Openclaw continuum LP review

content/learning-paths/laptops-and-desktops/openclaw_continuum/1_architecture.md

@@ -1,24 +1,25 @@
 ---
-title: Understand the Architecture and Local Data Boundaries
+title: Understand the architecture and local data boundaries
+description: Map OpenClaw Arm Continuum's Telegram request flow, local data boundaries, and shared inference API across NVIDIA DGX Spark and CPU-only Arm systems.
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Transition from Inference to an Assistant
+## Transition from inference to an assistant
 
 Running a local LLM gives you private text generation, but not a complete assistant. You still need an interface for questions, saved information, document searches, and reminders.
 
-In this Learning Path, you will deploy [OpenClaw Arm Continuum](https://github.com/odincodeshen/openclaw-arm-continuum) and use it from Telegram. You will save a household note, query a local document, search the web, and schedule a notification. Inference, embeddings, documents, vector memory, and task state remain on hardware you control.
+In this Learning Path, you'll deploy [OpenClaw Arm Continuum](https://github.com/odincodeshen/openclaw-arm-continuum) and use it from Telegram. You'll save a household note, query a local document, search the web, and schedule a notification. Inference, embeddings, documents, vector memory, and task state remain on hardware that you control.
 
-Telegram is the messaging interface for this tutorial. The runtime can support another platform through a gateway that translates its messages and events.
+Telegram is the messaging interface. The runtime can support another platform through a gateway that translates its messages and events.
 
-OpenClaw provides the foundation for the assistant. The reference runtime connects it to Telegram, local generation through vLLM or llama.cpp, Ollama embeddings, Qdrant memory, browser search, and scheduled tasks. It routes each request to the relevant local service or tool.
+OpenClaw provides the foundation for the assistant. The reference runtime connects it to Telegram, local generation through vLLM or `llama.cpp`, Ollama embeddings, Qdrant memory, browser search, and scheduled tasks. It routes each request to the relevant local service or tool.
 
 ## Understand the data boundary
 
-Local-first does not mean that every byte stays offline. Telegram and web search use external services, while the core AI data remains under your control.
+Local-first doesn't mean that every byte stays offline. Telegram and web search use external services, while the core AI data remains under your control.
 
 | Data or operation | Location | External interaction |
 |---|---|---|
@@ -30,13 +31,13 @@ Local-first does not mean that every byte stays offline. Telegram and web search
 | External data lookup | Local skill | Public data service selected by the skill |
 | Browser search | Local Playwright worker | Search engine and selected public pages |
 
-The runtime does not use a public cloud LLM API. Telegram transports bot messages, and browser searches send requests to external websites.
+The runtime doesn't use a public cloud LLM API. Telegram transports bot messages, and browser searches send requests to external websites.
 
 {{% notice Note %}}
-This Learning Path uses synthetic or public data. Do not enter real personal, household, or organizational information. If the host already contains personal runtime data, set the environment variables in the next chapter.
+Don't enter real personal, household, or organizational information. Instead, use synthetic or public data. If the host already contains personal runtime data, set environment variables by following the instructions in [Configure and start the OpenClaw runtime on DGX Spark](/learning-paths/laptops-and-desktops/openclaw_continuum/3_dgx_runtime_deploy/).
 {{% /notice %}}
 
-## Trace the Application Request Path
+## Trace the application request path
 
 The architecture shows how Telegram requests reach local services and persistent data:
 
@@ -53,19 +54,23 @@ Cron schedule
 
 Slash commands follow fixed routes. For example, `/search` always selects browser search, while a plain-language weather question selects the weather skill.
 
+{{% notice Note %}}
+The runtime uses a text-first architecture, fixed skill routes, and one local LLM endpoint. This Learning Path doesn't cover multi-model routing, multi-agent handoffs, or hardware benchmarking.
+{{% /notice %}}
+
 ## Understand the shared API contract across Arm platforms
 
 The same workflow uses an inference engine suited to each platform:
 
 | Platform | Inference engine |
 |---|---|
 | NVIDIA DGX Spark | vLLM server |
-| Radxa Orion O6 | llama.cpp server |
+| Radxa Orion O6 | `llama.cpp` server |
 
 Both expose an OpenAI-compatible chat-completions API, so only the configured endpoint and model name change.
 
 ## What you've learned and what's next
 
 You now understand the runtime components, data boundary, and shared inference API.
 
-Next, you will deploy the baseline runtime on NVIDIA DGX Spark.
+Next, you'll prepare the NVIDIA DGX Spark and configure Ollama and Qdrant.