Observed Signal · Apr 4, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
ESLint Plugin to Catch AI Coding Mistakes
The author analyzed roughly 500 AI-generated coding mistakes and created eslint-plugin-llm-core, an ESLint plugin with 20 rules designed to catch recurring errors produced by LLM coding assistants. The plugin targets patterns such as async/await misuse (e.g., async callbacks to array methods that return Promise arrays), empty catch blocks, missing null checks, magic numbers, deep nesting, inconsistent error handling and other LLM-prone anti-patterns. Rules are educationally worded to teach correct patterns and complement typescript-eslint rather than replace it. The project is published on GitHub (pertrai1/eslint-plugin-llm-core) and npm (eslint-plugin-llm-core), with zero-config recommended rules and an install example (npm install -D eslint-plugin-llm-core). The author plans auto-fixes, broader logging-library detection, and ongoing research to validate impact on AI-generated code quality.
A new open-source linting tool that targets recurring LLM-generated code mistakes can improve developer productivity and reduce production bugs when teams use AI coding assistants, but it is a niche tooling release rather than an industry-shifting platform update.
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Key Takeaways & Evidence Grounding
- Author analyzed approximately 500 AI-generated coding mistakes and identified repeated bug patterns.
- Released eslint-plugin-llm-core, an ESLint plugin containing 20 rules targeting common LLM-generated code errors.
- Plugin rules include no-async-array-callbacks, no-empty-catch, prefer-early-return, no-magic-numbers, and others addressing LLM anti-patterns.
- Plugin is published on GitHub (pertrai1/eslint-plugin-llm-core) and npm as eslint-plugin-llm-core; installation example provided (npm install -D eslint-plugin-llm-core).
- Plugin is intended to complement typescript-eslint by focusing on observed LLM bug patterns and providing educational error messages.
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Five IDE Rules for Safe AI-Assisted Coding
The article describes five IDE/workflow rules to reduce risks from AI-assisted “vibe coding”: linting, consistent formatting, dependency security audits, privacy-compliance scanning, and testing. It recommends concrete tools and commands (ESLint --fix for TypeScript, Prettier with a sample .prettierrc, npm audit for dependency CVEs, vitest for unit tests) and shows how to integrate rules into AI assistants (Cursor rules, CLAUDE.md for Claude Code). The piece emphasizes privacy as a major blindspot when AI adds analytics, payments or tracking SDKs and promotes PageGuard (npx pageguard) for scanning sites and flagging privacy gaps (example output lists Google Analytics, Stripe.js, Firebase Auth, Sentry). The guidance encourages running these checks automatically inside the AI workflow so generated code ships with linting, formatting, security, compliance and tests applied.
AI-Assisted Code Review Pipeline Catches Skimmed Bugs
This article describes a practical AI-assisted code review pipeline that hands repetitive attention tasks to a Large Language Model (LLM) while preserving human judgment for design and architecture. The recommended design places deterministic gates first (formatter, linter, type checker, secret scanner) and runs an LLM reviewer only on the remaining semantic/intent-level issues. The LLM is scoped to a small list of high-value categories (swallowed errors, missing await, N+1 queries, off-by-one pagination, contradictions with PR intent), instructed to return JSON or remain silent if nothing is found, and kept non-blocking so humans can dismiss false positives. The author provides a GitHub Actions example that gates the AI job behind CI to control token costs and notes that, as of mid-2026, the per-PR cost is on the order of cents. Managed services (GitHub Copilot code review, third-party bots) exist but trade control for maintenance-free operation.
Developer Builds Autonomous Tailwind Linter to Clean AI CSS
Rashad Husanli published a DEV post (May 18, 2026) describing why he built aura-lint, a zero-dependency autonomous Tailwind linter that finds and auto-fixes messy AI-generated CSS. The tool includes an autonomous --fix mode that converts hardcoded pixel classes into standard Tailwind spacing using a division-by-4 formula, a configurable auralint.json for theme detection and hex-to-Tailwind mappings, and a plugin architecture to add project-specific rules. Husanli positions aura-lint as a local gatekeeper to avoid repeatedly sending cleanup prompts to LLMs and to keep UI code aligned with a project's design system. The project is published on GitHub (modoldern/aura-lint) and is presented as extensible and open-source-friendly.
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