The context is an ongoing question about optimization -- not my strong suit -- and the SciPy algorithms for this. See Scipy.optimization.anneal Problems for some additional confusion over simple things.

The new quote is this:

However, firing up Python, NumPy, SciPy and figuring out which solver to use is not convoluted? Keep on writing code and over engineering as opposed to using the minimum tech in order to get the job. After all, we are professionals.

It appears that using a packaged, proven optimizer is somehow "convoluted." Apparently, the Anaconda product never surfaced in a Google search. This seems to indicate that perhaps (a) Google was never used or (b) the author didn't get to page 4 of the search results, or (c) the author never tried another search beyond the single-word "scipy".

I'm guessing they did not google "Python simulated annealing" -- the actual subject -- because there are a fairly large number of existing solutions to this. Lots and lots of lecture notes and tutorials. It seems to be a rich area full of tutorials on both optimization and Python. Reading a few of these would probably have addressed all of the concerns.

Anaconda, BTW, appears to be an amazing product. It seems to be the gold standard for data science. (I know of organizations that have in-house variations on this theme They bundle Python plus numerous extra packages and a variety of installers for Mac OS X, Windows and Linux.)

The "Keep on writing code" complaint is peculiar. The optimization examples in SciPy seem to involve less than a half-dozen lines of code. Reading a CSV file can be digested down to four lines of code.

import cvs
with open("constrains.csv", newline="") as source;
    rdr= DictReader(source)
    data = list(rdr)

I can only guess that the threshold for "over engineering" is a dozen lines of code. Fewer lines are acceptable; more are bad.

I don't know what "using the minimum tech in order to get the job" means, but the context included an example spreadsheet that was somehow a solution to an instance of a problem. I'm guessing from this that "minimum tech" means "spreadsheet."

Read this: When spreadsheets go bad. There are a lot of war stories like this. (For information on the  original quote, read 'What is meant by "Now you have two problems"?')

I regret not asking follow-up questions.

The more complete story is this: rather than actually leverage SciPy, the author of the quote appears to be fixated on rewriting a classic Simulated Annealing textbook example into a spreadsheet because reasons. One of which is that more modern algorithms in SciPy aren't actually classic simulated annealing. The newer algorithms may be better, but since they're not literally from the textbook, this is a problem.

And my suggestion -- just use SciPy -- was dismissed as "convoluted", "over-engineering", and -- I guess -- unprofessional.