Docs: Incorrect casting instructions

Author: ashvardanian

README.md

@@ -448,7 +448,7 @@ console.log('Squared Euclidean Distance:', distance);
 ```
 
 Other numeric types and precision levels are supported as well.
-For double-precsion floating-point numbers, use `Float64Array`:
+For double-precision floating-point numbers, use `Float64Array`:
 
 ```js
 const vectorA = new Float64Array([1.0, 2.0, 3.0]);
@@ -457,11 +457,11 @@ const distance = cosine(vectorA, vectorB);
 ```
 
 When doing machine learning and vector search with high-dimensional vectors you may want to quantize them to 8-bit integers.
-You may want to project values from the $[-1, 1]$ range to the $[-100, 100]$ range and then cast them to `Uint8Array`:
+You may want to project values from the $[-1, 1]$ range to the $[-127, 127]$ range and then cast them to `Int8Array`:
 
 ```js
-const quantizedVectorA = new Uint8Array(vectorA.map(v => (v * 100)));
-const quantizedVectorB = new Uint8Array(vectorB.map(v => (v * 100)));
+const quantizedVectorA = new Int8Array(vectorA.map(v => (v * 127)));
+const quantizedVectorB = new Int8Array(vectorB.map(v => (v * 127)));
 const distance = cosine(quantizedVectorA, quantizedVectorB);
 ```
 

javascript/simsimd.ts

@@ -83,20 +83,20 @@ export const jaccard = (a: Uint8Array, b: Uint8Array): number => {
 };
 
 /**
- * @brief Computes the kullbackleibler similarity coefficient between two vectors.
+ * @brief Computes the Kullback-Leibler divergence between two vectors.
  * @param {Float64Array|Float32Array} a - The first vector.
  * @param {Float64Array|Float32Array} b - The second vector.
- * @returns {number} The Jaccard similarity coefficient between vectors a and b.
+ * @returns {number} The Kullback-Leibler divergence between vectors a and b.
  */
 export const kullbackleibler = (a: Float64Array | Float32Array, b: Float64Array | Float32Array): number => {
   return compiled.kullbackleibler(a, b);
 };
 
 /**
- * @brief Computes the jensenshannon similarity coefficient between two vectors.
+ * @brief Computes the Jensen-Shannon divergence between two vectors.
  * @param {Float64Array|Float32Array} a - The first vector.
  * @param {Float64Array|Float32Array} b - The second vector.
- * @returns {number} The Jaccard similarity coefficient between vectors a and b.
+ * @returns {number} The Jensen-Shannon divergence between vectors a and b.
  */
 export const jensenshannon = (a: Float64Array | Float32Array, b: Float64Array | Float32Array): number => {
   return compiled.jensenshannon(a, b);