Vibe coding gets your app built. Monitoring helps you keep it working.
AI has made it much easier to go from an idea to a working app. You describe what you want, try the result, and keep iterating until it does what you had in mind. But once other people start using it, you face a different question: How do you know your app is actually working for them? If you’re new to building software, this is a good time to get familiar with monitoring and telemetry. They give…
Vibe coding makes building an app straightforward. You provide an idea, test the outcome, and refine it until it meets your expectations. However, once users start interacting with your app, a new challenge arises: how do you ensure it functions as intended? For newcomers to software development, grasping monitoring and telemetry is crucial. These tools provide insights into your app's behavior after deployment, offering valuable information to your AI when issues arise.
Testing your app is essential, but it doesn't cover every possible scenario. Users might submit empty fields, access non-existent records, or trigger slow requests. Monitoring helps identify errors and latency issues, enabling you to investigate them without solely relying on user reports. Logs, errors, and telemetry serve as a useful introduction to this monitoring process.
Logs track events while your app runs, aiding in tracing its activities. Errors signal problems and may provide details about the failure location. Performance data measures execution times, and telemetry encompasses broader data about your app's behavior, including these signals. Connecting these data points yields valuable insights.
While monitoring isn't continuous, sufficient visibility is crucial to detect issues promptly and investigate them effectively. Your AI benefits from runtime context when troubleshooting. For instance, when a user reports that saving their profile failed, the AI needs to determine the cause—whether it's due to unexpected input, database issues, or a service request that didn't complete.
Relevant logs and telemetry around the failure provide a strong foundation for investigation. Similarly, when an app runs slowly, pinpointing the exact request that takes several seconds becomes an achievable goal.
A practical workflow involves identifying errors or slow requests, examining available logs and telemetry, asking the AI to explain the likely cause using this evidence, and having it make targeted fixes. Testing the affected flow afterward is essential to verify the solution. Additionally, refining prompts can enhance the AI's problem-solving process: "Investigate this error.
Explain what the logs show, identify the likely cause, and fix it." or "Analyze this slow request. Identify where the time is spent and suggest an improvement."
In Croft, built-in monitoring and telemetry are now available for every app in your workspace. There's no need for SDK installations or code modifications. Monitoring allows you to view errors and slow requests, while logs, errors, and telemetry are automatically accessible to your AI, providing the necessary context to diagnose problems and work on solutions.
This feature streamlines the process for beginners, eliminating a potential setup hurdle. You can develop your app within the same workspace as your AI and receive direct assistance in investigating its behavior. For more information, visit the Croft monitoring page.
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