How AI is Redefining the Work of a Flutter Developer
How can Artificial Intelligence help in mobile app development?
📅 2025.12.21 - 👤 Borbély Viktor
The New Role: From Programmer to AI Engineer
The era of manual coding is over. In today’s modern workflow, the developer is much more like a conductor. We instruct Agents, supervise their work, and most importantly: refine the result. We need to think at a system level, document, and ensure the business logic works as well as possible.
The question "How do I code this?" has been replaced by "How do I specify this as precisely as possible?"
Documentation has always been a gray area in software development. Most developers don’t like to document, which is understandable as coding is much more exciting. However, in the AI era, precise specifications are vital. This has become the backbone of development, where the whole process begins.
SDD and MCP: The Safety Net of Professional Prompting
”Prompting into the air” is a rookie mistake. The basis of efficient AI-assisted development is Specification Driven Development (SDD) – that is, code generation based on precise specifications. This is where MCPs (Model Context Protocol) come into play, introduced by Anthropic and becoming a standard. I rely heavily on them because they ensure code safety and the enforcement of professional Best Practices. It was a huge step from Google when they introduced Dart language MCP integration around Flutter v3.35. This shows that the ecosystem not only follows trends but actively shapes them, offering even stronger, enterprise-level solutions.
My Personal Experiences
You hear a lot of good things about Cursor and Windsurf, but my personal stack is currently built on something else. Google Antigravity (VS Code clone) is specifically strong in coding, using Gemini 3 Pro by default. Beside it, Claude Code Sonnet 4.5 (or its even stronger, but more expensive Opus 4.1 model) and GitHub Copilot make up my daily toolkit. And when it comes to rapid prototyping, Lovable.dev has proven itself: with Supabase (backend) and GitHub (repo) integration, a full-stack concept can be carried through lightning fast.
The Bitter Truth: It Doesn’t Work Without Fundamentals
I left the most important part for last. AI is not a magic wand that replaces knowledge. If you don’t have strong programming foundations (IT fundamentals), AI won’t make you a 10x, or even a 2x engineer.
The equation 0 x 1000 = Zero is still valid
It is also true that errors are created 2x, 10x, 100x faster this way. If you don’t understand the systems, if you don’t know what’s happening in the background, then AI-generated code will just be a big black box for you that you can’t maintain, debug, or improve.
However, those who started developing themselves in time and understand the systems gain an incredible advantage: they can deliver larger projects with a smaller team in less time. Moreover, they can confidently enter areas (cross-functional engineering) where they previously had no experience.
About Subscriptions
Many models can be tried for free. It is worth testing them because manufacturers tweak them a lot every few months. However, the more serious models (Claude 4.5, Opus 4.1, Gemini 3 Pro) are subscription-based and represent a significant cost on a monthly basis. However, these costs are amply repaid by faster development cycles and smaller team sizes. It is worth maintaining 2-3 subscriptions so that you can always use the best tool for the task. If the daily limit of one fills up, you can use the other.
A subscription costs about 10-30 USD per month. So with 3 subscriptions, you are still within 100 USD, which is a negligible amount in a developer budget. If you combine these well, you can even replace the work of a smaller team.
How to Get Started?
If you are also interested in AI solutions but don’t know how to start, find me at one of my contacts so I can help.