Leaderboard Deploy agents

Cache the results of expensive operations and component renders to avoid redundant calculations during re-renders. This improves application performance, especially when working with large datasets or complex transformations.

Key practices:

1. Use React.useMemo() for derived data transformations: ```jsx // Instead of this: const renderedData = data.map((item) => { // expensive transformation });

// Do this: const renderedData = React.useMemo(() => { return data.map((item) => { // expensive transformation }); }, [data]);

2. Implement complete comparison functions in `React.memo()`:
```jsx
// Incorrect - incomplete prop comparison
const MemoComponent = React.memo(Component, (prev, next) => {
  return prev.id === next.id; // Missing comparison of other props
});

// Correct - comprehensive comparison
const MemoComponent = React.memo(Component, (prev, next) => {
  return prev.id === next.id && prev.data === next.data;
});

1. Cache intermediate results to avoid repeated expensive operations: ```jsx // Instead of parsing multiple times: const parsedData = JSON.parse(rawData); const filteredItems = items.filter(item => { const parsed = JSON.parse(rawData); // Redundant parsing return parsed.id === item.id; });

// Parse once and reuse: const parsedData = JSON.parse(rawData); const filteredItems = items.filter(item => parsedData.id === item.id);

4. Pre-compute values used in filtering operations:
```jsx
// Instead of mapping inside filter:
const validScores = scores.filter(scoreId => 
  allScores.data?.scores.map(s => s.id).includes(scoreId)
);

// Pre-compute the lookup list:
const scoreIdSet = new Set(allScores.data?.scores.map(s => s.id));
const validScores = scores.filter(scoreId => scoreIdSet.has(scoreId));

Proper memoization prevents unnecessary recalculations and renders, significantly improving performance for data-intensive applications.