First off, I should note that I'm still not really sure what 'Bayesianism' means; I'm interpreting it here as "understanding of conditional probabilities as applied to decision-making".

No human can apply Bayesian reasoning exactly, quantitatively and unaided in everyday life. Learning how to approximate it well enough to tell a computer how to use it for you is a (moderately large) research area. From what you've described, I think you have a decent working qualitative understanding of what it implies for everyday decision-making, and if everyday decision-making is your goal I suspect you might be better-served reading up on common cognitive biases (I heartily recommend /Heuristics and Biases/ ed Kahneman and Tversky as a starting point). Learning probability theory in depth is certainly worthwhile, but in terms of practical benefit outside of the field I suspect most people would be better off reading some cognitive science, some introductory stats and most particularly some experimental design.

Wrt your goals, learning probability theory might make you a better programmer (depends what your interests are and where you are on the skill ladder), but it's almost certainly not the most important thing (if you would like more specific advice on this topic, let me know and I'd be happy to elaborate). I have examples similar to dhoe's, but the important bits of the troubleshooting process for me are "base rate fallacy" and "construct falsifiable hypotheses and test them before jumping to conclusions", not any explicit probability calculation.