Explore our latest articles on AI data annotation, crowdsourcing, and content verification. Stay informed about the trends shaping the future of machine learning and media integrity.
Our first recurring job scheduler, why drift detection compares a schedule against its own history instead of a fixed threshold, and the one honest limitation we shipped with rather than wait to solve.
AI Result Evaluation used to only work on Crowdee's own pipeline runs. Now it works on anything with a verdict and an explanation — including your own model — and we didn't need a new table to do it.
A confidence score tells you how sure the model is, not whether its explanation actually makes sense to a person. Here's how we built a crowd panel to rate that, and where the ratings go afterward.
Why authenticity and provenance are separate questions, how a new web-research stage type lets pipelines search the live web instead of reasoning from training data, and what verify-source-credibility actually returns.
Why 'manipulated' and 'synthetic' are different questions, which pipelines now score synthetic likelihood, and how we fixed a prompt bug that was silently capping what our own models could report.
Why open-ended discovery needed a different data model than our verification pipelines, how deduplication works, and how gathered content flows straight into verification.
