The composition of genius is complex, but with the right lens, we will eventually be able to see its full picture clearly.

Author: AI Will

In the rapidly evolving field of artificial intelligence and cutting-edge technology, we often say that talent is the most critical asset. It is everything for innovation, the starting and ending point of success. However, despite the significant progress we have made in building complex system models, the tools we use to understand and evaluate the most outstanding minds still seem astonishingly primitive.

For decades, we have relied on some rough proxy indicators: the number of paper citations, H-index, years of experience, the reputation of alma mater, and so on. These indicators are not without use, but they are one-dimensional. It is like trying to appreciate a grand symphony by only listening to the violin part; we can only capture a small part of the story while missing the harmonious rhythm, rich tempo, and the true soul of a contributor.

At the same time, the nature of innovation itself has evolved. A groundbreaking idea may now manifest in a “Pull Request” on GitHub, a popular open-source code repository, or a series of rapidly iterated, impactful papers presented at top conferences—far earlier than it solidifies into a patent or a widely cited journal article. How should we measure the impact of an engineer as a silent pillar of a large framework? How do we distinguish a researcher who merely publishes incremental results from another scholar who continuously aims for and successfully establishes themselves at the most competitive, defining conferences like NeurIPS or CVPR?

This significant gap—existing between how value is created and how value is measured—is one of the main bottlenecks in our industry. It affects how companies recruit, how investors conduct technical due diligence, and more importantly, it influences every practitioner's perception of their self-worth and career planning.

This is precisely why I am deeply attracted to a project and am fortunate to serve as its advisor: DINQ ( http:// dinq.io ).

I chose to participate in the DINQ project because it is not just another recruitment tool. It is an ambitious attempt to build a richer and deeper new language for understanding technology and research talent. It is an AI-native platform dedicated to bringing unprecedented clarity and depth to talent analysis through a simple Google Scholar or GitHub link.

The functions and significance of DINQ can be summarized in the following core principles, which I firmly believe represent its value:

1. A leap from “quantity” to “quality and context.”

DINQ does not just count the number of papers; it delves deeper into their context. It analyzes researchers' publication records through the prestige ranking of conferences, clearly distinguishing between a workshop paper and an oral presentation at a top conference. It weighs the importance of first authorship and depicts a complete career development trajectory based on the evolution of their work focus. It provides a detailed “technical fingerprint” of an individual's professional capabilities.

2. Treating “code” as an equally important “first-class citizen” as papers.

DINQ profoundly understands that modern innovation is not confined to PDF documents. Through integration with GitHub, it begins to quantify the impact of engineering contributions—the complexity of projects, the significance of codebases, and an individual's influence in the open-source ecosystem. It treats code as a body of work worthy of rigorous analysis, just like academic papers.

3. Confronting the core issue of “value.”

Perhaps DINQ's boldest and most striking feature is its model-based salary estimation. This is a highly challenging issue, yet one that must be addressed. By integrating dozens of data points from academic achievements to engineering impact, DINQ provides a data-driven benchmark for market value. This is not intended to reduce a person to a number but to initiate a more symmetrical and fair dialogue about individual value.

This is a tool designed for the following groups:

Recruiters and hiring managers, helping you ultimately find the true signal amidst the noise of information, identifying exceptional candidates whose resumes may not fully showcase their talents.

Venture capitalists and investors, empowering you to conduct a new kind of technical due diligence, gaining data-supported, deeper confidence in the technical depth of the teams you invest in.

Researchers and engineers, providing you with a holistic perspective to examine your professional identity, helping you measure your progress and articulate your value more effectively.

I believe we are at the beginning of a significant transformation in how we perceive “human capital.” The team behind DINQ is one of the most focused and thoughtful teams I have had the privilege to work with, and they are steadfastly committed to this mission.

I encourage the creators, investors, and thinkers in my network to explore DINQ for themselves. Use it to understand the industry landscape, discover those hidden treasures, and perhaps gain a new perspective on your own exceptional career journey.

This is not just a product; it is an important step toward the future. In this future, every contribution is seen, every talent is understood, and everyone's potential can be realized more fully and fairly than ever before.

The composition of genius is complex, but with the right lens, we will eventually be able to see its full picture clearly.

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