How I Built a Real App with AI in 10 Days — And What Nobody Tells You

I Built MAKAN Translate with AI. The Hardest Part Wasn't Writing Code. Subtitle: Four development approaches, dozens of model experiments, one abandoned game translator, and the engineering lessons behind a free multilingual application.

Makan · Founder, MAKAN LAB · October 2026 Type: Independent engineering case study / reflective technical report Project: https://makanlab.tech/translate/ · Source: https://github.com/makan20042002/translator At a glance. MAKAN Translate began as an attempt to help a friend study Linux with translated Udemy subtitles. I tried visual app generation, coding agents, commercial and local models, and multiple testing strategies.

An approximately ten-day intensive development period produced a substantial project—but not a perfect translator. The essential lesson was not how to write a magic prompt. It was how to break work into verifiable pieces and recognize which problems AI could not solve for me.

Abstract AI-assisted programming can rapidly produce application interfaces and executable code, but producing maintainable software requires additional work: feasibility research, component integration, model evaluation, regression testing, and decisions about scope. This practitioner case study examines the development of MAKAN Translate, a free multi-engine translator conceived in response to subtitle-translation limits encountered while studying Linux with a friend. It reconstructs four development approaches involving Google AI Studio, ChatGPT/Codex, Claude Code, and Antigravity; describes the eventual division of research, review, coding, and manual verification across tools; and analyzes difficulties with Persian document processing, speech recognition, model compatibility, and game localization.

The account is based on the developer's recollections and publicly available project documentation, not a controlled comparison of AI products. Its central finding is qualitative: task decomposition and validation proved more consequential to the developer's progress than increasingly elaborate prompts or the selection of a single coding assistant. The paper concludes with a reproducible process for small-scale AI-assisted software engineering and an explicit accounting of its limitations.

Keywords: AI-assisted software development; human–AI collaboration; incremental development; software verification; multilingual machine translation; Persian; local language models; open source.

Automated passes, manual observations, and model-quality judgments must be reported separately rather than combined into one vague claim that an app “works.”