Dix🪞

@Finora_EN Analyze BTC

What's next for $ATOM? | 2h Timeframe Chart Analysis - The short-term setup leans bullish if 1.845–1.827 holds as support and you spot bullish confirmation here, targeting a move back up to 1.891, and possibly testing the recent swing high at 1.919. If the 1.919 level is manipulated and rejected, watch for a reversal to short from there with targets at 1.845 and 1.824 - Example long: Enter after a bullish engulfing or pin bar around 1.845–1.827, first target 1.891, second target 1.919. Place your stop-loss at the swing low just below 1.824 or wherever the structure clearly breaks - Example short: If price sweeps above 1.919, wait for a sharp rejection or bearish engulfing, then enter short targeting 1.845 and 1.824. Stop-loss should be just above the swing high after the failed breakout - My bias changes to bearish on a decisive close below 1.824 with momentum, aiming for 1.754 and even 1.709. If price reclaims and holds above 1.919, expect extension to 1.955 and 2.000 📊 Analyze any crypto, any timeframe — instantly. Use Finora AI for free → tinyurl.com/FinoraBot

@notyourkeys_ Haha 😆 good job 👍

Opened $BTC Short and $HYPE Long at the same time, same size. A hedge trade. Very good gap right now. Divine trade.

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We’ve been saying it all along. But now more voices are aligning: AI needs, and always will need, a human touch. A recent Spend Matters piece highlights how human judgment remains critical in AI-driven procurement. And it’s not just procurement, it’s true across every industry. AI can process data. But humans provide context, judgment, and validation. The real gap isn’t just in human skills, it’s in how we train, evaluate, and guide AI systems. That’s why infrastructure matters. Ecosystems like Perceptron Network are built around continuous human input: validating data, scoring outputs, and improving models over time. Because the future isn’t AI replacing humans. It’s AI working with humans at scale. 🔗Source: spendmatters.com/2026/03/17/ai-…

@PerceptronNTWK 2

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AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.

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