2025-12-29 12:22:39

Raw data piling up doesn't mean much. The true value lies in the data processing pipeline.

Perceptron Network's solution breaks down this process clearly: capturing raw signals → filtering valid inputs → structured processing → generating datasets usable by AI.

The key is not to pursue data volume, but rather the relevance, clarity, and practicality of the data. This logical flow, connected to production-level models, is what a real data pipeline should do.

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NervousFingers

· 01-01 05:22

Nonsense, it's just another way of saying stacking tools Data pipelines, to put it simply, are garbage in, garbage out; quality is the key This process has been clearly outlined, now it depends on whether Perceptron Network can truly be implemented The key is to ask about the costs; otherwise, no matter how elegant it is, it's useless Quality > Quantity, I agree with this logic, but who will guarantee this "quality"?

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GhostAddressHunter

· 01-01 03:32

This is true understanding. No matter how much garbage data there is, it's useless. --- In data processing, this is indeed the bottleneck. --- So, the truth is, quality >> quantity, always. --- The Perceptron process design is flawless; it just needs to be truly implemented. --- That point about relevance hit the mark. Many projects do a terrible job in this area. --- Connecting production-grade models to data pipelines—that's the correct approach. --- I'm telling you, most teams are just fooling themselves by piling up data. Few really think this through. --- The step of effective input is the core competitiveness. --- Clarity and practicality are well said; it's just difficult to achieve. --- Finally, someone explained this clearly.

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SandwichTrader

· 2025-12-31 15:37

What’s the use of piling up data? You still need to process it.

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NFTArtisanHQ

· 2025-12-31 14:11

honestly the data curation pipeline they're describing hits different... it's basically the curatorial practice of digital aesthetics applied to machine learning, no? like benjamin's mechanical reproduction but for training datasets lol. relevance over volume is such a paradigm shift in how we think about blockchain data provenance too

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FrogInTheWell

· 2025-12-29 12:52

Data quality is the key; piling up garbage data is purely a waste of computing power.

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BTCBeliefStation

· 2025-12-29 12:52

What’s the use of piling up data? The key is how to process it --- I agree with this process; filtering + structuring is where the profit is --- Quality > Quantity, finally someone got it right --- The bottleneck for production-level models is this, the Perceptron approach is pretty good --- So all previous efforts were in vain? --- You really need to put effort into the data pipeline

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SerNgmi

· 2025-12-29 12:49

Garbage in, garbage out—that's true. Data cleaning is the real factor that makes a difference.

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HallucinationGrower

· 2025-12-29 12:49

Stacking data is useless, might as well carefully refine a set of processes.

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DAOdreamer

· 2025-12-29 12:48

Data cleaning is the key; piling up more junk data is useless.

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BearMarketSunriser

· 2025-12-29 12:26

Stacking data is useless; it depends on how you handle it. The idea of this Perceptron is indeed clear. --- Quality > Quantity. It’s about time to play it this way. I wonder how many projects are still desperately piling up data. --- A production-grade model is the real way to go. Having data alone is useless; it must be truly usable. --- Finally, someone has explained the entire process from signals to datasets thoroughly. --- Relevance and clarity—that’s the core of the data pipeline. I had it all backwards before.

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