Is a Non-Developer Who Does Vibe Coding a Developer? (3) (opens in new tab)
AI coding agents have helped a non-developer move from creating small local HTML tools to building shared dashboards, integrations, automations, and repeatable workflows. The major change was not writing more code, but learning to define data, permissions, inputs, outputs, exceptions, and validation criteria. The author concludes that AI is making more kinds of work executable and structured, expanding questions about “development” beyond professional developers. ## From Local HTML to Shared Tools - Early tools were standalone HTML files used locally in a browser. - Sharing them introduced deployment, URLs, version updates, and maintenance concerns. - Once users needed persistent data and changing states, the problem expanded from UI design to: - Data storage - Access and edit permissions - Change history and rollback - Backups and operational responsibility - Google Sheets became a practical lightweight database because it already provided: - Collaboration and familiar interfaces - Permission management - Revision history - The author progressed from manually pasting Apps Script code to using `clasp` and Apps Script APIs for deployment and execution. - The key shift was learning to evaluate where data should live and which tools already work safely within an organization. ## Security as a Daily Habit - Connecting work tools through webhooks introduced the need to protect tokens and webhook URLs. - The author began using `.env` files and `.gitignore` to prevent secrets from entering source code or Git repositories. - Requests to AI agents increasingly included security requirements: - Read secrets from environment variables - Avoid printing sensitive values in logs - Use placeholders instead of real credentials - Small automations connected to external systems naturally required thinking about secret management, execution environments, and access control. ## Turning Manual Tasks into Workflows - Tasks such as copying files, organizing folders, converting documents, editing videos, and extracting audio or summaries were delegated to AI agents. - Delegating these tasks required explicit definitions of: - Input files - Output names and formats - Whether existing files may be overwritten - Failure conditions - Verification requirements - Informal, intuitive actions became structured work specifications. - The author found that understanding completion criteria and input/output formats was often more important than memorizing individual commands. ## Skills and Feedback Loops - Weekly meeting-note preparation revealed recurring editing patterns and implicit business rules. - These rules were encoded into Codex and Claude skills covering: - Note structure - Action-item extraction - PMO-related signals - Situations where the AI should ask questions instead of making assumptions - Skills functioned as stored decision criteria, not merely collections of prompts. - Comparing AI-generated drafts with the author’s final revisions enabled continuous refinement. - Deleting local data for security reasons accidentally removed useful conversation context, temporarily reducing skill quality and demonstrating the importance of preserving relevant operational knowledge. ## Using Outputs as New Inputs - Google Analytics reporting became more frequent and detailed through MCP-based API access and reusable reporting skills. - MCP provided the data connection, while skills preserved the recurring monthly report structure. - The valuable work remained interpreting changes between periods and deciding whether a change was meaningful. - Combining AI-generated metrics with the author’s contextual knowledge helped surface signals requiring further investigation in near real time. The author recommends focusing less on how much code AI can generate and more on how work can be clearly structured for AI execution. As agents become more capable, everyone—not only developers—will increasingly define inputs, outputs, permissions, security controls, repetition, and validation as part of everyday work.
