If your site has gone through three language reconstructions before ever meeting a real user, you'd probably wonder if you got it wrong from the start.
I'm that person who got it wrong.
Three Language Iterations, Each One Hurt
My site went through three phases: Chinese-only → Chinese + English → English-only.
Every iteration wasn't "just translate it." It was a 1.8x multiplier on code volume. Routing, components, SEO metadata, database fields — everything doubled.
It started simple. I wanted a small portfolio site to showcase my work as a freelancer. So I built it in Chinese. Then the project pivoted toward a global audience, so I added English. Then came the Vue-to-Next.js migration, and that's when it hit me: bilingual isn't a translation task, it's a code bloat problem.
I had to ask myself some hard questions.
Can I afford the growing maintenance cost?
A 1.8x codebase means more people, more servers. For a Chinese-language site, there's ICP filing and hosting compliance — the kind of hidden costs that don't show up on day one but pile up fast.
Where are my customers actually from?
I built a growth system to pull Google search data and reverse-engineer the answer. The verdict: Australia, US, UK, Canada. That left exactly one language to focus on.
What's the real ROI of multi-language?
When I first launched bilingual mode, I was excited — my site felt "international"! But as product thinker Liang Ning once said, avoid self-congratulatory products.
Was bilingual self-congratulatory?
For a small site like mine, Google Translate handles global content discovery well enough. I don't need a perfect product. I need a 60-score product that works. Multi-language, at this stage, is pure overhead.
So my site went through "Chinese → bilingual → English" without ever meeting a real user. Was it a waste of time? Yes. But it was necessary.
The most expensive thing in a startup isn't development. It's the wrong direction.
I Built a Company With 4 AIs
Direction settled: English only. But how to execute with almost no budget?
At first I was just using DeepSeek for fun — asking it "is today a good day to work?" kind of stuff. Then I started using Claude + GLM for development, and eventually ChatGPT and Gemini joined the mix.
Honestly, I didn't think of them as a team. Just four tools.
The moment it clicked was during a visual redesign.
I wanted to upgrade the site's look. Simple plan: have Gemini generate the new design. But the API kept failing. And asking Gemini directly was useless — it had zero context about the project.
That's when I hit a wall: AI is powerful, but it has no idea what you're doing.
I went back to ChatGPT. We started "discussing." It felt like two people who've never met, remotely coordinating a group of interns.
The final workflow was primitive:
- Claude — builds the visual preview framework
- ChatGPT — provides strategy and prompts
- Gemini — generates design code
- Me — copy, paste, tweak, debug
No API automation. No elegant architecture. Just human + AI patchwork.
And somehow, we finished the redesign. Not perfect. But it went from "unusable" to "usable."
That's when it hit me: we weren't "using AI." We were working alongside a team of AIs with different personalities.
- ChatGPT is the strategist — breaks down problems, gives direction.
- Claude is the architect — builds things, makes them work.
- GLM is the solid engineer — slower, but rarely needs rework.
- Gemini is the designer — sometimes abstract, but delivers surprising results.
They're like different people in a company. No office, no conference room. Just chat windows.
And I became the middleman — the one who coordinates everything.
I used to think AI was a tool. Now it feels more like: a team of remote employees.
So How Much Did This Actually Cost?
Everyone wants to know: what's the real price tag for a 60-score AI project?
Here's my ledger:
- Courses: Free tutorials from the web — $0
- API costs: GLM 5.1 and 5.2 only — about $45 total
- Tools: DIY everything, open-source everything. Maybe $3 for external API calls
- Domain: $2
- Everything else: Mostly my time.
ChatGPT calculated what this would have cost in 2022: servers $450, frontend outsourcing $700, design $400, development $2800+. At least $4000 if you don't count your own labor.
Today's actual cash outlay? Roughly $70.
Looking back at this math, something clicked. Starting up used to be about "not enough money." Now the bottleneck is "not enough time." So don't worry about AI taking your job.
Time is the one resource that's equally distributed. Even if you don't build your own thing, someone who's already validated their product will need you. Every indie hacker who hits a growth phase runs into the same wall: not enough hours, even working 24/7.
That's why hiring someone who understands AI becomes inevitable.
And yes, this is where I pitch. Our project is an AI talent marketplace. Because I genuinely believe the future works like the movie In Time — the most valuable currency is time itself.
The Thread That Connects These Three Stories
Put them together and one line runs through all three: make the right call, then validate it at the lowest possible cost.
The language lesson taught me that no amount of good technology saves a wrong direction. The 4-AI team showed me that resource constraints aren't the real limit — you just need a better combination. And the $70 price tag proved that the cost of being wrong in this era is so low, you have no excuse not to try.
The most expensive thing in a startup isn't development. It's the wrong direction.
But in the age of AI, the cheapest thing is finding out you're wrong — and correcting it — for pocket change.
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