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Still thinking about this DAVOON dinner at 4 Charles Prime Rib. A steakhouse that's WORTH IT. #newyorkcity #restaurant #foodreview #steak #nycrestaurants #4charlesprimerib #dinnerwithme #dessert Rating Vibe: 10 Staff: 10 Bougie Pretentiousness Factor: 0 Dinner Food: 8.75 Dessert: 20 Overall Experience: 9

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Bethenny Frankel

1,404,833 subscribers

43,662 views • 1 year ago •via X (Twitter)

Food#newyorkcity#restaurant#foodreview#steak#nycrestaurants#4charlesprimerib#dinnerwithme#dessert
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Meko Tanaka's profile picture
Meko Tanaka1 year ago

Sooo much adderall she can’t even eat even tho she is starving!! LOL🤣🤣🤣 I get it !! LOL

RedDeer.Games's profile picture
RedDeer.Games2 years ago

Run the best restaurant in the middle of the city, complete orders, learn new recipes, and improve your cooking skills! 🍣 COOKING is available now on #NintendoSwitch! 🥢 Check it out 👉 #indiegames #cooking

GOLDIE✨ AKA✨BONNIE ✨'s profile picture
GOLDIE✨ AKA✨BONNIE ✨1 year ago

Looking good 🙋👍

HONDO's profile picture
HONDO1 year ago

So good

Rockery hudpeck's profile picture
Rockery hudpeck1 year ago

Some meals you can’t put a price tag on. This is one of those places that you do not care about the price

Celso1210's profile picture
Celso12101 year ago

They take ebt I’m there!

🥐🍞 Knu Deli®️🥑 🏜's profile picture
🥐🍞 Knu Deli®️🥑 🏜1 year ago

🤔 is it legit ?? 🥩🥩🍷 @jwalkermobile .

Jeremiah’s Right's profile picture
Jeremiah’s Right1 year ago

Love that spot

Lookingup52's profile picture
Lookingup521 year ago

Looks de,iciois! So fun watching your reaction!🩷😊 Yum!

Natasha Tori's profile picture
Natasha Tori1 year ago

I'm going thank you for introducing me . I walk past this all the time .

Kristen Michelle's profile picture
Kristen Michelle1 year ago

Many hungry people around the world; can you be anymore about you right now?? Beth

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77,653 views • 1 month ago

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The Katie Miller Podcast

165,902 views • 9 months ago

He’s back! He’s 30 now and, since our last conversation, has gone from being semi-retired to now retired, as his investments are doing well. He worked for a NY-based hedge fund/family office. We discussed how he and his boss analyzed over 60 industries, the companies he looked at, and the investments he made. The best part about this conversation is that it shows how he thinks and analyzes companies and industries. (A very clear thinker, for sure!) To sum up, the game of investing and buying high-quality businesses is simple when you focus on the basics. Enjoy. TIMESTAMPS: 0:00 Intro 1:43 Defining retirement: cost of living vs net worth 3:04 Immigrant upbringing & discovering value investing 4:40 From NYU to Wall Street & joining a family office 7:58 Learning to analyze businesses & six key questions 10:33 Carvana deep dive: customer experience, lending & unit economics 17:03 Filtering noise, short reports & thinking in 10-year terms 22:21 Studying great long-term hedge funds & idea sourcing via 13F filings 25:17 Sponsor: CapitalPad - a marketplace for small business deals 26:19 What really matters: returns, time horizon & simple return math 36:16 When to sell: being wrong, better opportunities & thesis playing out 43:21 Leaving Wall Street for Buddhism 48:41 Enlightenment, meditation & inner transformation 55:21 Blending investing with spirituality
1:00:49

He’s back! He’s 30 now and, since our last conversation, has gone from being semi-retired to now retired, as his investments are doing well. He worked for a NY-based hedge fund/family office. We discussed how he and his boss analyzed over 60 industries, the companies he looked at, and the investments he made. The best part about this conversation is that it shows how he thinks and analyzes companies and industries. (A very clear thinker, for sure!) To sum up, the game of investing and buying high-quality businesses is simple when you focus on the basics. Enjoy. TIMESTAMPS: 0:00 Intro 1:43 Defining retirement: cost of living vs net worth 3:04 Immigrant upbringing & discovering value investing 4:40 From NYU to Wall Street & joining a family office 7:58 Learning to analyze businesses & six key questions 10:33 Carvana deep dive: customer experience, lending & unit economics 17:03 Filtering noise, short reports & thinking in 10-year terms 22:21 Studying great long-term hedge funds & idea sourcing via 13F filings 25:17 Sponsor: CapitalPad - a marketplace for small business deals 26:19 What really matters: returns, time horizon & simple return math 36:16 When to sell: being wrong, better opportunities & thesis playing out 43:21 Leaving Wall Street for Buddhism 48:41 Enlightenment, meditation & inner transformation 55:21 Blending investing with spirituality

PrivateEquityGuy (Mikk Markus)

502,060 views • 10 months ago

My must-watch interview with Vice President JD Vance: 0:00 - Moving into the Vice President’s residence 3:15 - One surprise about being Vice President 4:34 - Who makes breakfast? 5:13 - What do VP's children think he does all day? 7:09 - Public appearances with children 10:36 - Dad icks 11:56 - Best dad superpower 13:05 - The Vance Family house vibe 14:21 - Handling toddler tantrums in public 15:24 - Keeping marriage solid while in the limelight 16:50 - What men today get wrong about marriage and fatherhood 19:02 - Second Lady Usha Vance's debating skills 19:43 - Harder to negotiate with: children at bedtime or Senate Democrats? 20:05 - Who do the children take after? 20:42 - White House schedule vs. the Senate 21:45 - Daily routine from the gym to diet 23:37 - Favorite fictional vice president 24:56 - Cabinet confidential 28:40 - Would You Rather questions 30:22 - Best dad joke 30:53 - Favorite JD Vance meme 31:40 - One thing that would be a political scandal if the public knew 32:09 - At what age can children watch The Lord of the Rings? 33:05 - Why was the Vice President running after his kids at Disneyland? 34:17 - Favorite TV shows right now 35:19 - Latest book club reads 35:56 - Guilty pleasure TV show 36:49 - Current song playlist 38:22 - Rapid-fire food questions 39:01 - Opinion on cargo shorts 39:52 - Read the instructions or wing it? 39:57 - Last search using AI 40:38 - Hill willing to die on 42:21 - Dream vacation destination 43:11 - Sauna or cold plunge? 43:25 - What does a perfect day look like? 43:52 - If you could have dinner with anyone, living or dead, who would you invite?
44:40

My must-watch interview with Vice President JD Vance: 0:00 - Moving into the Vice President’s residence 3:15 - One surprise about being Vice President 4:34 - Who makes breakfast? 5:13 - What do VP's children think he does all day? 7:09 - Public appearances with children 10:36 - Dad icks 11:56 - Best dad superpower 13:05 - The Vance Family house vibe 14:21 - Handling toddler tantrums in public 15:24 - Keeping marriage solid while in the limelight 16:50 - What men today get wrong about marriage and fatherhood 19:02 - Second Lady Usha Vance's debating skills 19:43 - Harder to negotiate with: children at bedtime or Senate Democrats? 20:05 - Who do the children take after? 20:42 - White House schedule vs. the Senate 21:45 - Daily routine from the gym to diet 23:37 - Favorite fictional vice president 24:56 - Cabinet confidential 28:40 - Would You Rather questions 30:22 - Best dad joke 30:53 - Favorite JD Vance meme 31:40 - One thing that would be a political scandal if the public knew 32:09 - At what age can children watch The Lord of the Rings? 33:05 - Why was the Vice President running after his kids at Disneyland? 34:17 - Favorite TV shows right now 35:19 - Latest book club reads 35:56 - Guilty pleasure TV show 36:49 - Current song playlist 38:22 - Rapid-fire food questions 39:01 - Opinion on cargo shorts 39:52 - Read the instructions or wing it? 39:57 - Last search using AI 40:38 - Hill willing to die on 42:21 - Dream vacation destination 43:11 - Sauna or cold plunge? 43:25 - What does a perfect day look like? 43:52 - If you could have dinner with anyone, living or dead, who would you invite?

The Katie Miller Podcast

1,664,342 views • 1 year ago

Tuesday - Louis CK Louie is 58 now but moves like he's 85. He's got the darkest thickest under-eye bags I've ever seen, they're even apparent from below. He will not make it to 70 and if he does he will be so physically repulsive he won't even have to tell jokes any more — which is a shame because in my opinion he's the greatest living stand up by far. I am sick and tired of pretending there's any competition in this genre. This was the THIRD time I had heard him do this set, which barely cracks his top ten hours, and I was laughing my ass off the entire time. EVEN WITH THIS LOUD DRUNK BITCH BESIDE ME.. it made me realize how formulaic Shane Gillis and every other big up-and-comer feels in comparison right now, like they're all rolling off a conveyor belt at the comedy factory where you take a PREMISE, add a PUNCHLINE, and a CALL BACK, with THREE TAGS, and mechanically deploy this stuff over and over with no real through-line or heart in it at all. Louie is relentlessly personal and so his sets evolve with him. I saw him for the first time last year in Indianapolis, and was "severely disappointed." He seemed stilted, nervous, coasting on absurdist buzzword shit tailored for drunken simpletons. He'd say "Vagina" with no context and these hicks would lose their minds. I drove 4 hours to see him that day and couldn’t believe how empty his material was. Jokes that could’ve been written by anybody. There was one prick of personal truth — when he talked about putting his Dad away in a Home. He's been writing about his absentee father his whole life. It’s obviously a great source of anxiety and frustration. So it's no surprise that he leaned in to that thread, sharpened it over the course of the tour, and now the whole set circles around this theme of aging: "All my fat friends are dead. If you have a friend that's older than 50 you should say goodbye." Says big tall egg-shaped Louie, who looks like two Carlins combined. My date was way too drunk to appreciate the occasion. We were in front row seats at the Hollywood Bowl. She bought them for us at dinner, 1 hour before. Different Asian girl from Monday. She had never been to a stand up show in her life, and so she didn't really "get" comedy, and she wouldn't stop fucking talking. Pulling her phone out and texting God knows who. The security guy whose job it was to prevent me from climbing on stage and whipping my cock out had to reprimand her: "Please put your phone away" in the middle of the set. I could not believe it. We're here in the front row beneath the GOAT and he's KILLING, in a stadium of 20,000, and you're texting on your god damn phone? Who? Who could it be? It was a homeless guy from reddit. Not me, a different homeless guy. At one point she actually asked if she could step out and do some blow. I said are you kidding? There's like 10 minutes left, just shut the fuck up and enjoy it. I'm starting to worry that females can't appreciate stand up like I can. Overall rating: 9/10. He’s probably gonna die soon but for now he’s still got it. Mark Normand tonight, Dave Chappelle Thursday, Mulaney Friday.
0:36

Tuesday - Louis CK Louie is 58 now but moves like he's 85. He's got the darkest thickest under-eye bags I've ever seen, they're even apparent from below. He will not make it to 70 and if he does he will be so physically repulsive he won't even have to tell jokes any more — which is a shame because in my opinion he's the greatest living stand up by far. I am sick and tired of pretending there's any competition in this genre. This was the THIRD time I had heard him do this set, which barely cracks his top ten hours, and I was laughing my ass off the entire time. EVEN WITH THIS LOUD DRUNK BITCH BESIDE ME.. it made me realize how formulaic Shane Gillis and every other big up-and-comer feels in comparison right now, like they're all rolling off a conveyor belt at the comedy factory where you take a PREMISE, add a PUNCHLINE, and a CALL BACK, with THREE TAGS, and mechanically deploy this stuff over and over with no real through-line or heart in it at all. Louie is relentlessly personal and so his sets evolve with him. I saw him for the first time last year in Indianapolis, and was "severely disappointed." He seemed stilted, nervous, coasting on absurdist buzzword shit tailored for drunken simpletons. He'd say "Vagina" with no context and these hicks would lose their minds. I drove 4 hours to see him that day and couldn’t believe how empty his material was. Jokes that could’ve been written by anybody. There was one prick of personal truth — when he talked about putting his Dad away in a Home. He's been writing about his absentee father his whole life. It’s obviously a great source of anxiety and frustration. So it's no surprise that he leaned in to that thread, sharpened it over the course of the tour, and now the whole set circles around this theme of aging: "All my fat friends are dead. If you have a friend that's older than 50 you should say goodbye." Says big tall egg-shaped Louie, who looks like two Carlins combined. My date was way too drunk to appreciate the occasion. We were in front row seats at the Hollywood Bowl. She bought them for us at dinner, 1 hour before. Different Asian girl from Monday. She had never been to a stand up show in her life, and so she didn't really "get" comedy, and she wouldn't stop fucking talking. Pulling her phone out and texting God knows who. The security guy whose job it was to prevent me from climbing on stage and whipping my cock out had to reprimand her: "Please put your phone away" in the middle of the set. I could not believe it. We're here in the front row beneath the GOAT and he's KILLING, in a stadium of 20,000, and you're texting on your god damn phone? Who? Who could it be? It was a homeless guy from reddit. Not me, a different homeless guy. At one point she actually asked if she could step out and do some blow. I said are you kidding? There's like 10 minutes left, just shut the fuck up and enjoy it. I'm starting to worry that females can't appreciate stand up like I can. Overall rating: 9/10. He’s probably gonna die soon but for now he’s still got it. Mark Normand tonight, Dave Chappelle Thursday, Mulaney Friday.

Worst Boyfriend Ever

817,929 views • 4 months ago

Introducing the Content Machine! This was the first time I walked through the mechanics of our anti-slop content system & how we drive 10,000,000+ impressions at tenex with a marketing team of...2. Thanks to claire vo 🖤 for having me on her show to share. Full-writeup & interview below... What is the Content Machine? A directory of daisy-chained skills that turn what you already say into publish-ready content. This is the key way to avoid turning into a slop cannon. It mines the places you already talk (Slack, Notion, Gmail, Linear, GitHub) plus what the internet is saying, finds the ideas worth writing, interviews you to extract the story, drafts it, and edits it to a 9/10 bar before you post. What are the principles of this system? 1) It is not a "write me a post" prompt. The core belief baked into it: the raw material must come from you. 2) The machine never invents your voice and never fabricates your insight. It does the research, the structure, and the editing, so your time goes only to the part only you can do. It focuses the human on the first & final mile of the content process. How is it structured? A two-layer split between the process & the person - The process layer is generic and shared: the pipeline, the content-type specs, the copywriting references, the onboarding flow. That is what lives in git and what gets shipped to other people. - The personal layer is yours and never leaves your machine: content-machine.config.md, creators/ / (profile, style guide, lessons), projects/, published/, oracle-reports/. All gitignored, and the desktop build script refuses to package any of it. What is the 10-step pipeline? 1) Creator Select. Multi-creator by design. It figures out who this run is for and loads their profile, style guide, and content lessons. Everything downstream is scoped to that person. - Onboarding (first run only). Scaffolds the workspace, auto-creates your Notion Vault, connects your sources, and builds your voice guide one of three ways: import a guide you already have, feed it writing samples, or sit for a short voice interview. 2) The Oracle. Two idea engines running in parallel: - Oracle scans what you wrote in the last 7 days across Slack, Notion, Gmail, Linear, and Git, hunting for "spikes," moments where you naturally said something worth expanding. - The Internet Reader scans what the world is saying, pulled only from the source list in your profile (handles, labs, outlets, keyword watchlist), plus a social sweep across Reddit, X, YouTube, Hacker News, and more (thanks /last30days & Matt Van Horn). - Every idea is scored 0 to 10 (POV strength 25%, story potential 25%, emotional intensity 20%, lesson/framework 20%, depth 10%). Everything qualifying gets written to The Vault, a Notion database that is the durable idea bank. 2.5) Research. Before you get interviewed, a research agent builds a sourced brief: - key facts with links - current developments - what has already been said in-market - contrarian angles - open questions only you can answer. 3) Interview Panel. Six interviewer personas (Ferriss, Rogan, Larry King, Stern, Barbaro, Barbara Walters) ask you one question at a time, each chasing a different dimension: tactics, story, core truth, the hidden thing, clarity, emotional depth. It pushes back on vague answers and will not advance until it has 2 to 3 specific stories with real details. 4) Production. Your interview becomes a raw markdown file: stories, core insights, quotable moments, the emotional anchor, surprising reveals, the "so what." Your exact words are preserved. This file is the source of truth for everything that follows. 5) Refinement. Now it drafts, and only now. It must read your style guide, your content lessons, and the spec for the chosen format (LinkedIn post, X thread, long post, playbook, podcast promo, reaction post, article, and so on). 6) Writer's Council. Six reviewers score the draft: Morgan Housel (will this matter in 10 years), Tim Urban (is it confusing), Shaan Puri (would I stop scrolling, plus three alternate hooks), Greg Isenberg (what can someone steal), David Perell (is it personal, observational, playful), and a Slop Detector hunting AI tells. Each gives what's working, what needs work, a fix, and a score. 7) Revision Loop. Under 9/10 goes back around. The smart part: fixes get sorted into editorial (the machine rewrites it itself) and information gaps (only you have the answer), and information gaps route back to the Interview Panel with targeted questions rather than letting the machine make something up. Max 3 editorial cycles. At 9/10 the piece becomes the anchor. 8) Repurposing Engine. One anchor fans out into 10+ derivatives: X article, LinkedIn article, short X posts, short LinkedIn posts, a playbook if there is a framework in it. Each one is written native to its platform with a fresh hook, not cross-posted, and each runs the full council and revision loop to 9/10 on its own. This is the multiplier. 9) Distribution (optional, off by default). UTM tagging, a scheduled publishing queue, CRM capture of every touchpoint, attribution reporting back to pipeline, and marking the Vault row as Published. 10) The Learning Loop, always running. After you approve a piece it diffs your first draft against the final, extracts the pattern, and asks you to confirm it. Confirmed lessons go into content-lessons.md and override the style guide. Once a lesson proves out across a few projects it graduates into the style guide itself. Your first drafts get better over time instead of you re-explaining preferences. P.S. i'm thinking about opensourcing this. should i do it?
42:58

Introducing the Content Machine! This was the first time I walked through the mechanics of our anti-slop content system & how we drive 10,000,000+ impressions at tenex with a marketing team of...2. Thanks to claire vo 🖤 for having me on her show to share. Full-writeup & interview below... What is the Content Machine? A directory of daisy-chained skills that turn what you already say into publish-ready content. This is the key way to avoid turning into a slop cannon. It mines the places you already talk (Slack, Notion, Gmail, Linear, GitHub) plus what the internet is saying, finds the ideas worth writing, interviews you to extract the story, drafts it, and edits it to a 9/10 bar before you post. What are the principles of this system? 1) It is not a "write me a post" prompt. The core belief baked into it: the raw material must come from you. 2) The machine never invents your voice and never fabricates your insight. It does the research, the structure, and the editing, so your time goes only to the part only you can do. It focuses the human on the first & final mile of the content process. How is it structured? A two-layer split between the process & the person - The process layer is generic and shared: the pipeline, the content-type specs, the copywriting references, the onboarding flow. That is what lives in git and what gets shipped to other people. - The personal layer is yours and never leaves your machine: content-machine.config.md, creators/ / (profile, style guide, lessons), projects/, published/, oracle-reports/. All gitignored, and the desktop build script refuses to package any of it. What is the 10-step pipeline? 1) Creator Select. Multi-creator by design. It figures out who this run is for and loads their profile, style guide, and content lessons. Everything downstream is scoped to that person. - Onboarding (first run only). Scaffolds the workspace, auto-creates your Notion Vault, connects your sources, and builds your voice guide one of three ways: import a guide you already have, feed it writing samples, or sit for a short voice interview. 2) The Oracle. Two idea engines running in parallel: - Oracle scans what you wrote in the last 7 days across Slack, Notion, Gmail, Linear, and Git, hunting for "spikes," moments where you naturally said something worth expanding. - The Internet Reader scans what the world is saying, pulled only from the source list in your profile (handles, labs, outlets, keyword watchlist), plus a social sweep across Reddit, X, YouTube, Hacker News, and more (thanks /last30days & Matt Van Horn). - Every idea is scored 0 to 10 (POV strength 25%, story potential 25%, emotional intensity 20%, lesson/framework 20%, depth 10%). Everything qualifying gets written to The Vault, a Notion database that is the durable idea bank. 2.5) Research. Before you get interviewed, a research agent builds a sourced brief: - key facts with links - current developments - what has already been said in-market - contrarian angles - open questions only you can answer. 3) Interview Panel. Six interviewer personas (Ferriss, Rogan, Larry King, Stern, Barbaro, Barbara Walters) ask you one question at a time, each chasing a different dimension: tactics, story, core truth, the hidden thing, clarity, emotional depth. It pushes back on vague answers and will not advance until it has 2 to 3 specific stories with real details. 4) Production. Your interview becomes a raw markdown file: stories, core insights, quotable moments, the emotional anchor, surprising reveals, the "so what." Your exact words are preserved. This file is the source of truth for everything that follows. 5) Refinement. Now it drafts, and only now. It must read your style guide, your content lessons, and the spec for the chosen format (LinkedIn post, X thread, long post, playbook, podcast promo, reaction post, article, and so on). 6) Writer's Council. Six reviewers score the draft: Morgan Housel (will this matter in 10 years), Tim Urban (is it confusing), Shaan Puri (would I stop scrolling, plus three alternate hooks), Greg Isenberg (what can someone steal), David Perell (is it personal, observational, playful), and a Slop Detector hunting AI tells. Each gives what's working, what needs work, a fix, and a score. 7) Revision Loop. Under 9/10 goes back around. The smart part: fixes get sorted into editorial (the machine rewrites it itself) and information gaps (only you have the answer), and information gaps route back to the Interview Panel with targeted questions rather than letting the machine make something up. Max 3 editorial cycles. At 9/10 the piece becomes the anchor. 8) Repurposing Engine. One anchor fans out into 10+ derivatives: X article, LinkedIn article, short X posts, short LinkedIn posts, a playbook if there is a framework in it. Each one is written native to its platform with a fresh hook, not cross-posted, and each runs the full council and revision loop to 9/10 on its own. This is the multiplier. 9) Distribution (optional, off by default). UTM tagging, a scheduled publishing queue, CRM capture of every touchpoint, attribution reporting back to pipeline, and marking the Vault row as Published. 10) The Learning Loop, always running. After you approve a piece it diffs your first draft against the final, extracts the pattern, and asks you to confirm it. Confirmed lessons go into content-lessons.md and override the style guide. Once a lesson proves out across a few projects it graduates into the style guide itself. Your first drafts get better over time instead of you re-explaining preferences. P.S. i'm thinking about opensourcing this. should i do it?

Alex Lieberman

115,631 views • 1 month ago

UNDERCOVER REPORTER EXPOSES WHORLTON HALL In 2019, BBC Panorama (BBC Panorama) sent an undercover reporter, Olivia Davies, to work 38 days as a carer inside Whorlton Hall. It was a 17 bed hospital in County Durham for adults with learning disabilities and autism. @NHS funded. Privately run by Cygnet Health Care. The footage showed staff mocking, taunting, restraining and threatening patients. One of them was Alex, a 20 year old autistic woman who had been there almost a year. Her family had no idea until the BBC showed them the tape. Care Quality Commission) had already inspected Whorlton Hall in 2018, off the back of a whistleblower raising concerns. They found some issues, including heavy use of restraint. They did not properly investigate the group of staff at the centre of it. A later independent review of the regulator's own conduct found the CQC had also sat on a 2015 report flagging serious concerns instead of publishing it, which would have triggered closer scrutiny years earlier. So the regulator had the warning. Twice. It still took a journalist with a hidden camera to actually do something about it. Whorlton Hall traces back through the same corporate lineage as Winterbourne View, the hospital Panorama exposed for abusing people with learning disabilities back in 2011. Same type of unit. Same broadcaster catching it on tape. 8 years apart. Nobody in the system managed to learn a single lesson in between. Whorlton Hall closed after the programme aired. Durham Police (Durham Constabulary) opened a criminal investigation. 9 former staff were charged with 27 counts of ill treatment or wilful neglect. The trial concluded in 2023. 10 charges were upheld. 4 men were convicted. In 2024 they got suspended sentences. The judge described the place as an unpredictable and inherently frightening place to live. Suspended sentences for filmed cruelty against some of the most vulnerable adults in the country. That's the going rate. The hospital closed. The pattern didn't. SOURCES BBC Panorama BBCNews Care Quality Commission and others
1:30

UNDERCOVER REPORTER EXPOSES WHORLTON HALL In 2019, BBC Panorama (BBC Panorama) sent an undercover reporter, Olivia Davies, to work 38 days as a carer inside Whorlton Hall. It was a 17 bed hospital in County Durham for adults with learning disabilities and autism. @NHS funded. Privately run by Cygnet Health Care. The footage showed staff mocking, taunting, restraining and threatening patients. One of them was Alex, a 20 year old autistic woman who had been there almost a year. Her family had no idea until the BBC showed them the tape. Care Quality Commission) had already inspected Whorlton Hall in 2018, off the back of a whistleblower raising concerns. They found some issues, including heavy use of restraint. They did not properly investigate the group of staff at the centre of it. A later independent review of the regulator's own conduct found the CQC had also sat on a 2015 report flagging serious concerns instead of publishing it, which would have triggered closer scrutiny years earlier. So the regulator had the warning. Twice. It still took a journalist with a hidden camera to actually do something about it. Whorlton Hall traces back through the same corporate lineage as Winterbourne View, the hospital Panorama exposed for abusing people with learning disabilities back in 2011. Same type of unit. Same broadcaster catching it on tape. 8 years apart. Nobody in the system managed to learn a single lesson in between. Whorlton Hall closed after the programme aired. Durham Police (Durham Constabulary) opened a criminal investigation. 9 former staff were charged with 27 counts of ill treatment or wilful neglect. The trial concluded in 2023. 10 charges were upheld. 4 men were convicted. In 2024 they got suspended sentences. The judge described the place as an unpredictable and inherently frightening place to live. Suspended sentences for filmed cruelty against some of the most vulnerable adults in the country. That's the going rate. The hospital closed. The pattern didn't. SOURCES BBC Panorama BBCNews Care Quality Commission and others

Artur Nadolny

73,379 views • 1 month ago

My first interview with Peter Beck, Founder & CEO of Rocket Lab. Rocket Lab is scaling launch cadence faster than SpaceX scaled Falcon 9. 0:44 The biggest bottleneck to increasing launch cadence and mass to orbit 1:25 Growth of new space startups 3:05 Why governments have been ineffective at scaling launch 3:53 Tall Poppy Syndrome 4:38 Starting Rocket Lab without having $100 million 7:24 Scaling Electron vs focusing on Neutron 9:17 Rocket Lab hustle 11:10 Creating a culture of “F*ck it, let’s do it” 11:40 Worst supplier experiences 13:54 Having an engine explode before an important meeting 18:52 Rocket Lab’s first mission to the moon 20:55 Chewing glass and forcing the outcome to be good 22:57 Similarities in how Elon and Peter operate 25:05 Elon time and how to structure timelines 27:13 Designing a culture where everyone runs towards the fire 28:44 Making the Ferrari of rockets — the importance of creating beautiful things 31:20 “You don’t need to equate a price tag to beauty” 32:12 Why Peter hates launch day 34:48 “After a launch failure this place is a morgue” 35:22 Moving forward after a failure 36:40 Transitioning from an R&D organization to scaling rocket production 38:07 Production hell with rockets 39:09 Taking learnings from Electron to Neutron 41:03 Having dinner with Elon 42:00 Keeping the hiring bar extraordinarily high 44:05 “My job is to fix sh*t” 44:56 Rocket Lab’s first NASA launch 45:55 “The great thing about America is anything is possible” 47:40 Coming to Silicon Valley — meeting with Vinod Khosla 51:22 Why he decided to take $RKLB public 54:12 Creating a company that will outlive you 55:41 Turning Rocket Lab into a profitable business 57:23 The best space companies will all build their own rockets 1:00:35 Scaling Neutron 1:01:32 Why they’re using carbon fiber instead of steel 1:02:56 “Going public was a great capital unlock” 1:04:10 The importance of relentless optimism
1:05:18

My first interview with Peter Beck, Founder & CEO of Rocket Lab. Rocket Lab is scaling launch cadence faster than SpaceX scaled Falcon 9. 0:44 The biggest bottleneck to increasing launch cadence and mass to orbit 1:25 Growth of new space startups 3:05 Why governments have been ineffective at scaling launch 3:53 Tall Poppy Syndrome 4:38 Starting Rocket Lab without having $100 million 7:24 Scaling Electron vs focusing on Neutron 9:17 Rocket Lab hustle 11:10 Creating a culture of “F*ck it, let’s do it” 11:40 Worst supplier experiences 13:54 Having an engine explode before an important meeting 18:52 Rocket Lab’s first mission to the moon 20:55 Chewing glass and forcing the outcome to be good 22:57 Similarities in how Elon and Peter operate 25:05 Elon time and how to structure timelines 27:13 Designing a culture where everyone runs towards the fire 28:44 Making the Ferrari of rockets — the importance of creating beautiful things 31:20 “You don’t need to equate a price tag to beauty” 32:12 Why Peter hates launch day 34:48 “After a launch failure this place is a morgue” 35:22 Moving forward after a failure 36:40 Transitioning from an R&D organization to scaling rocket production 38:07 Production hell with rockets 39:09 Taking learnings from Electron to Neutron 41:03 Having dinner with Elon 42:00 Keeping the hiring bar extraordinarily high 44:05 “My job is to fix sh*t” 44:56 Rocket Lab’s first NASA launch 45:55 “The great thing about America is anything is possible” 47:40 Coming to Silicon Valley — meeting with Vinod Khosla 51:22 Why he decided to take $RKLB public 54:12 Creating a company that will outlive you 55:41 Turning Rocket Lab into a profitable business 57:23 The best space companies will all build their own rockets 1:00:35 Scaling Neutron 1:01:32 Why they’re using carbon fiber instead of steel 1:02:56 “Going public was a great capital unlock” 1:04:10 The importance of relentless optimism

Ti Morse

165,782 views • 6 months ago

Three days ago I asked myself a dumb question. It was so stupid I was actually ashamed to Google it. Can AI earn money while I sleep? Not saving time. Not automating routine. I mean putting real money into my account while I am not looking at the screen. Everyone says ClawdBot will change how we work. Automation. Task management. Smart replies. But I was sitting in my kitchen thinking about something else entirely. You know that feeling when you look at a tool and realize everyone is using only 1% of its potential? It is like being given a race car and only using it to drive to the store for bread. I decided to test it. I started a notebook. I record everything. > Day One I started with something simple. I gave Clawdbot a task. Find wallets on Polymarket where the numbers do not add up. Where the profit is too high for the win rate. Where the result smells like a system rather than luck. It thought for 14 minutes. I had time to pour a coffee and forget about it. Then the screen flashed. 4 addresses. I scrolled through the first three in a minute. Big bets on politics. They guessed the election. Classic. On the fourth one I stopped. Not because it was the most profitable but because I did not understand what I was looking at. The wallet was not trading politics or sports or anything people write reviews about. It was trading the weather. I read it three times. Weather. Will it be 9 degrees in London tomorrow? Will it rain in Tokyo? These are markets I would not even click on by accident. Then I looked at the numbers. > It started with $27. It is now at $63,853. $27 is two trips to McDonald's. It is nothing. $63,853 is a new car or a down payment on an apartment. It is two years of someone's salary. Between those two numbers was only one thing. Thousands of bets on rain. I closed the tab. Opened it again. Checked if it was a glitch. Real dollars. On markets that look like a bad joke. > Day Two I could not get that wallet out of my head. I went to look at its transaction history. I expected to find one big win that explained everything. A lucky hurricane forecast. Instead I saw thousands of small bets. Boring. "Will the temperature in New York be above 15 degrees?" Then I noticed the detail that finally broke my brain. Its win rate: 33%. It loses more often than it wins. 2 out of 3 bets go to zero. Any normal person with that result would be posting about how the market is unfair. Yet this wallet is sitting on $63,000 in profit. How? I started deconstructing the trades. After an hour I got it. When it loses, it loses 10 or 20 cents. When it wins, it takes $1.00. Loses 9 times in a row? Lost $1.80. Wins 1 time? Got $10.00. > This is not trading. It is math that works as long as you do not interfere with your emotions. Here is how it works. Weather is one of the most predictable things on the planet. Governments invest billions in satellites. Data is updated every 2 or 3 hours. Precision to a tenth of a degree. This data is public. But Polymarket is not a weather station. It updates its markets with a delay of 6 or 8 hours. Imagine the situation. 6 AM. The weather service updated the forecast. The probability that London reaches 9 degrees tomorrow rose to 80%. Algorithms everywhere already recalculated the data. But on Polymarket the YES button is still sitting there for 10 cents. Because the market has not woken up yet. This bot sees the difference. It buys YES for 10 cents when the real probability is already 80%. It is not guessing. It is buying what is essentially already known. It just waits a day and collects the dollar. 10 cents turn into a dollar. On information available to anyone who can read weather APIs. That evening I called a friend. He has been trading for 3 years. He sits in analytical chats. Draws support levels. I asked him: "How was the last month?" "I broke even. The market is tough right now. Too much noise." I looked at the screen. A bot betting on rain with a 33% win rate. Profit: $63,853. My friend with 3 years of experience and hundreds of hours of analysis. Profit: $0. Who is doing it wrong? I am not asking you to take my word for it. The blockchain does not lie: > Day Three I decided to dig deeper. I looked at the wallet description. I expected something complex. A hedge fund. A team of developers. Secret data sources. I found one line: Claude plus public weather APIs. Ordinary Claude. The one on your phone. Connected to free weather services. No secret stations. No insiders. No millions for infrastructure. Just an AI doing what any of us could do. But we are too lazy. Or bored. Or we think it is too simple to work. If someone already built this with basic Claude and free APIs... What happens when Clawdbot gets direct access to trading? > Day Four I watched the wallet in real time. First bet: loss. Second bet: loss. Third bet: loss. I thought: this is it. The statistics are collapsing. Fourth bet: loss. Fifth bet: loss. Down $12 in an hour. I was ready to write a post about how I overestimated this. Sixth bet: Temperature in Chicago. Win. +$87. Seventh bet: Win. +$94. By evening: 9 losses. 5 wins. Daily total: +$385. No emotions. No posts about injustice. No strategy changes after a loss. Just the next bet. I wrote to my friend. The one who has been trading for 3 years. "How was your day?" "Down $200. Market makers caught my stop loss again." I looked at the screen. A bot with no posts and no loud claims. +$385 for the day on rain bets. My friend with 3 years of experience and dozens of books. Minus $200 and a post about how the system is against him. > Day Five I woke up with a thought that kept me up all night. It finally hit me. It is not about the weather. It is not about APIs. It is not that the bot is "smarter". > It is about what the bot does NOT have: an ego that hates being wrong. No urge to revenge-trade. No boredom from repetition. My friend trades against the market. He tries to be smarter than the crowd. This bot trades against human nature. And nature loses every day. Clawdbot found me this wallet in 14 minutes. The weather bot turned $27 into $63,000 on markets everyone else thinks are trash. Both use the same principle. Do something simple. Remove emotions. Repeat. I do not know when Clawdbot will start trading on its own. Maybe in a month. Maybe in a year. But I know one thing. While we discuss if it is possible... Someone already set up their bot and went to live their life. Right now as you read this. Somewhere a weather service updated a forecast. Polymarket is sleeping. The bot is already entering a position. And my friend is writing a post about how market makers do not let honest people earn. Guess who wakes up tomorrow with money in their account?
0:25

Three days ago I asked myself a dumb question. It was so stupid I was actually ashamed to Google it. Can AI earn money while I sleep? Not saving time. Not automating routine. I mean putting real money into my account while I am not looking at the screen. Everyone says ClawdBot will change how we work. Automation. Task management. Smart replies. But I was sitting in my kitchen thinking about something else entirely. You know that feeling when you look at a tool and realize everyone is using only 1% of its potential? It is like being given a race car and only using it to drive to the store for bread. I decided to test it. I started a notebook. I record everything. > Day One I started with something simple. I gave Clawdbot a task. Find wallets on Polymarket where the numbers do not add up. Where the profit is too high for the win rate. Where the result smells like a system rather than luck. It thought for 14 minutes. I had time to pour a coffee and forget about it. Then the screen flashed. 4 addresses. I scrolled through the first three in a minute. Big bets on politics. They guessed the election. Classic. On the fourth one I stopped. Not because it was the most profitable but because I did not understand what I was looking at. The wallet was not trading politics or sports or anything people write reviews about. It was trading the weather. I read it three times. Weather. Will it be 9 degrees in London tomorrow? Will it rain in Tokyo? These are markets I would not even click on by accident. Then I looked at the numbers. > It started with $27. It is now at $63,853. $27 is two trips to McDonald's. It is nothing. $63,853 is a new car or a down payment on an apartment. It is two years of someone's salary. Between those two numbers was only one thing. Thousands of bets on rain. I closed the tab. Opened it again. Checked if it was a glitch. Real dollars. On markets that look like a bad joke. > Day Two I could not get that wallet out of my head. I went to look at its transaction history. I expected to find one big win that explained everything. A lucky hurricane forecast. Instead I saw thousands of small bets. Boring. "Will the temperature in New York be above 15 degrees?" Then I noticed the detail that finally broke my brain. Its win rate: 33%. It loses more often than it wins. 2 out of 3 bets go to zero. Any normal person with that result would be posting about how the market is unfair. Yet this wallet is sitting on $63,000 in profit. How? I started deconstructing the trades. After an hour I got it. When it loses, it loses 10 or 20 cents. When it wins, it takes $1.00. Loses 9 times in a row? Lost $1.80. Wins 1 time? Got $10.00. > This is not trading. It is math that works as long as you do not interfere with your emotions. Here is how it works. Weather is one of the most predictable things on the planet. Governments invest billions in satellites. Data is updated every 2 or 3 hours. Precision to a tenth of a degree. This data is public. But Polymarket is not a weather station. It updates its markets with a delay of 6 or 8 hours. Imagine the situation. 6 AM. The weather service updated the forecast. The probability that London reaches 9 degrees tomorrow rose to 80%. Algorithms everywhere already recalculated the data. But on Polymarket the YES button is still sitting there for 10 cents. Because the market has not woken up yet. This bot sees the difference. It buys YES for 10 cents when the real probability is already 80%. It is not guessing. It is buying what is essentially already known. It just waits a day and collects the dollar. 10 cents turn into a dollar. On information available to anyone who can read weather APIs. That evening I called a friend. He has been trading for 3 years. He sits in analytical chats. Draws support levels. I asked him: "How was the last month?" "I broke even. The market is tough right now. Too much noise." I looked at the screen. A bot betting on rain with a 33% win rate. Profit: $63,853. My friend with 3 years of experience and hundreds of hours of analysis. Profit: $0. Who is doing it wrong? I am not asking you to take my word for it. The blockchain does not lie: > Day Three I decided to dig deeper. I looked at the wallet description. I expected something complex. A hedge fund. A team of developers. Secret data sources. I found one line: Claude plus public weather APIs. Ordinary Claude. The one on your phone. Connected to free weather services. No secret stations. No insiders. No millions for infrastructure. Just an AI doing what any of us could do. But we are too lazy. Or bored. Or we think it is too simple to work. If someone already built this with basic Claude and free APIs... What happens when Clawdbot gets direct access to trading? > Day Four I watched the wallet in real time. First bet: loss. Second bet: loss. Third bet: loss. I thought: this is it. The statistics are collapsing. Fourth bet: loss. Fifth bet: loss. Down $12 in an hour. I was ready to write a post about how I overestimated this. Sixth bet: Temperature in Chicago. Win. +$87. Seventh bet: Win. +$94. By evening: 9 losses. 5 wins. Daily total: +$385. No emotions. No posts about injustice. No strategy changes after a loss. Just the next bet. I wrote to my friend. The one who has been trading for 3 years. "How was your day?" "Down $200. Market makers caught my stop loss again." I looked at the screen. A bot with no posts and no loud claims. +$385 for the day on rain bets. My friend with 3 years of experience and dozens of books. Minus $200 and a post about how the system is against him. > Day Five I woke up with a thought that kept me up all night. It finally hit me. It is not about the weather. It is not about APIs. It is not that the bot is "smarter". > It is about what the bot does NOT have: an ego that hates being wrong. No urge to revenge-trade. No boredom from repetition. My friend trades against the market. He tries to be smarter than the crowd. This bot trades against human nature. And nature loses every day. Clawdbot found me this wallet in 14 minutes. The weather bot turned $27 into $63,000 on markets everyone else thinks are trash. Both use the same principle. Do something simple. Remove emotions. Repeat. I do not know when Clawdbot will start trading on its own. Maybe in a month. Maybe in a year. But I know one thing. While we discuss if it is possible... Someone already set up their bot and went to live their life. Right now as you read this. Somewhere a weather service updated a forecast. Polymarket is sleeping. The bot is already entering a position. And my friend is writing a post about how market makers do not let honest people earn. Guess who wakes up tomorrow with money in their account?

Blaze

29,808 views • 7 months ago

MEET THE NVIDIA KILLER: OpenAI bet $10 BILLION on this company that makes chips 20x faster than Nvidia's. If this plays out as expected, it’s over for Nvidia. Cerebras Systems just locked in 750 megawatts of computing power to OpenAI through 2028. For reference: that's equivalent to the annual power consumption of 600,000 US homes. The deal? Over $10 billion. Here's what nobody understands: Cerebras doesn't make normal chips. Nvidia sells you thousands of tiny chips that you connect together. Cerebras makes ONE chip. A single wafer-scale processor the size of a dinner plate. 900,000 AI cores. 4 trillion transistors. All on one piece of silicon. The result? When OpenAI tested it, Cerebras ran inference 20X FASTER than Nvidia GPUs. That's not incremental improvement. That's a different category of performance. But here's where the story gets wild: Four months ago, Cerebras was a struggling company. Their IPO filing revealed that 87% of their revenue came from ONE customer: G42, a UAE-based AI firm. The US government launched a national security review. G42 had ties to Huawei. Ties to China. The IPO collapsed. Investors panicked. Cerebras withdrew their filing in October 2025. Most startups would've been dead. Instead, Cerebras did the opposite. They raised $1.1 billion at an $8.1 billion valuation. Kicked G42 out of the cap table entirely. Got CFIUS clearance. Then landed the OpenAI deal. Now they're raising ANOTHER $1 billion at a $22 billion valuation. They more than DOUBLED their valuation in 4 months. From near-death to $22 billion. While getting rid of their biggest customer. Why OpenAI chose them: ChatGPT has 900 million weekly users. Sam Altman keeps saying they have a "severe shortage" of compute. They need SPEED, not just power. When you ask ChatGPT a question, there's a loop happening: You send request → model thinks → sends response back Nvidia chips are fast at training models. Cerebras chips are built specifically for inference. For real-time responses. For the exact bottleneck OpenAI is trying to solve. Sachin Katti from OpenAI said it best: "Cerebras adds a dedicated low-latency inference solution to our platform. That means faster responses, more natural interactions, and a stronger foundation to scale real-time AI to many more people." In other words: "We need this to scale ChatGPT." The competitive landscape just shifted: Nvidia announced a $100 billion deal with OpenAI in September. But it's still not finalized. Meanwhile, Cerebras closed their deal before Thanksgiving. And it's ALREADY being deployed. Here's the part that should terrify Nvidia: In December, Nvidia bought Groq for $20 billion. Groq makes fast inference chips. Just like Cerebras. So why would Nvidia spend $20 billion buying a competitor to something they supposedly already dominate? Because they know what's coming. Inference is the new battleground. And Cerebras is winning it. The IPO is coming Q2 2026. After this OpenAI deal, Cerebras now has: ✓ IBM contracts ✓ Department of Energy contracts ✓ OpenAI locked in for 3 years ✓ $22 billion valuation ✓ CFIUS clearance ✓ Zero customer concentration risk They went from 87% revenue dependency on one customer to the most diversified chip company outside Nvidia. In four months. The lesson? Smart money doesn't follow headlines. It follows where the AI leaders are actually spending. OpenAI didn't announce this deal for publicity. They need Cerebras hardware to scale ChatGPT. That's a $10 billion vote of confidence. While everyone's watching Nvidia stock, the real war is happening in inference. And the company with ONE giant chip just beat the company with thousands of tiny ones. What do you think happens when Cerebras IPOs?
1:03

MEET THE NVIDIA KILLER: OpenAI bet $10 BILLION on this company that makes chips 20x faster than Nvidia's. If this plays out as expected, it’s over for Nvidia. Cerebras Systems just locked in 750 megawatts of computing power to OpenAI through 2028. For reference: that's equivalent to the annual power consumption of 600,000 US homes. The deal? Over $10 billion. Here's what nobody understands: Cerebras doesn't make normal chips. Nvidia sells you thousands of tiny chips that you connect together. Cerebras makes ONE chip. A single wafer-scale processor the size of a dinner plate. 900,000 AI cores. 4 trillion transistors. All on one piece of silicon. The result? When OpenAI tested it, Cerebras ran inference 20X FASTER than Nvidia GPUs. That's not incremental improvement. That's a different category of performance. But here's where the story gets wild: Four months ago, Cerebras was a struggling company. Their IPO filing revealed that 87% of their revenue came from ONE customer: G42, a UAE-based AI firm. The US government launched a national security review. G42 had ties to Huawei. Ties to China. The IPO collapsed. Investors panicked. Cerebras withdrew their filing in October 2025. Most startups would've been dead. Instead, Cerebras did the opposite. They raised $1.1 billion at an $8.1 billion valuation. Kicked G42 out of the cap table entirely. Got CFIUS clearance. Then landed the OpenAI deal. Now they're raising ANOTHER $1 billion at a $22 billion valuation. They more than DOUBLED their valuation in 4 months. From near-death to $22 billion. While getting rid of their biggest customer. Why OpenAI chose them: ChatGPT has 900 million weekly users. Sam Altman keeps saying they have a "severe shortage" of compute. They need SPEED, not just power. When you ask ChatGPT a question, there's a loop happening: You send request → model thinks → sends response back Nvidia chips are fast at training models. Cerebras chips are built specifically for inference. For real-time responses. For the exact bottleneck OpenAI is trying to solve. Sachin Katti from OpenAI said it best: "Cerebras adds a dedicated low-latency inference solution to our platform. That means faster responses, more natural interactions, and a stronger foundation to scale real-time AI to many more people." In other words: "We need this to scale ChatGPT." The competitive landscape just shifted: Nvidia announced a $100 billion deal with OpenAI in September. But it's still not finalized. Meanwhile, Cerebras closed their deal before Thanksgiving. And it's ALREADY being deployed. Here's the part that should terrify Nvidia: In December, Nvidia bought Groq for $20 billion. Groq makes fast inference chips. Just like Cerebras. So why would Nvidia spend $20 billion buying a competitor to something they supposedly already dominate? Because they know what's coming. Inference is the new battleground. And Cerebras is winning it. The IPO is coming Q2 2026. After this OpenAI deal, Cerebras now has: ✓ IBM contracts ✓ Department of Energy contracts ✓ OpenAI locked in for 3 years ✓ $22 billion valuation ✓ CFIUS clearance ✓ Zero customer concentration risk They went from 87% revenue dependency on one customer to the most diversified chip company outside Nvidia. In four months. The lesson? Smart money doesn't follow headlines. It follows where the AI leaders are actually spending. OpenAI didn't announce this deal for publicity. They need Cerebras hardware to scale ChatGPT. That's a $10 billion vote of confidence. While everyone's watching Nvidia stock, the real war is happening in inference. And the company with ONE giant chip just beat the company with thousands of tiny ones. What do you think happens when Cerebras IPOs?

Ricardo

28,088 views • 8 months ago

gave my dad a polymarket tutorial on New Year's Eve he's 58, never used crypto in his life thought i was explaining how to lose money two months later he texts me a screenshot $34,200 profit "is this normal son?" /// new year's eve party at my parents house everyone drinking dad corners me in the kitchen "so what is it you actually do again?" third time explaining this year "prediction markets, betting on outcomes with AI bots" he laughs "sounds like a good way to go broke" /// i pull out my phone: show him my polymarket dashboard $67K profit last 6 weeks he stops laughing "wait that's real money?" "yeah" "how" /// i give him the 2-minute version: binance updates BTC price every 10 milliseconds polymarket updates every 500 milliseconds there's a gap bots exploit the gap "so it's like... front-running?" "basically yeah" he's interested now /// january 2nd: dad calls me "can you set me up with one of those bots?" "dad you don't know anything about crypto" "i know how to follow instructions" he's serious /// i go over to help: spend 3 hours setting everything up OpenClaw on his old desktop polymarket account $800 from his savings "just let it run, don't touch anything" "that's it?" "that's it" /// week 1: he calls me every day "it made another trade" "yeah that's what it does" "but i didn't do anything" "correct" "this feels illegal" "it's not, it's just fast" /// week 2: calls stop i text him "how's the bot?" "good" "how good?" "$4,100" wait what /// i drive over: he's in his office watching the terminal like it's the super bowl $800 → $4,100 in two weeks "dad are you... are you ok?" "this is better than my retirement account" he's not wrong /// week 4: he stops calling me starts texting screenshots instead $4,100 → $9,800 $9,800 → $14,200 $14,200 → $19,100 "dad you need to take some profits" "why" "risk management" "it hasn't lost a trade yet" he's becoming a degen in real-time /// week 8: valentine's day dinner my mom mentions something "your father's been acting weird" "how so?" "he keeps checking his computer during meals" "oh no" /// i text him: "mom says you're obsessed" "tell her i'm up $34K" "absolutely not" "fine but i am" /// yesterday: he texts me at 11 PM screenshot $34,200 total profit "is this normal son?" "no dad this is insane" "so should i stop?" "probably" "ok" 2 minutes later "i'm not stopping" /// this morning: my mom calls "your father wants to quit his job" "WHAT" "he showed me some computer thing with a lot of money" "how did he explain it?" "he said a robot makes him $800 per day while he sleeps" she's not wrong /// called dad: "you can't quit your job" "why not" "you've been doing this for 8 weeks" "and i made $34K, your mom made $0 at her job in 8 weeks" "that's not how this works" "the bot doesn't care how long i've been doing this" he has a point /// the evolution: january 1: "sounds like a scam" january 15: "it made another trade!" february 1: cautiously optimistic february 14: checking it at dinner march 1: "should i quit my job?" /// most 58-year-olds: - scared of technology - think crypto is a scam - planning retirement in 7 years - saving $500/month if lucky my dad: - running autonomous AI bot - $34K in 8 weeks - considering quitting his job - robot makes more than his salary /// he just texted again "talked to your uncle, he wants one too" oh god it's spreading /// copytrade before your dad outperforms you: should i talk him out of quitting or is he actually onto something?
0:25

gave my dad a polymarket tutorial on New Year's Eve he's 58, never used crypto in his life thought i was explaining how to lose money two months later he texts me a screenshot $34,200 profit "is this normal son?" /// new year's eve party at my parents house everyone drinking dad corners me in the kitchen "so what is it you actually do again?" third time explaining this year "prediction markets, betting on outcomes with AI bots" he laughs "sounds like a good way to go broke" /// i pull out my phone: show him my polymarket dashboard $67K profit last 6 weeks he stops laughing "wait that's real money?" "yeah" "how" /// i give him the 2-minute version: binance updates BTC price every 10 milliseconds polymarket updates every 500 milliseconds there's a gap bots exploit the gap "so it's like... front-running?" "basically yeah" he's interested now /// january 2nd: dad calls me "can you set me up with one of those bots?" "dad you don't know anything about crypto" "i know how to follow instructions" he's serious /// i go over to help: spend 3 hours setting everything up OpenClaw on his old desktop polymarket account $800 from his savings "just let it run, don't touch anything" "that's it?" "that's it" /// week 1: he calls me every day "it made another trade" "yeah that's what it does" "but i didn't do anything" "correct" "this feels illegal" "it's not, it's just fast" /// week 2: calls stop i text him "how's the bot?" "good" "how good?" "$4,100" wait what /// i drive over: he's in his office watching the terminal like it's the super bowl $800 → $4,100 in two weeks "dad are you... are you ok?" "this is better than my retirement account" he's not wrong /// week 4: he stops calling me starts texting screenshots instead $4,100 → $9,800 $9,800 → $14,200 $14,200 → $19,100 "dad you need to take some profits" "why" "risk management" "it hasn't lost a trade yet" he's becoming a degen in real-time /// week 8: valentine's day dinner my mom mentions something "your father's been acting weird" "how so?" "he keeps checking his computer during meals" "oh no" /// i text him: "mom says you're obsessed" "tell her i'm up $34K" "absolutely not" "fine but i am" /// yesterday: he texts me at 11 PM screenshot $34,200 total profit "is this normal son?" "no dad this is insane" "so should i stop?" "probably" "ok" 2 minutes later "i'm not stopping" /// this morning: my mom calls "your father wants to quit his job" "WHAT" "he showed me some computer thing with a lot of money" "how did he explain it?" "he said a robot makes him $800 per day while he sleeps" she's not wrong /// called dad: "you can't quit your job" "why not" "you've been doing this for 8 weeks" "and i made $34K, your mom made $0 at her job in 8 weeks" "that's not how this works" "the bot doesn't care how long i've been doing this" he has a point /// the evolution: january 1: "sounds like a scam" january 15: "it made another trade!" february 1: cautiously optimistic february 14: checking it at dinner march 1: "should i quit my job?" /// most 58-year-olds: - scared of technology - think crypto is a scam - planning retirement in 7 years - saving $500/month if lucky my dad: - running autonomous AI bot - $34K in 8 weeks - considering quitting his job - robot makes more than his salary /// he just texted again "talked to your uncle, he wants one too" oh god it's spreading /// copytrade before your dad outperforms you: should i talk him out of quitting or is he actually onto something?

ZER

12,783 views • 6 months ago

I made this product launch video over the weekend with just prompts It's all vibe coded There's something you should know, though: Like everyone else, a few days ago my timeline started getting full of videos like this when Remotion launched their Claude skill, so I decided to give it a go I was captivated by all the examples, so I started like everyone was saying: "just write a prompt" I typed the prompt, and it created an extremely bland, untasteful, stock-looking video 10 prompts in and it was not getting better. It was very, very bland. But at least it was something, so I kept going at it I ended up spending my entire weekend on this, 2-3 days of work. Only to realize my original reference videos that inspired me to get started were all fake Everyone was outright lying about their results. They all claimed "I made this with just one prompt", but it was just bait, they didn't really use Remotion or code at all, it was just a normal, human-made motion video Then you expand the X post and read the replies and they're all like "haha joke" in the comments, but their main post already got 1.5 million views and bamboozled everyone who didn't read further And this is a problem: when a viral trend happens, these posts flood your timeline, and you only realize that they're all noise and bait (and that they haven't even used the tools they claim) when you click through the post and read its comments. But 90% of people (like me, initially) just see the post on their timeline while scrolling, and assume it's all real. You don't go in to check every single post you see: you just like it, or save it for later, and carry on with your day, thinking what you saw was the real thing, and that it's all outstanding results, and that motion designers are really done And it's so anxiety inducing, because everyone is hyping their results, but most of it is just not true. I have stopped reading X lately because going in makes me so anxious, everyone is claiming extraordinary outlier results just for the views and clicks, and you feel like you're lagging behind and you're not good enough because you don't get those results So for this video I decided to actually take the tech out for a spin, and see what results I could really get out of it I used Remotion and Claude Code 4.5, but contrary to what everyone was claiming, this video was not "just a prompt". It was fully vibe coded, but it required much more than a prompt. It was multiple days worth of work Here's what I learned: - Making vibe coded videos with Remotion is ~10-20x slower than building app code. I've been wasting my Claude limits on this video - Everything takes a lot of manual work and reprompting. You often need to go frame by frame correcting tiny things - It makes very silly mistakes - Even Opus 4.5 has very very limited knowledge of spatial / visual things. It doesn't understand well z-indexes, layers, compositions, proportions, temporal coherence, etc. Claude Code feels extremely dumb when creating code for Remotion videos, which surprised me a lot, beacuse I had been mind blown by how incredibly well it worked with my Ruby on Rails SaaS codebases - You need to have some design knowledge to adjust things manually, you need to ask for exactly what you want, in the technical jargon it expects. You can't just say "make this more beautiful" or "animate this better" because it just creates slop - Right now vibe coded videos are promising, but I think I could have done this video faster just by doing it manually in After Effects. It really took that much work - If you have a creative idea for something you want to animate, it takes multiple hours of back and forth prompting to create just one or two seconds worth of **good** animation - Tip: PARAMETERIZE everything! It tends to hardcode magic numbers everywhere in the code, so if you change something earlier in the video timeline, everything else breaks. You want to essentially be creating "key frames" with code by telling it to parameterize every frame where something important happens, and calculate the rest of the keyframes based off that. This comes in handy when you need, for example, to adjust keyframes to match the music So in summary: vibe coded videos are promising, but right now it only works for very stock-looking videos unless you put in a ton of effort Maybe actually useful for 1-2 second web animations though, I'll try that next It will obviously get better, this feels like the quality of code generation in 2023-2024, you need to hold its hand and correct it at every step along the way. But even if video code generation was better, you would still need someone with motion design knowledge to at least set the creative direction, lay out the overall script and composition, etc. It's not completely hands-off unless you want slop And a word on caution: especially here on X, there's 90% hype and 10% reality, nothing is what it seems. Do not believe what you see online, people are constantly baiting and then just laughing it off in the comments
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I made this product launch video over the weekend with just prompts It's all vibe coded There's something you should know, though: Like everyone else, a few days ago my timeline started getting full of videos like this when Remotion launched their Claude skill, so I decided to give it a go I was captivated by all the examples, so I started like everyone was saying: "just write a prompt" I typed the prompt, and it created an extremely bland, untasteful, stock-looking video 10 prompts in and it was not getting better. It was very, very bland. But at least it was something, so I kept going at it I ended up spending my entire weekend on this, 2-3 days of work. Only to realize my original reference videos that inspired me to get started were all fake Everyone was outright lying about their results. They all claimed "I made this with just one prompt", but it was just bait, they didn't really use Remotion or code at all, it was just a normal, human-made motion video Then you expand the X post and read the replies and they're all like "haha joke" in the comments, but their main post already got 1.5 million views and bamboozled everyone who didn't read further And this is a problem: when a viral trend happens, these posts flood your timeline, and you only realize that they're all noise and bait (and that they haven't even used the tools they claim) when you click through the post and read its comments. But 90% of people (like me, initially) just see the post on their timeline while scrolling, and assume it's all real. You don't go in to check every single post you see: you just like it, or save it for later, and carry on with your day, thinking what you saw was the real thing, and that it's all outstanding results, and that motion designers are really done And it's so anxiety inducing, because everyone is hyping their results, but most of it is just not true. I have stopped reading X lately because going in makes me so anxious, everyone is claiming extraordinary outlier results just for the views and clicks, and you feel like you're lagging behind and you're not good enough because you don't get those results So for this video I decided to actually take the tech out for a spin, and see what results I could really get out of it I used Remotion and Claude Code 4.5, but contrary to what everyone was claiming, this video was not "just a prompt". It was fully vibe coded, but it required much more than a prompt. It was multiple days worth of work Here's what I learned: - Making vibe coded videos with Remotion is ~10-20x slower than building app code. I've been wasting my Claude limits on this video - Everything takes a lot of manual work and reprompting. You often need to go frame by frame correcting tiny things - It makes very silly mistakes - Even Opus 4.5 has very very limited knowledge of spatial / visual things. It doesn't understand well z-indexes, layers, compositions, proportions, temporal coherence, etc. Claude Code feels extremely dumb when creating code for Remotion videos, which surprised me a lot, beacuse I had been mind blown by how incredibly well it worked with my Ruby on Rails SaaS codebases - You need to have some design knowledge to adjust things manually, you need to ask for exactly what you want, in the technical jargon it expects. You can't just say "make this more beautiful" or "animate this better" because it just creates slop - Right now vibe coded videos are promising, but I think I could have done this video faster just by doing it manually in After Effects. It really took that much work - If you have a creative idea for something you want to animate, it takes multiple hours of back and forth prompting to create just one or two seconds worth of **good** animation - Tip: PARAMETERIZE everything! It tends to hardcode magic numbers everywhere in the code, so if you change something earlier in the video timeline, everything else breaks. You want to essentially be creating "key frames" with code by telling it to parameterize every frame where something important happens, and calculate the rest of the keyframes based off that. This comes in handy when you need, for example, to adjust keyframes to match the music So in summary: vibe coded videos are promising, but right now it only works for very stock-looking videos unless you put in a ton of effort Maybe actually useful for 1-2 second web animations though, I'll try that next It will obviously get better, this feels like the quality of code generation in 2023-2024, you need to hold its hand and correct it at every step along the way. But even if video code generation was better, you would still need someone with motion design knowledge to at least set the creative direction, lay out the overall script and composition, etc. It's not completely hands-off unless you want slop And a word on caution: especially here on X, there's 90% hype and 10% reality, nothing is what it seems. Do not believe what you see online, people are constantly baiting and then just laughing it off in the comments

Javi

313,364 views • 7 months ago