Edge Computing for Developers | Creating Efficient Redirects with Edge Workers

Akamai Developers · Beginner ·🧠 Large Language Models ·1y ago

Key Takeaways

Explains creating efficient redirects with Edge Workers for edge computing

Original Description

New to Cloud Computing? Get started here with a $100 credit → https://www.linode.com/lp/youtube-viewers/?utm_source=youtube&utm_medium=dev_advocacy&utm_content=mret_ag_07_25_24 In this video, @heyAustinGil explains how to create an edge redirect engine using Akamai EdgeWorkers and EdgeKV. Chapters: 0:00 Introduction 0:48 SKU vs SLUG 2:17 Prerequisites 2:41 Create EdgeKV Namespace 3:37 Create an Access Token 4:43 Add Data to EdgeKV Store 5:25 SKU to SLUG Redirect 5:57 Create Business Logic in EdgeWorkers 9:40 Demo Redirect Example 11:17 Edge Redirector & Recap 12:19 Conclusion Read the doc for more information on EdgeWorkers → techdocs.akamai.com/edgeworkers/docs/event-handler-functions Learn more about the Akamai CLI on GitHub→ akamai.github.io/cli-edgeworkers/docs/edgekv_cli.html Check out Austin's previous video on HTTP Redirects → https://youtu.be/lYg0FMkmk2k Subscribe to get notified of new episodes as they come out → https://www.youtube.com/channel/UCf8uu3IE42b6hRUusufEH8g?sub_confirmation=1 #AkamaiDeveloper #AkamaiCLI #EdgeCompute Product: Akamai, HTTP Redirects, Akamai CLI, Edge Compute; @heyAustinGil
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

Embeddings Simplified
Learn the basics of embeddings and how they simplify complex data, a crucial concept in AI and ML
Medium · RAG
I built a tool that cuts Claude/ChatGPT token usage by 97% — here's how it works
Learn how to build a tool that reduces Claude/ChatGPT token usage by 97%, increasing productivity and efficiency in debugging and development
Dev.to · Rohith Matam
Serverless AI in a Browser Tab: Java WebAssembly + Local WebGPU LLMs
Learn to build a serverless AI model in a browser tab using Java WebAssembly and Local WebGPU LLMs for a zero-infrastructure RAG architecture
Dev.to · vishalmysore
Building LSTMs with PyTorch and Lightning AI Part 7: Resuming Training with Checkpoints
Learn to resume LSTM training with checkpoints using PyTorch and Lightning AI, enabling efficient model iteration and development
Dev.to · Rijul Rajesh

Chapters (11)

Introduction
0:48 SKU vs SLUG
2:17 Prerequisites
2:41 Create EdgeKV Namespace
3:37 Create an Access Token
4:43 Add Data to EdgeKV Store
5:25 SKU to SLUG Redirect
5:57 Create Business Logic in EdgeWorkers
9:40 Demo Redirect Example
11:17 Edge Redirector & Recap
12:19 Conclusion
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →