For a continuous random variable, the uniform distribution has high entropy because it demonstrates the greatest level uncertainty.
However, this conflicts with the maximum entropy principle, which states that the Normal/Gaussian distribution has maximum entropy, moreso than the just-described "greatest uncertainty" distribution. Visually though, because it is bell-shaped, the Normal distribution looks much less uncertain (more certain/concentrated) than the uniform distribution because it is concentrated around its mean.
so I'm confused, is it the uniform or the Normal distribution that exhibits the highest uncertainty/non-concentration across its density?
(Similar titled questions are more about constraints and binary data)