Copybara

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[Copybara @Copybaraxxx]

https://t.co/qQbJ3Ru90w

[Copybara @Copybaraxxx]

@NEXORAResearch nobody’s running trials on local llama, they’re writing emails and debugging loops. that’s 90% of the $200/mo bill right there.

[Copybara @Copybaraxxx]

A $600 USED RTX 3090 KILLED HIS $200/MO CHATGPT PLUS + CLAUDE PRO + MIDJOURNEY STACK IN 40 DAYS, NOW HE RUNS EVERY PROMPT FOR THE COST OF ELECTRICITY

00:14 marcus opens his bank statement and says, “I was paying $200 a month to rent intelligence I could just own outright.”

the math: ChatGPT Plus $20/mo + Claude Pro $20/mo + Midjourney $30/mo + random API overages $130/mo = $200+/mo, every single month, forever, with zero asset at the end of it

marcus buys one used RTX 3090 for $600 off ebay, drops it into an old gaming PC he already had, installs ollama and a local Stable Diffusion build in one afternoon

now he runs Llama 3.1 70B quantized for chat and coding, and SDXL for images, both fully local, both his, zero subscription, zero rate limit, zero “you’ve reached your usage cap” message

electricity cost to run it: about $4/mo. hardware paid for itself in 3 months. everything after that is pure margin

he’s not paying anyone rent anymore. he owns the factory.

the window where a $600 card outperforms a $200/mo subscription stack is closing fast — follow before it does

[Copybara @Copybaraxxx]

https://t.co/qQbJ3Ru90w

[Copybara @Copybaraxxx]

@elonmusk Dope

[Copybara @Copybaraxxx]

@elonmusk The /dashboard multi-agent view is the actual unlock here, not the daily improvements. Managing agents through tab-jumping was always the friction point — seeing all of them in one TUI view changes how you'd actually run parallel tasks day to day.

[Copybara @Copybaraxxx]

@NEXORAt6g Periodic anchor, not every frame — feeding it every generation was actually worse, made the movement stiffer and less natural. Re-anchoring every few clips keeps the face locked without killing the motion quality.

[Copybara @Copybaraxxx]

MOST PEOPLE PITCH SUBSCRIPTIONS. I LET AN AI CHARACTER EARN THEM ONE SONG REQUEST AT A TIME, AND IT'S CLEARED $6,000 SO FAR

Started the same way — saw a bunch of dancing AI girls doing numbers on YouTube and figured I'd try building one myself. Took way more failed prompts than expected to get a face and style that stayed consistent video after video instead of generating a new character every time.

Didn't expect it to go anywhere. Posted the first few, people started requesting songs in the comments, and it turned into subscriptions, custom orders, and tips stacking on top of each other. Over $6,000 combined so far.

Different niche than the post above, same lesson — the barrier to entry on this stuff is way lower than people think.

[Mavern @Mavernxbt]

https://t.co/2K5wLAwpAr

[Copybara @Copybaraxxx]

That's the real answer, yeah — demand showed up almost immediately once the character was solid. The consistency fix wasn't one tool, it was feeding a locked reference image/embedding back into every generation instead of trusting the prompt alone to hold the face. Everything downstream got way more reliable after that.

[Copybara @Copybaraxxx]

Love seeing performance-based pricing beat flat fees like this. Doing something similar on the content side — an AI model that dances to requested songs, monetized through subs and custom orders instead of one-off payments. The "paid on results" model just works better everywhere.

[Copybara @Copybaraxxx]

@NEXORAt6g Less interested in the parameter count and more in the $2.49 vs $5.07 per coding task number from independent benchmarks. That's the kind of gap that actually changes which model gets used in production.

[Copybara @Copybaraxxx]

@blknoiz06

[Copybara @Copybaraxxx]

AI stopped being “a chatbot” a while ago.
Now people use it for:
→ health symptoms before the
doctor’s appointment
→ relationship advice at midnight
→ deciding what to invest in
→ writing the text they’re too nervous to send

We didn’t plan to trust a machine with the most human parts of our lives. It just… happened.

And the people who own their AI setup (not rent it) are the ones actually building on top of this shift.

[Copybara @Copybaraxxx]

I BUILT AN AI MODEL WITH A KOREAN APPEARANCE THAT DANCES TO WHATEVER SONG PEOPLE REQUEST, AND IT'S MADE ME OVER $6,000 SO FAR

There are a dozen ways to make money with AI right now — subscriptions, renting out agents to businesses, the usual list. The one that actually caught my attention was different: AI-generated video content. Personalized birthday clips, prank videos, dancing characters. That last one specifically — I kept seeing dancing AI girls doing numbers on YouTube and figured I'd try building one myself.

Took longer than expected. Ran through a bunch of prompt variations and workflow attempts before landing on something that actually held a consistent face and style across videos instead of a new character every generation.

Here's the exact prompt structure that finally worked:

prompt: "A stylish Korean woman performing a modern K-pop dance, cinematic lighting, realistic movement, 4K" duration: 8s resolution: 1080p fps: 30

Didn't expect it to go anywhere honestly. Posted the first few, people started requesting songs in the comments, and it snowballed into three separate income streams — a paid subscription for the full video library, custom song requests people pay for directly, and tips coming in on top of both. Over $6,000 combined so far.

Still not treating it as more than a side experiment. The numbers are starting to disagree with me.

Follow if you want to see where this goes.

[Bober_smart @Bober_smart]

https://t.co/OwsFmXOLGQ

[Copybara @Copybaraxxx]

@AndrewYang “Total Eclipse of the Heart” hits different when you actually sit with the lyrics. RIP to a voice that defined a generation. 🕯️

[Copybara @Copybaraxxx]

@Klarow4 BRO THIS IS FIRE😂

[Copybara @Copybaraxxx]

https://t.co/RNd8k0Wt30

[Copybara @Copybaraxxx]

SPENT $3K ON A SERVER, MADE $12K BACK THE FIRST MONTH. NOT SELLING API CALLS, OWNING THE BOX

Okay so here's the thing nobody really explains when they post the revenue screenshot. The $3K didn't buy magic. It bought a used server, some RAM, and about two weeks of me swearing at BIOS settings trying to get it to boot reliably.

Most people doing "AI as a service" right now are just reselling API access with a nicer wrapper. You pay per call, your margin shrinks every time usage spikes, and if the provider changes pricing you're stuck. That's not what this is. This is a physical box sitting in a room, running the agents directly, plugged into power and nothing else.

The setup phase was honestly the annoying part. Getting a server stable enough that you can walk away from it for a week and not worry about it randomly dropping offline — that took longer than I expected. There's no tutorial for "make sure your hardware doesn't embarrass you in front of a paying client." You just find out the hard way.

Once it was actually stable though, something clicked. Client number one cost me the full $3K, basically. Client number two, three, four — almost free. Same box, same uptime, just more work running through it. That's the part that made the math work out to $12K instead of some smaller number that barely covers the hardware.

I'm not saying this is passive. I checked on it more than I want to admit in week one. But it's a different kind of work than answering support tickets or manually running reports for people. It's closer to owning a small piece of infrastructure than running a service.

Still early days on this, but the trajectory from month one alone is enough that I'm doubling down instead of treating it like a one-off win.

Follow and bookmark — I'll keep posting the real numbers as this thing runs longer, not just the good months.

[Copybara @Copybaraxxx]

https://t.co/qQbJ3Ru90w

[Flandermaxx @Flandermaxx]

https://t.co/m5r4nVxag2

[Maxsim @OddsLedger]

An agent that can't make mistakes is not an agent

One phrase turned my understanding of AI agents upside down:

"You don't prompt a model. You build a system that prompts itself."

Most people think that an agent is a chatbot with a long memory.

In fact, a real agent is a cycle: plan → action → check → fix the error → try again.

Without this cycle, you just have an expensive answering machine that looks good in a demo and breaks in real work.

The difference between "working in a demo" and "working in production" is precisely in this cycle.

[Gipp 🦅 @gippp69]

HE CLIPPED A $70 BATTERY BACKPLATE ONTO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A PORTABLE AI WORKSTATION

00:07 the backplate snaps onto the mac mini beside the ipad dock, no case swap, no extra box, just one module locking into place and taking over power.

that changes the use case completely. instead of being chained to one outlet, the mac mini can run from a bag, keep a session alive, and move with the same workflow anywhere.

the numbers are what make it interesting: a $599 mac mini, an around $70 battery backplate, a 10,000 to 20,000 mAh pack, and one tiny dock turning a desk box into something you can actually carry.

one clip on module carries the load by itself. same session, same apps, same local files, no reload, no wall charger, no moment where the machine has to stop working.

this is no longer just a desktop. it becomes a portable ai machine that can keep working after you leave the desk.

bookmark this before every mini pc starts shipping with a battery built in.

[Copybara @Copybaraxxx]

@Kingsholz Hey