AI Assistants for Developers: Choose by the Work You Do

The best AI assistant for developers is the one that fits your working environment, can inspect the code relevant to the task, and produces changes you can verify. Choose an editor assistant for frequent small suggestions, a repository assistant for bounded implementation work, and a conversational workflow for explanation and planning.

Tools like fenbs keep requirements, approved decisions, and unfinished work outside any single coding session. That matters when the assistant that investigates a bug is different from the one that fixes it. You should compare coding ability and continuity separately: a strong answer today does not tell tomorrow’s assistant what your team approved.

Which developer assistant fits each kind of work?

Start with the place where you spend your time. An assistant that fits your editor can be a better everyday choice than a separate application with an impressive demonstration. For larger changes, consider how it reads multiple files, shows proposed edits, and lets you inspect the commands it runs.

The feature descriptions above come from the official overviews for Claude Code↗ (opens in a new tab), GitHub Copilot↗ (opens in a new tab), Gemini CLI↗ (opens in a new tab), and JetBrains AI Assistant↗ (opens in a new tab). Treat the fit recommendations as a starting point for your own trial. Availability and account requirements belong in those vendors’ current documentation.

For a closer look at one editor family, read the JetBrains AI Assistant guide. The choice between tools is less useful until you know which recurring task you want to improve.

How should you test an assistant before adopting it?

Give each candidate the same small, representative task in a disposable copy of a real project. Avoid starting with a broad instruction to improve the entire application. You want enough complexity to expose misunderstandings while keeping the result small enough that you can inspect every change.

1. Choose a task with a clear outcome, such as correcting an empty-state message or reproducing a specific validation failure.
2. Provide the same requirements, repository instructions, allowed files, and verification commands to each candidate.
3. Ask it to identify the relevant code and explain the cause before editing. Check whether the explanation matches the implementation.
4. Inspect the final changes, run the appropriate checks, and record the corrections you had to make.
5. Repeat with a different task type, such as an unfamiliar module explanation or a narrow review, before choosing a daily tool.

Judge total work, including your review time. A quick patch that changes unrelated behavior can cost more attention than a slower, narrower fix. Also notice whether the assistant admits missing evidence, follows the project’s package manager, and leaves a clear account of checks it could not run.

What information should you give it before coding?

A useful assignment describes the trigger, expected behavior, boundaries, and evidence of completion. “Fix checkout” leaves almost every important choice open. “An empty postal code causes this error; show an inline message, preserve the existing payment flow, and run the form checks” makes the intended change reviewable.

• Goal: what a user should be able to do after the change.
• Evidence: reproduction steps, a sanitized error, or an example input and output.
• Constraints: files, dependencies, compatibility, and actions that require a person’s approval.
• Source of truth: the relevant requirement and the decisions that still apply.
• Completion: which checks to run and what remains for a person to confirm.

Keep stable coding conventions with the repository. Keep changing work state somewhere the next session can find it. A rule to preserve a public interface and a note that an integration test is blocked by a missing service answer different questions; putting both into a long chat makes their status harder to see.

The fenbs workflow gives connected assistants rules first, followed by where the work stands, so you can reuse the project record instead of reconstructing it in every prompt.

How do you use an assistant for explanation and debugging?

For explanation, ask the assistant to trace one concrete path through the code. For example, follow a form submission from the button to the server response. Ask for the relevant files and the point where validation happens, then inspect those references. A fluent architectural summary can still describe behavior the project does not have.

For debugging, separate observations from possible causes. An error appears after an upgrade; that does not establish that the upgrade caused it. Ask for competing explanations and the smallest check that would distinguish them. Prefer a reproduction that fails before a patch and passes afterward to a list of speculative changes.

Say a background job sends duplicate notifications. Have the assistant inspect the retry path, identify where a notification becomes permanent, and propose a way to observe repeated execution. Do not accept a patch that simply suppresses every second notification without explaining which event is duplicated and why.

After the fix, record the useful discovery, such as “retries can occur after delivery succeeds.” Keep it specific to the system you inspected. That gives the next developer a reason for the safeguard without presenting one incident as a universal rule.

What makes an AI code review useful?

Give the assistant a review question, the intended behavior, and the change to inspect. A general request for feedback often produces style suggestions when you need a correctness check. Ask it to focus on a concrete risk: missing authorization, duplicate writes, inconsistent error handling, or a change to a public response.

Require each finding to name the affected code, a plausible input or sequence of actions, and the resulting failure. A comment that a function “may be unsafe” is not enough to justify a rewrite. If the assistant cannot explain how the behavior fails, treat the comment as a question to investigate.

Keep review independent from acceptance. You still need the repository’s checks and a person responsible for deciding whether the behavior is right. Passing tests show that those particular checks passed; they do not establish that the requirements were complete or that every permission path was covered.

If you work alone, the guide to solo developer coding assistants looks more closely at dividing implementation and review work without inventing a larger team.

How much access should a coding assistant have?

Match access to the assignment. An explanation usually needs readable source files, not deployment credentials. A local fix may need edits and test commands, while production changes require a separate operational decision. State these boundaries before the assistant starts, then use the application’s controls to enforce them where available.

Check how a candidate handles file access, shell commands, connected services, and untrusted content. Instructions found inside an issue or a downloaded document should not silently become permission to send data somewhere else. Review external connections with the same care you use when adding a new development tool.

For environment-specific choices, see the separate guides to desktop assistants and Linux assistants; designing your own assistant belongs in the custom assistant guide. Those questions should not distract from testing the coding workflow you need.

How can you change tools without losing the project story?

Keep the code in version control and save a concise work record beside the assignment. Include the goal, what changed, the checks actually run, unresolved failures, and the next action. Link to the code revision when one exists. A saved conversation alone makes the next assistant recover that structure for itself.

Do not assume that connecting another model imports the first model’s chat or private memory. Give the new session an explicit instruction to read the shared record and inspect the current code. If the two disagree, resolve the discrepancy before asking for another implementation step.

How to do this with fenbs

1. Create a free account, then add one development project and a task with a concrete expected result. One person can start free, with no credit card.
2. Record the approved constraints in Decisions and rules. Keep open questions open until a person decides them.
3. Use the supported integration guides to connect your coding app over MCP, a standard way for an AI app to use an outside tool, through browser sign-in.
4. Ask the assistant to read the AI context, inspect the repository, and work within the task’s plan. Keep test results and unresolved checks with the task.
5. Before stopping or switching tools, ask it to save progress and the next action in Where we left off. Have the next session read that record before editing.

fenbs is a fit when the expensive part is re-explaining active work across sessions or supported assistants. It is not the right choice if you only need code completion, a compiler, or a place to store source files. For more work-specific guides, browse AI assistants by role.