Neheart

Degen gnoma

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https://t.co/dD23yXeMxT

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@AnLe4a0 wow this a real smart moove

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@N01ennn ARK cards now like a fresh a breath of air

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@kardinall @LyckyOus how say one smart dude dont worry be happy

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@kardinall @LyckyOus yes if you add some safety instructions and protect storege space with cloud memory

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@LyckyOus yes because im bored to record all, now claude do it by himself

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@qytrix she fix my car?

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@kardinall the four-part loop breakdown is the useful bit, most people would stop at "ai answers phones" and miss why it actually holds together

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@Kirratr wery interest loops so useful

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$18,720 A MONTH IS THE CEILING ON A GIRL WHO DOES NOT EXIST, AND SHE COSTS $57 A MONTH TO RUN.

Someone built her in a rented flat and has never shown his own face. She's a blend of two strangers' features, a voice made of five text files Claude reads before it answers anyone, and a following of over 40,000 people who think she's real.

The revenue doesn't come from ads or brand deals. It comes from men who pay to feel like the only one she's talking to. One live account built this exact way is 11 days old, already past $2,000 in revenue against $330 spent, and still climbing.

Running her costs less than most gym memberships. A stack of paid tools adds up to roughly $57 a month, and a subscription platform handles the billing. Everything above that number is margin.

No studio. No writer on payroll. No manager checking messages at 2 AM. One person, one folder of text, and a model that never sleeps runs the entire operation alone.

A human earning this needs a team. She needs a folder.

[Kardinall @kardinall]

https://t.co/96ym5qiHOW

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@kardinall you are right

[Neheart @neheart]

A LOOP HAS ONLY ONE JOB: SOMETHING ELSE HAS TO START IT, NOT YOU.

Most people call it a loop the moment they write a longer prompt. That is not a loop, that is just a slower chat. One creator who runs her own content pipeline put it bluntly: if you are still the one opening the app and typing the instruction, you do not have a loop yet.

A real loop needs a trigger that is not your hands on a keyboard. A clock. A file changing. A cron job firing at a set time with an agent sitting inside it instead of plain code. She runs one that watches her niche all day and drops fresh video ideas straight into her inbox. She never opens anything. The clock starts, the agent works, the result is just there when she checks.

Saved instructions do not count either. A saved prompt still waits for you to hit send. It only runs faster once you do. The chat is still the bottleneck, just a shorter one.

Here is the whole test, and you can set it up in one afternoon: tell your AI to message you every Sunday at 6 PM with 3 things to prep for the week, then close the app completely.

If that message actually lands Sunday at 6 PM without you touching anything, you built a loop. If it does not, you just wrote a nicer prompt.

The proof it is real is that you are not there when it starts.

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https://t.co/dD23yXeMxT

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@0x_Anni 22 year old outproducing Pixar's budget with a prompt pipeline before lunch is the real plot twist here, not the movie.

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@LyckyOus ohhh i see filters((

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@qytrix she repair my car?

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@LyckyOus Yes you are right

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IT NOW TAKES FOUR HOURS AND ONE RENTED GPU BOX TO DO WHAT USED TO COST A LAB $100 MILLION.

Every large model, GPT, Claude, whatever you type into, is the output of five separate stages: collect and clean trillions of the assets, burn weeks of compute teaching it to predict the next word, fine tune it on curated examples until it acts like an assistant, run it through human preference training until it stops being a liability, then compress and ship it so it answers in seconds instead of minutes. Frontier labs run that whole chain across months and hundreds of millions of dollars, and almost nobody outside those labs has ever seen it happen end to end.

Andrej Karpathy just gave that pipeline away. It is called NanoChat, and it runs tokenization, pretraining, fine tuning, and deployment into a working chat interface as one script.

One creator provisioned an 8xH100 cluster in the cloud with a single click, kicked it off, and had a model answering him in a browser tab four hours later. He met Karpathy the same morning and told him about it. Karpathy is folding NanoChat into the capstone project of his first course at Eureka Labs.

None of the five stages disappeared. Data still decides what the model can know. Pretraining still burns the compute. Fine tuning still shapes its manners. Preference training still decides what it refuses. Deployment still decides how fast it answers you.

What changed is who gets to run all five in an afternoon instead of a data center.

The pipeline did not get cheaper. It got small enough to fit in one rented box.

[CyrilXBT @cyrilXBT]

https://t.co/2WSbm5U8Ql

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@nikotin30x hahah so funny vid