Show HN: CLI tool for detecting non-exact code duplication with embedding models
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Re: Show HN: CLI tool for detecting non-exact code duplication with embedding models
#2It finds similar-looking code with embeddings. This detects more than just copy-paste clones or even clones with minor changes. Similar code is often not a clone to refactor, and this is a trade-off. Initial results need to be verified, but coding agents can do this quickly. Example prompts are available on https://slopo.dev
Additionally, similar code distant in the codebase is ranked higher to focus on less obvious duplication.
The results differ a lot depending on the codebase. I noticed that sometimes most of the detected duplicates are false positives, but the remaining ones are strong candidates to refactor or even bugs. Sometimes it reveals much more real duplication.
Re: Show HN: CLI tool for detecting non-exact code duplication with embedding models
#3I built Slopo to solve one specific problem: finding similar code that is hardest to detect by other tools, coding AI agents, and humans. It finds similar-looking code with embeddings. This detects more than just copy-paste clones or even clones with minor changes. Similar code is often not a clone to refactor, and this is a trade-off. Initial results need to be verified, but coding agents can do this quickly. Exampl…
Re: Show HN: CLI tool for detecting non-exact code duplication with embedding models
#4I built Slopo to solve one specific problem: finding similar code that is hardest to detect by other tools, coding AI agents, and humans. It finds similar-looking code with embeddings. This detects more than just copy-paste clones or even clones with minor changes. Similar code is often not a clone to refactor, and this is a trade-off. Initial results need to be verified, but coding agents can do this quickly. Exampl…
If it did PHP I would love to run it over WordPress. What would it take to add that?
Re: Show HN: CLI tool for detecting non-exact code duplication with embedding models
#5How do you handle chunking and parsing for different languages to make sure the embeddings capture semantic meaning effectively? For instance, do you chunk by functions/classes, or use a fixed token window? If a function is too long or too short, it can drastically skew the embedding similarity.