From a Billing Nightmare to an AI-Powered Lifesaver
I didn’t set out to build a code migration tool this year. I just wanted to fix a legacy PHP billing system that had survived too many CTOs and too little documentation.
The deeper I went, the more I realized I wasn’t looking at code—I was looking at digital archaeology. Files like invoice2.php, super_invoice_final.php, and functions referencing variables that no longer existed. It wasn’t code; it was a curse.
I love Go. It’s fast, clean, and built for the kind of backend sanity this system needed. But manually migrating that much tangled logic? Weeks of soul-draining work.
So I turned to OpenAI’s Codex.
The First Shot: Codex Actually Worked
I had used Codex before to generate helper functions or nudge me out of syntax fog. But converting a codebase from PHP to Go? That was new territory.
I started small—feeding it a tax calculation function. It responded with a basic Go translation that wasn’t perfect, but tweakable. My heart skipped. I thought, “Wait… this might actually work.”
Next thing I knew, I was building a CLI tool. Input a PHP file → chunk it → convert with Codex → output a Go module.
I named it Migrator.ai (because naming things makes chaos feel controlled).
The Pain: Rate Limits, Tokens & Legacy Landmines
Then came reality.
- API Rate Limits: Hit them. Often.
- Token Limits: Codex couldn’t handle big files, so I had to break them into chunks—while preserving logic across split files.
Legacy PHP doesn’t chunk neatly. Some logic spans three files and calls variables from a different directory. It was like slicing a cake with bombs inside.
I wrote a pre-parser to divide code by function or class. Even then, Codex hallucinated variable names or misunderstood deeply nested logic.
I was part developer, part therapist.
Smarter Prompts, Better Results
The breakthrough? Prompt engineering.
Instead of generic requests like “Convert this PHP to Go,” I began writing contextual prompts:
“This function calculates tax under Indian GST rules using global session variables. Rewrite in idiomatic Go with structs and error handling.”
The difference was night and day.
I also added feedback loops. If Codex got something wrong, I’d send it back with:
“That logic is incorrect because X—can you revise it?”
It didn’t learn per se, but it started outputting more useful translations. Less like autocomplete, more like a conversation.
You Still Have to Test Everything
AI gives you code. It doesn’t give you trust.
So I built a testing suite using real data from the old system. For every migrated function:
- Run it in PHP.
- Run it in Go.
- Compare the outputs.
The mismatches were enlightening:
- Missing
round()calls caused minor but critical invoice mismatches. - Timezone bugs crept in.
- Floats were silently treated as integers.
When billing is involved, ₹0.01 errors matter. You don’t want to explain those to thousands of customers.
Victory (After Debugging Until 3AM)
After three weeks, two existential crises, and one UTF-8 encoding meltdown, it finally worked.
Migrator.ai could process a legacy PHP module and output Go code that was about 85% production-ready.
We used it to successfully migrate three major modules. The billing system is now:
- Fully containerized
- Built in Go
- Tested and trusted
And I’m no longer afraid of touching it.
What I Learned
- Codex is powerful, not perfect. Treat it like a fast, naive junior dev.
- Prompt context is everything. Be specific, detailed, and human.
- Legacy code is a minefield. Chunking it safely takes more time than you think.
- Testing is your safety net. Assume nothing. Validate everything.
- Don’t over-automate. Sometimes it’s faster to just rewrite by hand.
Read more about tech blogs . To know more about and to work with industry experts visit internboot.com .
Would I Do It Again?
Yes—but only if you’re okay with a little suffering.
Building your own AI-powered migration tool isn’t plug-and-play.
But if you’re dealing with messy, undocumented legacy systems, it’s worth it. Even rough around the edges, Migrator.ai saved me weeks of manual work—and helped future-proof critical infrastructure.
I’m still deciding whether to open-source it. It’s scrappy, full of TODOs, and feels like a side project from a sleepless weekend. But maybe—just maybe—it’ll help someone else dig out of their own technical debt cave.
