diff --git a/docs/v3/documentation/introduction/overview.mdx b/docs/v3/documentation/introduction/overview.mdx
index f3b631b7..10ce0775 100644
--- a/docs/v3/documentation/introduction/overview.mdx
+++ b/docs/v3/documentation/introduction/overview.mdx
@@ -7,7 +7,7 @@ sidebarTitle: "Overview"
 Honcho is an open source memory library with a managed service for building stateful agents. Use it with any model, framework, or architecture. It enables agents to build and maintain state about any entity--users, agents, groups, ideas, and more. And because it's a continual learning system, it understands entities that change over time. Using Honcho as your memory system will earn your agents higher retention, more trust, and help you build data moats to out-compete incumbents.
 
 <Note>
-Honcho has defined the Pareto Frontier of Agent Memory. Watch the [video](https://x.com/honchodotdev/status/2002090546521911703?s=20), check out our [evals page](https://evals.honcho.dev/), and read the [blog post](https://blog.plasticlabs.ai/research/Benchmarking-Honcho) for more detail.
+  Honcho has defined the Pareto Frontier of Agent Memory. Watch the [video](https://x.com/honchodotdev/status/2002090546521911703?s=20), check out our [evals page](https://evals.honcho.dev/), and read the [blog post](https://blog.plasticlabs.ai/research/Benchmarking-Honcho) for more detail.
 </Note>
 
 <CardGroup cols={2}>
@@ -42,7 +42,7 @@ Break free from this cycle. Honcho is a general solution to context engineering,
 ## How Honcho Works
 
 <Note>
-Honcho is a memory system that reasons. Read more on the approach [here](https://blog.plasticlabs.ai/blog/Memory-as-Reasoning).
+  Honcho is a memory system that reasons. Read more on the approach [here](https://blog.plasticlabs.ai/blog/Memory-as-Reasoning).
 </Note>
 
 Honcho has four storage primitives that work together:
@@ -65,9 +65,9 @@ Honcho has four storage primitives that work together:
 - **Workspaces** - Top-level containers that isolate different applications or environments
 - **Peers** - Any entity that persists but changes over time (users, agents, objects, and more)
 - **Sessions** - Interaction threads between peers with temporal boundaries
-- **Messages** - Units of data that trigger reasoning (conversations, events, activity, documents, and more)
+- **Messages** - Units of data that trigger reasoning (conversations, events, activity, documents, and more) 
 
-When you write messages to Honcho, they're stored and processed in the background. Custom reasoning models perform formal logical [*reasoning*](/v3/documentation/core-concepts/reasoning) to generate conclusions about each peer. These conclusions are stored as [*representations*](/v3/documentation/core-concepts/representation) that you can query to provide rich context for your agents.
+When you write messages to Honcho, they're stored and processed in the background. Custom reasoning models perform formal logical [_reasoning_](/v3/documentation/core-concepts/reasoning) to generate conclusions about each peer. These conclusions are stored as [_representations_](/v3/documentation/core-concepts/representation) that you can query to provide rich context for your agents.
 
 ![Honcho Architecture](/images/architecture.png)
 
@@ -75,7 +75,7 @@ The diagram above shows the flow: agents write messages to Honcho, which trigger
 
 ## Why Reasoning?
 
-Traditional RAG systems retrieve what was explicitly said, but they miss what matters most—the insights only accessible by *rigorously thinking* about your data. Without reasoning, you're leaving latent information on the table. Static retrieval can't surface implicit connections, struggles when new information contradicts old data, and fails when you need to make predictions under uncertainty.
+Traditional RAG systems retrieve what was explicitly said, but they miss what matters most—the insights only accessible by _rigorously thinking_ about your data. Without reasoning, you're leaving latent information on the table. Static retrieval can't surface implicit connections, struggles when new information contradicts old data, and fails when you need to make predictions under uncertainty.
 
 Honcho uses formal logic to extract all that latent information. This reasoning is AI-native—it performs the rigorous, compute-intensive thinking that humans struggle with, instantly and consistently. The result is memory that goes beyond simple RAG recall to provide exhaustive context for statefulness.
 
@@ -100,4 +100,4 @@ Welcome to Honcho. We're excited to have you at the frontier of AI with us 🫡.
   <Card title="Reasoning" icon="gears" href="/v3/documentation/core-concepts/reasoning">
     Learn how Honcho reasons about data to build memory
   </Card>
-</CardGroup>
+</CardGroup>
\ No newline at end of file