diff --git a/packages/website/docs/Features/Node-Packages.md b/packages/website/docs/Features/Node-Packages.md
index 6a55c6dd..ab590c32 100644
--- a/packages/website/docs/Features/Node-Packages.md
+++ b/packages/website/docs/Features/Node-Packages.md
@@ -22,7 +22,7 @@ argument allows you to pass an environment previously created by another `run`
 call. Passing this environment will mean that all previously declared variables
 in the previous environment will be made available.
 
-The return type of `run` is a bit complicated, and comes from auto generated js
+The return type of `run` is a bit complicated, and comes from auto generated `js`
 code that comes from rescript. We highly recommend using typescript when using
 this library to help navigate the return type.
 
diff --git a/packages/website/docs/Internal/Processing-Confidence-Intervals.md b/packages/website/docs/Internal/Processing-Confidence-Intervals.md
index cc193e8f..004e3bb7 100644
--- a/packages/website/docs/Internal/Processing-Confidence-Intervals.md
+++ b/packages/website/docs/Internal/Processing-Confidence-Intervals.md
@@ -1,8 +1,9 @@
 ---
 title: Processing Confidence Intervals
+author: Nuño Sempere
 ---
 
-This page explains what we are doing when we take a 95% confidence interval, and we get a mean and a standard deviation from it.
+This page explains what we are doing when we take a 90% confidence interval, and we get a mean and a standard deviation from it.
 
 ## For normals
 
@@ -21,13 +22,10 @@ module Normal = {
 We know that for a normal with mean $\mu$ and standard deviation $\sigma$,
 
 $$
-
-a \cdot Normal(\mu, \sigma) = Normal(a\cdot \mu, |a|\cdot \sigma)
-
-
+a \cdot Normal(\mu, \sigma) = Normal(a \cdot \mu, |a| \cdot \sigma)
 $$
 
-We can now look at the inverse cdf of a $Normal(0,1)$. We find that the 95% point is reached at $1.6448536269514722$. ([source](https://stackoverflow.com/questions/20626994/how-to-calculate-the-inverse-of-the-normal-cumulative-distribution-function-in-p)) This means that the 90% confidence interval is $[-1.6448536269514722, 1.6448536269514722]$, which has a width of $2 \cdot 1.6448536269514722$.
+We can now look at the inverse cdf of a $Normal(0,1)$. We find that the 90% point is reached at $1.6448536269514722$. ([source](https://stackoverflow.com/questions/20626994/how-to-calculate-the-inverse-of-the-normal-cumulative-distribution-function-in-p)) This means that the 90% confidence interval is $[-1.6448536269514722, 1.6448536269514722]$, which has a width of $2 \cdot 1.6448536269514722$.
 
 So then, if we take a $Normal(0,1)$ and we multiply it by $\frac{(high -. low)}{(2. *. 1.6448536269514722)}$, it's 90% confidence interval will be multiplied by the same amount. Then we just have to shift it by the mean to get our target normal.
 
diff --git a/packages/website/docs/Introduction.md b/packages/website/docs/Introduction.md
index 021bd845..91cfd919 100644
--- a/packages/website/docs/Introduction.md
+++ b/packages/website/docs/Introduction.md
@@ -13,3 +13,4 @@ Squiggle is an _estimation language_, and a syntax for _calculating and expressi
 - [Squiggle functions source of truth](https://www.squiggle-language.com/docs/Features/Functions)
 - [Known bugs](https://www.squiggle-language.com/docs/Discussions/Bugs)
 - [Original lesswrong sequence](https://www.lesswrong.com/s/rDe8QE5NvXcZYzgZ3)
+- [Author your squiggle models as Observable notebooks](https://observablehq.com/@hazelfire/squiggle)