A price tracker is a useful student project because it brings backend development, APIs, databases, testing, and debugging together in one practical application. In a project write-up published on October 2, 2026, CSE student Kushal Kushwaha describes building one to learn how real-world applications are assembled—and using AI as an assistant without handing over the work of understanding and testing the code.
What a price tracker is meant to do
Kushwaha describes the project as an app intended to track product prices, compare changes over time, and help people decide when it might be a good time to buy. That goal makes it a practical way to explore how multiple parts of an application fit together, rather than treating backend development, APIs, or databases as isolated topics.
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The available write-up does not identify the programming language, framework, retailer integrations, database, hosting provider, alert channel, or performance results. Those details should not be assumed when using this project as a learning example.
What the project taught its creator
Backend development and application structure
The author says the project helped him learn backend development and practical application development. A tracker gives a student a concrete problem to work through: how information enters an application, how it is handled, and how the user-facing goal depends on the parts working together.
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APIs and databases
APIs and databases were among the learning areas he names. In a price-tracking project, these are relevant because the app’s purpose involves working with product-price information and comparing it over time. The article does not specify which APIs or database he used, so it offers a learning account rather than a reproducible technical blueprint.
Testing and debugging
Kushwaha also highlights testing and debugging as part of the experience. That is an important distinction between producing code and learning to build an application: understanding whether code works, finding errors, and making decisions about how to address them are part of the work, not optional polish.
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How AI fit into the learning process
The author says he used AI as a coding assistant to understand concepts, debug errors, explore different approaches, and improve parts of the code. He emphasizes that he did not simply copy and paste generated code; he focused on understanding how it worked, testing it, and making development decisions himself.
That approach keeps responsibility with the student. AI can help explain a concept or suggest a direction, but a learner still needs to inspect the result, test it, and decide whether it fits the application. The write-up describes this as Kushwaha’s method; it does not provide a measured comparison of AI-assisted and unassisted development.
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What a student can take from the example
- Choose a project with connected parts. A tracker’s goal naturally brings backend development, APIs, and data storage into the same learning exercise.
- Treat debugging and testing as learning activities. Do not stop at accepting code that appears to work; understand its behavior and check it.
- Use AI to support reasoning, not replace it. Ask for explanations or alternative approaches, then evaluate and test the code yourself.
- Separate the project idea from implementation claims. Kushwaha’s write-up supports the learning goals above, but does not establish a particular stack, retailer connection, hosting setup, or result.
What the write-up does—and does not—establish
Kushwaha’s account is useful as a student’s reflection on learning through a practical project. It identifies the project’s intended purpose and the subjects he says he explored. It does not provide enough implementation detail to reproduce the app, evaluate its accuracy or reliability, or compare specific technical choices. Read it as an account of what one student learned, not as a step-by-step build guide.
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Read Kushal Kushwaha’s DEV Community write-up.
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