After Brain Surgery, I’m Building Differently — Starting With Localization
AI Translation Is Fluent. And That’s the Problem.
A few years ago, I went through brain cancer surgery.
There’s still a visible hole in my MRI.
It changed how I think about time and leverage — and what deserves years of my life. I stopped wanting to grind on things that plateau. I started caring more about building things that are durable and genuinely useful.
That’s what brought me back to localization.
The Illusion of “Good Enough” AI Translation
A lot of teams plug in AI translation and ship.
At first glance, it looks impressive:
- Fluent sentences
- Clean grammar
- No obvious red flags
Until you look closer.
Real examples people have shipped:
- “Handle unforeseen test expression values” → “testiculation values.”
- Payment instructions translated into phrases that literally made no sense.
- Legal or sensitive documents where names, timelines, or meaning subtly shifted.
- Marketing copy that technically translated — but completely lost tone and cultural nuance.
The dangerous part isn’t broken grammar.
It’s confident, fluent output that’s slightly wrong.
The Hallucination Problem
Modern AI systems don’t just translate — they interpret.
When context is missing (which is extremely common in UI strings), they guess.
And that’s where problems begin.
A simple example:
In product teams, we constantly say “ship the feature.”
To us, it means “release.”
Without context, “ship” can become a literal boat.
Grammatically correct. Completely wrong.
Other patterns teams have encountered:
- Translating variable names that should never be touched.
- Hallucinating meaning into short UI labels.
- Softening or rephrasing legal text in ways that change liability.
- Choosing the wrong sense of ambiguous words (“Charge” as billing vs powering vs accusing).
- Rewriting tone instead of preserving it.
- Adding implied context that never existed in the source text.
The output sounds polished.
But it’s wrong in subtle ways.
And subtle errors are the most dangerous ones — because they survive QA.
If you don’t speak the target language, you won’t catch it.
Your users will.
And when your product feels slightly “off” in their language, trust drops instantly.
Why I Care About This
I previously worked on localization systems used by Airbnb, Intercom, and Tencent.
At that scale, you learn quickly:
Localization isn’t a copy task.
It’s product infrastructure.
When it’s done right:
- International expansion feels smooth.
- Activation improves in new markets.
- Support tickets decrease.
When it’s done poorly:
- Terminology becomes inconsistent.
- Tone drifts across markets.
- The product feels foreign — even when translated.
At scale, those small quality gaps compound.
What I’m Building Now
I’m building localization the way AI-native teams build software — with context, version control, and tight feedback loops.
If you’re shipping fast with AI coding tools, you already know the pattern:
Generation is easy.
Consistency is hard.
Context is everything.
Localization shouldn’t be the least deterministic part of your stack.
It should be structured, traceable, and resilient — just like your codebase.
The idea is simple:
You build your product.
We handle your localization.
Apps. SaaS. Marketing sites. Games.
Built to scale without subtle translation debt creeping into production.
Ask
If you’ve:
- Shipped AI-translated content and later found subtle errors
- Felt uneasy about quality but couldn’t prove it
- Hit friction expanding into new markets
- Are building fast with AI and want localization to keep up
I’d genuinely love to hear about it.
I’m offering free pilot localization work in exchange for building real, measurable case studies.
No hype.
Just something that actually ships.
