AI Coding Assistants.

An AI coding assistant uses a language model to draft, explain or change code from plain-language instructions and nearby project files. It can suggest a whole function, write tests or describe unfamiliar code.

Traditional autocomplete works from the language server and static analysis. It mainly suggests real symbols, methods and types that are valid at the cursor. Its suggestions are narrow but usually grounded in the codebase.

An assistant can invent new code, so it is more flexible but less reliable. It may call an API that does not exist, misread a requirement or write insecure code. Use autocomplete as a source of known facts, and treat AI output as a draft that still needs review and testing.

The major assistants differ in where they run, how much work they do alone, and which models they support.

• GitHub Copilot covers the editor and GitHub, and its cloud agent can take a task and open a pull request.
• Cursor and Windsurf are AI-focused editors with codebase search and agent features, and Windsurf is now called Devin Desktop.
• Claude Code, OpenAI Codex and Gemini CLI are terminal agents, and Claude Code and Codex also run in IDEs and the browser.
• JetBrains builds its AI tools into its IDEs.
• Aider works from the terminal, and Continue is an open-source option that works with the model you choose.

Choose by workflow rather than brand. An editor assistant suits quick suggestions while typing, and a terminal agent suits multi-file changes, test runs and automation.

Most coding assistants provide four interaction modes:

• Inline autocomplete shows a suggestion at the cursor, usually accepted with Tab.
• Chat answers questions or drafts code without changing files.
• Edit mode applies a requested change to selected code or a small set of files.
• Agent mode can search the repository, edit several files, run commands, and react to test failures.

Each mode gives the tool more freedom. Autocomplete is fast and has a small impact. Chat is useful for learning and discussing options. Edit mode is good for a focused change with a visible diff. Agent mode suits a clear, testable task, but it also has the largest risk.

Select the least powerful mode that can complete the job, then review everything it produces.

Use inline autocomplete when you already know what to write and only want to type it faster. Good examples include repeated test cases, simple mappings, boilerplate, and a loop whose pattern is clear from nearby code. You can accept only the useful part and keep working without changing focus.

Use chat when the problem needs discussion or extra context. It is better for explaining an error, comparing designs, understanding a library, or planning a change across several files. A useful rule is simple: choose autocomplete when the intent is already clear, and choose chat when you still need to reason about the intent. Chat also works well for safe exploration because its answer does not change project files unless you ask it to.

AI-generated code can look polished and still be wrong. A model predicts likely text, and nothing in that process proves the code meets your requirements. It may invent an API or miss an edge case. It may also use an old library pattern or add a security weakness. Its generated tests can pass while checking the wrong behavior.

You stay responsible for any code you commit, so treat the assistant as a fast contributor whose work always needs review. Read the full diff, confirm unfamiliar APIs and run suitable tests, with extra care around authentication, user input and secrets. If you cannot explain a generated line or show how it was verified, it is not ready to merge.

A hallucinated API or package is a name that the model invents even though it does not exist. It may suggest a believable method, configuration option, or dependency because the name fits common patterns. The generated code can appear correct until it is compiled or run.

• Use the compiler, type checker, and language server as the first check.
• For dynamic languages, run focused tests immediately.
• Confirm unfamiliar methods in the official documentation for the version your project uses.
• Before installing a suggested package, inspect its official registry page, repository, maintainers, release history, and usage. Attackers sometimes register names that models often invent.
• Require human approval for new dependencies, especially when an agent can run package-install commands.

AI assistants work best on familiar patterns whose results are easy to check. Examples include:

• boilerplate
• simple data transformations
• unit-test drafts
• documentation
• configuration
• common framework code
• translations between similar languages

They can also summarize code or explain a stack trace quickly.

These tasks have many public examples, so the model has strong patterns to follow. A compiler, test, or short review can usually verify the result.

Assistants are less reliable for new architecture, subtle concurrency, hidden business rules, and performance work that needs real measurements. Use them when the cost of checking the answer is low. Keep human judgment in charge when a mistake would be expensive or when correctness cannot be demonstrated with clear evidence.