feat: images

Signed-off-by: Hélia Barroso <[email protected]>

Author: heliapb

docs/assets/images/blog/community-dashboards/perses-dashboards-am.png

@@ -32,14 +32,16 @@ With the limitations of mixins in mind, the Perses community decided to take a d
 
 Introducing the [community-dashboards](https://github.com/perses/community-dashboards) repository. Maintained by community contributors, it offers the following dashboards in type-safe modular Go SDK based Dashboards as Code:
 
-<TODO - add image here>
+![Dashboard overview](../../assets/images/blog/community-dashboards/perses-dashboards-overview.png)
 
 The goal of the community-dashboards library isn't just to provide ready-to-use YAML dashboards (though you can use them as-is), but to empower users to import the project as a Go module, generate tailored subsets of dashboards, and customize or extend them to meet specific needs.
 
 A core strength of the Perses Community Dashboards is its modular, reusable panel library. These panels—such as Go CPU Usage and Memory Consumption—serve as building blocks that can be composed into new dashboards. Instead of starting from scratch, users can leverage these community-maintained components to quickly build dashboards suited to their applications and workloads.
 
 This approach also lowers the barrier to achieving consistency across dashboards. By reusing the same patterns to build and display common metrics, dashboards remain aligned across teams and services. This makes it easier to compare environments, onboard new engineers, and scale observability practices—without reinventing the wheel.
 
+![AM overview](../../assets/images/blog/community-dashboards/perses-dashboards-am.png)
+
 ## How to build composable dashboards - a practical example
 
 Here we have an example of how to define a panel of Time Series type below. More detailed info about the available option for Time Series Panels can be found [here](https://perses.dev/perses/docs/dac/go/panel/time-series/).
@@ -139,9 +141,10 @@ Going back to the idea of copy-pasting raw YAML or JSON, although it might seem
 
 Combining the use of panel libraries and the SDK, you get a flexible and scalable way to manage dashboards, while saving time, reducing errors, and staying consistent across your observability stack. This allows a community of dashboards users to have their lives made easier with these features. 
 
-Also considering the most commonly used dashboard across platforms and teams, like Kubernetes, Prometheus and exporters like Node Exporter, Blackbox Exporter, also projects like Thanos dashboards as it tends to be a power duo within the Prometheus ecosystem, we have from the get go some core dashboards that will help you in the long term in your observability journey.
+Also considering the most commonly used dashboard across platforms and teams, like **Kubernetes**, **Prometheus** and exporters like **Node Exporter**, **Blackbox Exporter**, also projects like **Thanos** dashboards as it tends to be a power duo within the Prometheus ecosystem, we have from the get go some core dashboards that will help you in the long term in your observability journey.
 
 ## Next Steps for the Community Dashboards
+
 We’re committed to expanding support for more CNCF projects, while continuing to enhance the existing panel libraries. By adding more features and improving the quality of reusable panels, we enable users to gain deeper insights from their dashboards—because the faster you can pinpoint an issue in your telemetry data, the better.
 
 Also we aim to ensure proper documentation for all dashboards.Whether you're importing full dashboards or using panel libraries as building blocks, we want to make the process as seamless as possible. That means clear, well-commented code and guidance, so modularity is not only guaranteed but also easy to work with, without needing to dig through the entire codebase.