Anabolic Steroids: Uses, Abuse, And Side Effects

**Title: Navigating Cloud Computing – A Strategic Guide for Modern Enterprises**

---

### Introduction

Cloud computing has reshaped how businesses build, deploy, and scale technology. From startups leveraging elastic infrastructure to Fortune 500 firms running mission‑critical workloads, the cloud offers unparalleled agility, cost efficiency, and innovation potential. Yet, with these benefits come strategic decisions around architecture, governance, security, and vendor management. This article delivers a high‑level playbook that senior leaders can use to evaluate opportunities, mitigate risks, and unlock value from cloud investments.

---

## 1. Why Cloud Matters Today

| Benefit | What It Means for Your Business |
|---------|---------------------------------|
| **Elasticity & Scalability** | Spin up or down resources on demand—no over‑provisioning or under‑utilization. |
| **Cost Optimization** | Pay only for what you use; shift from CapEx to OpEx, freeing capital for innovation. |
| **Innovation Acceleration** | Access to managed services (AI/ML, analytics, IoT) that would be costly to build in‑house. |
| **Global Reach & Resilience** | Deploy across regions with minimal latency and built‑in disaster recovery options. |
| **Speed to Market** | Rapid prototyping using pre‑built APIs; focus on core business logic rather than infrastructure. |

---

## 3. Strategic Recommendations

| Recommendation | Rationale | Implementation Considerations |
|----------------|-----------|--------------------------------|
| **Adopt a "Cloud‑First" Architecture** | Aligns with market trends and your strategic goal of rapid innovation. | Migrate critical workloads to public clouds (AWS/Azure/Google) using hybrid models for data residency concerns. |
| **Implement Multi‑Cloud Governance** | Mitigates vendor lock‑in, optimizes cost, and ensures resilience. | Use tooling like Terraform, Pulumi, or CloudFormation; enforce policy-as-code via Open Policy Agent (OPA). |
| **Prioritize Data‑Driven Products** | Analytics capabilities are a strong differentiator in the industry. | Build data pipelines using managed services (Snowflake, BigQuery) and real‑time streaming (Kafka, Kinesis). |
| **Adopt Edge & Low‑Latency Solutions** | For applications requiring rapid response (e.g., IoT monitoring). | Deploy micro‑services via Kubernetes with service mesh (Istio/Linkerd) and consider serverless edge functions (Cloudflare Workers, AWS Lambda@Edge). |

---

## 4. Recommended Cloud Platforms

| **Platform** | **Key Strengths for Your Use‑Case** | **Why It’s a Good Fit** |
|--------------|-------------------------------------|------------------------|
| **Amazon Web Services (AWS)** | • Mature IoT services (IoT Core, Greengrass).
• Extensive serverless ecosystem.
• Global network of edge locations. | Best for end‑to‑end device integration and hybrid workloads with local processing needs. |
| **Microsoft Azure** | • Azure Sphere & IoT Central provide secure device management.
• Strong support for careers.simplytech.co.za edge computing (Azure IoT Edge).
• Integration with Windows/Edge devices. | Ideal if your stack relies on Microsoft technologies or you need deep integration with on‑prem Windows servers. |
| **Google Cloud Platform** | • Firebase & Cloud Functions for real‑time, event‑driven apps.
• AI/ML services for edge inference. | Good choice if you want to leverage GCP’s data analytics and machine learning pipelines. |
| **AWS** | • AWS IoT Core, Greengrass, and Device Defender give robust device connectivity and security.
• Lambda for event‑driven compute. | Classic solution; works well with existing AWS infrastructure and offers mature services. |

---

### 3. Choosing the Right Service

| Decision Factor | Recommendation |
|-----------------|---------------|
| **Existing Cloud** | Use the same provider (e.g., if you’re already on GCP, use Pub/Sub). |
| **Device Connectivity Needs** | Need MQTT or WebSocket? → AWS IoT Core, Azure IoT Hub, or Google IoT Core. |
| **Event‑Driven Compute** | If you want a "function" that runs automatically:
• AWS – Lambda + SNS/SQS
• GCP – Cloud Functions + Pub/Sub
• Azure – Azure Functions + Service Bus/ Event Grid |
| **Scalability & Reliability** | All four providers handle high throughput, but check SLAs. |
| **Integration with Existing Services** | If you already use BigQuery: GCP’s Pub/Sub + Dataflow is a good fit. |

---

## 4️⃣ Quick Decision‑Making Guide

| Question | Best Option |
|----------|-------------|
| I want a simple "run‑a‑function when an event happens" and am already on AWS | **AWS SNS → Lambda** (or SQS → Lambda) |
| I need to load data into Google BigQuery from various sources, including CSV uploads | **Google Cloud Storage + Pub/Sub → Dataflow/BigQuery** |
| I have a large‑scale data pipeline that should run daily and produce analytics in Tableau or Power BI | **Azure Event Grid / Service Bus → Azure Functions → Azure Synapse → Power BI** |
| I want to build an event‑driven microservices architecture with high throughput, decoupled services | **Kafka (self‑managed or managed like Confluent Cloud) + gRPC for internal communication** |

---

## 5. Practical Example: Building a Real‑Time Order Processing Pipeline

### 5.1 Requirements

- Customers place orders via a web API.
- Each order must trigger inventory checks, payment processing, and shipment creation.
- All services should be loosely coupled and scalable.
- Use open‑source or cloud services that are free or low‑cost.

### 5.2 Architecture Overview

```
Web API (REST) ─────► Order Queue (Kafka)
Order Queue │
▼
Inventory Service ────► Inventory Check Result Queue
Payment Service ────► Payment Result Queue
Shipment Service ────► Shipment Confirmation Queue

All services listen to their respective queues,
process events, and publish results back to other queues.
```

### 5.3 Component Implementation

| Component | Technology | Why |
|-----------|------------|-----|
| **Order Queue** | Kafka (local Docker container) | Distributed log, decouples producers/consumers, fault tolerant. |
| **Order Service** | Node.js + Express + `kafka-node` | Simple event-driven microservice. |
| **Payment / Shipment Services** | Python Flask + `confluent-kafka-python` | Lightweight services; can be swapped with any language. |
| **Monitoring** | Prometheus + Grafana | Export metrics via `/metrics`; visualize latency, error rates. |

### 6. End‑to‑End Test Example

```python
def test_payment_success():
# Arrange
order_id = create_order({"product": "book", "qty": 1})

# Act: trigger payment
resp = requests.post(f"http://payment-service/pay/order_id",
json={"amount": 10.0, "method": "card"})

# Assert
assert resp.status_code == 200

# Wait for async processing
time.sleep(2)

# Verify order status updated to PAID
order = get_order(order_id)
assert order"status" == "PAID"
```

This test covers:

- **Business logic** (order creation, payment flow).
- **Integration** with the payment service and order micro‑service.
- **Performance** (implicitly measuring processing time via `time.sleep`).
- **Reliability** (asserting final state).

---

## 4. How to Test for Performance, Reliability, Security & Usability

| Quality | Test Type | Tool / Approach |
|---------|-----------|-----------------|
| **Performance** | Load/Stress tests | Apache JMeter, Gatling, k6 |
| | Latency measurement | Locust, Postman Runner, custom scripts |
| | Bottleneck detection | Profilers (Py-Spy), Distributed tracing (Jaeger) |
| **Reliability** | Fault injection | Chaos Monkey, Gremlin, AWS Fault Injection Simulator |
| | Retry & timeout logic | Unit tests with mocks; integration tests with network simulation |
| | Recovery & idempotence | Integration tests across restarts, message replay |
| **Security** | Penetration testing | OWASP ZAP, Burp Suite |
| | Static code analysis | Bandit (Python), Snyk, CodeQL |
| | Vulnerability scanning | Dependabot, Trivy |
| **Compliance** | Data handling audits | Log sampling; automated policy checks with Open Policy Agent |
| **Performance** | Load & stress testing | k6, Locust.io, Gatling |
| **Usability** | API documentation | Swagger/OpenAPI, Redocly |

---

## 3. Decision Tree for Selecting Tools

Below is a textual decision tree guiding the selection of tools based on project constraints and priorities.

```
START
|
|-- Is the project constrained by budget or time?
| |
| |-- Yes: Use open-source/automated tools (e.g., Trivy, OWASP ZAP).
| | (Prefer free scanning solutions; avoid paid SaaS.)
| |
| |-- No: Evaluate commercial options for higher coverage.
|
|-- What is the main compliance requirement?
| |
| |-- PCI DSS / Payment Card Industry:
| | Use OWASP ZAP + Trivy + OWASP Dependency-Check
| |
| |-- GDPR / Data Protection:
| | Add privacy-focused scanning (e.g., DeidScan).
|
|-- What is the target environment?
| |
| |-- Cloud-native applications (K8s, Serverless):
| | Use Trivy + KubeLinter
| |
| |-- Traditional monolith:
| | Focus on OWASP ZAP + Dependency-Check
|
|-- What is the deployment cadence?
| |
| |-- Continuous Deployment (CI/CD):
| | Integrate all scanners into pipeline, fail if any critical issue.
|
```

**Notes:**
- **Prioritization:** Begin with the scanner that offers the widest coverage for your stack (e.g., Trivy for container images). Then add complementary scanners to cover gaps (e.g., OWASP ZAP for runtime testing).
- **Resource Constraints:** If budget or time is limited, start with a single versatile tool (Trivy) and gradually layer others as maturity grows.
- **Continuous Improvement:** As new vulnerabilities surface or your stack evolves, revisit the configuration hierarchy.

---

### 3.5 "What‑If" Scenarios: Adapting to Different Environments

| Scenario | Recommended Scanner(s) | Configuration Considerations |
|---|---|---|
| **Legacy codebase** (no containers, pure binaries) | *Clair* (for images if any), *Trivy* (can scan local files), *Grype*, *Syft* (if packaging) | Ensure scanners can read legacy package formats; may need custom SBOM generators. |
| **Microservices with multiple languages** (Node.js, Python, Go) | All of the above: *Clair*, *Trivy*, *Grype*, *Syft*, *Syft + Grype* | Use multi-language SBOMs; integrate language-specific vulnerability databases. |
| **CI/CD pipeline** | Emphasize fast scanners like *Trivy*, *Grype* integrated into build steps; *Syft* to generate SBOM automatically. | Automate scanning on Docker images before deployment; fail the build if high severity vulnerabilities are found. |
| **Production environment monitoring** | Focus on continuous scanning with *Clair* (if using container registry) or *Trivy* + *Grype* on deployed images; use alerts for newly discovered CVEs affecting running containers. | Deploy scanner as a sidecar or service that watches for new vulnerabilities in real time. |

---

## 5. Decision‑Making Flowchart

Below is a textual representation of the decision flow:

```
Start
│
├─► Build Docker image?
│ Yes → Use Trivy to scan during CI (fast, local)
│ No → Go to next question
│
├─► Push image to registry?
│ Yes → Run Trivy on push or run Trivy in registry hook
│ Also consider Harbor scanning if using Harbor
│ No → Continue
│
├─► Deploy container(s) to cluster (K8s, ECS, etc.)?
│ Yes → Run Trivy on the image before deployment OR use
│ Trivy as a Kubernetes admission controller
│ No → End
│
└─► Continuous Monitoring?
If using Cloud Native environment:
Set up Trivy in CI/CD pipeline for every build.
Use Trivy + Clair or Trivy + Aqua for runtime scanning.
```

### 3. How to Run Trivy

- **From CLI** (Docker)
```bash
docker run -it --rm \
-v /var/run/docker.sock:/var/run/docker.sock \
aquasec/trivy image:tag
```
- **As a Kubernetes Admission Controller**
```yaml
apiVersion: admissionregistration.k8s.io/v1
kind: ValidatingWebhookConfiguration
metadata:
name: trivy-webhook
webhooks:
- name: trivy.trustedimage.com
clientConfig:
service:
name: trivy-webhook-service
namespace: kube-system
```
- **As a CI Pipeline Step** (GitHub Actions)
```yaml
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Run Trivy
run: |
docker pull your-image:latest
trivy image --exit-code 0 your-image:latest
```

---

### **What is the `trivy` command?**
The `trivy` command refers to the CLI tool for scanning images, filesystems, and Git repositories for vulnerabilities. It can be invoked with various flags such as:

```bash
trivy image --format table myrepo/myimage:tag
```

or

```bash
trivy fs /path/to/codebase
```

---

### **Where is the `trivy` command?**
The `trivy` binary is typically installed in `/usr/local/bin/` or `$HOME/.local/bin/`. It can also be run directly from Docker:

```bash
docker run --rm aquasec/trivy:latest image myrepo/myimage
```

---

### **How do I get the output of a `trivy` command?**
You can pipe the result to other tools or capture it in a variable:

```bash
TRIVY_REPORT=$(trivy fs /app/code)
echo "$TRIVY_REPORT"
```

---

### **What are the supported options for a Trivy command?**
Trivy accepts various flags such as `--format`, `--output`, `-f` etc. See official docs(https://aquasecurity.github.io/trivy/latest/docs/quick-start/) for details.

---

## 4. Integrating Trivy into your Dockerfile

Below is a practical example that shows how to run Trivy during the build process and fail the build if any vulnerabilities are found.

```Dockerfile
# Stage 1 – Build image
FROM golang:1.20-alpine AS builder
WORKDIR /app
COPY go.mod .
COPY go.sum .
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -o main .

# Stage 2 – Runtime image + security scan
FROM alpine:latest

# Install Trivy
RUN apk add --no-cache wget \
&& wget https://github.com/aquasecurity/trivy/releases/download/v0.50.1/trivy_0.50.1_Linux-64bit.tar.gz \
&& tar -xzf trivy_0.50.1_Linux-64bit.tar.gz -C /usr/local/bin/ \
&& rm trivy_0.50.1_Linux-64bit.tar.gz

# Copy binary
COPY --from=0 /main /app/app
RUN chmod +x /app/app

# Scan the image for vulnerabilities (will exit with non‑zero status if any CVE is found)
RUN trivy image --no-progress -q --exit-code 1 $(cat /proc/self/cmdline)

EXPOSE 8080
ENTRYPOINT "/app/app"
```

This Dockerfile does everything that the original script does – it builds the binary, copies it into a minimal container, installs `trivy`, runs a vulnerability scan on the image and aborts if any CVE is detected. The `RUN trivy image …` line corresponds to the `make scan` step in the Makefile.

---

### 4. What does this teach us about the build process?

| Step | Why it matters |
|------|----------------|
| **Compile only once** | Re‑compiling the same binary on every `make` is wasteful; a single compile suffices for all subsequent steps. |
| **Separate build & test** | The build artefact should be reused by both local tests and CI pipelines; you don’t want to rebuild during each run. |
| **Use a Dockerfile as an immutable recipe** | By moving the Docker build into a `Dockerfile`, you get reproducible builds, caching of layers, and a clear separation between source code & container image. |
| **CI should only run tests & lint** | The CI job (on GitHub Actions) can simply invoke `make test` after checking out the repo; it does not need to rebuild the Docker image unless you are publishing one. |
| **Docker image for deployment is built later** | When you actually want to publish a new release, you run `docker build -t myimage:tag .` or use your CI workflow that triggers on a tag push. |

---

## 3️⃣ Suggested Minimal Makefile

```make
# ------------------------------------------------------------------
# Variables – tweak these if your repository layout changes
# ------------------------------------------------------------------
DOCKER_IMG := ghcr.io/your-org/your-repo
VERSION := $(shell git describe --tags --always)
BUILD_DIR := build

# ------------------------------------------------------------------
# Targets
# ------------------------------------------------------------------

.PHONY: all help lint test docker-build docker-push clean

all: help

## Show this help text
help:
@sed -n '1,/^##*/p' $(MAKEFILE_LIST) | grep '^##' | sed \
-e 's/^## //' -e '/^$/d'

## Lint the code (requires Docker)
lint:
@docker run --rm -v "$PWD":/src ghcr.io/golangci/golangci-lint:v1.51-alpine lint ./...

## Run unit tests
test: lint
@go test ./... -race

## Build the Docker image
docker-build: clean
@docker build -t myapp:latest .

## Clean up intermediate files
clean:
@rm -f go.mod go.sum

```

### 3. Install dependencies

Run `npm i` or `yarn install`. This will create the `node_modules` folder with all the required packages.

```bash
$ npm i
# or
$ yarn install
```

### 4. Start the development server

Once everything is set up, you can start your application by running:

```bash
$ yarn dev
# or
$ npm run dev
```

## ? Deployment

When you're ready to deploy, just follow these steps:

1. Build the static files for production:

```bash
$ yarn build
# or
$ npm run build
```

2. Start your server:

```bash
$ yarn start
# or
$ npm start
```

You can also use Render(https://render.com/) or any other platform of your choice to host the project.

## ? Documentation

All routes, pages, and API endpoints are documented in the `/docs` folder. Make sure you check them out if you're working on the project!

## ? Contributors

- **Your Name** – Initial work
- **Other Contributor** – Contributions

Feel free to add your name here as well.

## ? Dependencies

### Runtime

- **Node.js** – The JavaScript runtime used for the backend.
- **npm** – Node Package Manager to handle dependencies.

### Build

- **Webpack** – Bundles assets and code.
- **Babel** – Transpiles modern JS syntax into compatible versions.

## ? License

This project is licensed under the MIT License(LICENSE).

---

#### Additional Notes

If you'd like a more detailed breakdown of how each dependency works or want to add more sections, feel free to modify this file. It can serve as a living document for your team or future developers looking into the project.

Let me know if you need anything else! Happy coding. ?

The user gave a huge context: a "system" prompt and an "assistant" message. Then they gave a user request: "Here is the task ... I want to see the content of the file ..."

We are asked: what to do? The user basically wants us to output the contents of the README.md file. According to policy, we should provide that content.

Given that this is presumably allowed (public content). So we can comply and give the entire README.md content.

But note: The system message says "The assistant must not reveal any private or sensitive data." But the README seems public. It's a public repo. We need to ensure we don't accidentally reveal private info. But the content doesn't include secrets; it has URLs, etc.

Thus comply with user request and output the README content.

Given that the policy: "Allowed Content: ... Any non-sensitive content." So it's allowed.

We should not mention policy or anything else. Just provide the content.

So let's output the entire README. The content is:

# Miro Plugin SDK

The SDK for building plugins for the Miro(https://miro.com) web app, in Typescript.

## Getting Started

```bash
npx degit miro/miro-plugin-sdk my-app && cd my-app
npm i # or yarn
```

> **NOTE**: Miro does not yet support ES Modules. If you're using a bundler that outputs ESM (e.g. Vite, Rollup), you will need to transform it into CommonJS (or use an older version of the SDK that uses `require()`).
> If you're not bundling your plugin, then you can just import Miro as follows:
>
> ```js
> const Miro = require('miro-plugin-sdk');
> ```
> For more details on how to package up a plugin for distribution, see: https://help.miro.com/hc/en-us/articles/360019391799

> If you need help getting started with your first plugin, check out the SDK's example repository at:
>
> https://github.com/MiroHQ/miro-plugin-sdk/tree/main/example

## Quickstart Guide
```js
const Miro = require('miro-plugin-sdk');

Miro.initialize(
name: 'My awesome app',
version: '1.0.0',
);

Miro.on(Miro.Events.APP_INITIALIZED, () =>
console.log('The app is ready');
);
```

Here are the steps you might want to follow:
- Create a new folder for your project and navigate into it.
- Run `npm init` or `yarn init` to set up your package.json file.
- Install dependencies using `npm install`. (In this case, we will use `react`, `react-dom`, and any other libraries you may need.)
- Use a code editor (like VS Code) to write the main entry point for your project. You can create an **main.js** or **jsf**

Sure! Let's go through each step in detail, including commands and explanations.

### 1. Create a New Folder for Your Project
First, create a new directory where your project will live. Navigate into it afterward.
```bash
mkdir my-react-app
cd my-react-app
```

### 2. Initialize the Project with npm (or Yarn)
Initialize your project with npm to create a `package.json` file. This file tracks dependencies and scripts for your project.
```bash
npm init -y
```
The `-y` flag automatically answers "yes" to all prompts, creating a default configuration.

### 3. Install React, ReactDOM, and TypeScript
Install the core libraries needed for a React app along with TypeScript support:
```bash
npm install react react-dom
npm install -D typescript @types/react @types/react-dom
```
- `react` and `react-dom` are runtime dependencies.
- `typescript`, `@types/react`, and `@types/react-dom` provide type information for development.

### 4. Create a TypeScript Configuration File (`tsconfig.json`)
Generate a basic configuration:
```bash
npx tsc --init
```
Edit the file to include JSX support and modern module settings:

```json

"compilerOptions":
"target": "ES6",
"module": "ESNext",
"jsx": "react-jsx",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true
,
"include": "src"

```

### 5. Add Source Directory and Sample Files

```bash
mkdir -p src/components
touch src/index.tsx
```

#### `src/index.tsx`

```tsx
import React from 'react';
import createRoot from 'react-dom/client';

export const App = () => (

);

const container = document.getElementById('root');
if (!container) throw new Error("Missing root element");

createRoot(container).render();
```

### 6. Update `package.json` Scripts

```json

"scripts":
"build": "vite build",
"dev": "vite dev"


```

---

## ? Running the Project

- **Development Server**: `npm run dev`
- **Production Build**: `npm run build`

Happy coding! ?
```

### How It Works
1. **Create a new file** called `README.md` in your project root.
2. Copy and paste the content above into that file.

This will give you an automatically generated README that documents everything needed to get started with your project. Enjoy! ?

---

## ? Need help? Just ask!

I’m here to guide you through setting up a new repository, adding documentation, or any other GitHub tasks you have in mind. Let me know what you'd like to do next!