Two different formulas for standard error of difference between two means I mostly see this formula when searching for a formula for the estimate of the standard error in difference between two means, and it is also used in this video.
$$\Delta=\sqrt{s_1^2/N_1+s_2^2/N_2}$$
But I've also seen this one (and this is the one my book uses):
$$\Delta'=\sqrt{\dfrac{\left(N_1-1\right)s_1^2+\left(N_2-1\right)s_2^2}{N_1+N_2-2}\left(\dfrac{1}{N_1}+\dfrac{1}{N_2}\right)}$$
As these are two very different formulas, how come they are used seemingly interchangeably?
 A: In both scenarios $\sigma_{1}$ and $\sigma_{2}$ are unknown.  The bottom formula is using the assumption that $\sigma_{1} = \sigma_{2}$ and attempting to estimate that shared variance by pooling all observations together and calculating a weighted mean.  Thus, the factor on the left plays the role of both $s_{1}^{2}$ and $s_{2}^{2}$ in the bottom equation.  This method is usually used when you have small sample sizes and the equal variance assumption is plausible.
A: There are two different versions of the two-sample t test in common usage.
Pooled. The assumption, often unwarranted in practice, is made that the two populations have the same variance $\sigma_1^2 = \sigma_2^2.$ In that case one seeks to estimate the common population variance, using both of the sample variances, to obtain what is called a pooled estimate $s_p^2$. 
If the two sample sizes are equal, $n_1 = n_2,$ then this is simply $(s_1^2 + s_2^2)/2.$ But if sample sizes differ, then greater weight is put on the sample variance from the larger sample. The weights use the degrees of freedom $\nu_i = n_i - 1)$ instead of the $n_i.$
The first factor under the radical in your $\Delta^\prime$ is $s_p^2.$ Under the assumption of equal population variances,
the standard deviation of $\bar X_1 - \bar X_2$ (estimated standard error)
is your $\Delta^\prime$. 
Consequently, the $T$-statistic is
$T = (\bar X_1 - \bar X_2)/\Delta^\prime$. Under the null hypothesis that population means $\mu_1$ and $\mu_2$ are equal, this $T$-statistic has Student's T distribution with $n_1 + n_2 - 2$ degrees of freedom.
Separate variances (Welch). The assumption of equal population variances is not made. Then the variance of $\bar X_1 - \bar X_2$
is $\sigma_1^2/n_1 + \sigma_2^2/n_2.$ This variance is estimated by $s_1^2/n_1 + s_2^2/n_2.$ So the (estimated) standard error is
$\Delta = \sqrt{s_1^2/n_1 + s_2^2/n_2}.$ So your first formula
is has typos and is incorrect. This may account for "ludicrous" difference you are getting. If $n_1 - n_2$, then you should get $\Delta = \Delta^\prime.$ But the two (estimated) standard errors will not necessarily be equal if sample sizes differ.
An crucial difference between the pooled and Welch t tests is that the Welch test uses a rather complicated formula involving both sample sizes and sample variances for the degrees of freedom (DF). The Welch DF is always between the minimum of $n_1 - 1$ and $n_2 - 1$ on the one hand and $n_1 + n_2 - 2$ on the other. So if both sample sizes are moderately large both $T$-statistics will be nearly normally distributed when $\mu_1 = \mu_2.$
The Welch $T$-statistic is only approximate, but simulation studies have shown that it is a very accurate approximation over a large variety of sample sizes (equal and not) and population variances (equal or not). 
The current consensus among applied statisticians is always to use the Welch t test and not worry about whether population variances are equal. Most statistical computer packages use the Welch procedure by default and the pooled procedure only if specifically requested.
