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this guy turned a raw 10-second phone clip into a multi-character media engine and pulled $19,400 in agency retainers this month he didn't rent a studio, buy expensive lighting, or hire actors → original clip: record yourself in a regular alleyway wearing black clothing → google flow upload: drop... the raw mp4 file directly into the layout engine → omni flash prompt: "change clothing to premium white galabeya, preserve body weight and hand tracking" → asset injection: point at the empty space → render a white toyota sedan with exact matching shadows 30 minutes from zero to a flawless, physics-locked side-by-side demonstration video. earnings breakout from this single infrastructure setup: > brand deal 1 (local automotive): $8,500 > tech platform promo: $6,000 > dynamic ad variations contract: $4,900 most content agencies are still sending 5-man production crews to locations and charging clients $20,000 to shoot one clean commercial. he bypassed the entire physical production bottleneck using consumer hardware and a single web window.show more

Ridark

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46,419 görüntüleme • 3 ay önce •via X (Twitter)

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This guy is turning every frame of his bedroom into a high-end commercial set and printed $28,700 in commissions this month by selling these "live-edit" assets to digital stores. The workflow that prints money from a single bedroom cam: > raw footage: record 10 seconds of you holding a plain steel tumbler > Gemini Omni prompt: "transform steel tumbler to glass bottle with glowing orange liquid, match physics and hand-grip" > environment swap: "turn toy shelf into a functioning aquarium with floating goldfish" > lighting pass: "open window curtains, cast realistic sunlight across the desk, add lamp glow" The results are indistinguishable from professional VFX work that usually requires a 10-person crew and weeks of post-production. Earnings breakout: > 5 custom ad assets for a beverage brand: $12,500 > 4 interactive shop interior renders: $9,200 > direct licensing of "live-aquarium" loop: $7,000 Most product photographers are still fighting with studio lights, reflection management, and expensive glass props. He bypassed the physical studio setup for the cost of a Google AI Pro subscription.

Gipp 🦅

17,793 görüntüleme • 3 ay önce

A 23-year-old game modder from Prague uploaded one image of a battle axe into Claude Opus 4.8 and built a $6,200/month 3D asset factory without opening a single modeling tab he let the model write the entire geometry script for Blender from scratch the workflow that prints ready-to-sell assets: > reference upload: drop a high-res weapon concept into the chat window > reasoning layer: Claude Opus 4.8 analyzes the edges, weight distribution, and scale > code generation: the model spits out a flawless python script in 13 minutes using 5,000 tokens > render loop: the code executes inside Blender, auto-generating complex 3D meshes with surface imperfections he found a minor shading bug, fixed it manually in 40 seconds, and uploaded the finished axe to TurboSquid and Sketchfab Earnings breakout: Week 1 > $1,120 Week 2 > $1,450 Week 3 > $1,780 Week 4 > $1,850 1 month > $6,200 net profit > most 3D artists are still spending 14 hours extruding vertices manually and crying about industry layoffs he bypassed the entire pipeline for the price of an API key

A 23-year-old game modder from Prague uploaded one image of a battle axe into Claude Opus 4.8 and built a $6,200/month 3D asset factory without opening a single modeling tab he let the model write the entire geometry script for Blender from scratch the workflow that prints ready-to-sell assets: > reference upload: drop a high-res weapon concept into the chat window > reasoning layer: Claude Opus 4.8 analyzes the edges, weight distribution, and scale > code generation: the model spits out a flawless python script in 13 minutes using 5,000 tokens > render loop: the code executes inside Blender, auto-generating complex 3D meshes with surface imperfections he found a minor shading bug, fixed it manually in 40 seconds, and uploaded the finished axe to TurboSquid and Sketchfab Earnings breakout: Week 1 > $1,120 Week 2 > $1,450 Week 3 > $1,780 Week 4 > $1,850 1 month > $6,200 net profit > most 3D artists are still spending 14 hours extruding vertices manually and crying about industry layoffs he bypassed the entire pipeline for the price of an API key

Mr. Buzzoni

24,497 görüntüleme • 3 ay önce

19 YEAR OLD IS RUNNING A YOUTUBE ANIMATION STUDIO FROM HIS BEDROOM. HE HAS NO ANIMATORS. HE CANNOT DRAW. HE IS PRINTING MONEY FROM CHILDREN'S RHYMES. Kids' YouTube channels routinely pull millions of views per video. The platform pays creators between $1,000 and $3,000 for every hundred thousand views in that niche. Before this year, getting a piece of that market required a 3D animation studio, voice actors, and a month of rendering. He does it in under three minutes. His entire studio is a GPT sol and a Claude window. He opens YouTube and finds the most popular nursery rhyme channels. He takes a screenshot of their video feed. He opens Claude. He has a custom tool called Higgsfield MCP connected directly to his chat. He pastes the screenshot. He writes one prompt. Analyze this YouTube channel and generate a week of original content in the exact same style and niche. He hits enter. A few minutes later, the chat window spits out a fully rendered, broadcast-ready 3D animation. A baby playing with blocks, perfectly lit, smoothly animated, and ready to be monetized. He uploads it to his channel. The next day, he does it again. The kids watching on their iPads don't know the video was generated by an AI analyzing a screenshot. The parents who just want five minutes of quiet don't care. The algorithm treats it like a Pixar short. A real animation studio spends $50,000 and two months to produce three minutes of content. He spends zero dollars, takes a screenshot, and goes to make coffee. While the rest of the internet argues about whether AI is a real job, a quiet group of teenagers is pulling market share from massive media companies with a single prompt. Most people will watch his tutorial and complain. A few will open a new tab.

19 YEAR OLD IS RUNNING A YOUTUBE ANIMATION STUDIO FROM HIS BEDROOM. HE HAS NO ANIMATORS. HE CANNOT DRAW. HE IS PRINTING MONEY FROM CHILDREN'S RHYMES. Kids' YouTube channels routinely pull millions of views per video. The platform pays creators between $1,000 and $3,000 for every hundred thousand views in that niche. Before this year, getting a piece of that market required a 3D animation studio, voice actors, and a month of rendering. He does it in under three minutes. His entire studio is a GPT sol and a Claude window. He opens YouTube and finds the most popular nursery rhyme channels. He takes a screenshot of their video feed. He opens Claude. He has a custom tool called Higgsfield MCP connected directly to his chat. He pastes the screenshot. He writes one prompt. Analyze this YouTube channel and generate a week of original content in the exact same style and niche. He hits enter. A few minutes later, the chat window spits out a fully rendered, broadcast-ready 3D animation. A baby playing with blocks, perfectly lit, smoothly animated, and ready to be monetized. He uploads it to his channel. The next day, he does it again. The kids watching on their iPads don't know the video was generated by an AI analyzing a screenshot. The parents who just want five minutes of quiet don't care. The algorithm treats it like a Pixar short. A real animation studio spends $50,000 and two months to produce three minutes of content. He spends zero dollars, takes a screenshot, and goes to make coffee. While the rest of the internet argues about whether AI is a real job, a quiet group of teenagers is pulling market share from massive media companies with a single prompt. Most people will watch his tutorial and complain. A few will open a new tab.

ZER

31,034 görüntüleme • 2 ay önce

Higgsfield MCP + Codex is f*cking wild 🤯 Here's how you can generate 17 on-brand Amazon listing images by uploading one product photo. One plain-English prompt, zero manual generation. All inside the Codex app. Perfect for DTC brands and agencies who need production-quality product shots without booking a shoot or briefing a designer. If you're prompting image generators one shot at a time: Tweaking the lighting, adjusting the angle, re-running generations until something usable comes out. This combination eliminates the entire loop: → Install the Higgsfield CLI and add the skills to Codex in 5 minutes → Drop one product photo into your project folder → Type one prompt: "make me Amazon listing images — hero, lifestyle, detail, angles" → Codex interprets it, Higgsfield skills fire automatically → Finished images land in an organized folder on your machine No manual prompt tweaking. No re-running generations. No briefing a designer. What you get: → Production-quality listing images from a single product photo → Lighting, angles, and reflections handled automatically → Everything organized into folders locally on your machine → A pipeline that runs in minutes, not days Setup takes five minutes. I put together a full guide showing the exact install, the skills setup, and the prompt to run it yourself. Want it for free? > Like this post > Comment "CODEX" And I'll send it over (must be following so I can DM)

Mike Futia

13,013 görüntüleme • 3 ay önce

19 YEAR OLD IS RUNNING A YOUTUBE ANIMATION STUDIO FROM HIS BEDROOM. HE HAS NO ANIMATORS. HE CANNOT DRAW. HE IS PRINTING MONEY FROM CHILDREN'S RHYMES. Kids' YouTube channels routinely pull millions of views per video. The platform pays creators between $1,000 and $3,000 for every hundred thousand views in that niche. Before this year, getting a piece of that market required a 3D animation studio, voice actors, and a month of rendering. He does it in under three minutes. His entire studio is a GPT sol and a Claude window. He opens YouTube and finds the most popular nursery rhyme channels. He takes a screenshot of their video feed. He opens Claude. He has a custom tool called Higgsfield MCP connected directly to his chat. He pastes the screenshot. He writes one prompt. Analyze this YouTube channel and generate a week of original content in the exact same style and niche. He hits enter. A few minutes later, the chat window spits out a fully rendered, broadcast-ready 3D animation. A baby playing with blocks, perfectly lit, smoothly animated, and ready to be monetized. He uploads it to his channel. The next day, he does it again. The kids watching on their iPads don't know the video was generated by an AI analyzing a screenshot. The parents who just want five minutes of quiet don't care. The algorithm treats it like a Pixar short. A real animation studio spends $50,000 and two months to produce three minutes of content. He spends zero dollars, takes a screenshot, and goes to make coffee. While the rest of the internet argues about whether AI is a real job, a quiet group of teenagers is pulling market share from massive media companies with a single prompt. Most people will watch his tutorial and complain. A few will open a new tab. full breakdown in the article below Bookmark and follow for more.

19 YEAR OLD IS RUNNING A YOUTUBE ANIMATION STUDIO FROM HIS BEDROOM. HE HAS NO ANIMATORS. HE CANNOT DRAW. HE IS PRINTING MONEY FROM CHILDREN'S RHYMES. Kids' YouTube channels routinely pull millions of views per video. The platform pays creators between $1,000 and $3,000 for every hundred thousand views in that niche. Before this year, getting a piece of that market required a 3D animation studio, voice actors, and a month of rendering. He does it in under three minutes. His entire studio is a GPT sol and a Claude window. He opens YouTube and finds the most popular nursery rhyme channels. He takes a screenshot of their video feed. He opens Claude. He has a custom tool called Higgsfield MCP connected directly to his chat. He pastes the screenshot. He writes one prompt. Analyze this YouTube channel and generate a week of original content in the exact same style and niche. He hits enter. A few minutes later, the chat window spits out a fully rendered, broadcast-ready 3D animation. A baby playing with blocks, perfectly lit, smoothly animated, and ready to be monetized. He uploads it to his channel. The next day, he does it again. The kids watching on their iPads don't know the video was generated by an AI analyzing a screenshot. The parents who just want five minutes of quiet don't care. The algorithm treats it like a Pixar short. A real animation studio spends $50,000 and two months to produce three minutes of content. He spends zero dollars, takes a screenshot, and goes to make coffee. While the rest of the internet argues about whether AI is a real job, a quiet group of teenagers is pulling market share from massive media companies with a single prompt. Most people will watch his tutorial and complain. A few will open a new tab. full breakdown in the article below Bookmark and follow for more.

Mayor.

57,974 görüntüleme • 27 gün önce

THIS IS F**KING GOLD this 23-year-old made $30,000 this month and barely opens video editing software. she didn’t get better at keyframing. she stopped doing the manual labor. here’s what she actually built: ChatGPT as the brain, Picsart Flow as the animator. ChatGPT studies what’s working across viral kids’ content and turns those patterns into detailed visual prompts for her own character, feeds them into Picsart Flow running Kling 3.0, sets the render parameters, outputs full Pixar-style 3D clips. all from her browser, under 5 minutes per clip. 30 minutes a day, total. she’s not animating frame by frame. she’s orchestrating a pipeline. kids’ channels pull hundreds of millions of views on short-form content without her building a single 3D model. the cross-platform ad revenue math is what should actually get your attention: $2,500–$7,000 per million views on YouTube under 10 minutes render time per clip 2 to 3 videos posted daily across channels $15,000/month in passive revenue she’s not selling animation skill anymore. she’s selling volume. the character concept is hers. everything from prompt structure to final render is the AI’s job. most creators are still trying to figure out 3D modeling tools. she skipped that entirely and automated the production. full breakdown in the article below Bookmark and follow for more.

THIS IS F**KING GOLD this 23-year-old made $30,000 this month and barely opens video editing software. she didn’t get better at keyframing. she stopped doing the manual labor. here’s what she actually built: ChatGPT as the brain, Picsart Flow as the animator. ChatGPT studies what’s working across viral kids’ content and turns those patterns into detailed visual prompts for her own character, feeds them into Picsart Flow running Kling 3.0, sets the render parameters, outputs full Pixar-style 3D clips. all from her browser, under 5 minutes per clip. 30 minutes a day, total. she’s not animating frame by frame. she’s orchestrating a pipeline. kids’ channels pull hundreds of millions of views on short-form content without her building a single 3D model. the cross-platform ad revenue math is what should actually get your attention: $2,500–$7,000 per million views on YouTube under 10 minutes render time per clip 2 to 3 videos posted daily across channels $15,000/month in passive revenue she’s not selling animation skill anymore. she’s selling volume. the character concept is hers. everything from prompt structure to final render is the AI’s job. most creators are still trying to figure out 3D modeling tools. she skipped that entirely and automated the production. full breakdown in the article below Bookmark and follow for more.

Mayor.

80,166 görüntüleme • 13 gün önce

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.

Ioannis Koukourakis

48,703 görüntüleme • 8 ay önce

She cleans garages, and her before/afters make $9,000 a month — but here's the twist: the videos aren't marketing for the business. They ARE the business. Most jobs, content is a side hustle bolted onto the work. Cleaning is the rare one where the viral clip and the sales pitch are the same thing. Someone watches a filthy concrete floor turn spotless and they don't think "nice video" — they think "my garage looks like the before, I need her." That's the whole game: a transformation clip is a portfolio, an ad, and proof you can trust, all in one. And it stacks three ways at once. The videos book her actual jobs — people see the reveal and hire her at full rate with zero ad spend, and that's the biggest slice by far. Cleaning-product and equipment brands pay her $2 - 4k a drop to be the bottle in her hand. And the raw views — often 5 to 10 million a clip — throw platform payouts on top. Three incomes from one phone she props in the corner and forgets about. The wild part is how little she actually does. She films the clean, hands the raw footage to AI, and it finds the reveal, trims it, captions it, schedules it. She's driving to the next job before the last one's even posted and that job got booked by a video that also earns ad money while it sells her services. One clip doing four things at once. Think about your own work for a second. If your job has a visible before and after — a lawn, a haircut, a repair, a room — your marketing budget is already zero. You just haven't pointed a camera at it yet. So tell me: what's a job where the finished result alone would make someone hire you on the spot? Drop it below. I'm collecting them — and the best ones are getting their own breakdown.

She cleans garages, and her before/afters make $9,000 a month — but here's the twist: the videos aren't marketing for the business. They ARE the business. Most jobs, content is a side hustle bolted onto the work. Cleaning is the rare one where the viral clip and the sales pitch are the same thing. Someone watches a filthy concrete floor turn spotless and they don't think "nice video" — they think "my garage looks like the before, I need her." That's the whole game: a transformation clip is a portfolio, an ad, and proof you can trust, all in one. And it stacks three ways at once. The videos book her actual jobs — people see the reveal and hire her at full rate with zero ad spend, and that's the biggest slice by far. Cleaning-product and equipment brands pay her $2 - 4k a drop to be the bottle in her hand. And the raw views — often 5 to 10 million a clip — throw platform payouts on top. Three incomes from one phone she props in the corner and forgets about. The wild part is how little she actually does. She films the clean, hands the raw footage to AI, and it finds the reveal, trims it, captions it, schedules it. She's driving to the next job before the last one's even posted and that job got booked by a video that also earns ad money while it sells her services. One clip doing four things at once. Think about your own work for a second. If your job has a visible before and after — a lawn, a haircut, a repair, a room — your marketing budget is already zero. You just haven't pointed a camera at it yet. So tell me: what's a job where the finished result alone would make someone hire you on the spot? Drop it below. I'm collecting them — and the best ones are getting their own breakdown.

Rich

28,429 görüntüleme • 1 ay önce

You’ve been lied to about what “sugar” actually is your entire life. Watch this 53-second clip and try not to drop your phone Dr. Mark Hyman revealed: - The entire Glycemic Index (the scale that measures how fast food turns into blood sugar) sets plain white bread at 100. - Pure table sugar? Only 65. Meaning: a sandwich can spike your blood sugar HARDER and FASTER than spooning straight sugar. He explains it perfectly: “Below the neck, your body literally cannot tell the difference between a bowl of breakfast cereal and a bowl of table sugar… or a loaf of bread.” He then drops the wild (public record) story of how, years ago, the World Health Organization wanted to cut recommended sugar from 10 % to 5 % of calories — and a high-level U.S. official flew to Geneva threatening to pull hundreds of millions in funding if they did. The guidelines stayed at 10 %. 53 seconds that will change how you look at every aisle in the supermarket forever. Watch till the end — the last 10 seconds are the biggest jaw-drop. Which part shocked you the most? Tell me in the replies, I read every single one 👇

You’ve been lied to about what “sugar” actually is your entire life. Watch this 53-second clip and try not to drop your phone Dr. Mark Hyman revealed: - The entire Glycemic Index (the scale that measures how fast food turns into blood sugar) sets plain white bread at 100. - Pure table sugar? Only 65. Meaning: a sandwich can spike your blood sugar HARDER and FASTER than spooning straight sugar. He explains it perfectly: “Below the neck, your body literally cannot tell the difference between a bowl of breakfast cereal and a bowl of table sugar… or a loaf of bread.” He then drops the wild (public record) story of how, years ago, the World Health Organization wanted to cut recommended sugar from 10 % to 5 % of calories — and a high-level U.S. official flew to Geneva threatening to pull hundreds of millions in funding if they did. The guidelines stayed at 10 %. 53 seconds that will change how you look at every aisle in the supermarket forever. Watch till the end — the last 10 seconds are the biggest jaw-drop. Which part shocked you the most? Tell me in the replies, I read every single one 👇

Camus

128,812 görüntüleme • 9 ay önce

📖SEEDANCE 2.0 JUST MADE EVERY FILM SCHOOL IRRELEVANT FOR SOLO CREATORS Solo creators with the right workflow are closing clients that used to require a full production studio. Seedance 2.0 inside Dreamina holds character consistency across scenes in a way no other tool at this price point comes close to. Same face, same costume, same lighting logic — frame after frame after frame. That's the feature that turns a single prompt session into a short film. The lava demon materializing inside a gothic cathedral. The girl in black holding her ground while everything burns around her. Two characters with completely different visual languages sharing the same atmospheric world — and Seedance holds both of them consistent across every cut. That's not a generation. That's a production. Here's the 7-step workflow that produced this: • Step 1 — Define the character before you define the scene. Write a complete physical description — face structure, hair, clothing, skin, posture. This becomes the anchor every future generation references. • Step 2 — Build the world separately from the character. Gothic cathedral, candlelight, fog, cracked stone, scattered bodies. Define the atmosphere as its own entity before you place anyone inside it. • Step 3 — Generate the reference frame. One image that establishes the visual language, the color grade, the lighting temperature. Lock this as your style reference before generating any video. • Step 4 — Feed the reference into Seedance's image-to-video pipeline with a motion prompt. Camera behavior only — slow push, hold, circle. The image handles the subject. The prompt handles the direction. • Step 5 — Generate four variations per scene. Delete the two that look generated. Keep the one where the character's face holds and the atmosphere feels physical rather than rendered. • Step 6 — Edit in CapCut or Premiere Pro. Add music that matches the emotional temperature of the grade — the visual already tells you what the sound should feel like. Dark orchestral, slow tempo, single instrument carrying the melody. • Step 7 — Save the character description and reference frame as a template. The next episode starts from the same character in the same world. Series content becomes a system, not a restart. How a freelancer sells this: Dark fantasy content for game studios, music artists, and fantasy brands is a real market with real budgets. A musician dropping an album needs a visual world. A game studio needs promotional cinematics. A fantasy brand needs a story. Where to find clients: • Music artists on SoundCloud and Spotify releasing dark, gothic, or cinematic albums — search by genre, find artists with 1k–50k listeners who have no visual content. They have the audience and the need but no production budget for traditional video. • Indie game studios on Itch and Steam launching fantasy or horror titles — they need promotional cinematics and trailers but can't afford a production company. A single free scene built from their game's character art opens every conversation. • Dark fantasy and gothic brands on Instagram and TikTok with strong photo content but zero video presence — jewelry brands, clothing labels, occult lifestyle brands. They have the aesthetic already built. You just add motion to it. • Fantasy and horror fiction authors on Instagram and Substack launching new books — they need visual teasers, trailers, and world-building content to build pre-launch audiences. Most have no idea this kind of production is accessible at this price point. • Tabletop RPG creators and Dungeon Masters on Patreon and Kickstarter — they build entire fantasy universes and need cinematic content for campaigns, promotional videos, and subscriber rewards. The niche is underserved and the creators inside it spend consistently on content tools and services. 📥Tomorrow I'll show you what's sitting right next to this opportunity. 🔖Save this if you are looking for practical AI methods that actually pay.

Zentrix⌚️

50,098 görüntüleme • 3 ay önce

DRONE VIDEOGRAPHERS CHARGE $10K FOR THIS SHOT. HE PULLS IT FROM GOOGLE EARTH AND A PROMPT You never buy a drone, book a pilot, or leave the house. You pick any city on Earth, trace the flight path you want, and let Gemini render it as real-looking FPV footage. Clients pay thousands for this shot. You make it from a screenshot Here is the exact process: 1. Open Google Earth. Find the city or building you want. Frame the angle you'd want a drone to start from and take a screenshot 2. Draw the path. On that screenshot, draw a red line showing exactly where the drone should fly through the scene. This line is what the AI follows 3. Open Gemini and drop in the screenshot. Use the video generation in the Gemini app, the part that animates a still image into motion. Nano Banana handles images, the video engine is what turns your shot into footage 4. Paste the prompt. Tell it to follow the red flight path through the city, fast smooth motion, banking around buildings, golden-hour light, motion blur, 9:16 vertical, real FPV drone look. Full prompt is in the comments 5. Generate and clean it up. One clip is a few seconds. Stitch a couple together for a full flythrough and you have a reel Set the prompt once and you can re-run it for any location on the planet Who pays for this: Real estate agents, hotels, restaurants and event venues all need aerial b-roll and almost none can afford a real drone shoot Pull listings or venues with flat, ground-level photos and zero aerial footage. Send a free sample flythrough of their own location, then charge per clip or a monthly rate for ongoing reels One agent with ten listings is a recurring client, fully online Full prompt in the comments Bookmark this

DRONE VIDEOGRAPHERS CHARGE $10K FOR THIS SHOT. HE PULLS IT FROM GOOGLE EARTH AND A PROMPT You never buy a drone, book a pilot, or leave the house. You pick any city on Earth, trace the flight path you want, and let Gemini render it as real-looking FPV footage. Clients pay thousands for this shot. You make it from a screenshot Here is the exact process: 1. Open Google Earth. Find the city or building you want. Frame the angle you'd want a drone to start from and take a screenshot 2. Draw the path. On that screenshot, draw a red line showing exactly where the drone should fly through the scene. This line is what the AI follows 3. Open Gemini and drop in the screenshot. Use the video generation in the Gemini app, the part that animates a still image into motion. Nano Banana handles images, the video engine is what turns your shot into footage 4. Paste the prompt. Tell it to follow the red flight path through the city, fast smooth motion, banking around buildings, golden-hour light, motion blur, 9:16 vertical, real FPV drone look. Full prompt is in the comments 5. Generate and clean it up. One clip is a few seconds. Stitch a couple together for a full flythrough and you have a reel Set the prompt once and you can re-run it for any location on the planet Who pays for this: Real estate agents, hotels, restaurants and event venues all need aerial b-roll and almost none can afford a real drone shoot Pull listings or venues with flat, ground-level photos and zero aerial footage. Send a free sample flythrough of their own location, then charge per clip or a monthly rate for ongoing reels One agent with ten listings is a recurring client, fully online Full prompt in the comments Bookmark this

Yarchi

53,401 görüntüleme • 3 ay önce

This guy got Claude to take control of Arcads and generate hyperrealistic UGC ads with a consistent AI actor, from a 7-word prompt, and now e-com brands pay him $1,500 per video to clone the setup. He got tired of watching DTC brands burn $4K on a photoshoot that dies the second a product angle changes, so he built a 2-prompt workflow that turns one product photo into scroll-stopping UGC without a single model, camera, or casting call. Here's the exact breakdown: → Claude is the director, Arcads is the studio. You never write a real prompt, you write one sentence → Type 7 words: "create a character sheet of a UGC actress on Arcads." Claude expands it into a 40-line spec: warm skin tone, high ponytail, gold hoops, white AirPods, athletic build, then takes control of Arcads itself → Arcads renders a 6-angle character sheet: front, back, profile, 3/4, two portraits, one consistent face locked across all of them → You download the grid and drop it back into Claude with one product photo → Second prompt, 8 words: "use Seedance 2.0 on Arcads to create natural UGC" → Claude writes the full shot list: timecodes, "1:03 AM clock zoom," the trackpad scroll, the arm pull that drags the product into frame, "delivery: casual, energetic," "ends on a wink + air kiss" → Arcads cuts the yellow Vans print onto an actor who was wearing all black in the grid. Real fabric folds, real light, zero warping → Final render: lip-sync locked to consonants, head not glued to the camera, a girl filming at 1am on her bed The key move 96% of people skip: you can't put the product on the actor before you lock the character sheet. If you drop a product photo onto a face that isn't locked across angles, the identity drifts frame to frame. The jaw resets. The skin tone shifts. The print slides off the fabric. The whole thing screams "AI" and your CTR dies in the first 2 seconds. His system locks the 6-view sheet first, so Arcads has one real human to dress, and the top lands with natural folds even though the grid never had it. One apparel brand ran 20 variants off a single product photo and found a winner in 36 hours, because the clips read like organic UGC, not a studio ad. Brands now pay him $1,500 per avatar build + $340/month to keep dropping fresh video batches on every new product. The entire thing runs on two prompts and one laptop. No photographer. No model agency. No product samples on set. Just two sentences, one product photo, and the discipline to lock the character before you render a single second of movement.

Shade

15,227 görüntüleme • 3 ay önce

Gemini 2.5 Flash demolishes my Galton Board test, I could not get 4omini, 4o mini high, or 03 to produce this. I found that Gemini 2.5 Flash understands my intents almost instantly, code produced is tight and neat. The prompt is a merging of various steps. It took me 5 steps to achieve this in Gemini 2.5 Flash, I gave up on OpenAI models after about half an hour. My iterations are obviously not exact. But people can test with this one prompt for more objective comparison. Please try this prompt on your end to confirm: -------------------------------------------------- Create a self-contained HTML file for a Galton board simulation using client-side JavaScript and a 2D physics engine (like Matter.js, included via CDN). The simulation should be rendered on an HTML5 canvas and meet the following criteria: 1. **Single File:** All necessary HTML, CSS, and JavaScript code must be within this single `.html` file. 2. **Canvas Size:** The overall simulation area (canvas) should be reasonably sized to fit on a standard screen without requiring extensive scrolling or zooming (e.g., around 500x700 pixels). 3. **Physics:** Utilize a 2D rigid body physics engine for realistic ball-peg and ball-wall interactions. 4. **Obstacles (Pegs):** Create static, circular pegs arranged in full-width horizontal rows extending across the usable width of the board (not just a triangle). The pegs should be small enough and spaced appropriately for balls to navigate and bounce between them. 5. **Containment:** * Include static, sufficiently thick side walls and a ground at the bottom to contain the balls within the board. * Implement *physical* static dividers between the collection bins at the bottom. These dividers must be thick enough to prevent balls from passing through them, ensuring accurate accumulation in each bin. 6. **Ball Dropping:** Balls should be dropped from a controlled, narrow area near the horizontal center at the top of the board to ensure they enter the peg field consistently. 7. **Bins:** The collection area at the bottom should be divided into distinct bins by the physical dividers. The height of the bins should be sufficient to clearly visualize the accumulation of balls. 8. **Visualization:** Use a high-contrast color scheme to clearly distinguish between elements. Specifically, use yellow for the structural elements (walls, top guides, physical bin dividers, ground), a contrasting color (like red) for the pegs, and a highly contrasting color (like dark grey or black) for the balls. 9. **Demonstration:** The simulation should visually demonstrate the formation of the normal (or binomial) distribution as multiple balls fall through the pegs and collect in the bins. Ensure the physics parameters (restitution, friction, density) and ball drop rate are tuned for a smooth and clear demonstration of the distribution. #OpenAI Sam Altman Greg Brockman AshutoshShrivastava Aidan McLaughlin

Gemini 2.5 Flash demolishes my Galton Board test, I could not get 4omini, 4o mini high, or 03 to produce this. I found that Gemini 2.5 Flash understands my intents almost instantly, code produced is tight and neat. The prompt is a merging of various steps. It took me 5 steps to achieve this in Gemini 2.5 Flash, I gave up on OpenAI models after about half an hour. My iterations are obviously not exact. But people can test with this one prompt for more objective comparison. Please try this prompt on your end to confirm: -------------------------------------------------- Create a self-contained HTML file for a Galton board simulation using client-side JavaScript and a 2D physics engine (like Matter.js, included via CDN). The simulation should be rendered on an HTML5 canvas and meet the following criteria: 1. Single File: All necessary HTML, CSS, and JavaScript code must be within this single `.html` file. 2. Canvas Size: The overall simulation area (canvas) should be reasonably sized to fit on a standard screen without requiring extensive scrolling or zooming (e.g., around 500x700 pixels). 3. Physics: Utilize a 2D rigid body physics engine for realistic ball-peg and ball-wall interactions. 4. Obstacles (Pegs): Create static, circular pegs arranged in full-width horizontal rows extending across the usable width of the board (not just a triangle). The pegs should be small enough and spaced appropriately for balls to navigate and bounce between them. 5. Containment: * Include static, sufficiently thick side walls and a ground at the bottom to contain the balls within the board. * Implement physical static dividers between the collection bins at the bottom. These dividers must be thick enough to prevent balls from passing through them, ensuring accurate accumulation in each bin. 6. Ball Dropping: Balls should be dropped from a controlled, narrow area near the horizontal center at the top of the board to ensure they enter the peg field consistently. 7. Bins: The collection area at the bottom should be divided into distinct bins by the physical dividers. The height of the bins should be sufficient to clearly visualize the accumulation of balls. 8. Visualization: Use a high-contrast color scheme to clearly distinguish between elements. Specifically, use yellow for the structural elements (walls, top guides, physical bin dividers, ground), a contrasting color (like red) for the pegs, and a highly contrasting color (like dark grey or black) for the balls. 9. Demonstration: The simulation should visually demonstrate the formation of the normal (or binomial) distribution as multiple balls fall through the pegs and collect in the bins. Ensure the physics parameters (restitution, friction, density) and ball drop rate are tuned for a smooth and clear demonstration of the distribution. #OpenAI Sam Altman Greg Brockman AshutoshShrivastava Aidan McLaughlin

RameshR

247,923 görüntüleme • 1 yıl önce

The simplest hustle I've seen this year. He pulled $1,800 in 5 minutes using Claude Meet Gianni. A regular dude with a laptop, sitting in his bedroom. No courses to sell, no team behind him, no fancy setup. What he does is honestly stupid simple. He opens Google Maps in his city. Scrolls around. Looks for one specific thing. Local businesses with great reviews but no website. Roofers. Plumbers. Landscapers. HVAC guys. Tree services. Half of them are still running their entire operation off a phone number and word of mouth. In the video he finds Aspen Roofing in Colorado Springs. 4.5 stars, 8 reviews, zero web presence. That's his target. He copies the company name. Pulls a few of their best Google reviews. Pastes everything into Claude with one prompt. «Build me a sexy, luxurious, high-converting website for this business. Use these reviews to understand their strengths.» Claude writes the entire site from scratch. He drops the code into Lovable, clicks Publish, and a real working website is live in one click. Hero section reads «Colorado's Premier Roofing Experience.» CTA button says «Get Your Free Roof Inspection.» Now the fun part. He emails or DMs the owner with the link. Says «I built you something, take a look.» The owner sees a professional site that already represents his brand. Buys it on the spot for $1,000 to $2,000. Plugs in his domain. Done. One website. 5 minutes of work. $1,800 in the pocket. And there are millions of local businesses in the US still running without a site. He's just stamping them out one after another.

winkle.

112,786 görüntüleme • 4 ay önce

Can't get enough of Seedance 2.5. The best part is it can generate 30s video in one single prompt, and the result is so realistic! So, here's another one that I made using CapCut. Prompt: [STYLE + CAMERA + ATMOSPHERE] Gritty, raw handheld 35mm film aesthetic with natural film grain. Harsh direct sunlight creating high-contrast shadows over a dramatic coastal cliff and open ocean. Continuous single-take handheld tracking shot (3rd-person / over-the-shoulder) with no cuts. Atmosphere: high-altitude wind, realistic coastal cliff and ocean physics, sudden wingsuit deployment. Audio: heavy rhythmic breathing, intense wind howl, fabric snap of wingsuit opening, high-speed air rush over open water, near-miss whooshes past yachts, soft landing roll on sand, distant ocean waves and beach ambient noise, final bite sounds. [IMAGE REFERENCES] Use the provided Hoshino character sheet as the single strict visual reference for the male character. Exact face, black hair, dark brown eyes, lean athletic build (178 cm), gold earrings, and overall facial structure locked from the reference. Outfit: modern high-performance cliff-jumping wingsuit — sleek, form-fitting design in matte charcoal black with sharp white paneling and subtle gold zipper accents (matching his minimalist aesthetic). The wingsuit is worn from the start with a matching technical backpack. Body proportions, posture, and face remain fully locked to the Hoshino reference. [TIMELINE SECOND BY SECOND] 0-3s: [Handheld medium] Hoshino stands on the edge of a high rocky cliff overlooking the open ocean, wearing his charcoal-and-white wingsuit and technical backpack. He looks straight into the camera with calm, confident intensity, then turns and launches into a clear, controlled forward somersault in slow motion as he leaves the cliff edge. 3-5s: [Continuous freefall] He completes the somersault and falls head-first toward the sea. At exactly 1.5 seconds into the fall he fully deploys the wingsuit with a sharp snap. The wing membranes inflate and he levels out into a smooth glide. 5-12s: [High-speed tracking] Camera stays locked behind him as he rockets at full speed just above the ocean surface along the coastline. He weaves tightly past sheer cliff faces, banking hard left and right, turquoise water and rocky walls streaking past at extreme velocity. 12-18s: [Low-level chaos] He drops lower, flying just above the water. He almost collides with a large luxury yacht that suddenly turns, banks hard to avoid it, then narrowly misses a smaller motor yacht. He dips under a yacht’s outstretched boom and threads between two more vessels. 18-23s: [Water-level action] Still flying extremely low over the sea, he dodges a startled seabird that dives across his path, skims past a group of people on a nearby yacht who scatter in surprise, and banks sharply to avoid an open yacht swim platform. The camera stays locked behind him through every near-miss. 23-26s: [Landing] He flares the wingsuit hard, touches down with both feet on the soft beach sand and immediately rolls forward to kill the speed. He stands up, peels off the wingsuit in one fluid motion and drops it on the sand beside him, revealing a clean fitted white undershirt underneath. 26-28s: [Beach level] He walks a few steps still wearing the backpack and stops right in front of a classic beachside hot-dog cart. The vendor hands him a steaming hot dog fresh off the grill. 28-30s: [Close continuous] He turns, looks directly into the camera, takes a big bite of the hot dog and chews with a calm, satisfied expression as the shot holds. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, ultra-detailed textures, cinematic lighting, perfect motion blur, high dynamic range, coherent physics (fabric, air, wingsuit membranes, impact, roll, near-misses with yachts, water spray), stable character locked to the Hoshino reference, realistic ocean reflections, cliff rock textures and wind, no artifacts, movie-level stability, pure single continuous take.

Can't get enough of Seedance 2.5. The best part is it can generate 30s video in one single prompt, and the result is so realistic! So, here's another one that I made using CapCut. Prompt: [STYLE + CAMERA + ATMOSPHERE] Gritty, raw handheld 35mm film aesthetic with natural film grain. Harsh direct sunlight creating high-contrast shadows over a dramatic coastal cliff and open ocean. Continuous single-take handheld tracking shot (3rd-person / over-the-shoulder) with no cuts. Atmosphere: high-altitude wind, realistic coastal cliff and ocean physics, sudden wingsuit deployment. Audio: heavy rhythmic breathing, intense wind howl, fabric snap of wingsuit opening, high-speed air rush over open water, near-miss whooshes past yachts, soft landing roll on sand, distant ocean waves and beach ambient noise, final bite sounds. [IMAGE REFERENCES] Use the provided Hoshino character sheet as the single strict visual reference for the male character. Exact face, black hair, dark brown eyes, lean athletic build (178 cm), gold earrings, and overall facial structure locked from the reference. Outfit: modern high-performance cliff-jumping wingsuit — sleek, form-fitting design in matte charcoal black with sharp white paneling and subtle gold zipper accents (matching his minimalist aesthetic). The wingsuit is worn from the start with a matching technical backpack. Body proportions, posture, and face remain fully locked to the Hoshino reference. [TIMELINE SECOND BY SECOND] 0-3s: [Handheld medium] Hoshino stands on the edge of a high rocky cliff overlooking the open ocean, wearing his charcoal-and-white wingsuit and technical backpack. He looks straight into the camera with calm, confident intensity, then turns and launches into a clear, controlled forward somersault in slow motion as he leaves the cliff edge. 3-5s: [Continuous freefall] He completes the somersault and falls head-first toward the sea. At exactly 1.5 seconds into the fall he fully deploys the wingsuit with a sharp snap. The wing membranes inflate and he levels out into a smooth glide. 5-12s: [High-speed tracking] Camera stays locked behind him as he rockets at full speed just above the ocean surface along the coastline. He weaves tightly past sheer cliff faces, banking hard left and right, turquoise water and rocky walls streaking past at extreme velocity. 12-18s: [Low-level chaos] He drops lower, flying just above the water. He almost collides with a large luxury yacht that suddenly turns, banks hard to avoid it, then narrowly misses a smaller motor yacht. He dips under a yacht’s outstretched boom and threads between two more vessels. 18-23s: [Water-level action] Still flying extremely low over the sea, he dodges a startled seabird that dives across his path, skims past a group of people on a nearby yacht who scatter in surprise, and banks sharply to avoid an open yacht swim platform. The camera stays locked behind him through every near-miss. 23-26s: [Landing] He flares the wingsuit hard, touches down with both feet on the soft beach sand and immediately rolls forward to kill the speed. He stands up, peels off the wingsuit in one fluid motion and drops it on the sand beside him, revealing a clean fitted white undershirt underneath. 26-28s: [Beach level] He walks a few steps still wearing the backpack and stops right in front of a classic beachside hot-dog cart. The vendor hands him a steaming hot dog fresh off the grill. 28-30s: [Close continuous] He turns, looks directly into the camera, takes a big bite of the hot dog and chews with a calm, satisfied expression as the shot holds. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, ultra-detailed textures, cinematic lighting, perfect motion blur, high dynamic range, coherent physics (fabric, air, wingsuit membranes, impact, roll, near-misses with yachts, water spray), stable character locked to the Hoshino reference, realistic ocean reflections, cliff rock textures and wind, no artifacts, movie-level stability, pure single continuous take.

MrDejie

198,144 görüntüleme • 1 ay önce

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

Blaze

55,926 görüntüleme • 4 ay önce

These Claude Code Skills are f*cking insane 🤯 A complete brand-to-ad pipeline that builds a permanent brand brain and ships hooks, briefs, scripts, and ad variations on command. All inside Claude Code. A system perfect for DTC brands and agencies who are burning hours every week on creative production that should take minutes. If you're still copy-pasting brand context into every Claude session, briefing hooks in Slack, writing ad scripts from scratch, and hand-building variations in Canva... This system eliminates the entire loop: → Brand DNA Builder scrapes any brand and outputs a full positioning file → Brand Voice Profiler extracts voice rules from the brand's actual copy → ICP Deep Dive runs autonomous research and pulls real customer language → Hook Writer generates 20 hooks across 10 DR frameworks → Brief Generator writes full creative briefs in your brand voice → Ad Script Writer produces UGC and produced video scripts → Variation Engine takes a winning ad and generates copy + image variants No Google Doc brand briefs. No copy-pasting context every session. No starting from a blank page. What you get: → A permanent brand brain Claude reads every time → Hooks, briefs, scripts, and variations that sound like your brand — not generic AI → A continuous test loop feeding directly into Meta Ads → One workflow that scales across every client or product Built 100% in Claude Code. I'm giving away the 3 foundational skills pack that every other skill in the system reads from. Want access for free? > Like this post > Comment "SKILLS" And I'll send it over (must be following so I can DM)

Mike Futia

25,725 görüntüleme • 4 ay önce

This 24-year-old solo founder generated $98,489 in 30 days without writing a single line of code or paying expensive Shopify app fees. He doesn’t have a dev agency. No team of UI/UX designers. No thousand-dollar monthly software stack. He built an automated "e-com engine" using an all-in-one AI store builder called Amboras. The system works like this: Upload 1 product photo → AI generates copy, pricing tiers, upsells & visual banners in seconds → Built-in e-com logic formats high-converting pages → Text-based AI agent manages reviews, countdown timers, and layouts on the fly. 3 ways creators & founders are monetizing this right now: > 1. Rapid Dropshipping & Product Testing: Launch fully functional product pages in minutes to test viral TikTok/Reels hooks with near-zero overhead. > 2. High-Ticket Agency Services: Build complete, high-converting stores for local clients in 24 hours instead of weeks. > 3. Conversion Rate Optimization (CRO): Charge brands monthly retainers to continuously inject automated upsells and dynamic review blocks via simple prompts. He didn’t spend weeks waiting on developers or setting up 15 different Shopify plugins. He took a unified AI workflow, automated the entire storefront architecture, and scaled a brand single-handedly. In the AI-native e-commerce era, execution speed and zero friction are the ultimate leverage.

Ridark

21,212 görüntüleme • 1 ay önce

I just built an AI-powered creative search engine with Gemini Embedding 2 + Claude Code 🤯 Drop in your UGC clips, product shots, and ad variations — then search through everything in plain English. "Show me all the unboxing clips." "Find product shots with natural lighting." "Which creator talked about sensitive skin?" All inside Claude Code. Perfect for DTC brands and agencies sitting on hundreds of creative files they can never find when they actually need them. If you're digging through a folder of random file names, scrubbing through raw footage to find that one clip, and relying on memory to track down what's already been shot... This system eliminates the entire loop: → Drop your videos, images, and docs into a project folder → One prompt to Claude Code — it builds the entire search app for you → Google's new Gemini Embedding 2 model actually watches your videos and looks at your images → It understands what's inside each file — not just the file name → Search in plain English and get back the actual assets with confidence scores No scrolling through folders. No relying on file names to find anything. No re-shooting footage you already have. What you get: → A searchable library of every creative asset your brand has ever produced → Natural language search across video, images, and documents at the same time → Results that show the actual files inline — play videos, view images, read docs → A system that gets smarter every time you add richer descriptions to your assets One free API key. No monthly subscriptions. Runs on your machine. I put together a full playbook with the exact build prompt, the setup process, and DTC/agency use cases to get this running in under 30 minutes. Want the full playbook? > Like this post > Comment "SEARCH" And I'll send it over (must be following so I can DM)

Mike Futia

12,283 görüntüleme • 6 ay önce