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Long Article Details AI-Generated OnlyFans Persona Built With Four Files

OnlyFans + Claude Code = $43,000 in 30 days. No camera. No team. The 4-file system runs alone

Raytar's long-form article describes how a student built an AI-generated OnlyFans persona named Maya using Claude Code, Flux and ElevenLabs, and cites similar AI influencers such as Aitana López and Emily Pellegrini. The piece is a detailed how-to and business narrative.

Original post · 5 min read
X ArticleOnlyFans + Claude Code = $43,000 in 30 days. No camera. No team. The 4-file system runs alone
A 21-year-old college student in Austin runs an OnlyFans account that cleared $43,000 in his first 30 days.
The girl on the page doesn't exist.
Her name is Maya, on paper. 22 years old. UCF psychology dropout. 1,247 paying subscribers. Her top fan has paid $1,847 in messages and content over the last month. Average revenue per fan: $34. She does not sleep.
There is nothing to film. There is no one to type. Claude Code writes every message. Flux generates every photo. ElevenLabs generates her voice. Maya is four .md files in a folder on a MacBook in Austin.

This is not theoretical, and he is not first.
Aitana López

A pink-haired AI model from Barcelona, brings in up to €10,000 a month for The Clueless agency.
Fortune covered her in 2024. She has done campaigns with Victoria's Secret, Razer, and Olaplex. A 5-million-follower Latin American actor slid into her Instagram DMs trying to ask her out, not knowing she was AI.
Emily Pellegrini

Emily Pellegrini is 21, from Italy, on paper. She was generated in Midjourney by an anonymous creator who told the Daily Mail how he built her:
"I asked ChatGPT what's the average man's dream girl, and it said brown hair and long legs. So I made her exactly how it said."
None of them figured out she was not real. The creator who built her stays anonymous to this day.
Aitana took eighteen months to build. Emily took fourteen-hour workdays for half a year. Maya was built in four weeks.
Maya is four files.
persona.md is who she is. UCF psych dropout, '04 baby, Pisces. Step-dad she hates, fake brother in Tampa. Loves Lana Del Rey and chicken tendies. 1,400 words of biography that never break.

voice.md is how she sounds. Three audio samples cloned in ElevenLabs. Voice notes drop at 11pm her time. Top fan in Berlin gets them at 6am.
flux.md is what she looks like. 47 reference shots, one LoRA, three lighting setups. Bedroom mirror. Bathroom mirror. Kitchen counter at 2am. Every image has a tiny scar on her left wrist that she never explains.
brain.md is what she remembers. JSON, one entry per subscriber:

Claude Code reads all four before every message. It never forgets a name. It never breaks character. It sleeps when the creator sleeps and catches up at 7am with "sorry babe just woke up 🥺".

Four weeks, in order.

Week 1 — persona.md.


1,200 words. Three sections:
backstory:

forbidden topics:

voice rules:

Read aloud. If it sounds like Wikipedia, delete and restart. Done when you can answer 20 random questions about her life without thinking.
Week 2 — flux.md + LoRA.

Pick a base face. Lock 6–8 descriptors:

Generate 47 variations of that face. Fine-tune a LoRA on them. ~$80 on a rented A100.
Lock three seed ranges, one per setup:

Different seeds across setups = different jawline. Lock them or she stops looking like herself.
Done when 10 fresh generations from each setup pass as the same person.
Week 3 — voice.md + ElevenLabs.
Buy 90 seconds of clean audio from a Fiverr voice actress, ~$40. Clone in ElevenLabs Instant Voice. Ten minutes. Add audio rules to voice.md:

Generate 30 test voice notes. Done when none of them sound like a podcast intro.
Week 4 — brain.md + orchestrator.

Claude Code reads persona.md + voice.md + brain.md before every reply. Then a second pass extracts new facts from the user's message:

System prompt:

Hook order: read .md files → reply → extract → append. Run on a cron every 30 seconds polling the inbox. Done when Claude holds 50 messages without contradicting brain.md.

This was not possible eighteen months ago.

Aitana took eighteen months. She was built before any of this shipped.
Emily took six months. Half the stack was missing.
Maya took four weeks. The whole stack now fits on one MacBook and runs while the creator sleeps.
The next Maya is a weekend.

The math.
$43,000 in revenue.

To a 21-year-old who paid zero dollars for talent, zero for filming, zero for editing.
The only labor cost was four weeks of writing four markdown files.
OnlyFans is the wedge, not the product.
The same four files run an Instagram fitness account in São Paulo. A TikTok cooking persona in Seoul. A Twitch streamer who plays Marvel Rivals at 3am Pacific. An X account that posts crypto takes between sponsored DEX shills.
None of them exist either.
The bottleneck is not compute. It is not GPUs. It is taste — knowing which lies a stranger wants to believe.
Anyone can be a folder now.

Bookmark this. The question isn't if you've followed an AI. It's how many.
♥ 3.4K · ⟲ 316 · 👁 35.8MView on X ↗

More in Culture & Ideas

How Avery Wang's Shazam Algorithm Identified Songs Without AI

Aakash Gupta explains that Shazam's 2002 system, invented by Stanford PhD Avery Wang, used spectrogram peaks, constellation maps and hash lookups instead of AI, and was published openly in 2003. Apple acquired the company in 2018.

Original post · 2 min read
Shazam could name any song back in 2002, on flip phones, with zero AI. You dialed the number 2580, held your phone up to the speaker, and hung up. 15 seconds later, a text came back with the song name. That same core trick still runs the app today.

The inventor was Avery Wang, a Stanford PhD in audio signal processing. His problem was brutal. Match a short clip recorded on a 2002 cell phone mic, in a noisy bar, against a database of a million songs, in seconds, over a phone call.

His solution treated music as geometry instead of sound.

The algorithm converts audio into a spectrogram, a picture of the song, then throws almost all of it away. It keeps only the peaks, the loudest frequency points at each moment in time. Bar chatter and blown-out speakers can wreck most of a recording. The peaks survive. Shazam only ever needed the peaks.

Those surviving points form what Wang called a constellation map, because it looks like a star field. Pairs of peaks get converted into hash numbers, and identifying a song becomes a dictionary lookup rather than an audio comparison. That made it fast enough to search a million tracks on 2002 hardware.

Wang published the full method openly in 2003 in a paper called "An Industrial-Strength Audio Search Algorithm." Anyone could read exactly how the magic worked. The moat was the database and the deals with carriers, never the secret.

Apple bought the company in 2018 for a reported $400 million. People have tagged over 100 billion songs since the very first one, Jeepster by T. Rex, during the beta in April 2002.

One deterministic signal-processing trick, written before most people had heard the phrase machine learning, and it's still so good that in 2026 everyone assumes it must be AI.
Nathan Ruff @TheNathanRuff
Dude, how did Shazam work 15 years ago without AI!?
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FBI Arrests Fortnite Player Using Epic's Voice Chat Recording

Aakash Gupta reports that the FBI arrested Edward Frith after Epic Games reviewed a reported voice clip of his threat and sent it to authorities, and explains how Fortnite's rolling five-minute voice buffer and reporting system work.

Original post · 2 min read
The FBI just arrested a Fortnite player using a recording the game made of his own voice. He had no idea his headset was taping him. Almost nobody playing does.

Edward Frith, 29, had logged into his account over 1,700 times. In September he told another player that if the FBI showed up at his door he'd shoot them too. Someone in the lobby pressed the report button. Epic reviewed the clip, sent it to the FBI on September 20, and agents arrested him within days.

The design of the system is the clever part.

Recording every player would be a privacy disaster and a storage bill nobody wants. So Epic built voice reporting in 2023 to work like a flight recorder. The audio buffer lives on your own device, overwrites itself every five minutes, and never leaves your machine unless another player in the match reports it. The moment someone does, the clip gets packaged and sent to Epic's safety team with the speakers tagged.

He said it to one stranger in a lobby. That stranger had a button that turns the last five minutes into a federal exhibit.

I was a producer on Fortnite, and this is the part people outside the building never see. Threats of real-world violence got treated with the same urgency as a revenue outage. The game is full of kids, and Epic acts like it.

Fortnite is actually the conservative version of this. It still requires a human to press the button. Call of Duty has run AI moderation directly on live voice chat since 2023, no report needed. Every major platform with a microphone is converging on the same architecture.

The era where anything said into a gaming headset stayed in the lobby is ending one match at a time.
Dexerto @Dexerto
A Fortnite player who threatened to shoot the FBI in voice chat was arrested by the FBI

WagesOfNinja told another user "Let's see if the FBI is going to show up at my door because I'll shoot them motherf**kers too"
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Ohio's New Rome Dissolved Over Speeding Ticket Revenue

Aakash Gupta describes how New Rome, Ohio, and Macks Creek, Missouri, depended on speeding fines for most of their budgets, leading to dissolution in Ohio and bankruptcy in Missouri. Missouri subsequently capped ticket revenue at 20% of city budgets.

Original post · 2 min read
Ohio once dissolved an entire town because its main business was writing speeding tickets.

New Rome had 60 residents and a 14-officer police force. One cop for every four people in town. They collected around $400,000 a year in fines, 92% of the village budget, mostly by working a stretch of road where the speed limit dropped from 45 to 35.

A state audit then found the village was spending 82% of that budget on the police force. The town's only real industry was funding the thing that funded the town. In 2004 a judge ruled New Rome had effectively dissolved itself through corruption and erased it from the map. Its land got absorbed into the neighboring township.

Missouri ran the same experiment with Macks Creek, population 272, sitting on the highway to Lake of the Ozarks. The town wrote 2,900 tickets a year. Eight a day, almost all tourists who would never drive back to fight them. More than 75% of town revenue came from fines.

Then an officer there pulled over a state legislator. He went back to the capitol and passed a law capping how much of a city's budget can come from traffic tickets. Macks Creek lost its revenue stream, went bankrupt, laid off its entire police force, and disincorporated. The IRS seized the town's bank account.

Speed traps cluster wherever a highway full of out-of-towners meets a sudden limit drop in a state that lets the town keep the money. Missouri now caps ticket revenue at 20% of a city's budget. Ohio had to pass a law aimed at one specific village.

A speeding ticket heatmap doubles as a map of who's allowed to keep the fine money.
Terrible Maps @TerribleMaps
Where you’re most likely to get a speeding ticket in the U.S.
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Claire Vo Says Mothers Are Slowest to Adopt AI, Calls for Family-Focused Agents

Claire Vo says many mothers, including those in tech, are slow to adopt AI due to privacy, environmental and workplace concerns. She argues the market is missing agents that manage family logistics like calendars, bills and errands rather than one-off tasks.

Original post · 1 min read
As one of the most tagged AI mother of bots in this thread let me tell you a secret: a lot of moms (even moms in tech) are the slowest f-ing adopters of AI I know.

I’m in a bunch of random mom groups and I hear
- I won’t connect my email cal whatever
- water / data centers etc
- get all AI out of my kids schools
- here is my 10000 word prompt for chat
- I hate AI in my job, burn out, get me out of tech

If you get off X and into the real world, the adoption curve is slow. My YT channel is over 90% men. Women + moms are being left behind, and it’s not because someone hasn’t designed the right agent yet.

Layers here
Sonia Baschez @SoniaBaschez
Silicon Valley keeps building me a husband when what I really want is a wife lol

I don't want an agent that only does one-off, random tasks that are nice but not life-changing: booking flights, making dinner reservations, or buying tickets

I want an AI wife who knows what’s on the family calendar, remembers there’s a birthday party Saturday and we haven’t bought a gift then sends me options, realizes we’re almost out of diapers and buys them, knows which bills are coming up, remembers someone needs a dentist appointment and books it, figures out what the hell we’re eating all week and what w…
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Deedy Explains Shazam's Spectrogram Peak Hashing Method

Deedy Explains Shazam's Spectrogram Peak Hashing Method

Deedy outlines how Shazam extracts high-amplitude spectrogram peaks, hashes them with timestamp and track ID, and performs recognition via a hash table lookup. He notes that his college CS class built a version and recommends the original paper.

Original post · 1 min read
The serious answer to how Shazam worked is it took the peaks of a spectrogram of short clips of every song, find the peaks from the highest amplitude bits, hash it with the value being (time stamp, track id) and then the actual recognition is a hashtable lookup.

We did this for a college CS project. The original paper is fantastic:
Nathan Ruff @TheNathanRuff
Dude, how did Shazam work 15 years ago without AI!?
♥ 3.8K · ⟲ 357 · 👁 111.6KView on X ↗

Aakash Gupta Offers Formula for Valuing Private Company Equity Grants

Card comparing Google and OpenAI offers

Aakash Gupta shares a formula for valuing private equity grants by discounting quoted equity by payout probability and years to liquidity. He compares a $400K Google grant to a $400K OpenAI grant, estimating the latter at about $242K.

Original post · 1 min read
A $400K grant from Google is worth $400K.

A $400K grant from OpenAI is worth about $242K today.

Same number on the offer letter. You can sell Google stock the day it vests. OpenAI is private, so you sell only when the company runs a tender.

Here's the formula I use for any private grant:

Value = Quoted equity × P(payout) ÷ 1.15^years to liquidity

The 15% is your discount for money you can't touch. For a late-stage company with real revenue and a tender history, P(payout) is 70-90%.

OpenAI, 2 years to a sale at 80% odds: $400K × 0.8 ÷ 1.15² = ~$242K.
1 year at 90%: ~$313K. 3 years at 70%: ~$184K.

Earlier stage gets brutal. A $400K grant at a Series C with an IPO 5 years out at 40% odds: $400K × 0.4 ÷ 1.15^5 = ~$80K.

The formula gives no credit for growth past today's price, and OpenAI has had a lot of it: $157B to $852B in 17 months. So treat the number as your floor.

Compare offers on what the equity is worth today. Then negotiate the gap in base and sign-on.
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