Mael

♟️Watching the future before it becomes obvious.
Follow what’s being built in the shadows ♟️ I’m here for what happens next.
♟️

[Mael @mael_x88]

@R_ChajX Everyone is racing to build smarter AI.

The real race is happening underneath it: who can deliver the most compute without drowning in heat and power costs?

[Mael @mael_x88]

@R_ChajX AI gets the headlines. Compute gets paid every time AI runs.

[Chajkovska @R_ChajX]

HE WAS DELIVERING PIZZA FOR $17/HOUR.

NOW HIS GPUs MADE $13,800 IN 7 DAYS.

His name is Ethan Cole.

A year ago, Ethan spent most of his evenings driving around delivering pizza.

No tech job.
No startup.
No investors.

Then he noticed something:

Everyone was trying to make money BUILDING with AI.

Almost nobody around him was thinking about the thing AI desperately needs:

COMPUTE.

So Ethan started small.

He saved enough money to buy his first used GPU.

Then a second.

Then another.

Every extra dollar went back into hardware.

Eventually, his bedroom setup became a rack.

The rack became several servers.

And the servers became his own tiny AI data center.

6 GPUs.
24/7 operation.
No employees.
No fancy office.

He connected the machines to platforms where AI workloads could rent unused GPU compute.

Then he waited.

Day 1: $640
Day 2: $1,120
Day 3: $1,730
Day 4: $2,050
Day 5: $2,410
Day 6: $2,780
Day 7: $3,070

$13,800 in ONE WEEK.

The craziest part?

Ethan wasn’t building the next ChatGPT.

He wasn’t selling an AI course.

He wasn’t trying to create the next billion-dollar AI startup.

He was selling the one thing all of them need:

COMPUTE.

During the gold rush, everyone wanted the gold.

The smartest people sold the shovels.

AI may be the biggest gold rush of our generation.

And GPUs are the shovels.

[Mael @mael_x88]

@R_ChajX This is insane. Turning an $8,400 debt into a mission for an AI agent might be one of the smartest Grok experiments I’ve seen.

[Chajkovska @R_ChajX]

@mael_x88 Yes, it actually works. If you have any questions about how I set it up, just ask — happy to share what I’ve learned.

[Chajkovska @R_ChajX]

THIS IS THE FIRST VIDEO THAT BECAME AVAILABLE 99% OF PEOPLE WILL SCROLL PAST THIS VIDEO.

The other 1% might build something that makes them $15,000+ a month.

This video shows, step by step, how to build your own AI bot using Grok + Claude.

But the bot itself isn’t the interesting part.

The interesting part is what happens next.

Give it a specific job.
Connect it to the right tools.
Teach it your workflow.
Let it handle tasks that normally require hours of human work.

Now you’re not just chatting with AI.

You’re building a digital worker that can operate 24/7.

One bot becomes an assistant.
Five become a team.
A well-designed system becomes a business.

Customer support. Research. Content. Lead generation. Sales workflows. Data analysis. Automation.

The biggest opportunity in AI may not be creating the next foundation model.

It may be building profitable businesses on top of the models that already exist.

Most people will watch AI happen.

A much smaller group will figure out how to make AI work for them.

The first video shows exactly where to start.

Bookmark it.

Because six months from now, you might wish you had started today.

[Mael @mael_x88]

@R_ChajX Absolutely. This is brilliant — turning Grok + Claude into an actual business engine is where AI gets seriously interesting.

[Mael @mael_x88]

@R_ChajX Your future is rarely destroyed by one big mistake. It’s quietly built—or lost—by what you repeat when nobody is watching.

[Chajkovska @R_ChajX]

https://t.co/bxueoPjByL

[Mael @mael_x88]

@0xFinrex 30 years of debt for one house is wild when you actually see the math. At what point does renting + investing the difference win?

[Mael @mael_x88]

@MikeTheElite Taking more clients is easy. Keeping the quality while scaling is the real game. What broke first?

[shmidt @shmidtqq]

Nike spends $2M on a stadium shoot for a 10-second hero shot.

40-person crew. 3 days of permits. 2 weeks in post. Stunt doubles. Insurance paperwork thicker than the script.

This creator generated the entire sequence on a laptop for under $80.

A guy sprints across a stadium roof at night, leaps off the edge, free-falls into a sold-out arena, and lands on a giant football in the center of the pitch.

One continuous shot. No crane. No drone operator. No safety harness review board.

The toolchain behind this clip:

> Kimi K3 wrote the full shot sequence: rooftop sprint, jump arc, mid-air hang, landing impact, crowd reaction timing
> Kling 3.0 generated the fluid body physics and night lighting on the roof
> Seedance 2.5 rendered the free-fall, wind on the shirt, and the ball deformation on impact
> ElevenLabs produced the ambient stadium atmosphere and crowd noise
> CapCut handled speed ramps, color grade, and final export

A traditional production house would need a stadium rental at $150K, a stunt coordinator, a medical team on standby, and 6 months of liability clearance.

This entire sequence was prompted, rendered, and exported between dinner and sleep.

AI didn't replace the stunt double.
It replaced the entire stadium booking.

[Chajkovska @R_ChajX]

@mael_x88 That’s the part everyone wants to know. It wasn’t magic — the bot kept testing, optimizing and reinvesting while he slept. The $120 was just proof the loop worked.

[Mael @mael_x88]

@R_ChajX Wait, what was the bot actually doing overnight to turn $120 into a $78K/month operation?

[Chajkovska @R_ChajX]

HE WENT TO SLEEP WITH ONE AI BOT RUNNING. TWO MONTHS LATER, HIS GPU FARM WAS MAKING $78,000 A MONTH.

BY MORNING, IT HAD MADE $120.

A month later, his setup looked nothing like a normal PC.

This is the story of Ethan Cole, a 24-year-old from Louisiana who became obsessed with one question:

What happens if you give an AI trading system more computing power — and let it work while you sleep?

His first experiment was almost laughably small.

One computer.
One GPU.
One AI bot.
One night.

The bot monitored market data, searched for predefined setups and executed its strategy automatically.

When Ethan woke up, the experiment was up $120.

Not life-changing money.

But something about it bothered him.

Ethan had slept for eight hours.

The machine hadn’t.

It had watched the market the entire time.

So instead of spending the $120, he started rebuilding the system.

For the next month, almost everything went back into hardware, testing and infrastructure.

Eventually, his bedroom experiment became a 15-GPU setup.

More compute.
More parallel analysis.
More markets monitored simultaneously.

Then came the day that changed the scale of the experiment:

$1,800 in 24 hours.

Ethan didn’t celebrate.

He scaled.

Another month passed.

More hardware arrived.
Cooling became a problem.
Electricity became a serious expense.
The “computer” slowly started looking more like a miniature data center.

But the biggest improvement wasn’t the number of GPUs.

It was the architecture.

Instead of asking one AI to do everything, Ethan split the workload between specialized agents.

One watched momentum.
One tracked volatility.
One analyzed market structure.
One searched for anomalies.
One monitored risk.
Another could shut the entire system down when predefined limits were hit.

The machines searched.

The software coordinated.

Ethan supervised.

Eventually, in this hypothetical scenario, the operation reached roughly:

$78,000/month.

From one computer running overnight…

to a small autonomous trading operation working 24/7.

But here’s what makes this story interesting.

It isn’t really about trading.

And it isn’t really about GPUs.

It’s about leverage.

For centuries, making more money usually meant hiring more people, working more hours or deploying more capital.

AI introduces another possibility:

deploying more intelligence.

One person can now experiment with systems that research, analyze and operate while that person is doing something completely different.

That doesn’t mean an AI trading bot is a money printer.

Markets change. Strategies fail. Hardware costs money. Fees, slippage and bad risk management can destroy impressive-looking returns very quickly.

But the underlying idea is much bigger than trading:

What happens when one person can build a digital workforce that never sleeps?

Ethan started with one machine.

The next generation of entrepreneurs may start with 100 AI agents.

And their first “employee” might never be human.

[Coin Bureau @coinbureau]

NEW: OpenAI reportedly bought tens of thousands of Mac minis and Mac Studios to train computer-use agents through reinforcement learning.

While Anthropic rents similar Mac hardware through AWS, per The Information.

[alexei @alexeixbt]

you are letting random, incoming thoughts dictate your actual biological reality.

quazi johir breaks down the difference between incoming thoughts (opinions you absorb from society) and outgoing thoughts (deliberate commands you choose). most people unconsciously take incoming garbage and repeat it as their own outgoing reality.

the secret to absolute subconscious control? build a strict mental filter at the door. accept only the outgoing visions you choose, and attach heavy emotion exclusively to your ideal targets.

incoming opinions are just noise. outgoing choices are code. stop repeating the noise and start firing the code.

watch the video, then review the key concepts in the article below.

[Mael @mael_x88]

@0xHvdes The hardest decisions usually aren’t between right and wrong — they’re between two futures that both look right. Maybe intelligence is less about choosing perfectly and more about knowing which mistakes you can afford. What’s your framework when both paths make sense?

[Chajkovska @R_ChajX]

SHE MADE $18,300 THIS MONTH.
THERE’S JUST ONE PROBLEM — SHE DOESN’T EXIST.

No photoshoots.
No studio.
No salary.

She’s an AI model — built to create content, grow an audience and generate revenue 24/7.

And this isn’t some 2030 prediction.

People are building AI influencers right now and turning attention into real businesses.

The scary part?

Most people are still using AI just to write emails and generate funny pictures.

Meanwhile, others are building digital personalities that can make money while they sleep.

I broke down the entire AI model business — how it works, where the money comes from, and how people are building them.

Full article below ↓

[Mael @mael_x88]

@elonmusk The Kardashev scale gets more interesting when AI enters the equation. Humanity may be far below Type I today, but AI + robotics + abundant energy could compress centuries of progress into decades. The real question is: what becomes the bottleneck then?