In the rapidly evolving fields of artificial intelligence and cutting-edge technology, it is often said that talent is the most critical asset. It is the essence of innovation, the starting and ending point of success. However, despite the tremendous progress we have made in building complex system models, the tools we use to understand and evaluate those most outstanding minds still seem remarkably primitive.

For decades, we have relied on some rough proxy indicators: the number of paper citations, the H-index, years of experience, the reputation of the alma mater, and so on. These indicators are not without value, but they are one-dimensional. It’s 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 rhythms, rich tempos, and the true soul of a contributor.

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

This huge gap—existing between how value is created and how value is measured—is one of the main bottlenecks in our industry. It affects the way companies recruit, the technical due diligence of investors, and more importantly, it impacts every practitioner’s understanding of their own value and career planning.

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

I choose 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. This 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 importance of DINQ can be summarized into the following core principles, which is also where I firmly believe its value lies:

1. The transition from “quantity” to “quality and background.”

DINQ is not just about counting the number of papers; it delves deeper into their context. It analyzes researchers’ publication records through the prestige level of conferences, clearly distinguishing between a workshop paper and a 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. What it provides is a detailed “technical fingerprint” of an individual’s professional capabilities.

2. Treat “code” as a first-class citizen equally important as the paper.

DINQ deeply understands that modern innovation is not limited to PDF documents. By integrating with GitHub, it begins to quantify the impact of engineering contributions — the complexity of projects, the importance of codebases, and the influence of individuals in the open-source ecosystem. It regards code as a body of work worthy of analysis with the same rigor as academic papers.

3. Confront the core issue of “value.”

Perhaps the boldest and most eye-catching feature of DINQ is its model-based salary estimation. This is a highly challenging issue, but it is also a problem that must be solved. By integrating dozens of data points from academic achievements to engineering impacts, 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 of people:

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

Venture capitalists and investors, empower yourselves to conduct a new kind of technical due diligence, gaining data support for a deeper level of 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 assess your progress and articulate your value more effectively.

I believe we are at the beginning of a significant transformation in how we understand “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 dedicated to this mission.

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

This is not just a product; it is an important step towards 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 as long as we have the right lens, we will eventually be able to see its entirety clearly.

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