docs: remove duplicate info froms README, remove flash run from instructions

01_getting_started/01_hello_world/README.md

@@ -8,41 +8,48 @@ Simple example demonstrating GPU-based serverless workers with automatic scaling
 
 ```bash
 uv sync
+uv tool install runpod-flash
 ```
 
 ### 2. Authenticate
 
 ```bash
-uv run flash login
+flash login
 ```
 
 Or create a `.env` file with `RUNPOD_API_KEY=your_api_key_here`.
 
-### 3. Run Locally
+### 3. Run the Example
 
 ```bash
-uv run flash run
+uv run gpu_worker.py
 ```
 
-Server starts at **http://localhost:8888**
+The function executes on a Runpod GPU and prints the result directly:
 
-### 4. Test the API
+```
+Testing GPU worker with payload: {'message': 'Testing GPU worker'}
+Result: {'status': 'success', 'message': 'Testing GPU worker', 'worker_type': 'GPU', ...}
+```
+
+First run takes 30-60 seconds (provisioning). Subsequent runs take 2-3 seconds.
+
+### Alternative: HTTP API Testing
+
+To test via HTTP endpoints instead:
 
-Visit **http://localhost:8888/docs** for interactive API documentation. QB endpoints are auto-generated by `flash run` based on your `@Endpoint` functions.
+```bash
+flash dev
+```
+
+Visit **http://localhost:8888/docs** for interactive API documentation.
 
 ```bash
 curl -X POST http://localhost:8888/gpu_worker/runsync \
   -H "Content-Type: application/json" \
   -d '{"message": "Hello GPU!"}'
 ```
 
-### Full CLI Documentation
-
-For complete CLI usage including deployment, environment management, and troubleshooting:
-- **[CLI Reference](../../CLI-REFERENCE.md)** - All commands and options
-- **[Getting Started Guide](../../docs/cli/getting-started.md)** - Step-by-step tutorial
-- **[Workflows](../../docs/cli/workflows.md)** - Common development patterns
-
 ## What This Demonstrates
 
 ### GPU Worker (`gpu_worker.py`)
@@ -133,14 +140,14 @@ The worker uses PyTorch to detect and report GPU information:
 
 ## Development
 
-### Test Worker Locally
+### Run the Worker
 ```bash
-python gpu_worker.py
+uv run gpu_worker.py
 ```
 
-### Run the Application
+### HTTP API Testing (Optional)
 ```bash
-flash run
+flash dev
 ```
 
 ## Next Steps

01_getting_started/01_hello_world/gpu_worker.py

@@ -1,6 +1,6 @@
-# gpu serverless worker -- detects available GPU hardware.
-# run with: flash run
-# test directly: python gpu_worker.py
+# GPU serverless worker -- detects available GPU hardware.
+# Run: uv run gpu_worker.py
+# Alternative: flash dev (for HTTP API testing)
 from runpod_flash import Endpoint, GpuType
 
 

01_getting_started/02_cpu_worker/README.md

@@ -8,41 +8,48 @@ Simple example demonstrating CPU-based serverless workers with automatic scaling
 
 ```bash
 uv sync
+uv tool install runpod-flash
 ```
 
 ### 2. Authenticate
 
 ```bash
-uv run flash login
+flash login
 ```
 
 Or create a `.env` file with `RUNPOD_API_KEY=your_api_key_here`.
 
-### 3. Run Locally
+### 3. Run the Example
 
 ```bash
-uv run flash run
+uv run cpu_worker.py
 ```
 
-Server starts at **http://localhost:8888**
+The function executes on a Runpod CPU worker and prints the result directly:
 
-### 4. Test the API
+```
+Testing CPU worker with payload: {'name': 'Testing CPU worker'}
+Result: {'status': 'success', 'message': 'Hello, Testing CPU worker!', 'worker_type': 'CPU', ...}
+```
+
+First run takes 30-60 seconds (provisioning). Subsequent runs take 2-3 seconds.
+
+### Alternative: HTTP API Testing
+
+To test via HTTP endpoints instead:
 
-Visit **http://localhost:8888/docs** for interactive API documentation. QB endpoints are auto-generated by `flash run` based on your `@Endpoint` functions.
+```bash
+flash dev
+```
+
+Visit **http://localhost:8888/docs** for interactive API documentation.
 
 ```bash
 curl -X POST http://localhost:8888/cpu_worker/runsync \
   -H "Content-Type: application/json" \
   -d '{"name": "Flash User"}'
 ```
 
-### Full CLI Documentation
-
-For complete CLI usage including deployment, environment management, and troubleshooting:
-- **[CLI Reference](../../CLI-REFERENCE.md)** - All commands and options
-- **[Getting Started Guide](../../docs/cli/getting-started.md)** - Step-by-step tutorial
-- **[Workflows](../../docs/cli/workflows.md)** - Common development patterns
-
 ## What This Demonstrates
 
 ### CPU Worker (`cpu_worker.py`)
@@ -135,14 +142,14 @@ The CPU worker scales to zero when idle:
 
 ## Development
 
-### Test Worker Locally
+### Run the Worker
 ```bash
-python cpu_worker.py
+uv run cpu_worker.py
 ```
 
-### Run the Application
+### HTTP API Testing (Optional)
 ```bash
-flash run
+flash dev
 ```
 
 ## When to Use CPU Workers

01_getting_started/02_cpu_worker/cpu_worker.py

@@ -1,6 +1,6 @@
-# cpu serverless worker -- lightweight processing without GPU.
-# run with: flash run
-# test directly: python cpu_worker.py
+# CPU serverless worker -- lightweight processing without GPU.
+# Run: uv run cpu_worker.py
+# Alternative: flash dev (for HTTP API testing)
 from runpod_flash import CpuInstanceType, Endpoint
 
 

01_getting_started/03_mixed_workers/README.md

@@ -40,31 +40,44 @@ Response
 
 **Prerequisites**: Complete the [repository setup](../../README.md#quick-start) first (clone, `make dev`, set API key).
 
-### Run This Example
+### Test Individual Workers
+
+Run the CPU and GPU workers directly:
 
 ```bash
 cd 01_getting_started/03_mixed_workers
-flash run
+
+# Test CPU preprocessing worker
+uv run cpu_worker.py
+
+# Test GPU inference worker
+uv run gpu_worker.py
 ```
 
-### Alternative: Standalone Setup
+First run takes 30-60 seconds (provisioning). Subsequent runs take 2-3 seconds.
+
+### Run the Full Pipeline
 
-If you haven't run the repository-wide setup:
+The pipeline endpoint (`/classify`) orchestrates multiple workers via HTTP. To test it:
+
+```bash
+flash dev
+```
+
+Server starts at http://localhost:8888
+
+### Setup (if needed)
 
 ```bash
 # Install dependencies
 uv sync
+uv tool install runpod-flash
 
 # Authenticate
-uv run flash login
+flash login
 # Or create .env file with RUNPOD_API_KEY=your_api_key_here
-
-# Run
-uv run flash run
 ```
 
-Server starts at http://localhost:8888
-
 ## Test the Pipeline
 
 ```bash

01_getting_started/03_mixed_workers/cpu_worker.py

@@ -1,7 +1,7 @@
-# cpu workers for text preprocessing and postprocessing.
-# part of the mixed CPU/GPU pipeline example.
-# run with: flash run
-# test directly: python cpu_worker.py
+# CPU workers for text preprocessing and postprocessing.
+# Part of the mixed CPU/GPU pipeline example.
+# Run: uv run cpu_worker.py
+# Alternative: flash dev (for HTTP API testing)
 from runpod_flash import CpuInstanceType, Endpoint
 
 

01_getting_started/03_mixed_workers/gpu_worker.py

@@ -1,7 +1,7 @@
-# gpu worker for ML inference (sentiment classification).
-# part of the mixed CPU/GPU pipeline example.
-# run with: flash run
-# test directly: python gpu_worker.py
+# GPU worker for ML inference (sentiment classification).
+# Part of the mixed CPU/GPU pipeline example.
+# Run: uv run gpu_worker.py
+# Alternative: flash dev (for HTTP API testing)
 from runpod_flash import Endpoint, GpuGroup
 
 

01_getting_started/03_mixed_workers/pipeline.py

@@ -1,6 +1,13 @@
-# classification pipeline: CPU preprocess -> GPU inference -> CPU postprocess.
-# demonstrates cross-worker orchestration via a load-balanced endpoint.
-# run with: flash run
+# Classification pipeline: CPU preprocess -> GPU inference -> CPU postprocess.
+# Demonstrates cross-worker orchestration via a load-balanced endpoint.
+# Run: uv run pipeline.py (local testing)
+# Alternative: flash dev (for HTTP route testing)
+import sys
+from pathlib import Path
+
+# Ensure sibling modules (cpu_worker, gpu_worker) are importable regardless of cwd
+sys.path.insert(0, str(Path(__file__).parent))
+
 from runpod_flash import Endpoint
 
 pipeline = Endpoint(name="01_03_classify_pipeline", cpu="cpu3c-1-2", workers=(1, 3))

01_getting_started/04_dependencies/README.md

@@ -29,27 +29,37 @@ Learn how to manage Python packages and system dependencies in Flash workers.
 
 ```bash
 cd 01_getting_started/04_dependencies
-flash run
-```
 
-Server starts at http://localhost:8888
+# Run any worker directly
+uv run gpu_worker.py
+uv run cpu_worker.py
+uv run mixed_worker.py
+```
 
-### Alternative: Standalone Setup
+First run takes 30-60 seconds (provisioning). Subsequent runs take 2-3 seconds.
 
-If you haven't run the repository-wide setup:
+### Setup (if needed)
 
 ```bash
 # Install dependencies
 uv sync
+uv tool install runpod-flash
 
 # Authenticate
-uv run flash login
+flash login
 # Or create .env file with RUNPOD_API_KEY=your_api_key_here
+```
+
+### Alternative: HTTP API Testing
 
-# Run
-uv run flash run
+To test via HTTP endpoints:
+
+```bash
+flash dev
 ```
 
+Server starts at http://localhost:8888
+
 ## GPU vs CPU Packaging
 
 GPU and CPU endpoints use different base Docker images, which affects how dependencies are resolved:
@@ -274,8 +284,8 @@ async def fetch_data(url: str):
 
 ```bash
 # Test locally first
-python gpu_worker.py
-python cpu_worker.py
+uv run gpu_worker.py
+uv run cpu_worker.py
 ```
 
 ### 4. Document Dependencies

01_getting_started/04_dependencies/cpu_worker.py

@@ -1,6 +1,6 @@
-# cpu workers demonstrating data science and zero-dependency patterns.
-# run with: flash run
-# test directly: python cpu_worker.py
+# CPU workers demonstrating data science and zero-dependency patterns.
+# Run: uv run cpu_worker.py
+# Alternative: flash dev (for HTTP API testing)
 from runpod_flash import CpuInstanceType, Endpoint
 
 

01_getting_started/04_dependencies/gpu_worker.py

@@ -1,6 +1,6 @@
-# gpu workers demonstrating Python and system dependency management.
-# run with: flash run
-# test directly: python gpu_worker.py
+# GPU workers demonstrating Python and system dependency management.
+# Run: uv run gpu_worker.py
+# Alternative: flash dev (for HTTP API testing)
 from runpod_flash import Endpoint, GpuGroup
 
 

01_getting_started/04_dependencies/mixed_worker.py

@@ -3,8 +3,8 @@
 #   - GPU images (runpod/pytorch:*) have numpy pre-installed
 #   - CPU images (python-slim) install numpy from the build artifact
 #
-# run with: flash run
-# test directly: python mixed_worker.py
+# Run: uv run mixed_worker.py
+# Alternative: flash dev (for HTTP API testing)
 from runpod_flash import CpuInstanceType, Endpoint, GpuType