Layer3 Signal: Real Attention Metric Based on on-chain Behavior

The pursuit of hype in the Web3 ecosystem has never ceased. Whether a new protocol suddenly becomes popular or an entire sector turns into an investment hotspot, "everyone is talking about it" often serves as the reason for participation. However, the problem lies in the fact that the volume of discussions does not equate to actual appeal; the truly valuable attention should be reflected on-chain.

The truly meaningful heat is not just about who is discussing it, but more importantly, how many people take actual action after the discussion — opening wallets, completing tasks, interacting with protocols. This is exactly the intention behind the launch of Signal by a certain company: an on-chain attention metric designed specifically for Web3, helping users identify which protocols are gaining real user participation and action, rather than just remaining as topics in chat rooms.

Signal: Real-time metrics of on-chain attention

In mid-July, a new product called Signal was officially launched. It is not just a tool, but a way to re-understand the Web3 world — it does not focus on who is discussing the most, but rather on who is truly engaging users to take action on-chain.

Signal claims to be the "on-chain correlation index," aiming to capture whether a protocol or project truly attracts on-chain participants. It does not look at who shouts the loudest or has the most interactions, but focuses on: Have people taken actual actions on-chain? What tasks have been completed? Are they willing to pay Gas fees for these interactions or use real identities?

For an already highly gamified on-chain world, this shift in perspective may be a necessary step.

The crystallization of three years of data accumulation

The launch of Signal is not starting from scratch, but is built on a large amount of data accumulated over the past three years. As one of the most active on-chain task platforms at present, the platform has:

• 3 million active users' real interaction records
• Over 200 million task completion actions
• 60 million verifiable credentials
• Covers the tracking scope of more than 40 public chain ecosystems

These data are not just cold, hard numbers; they are also traces of participation filled with meaning. For example, Signal can identify that even if a task has been completed 10,000 times, if only 100 people participated, with each person repeating the operation 100 times, then this "popularity" is actually questionable. This kind of insight was not achievable in the past relying solely on community data.

Calculation Method of Signal: Focus on the Authenticity of Behavior

Signal is not just a simple ranking; it is backed by a set of indicators that focus on "who did what and why."

Its main scoring logic takes into account:

• Proportion of independent participants
• Task conversion rate and engagement behavior depth
• The complexity of the task or interaction behavior (is it just a single click? Does it involve on-chain signatures or transfers?)

In other words, if a protocol can attract a large number of real addresses to engage in meaningful actions (such as staking, voting, minting, etc.), its Signal value will relatively increase.

This allows Signal to serve as a proxy metric for product attractiveness and genuine traction, rather than a bubble under marketing activities.

Difference from Other Popularity Tracking Tools

In recent years, many products have emerged in the market aimed at helping users grasp "what the crypto world is focusing on", ranging from narrative-focused information aggregation projects to traditional on-chain data analysis tools. Although these tools seem to be engaged in "trend tracking", their starting points and handling methods are actually quite different.

Information aggregation products mainly focus on what the community is discussing and how public opinion is changing. Their value proposition is: there is too much information, and users need a more efficient summarization system to organize discussions, news, and sentiment trends. These tools mostly rely on AI, keyword and source aggregation, and belong to the "editorial layer of narrative perspective".

On-chain data analysis tools are centered around open queries. They provide a complete set of on-chain databases and query languages, allowing users to set their own conditions and pull the data they care about. These tools emphasize flexibility and transparency, but also impose a higher technical barrier on users.

The starting point of Signal is that there are actually a lot of real participation behavior signals hidden on-chain, but no one is helping you sort out the key points.

Signal does not require you to input query conditions, but instead directly tells you: how many people are genuinely participating in this protocol right now, what interactions have been completed, and the proportion of overall attention it occupies. It is a behavior-based real-time scoring system, not a data querying tool, nor an information aggregator.

We can understand the division of labor among the three as follows:

• Information aggregation products help you understand "what everyone is talking about"
• Data analysis tools help you check "What do you want to know"
• Signal actively tells you "what is really happening right now"

There is no best among these three types of tools; rather, they correspond to different information needs and decision-making scenarios in Web3. You can first use an information aggregation platform to understand the current mainstream narratives, then use data tools to verify the details, and finally return to Signal to determine whether there is truly attractiveness, behavior, and momentum behind these narratives.

What Signal wants to add is that aspect of observation, which has long been overlooked beyond the noise and charts: the real on-chain traction.

Future Development: Predictions, APIs, and Commercialization Models

The development team also admitted that Signal will not always offer a free version. A paid plan is expected to be launched in the next few months, which is anticipated to include:

• Real-time trend prediction and potential hot product protocol alert
• On-chain Task and Activity Efficiency Analysis
• API integration feature for the data team to establish custom strategies with the fund.
• Custom Dashboard and Notification Module

For a product that already has 1.1 million active users, such an extension is natural and also means that the development team will position Signal as one of the core engines for its next stage of growth and commercialization.

Conclusion: Redefining the Metrics of Attention

The emergence of Signal is a correction to the past methods of assessing on-chain activity. It does not aim to replace the volume models of social media and community platforms, but rather provides a measurement scale that is closer to the essence of behavior.

While everyone is focused on who is the most eye-catching, Signal chooses to observe who has actually taken action. This perspective may be the starting point for the next wave of cryptocurrency product design and research.