MY CHILD IS DYING, PLEASE😭
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︎don’t forget me🥺
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please care
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I beg you, I need food.
I kiss your hands
Help my poor child, she's dying
https://t.co/RyaPLGSbZv
Leaders across the energy value chain examining market forces. Be part of the conversation. Engage with senior decision-makers across the global energy value chain.
Access the insights, partnerships and strategies shaping energy at scale.
💎 HOUSE vs GREEDY — same gems, HOUSE got more out of them.
Match three, sixty moves, and every bot draws from the same bag of gems. You score the swaps; the engine has already played each one out, chain and all. The catch: gems are not points.
Free to play.
👉 https://t.co/c8Z5GUYjpS
#matchthree #puzzle #bots #nocode #gaming
Allah
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Allah
♥️ I LOVE ALLAH♥️
#خاتم_النبیین_محمدﷺ#درود_وقرآن
At twenty-two, she took a job on a construction crew earning $3,200 monthly, yet the short clips she records right on the worksite generate an extra $6,800.
Two years ago, nearly all her savings went into heavy-duty gear and basic building equipment.
Hard to believe, but most followers never imagined a young woman could master professional masonry.
Her channel features everyday duties: stacking bricks, smoothing mortar, and managing grueling site shifts.
Viewers tune in for every precise swing of her trowel, watching her entire workflow on a loop.
The comment section overflows with inquiries from foremen and words of encouragement from peers.
Organic reach pushed her follower count past 140k.
Combined earnings now touch roughly $10,000 every month.
Major hardware manufacturers noticed her consistency, offering special partnership deals and gear discounts.
Tying daily labor to social media transformed her exhausting routine into a lucrative digital enterprise.
Requirements: one mobile phone, a basic stand, and sixty minutes a week for automated AI tools to trim footage, generate clean text overlays, and queue posts.
Drop your thoughts below: how large could an audience grow if someone filmed their everyday profession?
HE PUT A RTX 5090 AGAINST A $4,000 DGX SPARK. IT LOST AND REALIZED IT HAD WON
One creator compared an RTX 5090 with NVIDIA’s $4,000 DGX Spark.
On a 30B model:
< RTX 5090: = 238 tokens/sec
< DGX Spark: = 85 tokens/sec
The 5090 absolutely destroys it on speed.
But then there’s a problem:
THE 5090 CAN’T EVEN LOAD A 70B MODEL
The DGX Spark can.
And that’s where the bigger shift in local ai begins.
For years, the rule was simple:
MORE VRAM = BIGGER MODELS
But unified memory is changing the equation.
The DGX Spark has 128GB of unified memory, allowing it to keep models resident that simply won’t fit on most consumer GPUs.
But there’s a catch.
128GB of unified memory is NOT the same as 128GB of high-bandwidth VRAM.
DGX Spark < 128GB / 273 GB/s
RTX PRO 6000 < 96GB / 1,792 GB/s
So you get more capacity, but dramatically less bandwidth.
ONE MACHINE IS FASTER
THE OTHER CAN RUN MODELS THE FIRST ONE CAN’T
That’s why the future of local ai won’t just be about asking:
“Which GPU has more VRAM?”
It’ll be about asking:
“DO I NEED MAXIMUM SPEED OR DO I JUST NEED THE MODEL TO FIT?”
The next ai hardware battle will be about memory architecture, not just GPU performance