Over the past few months, I have been building Toket continuously. At first, it was simply a collection of AI tools I personally needed. An AI cost calculator. A Prompt Optimizer. A model directory. Over time, Toket expanded into Knowledge, Workspace, News, and GEO experiments. As the product grew, I realized something important: Building a feature and building a product are completely different challenges. GEO Revealed a New Challenge: Real User Experience Is More Complex Than APIs Recently, I have been researching GEO (Generative Engine Optimization). At first, the problem seemed simple: Create queries. Call models. Analyze responses. Generate reports. But after deeper research, the problem became much more complex. Today, GEO involves multiple AI systems: ChatGPT. Claude. Gemini. Perplexity. And various regional AI models. Each has different behaviors, interfaces, and response patterns. More importantly: API results do not always represent what real users experience. When users open ChatGPT, ask questions, and receive answers, many variables affect the outcome: Model versions. Context. System instructions. Product interfaces. User behavior. The real challenge of GEO is not only measurement. It is building an evaluation system close to real user experience. Understanding the Difference Between Engineering and Product Recently, while studying excellent GitHub projects, I also learned something important. Why do many talented developers build powerful systems without complete product interfaces? I did not fully understand this before. Now I do. Engineering and product solve different problems. Engineering asks: “Can this system work?” Product asks: “Can users understand and achieve their goals?” A tool can run successfully. But that does not mean ordinary users know: Where to start. What the result means. What to do next. AI Makes Development Easier, But Raises Product Expectations AI Coding has changed what one person can build. Today, a Builder can use: ChatGPT for product thinking. Codex for complex engineering analysis. Cursor for rapid implementation. A single person can now complete work that previously required multiple roles. But this creates a new challenge: The faster we build, the more important product discipline becomes. Toket is experiencing this transition as well. To explore quickly, the project accumulated compatibility layers and temporary solutions. They helped the product move faster. But the next stage requires: Simplifying architecture. Improving consistency. Reducing unnecessary complexity. Creating a clearer product experience. From Building More Features to Connecting Existing Capabilities This is also the focus of Toket’s August roadmap. The next stage is not about endlessly adding features. It is about: Connecting existing capabilities. Improving visual experience. Reducing technical debt. Validating real user needs. Finding the first business signals. The question has changed. Before: “Can I build this feature?” Now: “Does this create real value?” This may be the stage every independent Builder eventually faces. Toket’s Perspective In the AI era, building software is becoming easier. But productization is not becoming simpler. The most valuable AI products will not only have stronger models. They will not only have more features. They will understand user problems and turn complex technology into simple experiences. Toket continues to evolve. I will continue documenting: How AI collaborates in building products. How experiments become real user validation. How an independent AI product finds its direction. Building Toket.
Estimate task cost in the AI Cost Analysis or refine prompts in the Prompt Optimizer.