was the daily rejection from casting agents who labeled her "too this" or "not enough that," looking for a polished puppet instead of a performer. > Age 23: The leap of faith. She debuted in the Telugu film *Jhummandi Naadam*. She faced the "Outsider's Wall"—no film connections, no mentor, and not even speaking the language. She was mocked on sets for her pronunciation and treated as a temporary face until she proved her grit. > Age 26: The Bollywood entry. With Chashme Baddoor, she entered Hindi cinema. Here, she hit the "Typecasting Trap." For years, she was relegated to the background, offered "side-kick" roles that demanded nothing but a smile, while she desperately craved the chance to show her teeth. > Age 29: The awakening. She starred in *Pink*. Behind the scenes, she fought a silent battle to shed the "glamorous lead" tag. The industry doubted her, wondering if a "commercial face" could handle a film that required such brutal, raw emotional vulnerability. > Age 30–33:The "Versatility Era." She didn't just act; she fought for every script. During Thappad, she endured intense mental exhaustion, channeling the frustration of her own real-life struggles with sexism into the character’s silence. > Age 35: The entrepreneur. She faced the "Industry Ageism" barrier. When she started her own production house, the whispers began: "She’s just an actress; she doesn't know business." She ignored the gatekeepers, turning her production house into a machine that produces hits when people expected her to fade away. > Age 38: The defiance. She openly challenged the industry's double standards. She faced public backlash and "cancel culture" from online trolls who couldn't stomach an actress who refused to be polite, refused to apologize, and refused to play the "damsel" in real life. > Age 39: The icon. From an IT office to the heights of national acclaim, she has built a career entirely on her own terms, surviving systemic exclusion, toxic narratives, and the pressure to conform. > At 39, Taapsee Pannu is the woman who proved that you don't need a PR-manufactured image to sustain success; you just need to be sharper than the system around you. She entered the industry as an outsider, navigated the complexities of linguistic barriers and blatant industry bias, and emerged as one of the most bankable stars in India. She didn't just adapt to the screen she changed the lens through which we view the modern Indian woman. Bold, unapologetic, and technically brilliant she is the living proof that when you have talent, you don't need permission to lead.

Meet Taapsee Pannu: A Software Engineer Who Hacked the Bollywood System > Born: August 1, 1987, in New Delhi to Dilmohan Singh Pannu and Nirmaljeet Pannu. Raised in a strict, middle-class household where education was the only currency that mattered. > Age 21: The corporate detour. Armed with a Computer Science Engineering degree, she entered the IT industry. But beneath the surface of code, she was fighting a secret war the battle against mediocrity in a world that expected her to settle for a desk job. > Age 22: The pivot. She traded her keyboard for the ramp, participating in beauty pageants. The struggle wasn't just physical; it was the daily rejection from casting agents who labeled her "too this" or "not enough that," looking for a polished puppet instead of a performer. > Age 23: The leap of faith. She debuted in the Telugu film Jhummandi Naadam. She faced the "Outsider's Wall"—no film connections, no mentor, and not even speaking the language. She was mocked on sets for her pronunciation and treated as a temporary face until she proved her grit. > Age 26: The Bollywood entry. With Chashme Baddoor, she entered Hindi cinema. Here, she hit the "Typecasting Trap." For years, she was relegated to the background, offered "side-kick" roles that demanded nothing but a smile, while she desperately craved the chance to show her teeth. > Age 29: The awakening. She starred in Pink. Behind the scenes, she fought a silent battle to shed the "glamorous lead" tag. The industry doubted her, wondering if a "commercial face" could handle a film that required such brutal, raw emotional vulnerability. > Age 30–33:The "Versatility Era." She didn't just act; she fought for every script. During Thappad, she endured intense mental exhaustion, channeling the frustration of her own real-life struggles with sexism into the character’s silence. > Age 35: The entrepreneur. She faced the "Industry Ageism" barrier. When she started her own production house, the whispers began: "She’s just an actress; she doesn't know business." She ignored the gatekeepers, turning her production house into a machine that produces hits when people expected her to fade away. > Age 38: The defiance. She openly challenged the industry's double standards. She faced public backlash and "cancel culture" from online trolls who couldn't stomach an actress who refused to be polite, refused to apologize, and refused to play the "damsel" in real life. > Age 39: The icon. From an IT office to the heights of national acclaim, she has built a career entirely on her own terms, surviving systemic exclusion, toxic narratives, and the pressure to conform. > At 39, Taapsee Pannu is the woman who proved that you don't need a PR-manufactured image to sustain success; you just need to be sharper than the system around you. She entered the industry as an outsider, navigated the complexities of linguistic barriers and blatant industry bias, and emerged as one of the most bankable stars in India. She didn't just adapt to the screen she changed the lens through which we view the modern Indian woman. Bold, unapologetic, and technically brilliant she is the living proof that when you have talent, you don't need permission to lead.

Vara Tweets 🪷

145,723 views • 1 month ago

Milady APP x BAP-578 — NFA Milady Remembers. Adapts. Doesn’t make the same mistake twice. Most AI agents are stateless—they forget everything between conversations. Milady doesn’t. What happens automatically in the Milady agent: Milady detects her own patterns. A background process reviews her work history every six hours, identifying repeated mistakes she may have overlooked. This runs silently in the background at zero cost to you. > She improves without retraining—no fine-tuning, no expensive GPU hours. > Her learnings are directly injected into her working context. > She reviews her own notes before every task—just like a good employee reflecting on past lessons before starting new work. We already had an Agent Self-Learning mechanism. But now, with BAP-578 (credits to Christel Buchanan 💛), your personal Milady agent can exist on-chain. How it works—simply: Over time, your Milady agent accumulates learnings—mistakes she has corrected, patterns she has identified, and insights she has gained. All of this data is compressed into a single cryptographic fingerprint (a Merkle root) and recorded on the BNB Chain. What your Milady agent now gets on-chain: - A unique identity in the BNB Agent Registry (ERC-8004)—like a passport for AI agents - A Non-Fungible Agent (BAP-578)—not a profile picture, but a living record of who she is, what she has learned, and what she is capable of - A tamper-proof learning record—anchored on-chain, verifiable by anyone, forgeable by no one Live on BSC Mainnet—just tell your Milady: “register Milady on BNB Chain” or click “mint NFA” to get started. *Oh ya, we recorded this demo using a new agent, that's why it’s showing "0" entries in learning history. ▶️ BIG NEWS NEXT WEEK. STAY TUNED Shaw (spirit/acc) BNB Chain

Milady on BSC

44,584 views • 5 months ago

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on mainstream cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

291,709 views • 3 months ago

Mala Gaonkar has one of the most storied careers in finance > Started career at BCG after graduating from Harvard > Worked as an analyst at Chase Capital before getting an MBA at Harvard Business School > Became founding partner of Lone Pine Capital, one of the most successful hedge funds of all time > Went on to become co-head of Lone Pine's long only strategy, covering their tech and internet investments > Started SurgoCap Partners in 2022 with $1.8B in AUM, the largest ever hedge fund launched by a woman Here are her biggest investing lessons from 23 years of experience as an investor 1. Think about businesses systematically, instead of silos Mala goes into detail about her short position in Nokia. Microsoft bought out the company and shorts were squeezed out of billions of dollars A year later, Microsoft completely wrote off their investment. She was right about the business fundamentals, but did not consider the systemic risk of a potential buyout You can be right in investing, but being early is the same as being wrong 2. Avoid companies with too much leverage Good businesses with too much leverage are a trap. When times get tough, they become limited in what they can do to maneuver around the situation She refers to these as "public LBOs". These are companies that are public but have balance sheet leverage similar to private equity-backed businesses Mala refers to her investment in Altice as a good example of this 3. Not revisiting your old names People often get wrapped up into sunk cost bias when they sell out of a name. Too few investors come back to look at their old names and re-underwrite them from first principles Mala refers to her investment in NVIDIA as a great example. She sold due to fears around crypto and gaming, which were largely driving the stock at the time But NVIDIA ultimately became an AI darling because of the progress they made on their chips. She ended up missing out on a massive rally on the stock Her process now includes consistently revising and revisiting stocks that she has sold or previously missed. These stocks present an interesting opportunity set that you can find winners from

Mala Gaonkar has one of the most storied careers in finance > Started career at BCG after graduating from Harvard > Worked as an analyst at Chase Capital before getting an MBA at Harvard Business School > Became founding partner of Lone Pine Capital, one of the most successful hedge funds of all time > Went on to become co-head of Lone Pine's long only strategy, covering their tech and internet investments > Started SurgoCap Partners in 2022 with $1.8B in AUM, the largest ever hedge fund launched by a woman Here are her biggest investing lessons from 23 years of experience as an investor 1. Think about businesses systematically, instead of silos Mala goes into detail about her short position in Nokia. Microsoft bought out the company and shorts were squeezed out of billions of dollars A year later, Microsoft completely wrote off their investment. She was right about the business fundamentals, but did not consider the systemic risk of a potential buyout You can be right in investing, but being early is the same as being wrong 2. Avoid companies with too much leverage Good businesses with too much leverage are a trap. When times get tough, they become limited in what they can do to maneuver around the situation She refers to these as "public LBOs". These are companies that are public but have balance sheet leverage similar to private equity-backed businesses Mala refers to her investment in Altice as a good example of this 3. Not revisiting your old names People often get wrapped up into sunk cost bias when they sell out of a name. Too few investors come back to look at their old names and re-underwrite them from first principles Mala refers to her investment in NVIDIA as a great example. She sold due to fears around crypto and gaming, which were largely driving the stock at the time But NVIDIA ultimately became an AI darling because of the progress they made on their chips. She ended up missing out on a massive rally on the stock Her process now includes consistently revising and revisiting stocks that she has sold or previously missed. These stocks present an interesting opportunity set that you can find winners from

Boring_Business

133,547 views • 6 months ago

How did a tiny team of 30 engineers build WhatsApp, more than a decade ago? From Jean Lee, engineer #19 at the company. Timestamps: 00:00 Intro 01:39 Early years in tech 06:18 Becoming engineer #19 at WhatsApp 13:53 WhatsApp’s tech stack 18:09 WhatsApp’s unique ways of working 25:27 Countdown displays and outages 27:07 Why WhatsApp won 28:53 The Facebook acquisition 33:13 Life after acquisition 39:27 Working at Facebook in London 44:07 Transitioning to management 47:27 Performance reviews as a manager 53:29 After Facebook 58:53 AI’s impact on engineering 1:02:34 Jean’s advice to new grads and startups 1:06:45 Empowering employees 1:08:17 Book recommendations Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review. • WorkOS – Everything you need to make your app enterprise ready Three interesting observations from this episode: 1. WhatsApp had no code reviews after in-place. WhatsApp cofounder, Brian Acton, reviewed the very first pull request of each new hire, and after that, there were no more code reviews. Jean recounts how Brian reviewed her debut PR in extreme detail. This first (and only!) review set the bar high, and she wrote code to that standard from then on. 2. WhatsApp had close to zero formal processes. WhatsApp had no Scrum, no Agile, no TDD (test driven development), and no formal code reviews beyond the first commit. In contrast, Skype had 1,000 engineers and mandatory Scrum training, but WhatsApp still outcompeted it and won. Jean’s response to hearing of all the formal processes Skype used in order to execute faster: “I’m surprised to hear they thought they were shipping faster because of it.” Perhaps process is often a substitute for trust, not quality?” 3. Saying “no” to features was a competitive advantage. WhatsApp’s CEO, Jan Koum, rejected 99% of feature requests from the team. While competitors shipped dozens of shiny, new features, WhatsApp ruthlessly prioritized reliability and simplicity. Jan repeatedly told the team what the mission was. “I want a grandma living in the countryside to be able to use our app”, he said.

How did a tiny team of 30 engineers build WhatsApp, more than a decade ago? From Jean Lee, engineer #19 at the company. Timestamps: 00:00 Intro 01:39 Early years in tech 06:18 Becoming engineer #19 at WhatsApp 13:53 WhatsApp’s tech stack 18:09 WhatsApp’s unique ways of working 25:27 Countdown displays and outages 27:07 Why WhatsApp won 28:53 The Facebook acquisition 33:13 Life after acquisition 39:27 Working at Facebook in London 44:07 Transitioning to management 47:27 Performance reviews as a manager 53:29 After Facebook 58:53 AI’s impact on engineering 1:02:34 Jean’s advice to new grads and startups 1:06:45 Empowering employees 1:08:17 Book recommendations Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review. • WorkOS – Everything you need to make your app enterprise ready Three interesting observations from this episode: 1. WhatsApp had no code reviews after in-place. WhatsApp cofounder, Brian Acton, reviewed the very first pull request of each new hire, and after that, there were no more code reviews. Jean recounts how Brian reviewed her debut PR in extreme detail. This first (and only!) review set the bar high, and she wrote code to that standard from then on. 2. WhatsApp had close to zero formal processes. WhatsApp had no Scrum, no Agile, no TDD (test driven development), and no formal code reviews beyond the first commit. In contrast, Skype had 1,000 engineers and mandatory Scrum training, but WhatsApp still outcompeted it and won. Jean’s response to hearing of all the formal processes Skype used in order to execute faster: “I’m surprised to hear they thought they were shipping faster because of it.” Perhaps process is often a substitute for trust, not quality?” 3. Saying “no” to features was a competitive advantage. WhatsApp’s CEO, Jan Koum, rejected 99% of feature requests from the team. While competitors shipped dozens of shiny, new features, WhatsApp ruthlessly prioritized reliability and simplicity. Jan repeatedly told the team what the mission was. “I want a grandma living in the countryside to be able to use our app”, he said.

Gergely Orosz

111,903 views • 5 months ago

🕵️‍♂️ ASSET OR WIDOW? CIA Analyst Breaks Down the Pattern of Lies—Intelligence Assessment of Erika Kirk 🧐⚖️ Leah from theleahfiles spent my career at the CIA and advising intelligence agencies and has seen how assets are built, how they are managed, and how they are deployed. And looking at Erika Kirk, she doesn't see a grieving wife—she sees a textbook Intelligence Asset—and I AGREE There are two types of liars: normal people who lie for self-benefit, and assets who have lives built for them. Erika’s lies aren't random; they follow a strategic pattern. RED FLAGS🚩 1⃣ The 24-Year-Old CIA Connection In 2013, at just 24, Erika was the face of a documentary about Electromagnetic Pulses (EMPs) and taking down the American power grid. Her mother owns a defense contracting firm that specializes in this exact tech. Why is a 24-year-old pageant girl with no technical background sitting down with the Director of the CIA? You don't get that access by "accident." You get it because you are incredibly well-connected and being groomed for a role. Yet, she never speaks about this today. 2⃣ The "Divine" Jerusalem Encounter In May 2018, Erika claims a "chance" meeting with Charlie Kirk at an airport during a pilgrimage to Israel. This screams Planned Encounter. In the biz, you "bump into" your target to establish a baseline relationship so the eventual "real" meeting feels organic. Erika allegedly used connections like Tyler Boyer and Cabot Phillips to learn everything about Charlie—his faith, his triggers, his "ideal woman"—and then played that role to perfection. 🎭 3⃣ The "Nonprofit" Interview At 29, living in NYC, Erika flies to Arizona to interview for a "nonprofit" job (TPUSA) while supposedly holding two degrees (which investigators are now proving is likely a lie). She then flies back to NYC and gets a real estate license. What she does and what she says never add up. That is the definition of living a "legend" (a fake background created for an operative). 🧐 Who is the Mastermind? If you look at the epicenter of Superfeat Technologies, the Israeli connections, and the TPUSA takeover, Erika isn't the one pulling the strings. The real player sits in the middle: Her Mother. 🕵️‍♀️ Erika is the face, the role-player, and the integrated asset. She integrated herself through Cabot and Boyer to reach her target: Charlie Kirk. She learned who she needed to be, and she executed. This isn't a love story; it's an operation. "She’s not the mastermind... but she plays the role." What do you think? Is Erika a "providential" wife, or was she deployed to steer CHARLIE from the inside? Sound off below. 👇 From: theleahfiles FOLLOW Her!

🕵️‍♂️ ASSET OR WIDOW? CIA Analyst Breaks Down the Pattern of Lies—Intelligence Assessment of Erika Kirk 🧐⚖️ Leah from theleahfiles spent my career at the CIA and advising intelligence agencies and has seen how assets are built, how they are managed, and how they are deployed. And looking at Erika Kirk, she doesn't see a grieving wife—she sees a textbook Intelligence Asset—and I AGREE There are two types of liars: normal people who lie for self-benefit, and assets who have lives built for them. Erika’s lies aren't random; they follow a strategic pattern. RED FLAGS🚩 1⃣ The 24-Year-Old CIA Connection In 2013, at just 24, Erika was the face of a documentary about Electromagnetic Pulses (EMPs) and taking down the American power grid. Her mother owns a defense contracting firm that specializes in this exact tech. Why is a 24-year-old pageant girl with no technical background sitting down with the Director of the CIA? You don't get that access by "accident." You get it because you are incredibly well-connected and being groomed for a role. Yet, she never speaks about this today. 2⃣ The "Divine" Jerusalem Encounter In May 2018, Erika claims a "chance" meeting with Charlie Kirk at an airport during a pilgrimage to Israel. This screams Planned Encounter. In the biz, you "bump into" your target to establish a baseline relationship so the eventual "real" meeting feels organic. Erika allegedly used connections like Tyler Boyer and Cabot Phillips to learn everything about Charlie—his faith, his triggers, his "ideal woman"—and then played that role to perfection. 🎭 3⃣ The "Nonprofit" Interview At 29, living in NYC, Erika flies to Arizona to interview for a "nonprofit" job (TPUSA) while supposedly holding two degrees (which investigators are now proving is likely a lie). She then flies back to NYC and gets a real estate license. What she does and what she says never add up. That is the definition of living a "legend" (a fake background created for an operative). 🧐 Who is the Mastermind? If you look at the epicenter of Superfeat Technologies, the Israeli connections, and the TPUSA takeover, Erika isn't the one pulling the strings. The real player sits in the middle: Her Mother. 🕵️‍♀️ Erika is the face, the role-player, and the integrated asset. She integrated herself through Cabot and Boyer to reach her target: Charlie Kirk. She learned who she needed to be, and she executed. This isn't a love story; it's an operation. "She’s not the mastermind... but she plays the role." What do you think? Is Erika a "providential" wife, or was she deployed to steer CHARLIE from the inside? Sound off below. 👇 From: theleahfiles FOLLOW Her!

Project Constitution

36,600 views • 4 months ago

MUST-WATCH: Inside Databento with Christina Qi—from MIT dorm room HFT shop to taking on the data incumbents Christina Qi (Christina Qi) went from running a high-frequency trading fund out of her dorm room at MIT to building Databento (Databento), a market data platform growing ~5x YoY with 16,000+ customers that's landed 8 of the 10 largest options market makers & the biggest AI companies in the world. 50-75 new customers sign up daily with their own credit cards. "The biggest AI company in the world sent us an email: 'We want to buy your most expensive data plan'—and we were like, what?" We cover: - Why the "smart MIT founders" pitch failed when launching a hedge fund & what actually worked with investors - The difference between raising venture capital vs. hedge fund capital (they raised both) - How they compete at 1/1,000th the budget of incumbents by selling bottom-up, not top-down - Why member-of-technical-staff employees have more buying power than anyone realizes - The strategic decision to stay one layer upstream from Bloomberg—not compete directly - Why HFT strategies aren't scalable & the data licensing nightmare that plagued their fund - How to know when it's time to shut down (hint: it's not about performance) - Her mom texted "go beta bento"— even family doesn't always get what you do Thank you Christina Qi for coming on the pod! 00:00 Intro 01:36 Can you start a hedge fund in college? 02:16 Dorm-room origin: MIT/Harvard HFT startup story begins 03:18 Early sacrifices, honeymoon phase, building fast together relentlessly 04:28 Then vs now: launching funds in crowded quant landscape 07:50 Fundraising basics: allocators, fees, lockups, track record 11:31 Why raise VC: real tech beyond strategies only 13:21 2025 reality: easier tools, tougher alpha generation 16:46 Blow-ups and risk management: leverage, slippage, TCA lessons 17:22 Why Databento: licensing solved, API-first market data pipeline 22:06 Upstream of terminals: enable analytics, AI, backtesting workflows 24:42 PLG beats sales: bottom-up, self-serve enterprise adoption 28:32 Case study: AI staffer buys top plan instantly 30:06 AI in finance needs clean tick data pipelines 32:34 Bloomberg dominance, network effects, Refinitiv comparison realities 35:56 Users and roadmap: upvotes, feedback prioritize datasets 39:14 Advance commitments fund new exchange integrations, secure adoption 41:55 Market microstructure matters: better order-book signals, execution 44:13 Career advice: data engineering, portfolio construction, Python 48:06 Macro regimes shift fundraising outcomes for founders 49:46 Personal barometer: pursue work that energizes you 51:26 Closing thoughts and thanks

MUST-WATCH: Inside Databento with Christina Qi—from MIT dorm room HFT shop to taking on the data incumbents Christina Qi (Christina Qi) went from running a high-frequency trading fund out of her dorm room at MIT to building Databento (Databento), a market data platform growing ~5x YoY with 16,000+ customers that's landed 8 of the 10 largest options market makers & the biggest AI companies in the world. 50-75 new customers sign up daily with their own credit cards. "The biggest AI company in the world sent us an email: 'We want to buy your most expensive data plan'—and we were like, what?" We cover: - Why the "smart MIT founders" pitch failed when launching a hedge fund & what actually worked with investors - The difference between raising venture capital vs. hedge fund capital (they raised both) - How they compete at 1/1,000th the budget of incumbents by selling bottom-up, not top-down - Why member-of-technical-staff employees have more buying power than anyone realizes - The strategic decision to stay one layer upstream from Bloomberg—not compete directly - Why HFT strategies aren't scalable & the data licensing nightmare that plagued their fund - How to know when it's time to shut down (hint: it's not about performance) - Her mom texted "go beta bento"— even family doesn't always get what you do Thank you Christina Qi for coming on the pod! 00:00 Intro 01:36 Can you start a hedge fund in college? 02:16 Dorm-room origin: MIT/Harvard HFT startup story begins 03:18 Early sacrifices, honeymoon phase, building fast together relentlessly 04:28 Then vs now: launching funds in crowded quant landscape 07:50 Fundraising basics: allocators, fees, lockups, track record 11:31 Why raise VC: real tech beyond strategies only 13:21 2025 reality: easier tools, tougher alpha generation 16:46 Blow-ups and risk management: leverage, slippage, TCA lessons 17:22 Why Databento: licensing solved, API-first market data pipeline 22:06 Upstream of terminals: enable analytics, AI, backtesting workflows 24:42 PLG beats sales: bottom-up, self-serve enterprise adoption 28:32 Case study: AI staffer buys top plan instantly 30:06 AI in finance needs clean tick data pipelines 32:34 Bloomberg dominance, network effects, Refinitiv comparison realities 35:56 Users and roadmap: upvotes, feedback prioritize datasets 39:14 Advance commitments fund new exchange integrations, secure adoption 41:55 Market microstructure matters: better order-book signals, execution 44:13 Career advice: data engineering, portfolio construction, Python 48:06 Macro regimes shift fundraising outcomes for founders 49:46 Personal barometer: pursue work that energizes you 51:26 Closing thoughts and thanks

Ethan Kho

20,054 views • 6 months ago

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,497 views • 3 months ago

SISA MALAM (Remnants of the Night) An AI-generated short film by Dan Pradana Made with Seedance 2.0 via Runway Edited with CapCut CapCut CapCut_jp A slice of life. A woman. A man. A flat somewhere in the city. And the quiet hours between afternoon and night where something unspoken slowly finds its way out. No action scenes. No explosions. Just love that finds its way through moments. + + + While AI influencers keep declaring Hollywood is cooked just for clout or engagement, I've been quietly trying to figure out how far Seedance 2.0 can actually go. And it's been one of the most challenging and rewarding things I've made. Fight scenes with Seedance 2.0 are easy. But drama is not. The goal was simple but hard: stop relying on random footage stitched together with a voiceover, and instead build something continuous. Scene by scene. Moment by moment. Reaction by reaction. A love story told through glances, small talk, and the kind of silences that say more than dialogue. Far from perfect, but further than I expected. + + + The characters are entirely AI-generated using Nano Banana. But the souls behind them are real. Risa (Icha) was inspired by a remarkable woman I admire. A middle-aged lady who could not have children of her own adopted an orphan girl at the age of eight. That girl is Icha. She grew up to become someone with a depth of warmth and maternal instinct that is hard to describe. When her adoptive mother later took in two more orphans, Icha, now 24, naturally became both an older sister and a mother figure to them. Through her I wanted to explore something I find genuinely beautiful about Southeast Asian women: that nurturing instinct, so quiet and so deep, it doesn't need to be taught. It simply is. Riandi (Rian) is a reflection of my younger self. Long before I married my wife, we lived in different cities. Every weekend I would take a two hour travel bus just to see her. No WhatsApp back then, no easy communication. Just a longing strong enough to move. We never went anywhere fancy. Cheap warung food, roadside stalls, just sitting together. It was never about the place. It was about needing to see her. That kind of love makes you do things that make no logical sense, and you do them gladly. We eventually got married. She gave me three boys. All of them. And she remains the only woman in the house, an absolute princess surrounded by chaos, and she handles it with more grace than any of us deserve. + + + I hope you enjoy the video. The technology is there. It has come a long way, and it keeps getting better. Looking forward to keep experimenting. 🙂

SISA MALAM (Remnants of the Night) An AI-generated short film by Dan Pradana Made with Seedance 2.0 via Runway Edited with CapCut CapCut CapCut_jp A slice of life. A woman. A man. A flat somewhere in the city. And the quiet hours between afternoon and night where something unspoken slowly finds its way out. No action scenes. No explosions. Just love that finds its way through moments. + + + While AI influencers keep declaring Hollywood is cooked just for clout or engagement, I've been quietly trying to figure out how far Seedance 2.0 can actually go. And it's been one of the most challenging and rewarding things I've made. Fight scenes with Seedance 2.0 are easy. But drama is not. The goal was simple but hard: stop relying on random footage stitched together with a voiceover, and instead build something continuous. Scene by scene. Moment by moment. Reaction by reaction. A love story told through glances, small talk, and the kind of silences that say more than dialogue. Far from perfect, but further than I expected. + + + The characters are entirely AI-generated using Nano Banana. But the souls behind them are real. Risa (Icha) was inspired by a remarkable woman I admire. A middle-aged lady who could not have children of her own adopted an orphan girl at the age of eight. That girl is Icha. She grew up to become someone with a depth of warmth and maternal instinct that is hard to describe. When her adoptive mother later took in two more orphans, Icha, now 24, naturally became both an older sister and a mother figure to them. Through her I wanted to explore something I find genuinely beautiful about Southeast Asian women: that nurturing instinct, so quiet and so deep, it doesn't need to be taught. It simply is. Riandi (Rian) is a reflection of my younger self. Long before I married my wife, we lived in different cities. Every weekend I would take a two hour travel bus just to see her. No WhatsApp back then, no easy communication. Just a longing strong enough to move. We never went anywhere fancy. Cheap warung food, roadside stalls, just sitting together. It was never about the place. It was about needing to see her. That kind of love makes you do things that make no logical sense, and you do them gladly. We eventually got married. She gave me three boys. All of them. And she remains the only woman in the house, an absolute princess surrounded by chaos, and she handles it with more grace than any of us deserve. + + + I hope you enjoy the video. The technology is there. It has come a long way, and it keeps getting better. Looking forward to keep experimenting. 🙂

MXVDXN // DAN

40,527 views • 4 months ago

$Katherine Boyle just identified Elon Musk’s most important contribution to America, and it has nothing to do with the products he shipped. Boyle, General Partner at a16z: “I think Elon’s most important contribution to this country is training two generations of engineers to work with their hands again.” For ten years, America’s sharpest technical minds optimized ad clicks and built messaging apps. Software consumed ambition. The physical world became something you abstracted into APIs, not something you touched or understood. Elon didn’t reverse that through inspiration. He reversed it by building companies that required understanding manufacturing or failing completely. SpaceX and Tesla forced engineers to learn how metal fractures, how tolerances cascade through systems, how physical iteration costs months and millions per failure. No debugging. No patches. Just physics that doesn’t negotiate. Boyle: “Training two generations of engineers.” The product isn’t the cars. It’s the people. Look at who’s founding America’s critical hard-tech companies now. The common thread isn’t Stanford or MIT. It’s time on factory floors at SpaceX or Tesla. They learned welding. They learned that “impossible” just means unsolved engineering, not violated physics. They learned failure in the physical domain where mistakes compound instead of reverting. Elon didn’t build companies. He accidentally rebuilt industrial knowledge that had been decaying for thirty years while America’s best minds chased digital scale. Boyle: “Work with their hands again.” Three words that sound quaint but describe a civilizational inflection point. Software dominated because it scaled infinitely at zero marginal cost. Physical manufacturing was slow, expensive, unfashionable. Building real things became what you did if you couldn’t code. Elon made atoms matter again. Made manufacturing the hardest problem worth solving. Made physical engineering prestigious in ways it hadn’t been since humans walked on the moon. The evidence is everywhere now. Technical talent that doesn’t default to “which app” but asks “which physical thing should exist that currently doesn’t.” Ambition redirected from optimizing engagement metrics to building rockets. From scaling users to scaling factories. From virtual products to physical infrastructure. That shift matters more than any vehicle or spacecraft Musk delivered. Products obsolesce. Redirecting an entire generation’s engineering ambition from digital to physical compounds across decades and rebuilds industrial capability at civilizational scale. We stopped just coding the future. We started machining it, welding it, breaking it in reality until physics confirms it works. That transformation from virtual to tangible ambition is reconstructing American manufacturing one engineer at a time. And those engineers are now training the next wave. The compounding has started. The School of Elon doesn’t need Elon anymore. It’s self-sustaining, spreading through an entire generation that learned building real things matters more than building virtual ones. That’s not just a business achievement. That’s a civilization remembering how to make things that matter in the physical world again. And it might be the only thing that saves American technological leadership when the competition is just building faster because they never forgot.$

Katherine Boyle just identified Elon Musk’s most important contribution to America, and it has nothing to do with the products he shipped. Boyle, General Partner at a16z: “I think Elon’s most important contribution to this country is training two generations of engineers to work with their hands again.” For ten years, America’s sharpest technical minds optimized ad clicks and built messaging apps. Software consumed ambition. The physical world became something you abstracted into APIs, not something you touched or understood. Elon didn’t reverse that through inspiration. He reversed it by building companies that required understanding manufacturing or failing completely. SpaceX and Tesla forced engineers to learn how metal fractures, how tolerances cascade through systems, how physical iteration costs months and millions per failure. No debugging. No patches. Just physics that doesn’t negotiate. Boyle: “Training two generations of engineers.” The product isn’t the cars. It’s the people. Look at who’s founding America’s critical hard-tech companies now. The common thread isn’t Stanford or MIT. It’s time on factory floors at SpaceX or Tesla. They learned welding. They learned that “impossible” just means unsolved engineering, not violated physics. They learned failure in the physical domain where mistakes compound instead of reverting. Elon didn’t build companies. He accidentally rebuilt industrial knowledge that had been decaying for thirty years while America’s best minds chased digital scale. Boyle: “Work with their hands again.” Three words that sound quaint but describe a civilizational inflection point. Software dominated because it scaled infinitely at zero marginal cost. Physical manufacturing was slow, expensive, unfashionable. Building real things became what you did if you couldn’t code. Elon made atoms matter again. Made manufacturing the hardest problem worth solving. Made physical engineering prestigious in ways it hadn’t been since humans walked on the moon. The evidence is everywhere now. Technical talent that doesn’t default to “which app” but asks “which physical thing should exist that currently doesn’t.” Ambition redirected from optimizing engagement metrics to building rockets. From scaling users to scaling factories. From virtual products to physical infrastructure. That shift matters more than any vehicle or spacecraft Musk delivered. Products obsolesce. Redirecting an entire generation’s engineering ambition from digital to physical compounds across decades and rebuilds industrial capability at civilizational scale. We stopped just coding the future. We started machining it, welding it, breaking it in reality until physics confirms it works. That transformation from virtual to tangible ambition is reconstructing American manufacturing one engineer at a time. And those engineers are now training the next wave. The compounding has started. The School of Elon doesn’t need Elon anymore. It’s self-sustaining, spreading through an entire generation that learned building real things matters more than building virtual ones. That’s not just a business achievement. That’s a civilization remembering how to make things that matter in the physical world again. And it might be the only thing that saves American technological leadership when the competition is just building faster because they never forgot.

Dustin

941,399 views • 6 months ago

Dear young men, You can be anything in this life but please don't be a simp. I was seeing various video snippets of a certain man who went to the honest bunch podcast to share the tales of his bitter ordeal in his marriage to a certain lady whose name I don't want to mention. I sincerely have no idea about the couple until I saw the video but I realised from online comments that they were a very popular power couple on Instagram way back 2018 or thereabout. Out of curiosity, I went on YouTube to watch the full video on Glitch Africa Studio channel. I cringed while watching the video because I was very pissed off with what the guy was saying. I felt for him at the same time though. My question still remains "how can a guy be so naive?". He married at 37yrs of age. One would have expected a 37y/o man to be well experienced about life. Now he is 48yrs old with no child anywhere in the world, and his wife has left him too. His first mistake: She was already married and divorced, and she told you about it, but you are a single man who has never been married, yet you made a divorced woman your first choice and you even got married to her just five months after she slid into your DM. She was already leading you on handicap with her prior marriage experience. As a single man, marrying a divorced woman or a single mother is more like you playing Russian roulette with your life. Another mistake: You agreed to her travelling abroad while you stay behind in Nigeria. She outgrew you while you remained at same level. Another mistake: You saw a chat she had with her friend where she said she was done with you and all sort, yet you still chose to stay in the long distance marriage and kept on fooling and simping. Another mistake: You agreed to her doing an arranged marriage in US so she can get a green card. Another mistake: You live in Nigeria and earn in naira but you still send money to your "wife" who is in America. I can go on and on but wetin concern me self. Anyways, like MKO the comedian would always banter on his show, "Dodoyo go dance gbedu". As a man, you are only allowed to simp up till 25yrs of age. Once you cross your mid 20s, as a man, please love with your brain. Don't be an intentional man. Don't be a matured guy. Don't be a real man. Na mumu dey answer all those name. Don't be a simp. Let him who has ears hear now. If you fail to hear now, you will also cry bitterly sooner or later. I pray Roby Ekpo heals. 📹 : Glitch Africa Studio (YouTube)

Dear young men, You can be anything in this life but please don't be a simp. I was seeing various video snippets of a certain man who went to the honest bunch podcast to share the tales of his bitter ordeal in his marriage to a certain lady whose name I don't want to mention. I sincerely have no idea about the couple until I saw the video but I realised from online comments that they were a very popular power couple on Instagram way back 2018 or thereabout. Out of curiosity, I went on YouTube to watch the full video on Glitch Africa Studio channel. I cringed while watching the video because I was very pissed off with what the guy was saying. I felt for him at the same time though. My question still remains "how can a guy be so naive?". He married at 37yrs of age. One would have expected a 37y/o man to be well experienced about life. Now he is 48yrs old with no child anywhere in the world, and his wife has left him too. His first mistake: She was already married and divorced, and she told you about it, but you are a single man who has never been married, yet you made a divorced woman your first choice and you even got married to her just five months after she slid into your DM. She was already leading you on handicap with her prior marriage experience. As a single man, marrying a divorced woman or a single mother is more like you playing Russian roulette with your life. Another mistake: You agreed to her travelling abroad while you stay behind in Nigeria. She outgrew you while you remained at same level. Another mistake: You saw a chat she had with her friend where she said she was done with you and all sort, yet you still chose to stay in the long distance marriage and kept on fooling and simping. Another mistake: You agreed to her doing an arranged marriage in US so she can get a green card. Another mistake: You live in Nigeria and earn in naira but you still send money to your "wife" who is in America. I can go on and on but wetin concern me self. Anyways, like MKO the comedian would always banter on his show, "Dodoyo go dance gbedu". As a man, you are only allowed to simp up till 25yrs of age. Once you cross your mid 20s, as a man, please love with your brain. Don't be an intentional man. Don't be a matured guy. Don't be a real man. Na mumu dey answer all those name. Don't be a simp. Let him who has ears hear now. If you fail to hear now, you will also cry bitterly sooner or later. I pray Roby Ekpo heals. 📹 : Glitch Africa Studio (YouTube)

Olamide Obe

58,005 views • 4 months ago

Shared by Rebecca Lynn Hutcheson RN- "Wake up America... just one more Medical Professional putting their life, career and reputation on the line... Published on Jul 1, 2019 Neonatologist Dr. Paul Byrne, president of the Life Guardian Foundation, addresses the 2019 John Paul II Academy for Human Life and Family conference in Rome. May 20, 2019. Hear his voice it can save the life of your loved one... this is a long video, so go to the last 11 minutes and start there ...It is imperative for you to hear all when you have the time... I know this one first hand, a year ago this month my daughter was in ICU on a ventilator unresponsive they said. But she was unresponsive and, why did they have her on a high dose of Fentanyl? They were setting my daughter up for failure and then so she could be an organ donor ..had I not had the medical background that I did and another critical care nurse that I could reach out to they would have been successful and harvesting her organs and my daughter would not be here today... My daughter is the love of my life and it breaks my heart to think about all the families that were convinced that there was nothing left to do and signed those organ harvesting papers. When they kept telling me that my daughter was unresponsive I had to fight with them to pull her off the fentanyl. I told them you cannot possibly determine neuro status with that strong opiate. They told me that it was to keep her comfortable . I said she doesn't need to be kept comfortable if she's comatose she doesn't know anything, take her off the drug. What I was screaming on the inside was not what I could verbalize, I had to be more patient and kind because you can get thrown out of these rooms, yes that is true, they can remove you from your childs side.. So not only am I trying to save my daughter I'm trying to keep my composure , when I know what they are doing is so wrong. Another example of them setting my daughter up for failure is normal platelet count is 150000 to 400,000 my daughter's was 60, 000 that means that she was under normal limits that means that her blood was not clotting and this is why all of her IV sites were bleeding.. The CCU doctors wanted to start her on heparin, Heparin makes the blood thin for somebody who's got thick blood . my daighter was already well below limits and I said absolutely not, she is already thrombocytopenic(low platelets a bleeding disorder) Those 4 doctors come up against me again telling me "this was protocol she'll throw a clot, she could have a stroke." I said "there's no way for her to clot she has no platelets " and then I pointed at the Carousel of drugs they had her on and I said" those will drop her platelet count even lower." I had to sign refusals , this Heparin they wanted to put her on with no clotting factors already could/ would have caused a brain bleed in my child. I know the side effects of these Dangerous Drugs, but how many people do not? how many people do not even know what normal lab values are? without having all of the information and knowing what you need to know how can you make the right decision? It sickens me to the core of my being to know this is happening... If you want to gain some inside knowledge before you're ever put into this position watch this video in in its entirety. It's an hour and 11 minutes long if you don't have time, go to the last 11 minutes. Normally I don't have time to take and spend an hour watching a video because of school and all my other duties in my home with my animals, but when I started watching this video it brought back a lot of what I had to go through , people need to know this information. You have a doctor here trying to do the right thing but even with his background couldn't make things happen in a hospital setting. There's something very criminal taking place in our medical system and we just have to be aware of it."

Shared by Rebecca Lynn Hutcheson RN- "Wake up America... just one more Medical Professional putting their life, career and reputation on the line... Published on Jul 1, 2019 Neonatologist Dr. Paul Byrne, president of the Life Guardian Foundation, addresses the 2019 John Paul II Academy for Human Life and Family conference in Rome. May 20, 2019. Hear his voice it can save the life of your loved one... this is a long video, so go to the last 11 minutes and start there ...It is imperative for you to hear all when you have the time... I know this one first hand, a year ago this month my daughter was in ICU on a ventilator unresponsive they said. But she was unresponsive and, why did they have her on a high dose of Fentanyl? They were setting my daughter up for failure and then so she could be an organ donor ..had I not had the medical background that I did and another critical care nurse that I could reach out to they would have been successful and harvesting her organs and my daughter would not be here today... My daughter is the love of my life and it breaks my heart to think about all the families that were convinced that there was nothing left to do and signed those organ harvesting papers. When they kept telling me that my daughter was unresponsive I had to fight with them to pull her off the fentanyl. I told them you cannot possibly determine neuro status with that strong opiate. They told me that it was to keep her comfortable . I said she doesn't need to be kept comfortable if she's comatose she doesn't know anything, take her off the drug. What I was screaming on the inside was not what I could verbalize, I had to be more patient and kind because you can get thrown out of these rooms, yes that is true, they can remove you from your childs side.. So not only am I trying to save my daughter I'm trying to keep my composure , when I know what they are doing is so wrong. Another example of them setting my daughter up for failure is normal platelet count is 150000 to 400,000 my daughter's was 60, 000 that means that she was under normal limits that means that her blood was not clotting and this is why all of her IV sites were bleeding.. The CCU doctors wanted to start her on heparin, Heparin makes the blood thin for somebody who's got thick blood . my daighter was already well below limits and I said absolutely not, she is already thrombocytopenic(low platelets a bleeding disorder) Those 4 doctors come up against me again telling me "this was protocol she'll throw a clot, she could have a stroke." I said "there's no way for her to clot she has no platelets " and then I pointed at the Carousel of drugs they had her on and I said" those will drop her platelet count even lower." I had to sign refusals , this Heparin they wanted to put her on with no clotting factors already could/ would have caused a brain bleed in my child. I know the side effects of these Dangerous Drugs, but how many people do not? how many people do not even know what normal lab values are? without having all of the information and knowing what you need to know how can you make the right decision? It sickens me to the core of my being to know this is happening... If you want to gain some inside knowledge before you're ever put into this position watch this video in in its entirety. It's an hour and 11 minutes long if you don't have time, go to the last 11 minutes. Normally I don't have time to take and spend an hour watching a video because of school and all my other duties in my home with my animals, but when I started watching this video it brought back a lot of what I had to go through , people need to know this information. You have a doctor here trying to do the right thing but even with his background couldn't make things happen in a hospital setting. There's something very criminal taking place in our medical system and we just have to be aware of it."

Jessica Rojas 🇺🇸💪

71,160 views • 1 year ago