Orix goes beyond traditional linters. While standard scanners focus on syntax and known CVEs, Orix introduces the "AI Slop Analyzer." With the rise of AI coding assistants, codebases are filling with unreviewed LLM output—unimplemented TODOs, AI preambles ("As an AI..."), placeholder variables, and over-explained comments. Orix detects these lazy, low-effort patterns and AI-generated technical debt before they are merged, ensuring your codebase remains high-quality and human-readable.

Customer.io — Automate Messaging Everywhere — Startups Get 12 Months Free

Automate Messaging Everywhere — Startups Get 12 Months Free

Hey everyone! 👋🏼 I'm thrilled to launch Orix today. With the explosion of AI coding assistants, we noticed a new kind of technical debt creeping into our pull requests: "AI Slop". Developers are moving faster than ever, but sometimes unreviewed LLM outputs slip through the cracks, think generic placeholder variables (data2, foo), empty catch blocks, AI preambles ("As an AI language model..."), and excessive "step-by-step" narrative comments. Standard linters catch syntax errors, and security scanners catch known vulnerabilities, but nothing was catching these lazy, low-effort patterns. That’s why we built Orix. Alongside standard code quality and security checks, Orix features a dedicated AI Slop Analyzer that detects: - Unreviewed AI preambles and boilerplate - Unimplemented TODOs and empty stubs - Placeholder variable and function names - Over-explained, high-density AI comments I’d love to hear your thoughts! Have you noticed "AI slop" in your repos lately? What other lazy code patterns should Orix look out for? I'll be here all day to answer your questions. Looking forward to your feedback! 🚀

Customer.io — Automate Messaging Everywhere — Startups Get 12 Months Free

Automate Messaging Everywhere — Startups Get 12 Months Free

Orix - Code Quality & Security Scanner

Code Quality & Security Scanner

Code Quality & Security Scanner