Understand Any Codebase Faster with AI on Mac

Q: Can it explain code in any language?

Yes. The AI models Chapeta uses understand all major programming languages. The quality of explanation scales with how well-documented and well-structured the code is, but even dense, uncommented code gets useful breakdowns.

Q: Does it need the whole repo or just one file?

It works with whatever you give it. A single file is fine for understanding one function. For architectural questions or tracing data flow, letting Chapeta read multiple related files produces much better results.

Q: How is this different from inline AI in my editor?

Inline editor tools are great for completions and quick lookups. Chapeta is better for deeper exploration: multi-file tracing, architectural questions, and conversations where you build understanding across several files in sequence.

You inherited a repo, joined a new team, or opened a dependency you have never seen before.

3 steps 3 tools 20-45 minutes per unfamiliar module

Download Chapeta

The Problem

You inherited a repo, joined a new team, or opened a dependency you have never seen before. The function names are cryptic, there are no comments, and the logic branches in ways that make no sense until you trace every call. Reading unfamiliar code is one of the slowest parts of development, and it gets worse with every layer of abstraction.

How Chapeta Handles This

Chapeta reads files directly from your Mac and explains code in plain language. Point it at a file, a function, or an entire module and ask what it does. Because it can access related files through File Read, it traces imports and dependencies instead of guessing from a single snippet.

How to Explain Unfamiliar Code

3 steps to get it done

1

Open the file or attach it

Drag a source file into Chapeta, or tell it a file path. It reads the file contents directly from disk, so you do not need to copy-paste code into a chat box.
2

Ask what you need to know

Be specific: 'What does this function do?', 'Why is this mutex here?', 'Walk me through the data flow from line 40 to line 90.' General questions work too, but targeted ones give better results.
3

Follow up across files

Ask Chapeta to read imported modules, check type definitions, or trace a call chain. The conversation context carries forward, so it builds understanding across multiple files without losing track.

Example

You type

Read src/managers/SettingsManager.swift and explain the initialization flow. What does didSet on each property do, and which settings persist to UserDefaults?

Chapeta returns

## SettingsManager Initialization
The manager initializes as a singleton (`shared`) and loads all persisted values from UserDefaults in `init()`. Each property uses a computed getter/setter backed by UserDefaults keys.

## didSet Behavior
`didSet` fires on property changes AFTER initialization (Swift's `didSet` does not fire during the declaring type's `init`). When triggered:
- `selectedModelID`: updates ModelStore and posts a notification
- `temperature`: clamps to 0.0-2.0 range before saving
- `theme`: calls `Theme.shared.apply()` to update the UI immediately

## Persisted Settings
All 14 properties persist to UserDefaults with the prefix `chapeta.settings.`. Non-persisted computed properties: `isProUser` (derived from SubscriptionManager), `displayName` (derived from model metadata).

Without Chapeta

Open the file in your editor. Read it line by line. Cmd-click through imports to trace dependencies. Open a browser to look up unfamiliar APIs. Mentally reconstruct the data flow. For a complex class, this can take 30-60 minutes before you feel confident enough to make a change.

Time saved 20-45 minutes per unfamiliar module

Tools Used

Bash Terminal System Executes bash commands directly on your Mac. Instead of copying AI-generated commands and pasting them into Terminal, the AI runs them for you and shows the output inline. It handles everything from simple one-liners to complex piped commands and scripts. File Read File Operations Reads the contents of any file on your Mac and makes it available to the AI model. Supports text files, code files, configuration files, CSVs, JSON, XML, logs, PDFs (page-by-page with offset and limit), and any other text-based format. The AI can then analyze, summarize, search, transform, or answer questions about the file content. Grep File Operations Searches the contents of files for text patterns using ripgrep, a high-performance regex engine. Finds matching lines across multiple files, returning the file path, line number, and matching content. Uses smart-case (case-insensitive unless your pattern contains uppercase), respects .gitignore, and supports file type filtering and directory scoping.

Go Deeper

Jump into the related guide, tool, or skill when you need more depth.

FAQ

Can it explain code in any language?

Does it need the whole repo or just one file?

How is this different from inline AI in my editor?

Understand Any Codebase Faster with AI on Mac

The Problem

How Chapeta Handles This

How to Explain Unfamiliar Code

Open the file or attach it

Ask what you need to know

Follow up across files

Example

Without Chapeta

Tools Used

Related Skills

Go Deeper

FAQ

Related Workflows

Try the Explain Unfamiliar Code workflow in Chapeta

Understand Any Codebase Faster with AI on Mac

The Problem

How Chapeta Handles This

How to Explain Unfamiliar Code

Open the file or attach it

Ask what you need to know

Follow up across files

Example

Without Chapeta

Tools Used

Related Skills

Go Deeper

How to Search Your Codebase with AI

File Read Tool

Code Review Skill

FAQ

Related Workflows

Debug Error Messages

Generate Git Commit Messages

Review a Pull Request

Try the Explain Unfamiliar Code workflow in Chapeta