How I Ship AI Products in Days, Not Months

How I Ship AI Products in Days, Not Months

Most software projects die in planning meetings. Mine don't — because I've replaced most of the planning with shipping.

Over the past year I've launched 10+ AI-powered products: Telegram bots with voice interfaces, full-stack web apps with LLM backends, automation pipelines, and landing pages. The fastest took three days from idea to live URL.

Here's the workflow that makes it possible.

The Core Idea: Vibe Coding

Vibe coding isn't a specific framework or toolset. It's a philosophy: move at the speed of thought, cut everything that slows you down.

Traditional development has layers of friction — sprint planning, code reviews, architecture meetings, test coverage debates. All of that has value at scale. But for an MVP? It's deadweight.

Vibe coding replaces the ceremony with:

  1. A clear problem statement (one sentence)
  2. A working prototype in the fastest possible stack
  3. Real users within 72 hours

My Stack in 2024

I default to a small set of tools I know deeply:

  • Python + FastAPI for backends — zero boilerplate, async out of the box
  • Next.js for web frontends — fast to deploy, great DX
  • Telegram Bot API for conversational interfaces — no app store, instant distribution
  • Claude or GPT-4o depending on the task
  • Render or Railway for hosting — push and forget

The key is: I don't switch stacks for new projects. Every unfamiliar tool is a context switch that costs hours. Know your tools cold, then go fast.

A Real Example: The PDF Translator

A user asked me: "Can you build something that translates large English PDFs to Russian, keeping the structure intact?"

Here's how long each phase took:

PhaseTime
Architecture sketch30 min
Backend API (FastAPI + queue)6 hours
Frontend (Next.js upload + progress)4 hours
LLM integration (chunked translation)3 hours
Deploy + DNS1 hour
Total~15 hours

The result is running at pdf.valdas.online. It handles files up to 80MB and processes them in batches of 20 pages using GPT-4o-mini.

What Actually Slows Projects Down

After a dozen MVPs, the bottlenecks are always the same:

Scope creep before launch. The product grows in your head while you build. Cut it. The first version should do one thing.

Over-engineering infrastructure. You don't need Kubernetes for an MVP. You need a single server and a deploy script.

Waiting for perfect design. Use a component library or copy your own past work. Ship something that works, then polish.

Fear of "unfinished" code. Real users give you better feedback than code reviews.

The YouTube Channel Idea (Example Embed)

Here's how I structure a planning session — I sometimes record short explainers:

(Replace with your actual video ID in the Markdown.)

Conclusion

Speed is a feature. The sooner you're in front of real users, the sooner you learn whether the thing you're building is something people actually want.

Every week of planning is a week of not learning.

Ship first. Fix later. That's vibe coding.


If this resonates and you have an idea you want to turn into an MVP, reach out on Telegram. I build for founders, solo operators, and ambitious individuals who want results fast.

Valdas

Valdas

Vibe Coder · AI Product Builder based in Prague. I turn ideas into working AI products in days — Telegram bots, web apps, automation tools. Reach me on Telegram or follow on Medium.

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