Tech

AI for Developers

Ship better code faster with AI as your second pair of eyes

Developers use AI to accelerate every phase of the software development lifecycle — from writing and reviewing code to generating documentation and debugging production errors. AI acts as an always-available senior engineer who can explain an unfamiliar codebase, write tests for untested functions, and suggest architectural improvements without judgment or scheduling constraints.

Common challenges AI helps solve

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Spending hours debugging cryptic errors when the fix turns out to be trivial

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Writing repetitive boilerplate code that slows down feature development

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Maintaining comprehensive documentation that stays synchronized with code changes

Top use cases for Developers

Debug a production error

I am seeing this error in production: [paste full stack trace and error message]. Here is the relevant code: [paste code]. The app is a Next.js TypeScript application using Supabase. Identify the root cause, explain why it occurs in this specific context, and provide a corrected implementation with comments explaining the fix.

Write unit tests for a function

Write a complete Jest test suite for this TypeScript utility function: [paste function]. Cover all happy paths, null and undefined inputs, boundary values, and async error states. Name each test in the format 'should [expected behavior] when [condition]'. Do not mock any dependencies unless they involve network calls.

Refactor legacy code

Analyze this JavaScript function written in 2018: [paste function]. List all code smells, deprecated patterns, and performance issues. Then rewrite it using modern ES2022+ syntax, proper error handling, and TypeScript types. Maintain identical input and output behavior. Add a migration note explaining what changed and why.

Generate API documentation

Write complete OpenAPI 3.0 YAML documentation for this Express.js router file: [paste router]. Include: path, method, description, request body schema with field descriptions and validation constraints, success response with example JSON, and error responses for 400, 401, 403, and 500 status codes.

Explain an unfamiliar codebase

I have just joined a new team and need to understand this codebase quickly. Here is the main entry point file: [paste file]. Here is the project folder structure: [paste structure]. Explain: what this application does, the key architectural patterns used, the data flow for a typical request, and what I should read next to get productive.