Rebuilding My Blog in 2 Hours with Claude: A Tale of Lost Data and AI-Powered Recovery

2025-12-07

Rebuilding My Blog in 2 Hours with Claude: A Tale of Lost Data and AI-Powered Recovery

It's 1 AM. I'm staring at a blank screen where my blog used to be. The WordPress hosting expired, the database is gone, and two years of marketing insights have vanished into the digital void.

Sound familiar? Here's what happened next—and why it changed how I think about AI tooling.

The Problem: When Your Digital Home Disappears

I had moved my blog hosting, thinking I'd backed everything up. Spoiler: I hadn't. At least not completely.

What I lost:

9 carefully crafted blog posts on growth marketing
124 images (diagrams, charts, frameworks I'd spent hours creating)
All the internal linking structure
My author bio and site metadata

What I had:

A folder of random images on my Mac (might be related?)
A vague memory that the Wayback Machine exists
Claude Code open on my screen

Most people would have called it a night. Written it off as a lesson learned about backups. But I decided to try something different.

Enter Claude Code: Not Your Average Coding Assistant

Here's where this story gets interesting.

I asked Claude Code to help me rebuild the blog. Not just "help me debug this error" or "explain this function"—but actually rebuild an entire website from scattered pieces.

What happened next was less like using a tool and more like pair programming with someone who actually remembers everything.

Act 1: The Recovery Mission

Me: "My blog is gone. Let's recover it from the Wayback Machine."

Within minutes, Claude had:

Queried the Wayback Machine API
Found 71 unique archived URLs from bpitha.com
Identified 9 complete blog posts
Built a Python script to download and convert HTML to markdown

The kicker? It didn't just dump raw HTML. It intelligently extracted article content, removed navigation and footers, preserved formatting, and added proper frontmatter.

Time elapsed: 20 minutes

Act 2: The Image Hunt

Here's where it got impressive.

The recovered posts had 144 image references—all pointing to the Wayback Machine. Loading them was painfully slow. Claude noticed this without me mentioning it.

Claude: "I see we're referencing images through Wayback Machine which is slow. Let's host them locally."

It matched filenames from my local Mac backup, copied 124 images, updated all markdown references, and even fixed the spaces-in-filenames issue that would have broken everything.

But here's the part that made me pause: When some images didn't match exactly, it created a mapping system. When filenames had dashes vs spaces vs underscores, it normalized everything. When duplicate images appeared (a quirk of the HTML-to-markdown conversion), it found and fixed all 77 instances.

This wasn't following instructions. This was understanding the problem.

Time elapsed: 45 minutes total

Act 3: The Details That Matter

By now, I was just watching. Claude was:

Fixing 54 internal URLs that were routing through Wayback Machine
Converting absolute URLs to relative paths
Removing broken screenshot references
Updating my author bio across all posts
Cleaning up subscription forms that no longer worked

Each fix revealed another issue. Each issue got solved before I noticed it was a problem.

Time elapsed: 90 minutes total

Act 4: The Fresh Start

With everything recovered, we built something better:

Next.js 14 with App Router (modern, fast)
Markdown-based content (version controlled, portable)
Vercel deployment (auto-deploys on git push)
Custom domain setup (bpitha.com pointing to the new stack)
Clean, minimal design (because content matters more than flashiness)

Final time: 2 hours

What This Actually Means

Here's what struck me as I watched this unfold:

1. AI Tooling Has Crossed a Threshold

This wasn't autocomplete on steroids. Claude maintained context across 2 hours of conversation. It remembered decisions from 30 messages ago. It anticipated problems I hadn't articulated.

When I said "the images aren't loading," it didn't just fix that one image. It:

Checked all image references across all posts
Found patterns in the failures
Created a comprehensive fix
Verified the solution worked

That's not pattern matching. That's problem solving.

2. The Future of Work Looks Different Than I Thought

I've been in tech long enough to be skeptical of "AI will change everything" claims. But this wasn't about AI replacing developers.

It was about collapsing the gap between "I need this done" and "it's done."

The traditional path would have been:

Research Wayback Machine API (30 min)
Write scraping script (1 hour)
Debug encoding issues (30 min)
Manually fix image paths (2 hours)
Set up Next.js (1 hour)
Deploy to Vercel (30 min)
Fix all the things I missed (3 hours)

Total: ~8-10 hours minimum

With Claude: 2 hours, start to finish

But more importantly: I spent those 2 hours thinking about strategy, not syntax. About what I wanted, not how to implement it.

3. This Changes How We Should Think About Building

As someone who's scaled growth teams at OYO and HealthPlix, I'm obsessed with leverage. The question isn't "can we build this?" but "what's the highest leverage way to build this?"

AI coding tools are becoming the highest leverage option for entire categories of work.

Not because they're smarter than developers (they're not). But because they're:

Infinitely patient with tedious work
Consistent across boring, repetitive tasks
Able to maintain context for hours
Fast enough that iteration is nearly free

The Technical Stack (For Those Who Care)

What we built:

Frontend:

Next.js 14 with TypeScript
App Router for better performance
Server-side rendering for SEO

Content:

Markdown files with gray-matter for frontmatter
Remark for HTML conversion
File-based, no database needed

Hosting:

GitHub for version control
Vercel for deployment
Custom domain with auto-SSL

Migration Tools:

Python for web scraping
Wayback Machine CDX API
Custom scripts for content cleanup

Total cost: $0/month (Vercel free tier)

What I Learned

On AI Tooling:

The value isn't in AI writing perfect code. It's in AI maintaining context while you maintain vision.

On Technology Choices:

Simpler is better. Markdown files beat WordPress for 95% of blogs. Version control beats databases for content management. Static generation beats dynamic rendering for performance.

On Problem Solving:

When you remove the tedious parts of implementation, you have more cognitive space for strategy. That's the real productivity gain.

The Punchline

Two hours after starting, my blog was live. All content recovered. All images optimized. All links working.

But here's what matters more: I didn't spend those two hours frustrated by forgotten APIs or debugging CSS. I spent them thinking about content strategy and site architecture.

That's the shift.

AI tools aren't replacing developers. They're removing the parts of development that get in the way of actually building things.

And if you're in the business of growth, marketing, or product—being able to go from idea to implementation in 2 hours instead of 2 weeks changes what's possible.

What's Next?

I'm running an experiment: Using this same workflow to build micro-tools for growth analytics. Conversion calculators, attribution models, cohort analysis dashboards—built in hours, not weeks.

If you're curious about the technical details or want to see the code, the entire blog is open source: github.com/b-ai-wala/simple-blog

And if you're thinking about rebuilding something, simplifying your stack, or just want to see what AI-assisted development actually looks like in practice—well, you just read it.

Now get back to building. The tools are better than you think.

burhanuddin.pithawala

AI Leader & Growth Marketing Strategist. Currently heading AI Business at InterviewKickstart, transforming learning for thousands through AI-powered education. Ex Global Head of Marketing at OYO and Ex Growth Marketing Leader at HealthPlix. Helping startups crack growth through data-driven marketing, product strategy, and AI transformation.