Adjust all of the titles

Author: odinshen-git

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

@@ -1,12 +1,12 @@
 ---
-title: Understand the local-first OpenClaw-based architecture and data boundaries
+title: Understand the Architecture and Local Data Boundaries
 weight: 2
 
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-## From local inference to an operational assistant
+## Transition from Inference to an Assistant
 
 Running a local LLM gives you a private way to generate text, but it does not yet give you an assistant you can use throughout the day. You still need a convenient way to ask questions, save information, search your documents, retrieve current information, and receive reminders without returning to a terminal each time.
 
@@ -47,7 +47,7 @@ The runtime does not require a public cloud LLM API. However, content sent throu
 This Learning Path uses the default personal runtime configuration, but every exercise uses synthetic or public data. Do not enter real personal, household, or organizational information while following the tutorial. If the host already contains personal runtime data, use the optional demo isolation settings introduced in the next chapter.
 {{% /notice %}}
 
-## Follow the request path
+## Trace the Application Request Path
 
 In this Learning Path, Telegram requests follow this path:
 

content/learning-paths/laptops-and-desktops/openclaw_continuum/2_dgx_deploy.md

@@ -1,12 +1,12 @@
 ---
-title: Deploy an OpenClaw-based runtime with local vLLM on NVIDIA DGX Spark
+title: Deploy an OpenClaw-based Reference Runtime with vLLM on DGX Spark
 weight: 3
 
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-## Prepare the DGX Spark host
+## Prepare the DGX Spark Host Environment
 
 This section assumes Docker Engine, the Docker Compose plugin, the NVIDIA driver, and NVIDIA Container Toolkit are installed on DGX Spark. Cloning the project later in this Learning Path downloads the reference runtime source and configuration, but it does not install Ollama or Qdrant. Prepare these host services before you start the project containers.
 
@@ -35,7 +35,7 @@ docker run --rm --gpus all ubuntu nvidia-smi
 
 You do not need to install the vLLM Python package or start a vLLM server directly on the DGX Spark host. The project's `compose.yaml` pulls a container image that already includes vLLM and starts the local inference server for you. The NVIDIA driver and NVIDIA Container Toolkit are still required so that this container can access the GPU.
 
-## Install Ollama for local embeddings
+## Configure Ollama for Local Embeddings
 
 Unlike vLLM, Ollama is not included as a service in the project's `compose.yaml`. Install and run Ollama separately on the DGX Spark host before starting the reference runtime.
 
@@ -166,7 +166,7 @@ The response time can differ. The empty collection list is expected at this stag
 Ollama and Qdrant must be reachable from the project containers, but they should not be exposed to an untrusted network. Use the DGX Spark firewall or another host-level access control to restrict ports `11434`, `6333`, and `6334` to the local host and its Docker networks.
 {{% /notice %}}
 
-## Clone the tutorial release
+## Clone the Reference Repository
 
 Clone the repository and check out the release used by this Learning Path:
 
@@ -178,7 +178,7 @@ git checkout v1.2
 
 The checked-out tag fixes the source used by this Learning Path even when development continues on `main`. Container images and model artifacts that do not have an explicit version are resolved when you download them and can change independently of the source tag.
 
-## Create a private environment file
+## Configure Private Environment Variables
 
 Copy the DGX Spark environment template:
 
@@ -240,7 +240,7 @@ OPENCLAW_VISION_ENABLED=false
 Never commit `.env`. It contains the Telegram bot token and Gateway authentication token. The repository ignores this file, but you should still verify `git status` before publishing changes.
 {{% /notice %}}
 
-## Start the runtime
+## Initialize and Start the Runtime Stack
 
 The Gateway runs as user ID `1000` inside its container and needs write access to its persistent state directory. Prepare the directory before starting the stack:
 
@@ -258,7 +258,7 @@ docker compose --env-file .env -f compose.yaml up -d
 
 The first start can take several minutes while Docker images and model weights are downloaded and vLLM loads and compiles the model. A running container does not yet mean that its API is ready.
 
-Inspect service state:
+Verify Service Status and API Readiness:
 
 ```bash
 docker compose --env-file .env -f compose.yaml ps -a
@@ -341,7 +341,7 @@ The recent log should include a successful local completion request similar to:
 
 The request appearing in the local logs confirms the runtime path. The model's text alone is not evidence that inference was local.
 
-## Run the automated checks
+## Execute Test Suites
 
 Run the repository tests from the host:
 

content/learning-paths/laptops-and-desktops/openclaw_continuum/3_household_memory.md

@@ -1,20 +1,20 @@
 ---
-title: Validate local memory and deterministic routing with Telegram and Qdrant
+title: Validate Memory Persistence and Routing with Telegram and Qdrant
 weight: 4
 
 ### FIXED, DO NOT MODIFY
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-## Use a household assistant scenario
+## Define the Household Test Scenario
 
 The project provides a customizable runtime for use cases that need private, local-first AI processing. You can adapt its skills, data sources, workflows, and schedules to match your own requirements. To make these capabilities concrete, this chapter uses a shared household assistant as an example rather than prescribing a fixed application. You will save synthetic maintenance information and retrieve it later without sending the memory to a public cloud LLM. You can apply the same architecture to other scenarios that need locally controlled inference, memory, and retrieval.
 
 Telegram transports the original messages between you and the runtime. After the messages reach your host, Ollama, Qdrant, and the local LLM process the memory workflow without using a public cloud LLM API.
 
 This tutorial treats household data as shared. It does not implement separate access control for each family member.
 
-## Save a household memory
+## Store and Query Local Memory
 
 Send this command to the Telegram bot:
 
@@ -53,7 +53,7 @@ Telegram question
     -> Telegram answer
 ```
 
-## Verify the local collection
+## Verify Qdrant Vector Collections
 
 Confirm that the personal memory collection exists:
 
@@ -146,7 +146,7 @@ Look for the synthetic boiler memory in the returned payload. This second check
 
 This check is important. It verifies the storage location from the data layer instead of trusting the assistant to describe its own architecture.
 
-## Inspect the active agents and task history
+## Inspect Active Agents and Task Execution
 
 Send the following command to the Telegram bot:
 
@@ -164,7 +164,7 @@ To inspect recent tasks, send this command to the Telegram bot:
 
 Task history records which agent handled the request, its status, and runtime duration. The dispatcher selects skills and agents while using one configured LLM endpoint. Dynamic routing between multiple models is a possible direction for future development.
 
-## Ask for external weather data
+## Test External Skill Integration
 
 Send a weather question in plain language:
 

content/learning-paths/laptops-and-desktops/openclaw_continuum/4_workflows.md

@@ -1,12 +1,12 @@
 ---
-title: Validate document RAG, browser search, and proactive scheduling
+title: Validate Document RAG, Web Search, and Proactive Tasks
 weight: 5
 
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-## Upload and query a synthetic household document
+## Ingest and Query Document RAG
 
 Create a small text file on the device where you use Telegram. The source file can be in any directory that your Telegram client can access. Use the following synthetic tutorial content:
 
@@ -87,7 +87,7 @@ curl -sS -X POST \
 
 The returned payload should contain chunks from `household-maintenance.txt`. This confirms that the uploaded file is stored and indexed locally rather than relying only on the assistant's answer.
 
-## Run an explicit browser search
+## Execute Deterministic Web Search
 
 Use the browser agent when you intentionally need current public information that is not stored in local memory. Send this command to the Telegram bot:
 
@@ -124,7 +124,7 @@ Finally, send this command in Telegram:
 
 Confirm that the search task reports `browser_search_agent`. This verifies the selected agent as well as the external request path.
 
-## Create a proactive reminder
+## Schedule Proactive Cron Tasks
 
 Choose a time a few minutes in the future using the runtime timezone configured by `OPENCLAW_CRON_TIMEZONE`. Send this command to the Telegram bot to create a daily tutorial reminder:
 
@@ -160,7 +160,7 @@ To test without waiting, copy the job ID from `/cron list` and send:
 
 The result should be delivered as a Telegram push message.
 
-## Inspect cron from the Gateway dashboard
+## Inspect Cron From the Gateway Dashboard
 
 The Gateway dashboard listens on localhost. If you are working directly on the DGX Spark desktop, open:
 

content/learning-paths/laptops-and-desktops/openclaw_continuum/5_cpu_only.md

@@ -1,12 +1,12 @@
 ---
-title: Move the OpenClaw-based runtime to a CPU-only Armv9 system
+title: Port the Application Workflow to a CPU-Only Armv9 System
 weight: 6
 
 ### FIXED, DO NOT MODIFY
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-## Run the same workflow across different Arm systems
+## Overview of Cross-Platform Portability
 
 This runtime architecture is designed to work across Arm systems with different compute configurations. In the previous sections, you deployed it on NVIDIA DGX Spark, a heterogeneous CPU-GPU platform using vLLM for local generation. In this section, you will move the same application workflows to a CIX-based Radxa Orion O6 running Debian 12, with llama.cpp providing local generation on the Armv9 CPU.
 
@@ -21,7 +21,7 @@ The Telegram interface, local memory and RAG, browser search, scheduled workflow
 These backends were selected to build on the environments used in the earlier chapters and in [Run ERNIE-4.5 Mixture of Experts model on Armv9 with llama.cpp](/learning-paths/cross-platform/ernie_moe_v9/). They are not fixed architecture requirements. You can use another local inference backend that provides a compatible OpenAI chat-completions API.
 {{% /notice %}}
 
-## Check the CPU-only host
+## Verify System Requirements on Armv9 Host
 
 On Orion O6, confirm the operating system, architecture, CPU features, memory, and disk capacity:
 
@@ -46,7 +46,7 @@ $HOME/llama.cpp/build/bin/llama-server
 $HOME/models/ernie-4.5/ERNIE-4.5-21B-A3B-Thinking-Q4_0.gguf
 ```
 
-## Start the OpenAI-compatible llama.cpp server
+## Deploy llama.cpp OpenAI-Compatible Server
 
 Start the server on the host:
 
@@ -120,7 +120,7 @@ Confirm that the managed endpoint responds:
 curl http://127.0.0.1:8080/v1/models
 ```
 
-## Install Ollama and start Qdrant
+## Provision Supporting Local Services
 
 Install Ollama on the Orion O6 host:
 
@@ -173,7 +173,7 @@ Confirm that the local API responds:
 curl http://127.0.0.1:6333/collections
 ```
 
-## Configure the CPU-only profile
+## Configure the CPU-Only Runtime Environment
 
 Clone the same release on Orion O6:
 
@@ -227,7 +227,7 @@ OPENCLAW_SCRAPER_LIMIT=2
 OPENCLAW_WEB_CONTEXT_CHARS=1800
 ```
 
-## Start the CPU-only runtime
+## Launch the CPU-Only Application Stack
 
 Voice transcription is not used in this Learning Path. Keep it disabled in `.env`:
 
@@ -265,7 +265,7 @@ docker exec openclaw-browser-scraper python -c "import socket; print(socket.geth
 If this command cannot resolve the hostname, inspect the Orion host DNS configuration with `cat /etc/resolv.conf`. Then update `OPENCLAW_DNS_SERVER_1` and `OPENCLAW_DNS_SERVER_2` in `.env` with DNS servers that are reachable from your network, restart the stack, and run the check again.
 {{% /notice %}}
 
-## Validate a shared household budget assistant
+## Validate Shared Workflows on CPU
 
 The previous chapters used a household assistant to validate memory, document retrieval, browser search, and scheduled reminders. In this section, you continue the household scenario on the CPU-only deployment by creating a simple budget assistant that two household members can share.
 

content/learning-paths/laptops-and-desktops/openclaw_continuum/6_summary.md

@@ -1,12 +1,12 @@
 ---
-title: Review the local-first deployment across Arm platforms
+title: Review the Deployment Across Arm Platforms
 weight: 7
 
 ### FIXED, DO NOT MODIFY
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-## Compare the two Arm deployments
+## Compare Arm Deployment Architectures
 
 In this Learning Path, you built a local-first household assistant and used Telegram to validate persistent memory, document RAG, browser search, and scheduled notifications. You first ran these workflows with local vLLM inference on NVIDIA DGX Spark, then moved the same application experience to llama.cpp on the Armv9 CPU of a Radxa Orion O6.
 
@@ -25,7 +25,7 @@ The following comparison shows what stayed the same across the two Arm-based imp
 
 The exercise demonstrates software portability across different compute configurations. Each platform can use model and context settings appropriate to its available compute while preserving the same application-level contract.
 
-## Review the local and external data boundaries
+## Review Data Privacy Boundaries
 
 The runtime keeps the following state under your control:
 
@@ -44,7 +44,7 @@ External boundaries remain visible:
 
 For sensitive deployments, you should review network exposure, Telegram suitability, host access, backups, model provenance, and the contents of every enabled tool.
 
-## Understand the implementation boundaries
+## Identify Current System Scope
 
 This Learning Path uses a text-first architecture with deterministic skill routing and one configured local LLM endpoint.
 
@@ -56,7 +56,7 @@ It does not implement:
 
 The thin AgentRegistry and TaskDispatcher route explicit skills and preserve predictable command behavior within this scope.
 
-## Apply the architecture to other Arm deployments
+## Explore Other Arm Deployment Topologies
 
 The same endpoint-driven design can support additional deployment shapes:
 
@@ -66,7 +66,7 @@ The same endpoint-driven design can support additional deployment shapes:
 
 Each deployment changes the compute and trust boundary. It should not silently change where personal data is stored or which external services are contacted.
 
-## What you've learned
+## Key Takeaways and Next Steps
 
 You can now:
 

content/learning-paths/laptops-and-desktops/openclaw_continuum/_index.md

@@ -1,5 +1,5 @@
 ---
-title: Extend OpenClaw into a local-first AI assistant across Arm platforms
+title: Extend OpenClaw for a Local-First AI Assistant Across Arm Platforms
 
 description: Extend OpenClaw with local memory, document RAG, browser search, deterministic routing, and proactive scheduling, then move the same local-first runtime from NVIDIA DGX Spark with vLLM to a CPU-only Armv9 system with llama.cpp.