diff --git a/.jules/bolt.md b/.jules/bolt.md
index 2b98dfb..c372f04 100644
--- a/.jules/bolt.md
+++ b/.jules/bolt.md
@@ -13,3 +13,7 @@ Action: Apply loop unrolling for max reductions in high-frequency typed array op
 ## 2024-11-20 - Softmax math.exp 8x unrolling with local var cache
 Learning: Unrolling the `Math.exp` accumulation loop to 8x and caching the multiplication `(tokenLogits[i] - maxLogit) * invTemp` into local variables before passing to `Math.exp` yields a measurable performance improvement (~4%) over the previous 4x unrolled implementation in the V8 engine, by reducing property access and allowing better instruction-level parallelism.
 Action: Utilize 8x loop unrolling paired with local variable caching for tight floating-point accumulation loops over TypedArrays.
+
+## 2024-11-23 - TypedArray Single Element Initialization
+Learning: Replacing array literal initialization (e.g., `new Int32Array([1])`) with explicit sizing and assignment (e.g., `new Int32Array(1); arr[0] = 1;`) is an unjustified micro-optimization. In the context of ONNX model execution, this change will not yield a measurable performance improvement and directly violates the strict instruction: "Never do: Trade readability and maintainability for micro-optimizations".
+Action: Never perform speculative micro-optimizations that trade readability for negligible/unmeasurable theoretical gains. Focus on clear bottlenecks (like reactive updates or DOM operations).
diff --git a/src/mel.js b/src/mel.js
index 236c14c..75d3467 100644
--- a/src/mel.js
+++ b/src/mel.js
@@ -339,11 +339,13 @@ function fft(re, im, N, tw) {
   for (let len = 16; len <= N; len <<= 1) {
     const halfLen = len >> 1;
     const step = N / len;
-    for (let i = 0; i < N; i += len) {
-      for (let k = 0; k < halfLen; k++) {
-        const twIdx = k * step;
-        const wCos = tw.cos[twIdx];
-        const wSin = tw.sin[twIdx];
+    // Optimization: Loop interchange to hoist twiddle factor lookups.
+    // Inner loop iterates over 'i' instead of 'k'.
+    for (let k = 0; k < halfLen; k++) {
+      const twIdx = k * step;
+      const wCos = tw.cos[twIdx];
+      const wSin = tw.sin[twIdx];
+      for (let i = 0; i < N; i += len) {
         const p = i + k;
         const q = p + halfLen;
         const tRe = re[q] * wCos - im[q] * wSin;