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Software Development

Open Terminal enables the AI to interact with real codebases — cloning repos, running tests, reading errors, installing dependencies, and iterating on fixes.

Clone and explore a repo​

You: Clone https://github.com/user/project and give me an overview of the codebase. What's the architecture? Where are the entry points?

The AI:

  1. Runs git clone to pull the repo
  2. Scans the directory structure, reads key files (README, package.json, pyproject.toml, etc.)
  3. Identifies the tech stack, entry points, and major components
  4. Returns a structured summary with file counts, dependencies, and architecture notes

AI listing project files and describing the structure

Run the test suite and fix failures​

You: Run the tests. If anything fails, figure out why and fix it.

The AI:

  1. Detects the test framework (pytest, jest, go test, etc.)
  2. Installs dependencies if needed
  3. Runs the full test suite
  4. Reads failure output, traces the bug, edits the source code
  5. Re-runs the failing tests to confirm the fix

AI running tests and iterating on fixes

Iterative debugging

The AI sees the same terminal output a developer would — stack traces, assertion errors, log messages. Multiple rounds of "run → read error → fix → re-run" happen automatically.

Set up a development environment​

You: Set up this project so I can develop on it. Install all dependencies, create the database, and run the dev server.

The AI:

  1. Reads setup docs (README, Makefile, docker-compose.yml)
  2. Installs system packages and language dependencies
  3. Creates config files, sets up databases, runs migrations
  4. Starts the dev server and confirms it's working
  5. Reports the URL where you can access it

AI installing dependencies and running a project

Refactor with confidence​

You: Refactor the database queries in users.py to use async/await. Make sure the tests still pass.

The AI:

  1. Reads the current implementation
  2. Rewrites the code with your requested changes
  3. Runs the test suite to verify nothing broke
  4. If tests fail, adjusts the refactored code until they pass
  5. Shows you a git diff of what changed

AI debugging and fixing code errors automatically

Git workflows​

You: Show me what changed since the last release tag. Summarize the commits.

The AI works with Git directly:

  • git log, git diff, git blame to analyze history
  • Create branches, stage changes, make commits
  • Generate changelogs from commit history
  • Find when a bug was introduced with git bisect
  • Resolve merge conflicts

AI initializing a git repo and working with git

Write and run tests​

You: Write unit tests for the calculate_shipping() function in orders.py. Cover edge cases.

The AI:

  1. Reads the function to understand its logic and parameters
  2. Identifies edge cases (zero quantity, negative values, international vs domestic, free shipping threshold)
  3. Writes test cases using the project's existing test framework
  4. Runs them to verify they pass
  5. If any fail, it determines whether it's a test bug or a code bug

AI writing and running unit tests with pytest

Debug a specific issue​

You: Users are reporting that the login endpoint returns 500 sometimes. Here's the error from the logs: KeyError: 'session_token'. Find and fix it.

The AI:

  1. Searches the codebase for where session_token is used
  2. Reads the surrounding code to understand the flow
  3. Identifies the bug (e.g., missing key check when session expires)
  4. Writes the fix with proper error handling
  5. Adds a test case for the edge case
  6. Runs the tests to confirm

AI finding and fixing a bug in the codebase

Build and verify an API​

You: Create a REST API for managing a bookstore. I need CRUD for books, authors, and categories. Use FastAPI and SQLite.

The AI:

  1. Scaffolds the project structure
  2. Defines database models and schemas
  3. Implements all endpoints with validation
  4. Creates seed data
  5. Starts the server and tests every endpoint with curl
  6. Shows you the Swagger docs page

AI creating and running a web application

What languages and tools are available?​

The Docker image comes with common development tools pre-installed:

CategoryTools available
LanguagesPython, Node.js, Ruby, C/C++, Bash
Package managerspip, npm, gem, apt
Version controlGit
Editorsnano, vim
Build toolsmake, gcc, g++

The AI can install additional tools on the fly — Rust, Go, Java, Docker CLI, database clients, and anything else available via apt or language-specific package managers.