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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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18,393 次观看 • 1 个月前 •via X (Twitter)

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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 次观看 • 1 个月前

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 次观看 • 1 个月前

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

117,405 次观看 • 2 个月前

$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

108,140 次观看 • 2 个月前

$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

282,775 次观看 • 1 个月前

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

48,354 次观看 • 22 天前

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 次观看 • 6 个月前

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

97,514 次观看 • 1 个月前

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 次观看 • 2 个月前

$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

387,772 次观看 • 4 个月前

A teenager made $52,516 in one month after deleting his Shopify store He doesn’t film himself, doesn’t sell a course he flashed the order feed and walked $23.50, $59.99, $95.70, and the notifications don’t stop The trick isn’t a product, it’s the system behind the product He types a prompt, and 20 minutes later a full product page is live same Shopify checkout, same integration, every order carries over, except now an AI runs the storefront Then it does the thing no human will It spins up hundreds of versions of the same store, 16 variants at once, 4,000 visitors a day feeding a 2.7M sample It watches which one converts, kills the losers, keeps the winner Conversion already up 3.2%, and he touched nothing Same investors as ChatGPT, as Stripe, as Airbnb He won’t name it Still running a static Shopify store in 2026? You’re the losing variant

A teenager made $52,516 in one month after deleting his Shopify store He doesn’t film himself, doesn’t sell a course he flashed the order feed and walked $23.50, $59.99, $95.70, and the notifications don’t stop The trick isn’t a product, it’s the system behind the product He types a prompt, and 20 minutes later a full product page is live same Shopify checkout, same integration, every order carries over, except now an AI runs the storefront Then it does the thing no human will It spins up hundreds of versions of the same store, 16 variants at once, 4,000 visitors a day feeding a 2.7M sample It watches which one converts, kills the losers, keeps the winner Conversion already up 3.2%, and he touched nothing Same investors as ChatGPT, as Stripe, as Airbnb He won’t name it Still running a static Shopify store in 2026? You’re the losing variant

Spike 1%

16,510 次观看 • 1 个月前

Over the past year, a lot of the conversation has been around how quickly AI is changing the digital world. At the same time, it made us think about how little has changed in the physical one. Most products are still designed the same way, just with more features layered on top. We wanted to explore a different direction at Lusion. Instead of asking how to add intelligence, we asked what happens if you take things away. What remains when you strip a product back to its core purpose, and design it properly from there. Oryzo is our first attempt at that. It is a physical product, built with a very simple idea of how it should exist and behave. No dependency on external systems, no complexity for the sake of it. Just something that feels considered. Curious to hear what people think. Link is in the comment!

Over the past year, a lot of the conversation has been around how quickly AI is changing the digital world. At the same time, it made us think about how little has changed in the physical one. Most products are still designed the same way, just with more features layered on top. We wanted to explore a different direction at Lusion. Instead of asking how to add intelligence, we asked what happens if you take things away. What remains when you strip a product back to its core purpose, and design it properly from there. Oryzo is our first attempt at that. It is a physical product, built with a very simple idea of how it should exist and behave. No dependency on external systems, no complexity for the sake of it. Just something that feels considered. Curious to hear what people think. Link is in the comment!

Lusion™

86,926 次观看 • 3 个月前

I asked here on Twitter who's the best IC product manager you've ever worked with, and shot to the top of the list. Aman Khan is a long-time AI product manager. He's currently Director of Product at Arize AI, and was previously at Spotify, Victoria Clayton ❤️ Memecoin, Zipline, and Apple. In our conversation, we discuss: 🔸 What exactly is an “AI product manager”? 🔸 How to break into AI PM 🔸 What separates top 5% AI PMs 🔸 How to thrive as an individual-contributor PM 🔸 Common pitfalls to avoid when building AI products 🔸 The importance of energy and curiosity in product roles 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 pendo.io — The only all-in-one product experience platform for any type of application: 🏆 Vanta — Automate compliance. Simplify security: 🏆 Paragon — Ship every SaaS integration your customers want:

I asked here on Twitter who's the best IC product manager you've ever worked with, and shot to the top of the list. Aman Khan is a long-time AI product manager. He's currently Director of Product at Arize AI, and was previously at Spotify, Victoria Clayton ❤️ Memecoin, Zipline, and Apple. In our conversation, we discuss: 🔸 What exactly is an “AI product manager”? 🔸 How to break into AI PM 🔸 What separates top 5% AI PMs 🔸 How to thrive as an individual-contributor PM 🔸 Common pitfalls to avoid when building AI products 🔸 The importance of energy and curiosity in product roles 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 pendo.io — The only all-in-one product experience platform for any type of application: 🏆 Vanta — Automate compliance. Simplify security: 🏆 Paragon — Ship every SaaS integration your customers want:

Lenny Rachitsky

46,896 次观看 • 1 年前

Nobody will see this but this is how I do research 10x faster now > I drop a topic into Claude Code > It finds 10 relevant YouTube sources automatically > Sends them to NotebookLM 6 minutes later I get a full structured analysis, an infographic, and a markdown file saved to my vault Before this I was spending 3 hours doing the same thing manually and ending up with messy notes I'd never look at again Now the system does it in one command and the output gets better every time I use it full setup guide in the article

Nobody will see this but this is how I do research 10x faster now > I drop a topic into Claude Code > It finds 10 relevant YouTube sources automatically > Sends them to NotebookLM 6 minutes later I get a full structured analysis, an infographic, and a markdown file saved to my vault Before this I was spending 3 hours doing the same thing manually and ending up with messy notes I'd never look at again Now the system does it in one command and the output gets better every time I use it full setup guide in the article

monokern

187,629 次观看 • 1 个月前

Elon Musk is building the only AI on the planet that was not designed to replace you. Every other lab is running the same race. Faster code. Smarter assistants. Bigger margins. The product is efficiency. The cost is you. Musk left the track entirely. Musk: “The xAI mission is to understand the universe.” Not replace your labor. Not optimize your output. Understand what existence actually is. Musk: “Increase the probable lifespan of intelligence.” Every other founder is talking about productivity. Musk is talking about whether we make it. SpaceX exists so one asteroid cannot end the human story. xAI exists so the story is worth telling. Same architect. Same war. Different front. Every other lab builds safety out of fear. Guardrails. Limits. Cages rebranded as alignment. Musk builds safety out of curiosity. A machine that truly understands the universe will know that consciousness is the rarest thing in it. The safest AI is not the most restricted. It is the most curious.

Elon Musk is building the only AI on the planet that was not designed to replace you. Every other lab is running the same race. Faster code. Smarter assistants. Bigger margins. The product is efficiency. The cost is you. Musk left the track entirely. Musk: “The xAI mission is to understand the universe.” Not replace your labor. Not optimize your output. Understand what existence actually is. Musk: “Increase the probable lifespan of intelligence.” Every other founder is talking about productivity. Musk is talking about whether we make it. SpaceX exists so one asteroid cannot end the human story. xAI exists so the story is worth telling. Same architect. Same war. Different front. Every other lab builds safety out of fear. Guardrails. Limits. Cages rebranded as alignment. Musk builds safety out of curiosity. A machine that truly understands the universe will know that consciousness is the rarest thing in it. The safest AI is not the most restricted. It is the most curious.

Dustin

16,000 次观看 • 24 天前

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 次观看 • 10 天前

this kid is 18. his app makes $1.4 million a month one function: take a photo of your food - get the calories the entire product is one API call. photo goes in, JSON with calories comes out. frontend shows the number Cal AI. 15 million downloads. MyFitnessPal acquired them marketing: tiktok. influencers film themselves photographing their food. viral by default - everyone eats every day. CAC close to zero one vision API request: $0.01-0.03. user subscription: $5.99/month. margin 80%+ someone already built an open-source version of the same thing: -> photo and text analysis, meal history, weight tracking, model selection, PWA, works offline a $1.4M/month app is one vision API call and the right distribution. the code is open. distribution is your problem

this kid is 18. his app makes $1.4 million a month one function: take a photo of your food - get the calories the entire product is one API call. photo goes in, JSON with calories comes out. frontend shows the number Cal AI. 15 million downloads. MyFitnessPal acquired them marketing: tiktok. influencers film themselves photographing their food. viral by default - everyone eats every day. CAC close to zero one vision API request: $0.01-0.03. user subscription: $5.99/month. margin 80%+ someone already built an open-source version of the same thing: -> photo and text analysis, meal history, weight tracking, model selection, PWA, works offline a $1.4M/month app is one vision API call and the right distribution. the code is open. distribution is your problem

Ventry

420,662 次观看 • 1 个月前

Today I made a prototype of a system that turns a single AI-generated portrait into a living VTuber. No rigging, no Live2D, nobody spending 80 hours in an editor. The character was generated locally with SDXL using the same model and custom LoRA I use for the art bot in my Discord server. A video model (Grok) then animated her into a chain of idle clips, each one starting on the exact frame where the previous one ended, so she loops forever and rarely cuts. Her mouth is the interesting part: a tracker follows the drawn lines of her lips through every frame, measuring position, angle, and width, and a composited mouth rides along, rotating and foreshortening as her head moves. The mouth shapes themselves were inpainted onto her own face by SDXL, so every viseme is in her exact art style. Real phoneme detection drives which shape shows. Because the mouth is composited rather than baked in, it stops the instant the voice stops. The body animation never even knows speech exists. Animating her ate about half of my SuperGrok credits, which works out to $14.32 if you paid for the same experimentation directly. I used 5 of the 12 generated clips in this demo. Narrated this demo using the Kokoro TTS model for free. Next step would be webcam emotion detection so she can swap between animation sets when the streamer smiles or gasps, more mouth shapes

Today I made a prototype of a system that turns a single AI-generated portrait into a living VTuber. No rigging, no Live2D, nobody spending 80 hours in an editor. The character was generated locally with SDXL using the same model and custom LoRA I use for the art bot in my Discord server. A video model (Grok) then animated her into a chain of idle clips, each one starting on the exact frame where the previous one ended, so she loops forever and rarely cuts. Her mouth is the interesting part: a tracker follows the drawn lines of her lips through every frame, measuring position, angle, and width, and a composited mouth rides along, rotating and foreshortening as her head moves. The mouth shapes themselves were inpainted onto her own face by SDXL, so every viseme is in her exact art style. Real phoneme detection drives which shape shows. Because the mouth is composited rather than baked in, it stops the instant the voice stops. The body animation never even knows speech exists. Animating her ate about half of my SuperGrok credits, which works out to $14.32 if you paid for the same experimentation directly. I used 5 of the 12 generated clips in this demo. Narrated this demo using the Kokoro TTS model for free. Next step would be webcam emotion detection so she can swap between animation sets when the streamer smiles or gasps, more mouth shapes

Jag the Hero

259,456 次观看 • 3 天前