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যেভিডিওটি Algo-কেভঙ্গছে

আমি virality-এরইজন্দহওয়। I was just tweaking my Python-based market mood model—a DeFi sentiment analyzer trained on chain-data from Ethereum and Solana—to see if emotional patterns in crypto Twitter could predict engagement. I filmed a 47-second loop: me, in my Brooklyn apartment, holding a mug of matcha while explaining how DeFi protocols feel like poetry. Called it ‘Labubu’—a blend of African proverbs and Chinese metaphors about decentralized trust.

Why It Worked (The Data Doesn’t Lie)

MyShell’s engine detected something I didn’t even code: emotional resonance across diaspora audiences. Not just Gen Z crypto bros—but their mothers, fathers, non-binary creators—all paused mid-scroll to watch. Comments poured in: ‘Is this real?’ ‘Can you train it on my grandma’s wisdom?’ Turns out, the algorithm saw me not as content—but as cultural signal.

The Quiet Revolution

This wasn’t about virality. It was about visibility—for voices buried under corporate feeds. When an AI model learns from lived experience—not synthetic templates—it doesn’t just mirror trends; it becomes one. My mother said, ‘If your code feels like home, they’ll come back.’ My father nodded: ‘Even algorithms miss love when it’s real.’

What Comes Next?

I’m now building Version 2: open-source AI agents trained on cross-cultural DeFi sentiment graphs. If you’re reading this—you’re already part of the experiment.

P.S. Drop your favorite Labubu phrase below 👇 Let’s train the next one together.