designatlas.io

Overview

Designatlas.io is a practical example of how a product idea can move from concept to a live experience through clear problem definition, intentional scope decisions, and the thoughtful use of AI tools. More than a side project, it demonstrates that building products with AI is possible—not by handing over decisions, but by positioning AI as a collaborator and accelerator.

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Problem Definition

This project did not start from a personal pain point. I am not currently struggling with this problem myself. However, I consistently observe the same challenge among people who are new to UX, UI, or Product Design, as well as junior-level designers.

The issue is not a lack of resources. There are countless articles, videos, courses, and tools available. The real problem is the absence of direction. Many designers do not know where to start, what to learn first, or how UX, UI, and Product Design relate to one another over time.

This uncertainty often leads to confusion, loss of motivation, and eventually abandoning the learning process altogether. I defined the core problem as follows: this is not a knowledge gap, but a guidance gap.

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Solution Approach

The goal of Designatlas.io was not to create another learning platform. Instead, the focus was on simplifying the learning journey by providing clear, structured roadmaps that help designers understand what to learn, when to learn it, and why it matters.

Rather than overwhelming users with content, Designatlas aims to act as a compass. It offers a high-level learning structure that helps designers make better decisions about their growth, without forcing them into a rigid system.

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Intentional Scope Decisions

One of the most important decisions in this project was deliberately keeping the scope small. Designatlas was never meant to be a fully featured product at launch.

Features such as user accounts, saved progress, gamification, or community elements were intentionally excluded. Adding these too early would have shifted the focus away from the core value: clarity and direction.

The guiding principle was simple—deliver value first, then consider expansion if and when it becomes necessary.

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Building a Product with AI

designatlas.io also served as an experiment in building a real product with AI. Throughout the process, AI was positioned not as a decision-maker, but as a supportive partner that accelerated execution.

ChatGPT was used to help structure roadmaps, review content consistency, and explore different wording options for the landing page. Claude Code supported the technical side by enabling a clean and minimal front-end implementation through iterative prompts rather than a single, heavy specification.

For research and reference validation, I relied on Perplexity. It played a key role in quickly framing topics, checking assumptions, and grounding roadmap themes in reliable sources.

Crucially, all core decisions—such as which roadmaps to include, how learning should be sequenced, and which topics should be excluded—were made through product thinking and design experience. AI proposed options, but the final judgment always remained human.

This process reinforced a key insight: building products with AI is not about writing better prompts, but about making better decisions.

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Launch and Current State

Designatlas.io currently operates with basic SEO, lightweight analytics, and manual feedback collection. These choices were intentional. The objective was not to ship a perfect product, but to release something functional, useful, and open to iteration.

In its current form, Designatlas represents a starting point rather than a finished solution.

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Outcome

designatlas.io is more than an AI-assisted side project. It is a reflection of a product designer's approach to problem-solving, scope management, and collaboration with AI tools.

The project demonstrates that AI can meaningfully contribute to product creation when it is positioned correctly—not as a replacement for thinking, but as a force multiplier. When guided by clear intent and strong decision-making, AI becomes a practical partner in turning ideas into live products.