edge-computing
Edge Computing covers deployment patterns and best practices for running compute at the edge using platforms like Cloudflare Workers, Deno Deploy, and Vercel Edge Functions. Learn edge-side rendering, smart routing, caching strategies, and how to integrate edge databases like D1, Turso, and DynamoDB Global Tables for sub-50ms response times worldwide.
Edge Computing helps you build low-latency globally distributed applications across Cloudflare Workers, Deno Deploy, Vercel Edge Functions, and other edge platforms.
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travisjneuman/.claude/edge-computing · repository language: JavaScript
git clone https://github.com/travisjneuman/.claude
cp -r .claude/skills/edge-computing ~/.claude/skills/edge-computingnpx skillfed install travisjneuman/.claude/edge-computingFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is edge computing and how does it reduce latency?
Edge Computing covers deployment patterns for running compute closer to users via platforms like Cloudflare Workers, Deno Deploy, and Vercel Edge Functions. By executing code at edge locations worldwide rather than centralized data centers, you achieve sub-50ms response times globally. Edge Computing teaches you to build low-latency globally distributed applications by placing compute, rendering, and caching logic near end users.
How do Deno Deploy vs Vercel Edge Functions compare?
Edge Computing helps you compare edge platforms and choose the best runtime for your use case. Deno Deploy and Vercel Edge Functions are both serverless edge runtimes, but differ in language support, pricing, and ecosystem integration. Edge Computing covers the trade-offs between these platforms alongside Cloudflare Workers and other options, helping you select based on your application's specific requirements.
What edge caching strategies reduce origin load?
Edge Computing teaches edge-side rendering and caching patterns to minimize origin requests. Strategies include HTTP caching headers, smart invalidation, stale-while-revalidate, and request coalescing at edge nodes. Edge Computing shows how to implement these patterns on platforms like Cloudflare Workers with KV storage to serve cached responses at edge locations worldwide.
How do edge databases like Turso and D1 work?
Edge Computing covers setting up edge databases and state management using platforms like Cloudflare D1, Turso, and DynamoDB Global Tables. These systems replicate data across edge regions for low-latency reads while handling write coordination. Edge Computing teaches you to integrate edge databases into your edge functions for persistent state without round-trips to distant origins.
What are Cloudflare Workers KV and Durable Objects?
Edge Computing explains Cloudflare's edge storage options: KV provides eventually-consistent key-value storage replicated globally for fast reads, while Durable Objects offer strongly-consistent state with coordination guarantees. Edge Computing teaches when to use each for different patterns—KV for caches and configuration, Durable Objects for rate limiting, sessions, and coordinated state.
How do you handle cold starts and optimize edge functions?
Edge Computing covers edge computing constraints and optimization techniques including cold start mitigation. Strategies include code splitting, tree-shaking, minimizing dependencies, and leveraging platform-specific optimizations. Edge Computing addresses AWS Lambda@Edge cold starts and similar challenges across platforms, teaching you to write efficient edge functions that execute in milliseconds.
SKILL.md
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Edge Computing
Platforms
| Platform | Runtime | Cold Start | Limits |
|---|---|---|---|
| Cloudflare Workers | V8 isolates | ~0ms | 128MB, 30s CPU |
| Deno Deploy | V8 isolates | ~0ms | 512MB, 50ms CPU |
| Vercel Edge Functions | V8 isolates | ~0ms | 128MB, 25s |
| AWS Lambda@Edge | Node.js | ~100ms | 128MB, 5s (viewer) |
| Bun | JavaScriptCore | N/A (server) | No hard limits |
Cloudflare Workers
```typescript export default { async fetch(request: Request, env: Env): Promise<Response> { const url = new URL(request.url);
// KV storage
const cached = await env.KV.get(url.pathname);
if (cached) return new Response(cached, { headers: { 'Cache-Control': 'max-age=60' } });
// D1 database
const { results } = await env.DB.prepare('SELECT * FROM users WHERE id = ?')
.bind(url.searchParams.get('id'))
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