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Yes, use AI to help build and troubleshoot technical projects—but do not treat a working result as proof that you understand it. Kay Macfoy’s September 30, 2026 essay on DEV Community shows how quickly AI-assisted progress can turn into confusion when the person deploying the project has not read its configuration, code, and infrastructure closely.
What Macfoy’s experience shows
Macfoy began a Cloud Resume Challenge project in 2025. The challenge, as described in the essay, had 16 steps. AI helped generate the initial HTML, but Macfoy reports that ChatGPT chose Node.js even though the challenge specified Python. The project’s visitor counter initially worked, then stopped.
In the first outage, Macfoy eventually found that the Azure Function responsible for the counter lacked required storage configuration. After correcting it, the counter recovered. The author recalls it moving from around 103 to the mid-180s; these are approximate values from this individual project, not independently verified measurements.
A later issue involved Step 11. Macfoy says the deployment workflow in deploy.yml was configured incorrectly. The website stayed online, but the counter broke. The essay describes repeated troubleshooting in which commands and AI suggestions did not, by themselves, explain how the whole system fit together.
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That distinction is the heart of the account: software can appear to work even when the person using it does not know why. Macfoy’s story is one person’s experience, not a controlled test of AI tools or proof that AI inherently causes technical failures.
Where AI helped—and where it did not replace understanding
The essay is not an argument for avoiding AI. Macfoy reports using it for HTML, troubleshooting, tests, dependency updates, and infrastructure as code. The problem was relying on suggestions without being able to explain the project’s behavior or verify what a change would affect.
Macfoy later reports adding eight automated tests covering counter increments, initialization, CORS, unsupported methods, missing environment variables, and failure conditions. The author also reports upgrading dependencies and receiving a zero-known-vulnerabilities result from npm audit. That result describes the audit output, not proof that the project had no vulnerabilities of any kind.
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For infrastructure changes, Macfoy describes validating templates, running a what-if deployment, and using a disposable environment before considering the work finished. These are reported steps in the author’s project, not independently reproduced checks. Their value is practical: they create ways to inspect and challenge generated work before it affects a live deployment.
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Why reading the infrastructure mattered to the bill
Macfoy also reports that the project initially cost around $75 per month. After examining and removing resources, the author recalls reducing a roughly $74 bill to about $3, with additional savings after removing Azure Front Door. These approximate personal figures are not an Azure pricing estimate; costs depend on the resources, configuration, usage, and region involved.
The broader lesson is not that any particular Azure service is wasteful. It is that deploying a configuration without knowing what each resource does makes it harder to spot both operational problems and unnecessary expense. Macfoy reports that Azure’s exported ARM template was 3,813 lines long, which made deliberate validation especially important in this project.
A practical way to use AI without handing over the steering wheel
- Check the task’s constraints. Compare generated code and configuration with the project requirements before accepting them. In Macfoy’s example, that would have surfaced the mismatch between the required Python and the Node.js selection.
- Ask for explanations, then verify them. Use AI to identify likely files, settings, or failure points, but read the relevant code and configuration yourself. A plausible explanation is a lead to check, not evidence that the system is fixed.
- Test the behavior that matters. Add automated checks for expected behavior and important failures, such as initialization, unsupported requests, missing configuration, and error handling.
- Review deployment changes before applying them. Inspect the workflow and infrastructure configuration, validate templates, and use a preview or isolated environment when available. Do not assume that a successful deployment means every component is configured correctly.
- Know what you are paying to run. Identify the purpose of deployed resources and confirm they are needed for the project. Check actual billing rather than assuming a cost from someone else’s account will apply to yours.
When to stop prompting and start reading
Keep using AI when it helps you make progress, but switch from prompting to inspection when you cannot explain what a suggested change does, why a service is needed, or how to tell whether the fix worked. Macfoy’s conclusion is conditional: use AI to move faster while retaining enough understanding to know where the project is going.
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