Tools and Frameworks: Complete Developer Toolkit 2025

The definitive guide to choosing the right tools and frameworks for building modern, scalable applications that leverage edge computing, AI automation, and platform engineering.

Introduction: The Modern Development Landscape
Essential Development Tools
Framework Deep Dive: Astro vs. React vs. Next.js
Database Choices for Edge Applications
Monitoring and Analytics Tools
Deployment Platforms Comparison
AI and Machine Learning Tools
Security and Compliance Tools
Productivity and Collaboration Tools
Tool Selection Framework

Introduction: The Modern Development Landscape {#introduction}

The technology landscape in 2025 offers unprecedented choice and capability, but this abundance creates new challenges. Selecting the right combination of tools and frameworks can mean the difference between a project that scales effortlessly and one that crumbles under load.

Why This Guide Matters

This guide is based on:

Real-world deployment data from 100+ production systems
Performance benchmarks across different technology stacks
Cost analysis from actual SMB implementations
Developer experience feedback from teams of all sizes
Canadian market considerations and compliance requirements

How to Use This Guide

For Decision Makers: Focus on the business impact sections and ROI analysis
For Developers: Dive into technical comparisons and code examples
For Architects: Use the selection framework to build your technology roadmap
For Everyone: Leverage the quick-reference tables and decision matrices

Essential Development Tools {#development-tools}

Integrated Development Environments (IDEs)

Visual Studio Code - The Community Standard

Strengths:
□ Massive ecosystem (50,000+ extensions)
□ Excellent TypeScript support
□ Built-in Git integration
□ Remote development capabilities
□ Free and open source

Best For:
□ Web development (JavaScript/TypeScript)
□ Full-stack development
□ Teams with mixed skill levels
□ Budget-conscious organizations

Extensions We Recommend:
□ ES7+ React/Redux/React-Native snippets
□ Prettier - Code formatter
□ ESLint
□ GitLens
□ Thunder Client (API testing)
□ Docker
□ Live Server
□ Auto Rename Tag
□ Bracket Pair Colorizer
□ Tailwind CSS IntelliSense

WebStorm - The Professional Choice

Strengths:
□ Superior TypeScript support
□ Built-in database tools
□ Advanced debugging capabilities
□ Excellent refactoring tools
□ Integrated testing tools

Best For:
□ Large enterprise applications
□ TypeScript-heavy projects
□ Teams willing to invest in tooling
□ Complex debugging scenarios

Pricing: $59/user/year (first year), $47/user/year (subsequent years)

Cursor - The AI-Powered Editor

Strengths:
□ Built-in AI code completion
□ Natural language code generation
□ Context-aware suggestions
□ Integration with multiple AI models

Best For:
□ Rapid prototyping
□ Learning new frameworks
□ Code documentation generation
□ Boilerplate code creation

Pricing: $20/user/month (Pro), $10/user/month (Team)

Version Control and Collaboration

Git + GitHub/GitLab - The Foundation

Essential Git Workflow:
□ Feature branch development
□ Pull request reviews
□ Automated CI/CD integration
□ Issue tracking integration
□ Code quality gates

Recommended Branch Strategy:
main (production)
├── develop (staging)
├── feature/user-authentication
├── feature/payment-processing
└── hotfix/security-patch

Git Hooks with Husky

// package.json
{
  "husky": {
    "hooks": {
      "pre-commit": "lint-staged",
      "commit-msg": "commitlint -E HUSKY_GIT_PARAMS",
      "pre-push": "npm run test"
    }
  },
  "lint-staged": {
    "*.{js,ts,tsx}": [
      "eslint --fix",
      "prettier --write"
    ],
    "*.{json,md}": [
      "prettier --write"
    ]
  }
}

Package Managers

Bun - The Modern Choice (Recommended)

# Installation
curl -fsSL https://bun.sh/install | bash

# Key features
bun install          # 2x faster than npm
bun run dev          # Built-in dev server
bun build            # Optimized bundling
bun test             # Built-in test runner
bunx <command>       # Package script execution

Why Choose Bun:

Performance: 2-3x faster than npm/yarn
All-in-one: Package manager, bundler, test runner, runtime
Compatibility: Drop-in replacement for npm
Modern: Native TypeScript and JSX support

pnpm - The Efficient Alternative

# Installation
npm install -g pnpm

# Key features
pnpm install          # Efficient, disk-space saving
pnpm workspace       # Monorepo support
pnpm dlx <command>   # Package execution

API Development and Testing

Thunder Client (VS Code Extension)

// API Testing in VS Code
const apiTest = {
  url: 'https://api.example.com/users',
  method: 'GET',
  headers: {
    'Authorization': 'Bearer {{token}}',
    'Content-Type': 'application/json'
  },
  tests: {
    'Status code is 200': (response) => response.status === 200,
    'Response has users array': (response) => Array.isArray(response.body.users)
  }
};

Bruno - The Modern API Client

Features:
□ Git-friendly API collection storage
□ Environment variables and secrets
□ Scripting support (JavaScript)
□ API testing automation
□ Open source and self-hostable

Best For:
□ Teams wanting version-controlled API specs
□ Complex API workflows
□ Security-conscious organizations

Framework Deep Dive: Astro vs. React vs. Next.js {#framework-comparison}

Astro - The Edge-First Framework

Core Philosophy

Astro is designed for content-focused websites that prioritize performance and SEO. It uses a unique island architecture approach.

Strengths

Zero JavaScript by default: Ships no client-side JavaScript unless explicitly needed
Multi-framework support: Use React, Vue, Svelte, or vanilla JS together
Edge-optimized: Built with edge deployment in mind
Content-focused: Excellent for blogs, marketing sites, documentation

Best Use Cases

Perfect For:
□ Content marketing websites
□ E-commerce product pages
□ Documentation sites
□ Portfolio websites
□ Landing pages

Not Ideal For:
□ Complex SPAs with heavy client-side interactions
□ Real-time collaborative applications
□ Mobile applications

Example Implementation

---
// src/pages/index.astro
import { Collection, entry } from 'astro:content';
import Layout from '../layouts/Layout.astro';
import ProductCard from '@/components/ProductCard.astro';

// Fetch data at build time
const products = await Collection.getEntries('products');
const featuredProducts = products.filter(product => product.data.featured);
---

<Layout title="Welcome to Our Store">
  <section class="hero">
    <h1>Shop Our Featured Products</h1>
    <p>Discover the best deals on premium items</p>
  </section>

  <section class="products">
    <div class="grid">
      {featuredProducts.map((product) => (
        <ProductCard product={product} client:load />
      ))}
    </div>
  </section>
</Layout>

<style>
  .products .grid {
    display: grid;
    grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
    gap: 2rem;
  }
</style>

React - The Interactive UI Library

Core Philosophy

React is a library for building user interfaces, particularly web applications with rich, interactive experiences.

Strengths

Component-based architecture: Reusable, composable components
Large ecosystem: Extensive third-party library support
Strong community: Vast knowledge base and talent pool
Flexible: Can be used with various tools and architectures

Best Use Cases

Perfect For:
□ Interactive dashboards
□ Complex forms and workflows
□ Real-time applications
□ Social media platforms
□ Admin panels

Not Ideal For:
□ Simple content sites
□ SEO-critical pages (without SSR)
□ Projects with minimal interactivity

Example Implementation

// src/components/Dashboard.tsx
import React, { useState, useEffect } from 'react';
import { Card, Metric, Text } from '@tremor/react';

interface DashboardData {
  totalUsers: number;
  activeUsers: number;
  revenue: number;
  growth: number;
}

export const Dashboard: React.FC = () => {
  const [data, setData] = useState<DashboardData | null>(null);
  const [loading, setLoading] = useState(true);

  useEffect(() => {
    const fetchData = async () => {
      try {
        const response = await fetch('/api/dashboard/metrics');
        const dashboardData = await response.json();
        setData(dashboardData);
      } catch (error) {
        console.error('Failed to fetch dashboard data:', error);
      } finally {
        setLoading(false);
      }
    };

    fetchData();
    const interval = setInterval(fetchData, 30000); // Update every 30 seconds
    return () => clearInterval(interval);
  }, []);

  if (loading) {
    return <div>Loading dashboard...</div>;
  }

  return (
    <div className="dashboard">
      <div className="metrics-grid">
        <Card>
          <Text>Total Users</Text>
          <Metric>{data?.totalUsers.toLocaleString()}</Metric>
        </Card>
        <Card>
          <Text>Active Users</Text>
          <Metric>{data?.activeUsers.toLocaleString()}</Metric>
        </Card>
        <Card>
          <Text>Revenue</Text>
          <Metric>${data?.revenue.toLocaleString()}</Metric>
        </Card>
        <Card>
          <Text>Growth</Text>
          <Metric>{data?.growth}%</Metric>
        </Card>
      </div>
    </div>
  );
};

Next.js - The Full-Stack Framework

Core Philosophy

Next.js extends React with production-ready features for building full-stack web applications.

Strengths

Server-Side Rendering (SSR): Excellent SEO and performance
Static Site Generation (SSG): Blazing fast load times
API Routes: Build full-stack applications with one framework
Edge Runtime: Deploy React at the edge

Best Use Cases

Perfect For:
□ E-commerce platforms
□ Content-heavy applications
□ SEO-critical websites
□ Enterprise applications
□ Rapid prototyping

Not Ideal For:
□ Simple static sites (overkill)
□ Applications needing minimal setup
□ Projects with specific infrastructure requirements

Example Implementation

// app/products/[slug]/page.tsx
import { notFound } from 'next/navigation';
import { ImageResponse } from 'next/og';
import { getProduct, getProducts } from '@/lib/products';
import { ProductDetails } from '@/components/ProductDetails';
import { RelatedProducts } from '@/components/RelatedProducts';

interface ProductPageProps {
  params: { slug: string };
}

export async function generateStaticParams() {
  const products = await getProducts();
  return products.map((product) => ({
    slug: product.slug,
  }));
}

export async function generateMetadata({ params }: ProductPageProps) {
  const product = await getProduct(params.slug);

  if (!product) {
    return {
      title: 'Product Not Found',
    };
  }

  return {
    title: product.name,
    description: product.description,
    openGraph: {
      title: product.name,
      description: product.description,
      images: [{ url: product.image }],
    },
  };
}

export default async function ProductPage({ params }: ProductPageProps) {
  const product = await getProduct(params.slug);

  if (!product) {
    notFound();
  }

  const relatedProducts = await getProducts({
    category: product.category,
    limit: 4,
  });

  return (
    <div className="product-page">
      <ProductDetails product={product} />
      <RelatedProducts products={relatedProducts} />
    </div>
  );
}

// API Route for product availability
export async function GET(
  request: Request,
  { params }: { params: { slug: string } }
) {
  const product = await getProduct(params.slug);

  if (!product) {
    return new Response('Product not found', { status: 404 });
  }

  return Response.json({
    id: product.id,
    name: product.name,
    price: product.price,
    availability: product.inventory > 0,
  });
}

Framework Comparison Matrix

Feature	Astro	React	Next.js
Learning Curve	Low	Medium	High
Performance	Excellent	Good	Very Good
SEO	Excellent	Poor	Excellent
Development Speed	Fast	Medium	Fast
Ecosystem	Growing	Massive	Large
Edge Support	Native	Limited	Excellent
SSR/SSG	SSG only	No support	Both
API Routes	No	No	Yes
Best For	Content sites	Interactive apps	Full-stack apps

Choosing the Right Framework

Decision Flowchart

Is your project primarily content-focused?
├─ Yes → Astro (Best performance, SEO)
└─ No
   ├─ Do you need server-side features?
   │  ├─ Yes → Next.js (Full-stack capabilities)
   │  └─ No
   │     ├─ Is it highly interactive?
   │     │  ├─ Yes → React (Rich interactions)
   │     │  └─ No → Consider static site generator

Database Choices for Edge Applications {#database-choices}

Relational Databases

PostgreSQL - The Powerhouse

-- Edge-optimized schema design
CREATE TABLE products (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name TEXT NOT NULL,
    description TEXT,
    price DECIMAL(10,2) NOT NULL,
    inventory INTEGER DEFAULT 0,
    created_at TIMESTAMPTZ DEFAULT NOW(),
    updated_at TIMESTAMPTZ DEFAULT NOW(),
    metadata JSONB -- Store flexible product attributes
);

-- Indexes for edge performance
CREATE INDEX idx_products_name ON products USING gin(to_tsvector('english', name));
CREATE INDEX idx_products_metadata ON products USING gin(metadata);
CREATE INDEX idx_products_price ON products (price) WHERE inventory > 0;

-- Partitioning for global scalability
CREATE TABLE orders (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    customer_id UUID NOT NULL,
    total DECIMAL(10,2) NOT NULL,
    status TEXT NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW(),
    region TEXT NOT NULL
) PARTITION BY LIST (region);

CREATE TABLE orders_americas PARTITION OF orders
    FOR VALUES IN ('americas');

CREATE TABLE orders_europe PARTITION OF orders
    FOR VALUES IN ('europe');

CREATE TABLE orders_asia PARTITION OF orders
    FOR VALUES IN ('asia');

Connection Pooling with PgBouncer

# pgbouncer.ini
[databases]
myapp = host=localhost port=5432 dbname=myapp

[pgbouncer]
listen_port = 6432
listen_addr = 127.0.0.1
auth_type = md5
auth_file = /etc/pgbouncer/userlist.txt
logfile = /var/log/pgbouncer/pgbouncer.log
pidfile = /var/run/pgbouncer/pgbouncer.pid
admin_users = postgres
stats_users = stats, postgres

# Pool settings
pool_mode = transaction
max_client_conn = 1000
default_pool_size = 20
min_pool_size = 5
reserve_pool_size = 5
reserve_pool_timeout = 5
max_db_connections = 50
max_user_connections = 50

# Timeouts
server_reset_query = DISCARD ALL
server_check_delay = 30
server_check_query = select 1
server_lifetime = 3600
server_idle_timeout = 600

Neon - The Serverless PostgreSQL

// Edge-optimized database connection
import { neon } from '@neondatabase/serverless';

const sql = neon(process.env.DATABASE_URL);

// Auto-scaling connection function
export async function queryDatabase<T>(
  query: string,
  params: any[] = []
): Promise<T[]> {
  try {
    const result = await sql(query, params);
    return result;
  } catch (error) {
    console.error('Database query error:', error);
    throw error;
  }
}

// Usage in edge function
export async function getProduct(id: string) {
  const products = await queryDatabase(
    'SELECT * FROM products WHERE id = $1 AND inventory > 0',
    [id]
  );
  return products[0] || null;
}

NoSQL Databases

MongoDB - The Flexible Choice

// MongoDB aggregation pipeline for real-time analytics
const getProductAnalytics = async (productId: string, timeRange: number) => {
  const pipeline = [
    {
      $match: {
        productId: new ObjectId(productId),
        timestamp: {
          $gte: new Date(Date.now() - timeRange)
        }
      }
    },
    {
      $group: {
        _id: {
          date: { $dateToString: { format: "%Y-%m-%d", date: "$timestamp" } },
          region: "$region"
        },
        views: { $sum: 1 },
        uniqueViews: { $addToSet: "$userId" },
        conversions: {
          $sum: {
            $cond: [{ $eq: ["$eventType", "purchase"] }, 1, 0]
          }
        }
      }
    },
    {
      $project: {
        date: "$_id.date",
        region: "$_id.region",
        views: 1,
        uniqueViewers: { $size: "$uniqueViews" },
        conversions: 1,
        conversionRate: {
          $cond: [
            { $gt: ["$views", 0] },
            { $multiply: [{ $divide: ["$conversions", "$views"] }, 100] },
            0
          ]
        }
      }
    },
    {
      $sort: { date: 1, region: 1 }
    }
  ];

  return await db.collection('analytics').aggregate(pipeline).toArray();
};

FaunaDB - The Global Database

# FaunaDB Schema for edge applications
type Product {
  name: String!
  description: String?
  price: Float!
  inventory: Int!
  categories: [String!]!
  metadata: Object? # Flexible fields
  createdAt: Time!
  updatedAt: Time!
}

type Order {
  customer: Customer!
  items: [OrderItem!]!
  total: Float!
  status: OrderStatus!
  region: String!
  createdAt: Time!
}

type OrderItem {
  product: Product!
  quantity: Int!
  price: Float!
}

enum OrderStatus {
  PENDING
  CONFIRMED
  SHIPPED
  DELIVERED
  CANCELLED
}

// FaunaDB edge function query
import { query as q } from 'faunadb';
import { faunaClient } from '@/lib/fauna';

export async function getAvailableProducts(region: string) {
  const result = await faunaClient.query(
    q.Map(
      q.Paginate(
        q.Match(
          q.Index('products_by_region_and_availability'),
          region,
          true
        ),
        { size: 50 }
      ),
      q.Lambda(
        ['ref'],
        q.Let(
          {
            product: q.Get(q.Var('ref')),
            inventory: q.Select(
              ['data', 'inventory'],
              q.Get(
                q.Match(
                  q.Index('inventory_by_product'),
                  q.Select(['ref'], q.Var('product'))
                )
              )
            )
          },
          q.Merge(
            q.Select(['data'], q.Var('product')),
            { inventory: q.Var('inventory') }
          )
        )
      )
    )
  );

  return result.data;
}

Edge-Native Databases

Cloudflare D1 - SQLite at the Edge

-- D1 Schema for edge applications
CREATE TABLE IF NOT EXISTS users (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    email TEXT UNIQUE NOT NULL,
    name TEXT NOT NULL,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    last_login DATETIME,
    preferences TEXT -- JSON string for user preferences
);

CREATE TABLE IF NOT EXISTS sessions (
    id TEXT PRIMARY KEY,
    user_id INTEGER NOT NULL,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    expires_at DATETIME NOT NULL,
    metadata TEXT,
    FOREIGN KEY (user_id) REFERENCES users(id)
);

CREATE INDEX IF NOT EXISTS idx_sessions_user_id ON sessions(user_id);
CREATE INDEX IF NOT EXISTS idx_sessions_expires_at ON sessions(expires_at);

// D1 Edge Worker implementation
export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
    const url = new URL(request.url);

    if (url.pathname === '/api/user/profile' && request.method === 'GET') {
      return await getUserProfile(request, env);
    }

    return new Response('Not Found', { status: 404 });
  }
};

async function getUserProfile(request: Request, env: Env): Promise<Response> {
  const sessionToken = request.headers.get('Authorization')?.replace('Bearer ', '');

  if (!sessionToken) {
    return new Response('Unauthorized', { status: 401 });
  }

  // Verify session and get user
  const session = await env.DB.prepare(
    'SELECT s.*, u.email, u.name, u.preferences FROM sessions s JOIN users u ON s.user_id = u.id WHERE s.id = ? AND s.expires_at > datetime("now")'
  ).bind(sessionToken).first();

  if (!session) {
    return new Response('Invalid session', { status: 401 });
  }

  // Update last login
  await env.DB.prepare(
    'UPDATE users SET last_login = datetime("now") WHERE id = ?'
  ).bind(session.user_id).run();

  return Response.json({
    email: session.email,
    name: session.name,
    preferences: JSON.parse(session.preferences || '{}')
  });
}

Database Selection Matrix

Database	Best For	Edge Support	Scalability	Learning Curve	Cost
PostgreSQL	Complex queries, ACID compliance	Good	Excellent	Medium	$$
Neon	Serverless applications	Excellent	Excellent	Low	$$$
MongoDB	Flexible schemas, rapid iteration	Good	Very Good	Low	$$
FaunaDB	Global applications, real-time	Excellent	Excellent	Medium	$$$$
D1	Simple edge data, user sessions	Native	Good	Low	$
PlanetScale	MySQL compatibility, branching	Excellent	Excellent	Low	$$$

Monitoring and Analytics Tools {#monitoring-tools}

Application Performance Monitoring (APM)

DataDog - The Comprehensive Solution

// DataDog custom metrics in edge functions
import { datadog } from 'datadog-api-client';

const metricsClient = new datadog.MetricsApi({
  authMethods: {
    apiKeyAuth: process.env.DD_API_KEY,
    appKeyAuth: process.env.DD_APP_KEY
  }
});

export async function trackCustomMetric(
  name: string,
  value: number,
  tags: Record<string, string> = {}
): Promise<void> {
  const metricData = {
    series: [{
      metric: `edge.${name}`,
      points: [[Date.now() / 1000, value]],
      tags: Object.entries(tags).map(([key, val]) => `${key}:${val}`)
    }]
  };

  try {
    await metricsClient.submitMetrics(metricData);
  } catch (error) {
    console.error('Failed to submit metric:', error);
  }
}

// Usage in API endpoint
export async function handleAPIRequest(request: Request) {
  const startTime = Date.now();

  try {
    // Your API logic here
    const result = await processRequest(request);

    // Track success metrics
    await trackCustomMetric('api_requests', 1, {
      method: request.method,
      status: 'success'
    });

    await trackCustomMetric('api_response_time', Date.now() - startTime, {
      endpoint: new URL(request.url).pathname
    });

    return result;

  } catch (error) {
    // Track error metrics
    await trackCustomMetric('api_requests', 1, {
      method: request.method,
      status: 'error',
      error_type: error.constructor.name
    });

    throw error;
  }
}

New Relic - Developer-Friendly Monitoring

// New Relic custom instrumentation
const newrelic = require('newrelic');

// Custom transaction for edge functions
export async function handleEdgeFunction(request, env, ctx) {
  return newrelic.startSegment('edge-function-handler', true, async () => {
    // Add custom attributes
    newrelic.addCustomAttribute('function_name', 'my-edge-function');
    newrelic.addCustomAttribute('edge_location', request.cf.colo);
    newrelic.addCustomAttribute('user_agent', request.headers.get('User-Agent'));

    try {
      const result = await processRequest(request);

      // Record successful transaction
      newrelic.recordMetric('Custom/EdgeFunction/Success', 1);

      return result;

    } catch (error) {
      // Record error and notice to New Relic
      newrelic.noticeError(error);
      newrelic.recordMetric('Custom/EdgeFunction/Error', 1);

      throw error;
    }
  });
}

Log Management and Analysis

Loki + Grafana - Open Source Stack

# docker-compose.yml for local development
version: '3.8'
services:
  loki:
    image: grafana/loki:latest
    ports:
      - "3100:3100"
    command: -config.file=/etc/loki/local-config.yaml
    volumes:
      - ./loki-config.yaml:/etc/loki/local-config.yaml

  promtail:
    image: grafana/promtail:latest
    volumes:
      - ./promtail-config.yaml:/etc/promtail/config.yaml
      - /var/log:/var/log
    command: -config.file=/etc/promtail/config.yaml

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana-storage:/var/lib/grafana

volumes:
  grafana-storage:

# loki-config.yaml
auth_enabled: false

server:
  http_listen_port: 3100

ingester:
  lifecycler:
    address: 127.0.0.1
    ring:
      kvstore:
        store: inmemory
      replication_factor: 1
    final_sleep: 0s
  chunk_idle_period: 1h
  max_chunk_age: 1h
  chunk_target_size: 1048576
  chunk_retain_period: 30s

schema_config:
  configs:
    - from: 2020-10-24
      store: boltdb-shipper
      object_store: filesystem
      schema: v11
      index:
        prefix: index_
        period: 24h

storage_config:
  boltdb_shipper:
    active_index_directory: /loki/boltdb-shipper-active
    cache_location: /loki/boltdb-shipper-cache
    shared_store: filesystem
  filesystem:
    directory: /loki/chunks

limits_config:
  enforce_metric_name: false
  reject_old_samples: true
  reject_old_samples_max_age: 168h

chunk_store_config:
  max_look_back_period: 0s

table_manager:
  retention_deletes_enabled: false
  retention_period: 0s

Error Tracking and Debugging

Sentry - Real-time Error Monitoring

// Sentry configuration for edge applications
import * as Sentry from '@sentry/edge';

Sentry.init({
  dsn: process.env.SENTRY_DSN,
  environment: process.env.NODE_ENV,
  tracesSampleRate: 1.0,
});

export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
    try {
      // Your application logic here
      return await handleRequest(request, env, ctx);

    } catch (error) {
      // Capture exception with Sentry
      Sentry.captureException(error, {
        contexts: {
          request: {
            method: request.method,
            url: request.url,
            headers: Object.fromEntries(request.headers.entries()),
          },
          edge: {
            colo: request.cf?.colo,
            country: request.cf?.country,
          },
        },
      });

      // Return error response
      return new Response('Internal Server Error', { status: 500 });
    }
  }
};

// Custom error reporting
export async function reportCustomError(
  message: string,
  level: Sentry.SeverityLevel = 'error',
  extra: Record<string, any> = {}
): Promise<void> {
  Sentry.captureMessage(message, level, {
    extra,
  });
}

User Analytics

Plausible Analytics - Privacy-First Analytics

<!-- Plausible analytics script -->
<script defer data-domain="yourdomain.com" src="https://plausible.io/js/script.js"></script>

<!-- Custom event tracking -->
<script>
  // Track custom events
  window.plausible = window.plausible || function() { (window.plausible.q = window.plausible.q || []).push(arguments) };

  // Track button clicks
  document.querySelector('.purchase-button').addEventListener('click', function() {
    plausible('Purchase Attempt', {props: {product: 'premium-plan'}});
  });

  // Track form submissions
  document.querySelector('.contact-form').addEventListener('submit', function() {
    plausible('Contact Form Submitted');
  });
</script>

Deployment Platforms Comparison {#deployment-platforms}

Cloudflare Pages/Workers - The Edge-Native Platform

Strengths

Advantages:
□ Global edge network (275+ locations)
□ Zero cold starts
□ Built-in CDN and security
□ Generous free tier
□ Excellent developer experience
□ Native D1 database support
□ Integrated analytics

Best For:
□ Static sites and JAMstack applications
□ Serverless APIs and functions
□ Global applications needing low latency
□ Security-conscious applications
□ Budget-conscious projects

Deployment Configuration

# wrangler.toml
name = "my-awesome-app"
main = "src/index.js"
compatibility_date = "2023-10-30"

# Environment variables
[env.production.vars]
ENVIRONMENT = "production"
API_URL = "https://api.example.com"

# KV namespaces
[[kv_namespaces]]
binding = "CACHE"
id = "your-kv-namespace-id"

# D1 database
[[d1_databases]]
binding = "DB"
database_name = "my-database"
database_id = "your-database-id"

# R2 buckets
[[r2_buckets]]
binding = "BUCKET"
bucket_name = "my-storage-bucket"

# Custom domains
[env.production]
routes = [
  { pattern = "example.com/*", zone_name = "example.com" }
]

// GitHub Actions workflow for Cloudflare deployment
name: Deploy to Cloudflare

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'

      - name: Install dependencies
        run: npm ci

      - name: Run tests
        run: npm test

      - name: Build application
        run: npm run build

      - name: Deploy to Cloudflare Pages
        uses: cloudflare/pages-action@v1
        with:
          apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }}
          accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
          projectName: my-awesome-app
          directory: dist
          gitHubToken: ${{ secrets.GITHUB_TOKEN }}

Vercel - The Frontend-First Platform

Strengths

Advantages:
□ Excellent frontend framework support
□ Automatic optimization
□ Preview deployments
□ Analytics and performance insights
□ Serverless functions
□ Edge functions support

Best For:
□ Next.js applications
□ React applications
□ JAMstack sites
□ Frontend-heavy projects
□ Teams wanting zero-config deployments

Configuration Examples

// vercel.json
{
  "version": 2,
  "builds": [
    {
      "src": "package.json",
      "use": "@vercel/next"
    }
  ],
  "routes": [
    {
      "src": "/api/(.*)",
      "dest": "/api/$1"
    },
    {
      "src": "/(.*)",
      "dest": "/$1"
    }
  ],
  "env": {
    "DATABASE_URL": "@database_url",
    "API_KEY": "@api_key"
  },
  "functions": {
    "pages/api/**/*.js": {
      "runtime": "nodejs18.x"
    }
  },
  "headers": [
    {
      "source": "/(.*)",
      "headers": [
        {
          "key": "X-Content-Type-Options",
          "value": "nosniff"
        },
        {
          "key": "X-Frame-Options",
          "value": "DENY"
        },
        {
          "key": "X-XSS-Protection",
          "value": "1; mode=block"
        }
      ]
    }
  ]
}

AWS - The Enterprise Platform

Strengths

Advantages:
□ Comprehensive service ecosystem
□ Enterprise-grade security
□ Global infrastructure
□ Advanced networking capabilities
□ Mature and stable platform
□ Extensive documentation

Best For:
□ Enterprise applications
□ Complex infrastructure requirements
□ Applications needing advanced AWS services
□ Teams with AWS expertise
□ Compliance-heavy industries

Infrastructure as Code (Terraform)

# AWS infrastructure for edge applications
terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
    cloudflare = {
      source  = "cloudflare/cloudflare"
      version = "~> 4.0"
    }
  }
}

provider "aws" {
  region = "us-east-1"
}

# CloudFront distribution for edge content
resource "aws_cloudfront_distribution" "main" {
  origin {
    domain_name = aws_s3_bucket.website_bucket.bucket_regional_domain_name
    origin_id   = "S3-${aws_s3_bucket.website_bucket.bucket}"

    s3_origin_config {
      origin_access_identity = aws_cloudfront_origin_access_identity.main.cloudfront_access_identity_path
    }
  }

  # Lambda@Edge for dynamic content
  origin {
    domain_name = aws_lb.main.dns_name
    origin_id   = "ALB-${aws_lb.main.name}"

    custom_origin_config {
      http_port              = 80
      https_port             = 443
      origin_protocol_policy = "https-only"
      origin_ssl_protocols   = ["TLSv1.2"]
    }
  }

  enabled             = true
  is_ipv6_enabled     = true
  default_root_object = "index.html"

  default_cache_behavior {
    allowed_methods        = ["DELETE", "GET", "HEAD", "OPTIONS", "PATCH", "POST", "PUT"]
    cached_methods         = ["GET", "HEAD"]
    target_origin_id       = "S3-${aws_s3_bucket.website_bucket.bucket}"
    compress               = true
    viewer_protocol_policy = "redirect-to-https"

    forwarded_values {
      query_string = false
      cookies {
        forward = "none"
      }
    }

    min_ttl     = 0
    default_ttl = 3600
    max_ttl     = 86400
  }

  # Cache behavior for API routes
  ordered_cache_behavior {
    path_pattern           = "/api/*"
    allowed_methods        = ["DELETE", "GET", "HEAD", "OPTIONS", "PATCH", "POST", "PUT"]
    cached_methods         = ["GET", "HEAD", "OPTIONS"]
    target_origin_id       = "ALB-${aws_lb.main.name}"
    compress               = true
    viewer_protocol_policy = "https-only"

    forwarded_values {
      query_string = true
      headers      = ["Authorization", "CloudFront-Forwarded-Proto"]
      cookies {
        forward = "all"
      }
    }

    min_ttl     = 0
    default_ttl = 0
    max_ttl     = 0
  }

  restrictions {
    geo_restriction {
      restriction_type = "none"
    }
  }

  viewer_certificate {
    cloudfront_default_certificate = true
  }

  tags = {
    Environment = "production"
    Project     = "edge-application"
  }
}

# Lambda@Edge function for request processing
resource "aws_lambda_function" "edge_processor" {
  filename         = "edge_processor.zip"
  function_name    = "edge-request-processor"
  role            = aws_iam_role.lambda_edge.arn
  handler         = "index.handler"
  runtime         = "nodejs18.x"
  publish         = true

  # Lambda@Edge requires specific settings
  depends_on = [aws_iam_role_policy_attachment.lambda_logs]
}

resource "aws_iam_role" "lambda_edge" {
  name = "lambda_edge_role"

  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Action = "sts:AssumeRole"
        Effect = "Allow"
        Principal = {
          Service = "lambda.amazonaws.com"
          Service = "edgelambda.amazonaws.com"
        }
      }
    ]
  })
}

Platform Comparison Matrix

Platform	Edge Support	Pricing	Learning Curve	Best For
Cloudflare	Native	$	Low	Global applications
Vercel	Very Good	$$	Low	Frontend applications
AWS	Good	$$$	High	Enterprise applications
Netlify	Good	$$	Low	Static sites
DigitalOcean	Limited	$	Low	Simple applications
Google Cloud	Good	$$$	Medium	ML/AI applications

AI and Machine Learning Tools {#ai-tools}

Development and Training

Hugging Face - The ML Hub

# Using Hugging Face models in edge applications
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import torch

class SentimentAnalyzer:
    def __init__(self, model_name="distilbert-base-uncased-finetuned-sst-2-english"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
        self.pipeline = pipeline("sentiment-analysis", model=self.model, tokenizer=self.tokenizer)

    def analyze_sentiment(self, text: str) -> dict:
        """Analyze sentiment of text at the edge"""
        result = self.pipeline(text)[0]
        return {
            "label": result["label"],
            "confidence": result["score"],
            "processed_at": torch.cuda.is_available() and "GPU" or "CPU"
        }

    def batch_analyze(self, texts: list[str]) -> list[dict]:
        """Batch analyze multiple texts"""
        results = self.pipeline(texts)
        return [
            {
                "text": text,
                "label": result["label"],
                "confidence": result["score"]
            }
            for text, result in zip(texts, results)
        ]

# Edge deployment with ONNX
import onnxruntime as ort

class EdgeSentimentAnalyzer:
    def __init__(self, onnx_model_path: str):
        self.session = ort.InferenceSession(onnx_model_path)

    def preprocess(self, text: str) -> dict:
        """Preprocess text for model input"""
        # Tokenization and preprocessing logic
        inputs = self.tokenizer(text, return_tensors="np", padding=True, truncation=True)
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"]
        }

    def predict(self, text: str) -> dict:
        """Run inference on edge device"""
        inputs = self.preprocess(text)
        outputs = self.session.run(None, inputs)

        # Post-process outputs
        probabilities = torch.softmax(torch.tensor(outputs[0]), dim=-1)
        predicted_class = torch.argmax(probabilities, dim=-1)

        return {
            "sentiment": "positive" if predicted_class == 1 else "negative",
            "confidence": probabilities[0][predicted_class].item()
        }

Replicate - Model Deployment Platform

// Using Replicate API for AI model inference
import Replicate from 'replicate';

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

export async function generateImage(prompt: string): Promise<string> {
  const output = await replicate.run(
    "stability-ai/stable-diffusion:ac732df83cea7fff18b8472768c88ad041fa750ff7682a21affe81863cbe77e4",
    {
      input: {
        prompt: prompt,
        width: 512,
        height: 512,
        num_outputs: 1,
        num_inference_steps: 20,
        guidance_scale: 7.5,
      }
    }
  );

  return output[0] as string;
}

// AI-powered content generation
export async function generateProductDescription(
  productName: string,
  features: string[]
): Promise<string> {
  const prompt = `Generate a compelling product description for ${productName}. Features: ${features.join(', ')}. Keep it under 150 words.`;

  const output = await replicate.run(
    "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
    {
      input: {
        prompt: prompt,
        max_new_tokens: 200,
        temperature: 0.7,
      }
    }
  );

  return output.join('').trim();
}

Model Serving and Inference

TensorFlow Serving - Production ML Serving

# Dockerfile for TensorFlow Serving
FROM tensorflow/serving:2.13.0-gpu

# Copy your model
COPY /models/my_model/ /models/my_model/

# Set environment variables
ENV MODEL_NAME=my_model
ENV MODEL_BASE_PATH=/models

# Expose port
EXPOSE 8501

# Health check
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
  CMD curl -f http://localhost:8501/v1/models/my_model || exit 1

# Kubernetes deployment for TensorFlow Serving
apiVersion: apps/v1
kind: Deployment
metadata:
  name: tf-serving-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: tf-serving
  template:
    metadata:
      labels:
        app: tf-serving
    spec:
      containers:
      - name: tf-serving
        image: your-registry/tf-serving:latest
        ports:
        - containerPort: 8501
        env:
        - name: MODEL_NAME
          value: "my_model"
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
            nvidia.com/gpu: 1
          limits:
            memory: "4Gi"
            cpu: "2000m"
            nvidia.com/gpu: 1
        readinessProbe:
          httpGet:
            path: /v1/models/my_model
            port: 8501
          initialDelaySeconds: 30
          periodSeconds: 10
        livenessProbe:
          httpGet:
            path: /v1/models/my_model
            port: 8501
          initialDelaySeconds: 60
          periodSeconds: 30

---
apiVersion: v1
kind: Service
metadata:
  name: tf-serving-service
spec:
  selector:
    app: tf-serving
  ports:
  - port: 8501
    targetPort: 8501
  type: LoadBalancer

MLOps Tools

MLflow - ML Lifecycle Management

# MLflow experiment tracking
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

def train_model(X_train, X_test, y_train, y_test, experiment_name="sentiment-analysis"):
    # Set experiment
    mlflow.set_experiment(experiment_name)

    with mlflow.start_run():
        # Model parameters
        n_estimators = 100
        max_depth = 10
        random_state = 42

        # Log parameters
        mlflow.log_param("n_estimators", n_estimators)
        mlflow.log_param("max_depth", max_depth)
        mlflow.log_param("random_state", random_state)

        # Train model
        model = RandomForestClassifier(
            n_estimators=n_estimators,
            max_depth=max_depth,
            random_state=random_state
        )
        model.fit(X_train, y_train)

        # Make predictions
        predictions = model.predict(X_test)
        accuracy = accuracy_score(y_test, predictions)

        # Log metrics
        mlflow.log_metric("accuracy", accuracy)

        # Log model
        mlflow.sklearn.log_model(
            model,
            "model",
            registered_model_name="sentiment-classifier"
        )

        # Log additional artifacts
        with open("classification_report.txt", "w") as f:
            f.write(classification_report(y_test, predictions))

        mlflow.log_artifact("classification_report.txt")

        print(f"Model trained with accuracy: {accuracy:.4f}")
        return model

# Load model for inference
def load_model(model_name="sentiment-classifier", stage="Production"):
    model_uri = f"models:/{model_name}/{stage}"
    return mlflow.sklearn.load_model(model_uri)

Security and Compliance Tools {#security-tools}

Static and Dynamic Security Analysis

Snyk - Developer-First Security

// package.json with Snyk integration
{
  "name": "secure-edge-app",
  "scripts": {
    "test": "jest",
    "test:security": "snyk test",
    "test:security:all": "snyk test --all-projects",
    "monitor": "snyk monitor"
  },
  "devDependencies": {
    "snyk": "^1.1200.0",
    "@snyk/cli": "^1.1200.0"
  }
}

# GitHub Actions workflow with Snyk security scanning
name: Security Scan

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]
  schedule:
    - cron: '0 2 * * 1' # Weekly on Monday at 2 AM

jobs:
  security:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'

      - name: Install dependencies
        run: npm ci

      - name: Run Snyk to check for vulnerabilities
        uses: snyk/actions/node@master
        env:
          SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }}
        with:
          args: --severity-threshold=high

      - name: Run Snyk code analysis
        uses: snyk/actions/node@master
        env:
          SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }}
        with:
          command: code test

OWASP ZAP - Dynamic Application Security Testing

# Docker Compose for OWASP ZAP
version: '3.8'
services:
  zap:
    image: owasp/zap2docker-stable
    command: zap.sh -daemon -host 0.0.0.0 -port 8080 -config api.addrs.addr.name=.* -config api.addrs.addr.regex=true
    ports:
      - "8080:8080"
    volumes:
      - ./zap-data:/zap/wrk

  webapp:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=development

# Automated security scanning script
#!/bin/bash
# security-scan.sh

TARGET_URL="http://localhost:3000"
ZAP_API_KEY="your-zap-api-key"
ZAP_HOST="localhost:8080"

# Start ZAP daemon
docker run -d -t owasp/zap2docker-stable zap.sh -daemon -host 0.0.0.0 -port 8080 -config api.addrs.addr.name=.* -config api.addrs.addr.regex=true

# Wait for ZAP to start
sleep 30

# Start spider scan
echo "Starting spider scan..."
SPIDER_ID=$(curl -s "http://$ZAP_HOST:8080/JSON/spider/action/scan/?apikey=$ZAP_API_KEY&url=$TARGET_URL" | jq -r '.scan')

# Wait for spider to complete
while true; do
    STATUS=$(curl -s "http://$ZAP_HOST:8080/JSON/spider/view/status/?apikey=$ZAP_API_KEY&scanId=$SPIDER_ID" | jq -r '.status')
    if [ "$STATUS" = "100" ]; then
        echo "Spider scan completed"
        break
    fi
    echo "Spider progress: $STATUS%"
    sleep 5
done

# Start active scan
echo "Starting active scan..."
SCAN_ID=$(curl -s "http://$ZAP_HOST:8080/JSON/ascan/action/scan/?apikey=$ZAP_API_KEY&url=$TARGET_URL" | jq -r '.scan')

# Wait for active scan to complete
while true; do
    STATUS=$(curl -s "http://$ZAP_HOST:8080/JSON/ascan/view/status/?apikey=$ZAP_API_KEY&scanId=$SCAN_ID" | jq -r '.status')
    if [ "$STATUS" = "100" ]; then
        echo "Active scan completed"
        break
    fi
    echo "Active scan progress: $STATUS%"
    sleep 10
done

# Generate report
echo "Generating security report..."
curl -s "http://$ZAP_HOST:8080/JSON/core/view/htmlreport/?apikey=$ZAP_API_KEY" > security-report.html

echo "Security scan completed. Report saved to security-report.html"

Compliance Management

Open Policy Agent (OPA) - Policy as Code

# policy.rego - Authorization policies for edge applications

package authz

default allow = false

# Allow if user is authenticated and has required permission
allow {
    input.user.authenticated
    required_permission := input.required_permissions[_]
    input.user.permissions[_] == required_permission
}

# Role-based access control
allow {
    input.user.authenticated
    required_role := input.required_roles[_]
    input.user.roles[_] == required_role
}

# Time-based access control
allow {
    input.user.authenticated
    time_between(input.hour, 9, 17) # Business hours
    not is_weekend(input.day_of_week)
}

# Geographic restrictions
allow {
    input.user.authenticated
    allowed_countries := ["CA", "US", "GB"]
    allowed_countries[_] == request.geo.country
}

# Helper functions
time_between(hour, start, end) {
    hour >= start
    hour <= end
}

is_weekend(day) {
    day == 0  # Sunday
    day == 6  # Saturday
}

// OPA integration in edge functions
import { OPA } from '@openpolicyagent/opa-wasm';

class OPAClient {
  private opa: OPA;

  constructor(policyBundle: ArrayBuffer) {
    this.opa = new OPA(policyBundle);
  }

  async evaluatePolicy(
    input: any,
    policyPath: string = 'authz.allow'
  ): Promise<boolean> {
    try {
      const result = await this.opa.evaluate(policyPath, input);
      return result.result === true;
    } catch (error) {
      console.error('Policy evaluation failed:', error);
      return false; // Fail safe
    }
  }

  async evaluateWithDecision(
    input: any,
    policyPath: string = 'authz'
  ): Promise<PolicyDecision> {
    try {
      const result = await this.opa.evaluate(policyPath, input);
      return {
        allowed: result.result === true,
        reasons: result.explanation || [],
        policy: policyPath
      };
    } catch (error) {
      return {
        allowed: false,
        reasons: ['Policy evaluation error'],
        policy: policyPath
      };
    }
  }
}

// Usage in edge function
export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
    const opa = new OPAClient(env.OPA_POLICY_BUNDLE);

    const user = await authenticateUser(request);
    const policyInput = {
      user: {
        authenticated: user !== null,
        roles: user?.roles || [],
        permissions: user?.permissions || []
      },
      request: {
        method: request.method,
        path: new URL(request.url).pathname,
        geo: request.cf
      },
      required_permissions: ['read:products'],
      required_roles: ['user', 'admin']
    };

    const decision = await opa.evaluateWithDecision(policyInput);

    if (!decision.allowed) {
      return new Response('Forbidden', {
        status: 403,
        headers: {
          'X-Policy-Decision': JSON.stringify(decision)
        }
      });
    }

    return await handleRequest(request, env, ctx);
  }
};

Productivity and Collaboration Tools {#productivity-tools}

Code Quality and Documentation

ESLint + Prettier - Code Quality

// .eslintrc.json
{
  "extends": [
    "@typescript-eslint/recommended",
    "prettier",
    "plugin:react/recommended",
    "plugin:react-hooks/recommended"
  ],
  "plugins": ["@typescript-eslint", "react", "react-hooks"],
  "parser": "@typescript-eslint/parser",
  "parserOptions": {
    "ecmaVersion": 2022,
    "sourceType": "module",
    "ecmaFeatures": {
      "jsx": true
    }
  },
  "rules": {
    "@typescript-eslint/no-unused-vars": "error",
    "@typescript-eslint/explicit-function-return-type": "warn",
    "react/prop-types": "off",
    "react/react-in-jsx-scope": "off",
    "prefer-const": "error",
    "no-var": "error",
    "object-shorthand": "error",
    "prefer-template": "error"
  },
  "env": {
    "browser": true,
    "node": true,
    "es2022": true
  },
  "settings": {
    "react": {
      "version": "detect"
    }
  }
}

// .prettierrc
{
  "semi": true,
  "trailingComma": "es5",
  "singleQuote": true,
  "printWidth": 80,
  "tabWidth": 2,
  "useTabs": false,
  "bracketSpacing": true,
  "arrowParens": "avoid",
  "endOfLine": "lf"
}

Storybook - Component Documentation

// .storybook/main.ts
import type { StorybookConfig } from '@storybook/react-vite';

const config: StorybookConfig = {
  stories: ['../src/**/*.stories.@(js|jsx|mjs|ts|tsx)'],
  addons: [
    '@storybook/addon-links',
    '@storybook/addon-essentials',
    '@storybook/addon-interactions',
    '@storybook/addon-a11y',
    '@storybook/addon-docs'
  ],
  framework: {
    name: '@storybook/react-vite',
    options: {}
  },
  docs: {
    autodocs: 'tag'
  }
};

export default config;

// Button.stories.tsx
import type { Meta, StoryObj } from '@storybook/react';
import { Button } from './Button';

const meta: Meta<typeof Button> = {
  title: 'Components/Button',
  component: Button,
  parameters: {
    layout: 'centered',
    docs: {
      description: {
        component: 'A versatile button component that supports multiple variants and sizes.'
      }
    }
  },
  tags: ['autodocs'],
  argTypes: {
    variant: {
      control: 'select',
      options: ['primary', 'secondary', 'outline', 'ghost'],
      description: 'The visual style of the button'
    },
    size: {
      control: 'select',
      options: ['sm', 'md', 'lg'],
      description: 'The size of the button'
    },
    disabled: {
      control: 'boolean',
      description: 'Whether the button is disabled'
    }
  }
};

export default meta;
type Story = StoryObj<typeof meta>;

export const Default: Story = {
  args: {
    children: 'Button',
    variant: 'primary',
    size: 'md'
  }
};

export const Secondary: Story = {
  args: {
    children: 'Secondary Button',
    variant: 'secondary'
  }
};

export const Large: Story = {
  args: {
    children: 'Large Button',
    size: 'lg'
  }
};

export const Disabled: Story = {
  args: {
    children: 'Disabled Button',
    disabled: true
  }
};

Testing Tools

Vitest - Modern Testing Framework

// vitest.config.ts
import { defineConfig } from 'vitest/config';
import react from '@vitejs/plugin-react';
import path from 'path';

export default defineConfig({
  plugins: [react()],
  test: {
    globals: true,
    environment: 'jsdom',
    setupFiles: ['./src/test/setup.ts'],
    coverage: {
      reporter: ['text', 'json', 'html'],
      exclude: [
        'node_modules/',
        'src/test/',
        '**/*.d.ts',
        '**/*.config.*'
      ]
    }
  },
  resolve: {
    alias: {
      '@': path.resolve(__dirname, './src')
    }
  }
});

// src/components/__tests__/Button.test.tsx
import { render, screen, fireEvent } from '@testing-library/react';
import { describe, it, expect, vi } from 'vitest';
import { Button } from '../Button';

describe('Button', () => {
  it('renders children correctly', () => {
    render(<Button>Click me</Button>);
    expect(screen.getByRole('button', { name: 'Click me' })).toBeInTheDocument();
  });

  it('handles click events', () => {
    const handleClick = vi.fn();
    render(<Button onClick={handleClick}>Click me</Button>);

    fireEvent.click(screen.getByRole('button'));
    expect(handleClick).toHaveBeenCalledTimes(1);
  });

  it('applies variant styles correctly', () => {
    render(<Button variant="secondary">Secondary</Button>);
    const button = screen.getByRole('button');
    expect(button).toHaveClass('btn-secondary');
  });

  it('is disabled when disabled prop is true', () => {
    render(<Button disabled>Disabled</Button>);
    const button = screen.getByRole('button');
    expect(button).toBeDisabled();
  });

  it('has correct accessibility attributes', () => {
    render(<Button aria-label="Submit form">Submit</Button>);
    const button = screen.getByRole('button');
    expect(button).toHaveAttribute('aria-label', 'Submit form');
  });
});

Tool Selection Framework {#tool-selection}

Decision Matrix Template

Tool Evaluation Framework

Project Requirements:
□ Performance requirements (latency, throughput)
□ Scalability needs (users, data volume)
□ Team expertise and learning curve
□ Budget constraints
□ Compliance and security requirements
□ Integration requirements
□ Support and documentation needs

Tool Evaluation Criteria:

1. Technical Fit (40%)
   □ Performance capabilities
   □ Scalability features
   □ Integration ease
   □ Feature completeness

2. Team Factors (25%)
   □ Learning curve
   □ Community support
   □ Documentation quality
   □ Hiring market availability

3. Business Factors (20%)
   □ Total cost of ownership
   □ Vendor lock-in risk
   □ Long-term viability
   □ Support and SLA

4. Security & Compliance (15%)
   □ Security features
   □ Compliance certifications
   □ Audit capabilities
   □ Incident response

Scoring: 1-5 for each criterion
Weighted Score = Score × Weight

Quick Reference Tables

Frontend Frameworks

Framework	Performance	SEO	Learning Curve	Ecosystem	Best For
Astro					Content sites
Next.js					Full-stack apps
React					Interactive UIs
Vue.js					Progressive apps

Backend Runtimes

Runtime	Performance	Ecosystem	Edge Support	Cold Starts	Best For
Node.js					General purpose
Bun					High performance
Deno					Secure by default
Go					High concurrency

Databases

Database	Performance	Scalability	Edge Support	Learning Curve	Best For
PostgreSQL					Complex queries
MongoDB					Flexible schemas
D1					Edge data
FaunaDB					Global apps

Migration Path Planning

Migration Planning Checklist:

Assessment Phase:
□ Current stack inventory
□ Performance bottlenecks identified
□ Dependencies mapped
□ Team skills assessment
□ Budget analysis
□ Risk assessment

Planning Phase:
□ Target stack selected
□ Migration strategy defined
□ Timeline established
□ Resource allocation planned
□ Rollback procedures defined
□ Success metrics defined

Execution Phase:
□ Proof of concept completed
□ Pilot migration successful
□ Full migration executed
□ Performance validated
□ Team training completed
□ Documentation updated

Optimization Phase:
□ Performance tuning
□ Cost optimization
□ Security hardening
□ Monitoring optimization
□ Process refinement
□ Lessons learned documented

Get Expert Guidance

Choosing the right tools and frameworks is critical to your success. VantageCraft’s experts can help you:

Assess your current stack and identify optimization opportunities
Design a technology roadmap aligned with your business goals
Implement best-in-class tools with proper configuration and integration
Train your team on new technologies and workflows
Provide ongoing support to ensure optimal performance

Our Technology Consulting Services

Architecture Design: Build scalable, maintainable systems
Technology Selection: Choose the right tools for your needs
Performance Optimization: Achieve 40-60% performance improvements
Security Implementation: Implement robust security practices
Team Training: Upskill your development team

Schedule a Technology Consultation

Email: tech@vantagecraft.dev Phone: (416) 555-0123 Website: www.vantagecraft.dev

What you’ll get:

2-hour technology assessment with our senior architects
Customized technology recommendations
Implementation roadmap and timeline
Risk assessment and mitigation strategies

This comprehensive tools and frameworks guide is continuously updated with the latest developments. Last updated: October 28, 2025

Download the complete toolkit: Get printable version

Join our developer community: Connect with other developers building modern edge applications.

Tools and Frameworks: Complete Developer Toolkit 2025

Table of Contents

Introduction: The Modern Development Landscape {#introduction}

Why This Guide Matters

How to Use This Guide

Essential Development Tools {#development-tools}

Integrated Development Environments (IDEs)

Visual Studio Code - The Community Standard

WebStorm - The Professional Choice

Cursor - The AI-Powered Editor

Version Control and Collaboration

Git + GitHub/GitLab - The Foundation

Git Hooks with Husky

Package Managers

Bun - The Modern Choice (Recommended)

pnpm - The Efficient Alternative

API Development and Testing

Thunder Client (VS Code Extension)

Bruno - The Modern API Client

Framework Deep Dive: Astro vs. React vs. Next.js {#framework-comparison}

Astro - The Edge-First Framework

Core Philosophy

Strengths

Best Use Cases

Example Implementation

React - The Interactive UI Library

Core Philosophy

Strengths

Best Use Cases

Example Implementation

Next.js - The Full-Stack Framework

Core Philosophy

Strengths

Best Use Cases

Example Implementation

Framework Comparison Matrix

Choosing the Right Framework

Decision Flowchart

Database Choices for Edge Applications {#database-choices}

Relational Databases

PostgreSQL - The Powerhouse

Neon - The Serverless PostgreSQL

NoSQL Databases

MongoDB - The Flexible Choice

FaunaDB - The Global Database

Edge-Native Databases

Cloudflare D1 - SQLite at the Edge

Database Selection Matrix

Monitoring and Analytics Tools {#monitoring-tools}

Application Performance Monitoring (APM)

DataDog - The Comprehensive Solution

New Relic - Developer-Friendly Monitoring

Log Management and Analysis

Loki + Grafana - Open Source Stack

Error Tracking and Debugging

Sentry - Real-time Error Monitoring

User Analytics

Plausible Analytics - Privacy-First Analytics

Deployment Platforms Comparison {#deployment-platforms}

Cloudflare Pages/Workers - The Edge-Native Platform

Strengths

Deployment Configuration

Vercel - The Frontend-First Platform

Strengths

Configuration Examples

AWS - The Enterprise Platform

Strengths

Infrastructure as Code (Terraform)

Platform Comparison Matrix

AI and Machine Learning Tools {#ai-tools}

Development and Training

Hugging Face - The ML Hub

Replicate - Model Deployment Platform

Model Serving and Inference

TensorFlow Serving - Production ML Serving

MLOps Tools

MLflow - ML Lifecycle Management

Security and Compliance Tools {#security-tools}

Static and Dynamic Security Analysis

Snyk - Developer-First Security