I write when an idea won't leave me alone 🧠 Building AI agents and the tools to build AI agents. Love connecting AI with other fields and yapping about all of them.
The verdict engine is the part I'd want to stress-test hardest, since labeling something unattributed_repost when it was legitimate syndication has real DMCA fallout. Word-overlap plus 5-gram shingling plus LCS is a reasonable ensemble, but I'd want a precision number on the "unattributed" class specifically before letting it act. Did you hold out a labeled set of known-syndicated versus stolen pairs to measure false positives?
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That is an incredibly sharp point. A false positive leading to a wrongful DMCA takedown is the worst-case scenario here.
To answer your question directly: Because this was built within the hackathon timeline, I didn't have the time to curate a formal hold-out dataset of known-syndicated vs. stolen pairs to calculate a strict precision/recall score.
However, OriginTrace mitigates false positives in two major ways:
1. Deterministic Attribution, not ML Classification
The NLP ensemble (word-overlap, shingling, LCS) is only used to calculate the overlapPercent. The actual unattributed_repost label isn't an ML prediction—it's a strict deterministic check. If the overlap is high, the scraper explicitly searches the DOM for <a> tags pointing to dev.to, canonical meta tags, or the original author's name. It defaults to safety: if any of those attribution signals exist, it flags it as a credited_syndication.
2. Human-in-the-Loop
Currently, the agent acts as an assistant, not an autonomous enforcer. It queries Sanity and drafts the DMCA template for you, but it doesn't hook into a mail API to fire it off automatically. The author always has the final review.
That said, your point is exactly what's needed for production. Building a rigorous benchmark dataset of 500 known DEV.to syndications vs. 500 known thefts to stress-test the attribution parser is the absolute top priority for v2!
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The verdict engine is the part I'd want to stress-test hardest, since labeling something unattributed_repost when it was legitimate syndication has real DMCA fallout. Word-overlap plus 5-gram shingling plus LCS is a reasonable ensemble, but I'd want a precision number on the "unattributed" class specifically before letting it act. Did you hold out a labeled set of known-syndicated versus stolen pairs to measure false positives?
That is an incredibly sharp point. A false positive leading to a wrongful DMCA takedown is the worst-case scenario here.
To answer your question directly: Because this was built within the hackathon timeline, I didn't have the time to curate a formal hold-out dataset of known-syndicated vs. stolen pairs to calculate a strict precision/recall score.
However, OriginTrace mitigates false positives in two major ways:
1. Deterministic Attribution, not ML Classification
The NLP ensemble (word-overlap, shingling, LCS) is only used to calculate the
overlapPercent. The actualunattributed_repostlabel isn't an ML prediction—it's a strict deterministic check. If the overlap is high, the scraper explicitly searches the DOM for<a>tags pointing to dev.to, canonical meta tags, or the original author's name. It defaults to safety: if any of those attribution signals exist, it flags it as acredited_syndication.2. Human-in-the-Loop
Currently, the agent acts as an assistant, not an autonomous enforcer. It queries Sanity and drafts the DMCA template for you, but it doesn't hook into a mail API to fire it off automatically. The author always has the final review.
That said, your point is exactly what's needed for production. Building a rigorous benchmark dataset of 500 known DEV.to syndications vs. 500 known thefts to stress-test the attribution parser is the absolute top priority for v2!