Use AI to analyze test logs, identify and prioritize technical debt, and generate a task list for AI agents like Cursor or Devin to systematically fix issues. This creates a repeatable process for improving codebase health.
with Claire Vo
Run your test suite and pipe the output to a log file to capture all console logs and errors. For example, run yarn test > test_output.log.
Feed the generated log file to a large language model like Claude for analysis. Ask it to identify the most problematic areas, categorize them, and rank them by severity.
Analyze this test log file and create a prioritized task list for reducing test noise. Categorize issues by type and severity. Output in markdown checklist format.Use the AI's output to create a prioritized list of tasks in a markdown checklist file. Store this file in a dedicated directory, for example, agents/migrations, to track progress.
Use AI coding assistants like Cursor or Devin to address the identified issues. Assign one task at a time to an agent to ensure focused and manageable changes.
Once an AI agent completes a task, a human engineer should review the generated code. After approval, merge the change into the codebase and mark the corresponding task as complete on your checklist.
Follow Sahil Lavingia's method for delegating small but impactful maintenance tasks, like fixing an internal Slack report, to the AI agent Devin. This workflow shows how to use simple, clear prompts to fix code and update system prompts without distracting your engineering team.
Learn Gumroad CEO Sahil Lavingia's process for rapidly prototyping a UI with v0, then using the AI agent Devin to autonomously code, implement, and submit the feature for review, turning a multi-day task into a one-hour workflow.
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