Linters help developers enforce coding standards, catch potential errors, and maintain consistency across a codebase. They reduce stylistic debates and automate code quality checks, making reviews more efficient. But despite these benefits, many developers hate linters.
A strict linter can overwhelm developers with warnings, flagging issues that don’t always impact functionality. Some enforce rigid style rules, leading to unnecessary debates over formatting instead of focusing on code logic. Others disrupt workflows by blocking commits or cluttering pull requests with minor fixes.
So, how do you make linters work for you instead of against you? This blog explores why developers hate linters, how to use them effectively, and how AI code reviews solve what linters miss.
Reasons Why Developers Hate Linters
When used correctly, linters enforce consistency and catch potential errors early. But in many cases, they introduce unnecessary friction, slow down workflows, and create distractions that don’t always lead to better code.
1. Warning Fatigue and False Positives
Linters often generate an overwhelming number of warnings, many of which don’t impact functionality. When developers see too many non-critical alerts, they start ignoring them altogether. Over time, important warnings get lost in the noise.
For example, a linter might flag an unused variable, even though it exists for debugging purposes:
//JavaScript
function processData(input) {
let debugMode = true; // Linter error: 'debugMode' is defined but never used
return input.trim();
}
To avoid interruptions, developers may start disabling linter rules project-wide, making the tool ineffective.
2. Bikeshedding and Style Debates
Instead of eliminating style debates, linters sometimes shift them to rule configurations. Teams spend time arguing over indentation, naming conventions, and line lengths rather than focusing on architecture and logic.
A developer submitting an important feature update might have their PR blocked over minor formatting differences. This leads to unproductive discussions and slows down actual development progress.
3. Rigid Rules that Ignore Context
Linters enforce rules mechanically, without understanding the intent behind the code. This can lead to unnecessary refactoring, where developers modify readable and efficient code just to satisfy formatting rules.
Consider a case where a linter forces a single-line list comprehension, even when the multi-line format is clearer:
#Python
# Readable version
data = [
{"id": 1, "name": "Alice"},
{"id": 2, "name": "Bob"},
]
# Linter forces this:
data = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}] # Less readable
This type of enforcement reduces clarity instead of improving it.
4. Disrupting Git Blame and Code History
When linters auto-format large portions of code, they can obscure meaningful changes in version history. Running git blame on a file might show the last change as a formatting update rather than the actual commit where a bug was introduced.
To prevent this, teams often use .git-blame-ignore-revs to exclude automated formatting commits. However, GitHub needs to respect this setting, or developers must configure it locally:
echo "26125bcc894c7e3988985ba459967c0fb21f9194" >> .git-blame-ignore-revs
git config blame.ignoreRevsFile .git-blame-ignore-revs
This ensures that git blame remains useful for tracking real changes.
5. False Sense of Code Quality
Just because code passes all linting checks doesn’t mean it is well-structured, scalable, or efficient. Linters enforce surface-level consistency but don’t analyse deeper issues like performance bottlenecks or maintainability.
For example, a linter won’t flag this inefficient algorithm:
#Python
def find_duplicates(nums):
seen = set()
duplicates = set()
for num in nums:
if num in seen:
duplicates.add(num) # O(n) complexity, better than O(n²)
seen.add(num)
return list(duplicates)
Linters won’t detect performance inefficiencies or suggest optimized algorithms — this requires deeper code analysis.
Making Linters Work for You
So far, we have discussed that linters can be frustrating, but they don’t have to be. When configured correctly, they provide real value by automating code consistency and catching potential issues early.
The key is to strike a balance — using linters effectively without letting them slow down development.
1. Customize Rules for Your Team
Instead of using an off-the-shelf ruleset, configure linters based on your project’s needs. Enforcing unnecessary rules can lead to unnecessary PR discussions and ignored warnings.
Keep the rules minimal, focusing only on those that improve readability, maintainability, and prevent real bugs.
For example, disabling overly strict rules like forcing single vs. double quotes can reduce distractions:
{
"rules": {
"quotes": "off", // Allow both single and double quotes
"no-console": "warn", // Flag console logs as warnings, not errors
"no-unused-vars": "error" // Keep important rules strict
}
}
This prevents linters from blocking development over minor style preferences.
2. Use Linters Incrementally
Applying linting rules to an entire codebase at once can overwhelm developers with thousands of errors. Instead, use incremental adoption by:
- Running linters only on new code changes.
- Introducing stricter rules gradually.
- Using pre-commit hooks to catch issues before they reach PR reviews.
For example, a “Hold the Line” strategy ensures linters only check modified files, improving code quality without forcing massive rewrites. In GitHub Actions, this can be automated:
name: Linter Check
on: pull_request
jobs:
lint:
runs-on: ubuntu-latest
steps:
- name: Run Linter on Changed Files
run: eslint $(git diff --name-only main...HEAD)
This approach ensures linters assist rather than obstruct development.
Balance Linters With Real Code Reviews
Linters should assist, not replace, code reviews. Use them to catch syntax errors and enforce consistency, but let developers focus on logic, performance, and security.
A well-balanced workflow separates what linters check from what human reviewers should evaluate.
For example:
- Let linters handle spacing, indentation, and unused variables.
- Let developers review architecture, scalability, and security.
This reduces unnecessary PR discussions while keeping the focus on actual code quality.
How AI Code Reviews Solve What Linters Can’t
Linters enforce coding standards, but they don’t analyse intent, architecture, or performance. They flag violations but can’t determine if a function is inefficient or if a refactor introduces unintended side effects. AI-powered code reviews bridge this gap.
Unlike static analysis, AI code reviews:
- Analyse entire repositories instead of single files.
- Detect logical issues, inefficiencies, and security risks.
- Offer context-aware suggestions that go beyond style enforcement.
For example, instead of flagging missing semicolons, an AI-powered review can detect an inefficient sorting algorithm and suggest a more optimized approach based on the codebase’s existing patterns.
Our AI Code Review Agent, for example, is designed to bring this level of intelligence to developer workflows.
Bito’s AI Code Review Agent
Bito’s AI Code Review Agent acts like an experienced reviewer, providing structured feedback on performance, security, and maintainability while integrating seamlessly with existing workflows.
By automating tedious code reviews and reducing human effort, it enables teams to merge PRs 89% faster, cut down regressions by 34%, and deliver a 14x return on every $1 spent.
How it improves code reviews:
- PR Summaries: Automatically generates a quick, high-level summary of pull requests, estimating review effort and categorizing changes.
- One-Click Code Fixes: Inline suggestions allow developers to accept fixes instantly, reducing back-and-forth in PRs.
- Changelist Overview: Provides a clear, structured table of modified files and key updates directly in PR comments.
- Security & Static Analysis: Integrates with Snyk, Whispers, detect-secrets, fbinfer, and Mypy to detect vulnerabilities and enforce best practices.
- Linter Integration: Works with ESLint, golangci-lint, and Astral Ruff to catch inconsistencies without disrupting workflows.
- Incremental Reviews: Focuses on new changes only, preventing unnecessary re-reviews of unchanged code.
Bito integrates with GitHub, GitLab, and Bitbucket, supporting both cloud and self-hosted deployments for security-conscious teams. It reduces PR cycle times by automating low-level feedback, letting developers focus on architecture and logic instead of minor formatting fixes.
Conclusion
Linters improve code consistency but often slow developers down with excessive warnings and rigid rules. They flag style violations but miss deeper issues like performance bottlenecks and logical errors. This is why developers hate linters — they enforce standards but don’t truly improve code quality.
AI code reviews provide a better alternative by understanding context, detecting inefficiencies, and offering actionable suggestions. Bito’s AI Code Review Agent automates this process, helping teams merge PRs faster and catch real issues without unnecessary friction.
Cut down PR cycles and improve code quality with AI-driven reviews.
by the Bito team. 
