No Shouting—Layer3 Signal: The Real On-Chain Attention Metric

The obsession with "hype" in the Web3 world has never stopped. Whether it's a sudden rise in popularity of a new protocol or an entire sector (like InfoFi) becoming the focus of capital attention, often the phrase "everyone is talking about it" becomes the reason to enter the market. But the problem is: volume ≠ pull power, mindshare should not only exist in the community, but should be visible on-chain. The real value of hype is not just who is talking, but how many people actually took action after the discussion — opened their wallets, completed tasks, and interacted with protocols. This is exactly why Layer3 launched Signal: an on-chain attention metric designed for Web3, helping you identify which protocols are gaining real user engagement and action, rather than just existing as topics in chat rooms.

Layer3 Launches Signal: Real-time Metrics of on-chain Attention

In mid-July, Layer3 officially launched its new product Signal. Rather than being a tool, it is more of a way to rethink the Web3 world — it doesn't matter how loudly you talk, but whether you have truly attracted users to "do something" on-chain. Signal claims to be the "onchain relevance index", aiming to capture whether a protocol or project has truly attracted on-chain participants. It's not just about who shouts the loudest or has the most interactions, but rather: have people "acted" on-chain? What have they accomplished? Are they willing to pay Gas or provide a real identity for these interactions? This perspective shift may be a necessary step for an already highly gamified on-chain world.

is not starting from scratch, but rather a refinement of three years of data accumulation.

The launch of Signal is not starting from scratch, but is built on the data foundation accumulated over the past three years with Layer3. As one of the most active on-chain task platforms currently, Layer3 has:

• Real interaction records of 3 million active users.
• Over 200 million task completion actions
• 60 million verifiable credentials
• Tracking across 40+ on-chain ecosystems

These data are not just cold, hard figures on a balance sheet, but rather filled with meaningful traces of participation. For example: Signal can recognize a task that has been completed 10,000 times, but if there are only 100 participants, each repeating the operation 100 times, then this "popularity" is actually worth questioning.

Such a judgment could not be made in the past relying solely on community data.

How to calculate Signal? It's not the completion number, but the authenticity of behavior.

Signal is not just a simple leaderboard; it has a set of metrics behind it that focus on "who did what and why." Its main scoring logic integrates:

• Proportion of independent participants
• Task conversion rate and the depth of initiation behavior
• The complexity of tasks or interactions (Is it just a single click? Does it include 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 stake, vote, mint, etc.), then its Signal value will relatively increase. This allows Signal to serve as a proxy for product traction and real traction, rather than a bubble under marketing activities.

They are both about "watching the trends", but what are we watching? - The fundamental differences between InfoFi, on-chain data tools and Layer3 Signal.

In recent years, there have been many products on the market aimed at helping users grasp "what the crypto world is paying attention to," ranging from InfoFi-style projects that focus on narrative integration to traditional on-chain data analysis tools. Although these tools may seem to be doing "hype tracking," their starting points and processing methods are actually quite different.

Products in the InfoFi track focus on understanding 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 digest system to organize discussions, news, and sentiment trends. Most of these tools rely on AI, keyword and source aggregation, belonging 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 threshold 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 helps you organize the key points.

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

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

• InfoFi products help you understand "what everyone is talking about".
• The data analysis tool helps you check "What do you want to know"
• Signal actively tells you "what is really happening right now"

There is no single best tool among these three categories; rather, they correspond to different information needs and decision-making scenarios in Web3. You can first use information aggregation platforms to understand the current mainstream narratives, then verify the details with data tools, and finally return to Signal to determine whether there is really traction, behavior, and momentum behind these narratives. Signal aims to supplement the long-ignored observation dimension beyond noise and charts: the real on-chain traction.

Next step: Prediction, API and commercialization model

Layer3 does not shy away from the fact that Signal will not always have a free version. A Premium Plan is expected to be launched in the coming months, which will include:

• Real-time trend prediction and potential hit protocol warning
• On-chain task and activity performance analysis (campaign benchmarking)
• API access functionality 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 Layer3 will take Signal as one of the core engines for its next stage of growth and commercialization.

Conclusion: Attention itself is also worth being measured more fairly.

The emergence of Signal is a correction to the past methods of judging on-chain activity. It does not aim to replace the volume models of Twitter, Discord, or Notion, but rather provides a measure that is closer to the essence of behavior. While everyone is busy arguing about who is the loudest, Layer3 chooses to observe who is really taking action. This perspective may very well be the starting point for the next wave of crypto product design and research.

Signal is now open for experience: