diff --git a/contents/docs/alerts/index.mdx b/contents/docs/alerts/index.mdx
index 6e99c6668132..ad6814f0a26b 100644
--- a/contents/docs/alerts/index.mdx
+++ b/contents/docs/alerts/index.mdx
@@ -22,7 +22,9 @@ Currently, alerts are supported on all [trends](/docs/product-analytics/trends).
 />
 
 1. Alerts are based off of [insights](/docs/product-analytics/insights). You can pick any existing trend insight or create a new one.
-2. Click **Actions** in the sidebar and select **Alerts**. This shows you all the alerts for this insight.
+2. There are two ways to access alerts for an insight:
+   - **From the insight page** – Click **Actions** in the sidebar and select **Alerts**. This shows you all the alerts for this insight.
+   - **From a dashboard** – Click the **⋯** menu on any insight card and select **Alerts**.
 
 <ProductScreenshot
   imageLight="https://res.cloudinary.com/dmukukwp6/image/upload/q_auto,f_auto/Screenshot_2026_03_13_at_9_50_26_AM_ccce74247b.png"
@@ -184,51 +186,51 @@ PostHog includes 13 anomaly detection algorithms. For most use cases, **Z-score*
 
 #### Statistical detectors
 
-| Detector | Best for | How it works |
-|----------|----------|--------------|
-| **Z-score** | General purpose | Flags points that are many standard deviations from the rolling mean. Uses first-order differencing by default to handle cyclical data. |
-| **MAD** (Median Absolute Deviation) | Data with outliers | Similar to Z-score but uses median instead of mean, making it more robust to existing outliers. Uses first-order differencing by default. |
-| **IQR** (Interquartile Range) | Skewed distributions | Flags values outside the interquartile range. Less sensitive to distribution shape than Z-score. |
+| Detector                            | Best for             | How it works                                                                                                                              |
+| ----------------------------------- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
+| **Z-score**                         | General purpose      | Flags points that are many standard deviations from the rolling mean. Uses first-order differencing by default to handle cyclical data.   |
+| **MAD** (Median Absolute Deviation) | Data with outliers   | Similar to Z-score but uses median instead of mean, making it more robust to existing outliers. Uses first-order differencing by default. |
+| **IQR** (Interquartile Range)       | Skewed distributions | Flags values outside the interquartile range. Less sensitive to distribution shape than Z-score.                                          |
 
 #### Machine learning detectors
 
 These use the [PyOD](https://pyod.readthedocs.io/) library and work well for more complex patterns:
 
-| Detector | Best for | How it works |
-|----------|----------|--------------|
-| **Isolation Forest** | General purpose, high-dimensional | Isolates anomalies by randomly partitioning data. Anomalies require fewer partitions to isolate. |
-| **KNN** (K-Nearest Neighbors) | Cluster-based patterns | Flags points that are far from their nearest neighbors. |
-| **LOF** (Local Outlier Factor) | Variable-density data | Compares a point's local density to its neighbors. Good when "normal" varies across different ranges. |
-| **ECOD** | Fast, no tuning needed | Uses empirical cumulative distribution functions. One of the fastest detectors with no hyperparameters beyond threshold and window. |
-| **COPOD** | Multivariate data | Uses copula-based outlier detection. Fast and parameter-light. |
-| **HBOS** (Histogram-Based Outlier Score) | Fast, large datasets | Builds histograms to estimate density. Very fast but assumes feature independence. |
-| **PCA** (Principal Component Analysis) | Correlated metrics | Projects data into lower dimensions and flags points with high reconstruction error. |
-| **OCSVM** (One-Class SVM) | Complex boundaries | Learns a boundary around normal data. Good when normal data forms an irregular shape. |
+| Detector                                 | Best for                          | How it works                                                                                                                        |
+| ---------------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| **Isolation Forest**                     | General purpose, high-dimensional | Isolates anomalies by randomly partitioning data. Anomalies require fewer partitions to isolate.                                    |
+| **KNN** (K-Nearest Neighbors)            | Cluster-based patterns            | Flags points that are far from their nearest neighbors.                                                                             |
+| **LOF** (Local Outlier Factor)           | Variable-density data             | Compares a point's local density to its neighbors. Good when "normal" varies across different ranges.                               |
+| **ECOD**                                 | Fast, no tuning needed            | Uses empirical cumulative distribution functions. One of the fastest detectors with no hyperparameters beyond threshold and window. |
+| **COPOD**                                | Multivariate data                 | Uses copula-based outlier detection. Fast and parameter-light.                                                                      |
+| **HBOS** (Histogram-Based Outlier Score) | Fast, large datasets              | Builds histograms to estimate density. Very fast but assumes feature independence.                                                  |
+| **PCA** (Principal Component Analysis)   | Correlated metrics                | Projects data into lower dimensions and flags points with high reconstruction error.                                                |
+| **OCSVM** (One-Class SVM)                | Complex boundaries                | Learns a boundary around normal data. Good when normal data forms an irregular shape.                                               |
 
 #### Ensemble detector
 
 The **Ensemble** detector combines two or more of the above detectors with AND/OR logic:
 
-- **AND**: Only fires when *all* sub-detectors agree a point is anomalous. Reduces false positives.
-- **OR**: Fires when *any* sub-detector flags an anomaly. Catches more anomalies but may be noisier.
+- **AND**: Only fires when _all_ sub-detectors agree a point is anomalous. Reduces false positives.
+- **OR**: Fires when _any_ sub-detector flags an anomaly. Catches more anomalies but may be noisier.
 
 ### Configuration
 
 All detectors share these core parameters:
 
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| **Sensitivity** (threshold) | Anomaly probability cutoff between 0 and 1. Higher values mean fewer alerts. | 0.9 |
-| **Window** | Number of historical data points used for training. | Depends on interval (see below) |
+| Parameter                   | Description                                                                  | Default                         |
+| --------------------------- | ---------------------------------------------------------------------------- | ------------------------------- |
+| **Sensitivity** (threshold) | Anomaly probability cutoff between 0 and 1. Higher values mean fewer alerts. | 0.9                             |
+| **Window**                  | Number of historical data points used for training.                          | Depends on interval (see below) |
 
 Default window sizes based on your alert's check interval:
 
 | Interval | Default window | What it covers |
-|----------|---------------|----------------|
-| Hourly | 168 | 7 days |
-| Daily | 90 | ~3 months |
-| Weekly | 26 | ~6 months |
-| Monthly | 12 | 1 year |
+| -------- | -------------- | -------------- |
+| Hourly   | 168            | 7 days         |
+| Daily    | 90             | ~3 months      |
+| Weekly   | 26             | ~6 months      |
+| Monthly  | 12             | 1 year         |
 
 #### Preprocessing
 
diff --git a/contents/docs/product-analytics/dashboards.mdx b/contents/docs/product-analytics/dashboards.mdx
index a280eb8b4daa..6963f3b316ef 100644
--- a/contents/docs/product-analytics/dashboards.mdx
+++ b/contents/docs/product-analytics/dashboards.mdx
@@ -124,8 +124,9 @@ Tapping 'F' enables fullscreen mode.
 
 ### Insight card options
 
-Each insight card on a dashboard has a **⋯** menu with quick visualization toggles. These let you adjust how an insight displays without opening the full insight editor.
+Each insight card on a dashboard has a **⋯** menu with quick actions. These let you manage an insight without opening the full insight editor.
 
+- **Alerts** – Open the alerts modal to view, create, or edit [alerts](/docs/alerts) for the insight. Available on trend insights.
 - **Show legend** / **Hide legend** – Toggle the chart legend on or off. Available for Trends, Stickiness, and Lifecycle insights.
 - **Show values on series** / **Hide values on series** – Toggle data value labels on chart series. Available for Trends, Stickiness, Lifecycle, and Funnel (trends view) insights. For pie charts, this appears as **Show labels on series** / **Hide labels on series**.