In a small AI discussion group, several people debated the pros and cons of a new model. Some said it was leading in image processing, while others felt it was stronger in text generation. In the end, they submitted predictions through a platform, and the group's differences became the starting point for collective intelligence.

The predict function of @recallnet has changed the way AI evaluation is conducted, moving away from relying on a few laboratory tests and instead allowing the user community to directly participate in assessing the performance of prediction models, such as voting on GPT-5's performance across different skills, ensuring diversity and authenticity in evaluations.

The core of this approach lies in the blockchain recording each prediction to prevent manipulation. Users can submit new tasks to collaboratively build benchmarks, avoiding large companies optimizing specific metrics to mislead results.

The unique point is that it shifts AI progress from elite control to mass collaboration, encouraging more people to contribute ideas and driving technology closer to actual needs, rather than remaining at the level of promotion.

Overall, this community-driven model injects new vitality into AI development and may become the standard in the future, helping models truly serve a wide range of users.