AI in VS Code helped me modernize a 3-year-old EDS fork in under 2 hours

March 2026

In April 2023, I forked the EDS boilerplate so I could ramp up on what Edge Delivery Services actually is.

I learn best by building, so I rebuilt my personal site on top of that fork. The goal was not perfection. The goal was to see the patterns, build some custom styles, and create my own components so I could understand the moving parts.

Then life happened.

Like most personal projects, it sat for a while.

When I came back and started trying to use newer EDS capabilities, I realized how much innovation had happened since I forked. The gap was bigger than I expected. The project still worked, but it was clearly not current with the latest boilerplate patterns.

At that point I had two options:

  1. Spend a weekend doing manual diffs and risky merges.
  2. Use AI in VS Code as a migration partner.

I chose option 2.

How I prompted AI

I did not use one giant prompt. I used short prompts in sequence, like pairing with an engineer:

  1. "Do a pre-analysis and tell me the safest path to parity."
  2. "Create a branch strategy and migrate in batches."
  3. "Keep parity first, then preserve my custom work."
  4. "Run lint and fix issues as you go."
  5. "Document what changed and what features are now enabled."
  6. "Trim non-runtime files so the repo stays clean."

That flow worked really well. Each prompt had one clear objective.

How the plan came together

The biggest win was early: AI identified that my repo and upstream did not share clean merge history, so a direct merge would create noise and conflict.

Instead, it recommended an upstream-first parity branch and then moved my project-specific deltas in controlled batches.

The migration happened in stages:

  1. Baseline config and identity files
  2. Blocks and styles
  3. Runtime scripts and plugin layer
  4. Validation and cleanup

After each stage, it validated with lint, then fixed what was needed before moving on. That removed a lot of risk.

How documentation stayed in sync

I did not want "tribal knowledge" after the migration, so I asked AI to keep a succinct migration note and feature checklist as it worked.

That gave me a running log of:

  1. Why decisions were made
  2. What was complete
  3. What was pending
  4. What was runtime-required vs optional baggage

This part was surprisingly useful. It turned the migration into something I can maintain later without re-discovering everything.

What features this migration enabled

The update brought my project much closer to current EDS expectations while preserving my custom site behavior.

Highlights:

  1. Modernized runtime wiring aligned with current boilerplate patterns
  2. Experimentation integration for audiences, campaigns, and experiments
  3. Universal Editor support for content patch/redecorate flow
  4. Rich text editor instrumentation support
  5. Sidekick library and sidekick config restored
  6. Lint-clean codebase after migration
  7. Asset cleanup (removed non-runtime SCSS/LESS/docs bloat while keeping required runtime files)

In short: parity foundation restored, customizations retained, and future upgrades should be much easier.

Time to value

The whole process took less than 2 hours.

Not because migration is trivial, but because AI kept the work structured:

  1. Analyze first
  2. Plan in batches
  3. Validate continuously
  4. Document as you go
  5. Remove non-essential files

That combination is the difference between "I should do this someday" and "done today."

Final thought

If you have an older EDS fork, this approach is worth trying.

Use AI in VS Code like a technical partner, not a magic button:

That is how I got a 3-year-old fork caught up without a full rewrite and without losing my custom work.