⚡ Bolt: [performance improvement] Optimize jaccardSimilarity memory allocation

[garridolecca]

💡 What: Replaced intermediate Set and Array allocations ([...a]) in the jaccardSimilarity utility function with manual iteration and counting. Uses the inclusion-exclusion principle (|A ∪ B| = |A| + |B| - |A ∩ B|) to compute the union size efficiently.

🎯 Why: The jaccardSimilarity function is called within the inner loop of clusterNewsCore, which has an algorithmic complexity of O(N²). The previous implementation allocated multiple new Sets and Arrays per call, causing high memory overhead and triggering frequent garbage collection cycles, which blocked the main thread (or worker) during large news clustering operations.

📊 Impact: Reduces memory allocations in jaccardSimilarity from O(N+M) to O(1). Time complexity is also improved from O(N+M) to O(min(N, M)) by iterating only over the smaller set. This significantly lowers GC pressure during the O(N²) clustering phase.

🔬 Measurement: Verified via npm run typecheck and npm run test:data. The optimization preserves the exact behavior of the original Jaccard similarity calculation while eliminating object allocations in the critical path.

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PR created automatically by Jules for task 1421925763801877681 started by @garridolecca

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