Engineering and Research are for the most part “measurable” in terms of progress. Yet, not everyone has the same amount of success using the same tools. The thing which distinguishes some high performers from the rest is the wizardry that goes into creating the mental frameworks around these tools. It is deeply personal to its creators, yet it offers a great deal to learn from.
Following researchers and engineers write about there systems in a lot of detail. I hate the fact that this is highly concentrated in the domain of CS and Machine Learning. But a large majority of the subsystems apply across domains.
Note : This list is regularly updated with new perspectives and resources from the internet.
Check out their writings here :