Your Python loops are probably slower than they need to be. One simple decorator can instantly bo...

[00:00:00]Your Python loops are probably slower than they need to be.

[00:00:03]One simple decorator can instantly boost performance, especially for recursive functions or repeated calculations.

[00:00:10]Meet functools.lru_cache.

[00:00:13]This isn't just for fancy data structures. It's a powerful tool built right into Python for memoization.

[00:00:20]Here's how it works. You wrap a function with @functools.lru_cache(maxsize=None).

[00:00:27]The first time your function runs with specific arguments, it computes and stores the result.

[00:00:32]The next time it's called with those *exact same arguments*, lru_cache skips the computation entirely and just returns the stored result.

[00:00:42]Think Fibonacci.

[00:00:44]Without lru_cache, fib(30) might recalculate fib(28) countless times.

[00:00:51]With it, fib(28) runs once, and subsequent calls are instant lookups.

[00:00:56]This dramatically cuts down redundant work, transforming exponential time complexity into something much more manageable for specific problem types.

[00:01:05]It's perfect for algorithms with overlapping subproblems or API calls where the response rarely changes.

[00:01:12]Just remember, maxsize=None means it stores *everything*, which can consume memory.

[00:01:19]For a bounded cache, set a specific maxsize.

[00:01:23]Want to debug your async code better?

[00:01:26]I'll cover