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SHE MADE $2,365 FROM ONE LINKEDIN POST. SAME MODEL WITH APPLE INTELLIGENCE MADE $18,240. her process: → copied blog post into Google Doc → exported as PDF → uploaded to Gumroad → gated original article with buy link → one year: $2,365 the AI version: → Apple Intelligence drafts... show more

Ridark

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18,460 views • 3 months ago •via X (Twitter)

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🚨a 19-year-old made $18,200 last year with an iPhone and one skill she could explain things clearly. that was it. built her first prompt pack in 3 hours on Gumroad. published it before it felt ready. made $340 in month one. Apple Intelligence wrote 80% of every draft > prompt packs: $6,840 > newsletter: $4,320 > one ebook: $3,060 > Medium guides: $2,190 > Notion templates: $1,830 four free platforms. month 12: $18,200 the product she spent 3 weeks perfecting made $190. the one she shipped in 11 days made $340. full system below👇

🚨a 19-year-old made $18,200 last year with an iPhone and one skill she could explain things clearly. that was it. built her first prompt pack in 3 hours on Gumroad. published it before it felt ready. made $340 in month one. Apple Intelligence wrote 80% of every draft > prompt packs: $6,840 > newsletter: $4,320 > one ebook: $3,060 > Medium guides: $2,190 > Notion templates: $1,830 four free platforms. month 12: $18,200 the product she spent 3 weeks perfecting made $190. the one she shipped in 11 days made $340. full system below👇

Fokki

10,545 views • 4 months ago

One iPhone with iOS 18.2. A Gumroad account. $18,200 in 12 months. Apple Intelligence wrote 80% of the first drafts. The formula is simple : > AI creates the raw material > you package and price it > the internet distributes. 5 revenue streams built on free platforms. Prompt packs on Gumroad generated $6,840. A weekly newsletter on Substack brought $4,320. One ebook on Amazon KDP added $3,060. Medium articles paid $2,190. Notion templates closed the gap at $1,830. The fastest product to ship is a prompt pack. 30 to 50 tested prompts for a specific use case, $9 to $15 on Gumroad, 3 hours to build. One guide published in month 4 made $340 in 90 days from 2 hours of work.

One iPhone with iOS 18.2. A Gumroad account. $18,200 in 12 months. Apple Intelligence wrote 80% of the first drafts. The formula is simple : > AI creates the raw material > you package and price it > the internet distributes. 5 revenue streams built on free platforms. Prompt packs on Gumroad generated $6,840. A weekly newsletter on Substack brought $4,320. One ebook on Amazon KDP added $3,060. Medium articles paid $2,190. Notion templates closed the gap at $1,830. The fastest product to ship is a prompt pack. 30 to 50 tested prompts for a specific use case, $9 to $15 on Gumroad, 3 hours to build. One guide published in month 4 made $340 in 90 days from 2 hours of work.

Lummox

13,989 views • 4 months ago

the whiteboard ad just became a PIXAR movie holding a pointer stick instead of a marker, same persona, same script, same product the board doesn't come pre-written anymore either. content appears live as she talks, the stick literally leads each word into existence at its tip before the text shows up quick workflow: - locked the same persona as an animated pixar character, same script, same product - built the board as a progressive reveal instead of fully written upfront, synced to the exact line being spoken - frame-chained all 3 clips the same way as the live-action version, last frame becomes the next clip's anchor - locked the voice across clips the same way too, timbre only, dialogue never repeats the one thing that took the most fixing: getting the stick to point at the right spot low on the board. RT + reply 'PIXAR' and i'll send you the full doc, every prompt, every fix (must follow so i can DM)

the whiteboard ad just became a PIXAR movie holding a pointer stick instead of a marker, same persona, same script, same product the board doesn't come pre-written anymore either. content appears live as she talks, the stick literally leads each word into existence at its tip before the text shows up quick workflow: - locked the same persona as an animated pixar character, same script, same product - built the board as a progressive reveal instead of fully written upfront, synced to the exact line being spoken - frame-chained all 3 clips the same way as the live-action version, last frame becomes the next clip's anchor - locked the voice across clips the same way too, timbre only, dialogue never repeats the one thing that took the most fixing: getting the stick to point at the right spot low on the board. RT + reply 'PIXAR' and i'll send you the full doc, every prompt, every fix (must follow so i can DM)

Lad

30,743 views • 1 month ago

A GUY IN CHINA CAN TURN ONE BASIC BODY CAM INTO A $3K/HOUR AI GIRL STREAM He moves on camera, and the girl underneath mirrors him almost perfectly. Same hands, same rhythm, same timing, different identity. That is the real unlock. The operator keeps the control, but the attention goes to the synthetic character. The article was about AI models becoming profitable assets, not pretty JPEGs. This clip shows the live version of that business. One man supplies the motion. AI supplies the face, body, voice, background and language. The platforms supply the lonely audience with a donation button. If one stream pulls $500, $1,000 or $3,000 in tips, the economics get stupid fast. No model split. No agency. No shoot. No creator schedule. Most people see a weird Chinese demo. The smarter read is worse: AI just turned human motion into monetizable identity. The man is not the product anymore. The character layered on top of him is.

A GUY IN CHINA CAN TURN ONE BASIC BODY CAM INTO A $3K/HOUR AI GIRL STREAM He moves on camera, and the girl underneath mirrors him almost perfectly. Same hands, same rhythm, same timing, different identity. That is the real unlock. The operator keeps the control, but the attention goes to the synthetic character. The article was about AI models becoming profitable assets, not pretty JPEGs. This clip shows the live version of that business. One man supplies the motion. AI supplies the face, body, voice, background and language. The platforms supply the lonely audience with a donation button. If one stream pulls $500, $1,000 or $3,000 in tips, the economics get stupid fast. No model split. No agency. No shoot. No creator schedule. Most people see a weird Chinese demo. The smarter read is worse: AI just turned human motion into monetizable identity. The man is not the product anymore. The character layered on top of him is.

Nekt0

12,034 views • 4 months ago

A 16-year-old kid from Shanghai borrowed $50 from his parents and disappeared for half a day. He came back with a 50-second Spider-Man movie made entirely with AI. It went viral overnight. His idea was simple: take a character everyone knows, give him a normal job and drop him into a new city. He turned Spider-Man into a delivery rider racing across Shanghai. Claude wrote the story as 5 shots. CapCut built the video. Make published it across 3 platforms. Then he repeated the same format with new deliveries, locations and problems. He did not copy a viral video. He copied the structure that made it spread. $50 in AI tools. Half a day of work. One format he can reuse indefinitely. The full system is in the article.

A 16-year-old kid from Shanghai borrowed $50 from his parents and disappeared for half a day. He came back with a 50-second Spider-Man movie made entirely with AI. It went viral overnight. His idea was simple: take a character everyone knows, give him a normal job and drop him into a new city. He turned Spider-Man into a delivery rider racing across Shanghai. Claude wrote the story as 5 shots. CapCut built the video. Make published it across 3 platforms. Then he repeated the same format with new deliveries, locations and problems. He did not copy a viral video. He copied the structure that made it spread. $50 in AI tools. Half a day of work. One format he can reuse indefinitely. The full system is in the article.

Fokki

38,988 views • 1 month ago

The past year has been the happiest period of my life. Genesis is a place of miracles. We set countless ambitious goals. None of them seemed possible with existing technology. One by one, we made them real. The density of breakthroughs coming from such a small team, across so many directions at once, feels almost rare in human history. What you see here is fully autonomous, 1x speed, run on the exact same model.

The past year has been the happiest period of my life. Genesis is a place of miracles. We set countless ambitious goals. None of them seemed possible with existing technology. One by one, we made them real. The density of breakthroughs coming from such a small team, across so many directions at once, feels almost rare in human history. What you see here is fully autonomous, 1x speed, run on the exact same model.

Zhou Xian

119,389 views • 4 months ago

$30,000 IN 4 MONTHS FROM AI MUSIC. THIS GUY JUST BROKE DOWN THE EXACT WORKFLOW IN 60 SECONDS chatgpt writes the lyrics. suno turns them into a full song instantly. distrokid sends it to spotify, apple music, tiktok and youtube automatically. every stream pays you suno costs $10 a month. distrokid is $22 a year. that's your entire setup cos someone did this working 2-3 hours a day and made $30,000 in four months with zero musical background a woman typed her poetry into suno, made an r&b song, it went viral and she signed a $3,000,000 record deal spotify just paid $11,000,000,000 to the music industry last year alone. 12,500 artists are making $100,000+ a year from one app the money is there. the tools are $10 a month. claude writes the prompts that make suno produce tracks that actually sell instead of generic garbage bookmark this and read the article below - it's worth it

$30,000 IN 4 MONTHS FROM AI MUSIC. THIS GUY JUST BROKE DOWN THE EXACT WORKFLOW IN 60 SECONDS chatgpt writes the lyrics. suno turns them into a full song instantly. distrokid sends it to spotify, apple music, tiktok and youtube automatically. every stream pays you suno costs $10 a month. distrokid is $22 a year. that's your entire setup cos someone did this working 2-3 hours a day and made $30,000 in four months with zero musical background a woman typed her poetry into suno, made an r&b song, it went viral and she signed a $3,000,000 record deal spotify just paid $11,000,000,000 to the music industry last year alone. 12,500 artists are making $100,000+ a year from one app the money is there. the tools are $10 a month. claude writes the prompts that make suno produce tracks that actually sell instead of generic garbage bookmark this and read the article below - it's worth it

starmex

108,683 views • 4 months ago

EVERY GIRL IN THIS LINE HAS A NUMBER PINNED TO HER DRESS. ONE ROOM, ONE NIGHT, ONE TABLE EACH. A FOLDER ON A LAPTOP RUNS THE IDENTICAL BUSINESS WITH NO ROOM AND NO CEILING. HERE IS THE ENGINE. the number is the tell. she is not being introduced, she is being indexed. you pick by digit. and this room has physics. one girl sits at one table. the venue holds what it holds. the night ends at 2am. the model caps out and everybody in it knows the number. the persona build sells the exact same thing and deletes all three limits. one character, three thousand simultaneous conversations, no shift, no travel, no room. nothing about the mechanic changed. scarcity, selection, attention that stops the moment you stop paying. ai did not invent any of that. it removed the walls. and here is the part operators actually copy from rooms like this. never present one option. present a row. the choosing is the product. that is why persona accounts post the same girl in four different looks and ask which one, and why the comment section does the selling for them. the room caps at twenty girls and one night. the folder does not cap at all. full template in the article below. 5 phases, every setting, every number.

Sensitive content

EVERY GIRL IN THIS LINE HAS A NUMBER PINNED TO HER DRESS. ONE ROOM, ONE NIGHT, ONE TABLE EACH. A FOLDER ON A LAPTOP RUNS THE IDENTICAL BUSINESS WITH NO ROOM AND NO CEILING. HERE IS THE ENGINE. the number is the tell. she is not being introduced, she is being indexed. you pick by digit. and this room has physics. one girl sits at one table. the venue holds what it holds. the night ends at 2am. the model caps out and everybody in it knows the number. the persona build sells the exact same thing and deletes all three limits. one character, three thousand simultaneous conversations, no shift, no travel, no room. nothing about the mechanic changed. scarcity, selection, attention that stops the moment you stop paying. ai did not invent any of that. it removed the walls. and here is the part operators actually copy from rooms like this. never present one option. present a row. the choosing is the product. that is why persona accounts post the same girl in four different looks and ask which one, and why the comment section does the selling for them. the room caps at twenty girls and one night. the folder does not cap at all. full template in the article below. 5 phases, every setting, every number.

CRYPTANSKY

2,786,079 views • 2 months ago

$4,800/year subscription stack vs $599 hardware Mac Mini M4 + Ollama + Claude Code Apple Stores ran out of Mac Minis. not because of marketing. because developers figured out the math. one env var redirects Claude Code to your $599 box: ANTHROPIC_BASE_URL= same workflow. $3/month in electricity. forever. the smart play isn't all-local or all-cloud. it's hybrid: / Mac Mini ($599) handles 80% coding, drafts, RAG, agent loops / one $20/month subscription covers the 20% that needs frontier reasoning / total monthly: $23 instead of $459 / annual savings: $5,232 ClaudeKit EngineerKit is $99 one-time if you want consistent output ( Apple positioned Mac Mini as "the most popular Mac ever." developers turned it into the cheapest AI server on earth. full setup guide in the article below

$4,800/year subscription stack vs $599 hardware Mac Mini M4 + Ollama + Claude Code Apple Stores ran out of Mac Minis. not because of marketing. because developers figured out the math. one env var redirects Claude Code to your $599 box: ANTHROPIC_BASE_URL= same workflow. $3/month in electricity. forever. the smart play isn't all-local or all-cloud. it's hybrid: / Mac Mini ($599) handles 80% coding, drafts, RAG, agent loops / one $20/month subscription covers the 20% that needs frontier reasoning / total monthly: $23 instead of $459 / annual savings: $5,232 ClaudeKit EngineerKit is $99 one-time if you want consistent output ( Apple positioned Mac Mini as "the most popular Mac ever." developers turned it into the cheapest AI server on earth. full setup guide in the article below

Madni Aghadi

285,517 views • 3 months ago

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

Noisy

48,763 views • 3 months ago

2025 was a crazy year. 2026 will be even less predictable. So at 20VC we thought it was time for Jason ✨👾SaaStr.Ai✨ Lemkin, Rory O'Driscoll to do a very special “20VC Big Fat Quiz of the Year”. For 2025: - Best Founder of the Year - Best Product of the Year - Best Fund of the Year - Best Breakout Company of the Year For 2026: - Google, Meta, Apple, Amazon, Microsoft, NVIDIA: Buy vs Sell? - Who will go public in 2026? - Biggest stock pick for 2026? - Will unemployment from AI show up in labour figures? Full List below 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:23 Founder of the Year 2025 08:33 Fund of the Year 21:27 Breakout Companies of 2025: Who Made the Biggest Impact? 28:35 Biggest Surprises of 2025 35:36 Predictions for 2026: Top Performing Tech Stocks of the Year 39:21 B2B Stocks to Watch 41:50 Why Salesforce Could Be the Buy of 2026 54:50 Google, Meta, Apple, NVIDIA, Microsoft: Buy One, Short One 01:01:40 IPO Speculations: Who Will Go Public in 2026 01:07:48 The Impact of AI on Employment

2025 was a crazy year. 2026 will be even less predictable. So at 20VC we thought it was time for Jason ✨👾SaaStr.Ai✨ Lemkin, Rory O'Driscoll to do a very special “20VC Big Fat Quiz of the Year”. For 2025: - Best Founder of the Year - Best Product of the Year - Best Fund of the Year - Best Breakout Company of the Year For 2026: - Google, Meta, Apple, Amazon, Microsoft, NVIDIA: Buy vs Sell? - Who will go public in 2026? - Biggest stock pick for 2026? - Will unemployment from AI show up in labour figures? Full List below 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:23 Founder of the Year 2025 08:33 Fund of the Year 21:27 Breakout Companies of 2025: Who Made the Biggest Impact? 28:35 Biggest Surprises of 2025 35:36 Predictions for 2026: Top Performing Tech Stocks of the Year 39:21 B2B Stocks to Watch 41:50 Why Salesforce Could Be the Buy of 2026 54:50 Google, Meta, Apple, NVIDIA, Microsoft: Buy One, Short One 01:01:40 IPO Speculations: Who Will Go Public in 2026 01:07:48 The Impact of AI on Employment

Harry Stebbings

363,733 views • 9 months ago

🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!

🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!

Kanika

38,795 views • 1 month ago

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Milk Road AI

37,459 views • 2 months ago

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.

Ihtesham Ali

99,384 views • 3 months ago

Eight weeks after returning to Apple in 1997, Steve Jobs walked into a product review meeting. Apple was 90 days from bankruptcy. They had 15 product categories, dozens of Mac variants, a digital camera, a TV set, printers, speakers. The company was trying to be everything to everyone and hemorrhaging a billion dollars a year doing it. Jobs asked one question: "Which ones do I tell my friends to buy?" Nobody had an answer. He cut 70% of the product roadmap on the spot. Then he said something that stuck: "I got out of the meeting with people who just had their projects cancelled, and they were three feet off the ground with excitement because they finally understood where the heck we were going." That's the part nobody talks about. The people whose work got killed weren't demoralized. They were relieved. Clarity did that. One year later Apple made $309 million in profit.

Eight weeks after returning to Apple in 1997, Steve Jobs walked into a product review meeting. Apple was 90 days from bankruptcy. They had 15 product categories, dozens of Mac variants, a digital camera, a TV set, printers, speakers. The company was trying to be everything to everyone and hemorrhaging a billion dollars a year doing it. Jobs asked one question: "Which ones do I tell my friends to buy?" Nobody had an answer. He cut 70% of the product roadmap on the spot. Then he said something that stuck: "I got out of the meeting with people who just had their projects cancelled, and they were three feet off the ground with excitement because they finally understood where the heck we were going." That's the part nobody talks about. The people whose work got killed weren't demoralized. They were relieved. Clarity did that. One year later Apple made $309 million in profit.

Founder Mode

37,901 views • 6 days ago

Elon Musk just put a five-year timeline on moving the majority of AI compute off the surface of the Earth. Musk: “5 years from now my prediction is we will launch and be operating every year more AI in space than the cumulative total on Earth.” Not decades from now. Five years. Dwarkesh Patel broke down the math live. 100 gigawatts of AI in orbit requires roughly 10,000 Starship launches per year. One launch every single hour. Musk confirmed it without flinching. The entire operation could run on as few as 20 or 30 physical Starships, each one cycling back to the pad every 30 hours. A fleet smaller than most regional airlines. Deploying more intelligence per year than the entire planet currently runs. Musk: “SpaceX is gearing up to do 10,000 launches a year. And maybe even 20 or 30,000 launches a year.” Every data center under construction right now. Every GPU cluster. Every billion-dollar AI facility going up across three continents. All of it combined would still fall short of what one company plans to put above the atmosphere every year. The reason no one else can follow him here is physics. AI scaling on the ground is already hitting hard ceilings. Grid capacity. Permitting. Cooling. The surface of the planet has a finite budget for how much power you can feed into compute. Space does not. Unobstructed solar at a scale Earth physically cannot provide. Musk: “On Earth you can get to around a terawatt a year of AI in space before you start having fuel supply challenges for the rocket.” A terawatt. That single number exceeds the entire electrical generation capacity of the United States. And the only constraint Musk names is not engineering. Not physics. Not capital. Fuel supply for the rockets. This is why SpaceX and xAI were never two separate visions. The rockets exist to move intelligence off the surface. The AI exists to justify building the rockets. One architecture split across two companies. Every other AI lab on the planet is fighting over the same finite pool of terrestrial power and real estate. Musk is not trying to win that fight. He is leaving the board entirely. Five years from now, the majority of functioning intelligence in the solar system may not be on this planet. It will be above it, running on sunlight, bound by no grid, governed by no jurisdiction. Earth becomes the secondary compute environment in its own solar system.

Elon Musk just put a five-year timeline on moving the majority of AI compute off the surface of the Earth. Musk: “5 years from now my prediction is we will launch and be operating every year more AI in space than the cumulative total on Earth.” Not decades from now. Five years. Dwarkesh Patel broke down the math live. 100 gigawatts of AI in orbit requires roughly 10,000 Starship launches per year. One launch every single hour. Musk confirmed it without flinching. The entire operation could run on as few as 20 or 30 physical Starships, each one cycling back to the pad every 30 hours. A fleet smaller than most regional airlines. Deploying more intelligence per year than the entire planet currently runs. Musk: “SpaceX is gearing up to do 10,000 launches a year. And maybe even 20 or 30,000 launches a year.” Every data center under construction right now. Every GPU cluster. Every billion-dollar AI facility going up across three continents. All of it combined would still fall short of what one company plans to put above the atmosphere every year. The reason no one else can follow him here is physics. AI scaling on the ground is already hitting hard ceilings. Grid capacity. Permitting. Cooling. The surface of the planet has a finite budget for how much power you can feed into compute. Space does not. Unobstructed solar at a scale Earth physically cannot provide. Musk: “On Earth you can get to around a terawatt a year of AI in space before you start having fuel supply challenges for the rocket.” A terawatt. That single number exceeds the entire electrical generation capacity of the United States. And the only constraint Musk names is not engineering. Not physics. Not capital. Fuel supply for the rockets. This is why SpaceX and xAI were never two separate visions. The rockets exist to move intelligence off the surface. The AI exists to justify building the rockets. One architecture split across two companies. Every other AI lab on the planet is fighting over the same finite pool of terrestrial power and real estate. Musk is not trying to win that fight. He is leaving the board entirely. Five years from now, the majority of functioning intelligence in the solar system may not be on this planet. It will be above it, running on sunlight, bound by no grid, governed by no jurisdiction. Earth becomes the secondary compute environment in its own solar system.

Dustin

13,346 views • 4 months ago

$Perplexity CEO Aravind Srinivas just shattered the greatest illusion of the AI arms race. The entire market is waiting for a single, god-like superintelligence to win the entire board. The physics of compute are forcing the exact opposite outcome. Models are not converging into a single monopoly. They’re violently fracturing into hyper-specialized execution nodes. Srinivas: “Towards the end of 2025, what happened was models started specializing. Even within coding, which you think might be a specialization, OpenAI’s Codex models and Anthropic’s Claude models are very different in terms of what they’re good at.” Bet your entire enterprise architecture on a single AI provider? You’re hardcoding your own ceiling. You don’t want a generalized model that’s “okay” at everything. You want a swarm of apex specialists. One ruthlessly optimized for syntax. One for visual synthesis. One for predictive reasoning. The future is not one AI. It’s the instantaneous orchestration of the absolute best compute for the exact task at hand. Platform lock-in is suicide. Srinivas: “Enterprise users are always selecting multiple different models all the time. That’s actually one of the value propositions of the Perplexity product. You don’t have to feel locked into one model provider, you don’t have to have one horse in the race.” Traditional tech giants are desperately trying to trap users inside their specific algorithmic ecosystem. Winning operators completely bypass the vendor war by becoming model-agnostic. When the foundational intelligence of the world is leapfrogging itself every three months, brand loyalty is a massive liability. The operators winning the next decade won’t care whether OpenAI, Anthropic, or Google trained the model. They’ll plug into an agnostic orchestration layer that autonomously routes to whichever specialized network currently dominates that exact sector of the board. The highest-leverage position is no longer building the intelligence. It’s directing the orchestra. Srinivas: “This is one particular skill, writing is another skill, being good at images and videos is another skill. You can hope that Perplexity figures out which model is best for what purpose, and you just have to come to the product and use it.” Multi-trillion-dollar hyperscalers burning billions fighting the model wars. Sovereign orchestrator bypasses the entire war. Harvests the output of all of them. You don’t need to be an expert in the underlying architecture of a dozen different foundation models. You just need to command the routing engine. When AI transitions from a monolithic product into a fractured grid of specialized utility nodes, the ultimate monopoly belongs to the orchestrator that abstracts the complexity. Foundation model builders became interchangeable plumbing.$

Perplexity CEO Aravind Srinivas just shattered the greatest illusion of the AI arms race. The entire market is waiting for a single, god-like superintelligence to win the entire board. The physics of compute are forcing the exact opposite outcome. Models are not converging into a single monopoly. They’re violently fracturing into hyper-specialized execution nodes. Srinivas: “Towards the end of 2025, what happened was models started specializing. Even within coding, which you think might be a specialization, OpenAI’s Codex models and Anthropic’s Claude models are very different in terms of what they’re good at.” Bet your entire enterprise architecture on a single AI provider? You’re hardcoding your own ceiling. You don’t want a generalized model that’s “okay” at everything. You want a swarm of apex specialists. One ruthlessly optimized for syntax. One for visual synthesis. One for predictive reasoning. The future is not one AI. It’s the instantaneous orchestration of the absolute best compute for the exact task at hand. Platform lock-in is suicide. Srinivas: “Enterprise users are always selecting multiple different models all the time. That’s actually one of the value propositions of the Perplexity product. You don’t have to feel locked into one model provider, you don’t have to have one horse in the race.” Traditional tech giants are desperately trying to trap users inside their specific algorithmic ecosystem. Winning operators completely bypass the vendor war by becoming model-agnostic. When the foundational intelligence of the world is leapfrogging itself every three months, brand loyalty is a massive liability. The operators winning the next decade won’t care whether OpenAI, Anthropic, or Google trained the model. They’ll plug into an agnostic orchestration layer that autonomously routes to whichever specialized network currently dominates that exact sector of the board. The highest-leverage position is no longer building the intelligence. It’s directing the orchestra. Srinivas: “This is one particular skill, writing is another skill, being good at images and videos is another skill. You can hope that Perplexity figures out which model is best for what purpose, and you just have to come to the product and use it.” Multi-trillion-dollar hyperscalers burning billions fighting the model wars. Sovereign orchestrator bypasses the entire war. Harvests the output of all of them. You don’t need to be an expert in the underlying architecture of a dozen different foundation models. You just need to command the routing engine. When AI transitions from a monolithic product into a fractured grid of specialized utility nodes, the ultimate monopoly belongs to the orchestrator that abstracts the complexity. Foundation model builders became interchangeable plumbing.

Dustin

387,955 views • 6 months ago

Pranitha Subhash is one of the few Hindu actresses who embraces her faith proudly, without any hesitation. She openly celebrates Hindu customs and traditions without a second thought. Her Bheemana Amavasya pictures provoke the Left wing every year. And a particular community hates for the same reason. Ever since then, she has been their target, and now a fake post is posted on Reddit, with social media doing its work. "MightyKhan_Temujin" is the user who made that fake post on Reddit.

Pranitha Subhash is one of the few Hindu actresses who embraces her faith proudly, without any hesitation. She openly celebrates Hindu customs and traditions without a second thought. Her Bheemana Amavasya pictures provoke the Left wing every year. And a particular community hates for the same reason. Ever since then, she has been their target, and now a fake post is posted on Reddit, with social media doing its work. "MightyKhan_Temujin" is the user who made that fake post on Reddit.

Gudumba Satti 🇮🇳🕉🚩

19,585 views • 22 days ago

🚨 THIS GUY BUILT AN AI MARKETING TEAM WITH GROK 4.7 AND IT PULLED 20M+ VIEWS One post alone hit 7.5M views and brought in 3.3K new followers No agency. No content team. Just Grok 4.7 Bots with one job each: → Researcher finds viral formats in your niche and explains why they work → Creative Director turns them into production-ready concepts → UGC Creator generates the visuals with the same AI avatar every time → Publisher connects to Postiz, writes the captions and fills the calendar And the Bots keep working on Grok's cloud computer while your laptop is closed He still picks the ideas and approves every post. AI just handles the repetitive stuff Full workflow + every prompt is in the article 👇

Dean W. Perkins

30,853 views • 6 days ago

She is 18 a Stanford intern at Anthropic - built a game with AI made $2.2M and revealed the system at Stanford: 00:12 - game built by Opus 5 became #1 in App Store and Play Market 28:34 - Anthropic engineers build a game with one prompt 59:47 - game brought $100k in the first month from scratch after watching I gave Claude Code the game idea I had been holding in my head for years - today I already got my first sales on the App Store. Save & watch - the article below shows how 5 tools turn an idea into an AI product that makes money from day one.

She is 18 a Stanford intern at Anthropic - built a game with AI made $2.2M and revealed the system at Stanford: 00:12 - game built by Opus 5 became #1 in App Store and Play Market 28:34 - Anthropic engineers build a game with one prompt 59:47 - game brought $100k in the first month from scratch after watching I gave Claude Code the game idea I had been holding in my head for years - today I already got my first sales on the App Store. Save & watch - the article below shows how 5 tools turn an idea into an AI product that makes money from day one.

Noisy

126,778 views • 1 month ago