Discussions on sustainable ship recycling under the India–EU Trade and Technology Council are supporting closer cooperation and greater alignment with EU standards. The inclusion of two Indian ship-recycling facilities in the European List marks an important step in strengthening India’s presence in the EU ship-recycling ecosystem.
#ShipRecycling #IndianShipyards #IndiaEU #IndiaExports #DoC_GoI @PIB_India@RajeshAgrawal94
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♥️ I LOVE ALLAH♥️
#خاتم_النبیین_محمدﷺ#درود_وقرآن
With over 3 million requests processed, we are excited to continue building on @base alongside @baseapac, with support from @Nibel_eth.
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$200 in.
$100,000 out.
That's the trade a 22-year-old Indian developer just made.
Most people spend $200 on a course they never finish.
He spent it on AI tools.
Then built a game.
GTA India.
20,326 copies sold.
$5 each.
No shootouts.
No cartels.
No cops.
The missions are wedding logistics.
Hijack the limo convoy.
Fend off the rival family.
Get the bride to the temple on time.
Chaos everyone's seen in real life.
Turned into gameplay everyone wants to play.
While most developers were chasing Rockstar's formula, one person looked at his own culture and asked a different question.
What if the open world was mine?
Not LA.
Not Liberty City.
A wedding procession in traffic.
That's the insight most people miss.
You don't need a AAA budget to build a hit.
You need a story nobody else is telling.
AI didn't replace the creativity.
It removed the cost of testing it.
$200 used to be a rounding error.
Now it's a seed round.
The gap between an idea and a shipped product has never been this small.
Most people are still waiting for permission to build.
A smaller group is already shipping.
Save this. 👇
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QUANT SIGNAL SELECTION CAN TURN AI PIXEL PREDICTION INTO A COLD RULE FOR FINDING THE FEW INPUTS THAT ACTUALLY MOVE THE POSTERIOR
In 4K, the tempting story is clean:
compute 500,000 pixels,
let AI infer the other 7.5 million.
BOOKMARK this for signal selection.
The useful story is colder.
You do not compute everything.
You compute the few observations
that reduce uncertainty the most.
That is the difference between
a real quant signal
and a noisy dashboard.
A bad model tries to ingest every tick,
every ratio,
every headline,
every alternative data feed.
Then it hopes the neural net
finds money inside the pile.
A better model asks one question:
which small set of inputs
actually changes the posterior
before fees, latency, slippage,
and regime shifts eat the edge?
DLSS works because pixels are not independent.
Neighboring pixels, motion vectors,
depth, and rendered samples
carry structure.
The model can reconstruct the missing parts
because the scene has constraints.
Markets are meaner.
The missing data is often not missing
because you were clever enough to skip it.
It is missing because the counterparty has it,
the oracle resolves differently,
or the distribution changed
after everyone found the same feature.
So the rule is not:
use AI to predict the rest.
The rule is:
sample aggressively only where
the world has stable structure.
Then size as if the reconstruction
can fail exactly when it matters most.