diff --git a/1-js/12-generators-iterators/1-generators/01-pseudo-random-generator/solution.md b/1-js/12-generators-iterators/1-generators/01-pseudo-random-generator/solution.md
@@ -16,7 +16,7 @@ alert(generator.next().value); // 282475249
alert(generator.next().value); // 1622650073
```

-Please note, the same can be done with a regular function, like this:

```js run
function pseudoRandom(seed) {
@@ -35,4 +35,4 @@ alert(generator()); // 282475249
alert(generator()); // 1622650073
```

-That also works. But then we lose ability to iterate with `for..of` and to use generator composition, that may be useful elsewhere.
diff --git a/1-js/12-generators-iterators/1-generators/01-pseudo-random-generator/task.md b/1-js/12-generators-iterators/1-generators/01-pseudo-random-generator/task.md
@@ -1,29 +1,29 @@

-# Pseudo-random generator

-There are many areas where we need random data.

-One of them is testing. We may need random data: text, numbers, etc. to test things out well.

-In JavaScript, we could use `Math.random()`. But if something goes wrong, we'd like to be able to repeat the test, using exactly the same data.

-For that, so called "seeded pseudo-random generators" are used. They take a "seed", the first value, and then generate the next ones using a formula so that the same seed yields the same sequence, and hence the whole flow is easily reproducible. We only need to remember the seed to repeat it.

-An example of such formula, that generates somewhat uniformly distributed values:

```
next = previous * 16807 % 2147483647
```

-If we use `1` as the seed, the values will be:
1. `16807`
2. `282475249`
3. `1622650073`
-4. ...and so on...

-The task is to create a generator function `pseudoRandom(seed)` that takes `seed` and creates the generator with this formula.

-Usage example:

```js
let generator = pseudoRandom(1);