How to choose AI Coding tools: A popular guide for beginners

You may have heard many names:

Cursor、GitHub Copilot、Trae、Claude Code、Codex……

They all seem to be able to “write code with AI”, so why are there so many different types? Which one should a novice install?

Let me give you an answer that won’t go terribly wrong:

**If you are new to AI Coding, first choose an AI editor that can see files, changes and running results; wait until you can judge whether the AI ​​changes are correct, and then try the terminal, desktop or cloud Agent. **

This article will not be a “who is the strongest” ranking list. Rankings will soon become outdated, and what really affects the experience is often not the product name, but:

• Whether AI can understand the entire project;
• AI can only answer questions, or it can modify files directly;
• Whether the AI ​​can run commands, tests and browsers; -Whether each step requires your confirmation;
• After an error occurs, can it read the error by itself and continue to repair it.

Understand these five things and you’ll be able to choose your own tools instead of chasing hot spots all over again every few weeks.

1. First understand: AI Coding is not “making a chatbot spit out a piece of code”

Suppose you say to the AI:

Add a “forgot password” function to the login page.

Ordinary chat tools may reply with a sample code and then let you complete the rest of the work yourself:

1. Find which file the login page is in;
2. Determine whether the project uses React, Vue or other technologies;
3. Copy the code to the correct location;
4. Install missing dependencies;
5. Start the project;
6. Read the error report and continue to modify;
7. Check whether the original login function has been damaged.

Real AI Coding tools will gradually take over these tasks. Not only must it “answer”, but it must also have the contextandtools needed to complete the task.

Think of it like hiring someone to decorate:

• Code completion is like someone handing you tools;
• AI Q&A is like someone standing next to you giving advice;
• IDE Agent works with you on site like a master;
• After the terminal or cloud Agent tells you the goal, let the master complete a room independently.

The further you go, the more AI can do, but the more you need to learn to describe goals, limit scope, and accept results.

2. Don’t memorize the product first, first understand the four working methods

1. Code completion: you write, AI will write next

You enter:

function calculateTotal(items) {

AI guesses your intention and automatically fills in the following code. You click Tab to accept. If you are not satisfied, continue writing.

The characteristics of this method are:

• AI usually works around the current file and code near the cursor;
• People always control the rhythm; -Low learning cost;
• Suitable for repeated code, common functions and simple tests;
• Not suitable for independently completing a requirement that spans many files.

It’s like “smart input method”. It saves time, but it won’t take care of the entire task for you.

Suitable:

• First attempt at AI-assisted programming;
• Already know how to write code, just want to improve the speed;
• The company has many restrictions on automatically executing commands and modifying files.

2. Q&A and planning: AI reads first, you don’t let it move first

You can ask:

What is the login process for this project? Please find the entry file, explain it in plain language, and do not modify the code yet.

The AI ​​searches for items, reads files, and gives explanations, but doesn’t do anything directly.

This approach is suitable for:

• Take over an unfamiliar project; -Learn a certain piece of code;
• Compare several options before modifying;
• Troubleshooting the problem, but not sure what should be changed;
• Let AI write the implementation plan first.

It’s like a “technical consultant”. For novices, this step is very important: **Let AI help you understand it first, and then allow it to modify it. **

Cursor’s Ask mode, GitHub Copilot’s Ask or Plan mode, and the read-only or planning phase of most Agents all belong to this way of working.

3. IDE Agent: AI works with you in the editor

IDE is an “integrated development environment”. You can think of it as a workbench for developers to write code, view files, run and debug programs.

Cursor, Trae, and VS Code with Copilot Agent are all common AI editor experiences. They can usually:

• Search the entire project;
• Modify multiple files at the same time;
• Show comparison of code changes;
• Run install, build and test commands;
• Continue to modify after reading the error;
• Lets you accept or reject actions step by step.

For example, you can give it a clearly defined task:

Add “See only unfinished” filter in existing to-do applications. Read the project structure first and tell me which files you plan to modify; I will confirm it before proceeding. Once completed, start the project and run the existing tests without changing extraneous pages.

The advantage of IDE Agent is that it is “visible on the spot”: the project file is on the left, the code is in the middle, and the dialogue is on the right. It is also easy to compare before and after modifications.

Therefore, it is often the most appropriate starting point for newbies.

4. Terminal, desktop and cloud Agent: hand over complete tasks

The terminal is the window where developers enter commands. Tools such as Claude Code, Codex CLI, and Cursor CLI can read projects, edit files, execute tests, and use Git directly in the terminal.

Codex and Claude Code don’t just exist in the terminal either: they also come in IDE, desktop or cloud form. What matters is not the interface, but the way it works:

You provide goals and constraints, and the Agent collects context, takes action, checks the results, and reports back to you.

For example:

Fixed the issue of occasional white screen on the order details page.

Require:
1. Reproduce and find the root cause first, don’t just bet on short value;
2. Do not change the interface protocol;
3. Supplement tests that can cover this issue;
4. Run relevant tests and type checks;
5. Finally, list the modified files, root causes, and verification results.

This tool is particularly suitable for:

• Developers who are already accustomed to using terminals;
• Bug fixes and refactorings across multiple files;
• Verifiable tasks such as testing, documentation, and dependency upgrades;
• Arrange several independent tasks at the same time;
• Leave time-consuming work to the background of the cloud environment for execution.

But it also has higher requirements for novices. Since you are no longer directing the AI ​​line by line, you must be able to determine:

• Whether it understands the correct questions;
• Whether the executed command is safe;
• Whether the modification scope is out of control;
• Whether passing the test really means that the function is correct.

3. The same product may belong to several categories at the same time

This is the most common misunderstanding when choosing a model.

In the past, people often said that Cursor is an IDE tool, and Claude Code and Codex are terminal tools. This statement is no longer accurate enough.

• Cursor has different modes such as Ask and Agent, and also provides terminal CLI;
• GitHub Copilot has Ask, Plan, Edit, Agent and other working methods in the editor;
• Claude Code can be used in terminal, IDE, desktop and web;
• Codex works in terminal, IDE, desktop and cloud environments.

So don’t ask:

Who is stronger, Cursor or Codex?

First ask:

Do I need to keep an eye on the modifications for this task, or can it be left to the Agent to complete independently? Is the code local or in the cloud? Where do I want to review the results?

The product is the “toolbox”, and the model is the tool you hold in your hand at this moment.

4. Understand common choices with a table

This table is not a product ranking. The portals and modes a tool has today may continue to change, but the selection logic won’t become obsolete anytime soon.

5. Use three questions to make your first choice

Question 1: Can you understand what the AI ​​modified?

If not, choose an AI editor that clearly displays file trees and code differences, and start in Ask or Agent mode that requires confirmation.

Don’t hand over the entire project to a completely automated agent right from the start. It’s not because the Agent will definitely make mistakes, but because you temporarily lack the means to detect errors.

Question 2: Do you have a clear acceptance method for your tasks?

“Help me optimize the project” has no clear end point.

The following tasks are more suitable for Agent:

Add mobile phone number format verification to the registration form. If there is an error, a Chinese prompt will be displayed under the input box; the correct number can be submitted normally. After completion, run the test and verify both the error and correct conditions in the browser.

It contains:

• Modification object: registration form;
• Expected behavior: format verification and error prompts;
• Behavior that should not be broken: the correct number can still be submitted;
• Acceptance method: test plus browser check.

The more acceptable the task is, the more suitable it is to be handed over to an Agent with stronger execution capabilities.

Question 3: Where is code and data allowed to go?

The risks of personal practice projects and corporate production code are completely different. Before selecting a tool, make sure at least:

• Whether the project code is processed locally or will be uploaded to the cloud environment;
• Which files and networks the Agent can access;
• Whether your approval is required before running the command; -Whether keys, customer data and production accounts will be accessed;
• Whether the company restricts models, plugins, MCPs or external services.

**The function is more automatic, which does not mean it is more suitable in all projects. **

6. If you still don’t know what to choose, start with this default plan.

Step one: Choose an interface you understand

If you are already using VS Code, you can try its Copilot Agent first; if you are willing to change editors, you can try Cursor or Trae.

The point is not which of the three is “absolutely best”, but to confirm that it at least has:

• Project file browsing;
• Ask or read-only Q&A; -Multiple file modifications;
• Diff review;
• Terminal command confirmation;
• Run tests or preview pages.

Step 2: Prepare only one practice item

Don’t use your company’s important warehouses the first time. You can choose:

• a to-do list;
• A personal introduction page;
• A simple accounting page;
• A practice project with only a few files.

It’s best to save the initial version using Git first. This makes it easy to compare and restore even if the modification fails.

Step 3: Ask first, then change

When you open the project for the first time, you can copy the following paragraph:

Please read this project first and do not modify any files.

Tell me in language that novices can understand:
1. What does this project do?
2. How to start;
3. What are the 5 most important documents responsible for?
4. If I want to change the homepage title, where should I start?
5. Which commands or files are risky and require my confirmation before operation.

If the explanation is basically consistent with the project you saw, give it the first modification task:

Change the home page title to "My First AI Coding Project".

Require:
1. Make only the minimum changes required to accomplish this goal;
2. Before modifying, explain which file is to be modified;
3. Start the project or run related checks after modification;
4. Tell me how to see the results for myself;
5. Do not install unnecessary dependencies.

This task is very small, but it contains a complete AI Coding cycle:

Understand the project → explain the plan → modify the code → run verification → manual confirmation

Completing this cycle is more valuable than letting AI generate thousands of lines of code.

7. Six things that are really worth comparing when choosing tools

When you see a new product, don’t just watch the demo video. Use the following six dimensions to judge:

1. Context: What does it see?

Only view the current code snippet, or can I search the entire repository? Can you read documents, design drafts, issues, and browser errors?

Without enough context, no matter how smart the model is, it is easy to guess wrong.

2. Action: What can it do?

Can you only answer questions, or can you modify files, execute commands, operate Git, check the browser, and call external tools?

Being able to speak and being able to do it are two completely different abilities.

3. Control: Can you prevent incorrect operations?

Show Diff? Does command execution require confirmation? Can I restrict accessible directories, networks, and tools?

Newbies should give priority to tools with clear control boundaries.

4. Verification: Can it check its own work?

Writing the code is just an intermediate step. How well a tool can run tests, type checking, builds, and browser validation is often more important than “how fast the first build is.”

5. Environment: Where does it work?

• IDE: suitable for viewing and modifying at the same time;
• Terminal: suitable for local engineering and automation;
• Desktop application: suitable for task management, parallel work and visual review;
• Cloud: Suitable for background execution, but you need to understand the environment, permissions and data boundaries.

6. Cost and team: Can it be used for a long time?

Also consider subscription fees, model credits, network environment, team collaboration, corporate compliance, and whether colleagues can replicate your workflow.

Don’t choose a solution that your team can’t use stably just to be faster every once in a while.

8. The five most common pitfalls for novices

Pit 1: Only compare models, not tools and contexts

The results of AI Coding are not determined solely by the model. Project context, available tools, project rules, and acceptance methods all influence the results.

Pit 2: Turn on all automatic permissions from the beginning

Automatically approving all commands may seem like a no-brainer, but you may accidentally delete files, modify large amounts of code, or run scripts you don’t understand. Keep confirming first, and then gradually let go after becoming familiar with it.

Pitfall 3: Treating a vague big wish as a task

“Building an e-commerce website” is not a task suitable for novices to directly deliver. First break it into small goals such as home page, product list, shopping cart, login, etc. that can be independently checked.

Pit 4: When you see “test passed”, you think it must be correct.

Tests may be incomplete or may not cover real requirements at all. You still have to look at the Diff and work on key processes yourself.

Pitfall 5: Changing tools every day but not completing a closed loop

Changing tools will bring freshness, but it will not automatically improve AI coding capabilities. First use a tool to complete it:

Describe requirements → Modify → Run → Find problems → Repair → Acceptance

Then compare other tools and you will know the difference.

9. Tools will change, but this growth path will not.

AI Coding is moving from “completing a few lines of code” to “autonomous execution around tasks.” The IDE will not disappear, it will become more and more like a workbench where humans observe, review and take over the Agent.

For individuals, the more secure order of growth is:

1. Use completion to improve input speed;
2. Use Ask to understand unfamiliar code;
3. Use the IDE Agent to complete small but clear modifications;
4. Learn to check Diffs, logs, tests and pages;
5. Use terminal or desktop Agent to handle complete tasks;
6. Finally try the cloud, parallel tasks and automated processes.

What really needs to be upgraded is not just the tools, but your role:

From “let AI write code” to “give AI a clear task and be responsible for the results.”

10. Do a 15-minute exercise now

After reading this article, please don’t continue to worry about product rankings. Choose an AI editor that is already available and complete the following four things:

• Open a practice project;
• Let AI only read and interpret items;
• Let AI modify a page title;
• View the Diff and verify the page yourself.

Once completed, you have crossed the most important first threshold of AI Coding: you no longer just ask the AI ​​for code, but start managing development tasks that can be checked at once.

In the next article, we will actually run this process through:

AI Coding Quick Start: Develop a web application in 10 minutes

Authoritative information

• Cursor Agent Official Overview
• GitHub Copilot Chat and Agent Mode
• Claude Code Official Overview
• Claude Code’s Agent work cycle
• Codex CLI official document
• Codex IDE extension official document
• Codex Cloud official document