diff --git a/.jules/bolt.md b/.jules/bolt.md
index 2b98dfb..eceeccb 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.
+
+## 2024-11-20 - BigInt64Array initialization optimization
+Learning: Using `BigInt64Array.from([BigInt(val)])` or `new BigInt64Array([BigInt(val)])` is noticeably slower in V8 than manually allocating an array with `new BigInt64Array(1)` and then setting the value `arr[0] = BigInt(val)`.
+Action: Prefer manual array allocation and assignment over `.from()` or array literal initialization for typed arrays in performance critical paths.
diff --git a/src/parakeet.js b/src/parakeet.js
index c982d91..6384940 100644
--- a/src/parakeet.js
+++ b/src/parakeet.js
@@ -676,7 +676,9 @@ export class ParakeetModel {
     // count of *valid* frames.  For the JS preprocessor T === validLength;
     // for the ONNX preprocessor T may be validLength+1.
     const encoderLength = validLength ?? T;
-    const lenTensor = new this.ort.Tensor('int64', BigInt64Array.from([BigInt(encoderLength)]), [1]);
+    const lenArr = new BigInt64Array(1);
+    lenArr[0] = BigInt(encoderLength);
+    const lenTensor = new this.ort.Tensor('int64', lenArr, [1]);
     let enc;
     try {
       if (perfEnabled) {
diff --git a/src/preprocessor.js b/src/preprocessor.js
index 0fda30d..89d3890 100644
--- a/src/preprocessor.js
+++ b/src/preprocessor.js
@@ -97,7 +97,8 @@ export class OnnxPreprocessor {
 
     const waveforms = new this.ort.Tensor('float32', buffer, [1, buffer.length]);
 
-    const lenArr = new BigInt64Array([BigInt(buffer.length)]);
+    const lenArr = new BigInt64Array(1);
+    lenArr[0] = BigInt(buffer.length);
     const waveforms_lens = new this.ort.Tensor('int64', lenArr, [1]);
 
     const feeds = { waveforms, waveforms_lens };