7 timeless lessons of programming graybeards

The piece says experienced programmers know things that cannot be learned quickly from current trends. It frames those lessons as hard-earned responses to real limits in software systems, especially when code has to scale beyond a small test case.

Reading notes

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• Memory is still limited, so programmers need to manage allocation and cleanup instead of assuming garbage collection or cloud RAM will solve the problem.
• Networks are slow, so code should do as much work locally as possible and send only the smallest final result to remote services.
• Compilers and interpreters can have bugs, so debugging sometimes has to test the tools as well as the application code.
• Users notice delay quickly, and slow interfaces or heavy browser-side code can make an application feel unusable.
• The public web is slower than an office network, so demos can hide delays that real users on weaker connections will still face.
• Algorithmic complexity matters because code that looks fine on small inputs can become unusable when data grows.
• Libraries and APIs can hurt performance when they are used in hot paths, especially if they add repeated parsing or other hidden overhead.