diff --git a/01_getting_started/01_hello_world/__init__.py b/01_getting_started/01_hello_world/__init__.py
deleted file mode 100644
index 72afaa4..0000000
--- a/01_getting_started/01_hello_world/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Hello world example."""
diff --git a/01_getting_started/01_hello_world/gpu_worker.py b/01_getting_started/01_hello_world/gpu_worker.py
index fe8b0cd..6fbd837 100644
--- a/01_getting_started/01_hello_world/gpu_worker.py
+++ b/01_getting_started/01_hello_world/gpu_worker.py
@@ -1,46 +1,20 @@
-## Hello world: GPU serverless workers
-# In this part of the example code, we provision a GPU-based worker and have it
-# execute code. We can run the worker directly, or have it handle API requests
-# to the router function. It's registered to a subrouter in the __init__.py
-# file in this folder, and subsequently imported by main.py and attached to the
-# FastAPI app there.
+# GPU serverless worker -- detects available GPU hardware.
+# Run with: flash run
+# Test directly: python gpu_worker.py
+from runpod_flash import GpuGroup, LiveServerless, remote
 
-# Scaling behavior is controlled by configuration passed to the
-# `LiveServerless` class.
-from fastapi import APIRouter
-from pydantic import BaseModel
-from runpod_flash import (
-    GpuGroup,
-    LiveServerless,
-    remote,
-)
-
-# Here, we'll define several variables that change the
-# default behavior of our serverless endpoint. `workersMin` sets our endpoint
-# to scale to 0 active containers; `workersMax` will allow our endpoint to run
-# up to 3 workers in parallel as the endpoint receives more work. We also set
-# an idle timeout of 5 minutes so that any active worker stays alive for 5
-# minutes after completing a request.
 gpu_config = LiveServerless(
     name="01_01_gpu_worker",
-    gpus=[GpuGroup.ANY],  # Run on any GPU
+    gpus=[GpuGroup.ANY],
     workersMin=0,
     workersMax=3,
     idleTimeout=5,
 )
 
 
-# Decorating our function with `remote` will package up the function code and
-# deploy it on the infrastructure according to the passed input config. The
-# results from the worker will be returned to your terminal. In this example
-# the function will return a greeting to the input string passed in the `name`
-# key. The code itself will run on a GPU worker, and information about the GPU
-# the worker has access to will be included in the response.
 @remote(resource_config=gpu_config)
-async def gpu_hello(
-    input_data: dict,
-) -> dict:
-    """Simple GPU worker example with GPU detection."""
+async def gpu_hello(input_data: dict) -> dict:
+    """Simple GPU worker that returns GPU hardware info."""
     import platform
     from datetime import datetime
 
@@ -51,10 +25,7 @@ async def gpu_hello(
     gpu_count = torch.cuda.device_count()
     gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
 
-    message = input_data.get(
-        "message",
-        "Hello from GPU worker!",
-    )
+    message = input_data.get("message", "Hello from GPU worker!")
 
     return {
         "status": "success",
@@ -64,10 +35,7 @@ async def gpu_hello(
             "available": gpu_available,
             "name": gpu_name,
             "count": gpu_count,
-            "memory_gb": round(
-                gpu_memory,
-                2,
-            ),
+            "memory_gb": round(gpu_memory, 2),
         },
         "timestamp": datetime.now().isoformat(),
         "platform": platform.system(),
@@ -75,29 +43,6 @@ async def gpu_hello(
     }
 
 
-# We define a subrouter for our gpu worker so that our main router in `main.py`
-# can attach it for routing gpu-specific requests.
-gpu_router = APIRouter()
-
-
-class MessageRequest(BaseModel):
-    """Request model for GPU worker."""
-
-    message: str = "Hello from GPU!"
-
-
-@gpu_router.post("/hello")
-async def hello(
-    request: MessageRequest,
-):
-    """Simple GPU worker endpoint."""
-    result = await gpu_hello({"message": request.message})
-    return result
-
-
-# This code is packaged up as a "worker" that will handle requests sent to the
-# endpoint at /gpu/hello, but you can also trigger it directly by running
-# python -m workers.gpu.endpoint
 if __name__ == "__main__":
     import asyncio
 
diff --git a/01_getting_started/01_hello_world/main.py b/01_getting_started/01_hello_world/main.py
deleted file mode 100644
index e0e51c2..0000000
--- a/01_getting_started/01_hello_world/main.py
+++ /dev/null
@@ -1,71 +0,0 @@
-## Example 1: Hello world
-# This is an example of a simple Flash application.
-# It consists of an API router (this file) that routes requests to a local
-# endpoint on your machine to worker code. Worker code is executed on Runpod
-# infrastructure on serverless workers with GPUs.
-
-# Subrouters and associated worker function code are defined in the ./workers/
-# dir and attached to the main router in this file. By default, running
-# `flash run` will start the local API server on your machine serving requests
-# from port 8888.
-
-# We'll define a resource configuration for a GPU worker on a Runpod serverless
-# endpoint. The GPU worker will return information about the infrastructure
-# it executes on.
-
-import logging
-import os
-
-from fastapi import FastAPI
-from gpu_worker import gpu_router
-
-logger = logging.getLogger(__name__)
-
-# We define a simple FastAPI app to serve requests from localhost.
-app = FastAPI(
-    title="Flash Application",
-    description="Distributed GPU computing with Runpod Flash",
-    version="0.1.0",
-)
-
-# Attach gpu worker subrouters - this will route any requests to our
-# app with the prefix /gpu to the gpu subrouter. To see the subrouter in action,
-# start the app and execute the following command in another terminal window:
-# curl -X POST http://localhost:8888/gpu/hello -d '{"input": "hello"}' -H "Content-Type: application/json"
-app.include_router(
-    gpu_router,
-    prefix="/gpu",
-    tags=["GPU Workers"],
-)
-
-
-# The homepage for our main endpoint will just return a plaintext json object
-# containing the endpoints defined in this app.
-@app.get("/")
-def home():
-    return {
-        "message": "Flash Application",
-        "docs": "/docs",
-        "endpoints": {
-            "gpu_hello": "/gpu/hello",
-        },
-    }
-
-
-@app.get("/ping")
-def ping():
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    host = os.getenv("FLASH_HOST", "localhost")
-    port = int(os.getenv("FLASH_PORT", 8888))
-    logger.info(f"Starting Flash server on {host}:{port}")
-
-    uvicorn.run(
-        app,
-        host=host,
-        port=port,
-    )
diff --git a/01_getting_started/01_hello_world/mothership.py b/01_getting_started/01_hello_world/mothership.py
deleted file mode 100644
index e417f98..0000000
--- a/01_getting_started/01_hello_world/mothership.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
-Mothership Endpoint Configuration
-
-The mothership endpoint serves your FastAPI application routes.
-It is automatically deployed as a CPU-optimized load-balanced endpoint.
-
-To customize this configuration:
-- Modify worker scaling: change workersMin and workersMax values
-- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer
-- Change endpoint name: update the 'name' parameter
-
-To disable mothership deployment:
-- Delete this file, or
-- Comment out the 'mothership' variable below
-"""
-
-from runpod_flash import CpuLiveLoadBalancer
-
-# Mothership endpoint configuration
-# This serves your FastAPI app routes from main.py
-mothership = CpuLiveLoadBalancer(
-    name="01_01_hello_world-mothership",
-    # workersMin=1,
-    # workersMax=3,
-)
-
-# Examples of customization:
-
-# Increase scaling for high traffic
-# mothership = CpuLiveLoadBalancer(
-#     name="mothership-01_01_hello_world",
-#     workersMin=2,
-#     workersMax=10,
-# )
-
-# Use GPU-based load balancer instead of CPU
-# (requires importing LiveLoadBalancer)
-# from runpod_flash import LiveLoadBalancer
-# mothership = LiveLoadBalancer(
-#     name="mothership-01_01_hello_world",
-#     gpus=[GpuGroup.ANY],
-# )
-
-# Custom endpoint name
-# mothership = CpuLiveLoadBalancer(
-#     name="my-api-gateway",
-#     workersMin=1,
-#     workersMax=3,
-# )
-
-# To disable mothership:
-# - Delete this entire file, or
-# - Comment out the 'mothership' variable above
diff --git a/01_getting_started/02_cpu_worker/__init__.py b/01_getting_started/02_cpu_worker/__init__.py
deleted file mode 100644
index f927b6b..0000000
--- a/01_getting_started/02_cpu_worker/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""CPU worker example."""
diff --git a/01_getting_started/02_cpu_worker/cpu_worker.py b/01_getting_started/02_cpu_worker/cpu_worker.py
index 8c9f281..429d1dc 100644
--- a/01_getting_started/02_cpu_worker/cpu_worker.py
+++ b/01_getting_started/02_cpu_worker/cpu_worker.py
@@ -1,33 +1,20 @@
-## Hello world: CPU serverless workers
-# In this part of the example code, we provision a CPU-based worker (no GPUs) and have it execute code.
-# We can run the worker directly, or have it handle API requests to the router function.
-# It's registered to a subrouter in the __init__.py file in this folder, and subsequently
-# imported by main.py and attached to the FastAPI app there.
-from fastapi import APIRouter
-from pydantic import BaseModel
+# CPU serverless worker -- lightweight processing without GPU.
+# Run with: flash run
+# Test directly: python cpu_worker.py
 from runpod_flash import CpuInstanceType, CpuLiveServerless, remote
 
-# Scaling behavior is controlled by configuration passed to the `CpuLiveServerless` class.
-# Here, we'll define several variables that change the default behavior of our serverless endpoint.
-# `workersMin` sets our endpoint to scale to 0 active containers; `workersMax` will allow our endpoint
-# to run up to 5 workers in parallel as the endpoint receives more work.
-# We also set an idle timeout of 5 minutes so that any active worker stays alive for 5 minutes after completing a request.
 cpu_config = CpuLiveServerless(
     name="01_02_cpu_worker",
     instanceIds=[CpuInstanceType.CPU3C_1_2],
-    workersMin=0,  # Scale to zero
+    workersMin=0,
     workersMax=3,
-    idleTimeout=5,  # Leave workers alive for 5 minutes after they serve a request
+    idleTimeout=5,
 )
 
 
-# Decorating our function with `remote` will package up the function code and deploy it on the infrastructure
-# according to the passed input config.
-# In this example the function will return a greeting to the input string passed in the `name` key.
-# The results are displayed in your terminal, but the work was performed by CPU workers on runpod infra.
 @remote(resource_config=cpu_config)
 async def cpu_hello(input_data: dict) -> dict:
-    """Simple CPU worker example."""
+    """Simple CPU worker that returns a greeting."""
     import platform
     from datetime import datetime
 
@@ -43,24 +30,6 @@ async def cpu_hello(input_data: dict) -> dict:
     }
 
 
-cpu_router = APIRouter()
-
-
-class MessageRequest(BaseModel):
-    """Request model for CPU worker."""
-
-    name: str = "Flash expert"
-
-
-@cpu_router.post("/hello")
-async def hello(request: MessageRequest):
-    """Simple CPU worker endpoint."""
-    result = await cpu_hello({"message": request.name})
-    return result
-
-
-# This code is packaged up as a "worker" that will handle requests sent to the endpoint at
-# /cpu/hello, but you can also trigger it locally by running python -m workers.cpu.endpoint.
 if __name__ == "__main__":
     import asyncio
 
diff --git a/01_getting_started/02_cpu_worker/main.py b/01_getting_started/02_cpu_worker/main.py
deleted file mode 100644
index 1ea67d6..0000000
--- a/01_getting_started/02_cpu_worker/main.py
+++ /dev/null
@@ -1,60 +0,0 @@
-## Example 2: Hello world (with CPU workers)
-# This is an example of a simple Flash application.
-# It consists of an API router (this file) that routes requests to a local endpoint on your machine to
-# worker code. Worker code is executed on Runpod infrastructure on low-cost, cpu-based serverless workers.
-
-# Subrouters and associated worker function code are defined in the ./workers/ dir
-# and attached to the main router in this file. By default, running `flash run`
-# will start the local API server on your machine serving requests from port 8888.
-
-# There are two worker resource configurations for this app: a gpu and a cpu config.
-# A CPU-only configured worker allows you to execute code that doesn't require GPUs on
-# runpod infrastructure for a lower cost. It will return a simple greeting when you invoke the code directly
-# or send a request to its corresponding endpoint. The GPU worker will return information about the infrastructure
-# it executes on.
-
-import logging
-import os
-
-from cpu_worker import cpu_router
-from fastapi import FastAPI
-
-logger = logging.getLogger(__name__)
-
-# We define a simple FastAPI app to serve requests from localhost.
-app = FastAPI(
-    title="Flash Application",
-    description="Distributed GPU and CPU computing with Runpod Flash",
-    version="0.1.0",
-)
-
-# Attach gpu and cpu worker subrouters - this will route any requests to our app
-# with the prefix /cpu to our CPU subrouter. Try out the following command
-# in a separate terminal window after starting your app:
-# curl -X POST http://localhost:8888/cpu/hello -d '{"input": "hello"}' -H "Content-Type: application/json"
-app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"])
-
-
-# The homepage for our main endpoint will just return a plaintext json object containing the endpoints defined in this app.
-@app.get("/")
-def home():
-    return {
-        "message": "Flash Application",
-        "docs": "/docs",
-        "endpoints": {"cpu_hello": "/cpu/hello"},
-    }
-
-
-@app.get("/ping")
-def ping():
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    host = os.getenv("FLASH_HOST", "localhost")
-    port = int(os.getenv("FLASH_PORT", 8888))
-    logger.info(f"Starting Flash server on {host}:{port}")
-
-    uvicorn.run(app, host=host, port=port)
diff --git a/01_getting_started/02_cpu_worker/mothership.py b/01_getting_started/02_cpu_worker/mothership.py
deleted file mode 100644
index 106109a..0000000
--- a/01_getting_started/02_cpu_worker/mothership.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
-Mothership Endpoint Configuration
-
-The mothership endpoint serves your FastAPI application routes.
-It is automatically deployed as a CPU-optimized load-balanced endpoint.
-
-To customize this configuration:
-- Modify worker scaling: change workersMin and workersMax values
-- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer
-- Change endpoint name: update the 'name' parameter
-
-To disable mothership deployment:
-- Delete this file, or
-- Comment out the 'mothership' variable below
-"""
-
-from runpod_flash import CpuLiveLoadBalancer
-
-# Mothership endpoint configuration
-# This serves your FastAPI app routes from main.py
-mothership = CpuLiveLoadBalancer(
-    name="01_02_cpu_worker-mothership",
-    # workersMin=1,
-    # workersMax=3,
-)
-
-# Examples of customization:
-
-# Increase scaling for high traffic
-# mothership = CpuLiveLoadBalancer(
-#     name="mothership-01_02_cpu_worker",
-#     workersMin=2,
-#     workersMax=10,
-# )
-
-# Use GPU-based load balancer instead of CPU
-# (requires importing LiveLoadBalancer)
-# from runpod_flash import LiveLoadBalancer
-# mothership = LiveLoadBalancer(
-#     name="mothership-01_02_cpu_worker",
-#     gpus=[GpuGroup.ANY],
-# )
-
-# Custom endpoint name
-# mothership = CpuLiveLoadBalancer(
-#     name="my-api-gateway",
-#     workersMin=1,
-#     workersMax=3,
-# )
-
-# To disable mothership:
-# - Delete this entire file, or
-# - Comment out the 'mothership' variable above
diff --git a/01_getting_started/03_mixed_workers/__init__.py b/01_getting_started/03_mixed_workers/__init__.py
deleted file mode 100644
index 0e7f92c..0000000
--- a/01_getting_started/03_mixed_workers/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Mixed workers example."""
diff --git a/01_getting_started/03_mixed_workers/workers/cpu/endpoint.py b/01_getting_started/03_mixed_workers/cpu_worker.py
similarity index 75%
rename from 01_getting_started/03_mixed_workers/workers/cpu/endpoint.py
rename to 01_getting_started/03_mixed_workers/cpu_worker.py
index 1137e2a..2fbca79 100644
--- a/01_getting_started/03_mixed_workers/workers/cpu/endpoint.py
+++ b/01_getting_started/03_mixed_workers/cpu_worker.py
@@ -1,36 +1,28 @@
+# 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
 from runpod_flash import CpuInstanceType, CpuLiveServerless, remote
 
-# Preprocessing CPU worker (fast, cheap)
 cpu_config = CpuLiveServerless(
     name="01_03_mixed_workers_cpu",
-    instanceIds=[CpuInstanceType.CPU3G_2_8],  # Small instance - 2 vCPU, 8GB
+    instanceIds=[CpuInstanceType.CPU3G_2_8],
     idleTimeout=3,
 )
 
 
 @remote(resource_config=cpu_config)
 async def preprocess_text(input_data: dict) -> dict:
-    """
-    Preprocessing on CPU: cleaning and tokenization.
-
-    Why CPU:
-    - No ML operations
-    - Fast text operations
-    - 85% cheaper than GPU
-
-    Note: Input validation handled by Pydantic at API layer
-    """
+    """Preprocess text: cleaning and tokenization (cheap CPU work)."""
     import re
     from datetime import datetime
 
     text = input_data.get("text", "")
 
-    # Text cleaning
     cleaned_text = text.strip()
-    cleaned_text = re.sub(r"\s+", " ", cleaned_text)  # Remove extra whitespace
-    cleaned_text = re.sub(r"[^\w\s.,!?-]", "", cleaned_text)  # Remove special chars
+    cleaned_text = re.sub(r"\s+", " ", cleaned_text)
+    cleaned_text = re.sub(r"[^\w\s.,!?-]", "", cleaned_text)
 
-    # Simple tokenization (word count, sentence count)
     words = cleaned_text.split()
     sentences = len(re.split(r"[.!?]+", cleaned_text))
 
@@ -48,21 +40,13 @@ async def preprocess_text(input_data: dict) -> dict:
 
 @remote(resource_config=cpu_config)
 async def postprocess_results(input_data: dict) -> dict:
-    """
-    Postprocessing on CPU: formatting, aggregation, logging.
-
-    Why CPU:
-    - No ML operations
-    - Simple data formatting
-    - 85% cheaper than GPU
-    """
+    """Postprocess GPU results: formatting and aggregation (cheap CPU work)."""
     from datetime import datetime
 
     predictions = input_data.get("predictions", [])
     original_text = input_data.get("original_text", "")
     metadata = input_data.get("metadata", {})
 
-    # Find top prediction
     if predictions:
         top_prediction = max(predictions, key=lambda x: x["confidence"])
         confidence_level = (
@@ -76,8 +60,7 @@ async def postprocess_results(input_data: dict) -> dict:
         top_prediction = None
         confidence_level = "none"
 
-    # Format response
-    formatted_result = {
+    return {
         "status": "success",
         "text_preview": original_text[:100] + "..." if len(original_text) > 100 else original_text,
         "classification": {
@@ -94,17 +77,10 @@ async def postprocess_results(input_data: dict) -> dict:
         "worker_type": "CPU Postprocessing",
     }
 
-    return formatted_result
 
-
-# Test locally
 if __name__ == "__main__":
     import asyncio
 
-    from dotenv import find_dotenv, load_dotenv
-
-    load_dotenv(find_dotenv())  # Find and load root .env file
-
     async def test_workers():
         print("\n=== Testing Preprocessing ===")
         preprocess_result = await preprocess_text(
diff --git a/01_getting_started/03_mixed_workers/workers/gpu/endpoint.py b/01_getting_started/03_mixed_workers/gpu_worker.py
similarity index 80%
rename from 01_getting_started/03_mixed_workers/workers/gpu/endpoint.py
rename to 01_getting_started/03_mixed_workers/gpu_worker.py
index 2c2ff89..9ca7e02 100644
--- a/01_getting_started/03_mixed_workers/workers/gpu/endpoint.py
+++ b/01_getting_started/03_mixed_workers/gpu_worker.py
@@ -1,9 +1,12 @@
+# 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
 from runpod_flash import GpuGroup, LiveServerless, remote
 
-# GPU worker for ML inference (expensive, powerful)
 gpu_config = LiveServerless(
     name="01_03_mixed_inference",
-    gpus=[GpuGroup.ADA_24],  # RTX 4090 - 24GB
+    gpus=[GpuGroup.ADA_24],
     workersMin=0,
     workersMax=3,
     idleTimeout=5,
@@ -12,14 +15,7 @@
 
 @remote(resource_config=gpu_config, dependencies=["torch"])
 async def gpu_inference(input_data: dict) -> dict:
-    """
-    GPU inference: classification with mock model.
-
-    Why GPU:
-    - ML model inference
-    - Matrix operations
-    - Worth the cost for compute-intensive tasks
-    """
+    """GPU inference: mock sentiment classification."""
     import random
     from datetime import datetime
 
@@ -28,7 +24,6 @@ async def gpu_inference(input_data: dict) -> dict:
     cleaned_text = input_data.get("cleaned_text", "")
     word_count = input_data.get("word_count", 0)
 
-    # GPU info
     gpu_available = torch.cuda.is_available()
     if gpu_available:
         gpu_name = torch.cuda.get_device_name(0)
@@ -37,21 +32,14 @@ async def gpu_inference(input_data: dict) -> dict:
         gpu_name = "No GPU (running locally)"
         gpu_memory = 0
 
-    # Mock model inference (in real scenario, load actual model)
-    # Simulating sentiment analysis classification
     predictions = []
-
-    # Positive sentiment indicators
     positive_words = ["good", "great", "excellent", "love", "best", "happy", "wonderful"]
     negative_words = ["bad", "terrible", "worst", "hate", "poor", "awful", "horrible"]
-
     text_lower = cleaned_text.lower()
 
-    # Simple sentiment scoring
     positive_score = sum(1 for word in positive_words if word in text_lower)
     negative_score = sum(1 for word in negative_words if word in text_lower)
 
-    # Create predictions based on word presence
     if positive_score > negative_score:
         predictions = [
             {"label": "positive", "confidence": 0.75 + random.uniform(0, 0.24)},
@@ -71,12 +59,10 @@ async def gpu_inference(input_data: dict) -> dict:
             {"label": "negative", "confidence": random.uniform(0.10, 0.30)},
         ]
 
-    # Normalize confidences to sum to 1.0
     total = sum(p["confidence"] for p in predictions)
     for p in predictions:
         p["confidence"] = round(p["confidence"] / total, 4)
 
-    # Simulate GPU tensor operations (in real scenario, this would be model forward pass)
     if gpu_available:
         dummy_tensor = torch.randn(100, 100, device="cuda")
         _ = torch.matmul(dummy_tensor, dummy_tensor)
@@ -99,14 +85,9 @@ async def gpu_inference(input_data: dict) -> dict:
     }
 
 
-# Test locally
 if __name__ == "__main__":
     import asyncio
 
-    from dotenv import find_dotenv, load_dotenv
-
-    load_dotenv(find_dotenv())  # Find and load root .env file
-
     test_payload = {
         "cleaned_text": "This is a great product! I love it.",
         "word_count": 8,
diff --git a/01_getting_started/03_mixed_workers/main.py b/01_getting_started/03_mixed_workers/main.py
deleted file mode 100644
index 8c29aa8..0000000
--- a/01_getting_started/03_mixed_workers/main.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import logging
-import os
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, field_validator
-from workers.cpu import cpu_router
-from workers.cpu.endpoint import postprocess_results, preprocess_text
-from workers.gpu import gpu_router
-from workers.gpu.endpoint import gpu_inference
-
-logger = logging.getLogger(__name__)
-
-
-app = FastAPI(
-    title="Mixed GPU/CPU Flash Application",
-    description="Cost-effective ML pipeline combining CPU preprocessing with GPU inference",
-    version="0.1.0",
-)
-
-# Include individual worker routers
-app.include_router(gpu_router, prefix="/gpu", tags=["GPU Workers"])
-app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"])
-
-
-class ClassifyRequest(BaseModel):
-    """Request model for complete classification pipeline."""
-
-    text: str
-
-    @field_validator("text")
-    @classmethod
-    def validate_text(cls, v):
-        if not v or not v.strip():
-            raise ValueError("Text cannot be empty")
-        if len(v) < 3:
-            raise ValueError(
-                'Text too short (minimum 3 characters). Example: {"text": "Hello world"}'
-            )
-        if len(v) > 10000:
-            raise ValueError(f"Text too long (maximum 10,000 characters). Got {len(v)} characters.")
-        return v
-
-
-@app.post("/classify")
-async def classify_text(request: ClassifyRequest):
-    """
-    Complete ML pipeline: CPU → GPU → CPU
-
-    Pipeline stages:
-    1. CPU Preprocessing: Validate and clean text (fast, cheap)
-    2. GPU Inference: Run ML model (expensive, necessary)
-    3. CPU Postprocessing: Format results (fast, cheap)
-
-    This architecture minimizes GPU usage and maximizes cost-effectiveness.
-    """
-    try:
-        # Stage 1: Preprocess on CPU (fast, cheap)
-        # Note: Input validation already done by Pydantic at API layer
-        preprocess_result = await preprocess_text({"text": request.text})
-
-        # Stage 2: GPU inference (expensive, necessary)
-        gpu_result = await gpu_inference(
-            {
-                "cleaned_text": preprocess_result["cleaned_text"],
-                "word_count": preprocess_result["word_count"],
-            }
-        )
-
-        # Stage 3: Postprocess on CPU (fast, cheap)
-        final_result = await postprocess_results(
-            {
-                "predictions": gpu_result["predictions"],
-                "original_text": request.text,
-                "metadata": {
-                    "word_count": preprocess_result["word_count"],
-                    "sentence_count": preprocess_result["sentence_count"],
-                    "model": gpu_result["model_info"],
-                },
-            }
-        )
-
-        return final_result
-
-    except Exception as e:
-        logger.error(f"Pipeline error: {e}")
-        raise HTTPException(status_code=500, detail=f"Processing error: {e!s}") from e
-
-
-@app.get("/", tags=["Info"])
-def home():
-    return {
-        "message": "Mixed GPU/CPU Flash Application",
-        "description": "Cost-effective ML pipeline combining CPU and GPU workers",
-        "docs": "/docs",
-        "pipeline": {
-            "classify": "POST /classify - Complete pipeline (CPU → GPU → CPU)",
-            "cpu_preprocess": "POST /cpu/preprocess - CPU text preprocessing",
-            "gpu_inference": "POST /gpu/inference - GPU ML inference",
-            "cpu_postprocess": "POST /cpu/postprocess - CPU result formatting",
-        },
-        "architecture": {
-            "pattern": "CPU (cheap) → GPU (expensive) → CPU (cheap)",
-            "cost_savings": "85% vs GPU-only pipeline",
-        },
-    }
-
-
-@app.get("/health", tags=["Info"])
-def health():
-    return {
-        "status": "healthy",
-        "workers": {
-            "cpu_preprocess": "ready",
-            "gpu_inference": "ready",
-            "cpu_postprocess": "ready",
-        },
-    }
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    host = os.getenv("FLASH_HOST", "localhost")
-    port = int(os.getenv("FLASH_PORT", 8888))
-    logger.info(f"Starting Mixed GPU/CPU Flash server on {host}:{port}")
-
-    uvicorn.run(app, host=host, port=port)
diff --git a/01_getting_started/03_mixed_workers/mothership.py b/01_getting_started/03_mixed_workers/mothership.py
deleted file mode 100644
index 513d841..0000000
--- a/01_getting_started/03_mixed_workers/mothership.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
-Mothership Endpoint Configuration
-
-The mothership endpoint serves your FastAPI application routes.
-It is automatically deployed as a CPU-optimized load-balanced endpoint.
-
-To customize this configuration:
-- Modify worker scaling: change workersMin and workersMax values
-- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer
-- Change endpoint name: update the 'name' parameter
-
-To disable mothership deployment:
-- Delete this file, or
-- Comment out the 'mothership' variable below
-"""
-
-from runpod_flash import CpuLiveLoadBalancer
-
-# Mothership endpoint configuration
-# This serves your FastAPI app routes from main.py
-mothership = CpuLiveLoadBalancer(
-    name="01_03_mixed_workers-mothership",
-    # workersMin=1,
-    # workersMax=3,
-)
-
-# Examples of customization:
-
-# Increase scaling for high traffic
-# mothership = CpuLiveLoadBalancer(
-#     name="mothership-01_03_mixed_workers",
-#     workersMin=2,
-#     workersMax=10,
-# )
-
-# Use GPU-based load balancer instead of CPU
-# (requires importing LiveLoadBalancer)
-# from runpod_flash import LiveLoadBalancer
-# mothership = LiveLoadBalancer(
-#     name="mothership-01_03_mixed_workers",
-#     gpus=[GpuGroup.ANY],
-# )
-
-# Custom endpoint name
-# mothership = CpuLiveLoadBalancer(
-#     name="my-api-gateway",
-#     workersMin=1,
-#     workersMax=3,
-# )
-
-# To disable mothership:
-# - Delete this entire file, or
-# - Comment out the 'mothership' variable above
diff --git a/01_getting_started/03_mixed_workers/pipeline.py b/01_getting_started/03_mixed_workers/pipeline.py
new file mode 100644
index 0000000..62b6869
--- /dev/null
+++ b/01_getting_started/03_mixed_workers/pipeline.py
@@ -0,0 +1,36 @@
+# Classification pipeline: CPU preprocess -> GPU inference -> CPU postprocess.
+# Demonstrates cross-worker orchestration via a load-balanced endpoint.
+# Run with: flash run
+from cpu_worker import postprocess_results, preprocess_text
+from gpu_worker import gpu_inference
+from runpod_flash import CpuLiveLoadBalancer, remote
+
+pipeline_config = CpuLiveLoadBalancer(
+    name="01_03_classify_pipeline",
+    workersMin=1,
+)
+
+
+@remote(resource_config=pipeline_config, method="POST", path="/classify")
+async def classify(text: str) -> dict:
+    """Complete ML pipeline: CPU preprocess -> GPU inference -> CPU postprocess."""
+    preprocess_result = await preprocess_text({"text": text})
+
+    gpu_result = await gpu_inference(
+        {
+            "cleaned_text": preprocess_result["cleaned_text"],
+            "word_count": preprocess_result["word_count"],
+        }
+    )
+
+    return await postprocess_results(
+        {
+            "predictions": gpu_result["predictions"],
+            "original_text": text,
+            "metadata": {
+                "word_count": preprocess_result["word_count"],
+                "sentence_count": preprocess_result["sentence_count"],
+                "model": gpu_result["model_info"],
+            },
+        }
+    )
diff --git a/01_getting_started/03_mixed_workers/workers/__init__.py b/01_getting_started/03_mixed_workers/workers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/01_getting_started/03_mixed_workers/workers/cpu/__init__.py b/01_getting_started/03_mixed_workers/workers/cpu/__init__.py
deleted file mode 100644
index a522189..0000000
--- a/01_getting_started/03_mixed_workers/workers/cpu/__init__.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from fastapi import APIRouter
-from pydantic import BaseModel, field_validator
-
-from .endpoint import postprocess_results, preprocess_text
-
-cpu_router = APIRouter()
-
-
-class PreprocessRequest(BaseModel):
-    """Request model for text preprocessing."""
-
-    text: str
-
-    @field_validator("text")
-    @classmethod
-    def validate_text(cls, v):
-        if not v or not v.strip():
-            raise ValueError("Text cannot be empty")
-        if len(v) < 3:
-            raise ValueError(
-                'Text too short (minimum 3 characters). Example: {"text": "Hello world"}'
-            )
-        if len(v) > 10000:
-            raise ValueError(f"Text too long (maximum 10,000 characters). Got {len(v)} characters.")
-        return v
-
-
-@cpu_router.post("/preprocess")
-async def preprocess_endpoint(request: PreprocessRequest):
-    """
-    Preprocess text before GPU inference.
-
-    Fast, cheap CPU operation for validation and cleaning.
-    """
-    result = await preprocess_text({"text": request.text})
-    return result
-
-
-class Prediction(BaseModel):
-    """Individual prediction with label and confidence."""
-
-    label: str
-    confidence: float
-
-    @field_validator("confidence")
-    @classmethod
-    def validate_confidence(cls, v):
-        if not 0.0 <= v <= 1.0:
-            raise ValueError(f"Confidence must be between 0.0 and 1.0, got {v}")
-        return v
-
-
-class PostprocessRequest(BaseModel):
-    """Request model for result postprocessing."""
-
-    predictions: list[Prediction]
-    original_text: str
-    metadata: dict
-
-    @field_validator("predictions")
-    @classmethod
-    def validate_predictions_not_empty(cls, v):
-        if not v:
-            raise ValueError(
-                'Predictions cannot be empty. Example: [{"label": "positive", "confidence": 0.9}]'
-            )
-        return v
-
-
-@cpu_router.post("/postprocess")
-async def postprocess_endpoint(request: PostprocessRequest):
-    """
-    Postprocess GPU inference results.
-
-    Fast, cheap CPU operation for formatting and aggregation.
-    """
-    # Convert Pydantic models to dicts for remote serialization
-    result = await postprocess_results(
-        {
-            "predictions": [pred.model_dump() for pred in request.predictions],
-            "original_text": request.original_text,
-            "metadata": request.metadata,
-        }
-    )
-    return result
diff --git a/01_getting_started/03_mixed_workers/workers/gpu/__init__.py b/01_getting_started/03_mixed_workers/workers/gpu/__init__.py
deleted file mode 100644
index 01fe478..0000000
--- a/01_getting_started/03_mixed_workers/workers/gpu/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from fastapi import APIRouter
-from pydantic import BaseModel, Field, field_validator
-
-from .endpoint import gpu_inference
-
-gpu_router = APIRouter()
-
-
-class InferenceRequest(BaseModel):
-    """Request model for GPU inference."""
-
-    cleaned_text: str
-    word_count: int = Field(ge=0)
-
-    @field_validator("cleaned_text")
-    @classmethod
-    def validate_text_not_empty(cls, v):
-        if not v or not v.strip():
-            raise ValueError("Cleaned text cannot be empty")
-        return v
-
-
-@gpu_router.post("/inference")
-async def inference_endpoint(request: InferenceRequest):
-    """
-    Run ML inference on GPU.
-
-    Expensive GPU operation - only use after CPU preprocessing.
-    """
-    result = await gpu_inference(
-        {
-            "cleaned_text": request.cleaned_text,
-            "word_count": request.word_count,
-        }
-    )
-    return result
diff --git a/01_getting_started/04_dependencies/__init__.py b/01_getting_started/04_dependencies/__init__.py
deleted file mode 100644
index f8afef5..0000000
--- a/01_getting_started/04_dependencies/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Dependencies example."""
diff --git a/01_getting_started/04_dependencies/workers/cpu/endpoint.py b/01_getting_started/04_dependencies/cpu_worker.py
similarity index 96%
rename from 01_getting_started/04_dependencies/workers/cpu/endpoint.py
rename to 01_getting_started/04_dependencies/cpu_worker.py
index 980b104..9abac4e 100644
--- a/01_getting_started/04_dependencies/workers/cpu/endpoint.py
+++ b/01_getting_started/04_dependencies/cpu_worker.py
@@ -1,3 +1,6 @@
+# CPU workers demonstrating data science and zero-dependency patterns.
+# Run with: flash run
+# Test directly: python cpu_worker.py
 from runpod_flash import CpuInstanceType, CpuLiveServerless, remote
 
 # Worker with data science dependencies
@@ -119,10 +122,6 @@ async def minimal_process(input_data: dict) -> dict:
 if __name__ == "__main__":
     import asyncio
 
-    from dotenv import find_dotenv, load_dotenv
-
-    load_dotenv(find_dotenv())  # Find and load root .env file
-
     async def test():
         print("\n=== Testing Data Dependencies ===")
         data_result = await process_data({"data": [[1, 2], [3, 4], [5, 6]]})
diff --git a/01_getting_started/04_dependencies/workers/gpu/endpoint.py b/01_getting_started/04_dependencies/gpu_worker.py
similarity index 96%
rename from 01_getting_started/04_dependencies/workers/gpu/endpoint.py
rename to 01_getting_started/04_dependencies/gpu_worker.py
index 624acf3..64970bf 100644
--- a/01_getting_started/04_dependencies/workers/gpu/endpoint.py
+++ b/01_getting_started/04_dependencies/gpu_worker.py
@@ -1,3 +1,6 @@
+# GPU workers demonstrating Python and system dependency management.
+# Run with: flash run
+# Test directly: python gpu_worker.py
 from runpod_flash import GpuGroup, LiveServerless, remote
 
 # Worker with ML dependencies (versioned)
@@ -109,10 +112,6 @@ async def process_with_system_deps(input_data: dict) -> dict:
 if __name__ == "__main__":
     import asyncio
 
-    from dotenv import find_dotenv, load_dotenv
-
-    load_dotenv(find_dotenv())  # Find and load root .env file
-
     async def test():
         print("\n=== Testing ML Dependencies ===")
         ml_result = await process_with_ml_libs({})
diff --git a/01_getting_started/04_dependencies/main.py b/01_getting_started/04_dependencies/main.py
deleted file mode 100644
index 11d07d0..0000000
--- a/01_getting_started/04_dependencies/main.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import logging
-import os
-
-from fastapi import FastAPI
-from workers.cpu import cpu_router
-from workers.gpu import gpu_router
-
-logger = logging.getLogger(__name__)
-
-
-app = FastAPI(
-    title="Dependency Management Examples",
-    description="Learn how to manage Python and system dependencies in Flash workers",
-    version="0.1.0",
-)
-
-app.include_router(gpu_router, prefix="/gpu", tags=["GPU Workers"])
-app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"])
-
-
-@app.get("/", tags=["Info"])
-def home():
-    return {
-        "message": "Flash Dependency Management Examples",
-        "description": "Examples of Python and system dependency management",
-        "docs": "/docs",
-        "examples": {
-            "ml_deps": "POST /gpu/ml-deps - ML dependencies (torch, pillow, numpy)",
-            "system_deps": "POST /gpu/system-deps - System dependencies (ffmpeg, libgl1)",
-            "data_deps": "POST /cpu/data - Data science dependencies (pandas, scipy)",
-            "minimal": "POST /cpu/minimal - No dependencies (fastest)",
-        },
-        "concepts": [
-            "Version pinning (torch==2.1.0)",
-            "Version constraints (>=, <, ~=)",
-            "System packages via apt",
-            "Minimal dependencies for fast cold start",
-        ],
-    }
-
-
-@app.get("/health", tags=["Info"])
-def health():
-    return {
-        "status": "healthy",
-        "workers": {
-            "ml_deps": "GPU worker with torch, pillow, numpy",
-            "system_deps": "GPU worker with ffmpeg, libgl1",
-            "data_deps": "CPU worker with pandas, scipy",
-            "minimal": "CPU worker with no dependencies",
-        },
-    }
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    host = os.getenv("FLASH_HOST", "localhost")
-    port = int(os.getenv("FLASH_PORT", 8888))
-    logger.info(f"Starting Dependency Management server on {host}:{port}")
-
-    uvicorn.run(app, host=host, port=port)
diff --git a/01_getting_started/04_dependencies/mothership.py b/01_getting_started/04_dependencies/mothership.py
deleted file mode 100644
index dcc91aa..0000000
--- a/01_getting_started/04_dependencies/mothership.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
-Mothership Endpoint Configuration
-
-The mothership endpoint serves your FastAPI application routes.
-It is automatically deployed as a CPU-optimized load-balanced endpoint.
-
-To customize this configuration:
-- Modify worker scaling: change workersMin and workersMax values
-- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer
-- Change endpoint name: update the 'name' parameter
-
-To disable mothership deployment:
-- Delete this file, or
-- Comment out the 'mothership' variable below
-"""
-
-from runpod_flash import CpuLiveLoadBalancer
-
-# Mothership endpoint configuration
-# This serves your FastAPI app routes from main.py
-mothership = CpuLiveLoadBalancer(
-    name="01_04_dependencies-mothership",
-    # workersMin=1,
-    # workersMax=3,
-)
-
-# Examples of customization:
-
-# Increase scaling for high traffic
-# mothership = CpuLiveLoadBalancer(
-#     name="mothership-01_04_dependencies",
-#     workersMin=2,
-#     workersMax=10,
-# )
-
-# Use GPU-based load balancer instead of CPU
-# (requires importing LiveLoadBalancer)
-# from runpod_flash import LiveLoadBalancer
-# mothership = LiveLoadBalancer(
-#     name="mothership-01_04_dependencies",
-#     gpus=[GpuGroup.ANY],
-# )
-
-# Custom endpoint name
-# mothership = CpuLiveLoadBalancer(
-#     name="my-api-gateway",
-#     workersMin=1,
-#     workersMax=3,
-# )
-
-# To disable mothership:
-# - Delete this entire file, or
-# - Comment out the 'mothership' variable above
diff --git a/01_getting_started/04_dependencies/workers/__init__.py b/01_getting_started/04_dependencies/workers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/01_getting_started/04_dependencies/workers/cpu/__init__.py b/01_getting_started/04_dependencies/workers/cpu/__init__.py
deleted file mode 100644
index 180925f..0000000
--- a/01_getting_started/04_dependencies/workers/cpu/__init__.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from fastapi import APIRouter
-from pydantic import BaseModel, field_validator
-
-from .endpoint import minimal_process, process_data
-
-cpu_router = APIRouter()
-
-
-class DataRequest(BaseModel):
-    """Request model for data processing."""
-
-    data: list[list[int]]
-
-    @field_validator("data")
-    @classmethod
-    def validate_two_columns(cls, v):
-        if not v:
-            raise ValueError("Data cannot be empty")
-        if len(v) < 2:
-            raise ValueError(
-                f"Need at least 2 rows to compute statistics, got {len(v)}. "
-                f'Example: {{"data": [[1, 2], [3, 4]]}}'
-            )
-        for i, row in enumerate(v):
-            if len(row) != 2:
-                raise ValueError(
-                    f"Row {i} has {len(row)} columns, expected exactly 2. "
-                    f'Example: {{"data": [[1, 2], [3, 4]]}}'
-                )
-        return v
-
-
-class TextRequest(BaseModel):
-    """Request model for text processing."""
-
-    text: str
-
-
-@cpu_router.post("/data")
-async def data_endpoint(request: DataRequest):
-    """Test worker with data science dependencies (pandas, numpy, scipy)."""
-    result = await process_data({"data": request.data})
-    return result
-
-
-@cpu_router.post("/minimal")
-async def minimal_endpoint(request: TextRequest):
-    """Test worker with NO dependencies (fastest cold start)."""
-    result = await minimal_process({"text": request.text})
-    return result
diff --git a/01_getting_started/04_dependencies/workers/gpu/__init__.py b/01_getting_started/04_dependencies/workers/gpu/__init__.py
deleted file mode 100644
index c47de1e..0000000
--- a/01_getting_started/04_dependencies/workers/gpu/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from fastapi import APIRouter
-
-from .endpoint import process_with_ml_libs, process_with_system_deps
-
-gpu_router = APIRouter()
-
-
-@gpu_router.post("/ml-deps")
-async def ml_deps_endpoint():
-    """Test worker with ML dependencies (torch, pillow, numpy)."""
-    result = await process_with_ml_libs({})
-    return result
-
-
-@gpu_router.post("/system-deps")
-async def system_deps_endpoint():
-    """Test worker with system dependencies (ffmpeg, libgl1)."""
-    result = await process_with_system_deps({})
-    return result
diff --git a/02_ml_inference/01_text_to_speech/__init__.py b/02_ml_inference/01_text_to_speech/__init__.py
deleted file mode 100644
index 1a418c3..0000000
--- a/02_ml_inference/01_text_to_speech/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Text-to-speech example using Qwen3-TTS."""
diff --git a/02_ml_inference/01_text_to_speech/workers/gpu/endpoint.py b/02_ml_inference/01_text_to_speech/gpu_worker.py
similarity index 97%
rename from 02_ml_inference/01_text_to_speech/workers/gpu/endpoint.py
rename to 02_ml_inference/01_text_to_speech/gpu_worker.py
index 83bd818..87eaff9 100644
--- a/02_ml_inference/01_text_to_speech/workers/gpu/endpoint.py
+++ b/02_ml_inference/01_text_to_speech/gpu_worker.py
@@ -1,3 +1,7 @@
+# Qwen3-TTS text-to-speech GPU worker.
+# Run with: flash run
+# Test directly: python gpu_worker.py
+
 from runpod_flash import GpuGroup, LiveServerless, remote
 
 # GPU config for Qwen3-TTS - needs 24GB+ VRAM for 1.7B model
@@ -162,7 +166,7 @@ async def get_voices(input_data: dict) -> dict:
     }
 
 
-# Test locally with: python -m workers.gpu.endpoint
+# Test locally with: python gpu_worker.py
 if __name__ == "__main__":
     import asyncio
 
diff --git a/02_ml_inference/01_text_to_speech/main.py b/02_ml_inference/01_text_to_speech/main.py
deleted file mode 100644
index 2852d8e..0000000
--- a/02_ml_inference/01_text_to_speech/main.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import logging
-import os
-
-from fastapi import FastAPI
-from workers.gpu import gpu_router
-
-logger = logging.getLogger(__name__)
-
-
-app = FastAPI(
-    title="Qwen3-TTS API",
-    description="Text-to-Speech API using Qwen3-TTS on RunPod serverless GPUs",
-    version="1.0.0",
-)
-
-app.include_router(gpu_router, prefix="/gpu", tags=["TTS"])
-
-
-@app.get("/")
-def home():
-    return {
-        "message": "Qwen3-TTS API",
-        "docs": "/docs",
-        "endpoints": {
-            "tts": "/gpu/tts",
-            "tts_audio": "/gpu/tts/audio",
-            "voices": "/gpu/voices",
-        },
-    }
-
-
-@app.get("/ping")
-def ping():
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    host = os.getenv("FLASH_HOST", "localhost")
-    port = int(os.getenv("FLASH_PORT", 8888))
-    logger.info(f"Starting Flash server on {host}:{port}")
-
-    uvicorn.run(app, host=host, port=port)
diff --git a/02_ml_inference/01_text_to_speech/mothership.py b/02_ml_inference/01_text_to_speech/mothership.py
deleted file mode 100644
index 8b2390d..0000000
--- a/02_ml_inference/01_text_to_speech/mothership.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""
-Mothership Endpoint Configuration
-
-The mothership endpoint serves your FastAPI application routes.
-It is automatically deployed as a CPU-optimized load-balanced endpoint.
-
-To customize this configuration:
-- Modify worker scaling: change workersMin and workersMax values
-- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer
-- Change endpoint name: update the 'name' parameter
-
-To disable mothership deployment:
-- Delete this file, or
-- Comment out the 'mothership' variable below
-
-Documentation: https://docs.runpod.io/flash/mothership
-"""
-
-from runpod_flash import CpuLiveLoadBalancer
-
-# Mothership endpoint configuration
-# This serves your FastAPI app routes from main.py
-mothership = CpuLiveLoadBalancer(
-    name="02_01_text_to_speech-mothership",
-    workersMin=1,
-    workersMax=3,
-)
-
-# Examples of customization:
-
-# Increase scaling for high traffic
-# mothership = CpuLiveLoadBalancer(
-#     name="mothership",
-#     workersMin=2,
-#     workersMax=10,
-# )
-
-# Use GPU-based load balancer instead of CPU
-# (requires importing LiveLoadBalancer)
-# from runpod_flash import LiveLoadBalancer
-# mothership = LiveLoadBalancer(
-#     name="mothership",
-#     gpus=[GpuGroup.ANY],
-# )
-
-# Custom endpoint name
-# mothership = CpuLiveLoadBalancer(
-#     name="my-api-gateway",
-#     workersMin=1,
-#     workersMax=3,
-# )
-
-# To disable mothership:
-# - Delete this entire file, or
-# - Comment out the 'mothership' variable above
diff --git a/02_ml_inference/01_text_to_speech/workers/__init__.py b/02_ml_inference/01_text_to_speech/workers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/02_ml_inference/01_text_to_speech/workers/gpu/__init__.py b/02_ml_inference/01_text_to_speech/workers/gpu/__init__.py
deleted file mode 100644
index 6c8f26d..0000000
--- a/02_ml_inference/01_text_to_speech/workers/gpu/__init__.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import base64
-
-from fastapi import APIRouter
-from fastapi.responses import Response
-from pydantic import BaseModel, Field
-
-from .endpoint import generate_speech, get_voices
-
-gpu_router = APIRouter()
-
-
-class TTSRequest(BaseModel):
-    """Request model for text-to-speech generation."""
-
-    text: str = Field(..., description="The text to synthesize into speech")
-    speaker: str = Field(
-        default="Ryan",
-        description="Voice to use. Options: Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee",
-    )
-    language: str = Field(
-        default="Auto",
-        description="Language. Options: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian, Auto",
-    )
-    instruct: str | None = Field(
-        default=None,
-        description="Optional emotion/style instruction (e.g., 'Speak happily', 'Say it slowly and calmly')",
-    )
-
-
-@gpu_router.post("/tts")
-async def text_to_speech(request: TTSRequest) -> dict:
-    """
-    Generate speech from text using Qwen3-TTS.
-
-    Returns JSON with base64-encoded WAV audio.
-    """
-    payload = {
-        "text": request.text,
-        "speaker": request.speaker,
-        "language": request.language,
-    }
-    if request.instruct:
-        payload["instruct"] = request.instruct
-
-    result = await generate_speech(payload)
-    return result
-
-
-@gpu_router.post("/tts/audio")
-async def text_to_speech_audio(request: TTSRequest) -> Response:
-    """
-    Generate speech from text using Qwen3-TTS.
-
-    Returns raw WAV audio file directly.
-    """
-    payload = {
-        "text": request.text,
-        "speaker": request.speaker,
-        "language": request.language,
-    }
-    if request.instruct:
-        payload["instruct"] = request.instruct
-
-    result = await generate_speech(payload)
-
-    if result["status"] == "error":
-        return result
-
-    audio_bytes = base64.b64decode(result["audio_base64"])
-    return Response(
-        content=audio_bytes,
-        media_type="audio/wav",
-        headers={"Content-Disposition": "attachment; filename=speech.wav"},
-    )
-
-
-@gpu_router.get("/voices")
-async def list_voices() -> dict:
-    """Get available voices and languages."""
-    result = await get_voices({})
-    return result
diff --git a/02_ml_inference/02_text_generation/.env.example b/02_ml_inference/02_text_generation/.env.example
new file mode 100644
index 0000000..f28a2dd
--- /dev/null
+++ b/02_ml_inference/02_text_generation/.env.example
@@ -0,0 +1,5 @@
+# FLASH_HOST=localhost
+# FLASH_PORT=8888
+# LOG_LEVEL=INFO
+# RUNPOD_API_KEY=your_api_key_here
+# VLLM_MODEL=Qwen/Qwen2.5-3B-Instruct
diff --git a/02_ml_inference/02_text_generation/.flashignore b/02_ml_inference/02_text_generation/.flashignore
new file mode 100644
index 0000000..4ce0adc
--- /dev/null
+++ b/02_ml_inference/02_text_generation/.flashignore
@@ -0,0 +1,40 @@
+# Flash Build Ignore Patterns
+
+# Python cache
+__pycache__/
+*.pyc
+
+# Virtual environments
+venv/
+.venv/
+env/
+
+# IDE
+.vscode/
+.idea/
+
+# Environment files
+.env
+.env.local
+
+# Git
+.git/
+.gitignore
+
+# Build artifacts
+dist/
+build/
+*.egg-info/
+
+# Flash resources
+.runpod/
+
+# Tests
+tests/
+test_*.py
+*_test.py
+
+# Documentation
+docs/
+*.md
+!README.md
diff --git a/02_ml_inference/02_text_generation/.gitignore b/02_ml_inference/02_text_generation/.gitignore
new file mode 100644
index 0000000..9150d9c
--- /dev/null
+++ b/02_ml_inference/02_text_generation/.gitignore
@@ -0,0 +1,45 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+.venv/
+ENV/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Environment
+.env
+.env.local
+
+# Flash
+.runpod/
+dist/
+
+# OS
+.DS_Store
+Thumbs.db
+.flash/
diff --git a/02_ml_inference/02_text_generation/README.md b/02_ml_inference/02_text_generation/README.md
new file mode 100644
index 0000000..0e3e970
--- /dev/null
+++ b/02_ml_inference/02_text_generation/README.md
@@ -0,0 +1,45 @@
+# Text Generation with vLLM (Qwen)
+
+Serve a chat-style text generation worker with vLLM on Runpod Flash GPUs.
+
+## What You'll Learn
+
+- Configuring a GPU worker with `LiveServerless`
+- Serving a vLLM-backed model with `@remote`
+- Caching model initialization to avoid repeated cold loads
+- Calling a `@remote` class method from a worker function
+
+## Quick Start
+
+### Prerequisites
+
+- Python 3.10+
+- Runpod API key
+
+### Setup
+
+```bash
+cd 02_ml_inference/02_text_generation
+pip install -r requirements.txt
+cp .env.example .env
+# Add RUNPOD_API_KEY to .env
+```
+
+### Run
+
+```bash
+flash run
+```
+
+Server starts at http://localhost:8888
+Visit http://localhost:8888/docs for interactive docs.
+
+## Common Issues
+
+- Cold start latency on first request is expected when worker is scaled to zero.
+- If you hit GPU memory errors, reduce `max_model_len` or use a larger GPU group.
+
+## References
+
+- https://docs.runpod.io/flash
+- https://docs.vllm.ai
diff --git a/02_ml_inference/02_text_generation/gpu_worker.py b/02_ml_inference/02_text_generation/gpu_worker.py
new file mode 100644
index 0000000..4da85ef
--- /dev/null
+++ b/02_ml_inference/02_text_generation/gpu_worker.py
@@ -0,0 +1,83 @@
+import asyncio
+import logging
+
+from runpod_flash import GpuGroup, LiveServerless, remote
+
+logger = logging.getLogger(__name__)
+
+gpu_config = LiveServerless(
+    name="02_02_text_generation_gpu",  # Name shown for this serverless worker deployment.
+    gpus=[GpuGroup.ADA_24],  # Request an NVIDIA L4 / 24GB-class GPU (Ada 24GB group).
+    workersMin=0,  # Keep 0 warm workers to reduce idle cost.
+    workersMax=3,  # Scale out to at most 3 workers under load.
+    idleTimeout=30,  # Keep workers running for n seconds before scaling back down
+)
+
+
+@remote(
+    resource_config=gpu_config,
+    dependencies=["vllm"],  # Extra package(s) installed in the worker image at runtime.
+)
+class MinimalVLLM:
+    _instance = None
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self) -> None:
+        self.MODEL = "Qwen/Qwen2.5-3B-Instruct"
+        self.SYSTEM_PROMPT = (
+            "You are a pirate-style rewriter. Rewrite user text in fun pirate voice. "
+            "Keep original meaning. Keep it concise. Output only the rewritten text."
+        )
+
+        import os
+
+        from transformers import AutoTokenizer
+        from vllm import LLM, SamplingParams
+
+        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = (
+            "spawn"  # Safer multiprocessing mode for CUDA workers.
+        )
+
+        self.llm = LLM(
+            model=self.MODEL,  # Hugging Face model id loaded by vLLM.
+            enforce_eager=True,  # Skip CUDA graph capture; faster cold start, usually lower throughput.
+            gpu_memory_utilization=0.6,  # Reserve ~60% of GPU memory for model/kv cache.
+            max_model_len=1024,  # Max context window handled per request.
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL)
+        self.sampling = SamplingParams(
+            temperature=0.6,  # Moderate creativity while preserving meaning.
+            top_p=0.9,  # Nucleus sampling to avoid low-probability tails.
+            max_tokens=500,  # Cap response length.
+        )
+        print("vLLM initialized successfully")
+
+    def piratize(self, text: str) -> str:
+        messages = [
+            {"role": "system", "content": self.SYSTEM_PROMPT},
+            {"role": "user", "content": text},
+        ]
+        prompt = self.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,  # Return plain prompt text for vLLM.
+            add_generation_prompt=True,  # Append assistant prefix so model continues as assistant.
+        )
+        out = self.llm.generate([prompt], self.sampling)
+        return out[0].outputs[0].text.strip()
+
+    def generate_single(self, prompt: str) -> str:
+        return self.piratize(prompt)
+
+
+async def piratize_text(text: str) -> str:
+    llm = MinimalVLLM()
+    return await llm.generate_single(text)
+
+
+if __name__ == "__main__":
+    sample = "Want to go get something to eat? man, I sure do enjoy sailing."
+    print(asyncio.run(piratize_text(sample)))
diff --git a/02_ml_inference/02_text_generation/pyproject.toml b/02_ml_inference/02_text_generation/pyproject.toml
new file mode 100644
index 0000000..40d5aea
--- /dev/null
+++ b/02_ml_inference/02_text_generation/pyproject.toml
@@ -0,0 +1,11 @@
+[project]
+name = "02_text_generation"
+version = "0.1.0"
+description = "Text generation with vLLM on Runpod Flash"
+requires-python = ">=3.10"
+dependencies = [
+    "runpod-flash",
+    "fastapi>=0.104.0",
+    "uvicorn>=0.24.0",
+    "pydantic>=2.0.0",
+]
diff --git a/02_ml_inference/02_text_generation/requirements.txt b/02_ml_inference/02_text_generation/requirements.txt
new file mode 100644
index 0000000..a73ed1a
--- /dev/null
+++ b/02_ml_inference/02_text_generation/requirements.txt
@@ -0,0 +1 @@
+runpod-flash
diff --git a/02_ml_inference/02_text_generation/uv.lock b/02_ml_inference/02_text_generation/uv.lock
new file mode 100644
index 0000000..089d805
--- /dev/null
+++ b/02_ml_inference/02_text_generation/uv.lock
@@ -0,0 +1,3096 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version < '3.13'",
+]
+
+[[package]]
+name = "02-text-generation"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "fastapi" },
+    { name = "pydantic" },
+    { name = "runpod-flash" },
+    { name = "uvicorn" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "fastapi", specifier = ">=0.104.0" },
+    { name = "pydantic", specifier = ">=2.0.0" },
+    { name = "runpod-flash" },
+    { name = "uvicorn", specifier = ">=0.24.0" },
+]
+
+[[package]]
+name = "aiodns"
+version = "4.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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diff --git a/02_ml_inference/README.md b/02_ml_inference/README.md
index cd41fed..763a6e5 100644
--- a/02_ml_inference/README.md
+++ b/02_ml_inference/README.md
@@ -4,21 +4,22 @@ Deploy machine learning models as production-ready APIs. Learn how to serve LLMs
 
 ## Examples
 
-### 01_text_generation _(coming soon)_
-LLM inference API with streaming support.
+### 01_text_to_speech
+Text-to-speech API using Qwen3-TTS on serverless GPU workers.
+
+### 02_text_generation
+vLLM-based text generation API with a cached Qwen model.
 
 **What you'll learn:**
-- Loading and serving LLMs (Llama, Mistral, etc.)
-- Streaming text generation
-- Model quantization for efficiency
-- Memory management for large models
+- Loading and serving LLMs with vLLM
+- Prompt templating for chat-style generation
+- Worker-level model caching for lower latency
+- Input validation and generation controls
 
 **Models covered:**
-- Llama 3, Llama 3.1, Llama 3.2
-- Mistral, Mixtral
-- Qwen, Phi, Gemma
+- Qwen/Qwen2.5-3B-Instruct
 
-### 02_image_generation _(coming soon)_
+### 03_image_generation _(coming soon)_
 Stable Diffusion image generation API.
 
 **What you'll learn:**
@@ -32,7 +33,7 @@ Stable Diffusion image generation API.
 - SDXL Turbo
 - ControlNet integration
 
-### 03_embeddings _(coming soon)_
+### 04_embeddings _(coming soon)_
 Text embedding API for semantic search and RAG.
 
 **What you'll learn:**
@@ -46,7 +47,7 @@ Text embedding API for semantic search and RAG.
 - OpenAI-compatible embeddings
 - Multilingual models
 
-### 04_multimodal _(coming soon)_
+### 05_multimodal _(coming soon)_
 Vision-language models (CLIP, LLaVA, etc.).
 
 **What you'll learn:**
diff --git a/03_advanced_workers/05_load_balancer/workers/cpu/endpoint.py b/03_advanced_workers/05_load_balancer/cpu_lb.py
similarity index 96%
rename from 03_advanced_workers/05_load_balancer/workers/cpu/endpoint.py
rename to 03_advanced_workers/05_load_balancer/cpu_lb.py
index 2dd86a5..55949c4 100644
--- a/03_advanced_workers/05_load_balancer/workers/cpu/endpoint.py
+++ b/03_advanced_workers/05_load_balancer/cpu_lb.py
@@ -1,3 +1,6 @@
+# CPU load-balanced endpoints with custom HTTP routes.
+# Run with: flash run
+# Test directly: python cpu_lb.py
 from runpod_flash import CpuLiveLoadBalancer, remote
 
 cpu_config = CpuLiveLoadBalancer(
@@ -95,7 +98,6 @@ async def transform_data(text: str, operation: str = "uppercase") -> dict:
     }
 
 
-# Test locally with: python -m workers.cpu.endpoint
 if __name__ == "__main__":
     import asyncio
 
diff --git a/03_advanced_workers/05_load_balancer/workers/gpu/endpoint.py b/03_advanced_workers/05_load_balancer/gpu_lb.py
similarity index 95%
rename from 03_advanced_workers/05_load_balancer/workers/gpu/endpoint.py
rename to 03_advanced_workers/05_load_balancer/gpu_lb.py
index f8c8d5c..a1dfd09 100644
--- a/03_advanced_workers/05_load_balancer/workers/gpu/endpoint.py
+++ b/03_advanced_workers/05_load_balancer/gpu_lb.py
@@ -1,3 +1,6 @@
+# GPU load-balanced endpoints with custom HTTP routes.
+# Run with: flash run
+# Test directly: python gpu_lb.py
 from runpod_flash import LiveLoadBalancer, remote
 
 gpu_config = LiveLoadBalancer(
@@ -68,7 +71,6 @@ async def gpu_info() -> dict:
     return info
 
 
-# Test locally with: python -m workers.gpu.endpoint
 if __name__ == "__main__":
     import asyncio
 
diff --git a/03_advanced_workers/05_load_balancer/main.py b/03_advanced_workers/05_load_balancer/main.py
deleted file mode 100644
index 7115e75..0000000
--- a/03_advanced_workers/05_load_balancer/main.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import logging
-import os
-
-from fastapi import FastAPI
-from workers.cpu import cpu_router
-from workers.gpu import gpu_router
-
-logger = logging.getLogger(__name__)
-
-
-app = FastAPI(
-    title="Load Balancer Example",
-    description="Demonstrates load-balancer endpoints with custom HTTP routes using Flash",
-    version="0.1.0",
-)
-
-# Include routers for unified app discovery
-app.include_router(gpu_router, prefix="/gpu", tags=["GPU Compute Service"])
-app.include_router(cpu_router, prefix="/cpu", tags=["CPU Data Service"])
-
-
-@app.get("/")
-def home():
-    return {
-        "message": "Load Balancer Example API",
-        "description": "Demonstrates load-balancer endpoints with custom HTTP routes",
-        "note": "Load-balancer endpoints are defined with @remote(resource, method=..., path=...)",
-        "docs": "/docs",
-        "gpu_endpoints": {
-            "health": "GET /gpu/health",
-            "compute": "POST /gpu/compute",
-            "info": "GET /gpu/info",
-        },
-        "cpu_endpoints": {
-            "health": "GET /cpu/health",
-            "validate": "POST /cpu/validate",
-            "transform": "POST /cpu/transform",
-        },
-    }
-
-
-@app.get("/ping")
-def ping():
-    """Health check endpoint for load balancer."""
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    host = os.getenv("FLASH_HOST", "localhost")
-    port = int(os.getenv("FLASH_PORT", 8000))
-    logger.info(f"Starting Load Balancer Example on {host}:{port}")
-
-    uvicorn.run(app, host=host, port=port)
diff --git a/03_advanced_workers/05_load_balancer/mothership.py b/03_advanced_workers/05_load_balancer/mothership.py
deleted file mode 100644
index 3e620be..0000000
--- a/03_advanced_workers/05_load_balancer/mothership.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
-Mothership Endpoint Configuration
-
-The mothership endpoint serves your FastAPI application routes.
-It is automatically deployed as a CPU-optimized load-balanced endpoint.
-
-To customize this configuration:
-- Modify worker scaling: change workersMin and workersMax values
-- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer
-- Change endpoint name: update the 'name' parameter
-
-To disable mothership deployment:
-- Delete this file, or
-- Comment out the 'mothership' variable below
-"""
-
-from runpod_flash import CpuLiveLoadBalancer
-
-# Mothership endpoint configuration
-# This serves your FastAPI app routes from main.py
-mothership = CpuLiveLoadBalancer(
-    name="03_05_load_balancer-mothership",
-    # workersMin=1,
-    # workersMax=3,
-)
-
-# Examples of customization:
-
-# Increase scaling for high traffic
-# mothership = CpuLiveLoadBalancer(
-#     name="mothership-03_05_load_balancer",
-#     workersMin=2,
-#     workersMax=10,
-# )
-
-# Use GPU-based load balancer instead of CPU
-# (requires importing LiveLoadBalancer)
-# from runpod_flash import LiveLoadBalancer
-# mothership = LiveLoadBalancer(
-#     name="mothership-03_05_load_balancer",
-#     gpus=[GpuGroup.ANY],
-# )
-
-# Custom endpoint name
-# mothership = CpuLiveLoadBalancer(
-#     name="my-api-gateway",
-#     workersMin=1,
-#     workersMax=3,
-# )
-
-# To disable mothership:
-# - Delete this entire file, or
-# - Comment out the 'mothership' variable above
diff --git a/03_advanced_workers/05_load_balancer/workers/__init__.py b/03_advanced_workers/05_load_balancer/workers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/03_advanced_workers/05_load_balancer/workers/cpu/__init__.py b/03_advanced_workers/05_load_balancer/workers/cpu/__init__.py
deleted file mode 100644
index e4d1cb5..0000000
--- a/03_advanced_workers/05_load_balancer/workers/cpu/__init__.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""CPU Load-Balancer Endpoints
-
-Load-balancer endpoints use the @remote decorator with method and path parameters.
-These decorators automatically create HTTP routes that are registered with the
-Load Balancer runtime.
-
-For the unified app discovery, we export a router that documents the endpoints.
-The actual HTTP handling is managed by the runpod-flash-lb framework.
-"""
-
-from fastapi import APIRouter
-from pydantic import BaseModel
-
-from .endpoint import cpu_health, transform_data, validate_data
-
-
-# Pydantic models for request validation
-class ValidateRequest(BaseModel):
-    """Request model for text validation."""
-
-    text: str
-
-
-class TransformRequest(BaseModel):
-    """Request model for text transformation."""
-
-    text: str
-    operation: str = "uppercase"
-
-
-# Export for unified app discovery
-cpu_router = APIRouter()
-
-
-@cpu_router.get("/health")
-async def get_cpu_health():
-    """CPU service health check."""
-    return await cpu_health()
-
-
-@cpu_router.post("/validate")
-async def post_validate(request: ValidateRequest):
-    """Validate and analyze text data."""
-    return await validate_data(request.text)
-
-
-@cpu_router.post("/transform")
-async def post_transform(request: TransformRequest):
-    """Transform text data with specified operation."""
-    return await transform_data(request.text, request.operation)
-
-
-__all__ = ["cpu_router"]
diff --git a/03_advanced_workers/05_load_balancer/workers/gpu/__init__.py b/03_advanced_workers/05_load_balancer/workers/gpu/__init__.py
deleted file mode 100644
index 1fe8c10..0000000
--- a/03_advanced_workers/05_load_balancer/workers/gpu/__init__.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""GPU Load-Balancer Endpoints
-
-Load-balancer endpoints use the @remote decorator with method and path parameters.
-These decorators automatically create HTTP routes that are registered with the
-Load Balancer runtime.
-
-For the unified app discovery, we export a router that documents the endpoints.
-The actual HTTP handling is managed by the runpod-flash-lb framework.
-"""
-
-from fastapi import APIRouter
-from pydantic import BaseModel, field_validator
-
-from .endpoint import compute_intensive, gpu_health, gpu_info
-
-
-class ComputeRequest(BaseModel):
-    """Request model for compute-intensive operations."""
-
-    numbers: list[int]
-
-    @field_validator("numbers")
-    @classmethod
-    def validate_numbers(cls, v: list[int]) -> list[int]:
-        """Validate that numbers list is not empty."""
-        if not v:
-            raise ValueError("numbers list cannot be empty")
-        return v
-
-
-# Export for unified app discovery
-gpu_router = APIRouter()
-
-
-@gpu_router.get("/health")
-async def get_gpu_health():
-    """GPU service health check."""
-    return await gpu_health()
-
-
-@gpu_router.post("/compute")
-async def post_gpu_compute(request: ComputeRequest):
-    """Perform compute-intensive operation on GPU."""
-    return await compute_intensive(request.model_dump())
-
-
-@gpu_router.get("/info")
-async def get_gpu_info():
-    """Get GPU device information."""
-    return await gpu_info()
-
-
-__all__ = ["gpu_router"]
diff --git a/05_data_workflows/01_network_volumes/workers/cpu/endpoint.py b/05_data_workflows/01_network_volumes/cpu_worker.py
similarity index 50%
rename from 05_data_workflows/01_network_volumes/workers/cpu/endpoint.py
rename to 05_data_workflows/01_network_volumes/cpu_worker.py
index 46f6064..ba7fb15 100644
--- a/05_data_workflows/01_network_volumes/workers/cpu/endpoint.py
+++ b/05_data_workflows/01_network_volumes/cpu_worker.py
@@ -1,8 +1,12 @@
-from fastapi.exceptions import HTTPException
-from fastapi.responses import Response
-from runpod_flash import CpuLiveServerless, remote
-
-from .. import volume
+# CPU worker with network volume for listing and serving generated images.
+# Run with: flash run
+# Test directly: python cpu_worker.py
+from runpod_flash import CpuLiveServerless, NetworkVolume, remote
+
+volume = NetworkVolume(
+    name="flash-05-volume",
+    size=50,
+)
 
 cpu_config = CpuLiveServerless(
     name="cpu_worker",
@@ -14,9 +18,9 @@
 
 @remote(resource_config=cpu_config)
 async def list_images_in_volume() -> dict:
+    """List generated images from the shared volume."""
     import os
 
-    """List generated images from the shared volume."""
     image_dir = "/runpod-volume/generated_images"
     images = os.listdir(image_dir)
 
@@ -26,29 +30,29 @@ async def list_images_in_volume() -> dict:
     }
 
 
-@remote(resource_config=cpu_config, dependencies=["fastapi"])
-async def get_image_from_volume(file_id: str) -> "Response":
+@remote(resource_config=cpu_config)
+async def get_image_from_volume(file_id: str) -> dict:
+    """Get image metadata from the shared volume."""
+    import base64
     from pathlib import Path
 
-    from fastapi.responses import Response
-
     image_path = Path(f"/runpod-volume/generated_images/{file_id}")
 
     if not image_path.is_file():
-        raise HTTPException(status_code=404, detail="file not found")
+        return {"status": "error", "error": "file not found"}
 
     image_bytes = image_path.read_bytes()
-    return Response(
-        content=image_bytes,
-        media_type="image/png",
-        headers={"Content-Disposition": f'inline; filename="{image_path.name}"'},
-    )
+    return {
+        "status": "success",
+        "filename": image_path.name,
+        "size_bytes": len(image_bytes),
+        "image_base64": base64.b64encode(image_bytes).decode("utf-8"),
+    }
 
 
-# Test locally with: python -m workers.cpu.endpoint
 if __name__ == "__main__":
     import asyncio
 
-    print("checking cpu worker by listing image files in /runpod-volume/generated_images/")
+    print("Checking cpu worker by listing image files in /runpod-volume/generated_images/")
     result = asyncio.run(list_images_in_volume())
     print(f"Result: {result}")
diff --git a/05_data_workflows/01_network_volumes/workers/gpu/endpoint.py b/05_data_workflows/01_network_volumes/gpu_worker.py
similarity index 77%
rename from 05_data_workflows/01_network_volumes/workers/gpu/endpoint.py
rename to 05_data_workflows/01_network_volumes/gpu_worker.py
index e63c480..8fa4f24 100644
--- a/05_data_workflows/01_network_volumes/workers/gpu/endpoint.py
+++ b/05_data_workflows/01_network_volumes/gpu_worker.py
@@ -1,15 +1,19 @@
-## GPU worker with network volumes
-# In this example, a GPU worker runs Stable Diffusion and writes outputs to the shared volume.
+# GPU worker with network volume for Stable Diffusion image generation.
+# Run with: flash run
+# Test directly: python gpu_worker.py
 import logging
 
-from runpod_flash import GpuGroup, LiveServerless, remote
-
-from .. import volume
+from runpod_flash import GpuGroup, LiveServerless, NetworkVolume, remote
 
 logger = logging.getLogger(__name__)
 
 MODEL_PATH = "/runpod-volume/models"
 
+volume = NetworkVolume(
+    name="flash-05-volume",
+    size=50,
+)
+
 gpu_config = LiveServerless(
     name="gpu_worker",
     gpus=[GpuGroup.ANY],
@@ -39,17 +43,15 @@ def __init__(self):
         self.pipe = StableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5",
             torch_dtype=torch.float16,
-            safety_checker=None,  # Disable to save memory
+            safety_checker=None,
             use_safetensors=True,
             requires_safety_checker=False,
-            low_cpu_mem_usage=True,  # Additional memory optimization
+            low_cpu_mem_usage=True,
         )
 
-        # Move to GPU and enable small memory optimizations.
         self.pipe = self.pipe.to("cuda")
-        self.pipe.enable_attention_slicing()  # Additional memory saving
+        self.pipe.enable_attention_slicing()
 
-        # Clean up any leftover memory
         gc.collect()
         torch.cuda.empty_cache()
 
@@ -68,22 +70,19 @@ async def generate_image(self, prompt: str) -> dict:
             height=512,
         ).images[0]
 
-        # Save output into the shared volume for the CPU worker to read.
         import datetime
         import os
 
         output_dir = "/runpod-volume/generated_images"
         os.makedirs(output_dir, exist_ok=True)
 
-        # Save image locally with timestamp.
         timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
         image_filename = f"sd_generated_{timestamp}.png"
         image_path = os.path.join(output_dir, image_filename)
         image.save(image_path)
         self.logger.info(f"Image saved to: {image_path}")
 
-        # Create response data
-        response_data = {
+        return {
             "prompt": prompt,
             "image_path": image_path,
             "timestamp": timestamp,
@@ -95,10 +94,8 @@ async def generate_image(self, prompt: str) -> dict:
             },
             "message": "Image generated and saved locally!",
         }
-        return response_data
 
 
-# Test locally with: python -m workers.gpu.endpoint
 if __name__ == "__main__":
     import asyncio
 
diff --git a/05_data_workflows/01_network_volumes/main.py b/05_data_workflows/01_network_volumes/main.py
deleted file mode 100644
index a00d80b..0000000
--- a/05_data_workflows/01_network_volumes/main.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import logging
-import os
-from contextlib import asynccontextmanager
-
-from fastapi import FastAPI
-from workers.cpu import cpu_router
-from workers.gpu import gpu_router
-from workers.gpu.endpoint import SimpleSD
-
-logger = logging.getLogger(__name__)
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # Load the GPU model once and share it across requests.
-    logger.info("Loading SimpleSD model...")
-    app.state.sd_model = SimpleSD()
-    logger.info("Model loaded successfully")
-
-    yield
-
-    # Clean up resources on shutdown.
-    logger.info("Cleaning up resources...")
-    if hasattr(app.state, "sd_model"):
-        del app.state.sd_model
-    logger.info("Cleanup complete")
-
-
-app = FastAPI(
-    lifespan=lifespan,
-    title="Flash Application",
-    description="Distributed GPU and CPU computing with Runpod Flash",
-    version="0.1.0",
-)
-
-# Wire GPU and CPU routers into the app.
-app.include_router(gpu_router, prefix="/gpu", tags=["GPU Workers"])
-app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"])
-
-
-@app.get("/")
-def home():
-    return {
-        "message": "Flash Application",
-        "docs": "/docs",
-    }
-
-
-@app.get("/ping")
-def ping():
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    port = int(os.getenv("PORT", 8888))
-    logger.info(f"Starting Flash server on port {port}")
-    logger.info(
-        "Try generating an image with a prompt by sending a POST request to http://localhost:8888/generate"
-    )
-    logger.info("List generated images by querying /images")
-
-    uvicorn.run(app, host="0.0.0.0", port=port)
diff --git a/05_data_workflows/01_network_volumes/workers/__init__.py b/05_data_workflows/01_network_volumes/workers/__init__.py
deleted file mode 100644
index ff2a525..0000000
--- a/05_data_workflows/01_network_volumes/workers/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from runpod_flash import NetworkVolume
-
-# Shared volume used by both GPU and CPU workers.
-volume = NetworkVolume(
-    name="flash-05-volume",
-    size=50,  # in GB
-)
diff --git a/05_data_workflows/01_network_volumes/workers/cpu/__init__.py b/05_data_workflows/01_network_volumes/workers/cpu/__init__.py
deleted file mode 100644
index fd2e234..0000000
--- a/05_data_workflows/01_network_volumes/workers/cpu/__init__.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from fastapi import APIRouter
-from fastapi.responses import HTMLResponse
-
-from .endpoint import get_image_from_volume, list_images_in_volume
-
-cpu_router = APIRouter()
-
-
-@cpu_router.get("/", response_class=HTMLResponse)
-async def browse_images() -> HTMLResponse:
-    result = await list_images_in_volume()
-    images = result.get("images", [])
-
-    items = "\n".join(f'<li><a href="/cpu/image/{image}">{image}</a></li>' for image in images)
-    body = items or "<li>No images found yet.</li>"
-
-    html = f"""<!doctype html>
-<html lang=\"en\">
-  <head>
-    <meta charset=\"utf-8\" />
-    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />
-    <title>Network Volume Images</title>
-    <style>
-      body {{ font-family: system-ui, sans-serif; padding: 24px; }}
-      h1 {{ font-size: 20px; margin-bottom: 12px; }}
-      ul {{ padding-left: 18px; }}
-      li {{ margin: 6px 0; }}
-    </style>
-  </head>
-  <body>
-    <h1>Generated Images</h1>
-    <ul>
-      {body}
-    </ul>
-  </body>
-</html>"""
-
-    return HTMLResponse(content=html)
-
-
-@cpu_router.get("/image")
-async def list_images():
-    # Simple index of files produced by the GPU worker.
-    result = await list_images_in_volume()
-    return result
-
-
-@cpu_router.get("/image/{file_id}")
-async def get_image(file_id: str):
-    # Serve a single image file from the shared volume.
-    result = await get_image_from_volume(file_id)
-    return result
diff --git a/05_data_workflows/01_network_volumes/workers/gpu/__init__.py b/05_data_workflows/01_network_volumes/workers/gpu/__init__.py
deleted file mode 100644
index 79a3ee5..0000000
--- a/05_data_workflows/01_network_volumes/workers/gpu/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from fastapi import APIRouter, Request
-from pydantic import BaseModel
-
-gpu_router = APIRouter()
-
-
-class GenerateRequest(BaseModel):
-    prompt: str
-
-
-@gpu_router.post("/generate")
-async def generate(request: GenerateRequest, req: Request):
-    """Simple GPU worker endpoint."""
-    # Use the shared model loaded in app startup.
-    sd_model = req.app.state.sd_model
-    result = await sd_model.generate_image(request.prompt)
-    return result
diff --git a/uv.lock b/uv.lock
index 5e44705..74aaf46 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -15,18 +15,18 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pycares" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/10/da/97235e953109936bfeda62c1f9f1a7c5652d4dc49f2b5911f9ae1043afa9/aiodns-4.0.0.tar.gz", hash = "sha256:17be26a936ba788c849ba5fd20e0ba69d8c46e6273e846eb5430eae2630ce5b1", size = 26204 }
+sdist = { url = "https://files.pythonhosted.org/packages/10/da/97235e953109936bfeda62c1f9f1a7c5652d4dc49f2b5911f9ae1043afa9/aiodns-4.0.0.tar.gz", hash = "sha256:17be26a936ba788c849ba5fd20e0ba69d8c46e6273e846eb5430eae2630ce5b1", size = 26204, upload-time = "2026-01-10T22:33:27.211Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d0/60/14ac40c03e8a26216e4f2642497b776e52f9e3214e4fd537628829bbb082/aiodns-4.0.0-py3-none-any.whl", hash = "sha256:a188a75fb8b2b7862ac8f84811a231402fb74f5b4e6f10766dc8a4544b0cf989", size = 11334 },
+    { url = "https://files.pythonhosted.org/packages/d0/60/14ac40c03e8a26216e4f2642497b776e52f9e3214e4fd537628829bbb082/aiodns-4.0.0-py3-none-any.whl", hash = "sha256:a188a75fb8b2b7862ac8f84811a231402fb74f5b4e6f10766dc8a4544b0cf989", size = 11334, upload-time = "2026-01-10T22:33:25.65Z" },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
@@ -43,110 +43,110 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/36/d6/5aec9313ee6ea9c7cde8b891b69f4ff4001416867104580670a31daeba5b/aiohttp-3.13.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d5a372fd5afd301b3a89582817fdcdb6c34124787c70dbcc616f259013e7eef7", size = 738950 },
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 [[package]]
@@ -1478,103 +1478,103 @@ source = { registry = "https://pypi.org/simple" }
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