The basic grouping pass felt fine, sliding through the list and collecting photos as I hit them.
Start by clarifying the requirements: how to handle incomplete groups (fewer than 3 photos) and whether the sort should be stable. Then outline a two-pass algorithm: first, scan the feed to group consecutive photos into chunks of up to 3, computing the max score for each group; second, sort all objects (including groups) by score descending. Discuss trade-offs like time complexity, memory usage, and stability.
Pro tip: Mention that grouping photos changes the number of objects, which can affect pagination or downstream ranking; propose a stable sort to preserve original order for ties, and consider streaming if the feed is large.
Ask how to handle incomplete groups (e.g., 1 or 2 photos at the end) and whether the sort should be stable. Confirm if grouping is based on consecutive photos in the original feed order.
Iterate through the feed, collecting consecutive Photo objects into a buffer. When the buffer reaches 3 photos or a non-photo is encountered, create a grouped object with max score and reset the buffer.
Decide whether to leave remaining photos ungrouped, group them anyway with max score, or drop them. Justify your choice based on product requirements (e.g., preserving content vs. consistency).
After grouping, sort all objects (original and grouped) by score descending. Use a stable sort to maintain relative order of equal scores, which is important for deterministic results.
Discuss time complexity (O(n log n) due to sorting) and space complexity (O(n) for output). Mention potential optimizations like streaming or in-place grouping if memory is constrained.
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