Upcoming Trainee Engineer @ Simform 🚀
Building, experimenting, and writing my way through tech.
AI • Web • Open Source • Challenges
If I find something interesting, I'll probably write about it. 😄
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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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!