⚡ Bolt: [performance improvement] Optimize search filtering and implement early returns

[MrAlokTech]

💡 What:

• Refactored renderPDFs to use explicit early returns (if (!condition) return false;) instead of eagerly evaluating all filter logic at the top of the function.
• Added a prepareSearchIndex function that calculates a _searchStr (lowercased combination of title, description, category, and author), _formattedDate, and _isNew flag during the initial database load (both from cache and network).
• Removed redundant date calculations and toLowerCase() calls from the render loop.

🎯 Why:

• In JavaScript, return A && B && C short-circuits, but if you evaluate A, B, and C into variables before the return statement, you lose the benefit of short-circuiting.
• Previously, matchesSearch was doing expensive string concatenation and includes() checks for every single item in the database on every keystroke, even if the item belonged to the wrong class or semester.
• This creates a massive performance bottleneck as the database grows, causing typing lag.

📊 Impact:

• Significantly reduces the CPU overhead of filtering the list.
• Changes an $O(N \times 4 \times \text{string_length})$ operation per keystroke into a vastly cheaper, early-exiting sequence. If an item doesn't match the current class or semester, the string search is completely skipped.
• Improves overall responsiveness and frames per second when users are rapidly typing in the search bar.

🔬 Measurement:

• Verified visually by loading the page and testing search logic—all items correctly render and filter as before.
• For profiling, you can artificially boost the mock database size to 5,000+ items and observe the main thread timeline in Chrome DevTools during search input. The time spent in renderPDFs will be drastically reduced compared to the baseline.

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PR created automatically by Jules for task 2719341189931633380 started by @MrAlokTech

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