fix: Code Rabbit grammatical catches

docs/v2/documentation/core-concepts/architecture.mdx

@@ -36,8 +36,8 @@ Honcho has a hierarchical data model centered around the entities below.
 ```
 
 A `Workspaces` has `Peers` & `Sessions`
-A `Peer` can be in multiple `Sessions` and a can send `Messages` in a `Session`.
+A `Peer` can be in multiple `Sessions` and can send `Messages` in a `Session`.
-A `Session` can have many `Peers` and has `Messages` sent by `Peers`.
+A `Session` can have many `Peers` and stores `Messages` sent by its `Peers`.
 
 ### <Icon icon="building" /> Workspaces
 
@@ -131,9 +131,9 @@ useful for an LLM to consume. There may be too many tokens that need to be
 compacted, key facts about what happened may be hard to piece together because
 they involve messages from across different sessions, etc.
 
-To solve this problem, Honcho has a reasoning layer that is always processing
+To solve this problem, Honcho has a reasoning layer that continually processes
-data that comes into Honcho to have the must informationaly dense and useful
+incoming data to form the most informationally dense and useful representations of `Peers`
-data that we can expose to agents. Currently, Honcho does the following tasks in
+that we can then expose to agents. Honcho does the following tasks in
 the reasoning engine.
 
 - **Fact Derivation**
@@ -142,16 +142,15 @@ the reasoning engine.
 - **Dreaming**
 
 
-So Honcho will reason about each `Message` it
+Honcho will reason about each `Message` it
 ingests to generate new facts and insights that are spelled out and easy to
 consume in an LLM prompt.
 
-We refer to this module of Honcho as the `Deriver`, because it constantly is
+We refer to this module of Honcho as the `Deriver`, because it's constantly
 deriving new insights from messages. The sum total of all these generated
-insights are what we refer to as a `Representation`, all the data related who
+insights are what we refer to as a `Representation`, all the data related to who
 and what a `Peer` is.
 
-
 Depending on the configuration of a `Peer` or `Session`, the deriver will behave
 differently and update different representations.
 

docs/v2/documentation/core-concepts/features/local-vs-global.mdx

@@ -1,6 +1,6 @@
 ---
 title: Local vs Global Representations
-description: Use Honcho that model directional relationships of Peers
+description: Model directional relationships between Peers in Honcho
 icon: location-pin
 ---
 
@@ -10,7 +10,7 @@ how one `Peer` thinks about another `Peer`.
 
 There are many use cases where you don't want every agent or human to know
 everything about another user such as games or multi-agent workflows. To
-illustrate further the following examples shows 2 conversations.
+illustrate this, the following examples shows 2 conversations.
 
 Conversation #1 (With Bob and Alice)
 ```
@@ -51,8 +51,8 @@ form a representation Alice based only on what they observe Alice do.
 This feature is illustrated in the graphic below:
 <img src="/images/local-vs-global-reps.png" alt="Peer Representations" />
 
-We can enable local representation for a peer by setting `observe_others=True`.
+We can enable local representation for a `Peer` by setting `observe_others=True`.
-This is show in the [Configure
+This is shown in the [Configure
 Reasoning](/v2/documentation/core-concepts/configuration) page.
 
 Now if we used Bob's local representation of Alice then Bob would only get

docs/v2/documentation/core-concepts/features/queue-status.mdx

@@ -4,11 +4,11 @@ description: Learn how to check the status of the Deriver
 icon: lines-leaning
 ---
 
-Whenever `Messages` are stored in Honcho a background process called the
+Whenever `Messages` are stored in Honcho, a background process called the
 [Deriver](/docs/v2/documentation/core-concepts/architecture#reasoning-layer) is
-triggered to reasoning about the conversation and generate insights.
+triggered to reason about the conversation and generate insights.
 
-The Deriver is an asynchronous process and depending on load may not immediately
+The Deriver is an asynchronous process and, depending on load may not immediately
 generated insights for the latest message you've sent. To help with this, Honcho
 provides several utilities to check the status of the Deriver.
 

docs/v2/documentation/core-concepts/features/storing-data.mdx

@@ -51,8 +51,8 @@ for their entire app. These are flexible components that work in any situation.
 
 ## Chat Bots
 
-A common use case for Honcho to is to build a Chatbot like ChatGPT or Claude.
+A common use case for Honcho to is to build a chatbot like ChatGPT or Claude.
-This this case you can simply
+In this case you can simply
 
 - Make a `Peer` for the User
 - Make a `Peer` for the AI

docs/v2/documentation/introduction/overview.mdx

@@ -38,7 +38,7 @@ insight = peer.chat("How should I explain this concept?")
 
 Designed for developers and agents alike:
 - **Natural Language Queries**: Chat with Honcho in natural language via the [Dialectic API](../core-concepts/architecture#dialectic-api) to get insights about your users and agents
-- **Automatic Context Management**: Smart conversation summaries to have infinite chates
+- **Automatic Context Management**: Smart conversation summaries to have infinite chats
 - **Native multi-agent support**: Sessions can natively have as many participants as you need
 - **Agent-first interfaces**: MCP connections and APIs designed for agents to consume and use as tools
 - **Provider Agnostic**: Works with any LLM or Agent Framework
@@ -91,8 +91,8 @@ Plug this into your prompt to get a quick overview of the user.
 This endpoint lets you chat with Honcho about any entity in your system. Honcho
 will leverage what it has remembered and learned about the entity to provide in-context actionable insights.
 
-This comes in handy when you want your agent to back-channel with Honcho to
+This is especially helpful when you want your agent to back-channel with Honcho to
-change it's behavior at runtime.
+change its behavior at runtime.
 
 Example Queries:
 - "What's the best way to explain technical concepts to this user?"

docs/v2/documentation/introduction/quickstart.mdx

@@ -249,11 +249,11 @@ const honcho = new Honcho({
 });
 
 // Get your Peers
-const alice = await client.peer("alice")
+const alice = await honcho.peer("alice")
-const bob = await client.peer("bob")
+const bob = await honcho.peer("bob")
 
 // Make a Session and add your peers
-const session = await client.session("session_1")
+const session = await honcho.session("session_1")
 await session.addPeers([alice, bob])
 
 // Add messages sent by your Peers
@@ -303,7 +303,7 @@ behavior and can be toggled off via [the configuration](/v2/documentation/core-c
   <Card title="Start Building" icon="brain" href="https://app.honcho.dev">
     Sign up for Managed Honcho and get started building agents now.
   </Card>
-  <Card title="Guides" icon="book" href="/v2/documentation/guides/overview">
+  <Card title="Guides" icon="book" href="/v2/guides/overview">
     Check out spellbooks to see different examples apps built with Honcho
     </Card>
 </CardGroup>

docs/v2/documentation/introduction/vibecoding.mdx

@@ -5,10 +5,10 @@ description: "Universal starter prompt for building with Honcho"
 sidebarTitle: 'Vibecoding Setup'
 ---
 
-These docs are designed to be easily consumable for LLMs. each page has a button
+These docs are designed to be easily consumable for LLMs. Each page has a button
-the lets you copy the page as markdown or put directly into ChatGPT or Claude.
+the lets you copy the page as Markdown or paste directly into ChatGPT or Claude.
 
-Additionally, we follow the llms.txt standard. There is both an llms.txt and
+Additionally, we follow the llms.txt standard. There are both an llms.txt and
 llms-full.txt available.
 
 - [llms.txt](/llms.txt)