@DaleStudy

Great job on implementing a variety of solutions across different problems! Your code is generally clear and follows good practices. Here are some constructive suggestions to enhance your solutions further:

1. Time/Space Complexity Annotations: Adding comments like # TC: O(n), SC: O(n) will help clarify the efficiency of your approaches, especially for larger inputs.

2. Contains Duplicate: Your hash-based method is efficient. To improve readability, you could use if nums[i] in count_dict: directly without keys() since in checks keys by default. Also, consider using a set for even cleaner code: return len(set(nums)) < len(nums).

3. Longest Consecutive Sequence: Your Hash approach is optimal. The commented sorting method is less efficient (O(n log n)) but worth noting. Your current implementation correctly achieves O(n) time.

4. Top K Frequent Elements: Using sorted() is fine, but for large datasets, Python's collections.Counter with most_common() can be more concise and potentially faster.

5. Two Sum: Your current solution works but has O(n^2) complexity due to in and index() on slices. A more optimal approach is using a dictionary to store visited numbers and their indices, achieving O(n).

6. House Robber: Your DP implementation is clear and efficient. For readability, consider adding comments explaining the DP relation: max loot up to house i.

7. Code Style: Always include necessary imports at the top (e.g., from typing import List) for completeness. Consistent indentation and spacing improve readability.

Overall, you're on the right track! Incorporating these suggestions will make your solutions more efficient and easier to understand. Keep up the excellent work!