CodeWithWho@CodeWithWho
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CodeWithWho
@CodeWithWho
Building software that solves real problems. Software Engineer | Frontend | Backend Shipping ideas to the internet.
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- CodeWithWho@CodeWithWhoTaking every customer from one list and rescanning a huge second dataset each time works, but it scales badly. #softwareengineering #algorithms Watch:000
- CodeWithWho@CodeWithWhoIntersection means unique common values. If 3 is already in the result, another 3 should not be added again. #arrays #algorithms Watch:0003
- CodeWithWho@CodeWithWhoOnce A[i] matches something in B, existence is confirmed. Add it and stop scanning for that candidate. #algorithms #python Watch:0002
- CodeWithWho@CodeWithWhoOne value from A can force a full scan of B when no match exists. That's where repeated work begins. #algorithms #optimization Watch:0006
- CodeWithWho@CodeWithWhoBrute force: take one unique value from A, scan B for a match, add it if found, then repeat. #algorithms #arrays Watch:0005
- CodeWithWho@CodeWithWhoAlgorithm choice depends on data shape. 1M vs 1M and 100 vs 10M may deserve completely different strategies. #softwareengineering #algorithms Watch:00113
- CodeWithWho@CodeWithWhon << m could mean 100 values vs 1,000,000. Same problem, very different data shape. #algorithms #optimization Watch:0007
- CodeWithWho@CodeWithWhon ≈ m means roughly the same scale, not exactly equal. Example: 1,000 vs 1,200 elements. #algorithms Watch:0005
- CodeWithWho@CodeWithWhoIf the problem tells you the arrays are sorted, use that information. Sorted order can eliminate unnecessary work. #algorithms #optimization Watch:0005
- CodeWithWho@CodeWithWhoArray intersection = values that exist in both A and B, with no duplicates in the result. #algorithms #arrays Watch:0027
- CodeWithWho@CodeWithWhoBrute force remembers used positions. Hashing remembers remaining quantity. The better representation removes repeated discovery. #optimization #hashtables Watch:0004
- CodeWithWho@CodeWithWhoEvery character in L may scan all of M: O(l × m), or roughly O(n²) when both inputs are similar in size. #bigO #algorithms Watch:00017
- CodeWithWho@CodeWithWhoBrute force repeatedly searches M. Hashing builds inventory once and consumes it. Same answer, much less repeated work. #algorithms #hashtables Watch:0004
- CodeWithWho@CodeWithWhoCount already 0? Fail immediately. Same logic applies to stock, seats, credits and quotas in real systems. #softwareengineering #hashtables Watch:0027
- CodeWithWho@CodeWithWhoSecond 'a'? No search. Read char_count['a'], decrement, continue. That's the hash-table advantage. #optimization #hashtables Watch:0003
- CodeWithWho@CodeWithWhoLetter demand just decrements inventory: a 2→1, b 1→0. No magazine rescan. #algorithms #hashtables Watch:0007
- CodeWithWho@CodeWithWhoOptimized debugger: watch the current char and char_count. Those two values show supply being built and consumed. #python #debugging Watch:0001
- CodeWithWho@CodeWithWhoM='aabc' becomes a→2, b→1, c→1 in the debugger. Supply is built once before demand starts. #python #hashtables Watch:0002
- CodeWithWho@CodeWithWhoTicket inventory uses the same idea: Standard→120, VIP→15, then bookings consume the counts. Resource → quantity → demand. #softwareengineering Watch:0005
- CodeWithWho@CodeWithWhoCount already zero and demand asks for another unit? Fail immediately. No need to search for inventory we already know is gone. #hashtables #optimization Watch:0003
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