Many people look at @dgrid_ai and start with the technology, like distributed computing and AI scheduling, but my personal feeling is that these aren't actually the most critical factors.

What really determines whether this project can succeed is whether the network itself can achieve scale. Are there enough compute providers? Is there stable demand? And can the two sides continuously match?

From a user's perspective, it's actually very practical. You don't care about technical details; you care about whether it works, if the price is reasonable, and if the results are stable.

In my experience using it, I can feel it's moving in this direction, but it's still in a fairly early stage, and the ecosystem hasn't fully expanded yet.

However, one thing is certain: once this type of project crosses a critical threshold, it enters a positive feedback loop—more resources lead to more demand, which in turn attracts even more resources.

So dgrid gives me the impression of a typical network-based project. Its value won't grow linearly; it depends on whether it can achieve scale effects.

This type of project requires patience, but once it takes shape, it will have enormous potential.

Its key isn't technology; it's whether the network can actually get off the ground.

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