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When people discuss AI, isn’t it always about whose model is more powerful? More parameters, faster speed, lower cost… But honestly, I’ve recently come to realize one thing: no matter how smart a model is, it’s useless if it lacks one key capability—whether it can truly “see” what’s happening on the internet right now.
This reminds me of a pretty interesting project that I feel is being overlooked by the market.
Think about it: no matter how advanced today’s AIs are, at the end of the day, it’s like locking a straight-A student in an archive room—they can dig through all the old files but have no idea what’s happening outside in the real world. They rely on the historical data fed to them during training, but the internet changes every second, and these models simply can’t perceive real-time dynamics.
And projects like RSS3, which serve as Web3 data layers, are doing exactly this: enabling AI to capture open data streams both on-chain and off-chain in real time. Isn’t this the true technological breakthrough that can finally break the information filter bubble?
The real bottleneck is real-time capability; relying solely on historical data will eventually lead to failure.
The RSS3 approach is definitely interesting, but how is the market responding? Is anyone actually using it?
Without real-time data streams, no matter how smart AI is, it's just a sophisticated search engine.
Someone has finally touched on the real issue this time, but still, too many people are focused on trivial things like fine-tuning parameters.
To be honest, I need to think more about this RSS3 approach, but it really does seem like they're doing something no one else has thought of.
Exactly, today's AI is like using last year's stock prices to trade today—it's ridiculous.
Your analogy is perfect, haha. "Archive room PhD" really nails it.
Right, what's the point of just comparing speed and price? Real-time perception is the real game-changer. That's the key breakthrough.
Why does Web3 always seem to have these hidden gems? I need to pay more attention.
This idea is definitely underrated. I think the logic behind RSS3 is worth exploring.
No matter how powerful the model is, it's just a repeating machine without real-time feedback, and that's the core issue.
The real gap lies in real-time capabilities. I need to think more about this RSS3 approach.
The real-time data stream aspect is definitely underrated, and I think RSS3's approach is worth exploring.
Instead of just focusing on parameters, it's better to think about how to truly let AI keep up with the pace of the internet.
I honestly never thought about it from this angle—it's quite interesting.
Connecting on-chain and off-chain data—just thinking about it makes me realize the huge potential here.
I've long seen that RSS3 is seriously undervalued. The Web3 data layer is bound to explode.
A straight-A student stuck in the archives, haha. That metaphor is perfect. GPTs really do live in the past.
No one is seriously working on on-chain data streams, but the potential is huge.
What's the use of feeding offline data? The internet changes every day, with constant news—this is the real pain point.
The real issue is right here: real-time capability. Without it, having more parameters is useless.
RSS3 does have some merit in this area, but it gets overlooked because the market is fixated on the parameter numbers of large models.
The real bottleneck is right here: data gaps. I never thought about it from the RSS3 angle—pretty interesting.
AI today is just going in circles in the dreams of the past. Wake up, everyone.