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THIS WHOLE 3-NODE CLUSTER RUNS PROXMOX, CEPH AND KUBERNETES - ON 10 WATTS TOTAL. that clip is three zima boards wired to one little switch, and the power meter reads 10 watts for the entire thing. on those three tiny fanless boards: proxmox running a real cluster, three nodes...

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RetroChainer

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41,560 次观看 • 5 天前 •via X (Twitter)

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THIS IS WHERE "THE CLOUD" ACTUALLY LIVES - A ROOM OF DELL STORAGE ARRAYS YOU RENT A SLIVER OF EVERY MONTH. that clip is a real datacenter aisle, rack after rack of dell powerstore arrays, every hexagon panel packed with drives. this is "the cloud." not a metaphor, this room, plus the power, cooling and staff keeping it alive. when you pay for cloud storage, you rent a few gigabytes of these arrays. forever. the bill grows with every photo. here's the part they'd rather you not connect. these enterprise drives run on a schedule. data centers swap them out long before they fail, by the pallet. those decommissioned drives get tested and resold as recertified, the same 20tb enterprise disk for a fraction of new. so the exact hardware humming in this room ends up in a nas on your desk, minus the monthly rent. no cloud subscription, no data on someone else's array, no bill that grows with your library. the uncomfortable part isn't that the cloud is huge. it's that "the cloud" was always just this room, and you can buy a piece of it once instead of renting it forever. follow RetroChainer for what the cloud doesn't want you to picture, and bookmark this before your next storage renewal.
1:10

THIS IS WHERE "THE CLOUD" ACTUALLY LIVES - A ROOM OF DELL STORAGE ARRAYS YOU RENT A SLIVER OF EVERY MONTH. that clip is a real datacenter aisle, rack after rack of dell powerstore arrays, every hexagon panel packed with drives. this is "the cloud." not a metaphor, this room, plus the power, cooling and staff keeping it alive. when you pay for cloud storage, you rent a few gigabytes of these arrays. forever. the bill grows with every photo. here's the part they'd rather you not connect. these enterprise drives run on a schedule. data centers swap them out long before they fail, by the pallet. those decommissioned drives get tested and resold as recertified, the same 20tb enterprise disk for a fraction of new. so the exact hardware humming in this room ends up in a nas on your desk, minus the monthly rent. no cloud subscription, no data on someone else's array, no bill that grows with your library. the uncomfortable part isn't that the cloud is huge. it's that "the cloud" was always just this room, and you can buy a piece of it once instead of renting it forever. follow RetroChainer for what the cloud doesn't want you to picture, and bookmark this before your next storage renewal.

RetroChainer

33,053 次观看 • 4 天前

100 MAC MINIS ON ONE METAL SHELF. IN THE CLOUD, EACH OF THOSE RENTS FOR $120 TO $500 A MONTH. that clip is a homemade rack: wire shelving packed with mac minis, each running macos, wired into one cluster. why macs and not a normal server? two reasons. you can only build, sign, and notarize ios and mac apps on apple hardware. cloud providers know it and charge for it. and apple silicon sips power and runs near-silent, so you can stack a lot of them on a shelf in a room, not a datacenter. the rent they're escaping: a single mac in the cloud runs ~$120 to $500 a month (macstadium up to aws) one engineer documented saving $4,000+ a month by self-hosting mac minis instead of cloud ci runners a used or new mini is a few hundred dollars once, then a few dollars of electricity what a shelf like this actually runs: ios and mac build farms, render jobs, app and device testing automation, and increasingly local ai inference. own the boxes, rent nothing. the uncomfortable part: the cloud was never the only option. it was the convenient one. the people who did the math bought the shelf. the honest caveat: it's real capex up front, real heat and power, and you become the sysadmin. no rented rack means no one to call at 3am. worth it at scale, overkill for one build a week. no rack rental, no per-hour metering, no fleet you don't own. save this before your cloud invoice renews again.
0:25

100 MAC MINIS ON ONE METAL SHELF. IN THE CLOUD, EACH OF THOSE RENTS FOR $120 TO $500 A MONTH. that clip is a homemade rack: wire shelving packed with mac minis, each running macos, wired into one cluster. why macs and not a normal server? two reasons. you can only build, sign, and notarize ios and mac apps on apple hardware. cloud providers know it and charge for it. and apple silicon sips power and runs near-silent, so you can stack a lot of them on a shelf in a room, not a datacenter. the rent they're escaping: a single mac in the cloud runs ~$120 to $500 a month (macstadium up to aws) one engineer documented saving $4,000+ a month by self-hosting mac minis instead of cloud ci runners a used or new mini is a few hundred dollars once, then a few dollars of electricity what a shelf like this actually runs: ios and mac build farms, render jobs, app and device testing automation, and increasingly local ai inference. own the boxes, rent nothing. the uncomfortable part: the cloud was never the only option. it was the convenient one. the people who did the math bought the shelf. the honest caveat: it's real capex up front, real heat and power, and you become the sysadmin. no rented rack means no one to call at 3am. worth it at scale, overkill for one build a week. no rack rental, no per-hour metering, no fleet you don't own. save this before your cloud invoice renews again.

RetroChainer

21,055 次观看 • 27 天前

EVERYONE'S BUYING THE M4 MAC MINI AS A CHEAP DESKTOP. HERE'S WHY IT ENDS UP UNUSED. the pitch is irresistible: a tiny mac that runs cool, quiet, and cheap. then the receipt shows up. the mac mini ships with no screen, no keyboard, no mouse. add a monitor, a decent keyboard and mouse, and more storage, and the "$799 mac" is past $1,000 fast. and macos isn't windows. if your world is gaming, specific windows apps, or the setup you already know, the mini fights you the whole way. that's how so many end up back on a pc, the mini in a drawer. so what is the mac mini actually great at? not being your main desktop. it's the box you never look at. headless in a corner, it's a near-silent always-on server: run local ai models on it, host your automations, keep your files, and reach it from the laptop you already use. it earns its keep by disappearing. the honest turn: the mac mini isn't a cheap desktop. it's a cheap server pretending to be one. buy it for the job it's quietly brilliant at, not the one the ads imply. want a main computer? price the whole setup first. want a silent little box that runs ai and never bothers you? this is the one. no monitor in the box, no keyboard, no "just $599". save this before you buy the sticker price instead of the real one.
0:34

EVERYONE'S BUYING THE M4 MAC MINI AS A CHEAP DESKTOP. HERE'S WHY IT ENDS UP UNUSED. the pitch is irresistible: a tiny mac that runs cool, quiet, and cheap. then the receipt shows up. the mac mini ships with no screen, no keyboard, no mouse. add a monitor, a decent keyboard and mouse, and more storage, and the "$799 mac" is past $1,000 fast. and macos isn't windows. if your world is gaming, specific windows apps, or the setup you already know, the mini fights you the whole way. that's how so many end up back on a pc, the mini in a drawer. so what is the mac mini actually great at? not being your main desktop. it's the box you never look at. headless in a corner, it's a near-silent always-on server: run local ai models on it, host your automations, keep your files, and reach it from the laptop you already use. it earns its keep by disappearing. the honest turn: the mac mini isn't a cheap desktop. it's a cheap server pretending to be one. buy it for the job it's quietly brilliant at, not the one the ads imply. want a main computer? price the whole setup first. want a silent little box that runs ai and never bothers you? this is the one. no monitor in the box, no keyboard, no "just $599". save this before you buy the sticker price instead of the real one.

RetroChainer

12,415 次观看 • 26 天前

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.
1:14

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.

RetroChainer

41,717 次观看 • 5 天前

THIS $1,300 CARD HAS THE SAME 32GB OF VRAM AS A $2,000+ RTX 5090 - FOR LOCAL AI, THAT'S THE SPEC THAT DECIDES EVERYTHING. that clip is the gigabyte radeon ai pro r9700 ai top, 32gb of vram, built for local ai. the pitch is simple: the same memory capacity as an rtx 5090, for roughly a third of what a 5090 actually sells for. here's why that matters, and where it doesn't. for ai, vram is the gate. a model either fits in memory or it doesn't. 32gb loads models a 16gb card can't touch, full stop. so for running local llms and image models, this card does the one thing that counts, cheap. honest line: this is not a 5090 for gaming. the 5090 is far faster at raster and ray tracing. they only match on how much vram they carry. if you want frames, buy the 5090. if you want to fit big models locally, you just found the same 32gb for a third of the money. no $2,000+ price tag, no cloud gpu rental, no model too big for 16gb. the uncomfortable part isn't that amd is cheaper. it's that for local ai you were about to overpay for gaming power you'd never use. follow RetroChainer for more on paying for the spec that matters, and bookmark this before your next gpu.
1:52

THIS $1,300 CARD HAS THE SAME 32GB OF VRAM AS A $2,000+ RTX 5090 - FOR LOCAL AI, THAT'S THE SPEC THAT DECIDES EVERYTHING. that clip is the gigabyte radeon ai pro r9700 ai top, 32gb of vram, built for local ai. the pitch is simple: the same memory capacity as an rtx 5090, for roughly a third of what a 5090 actually sells for. here's why that matters, and where it doesn't. for ai, vram is the gate. a model either fits in memory or it doesn't. 32gb loads models a 16gb card can't touch, full stop. so for running local llms and image models, this card does the one thing that counts, cheap. honest line: this is not a 5090 for gaming. the 5090 is far faster at raster and ray tracing. they only match on how much vram they carry. if you want frames, buy the 5090. if you want to fit big models locally, you just found the same 32gb for a third of the money. no $2,000+ price tag, no cloud gpu rental, no model too big for 16gb. the uncomfortable part isn't that amd is cheaper. it's that for local ai you were about to overpay for gaming power you'd never use. follow RetroChainer for more on paying for the spec that matters, and bookmark this before your next gpu.

RetroChainer

38,844 次观看 • 5 天前

Someone built Kubernetes that runs entirely in your browser. This might be the easiest way to learn Kubernetes. - No Docker. - No Minikube. - No Kind. - No EKS. - No installation. Just open a webpage and start exploring Pods, Deployments, ReplicaSets, Nodes, scheduling, networking, and more. The engineering behind it is incredible. Kubernetes is written in Go, but instead of compiling it to the browser, the developer rewrote major Kubernetes components in TypeScript so they could run natively inside the browser. This is one of the best examples of using AI I've seen. AI helped port thousands of lines of Kubernetes code, while the developer manually reviewed everything and validated it with extensive tests to make sure it behaves like a real cluster. ⚠️ It's not for running production workloads. It's built for learning, teaching, experimenting, and preparing for DevOps interviews. Imagine onboarding new engineers or teaching Kubernetes without asking everyone to spend an hour installing Docker, configuring Minikube, or creating an EKS cluster. I genuinely think projects like this will change how we learn infrastructure. Interactive labs > Static documentation. Blog Post:
0:42

Someone built Kubernetes that runs entirely in your browser. This might be the easiest way to learn Kubernetes. - No Docker. - No Minikube. - No Kind. - No EKS. - No installation. Just open a webpage and start exploring Pods, Deployments, ReplicaSets, Nodes, scheduling, networking, and more. The engineering behind it is incredible. Kubernetes is written in Go, but instead of compiling it to the browser, the developer rewrote major Kubernetes components in TypeScript so they could run natively inside the browser. This is one of the best examples of using AI I've seen. AI helped port thousands of lines of Kubernetes code, while the developer manually reviewed everything and validated it with extensive tests to make sure it behaves like a real cluster. ⚠️ It's not for running production workloads. It's built for learning, teaching, experimenting, and preparing for DevOps interviews. Imagine onboarding new engineers or teaching Kubernetes without asking everyone to spend an hour installing Docker, configuring Minikube, or creating an EKS cluster. I genuinely think projects like this will change how we learn infrastructure. Interactive labs > Static documentation. Blog Post:

Nandkishor

90,313 次观看 • 1 个月前

She makes $1,000s on Etsy from thrift-store junk she never even buys. She doesn't buy the item. She photographs it. A weird vintage bust, a gas mask, something nobody else is selling she snaps it on the shelf and walks out. AI turns the photo into a 3D model. The printer runs overnight. The listing is up by morning. She never learned to 3D model. The AI did that part. The edge isn't the printer. Everyone has a printer. The edge is that thrift stores are full of one-of-one shapes nobody has digitized yet and she's the one photographing them. The whole supply chain: a phone, a $200 printer, and a shelf she doesn't pay for.
0:25

She makes $1,000s on Etsy from thrift-store junk she never even buys. She doesn't buy the item. She photographs it. A weird vintage bust, a gas mask, something nobody else is selling she snaps it on the shelf and walks out. AI turns the photo into a 3D model. The printer runs overnight. The listing is up by morning. She never learned to 3D model. The AI did that part. The edge isn't the printer. Everyone has a printer. The edge is that thrift stores are full of one-of-one shapes nobody has digitized yet and she's the one photographing them. The whole supply chain: a phone, a $200 printer, and a shelf she doesn't pay for.

RetroChainer

422,930 次观看 • 1 个月前

Elon Musk explains the “series of miracles” xAI had to achieve to bring one gigawatt of power online for its supercomputer cluster in Memphis The xAI team coordinated dozens of gas turbines, faced permit challenges in Tennessee, and ultimately moved the power generation just across the border into Mississippi. From there, they had to run high-voltage transmission lines and build an entire power plant on the Mississippi side and it was very difficult to build that But the deeper point is even more important Most people calculate AI power needs by looking only at the GPUs. That is a serious mistake. A real data center also requires substantial power for networking, CPUs, storage, and most critically cooling In Memphis, where summer temperatures are extreme, peak cooling demand can add roughly 40% more power. On top of that, operators must maintain extra capacity so generators can be taken offline for servicing without shutting down the entire system. This redundancy typically adds another 20 to 25 percent When all of these factors are included, Elon stated that approximately 110,000 GB300 GPUs require roughly 330 megawatts of actual power generation This is why building and powering frontier AI data centers is far more difficult and far more capital intensive than it appears from the outside
2:43

Elon Musk explains the “series of miracles” xAI had to achieve to bring one gigawatt of power online for its supercomputer cluster in Memphis The xAI team coordinated dozens of gas turbines, faced permit challenges in Tennessee, and ultimately moved the power generation just across the border into Mississippi. From there, they had to run high-voltage transmission lines and build an entire power plant on the Mississippi side and it was very difficult to build that But the deeper point is even more important Most people calculate AI power needs by looking only at the GPUs. That is a serious mistake. A real data center also requires substantial power for networking, CPUs, storage, and most critically cooling In Memphis, where summer temperatures are extreme, peak cooling demand can add roughly 40% more power. On top of that, operators must maintain extra capacity so generators can be taken offline for servicing without shutting down the entire system. This redundancy typically adds another 20 to 25 percent When all of these factors are included, Elon stated that approximately 110,000 GB300 GPUs require roughly 330 megawatts of actual power generation This is why building and powering frontier AI data centers is far more difficult and far more capital intensive than it appears from the outside

X Freeze

140,274 次观看 • 3 个月前

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.
0:41

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,698 次观看 • 1 个月前

THIS FRENCH GUY RUNS OPENCLAW AND HERMES AI AGENTS ON A HOMELAB THAT REPLACED GOOGLE DRIVE AND ICLOUD ENTIRELY ON JUST 10 WATTS The project: host his own local AI agents, OpenClaw and Hermes, ditch cloud storage subscriptions entirely by running his own NAS, and use the whole build to prep for his K8s certification – all running on 4 Raspberry Pis. Today's upgrade: a PoE module. One Ethernet cable now does two jobs – powers the Raspberry Pi AND gives it network access at the same time. No separate power adapters, no cable clutter, no juggling connections during maintenance. Total power draw for all 4 Raspberry Pis combined: max 10 watts. He's not renting cloud storage anymore. He's not paying monthly subscriptions for space he already owns the hardware for. His files, his AI agents, his data – all sitting on hardware in his own home, on a fraction of the power a lightbulb uses. Next up: blocking ads network-wide across his entire home, using one of the automation services running on the same setup. This is what "own your data" actually looks like when someone builds it instead of just talking about it. Bookmark this post. Full build in the video below.
0:45

THIS FRENCH GUY RUNS OPENCLAW AND HERMES AI AGENTS ON A HOMELAB THAT REPLACED GOOGLE DRIVE AND ICLOUD ENTIRELY ON JUST 10 WATTS The project: host his own local AI agents, OpenClaw and Hermes, ditch cloud storage subscriptions entirely by running his own NAS, and use the whole build to prep for his K8s certification – all running on 4 Raspberry Pis. Today's upgrade: a PoE module. One Ethernet cable now does two jobs – powers the Raspberry Pi AND gives it network access at the same time. No separate power adapters, no cable clutter, no juggling connections during maintenance. Total power draw for all 4 Raspberry Pis combined: max 10 watts. He's not renting cloud storage anymore. He's not paying monthly subscriptions for space he already owns the hardware for. His files, his AI agents, his data – all sitting on hardware in his own home, on a fraction of the power a lightbulb uses. Next up: blocking ads network-wide across his entire home, using one of the automation services running on the same setup. This is what "own your data" actually looks like when someone builds it instead of just talking about it. Bookmark this post. Full build in the video below.

SCOTTY BEAM

18,175 次观看 • 1 个月前

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure
0:24

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure

Seth Thompson

36,271 次观看 • 3 个月前

THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation
0:29

THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation

Noisy

42,207 次观看 • 2 个月前

Jeff Bezos on the exact moment he realized he would never be a great physicist: "I really was determined to be a theoretical physicist. It's why I went to Princeton in the first place. And then I realized I was going to be a mediocre theoretical physicist. There were a few people in my classes, in quantum mechanics and so on, who could effortlessly do things that were so difficult for me." "Theoretical physics is not one of those fields where only the top few percent move the state of the art forward. It's one of those things where your brain has to be wired in a certain way." "My friend Joe and I were working on a very difficult partial differential equations problem set one night, and there was one problem we worked on for three hours. We made no headway whatsoever. And we looked up at each other at the same time and we said, Yasantha." "He stared at it for just a few seconds, maybe 10 seconds, and he said, cosine. And I said, what do you mean, cosine? He said, that's the answer. And I said, no, no, no, come on. And he said, let me show you. And he took out some paper and wrote down three pages of equations, everything canceled out, and the answer was cosine." "I said, Yasantha, did you do that in your head? And he said, oh no, that would be impossible. A few years ago I solved a similar problem, and I could map this problem onto that problem, and then it was immediately obvious that the answer was cosine." "You have an experience like that, you realize maybe being a theoretical physicist isn't what the universe wants you to be."
3:10

Jeff Bezos on the exact moment he realized he would never be a great physicist: "I really was determined to be a theoretical physicist. It's why I went to Princeton in the first place. And then I realized I was going to be a mediocre theoretical physicist. There were a few people in my classes, in quantum mechanics and so on, who could effortlessly do things that were so difficult for me." "Theoretical physics is not one of those fields where only the top few percent move the state of the art forward. It's one of those things where your brain has to be wired in a certain way." "My friend Joe and I were working on a very difficult partial differential equations problem set one night, and there was one problem we worked on for three hours. We made no headway whatsoever. And we looked up at each other at the same time and we said, Yasantha." "He stared at it for just a few seconds, maybe 10 seconds, and he said, cosine. And I said, what do you mean, cosine? He said, that's the answer. And I said, no, no, no, come on. And he said, let me show you. And he took out some paper and wrote down three pages of equations, everything canceled out, and the answer was cosine." "I said, Yasantha, did you do that in your head? And he said, oh no, that would be impossible. A few years ago I solved a similar problem, and I could map this problem onto that problem, and then it was immediately obvious that the answer was cosine." "You have an experience like that, you realize maybe being a theoretical physicist isn't what the universe wants you to be."

Jaynit

112,414 次观看 • 21 天前

PEDOGATE 2020 HD | In-Depth Exploration A reupload of part one in a three-part series (although I never set out to make a series, so technically this video can serve as a standalone) that outlines the satanic headquarters in Washington DC, awful algorithms on YouTube and what happens to a human soul that gives in to sin and temptation. Most of this video is theoretical, however some of it is purely evidence based, and none of this video is or ever was created with intent to harm. On the contrary, this video helps people follow Christ and his church. It serves to help the world through improving problematic imagery on search engines. And it holds the powerful accountable to the highest standard. That of Christ, who said, “See that you do not despise one of these little ones”. He also says that those who cause children to stumble will be punished. Since this video was made in 2020 the same issues are no longer present among these search engines or on the algorithms on YouTube. This is a good thing and needs to be celebrated. But let us never forget where the evil was on the internet. And how I found it by accident, bringing everyone along for the ride. More than ever this video needs to be seen. Enjoy. And share. Christ is KING 🇻🇦🇻🇦🇻🇦🇻🇦
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PEDOGATE 2020 HD | In-Depth Exploration A reupload of part one in a three-part series (although I never set out to make a series, so technically this video can serve as a standalone) that outlines the satanic headquarters in Washington DC, awful algorithms on YouTube and what happens to a human soul that gives in to sin and temptation. Most of this video is theoretical, however some of it is purely evidence based, and none of this video is or ever was created with intent to harm. On the contrary, this video helps people follow Christ and his church. It serves to help the world through improving problematic imagery on search engines. And it holds the powerful accountable to the highest standard. That of Christ, who said, “See that you do not despise one of these little ones”. He also says that those who cause children to stumble will be punished. Since this video was made in 2020 the same issues are no longer present among these search engines or on the algorithms on YouTube. This is a good thing and needs to be celebrated. But let us never forget where the evil was on the internet. And how I found it by accident, bringing everyone along for the ride. More than ever this video needs to be seen. Enjoy. And share. Christ is KING 🇻🇦🇻🇦🇻🇦🇻🇦

Mouthy Buddha

122,290 次观看 • 1 年前

Togi reveals the drug stack he was on during his most viral video "At the time during Togi vs Cabo I was on three grams of steroids, I was taking roughly 30 milligrams of Xanax a day, and maybe sixty 10 milligram Ritalins a day, so 600 milligrams of Ritalin." "A lot of people don't believe me when I say that because you would die. But I have video proof of it, waking up and taking four bars... The fact that I'm alive is a blessing." "On top of that, doing such an aggressive bender and managing to film and edit the whole thing in the midst of it, no one will ever ever ever do something like that ever again in the history of the world. And I'm so proud of that."
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Togi reveals the drug stack he was on during his most viral video "At the time during Togi vs Cabo I was on three grams of steroids, I was taking roughly 30 milligrams of Xanax a day, and maybe sixty 10 milligram Ritalins a day, so 600 milligrams of Ritalin." "A lot of people don't believe me when I say that because you would die. But I have video proof of it, waking up and taking four bars... The fact that I'm alive is a blessing." "On top of that, doing such an aggressive bender and managing to film and edit the whole thing in the midst of it, no one will ever ever ever do something like that ever again in the history of the world. And I'm so proud of that."

Kai

37,612 次观看 • 3 个月前

From Dan Lorenc on the malware attack that almost took down the entire internet last year: “There’s a popular compression library that’s used in almost every piece of software. And it had been maintained by one person in his spare time for the last 20 years. And then a couple years ago, somebody just decided to start helping him. They jumped in, fixed a bunch of bugs, and did a lot of great work. And then that first person got tired of working on it. So he handed the whole project over to this other person. It turned out that other person was just a pseudonym and was not a real person. And within six months of getting control of the project, they had put in a carefully orchestrated set of malware that was really hard to detect and no one noticed. And because it was so widely used, the exploit would've basically given that person remote access to any computer running that piece of software, which was basically everything connected to the Internet. But because it was open source and the code was transparent, some random engineer just happened to be running some benchmarks on a weekend. And he noticed that program was a little bit slower than it used to be, and that it was making a weird cryptographic operation to check something. And right before this thing got widely deployed across every device, he dug in, and discovered that there was a backdoor put in. This was the closest thing to a full-blown internet crisis that we’ve ever had. And they still have no idea who did it. It was just an anonymous email account. No one ever traced it back to an individual. And that's the long game. This person spent years just doing good work and earning the communities trust.”
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From Dan Lorenc on the malware attack that almost took down the entire internet last year: “There’s a popular compression library that’s used in almost every piece of software. And it had been maintained by one person in his spare time for the last 20 years. And then a couple years ago, somebody just decided to start helping him. They jumped in, fixed a bunch of bugs, and did a lot of great work. And then that first person got tired of working on it. So he handed the whole project over to this other person. It turned out that other person was just a pseudonym and was not a real person. And within six months of getting control of the project, they had put in a carefully orchestrated set of malware that was really hard to detect and no one noticed. And because it was so widely used, the exploit would've basically given that person remote access to any computer running that piece of software, which was basically everything connected to the Internet. But because it was open source and the code was transparent, some random engineer just happened to be running some benchmarks on a weekend. And he noticed that program was a little bit slower than it used to be, and that it was making a weird cryptographic operation to check something. And right before this thing got widely deployed across every device, he dug in, and discovered that there was a backdoor put in. This was the closest thing to a full-blown internet crisis that we’ve ever had. And they still have no idea who did it. It was just an anonymous email account. No one ever traced it back to an individual. And that's the long game. This person spent years just doing good work and earning the communities trust.”

The Peel

47,683 次观看 • 1 年前

This Skylit Atlas setting will change your trading career. With orbs v2 + nodes: 10 setting, its extremely easy to understand the micro-adjustments that dealers are making on the heatmap. Take a look at this replay of $SPY today and notice: Macro: - Obviously the 745 node was prominent... BUT Micro: - At ET, the 747 node had a value of +45 million. In just 12 minutes, the value became +150 million. This is not regular growth. An entry for calls from the deflection of 746 had very low risk and extremely high reward. - The 748/749 nodes were creating a reverse rug setup around ET. However, in 15 minutes this was quickly negated by market makers and they turned the negative gamma node into a positive gamma node, nullifying a reverse set up. What happened after? Price failed to break the CEILING of 749. There are so many micro events (even more than the 2 I described above). You should pay attention to on the heatmap, and not just the top 3 nodes. They tell you so much more about whats going on and where price will head. For now, learn the two concepts I explained above and keep that in mind as you learn from replay mode on other dates.
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This Skylit Atlas setting will change your trading career. With orbs v2 + nodes: 10 setting, its extremely easy to understand the micro-adjustments that dealers are making on the heatmap. Take a look at this replay of $SPY today and notice: Macro: - Obviously the 745 node was prominent... BUT Micro: - At ET, the 747 node had a value of +45 million. In just 12 minutes, the value became +150 million. This is not regular growth. An entry for calls from the deflection of 746 had very low risk and extremely high reward. - The 748/749 nodes were creating a reverse rug setup around ET. However, in 15 minutes this was quickly negated by market makers and they turned the negative gamma node into a positive gamma node, nullifying a reverse set up. What happened after? Price failed to break the CEILING of 749. There are so many micro events (even more than the 2 I described above). You should pay attention to on the heatmap, and not just the top 3 nodes. They tell you so much more about whats going on and where price will head. For now, learn the two concepts I explained above and keep that in mind as you learn from replay mode on other dates.

Tirth

16,929 次观看 • 1 个月前

Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.
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Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.

QVAC

11,382,628 次观看 • 1 个月前

We dropped a 30-page office lease into Box AI and asked Grok 4.5 one simple question: what would it actually cost us to walk away after three years? The lease is written to make you think the answer is easy: six months' rent (about $267K) stated right in the termination section. But the real cost is scattered in pieces across the whole document. The termination clause quietly points to a defined term that, when you chase it down, pulls in leftover build-out costs and broker fees from two other sections. The "free rent" you were given at signing is really taken back by a separate clause if you leave early. And there's a cleanup-and-restore obligation tucked into an exhibit at the very back. You only get the true number if you find all five and add them up — across 33 sections and 10 attachments. Grok 4.5 nailed it: ~$830K, about 3x the number the lease wants you to see, and it showed exactly where each piece came from. Then it kept going, pointing out the costs that have no cap, the auto-renewal trap that kicks in if you miss a deadline, and which terms to push back on before signing. That's the difference between skimming a contract and truly reading one and it's the kind of careful, connect-the-dots work over long documents that real business runs on. And this is the power of the latest reasoning models right now on working with unstructured enterprise data.
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We dropped a 30-page office lease into Box AI and asked Grok 4.5 one simple question: what would it actually cost us to walk away after three years? The lease is written to make you think the answer is easy: six months' rent (about $267K) stated right in the termination section. But the real cost is scattered in pieces across the whole document. The termination clause quietly points to a defined term that, when you chase it down, pulls in leftover build-out costs and broker fees from two other sections. The "free rent" you were given at signing is really taken back by a separate clause if you leave early. And there's a cleanup-and-restore obligation tucked into an exhibit at the very back. You only get the true number if you find all five and add them up — across 33 sections and 10 attachments. Grok 4.5 nailed it: ~$830K, about 3x the number the lease wants you to see, and it showed exactly where each piece came from. Then it kept going, pointing out the costs that have no cap, the auto-renewal trap that kicks in if you miss a deadline, and which terms to push back on before signing. That's the difference between skimming a contract and truly reading one and it's the kind of careful, connect-the-dots work over long documents that real business runs on. And this is the power of the latest reasoning models right now on working with unstructured enterprise data.

Box

2,003,440 次观看 • 1 个月前

7 steps and 2 clues created a woman who was bought a drink by a stranger. She doesn't exist. A stranger sat at a club bar last week. She picked out a man, started a conversation, bought him a round. He smiled back. The whole scene lived in a 6-second clip that pulled comments asking who she was. She was never there. No club, no drinks, no face that belongs to anyone. Here is the full build, 7 steps end to end. Open Pinterest, search “girl selfie,” save one image. Open Nano Banana Pro and swap the face with a single prompt. That photo now belongs to no one. Move the image into a node editor. Create 3 nodes: a reference image node, a text node, a video generator node. Drop the photo into the image node. Write 1 video prompt into the text node, the line that turns a still frame into motion. Connect text to text-in, image to image-in. Select Kling 2.6, set duration, switch sound on. Hit generate. 2 prompts. One face that exists nowhere. A 6-second clip people argued was real. The next person who buys you a drink at a bar might run on a text prompt.
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7 steps and 2 clues created a woman who was bought a drink by a stranger. She doesn't exist. A stranger sat at a club bar last week. She picked out a man, started a conversation, bought him a round. He smiled back. The whole scene lived in a 6-second clip that pulled comments asking who she was. She was never there. No club, no drinks, no face that belongs to anyone. Here is the full build, 7 steps end to end. Open Pinterest, search “girl selfie,” save one image. Open Nano Banana Pro and swap the face with a single prompt. That photo now belongs to no one. Move the image into a node editor. Create 3 nodes: a reference image node, a text node, a video generator node. Drop the photo into the image node. Write 1 video prompt into the text node, the line that turns a still frame into motion. Connect text to text-in, image to image-in. Select Kling 2.6, set duration, switch sound on. Hit generate. 2 prompts. One face that exists nowhere. A 6-second clip people argued was real. The next person who buys you a drink at a bar might run on a text prompt.

RGK

16,209 次观看 • 2 个月前