The AI-native platform DINQ has officially launched, aiming to establish a new standard for evaluating tech talent by integrating multidimensional data such as academic papers and open-source code contributions. This platform not only analyzes the quality of researchers' published papers, conference levels, and author identity weights, but also incorporates GitHub code contributions into the evaluation system to quantify engineering impact. Its core functions include mapping personal 'technical fingerprints', assessing career development trajectories, and providing salary estimates based on data models, helping recruiters identify undervalued talent, assisting investors in conducting technical due diligence, and offering career development references for practitioners. The DINQ team stated that the platform is committed to addressing the issue of information asymmetry in the current talent evaluation system and promoting the establishment of a fairer value measurement standard.