Show HN: AISlop, a CLI for catching AI generated code smells
Catches specific patterns (e.g., empty catch blocks, useless comments, duplicated helpers, dead code) in AI-generated code that pass syntax checks and tests but indicate poor quality. Operates locally without code transfer.
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Catches specific patterns (e.g., empty catch blocks, useless comments, duplicated helpers, dead code) in AI-generated code that pass syntax checks and tests but indicate poor quality. Operates locally without code transfer.
The proliferation of AI code generation tools introduces new quality control challenges. AISlop directly addresses the emerging developer pain point of 'AI-generated code smells' – syntactically correct but functionally suboptimal or inefficient code. This tool is critical for maintaining code quality and reducing technical debt in development workflows increasingly reliant on AI assistants. For B2B SaaS, this represents a nascent but growing market for AI-specific code quality and auditing tools. Integration into CI/CD pipelines or IDEs as a pre-commit hook offers significant value, ensuring AI-generated code adheres to organizational standards. The emphasis on local operation and no code transfer is a strong security and compliance selling point, crucial for enterprise adoption. This reflects a market trend towards specialized tooling to manage the outputs and implications of generative AI.
Hi, I’m Kenny, I’ve been building aislop. I starting working on this after using Claude Code, codex and opencode several times and noticing some slops. They aren’t syntax and passes most tests, they are patterns like empty catch blocks, useless comments, duplicated helpers, dead code and many more. So I built a tool to scan and check for these patterns and wired it into hooks so after each tool call, the agent checks for the slops.You can try it out with npx aislop scan.It’s all local and no code is transferred. Thank you.
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What is AISlop, a CLI for catching AI generated code smells?
AISlop, a CLI for catching AI generated code smells is analyzed by our AI as: Catches specific patterns (e.g., empty catch blocks, useless comments, duplicated helpers, dead code) in AI-generated code that pass syntax checks and tests but indicate poor quality. Operates locally without code transfer.. It focuses on The proliferation of AI code generation tools introduces new quality control challenges. AISlop directly addresses the emerging developer pain poin...
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Data for AISlop, a CLI for catching AI generated code smells was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was AISlop, a CLI for catching AI generated code smells publicly launched?
The initial public indexing or launch date for AISlop, a CLI for catching AI generated code smells within our tracked developer communities was recorded on May 29, 2026.
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AISlop, a CLI for catching AI generated code smells has achieved measurable traction, logging over 62 traction score and facilitating 51 recorded discussions or engagements.
Which technical categories define AISlop, a CLI for catching AI generated code smells?
Based on metadata extraction, AISlop, a CLI for catching AI generated code smells is categorized under topics such as: CLI, AI generated code, code smells, empty catch blocks.
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What are some commercial alternatives to AISlop, a CLI for catching AI generated code smells?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as PayCan, which offers overlapping value propositions.
How does the creator describe AISlop, a CLI for catching AI generated code smells?
The original author or development team describes the product as follows: "Hi, I’m Kenny, I’ve been building aislop. I starting working on this after using Claude Code, codex and opencode several times and noticing some slops. They aren’t syntax and passes most tests, the..."
Community Voice & Feedback
I’m eager to test this out. I have agent instructions to try to limit the worst of this already, but patterns still sneak through. I have a review agent run after every single edit looking for all of the following if you need more ideas for checks:- DRY principle violations, multiple definitions of the same helpers or utilities.- Changes that deviate from existing patterns and architecture already in the code, especially in nearby and related code- Comments that add no context or simply restate the field name.- Naming violations (enterprise factoryfactoryabstraction stuff, excessively long names, overly technical names, banned words like “seam”, “durable”, and no-value-qualifiers like “SaveGame” -> “Save”).- Tests that check implementations instead of correct business behavior.- Overly backwards-compatible unless asked for (this one is incredibly hard to keep under control, as AI loves to guard everything even if the previous code was never deployed and thus there is no contract break)- Un-necessary guard code (this is hard to control, most common case is the AI not relying on the serializer error handler and instead adding guards that the library already handles)- Changing public API contracts without express permission to do so (depends on the code, eg a library JAR or versioned REST service)- Meta references to previous code versions, to tasks or todos, or to instructions and other non-code context (e.g you tell the AI the adder should ignore negative numbers and that meta fact enters the comments or code)I usually hand review all changes myself but it’s incredibly tedious so I try to first pass with the review agent until it comes back clean. I hate wasting tokens on it though.
This is a great idea. Even if you're one of those developers squarely focused on getting the final result working, code quality still matters (to people and LLMs).Everyone should be doing regular code reviews and this helps a lot.
I was about to write what advantage it has over linters but then saw the built on section. Good work. We use megalinter with our flavour of go and vite rules, plus extensive e2e testing after each agent run. Quality of the spec driven agentic PRs are significantly better than the baseline. Megalinter is quite resource heavy and slow, so will definitely check this out
Gave it a try but there were a lot of false positives. SQLModel's exec method for example gets flagged every time thinking it's python's exec() function.
For anyone who wants something like this for Elixir, there is an open source hex package: https://hex.pm/packages/ex_slop
I don’t see if this is one of the covered cases, but one of the more common and nefarious patterns I run into is what you might call "sweeping exceptions under the rug." I think the agent’s motivation to get things running encourages these antipatterns of designing routines that are fault tolerant in a sort of maladaptive way: e.g. catching an error, logging a warning that something didn’t work, and continuing, but with now potentially missing/broken state.This has bitten me a couple of times, and it’s surprisingly annoying to nudge agents into good/resilient patterns or identify situations that should fail loudly, at least in my experience. The retry mechanisms they come up with on their own are often pretty terrible as well.I’ll note, though, that I have seen this from human engineers plenty of times, and at least the AI usually adds some logs rather than just totally silently absorbing an exception!
Petition to rename this “SlopCop”
I think a lot of the telltale signs of AI can be found in the comments. Besides the slop writing style, I've found AI comments to 1. be overly verbose, 2. unnecessarily describe before/after code state (# This function used to do foo, but now it does bar), and 3. reference its own internal "plan" (# This function is part of Stage 3 of the implementation of Use Case X from the requirements doc) WTF is Stage 3? - says code reader 2 years from now. Although I bet you can probably prompt these behaviors away.
Apparently I need to check in with a Doctor because code written by myself is seen as AI, and the lazy AI bits aren't. More Human than Human?
A linter with rules for AI-specific weirdness is absolutely a great idea, thank you! Are there any plans to support other languages besides javascript?
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