Several years ago, I built my first website in WordPress. I created pages, added content, and gradually worked on the site until it felt more complete. At the time, I wasn’t publishing blog posts. Everything went into pages.

The website took much longer to finish than I expected. When I finally decided to try Google AdSense, I hoped it would be the next step forward. Instead, my application was rejected again and again.

Each rejection was frustrating. I had already spent so much time building the site, and I struggled to understand what I needed to change.

The advice that made me feel I had to start over

A friend who worked in web development looked at my situation and suggested that I publish my article content as posts rather than keeping everything in pages. He thought I would need to recreate the content and delete the old versions.

That was difficult to hear. The thought of rebuilding work that had already taken so long left me discouraged. I wanted to improve the website, but I didn’t want to copy everything manually and begin again.

Looking back, there were two separate questions: how to organize my WordPress content, and why AdSense had rejected the site. Changing the first would not necessarily answer the second.

How AI helped me find a simpler approach

Before committing to a complete rebuild, I asked an AI assistant whether there was an easier way to turn existing pages into posts.

It suggested a WordPress plugin called Post Type Switcher.

The plugin lets you change an existing item’s content type, including converting a page into a post. It also supports bulk changes. That meant I had an alternative to manually recreating each article. See the plugin’s official description.

I used it to convert many of my pages into posts. For me, its value was practical: it made a task that had felt overwhelming much more manageable.

What this taught me about pages, posts, and AdSense

There is an important correction to the advice I originally received: AdSense does not require you to publish your content as WordPress posts rather than pages. Google’s published requirements focus on original, useful content, policy compliance, and a website that visitors can navigate. They do not specify a WordPress content type. Read Google’s eligibility requirements and site-readiness guidance.

Posts can help organize a growing collection of articles through categories and a blog archive. Pages still have a useful role for content such as About, Contact, and service information.

The right choice depends on what the content is for. Turning a page into a post does not improve the writing by itself, and it does not guarantee AdSense approval.

I also cannot conclude from this experience that using pages was the reason for my rejections. What I can say is that the plugin helped me reorganize existing content without manually recreating it.

What I recommend before converting content

If you are in a similar situation, Post Type Switcher may be worth considering. Start with a backup and test one item on a staging copy before making bulk changes. The plugin’s documentation notes that conflicts with other plugins can occur. Plugin documentation.

After a conversion, check these details:

• Layout: Does the article still display correctly, especially if it uses a page builder?
• Address: Has the URL changed? If so, redirect the old address to the new one.
• Navigation: Do menus and internal links still lead to the right place?
• Organization: Does the article appear in the intended category and blog archive?

You do not need to convert every page. Choose the content that belongs in your article collection, and keep other pages where they make sense.

A small tool made a difficult task feel possible

The most useful part of this experience was discovering that I had another option. I could reorganize what I had already written instead of assuming I needed to rebuild everything.

AI helped me find the tool, and Post Type Switcher helped me carry out the change. The experience also reminded me to check the reasoning behind advice, even when it comes from someone with more experience.

If your WordPress articles are currently stored as pages, this plugin could save you some work. If your goal is AdSense approval, review the quality and usefulness of the whole website as well. Content organization is one part of that work.

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Why Product Thinking Comes First

Have you ever launched a product that looked impressive… but didn’t truly solve anything meaningful?

Many teams move quickly into development. Features get built. Interfaces look polished. But somewhere along the way, the real problem gets lost.

That’s why product thinking matters.

Product thinking is a strategic mindset that starts before design and development. It asks a simple but powerful question:

What real problem are we solving — and for whom?

Instead of focusing only on features, it focuses on value.

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A Complete View of the Product Journey

Product thinking looks at the entire lifecycle of a product:

• Idea and validation
• User research
• Design and development
• Launch and iteration
• Long-term growth

It connects strategy with execution. It ensures that every decision serves a clear purpose.

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1. User-Centered by Design

At the heart of product thinking is empathy.

Teams invest time in understanding user behavior, frustrations, motivations, and goals. Rather than assuming what people need, they validate insights through research and feedback.

When products are built around real needs:

• Adoption increases
• Satisfaction improves
• Loyalty strengthens

Users don’t just use the product — they depend on it.

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2. Solving the Right Problems

Feature-first development often leads to complexity without clarity.

Product thinking slows the process down — in a productive way. It encourages teams to identify the core challenge before proposing solutions.

This approach ensures:

• Features are meaningful
• Resources are used wisely
• Development stays focused

Nothing is built “just because.” Everything has a reason.

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3. Alignment with Business Strategy

A product should support broader company objectives.

Product thinking connects product decisions with business goals such as:

• Revenue growth
• Market expansion
• Brand positioning
• Competitive differentiation

When strategy and product development move in the same direction, momentum builds.

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4. Cross-Functional Collaboration

Strong products are not created in isolation.

Product thinking brings together:

• Designers
• Engineers
• Marketing teams
• Sales and customer support

Each perspective strengthens the outcome. Collaboration reduces blind spots and improves decision-making across the board.

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5. Continuous Validation and Market Fit

Markets evolve. User expectations shift.

Product thinking embraces testing and iteration. Ideas are validated with real users before scaling. Assumptions are challenged early, not after launch.

This leads to better product-market fit and faster adaptation to change.

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6. Quality and Consistency

When teams share a user-focused mindset, the entire product feels cohesive.

Design, functionality, and messaging align under one clear principle: delivering value.

This consistency builds trust — and trust drives retention.

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7. Meaningful Innovation

Innovation is not about adding more. It’s about understanding deeper.

By focusing on underlying problems, teams discover smarter and often simpler solutions. These insights lead to differentiated features and stronger competitive positioning.

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8. Scalability and Long-Term Vision

Product thinking extends beyond launch day.

Products built on validated problems and strong foundations are easier to scale. As the user base grows, the solution evolves naturally.

This mindset supports sustainable, long-term success — not just short-term releases.

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The Strategic Advantage

Product thinking transforms development from a task-based process into a strategic discipline.

It helps organizations:

• Build what truly matters
• Reduce waste
• Deliver measurable value
• Innovate intentionally
• Scale with confidence

In today’s fast-moving, user-driven market, this approach is not optional — it is a competitive advantage.

If your goal is to build products that last, product thinking is where the journey begins.

Complete Google ADK CLI Command List

The current Google ADK Python CLI command tree includes api_server, conformance, create, deploy, eval, eval_set, migrate, optimize, run, test, and web. Your installed version may differ, so verify it locally with adk --help. Official ADK CLI reference

1. Display ADK help

adk --help

Shows every top-level command available in your installed ADK version.

For help with a specific command:

adk COMMAND --help

Example:

adk web --help

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2. Display the installed ADK version

adk --version

Confirms which ADK CLI version is active.

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3. Create a new agent project

adk create APP_NAME

Example:

adk create my_agent

Creates a new directory containing a prepopulated agent template.

Useful options:

adk create my_agent --model=MODEL_NAME

adk create my_agent --api_key=API_KEY

adk create my_agent \
  --project=PROJECT_ID \
  --region=REGION

Available options:

• --model: Model for the root agent
• --api_key: Google AI API key
• --project: Google Cloud project for Vertex AI
• --region: Google Cloud region for Vertex AI

Avoid placing API keys directly in shell history; environment variables or secret management are safer.

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4. Run an agent in the terminal

Interactive mode:

adk run path/to/my_agent

Single-query mode:

adk run path/to/my_agent "Hello"

This runs the agent directly in the terminal without starting a browser.

Useful options:

adk run path/to/my_agent --save_session

adk run path/to/my_agent --resume=SESSION_FILE.json

adk run path/to/my_agent --replay=REPLAY_FILE.json

adk run path/to/my_agent --state='{"language":"English"}'

adk run path/to/my_agent --timeout=30s

adk run path/to/my_agent --in_memory

adk run path/to/my_agent --jsonl

Important options:

• --save_session: Save the session when exiting
• --session_id: Set the saved session ID
• --resume: Continue a saved session
• --replay: Replay queries from a JSON file
• --state: Provide initial state as JSON
• --timeout: Limit the duration of a turn
• --in_memory: Avoid persistent session storage
• --jsonl: Produce machine-readable JSONL output
• --default_llm_model: Set the default model
• --session_service_uri: Select session storage
• --artifact_service_uri: Select artifact storage
• --memory_service_uri: Select memory storage

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5. Start the ADK development web interface

adk web

From the parent directory containing your agents:

adk web .

Specify the agents directory:

adk web path/to/agents

Specify a port:

adk web --port=8080 path/to/agents

Allow a frontend origin:

adk web \
  --allow_origins=https://example.com \
  path/to/agents

Enable debugging and automatic reload:

adk web \
  --log_level=DEBUG \
  --reload \
  --reload_agents \
  path/to/agents

Important options:

• --host: Binding address; default is 127.0.0.1
• --port: Server port
• --allow_origins: Allowed CORS origins
• -v or --verbose: Debug logging
• --log_level: Logging level
• --reload: Restart the server after code changes
• --reload_agents: Reload changed agents
• --url_prefix: Serve behind a path such as /adk
• --logo-text: Customize the UI logo text
• --logo-image-url: Customize the UI logo image
• --a2a: Enable the Agent-to-Agent endpoint
• --trace_to_cloud: Export Cloud Trace data
• --otel_to_cloud: Export OpenTelemetry data
• --default_llm_model: Supply a default model
• --extra_plugins: Enable additional plugins
• --trigger_sources: Enable sources such as Pub/Sub or Eventarc
• --session_service_uri: Configure session storage
• --artifact_service_uri: Configure artifact storage
• --memory_service_uri: Configure memory storage
• --use_local_storage: Store local ADK data under .adk

adk web is intended for development and testing—not as a production web interface.

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6. Start the ADK API server

adk api_server

Specify the agents directory:

adk api_server path/to/agents

Specify the host and port:

adk api_server \
  --host=127.0.0.1 \
  --port=8000 \
  path/to/agents

Allow requests from a frontend:

adk api_server \
  --allow_origins=https://example.com \
  path/to/agents

Automatically create missing sessions:

adk api_server \
  --auto_create_session \
  path/to/agents

Include the web interface:

adk api_server \
  --with_ui \
  path/to/agents

This exposes agents through REST endpoints for connection to a website, mobile application, backend, or test client. By default, it runs at http://localhost:8000. ADK API Server documentation

It supports most of the server options available to adk web, including:

• --host
• --port
• --allow_origins
• --log_level
• --reload
• --reload_agents
• --a2a
• --url_prefix
• --trigger_sources
• --session_service_uri
• --artifact_service_uri
• --memory_service_uri
• --trace_to_cloud
• --otel_to_cloud

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Deployment commands

7. Display deployment options

adk deploy --help

ADK currently provides deployment subcommands for:

• Cloud Run
• Agent Engine
• Google Kubernetes Engine

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8. Deploy to Cloud Run

adk deploy cloud_run \
  --project=PROJECT_ID \
  --region=REGION \
  path/to/my_agent

With an explicit service name:

adk deploy cloud_run \
  --project=PROJECT_ID \
  --region=REGION \
  --service_name=my-agent-service \
  --app_name=my_agent \
  path/to/my_agent

Development deployment with the ADK UI:

adk deploy cloud_run \
  --project=PROJECT_ID \
  --region=REGION \
  --service_name=my-agent-service \
  --with_ui \
  path/to/my_agent

The UI is intended only for development and testing. Production deployments should normally expose the API server without --with_ui.

Important options:

• --project: Google Cloud project
• --region: Cloud Run region
• --service_name: Cloud Run service name
• --app_name: ADK API application name
• --port: Server port
• --with_ui: Include the development UI
• --trace_to_cloud: Enable Cloud Trace
• --otel_to_cloud: Enable Google Cloud observability
• --log_level: Logging level
• --adk_version: ADK version deployed
• --a2a: Enable the A2A endpoint
• --allow_origins: Configure CORS
• --trigger_sources: Enable Pub/Sub or Eventarc triggers
• --session_service_uri: Configure session storage
• --artifact_service_uri: Configure artifact storage
• --memory_service_uri: Configure memory storage

Pass additional gcloud flags after --:

adk deploy cloud_run \
  --project=PROJECT_ID \
  --region=REGION \
  path/to/my_agent \
  -- \
  --no-allow-unauthenticated \
  --min-instances=2

Official Cloud Run deployment reference

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9. Deploy to Vertex AI Agent Engine

Using a Google Cloud project:

adk deploy agent_engine \
  --project=PROJECT_ID \
  --region=REGION \
  --display_name="My Agent" \
  path/to/my_agent

Using Express Mode:

adk deploy agent_engine \
  --api_key=API_KEY \
  path/to/my_agent

Update an existing Agent Engine resource:

adk deploy agent_engine \
  --project=PROJECT_ID \
  --region=REGION \
  --agent_engine_id=AGENT_ENGINE_ID \
  path/to/my_agent

Important options:

• --api_key: Express Mode API key
• --project: Google Cloud project
• --region: Google Cloud region
• --agent_engine_id: Update an existing deployment
• --display_name: Agent display name
• --description: Agent description
• --adk_app: Python file defining the ADK application
• --adk_app_object: Either root_agent or app
• --env_file: Environment-variable file
• --requirements_file: Python requirements file
• --agent_engine_config_file: Agent Engine configuration file
• --validate-agent-import: Validate imports before deployment
• --trace_to_cloud: Enable Cloud Trace
• --otel_to_cloud: Enable OpenTelemetry

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10. Deploy to Google Kubernetes Engine

adk deploy gke \
  --project=PROJECT_ID \
  --region=REGION \
  --cluster_name=CLUSTER_NAME \
  path/to/my_agent

Expose it through a load balancer:

adk deploy gke \
  --project=PROJECT_ID \
  --region=REGION \
  --cluster_name=CLUSTER_NAME \
  --service_type=LoadBalancer \
  path/to/my_agent

Important options:

• --project
• --region
• --cluster_name
• --service_name
• --app_name
• --port
• --service_type=ClusterIP
• --service_type=LoadBalancer
• --with_ui
• --trace_to_cloud
• --otel_to_cloud
• --log_level
• --adk_version
• --trigger_sources
• Storage and memory service URI options

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Evaluation and testing

11. Evaluate an agent

adk eval \
  path/to/my_agent/__init__.py \
  path/to/eval_set.json

Evaluate multiple sets:

adk eval \
  path/to/my_agent/__init__.py \
  eval_set_1.json \
  eval_set_2.json

Run selected cases:

adk eval \
  path/to/my_agent/__init__.py \
  eval_set.json:eval_1,eval_2

Print detailed results:

adk eval \
  --print_detailed_results \
  path/to/my_agent/__init__.py \
  eval_set.json

Important options:

• --config_file_path
• --print_detailed_results
• --eval_storage_uri
• --log_level
• --enable_features
• --disable_features

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12. Display evaluation-set commands

adk eval_set --help

The eval_set group manages collections of evaluation cases.

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13. Create an empty evaluation set

adk eval_set create \
  path/to/my_agent/__init__.py \
  EVAL_SET_ID

With Cloud Storage:

adk eval_set create \
  --eval_storage_uri=gs://BUCKET_NAME \
  path/to/my_agent/__init__.py \
  EVAL_SET_ID

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14. Add an evaluation case

Using a scenarios file:

adk eval_set add_eval_case \
  --scenarios_file=scenarios.json \
  path/to/my_agent/__init__.py \
  EVAL_SET_ID

Using a session input file:

adk eval_set add_eval_case \
  --session_input_file=session.json \
  path/to/my_agent/__init__.py \
  EVAL_SET_ID

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15. Generate evaluation cases

adk eval_set generate_eval_cases \
  --user_simulation_config_file=user-simulation.json \
  path/to/my_agent/__init__.py \
  EVAL_SET_ID

This uses the Vertex AI evaluation tooling to generate conversation scenarios dynamically.

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16. Run ADK JSON tests

adk test

Specify a folder:

adk test path/to/agents

Rebuild test files by running the real agent:

adk test --rebuild path/to/agents

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Conformance testing

17. Display conformance commands

adk conformance --help

Conformance tests check whether agent behavior remains consistent.

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18. Record conformance tests

Non-streaming:

adk conformance record tests none

SSE streaming:

adk conformance record tests sse

Bidirectional streaming:

adk conformance record tests bidi

This feature is marked as work in progress in the current reference.

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19. Run conformance tests

adk conformance test

Specify test folders:

adk conformance test tests/core tests/tools

Generate a Markdown report:

adk conformance test \
  --generate_report \
  --report_dir=reports

Choose replay mode:

adk conformance test \
  --mode=replay \
  tests

The documented live mode is not yet implemented in the current reference.

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Optimization and migration

20. Optimize the root-agent instructions

adk optimize \
  --sampler_config_file_path=sampler-config.json \
  path/to/my_agent/__init__.py

With a custom optimizer configuration:

adk optimize \
  --sampler_config_file_path=sampler-config.json \
  --optimizer_config_file_path=optimizer-config.json \
  --print_detailed_results \
  path/to/my_agent/__init__.py

This uses the GEPA optimizer to improve the root agent’s instructions based on evaluation data.

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21. Display migration commands

adk migrate --help

The migration group currently provides session-database migration.

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22. Migrate a session database

adk migrate session \
  --source_db_url=sqlite:///old-sessions.db \
  --dest_db_url=sqlite:///new-sessions.db

Migrates session data into the latest database schema.

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Setup commands related to ADK

These are package-management commands, not ADK subcommands.

Install Google ADK

pip install google-adk

Upgrade Google ADK

pip install --upgrade google-adk

Inspect the installed package

pip show google-adk

Confirm the executable location

which adk

Stop any running ADK server

Ctrl+C

Complete command tree

adk
├── api_server
├── conformance
│   ├── record
│   └── test
├── create
├── deploy
│   ├── agent_engine
│   ├── cloud_run
│   └── gke
├── eval
├── eval_set
│   ├── add_eval_case
│   ├── create
│   └── generate_eval_cases
├── migrate
│   └── session
├── optimize
├── run
├── test
└── web

For everyday development, the five most important commands are:

adk create my_agent
adk run my_agent
adk web .
adk api_server .
adk deploy cloud_run --project=PROJECT_ID --region=REGION my_agent