If you see all regression problems under the foil of maximum likelihood estimation, you might not know that ordinary least squares regression has a closed form solution
If you're interested in this you can read more in Mostly Harmless Econometrics [1] about adressing this with IV methods
[1] http://www.development.wne.uw.edu.pl/uploads/Main/recrut_eco...
Regarding, 2SLS models, I find them more useful to account for endogeneity in the model rather than measurement error. After all, measurement error is usually unobserved (otherwise you would just take it out). 2SLS “just” reweigh the point estimates by identifying the good variation in the instrumented variable using the instrument (for example, using draft lottery results to account for the endogenous choice to attend college).
[1] https://eli.thegreenplace.net/2014/derivation-of-the-normal-...
I almost certainly know something I'd consider "trivial" that you haven't encountered yet. I try to be really excited when that happens.
There's very little content either, it's literally a reformulation of the formula, no interesting graphs or geometric interpretation. What I expected from a title like "Linear Regression By Hand" was the minimization of some quadratic error function, by hand (i.e. using pencil and paper).