The First Tool: Trust Army & The Birth of Research2Earn

The Hidden Data: What Analysts Couldn’t Find

Before Trust Army, there were plenty of tools, dashboards, and data analytics platforms in Web3. Analysts had access to tokenomics sheets, explorer bots, and databases of on-chain metrics. But the problem wasn’t availability — it was understanding.

There was no clear, public methodology that showed users what really matters when evaluating a crypto project. No one explained what to look for, why it mattered, or how to cross-reference it all. The community was flooded with tools — but lacked guidance.

And even if you had the tools, you had to navigate closed ecosystems, contradictory data, or isolated sources. There was no reliable way to connect the dots. There was no framework to teach how and why this data could protect users and support better decisions.

From Theory to Action: Launching the First Research2Earn Model

We didn’t start Trust Army to play into airdrop culture. We started it because we believe in people. We believe that if you show them how to filter signals from noise, they’ll build better projects, better communities, and a safer Web3.

Our mission wasn’t to rethink incentives — it was to unlock responsibility. To teach people how to ask the right questions. Research2Earn wasn’t about “earn for filling a form.” It was about growing a network of valuable participants — the kind any serious founder wants in their community.

Yes, the skepticism was real. “Nobody’s going to do real research for tokens.” We heard that more than once. And early on, they weren’t completely wrong. Even with our carefully developed methodology, the first waves of participants treated the process like a formality. Submit basic data. Grab some $HAI. Move on.

But we kept going. Because we believed the system would adapt. That given the chance, people would choose to understand. And they did.

Trust Army: How We Turned Research into an Earnable Skill

Trust Army wasn’t launched as a final product — it was the first tool. An unfinished but functional prototype of what Research2Earn could become.

In the MVP, users moved through a Multi-Stage Academy. Each stage introduced concepts, validated knowledge through real tasks, and offered rewards for accuracy. The most reliable users turned into validators. The best of them still work alongside the Hacken Team, supporting deep-dive research and threat detection.

Over 4,000 users joined. Over 90,000 reports submitted. But what mattered wasn’t quantity. It was intent. Only 35% came to earn — 65% came to learn, build, and contribute.

The real innovation was in the process. Validation loops. Layered expertise. Incentives tuned to both accuracy and depth. Tasks split by difficulty. Error-rate-driven leveling systems. Validators emerging from the ranks.

And behind those numbers was something more important: a shift in mindset. A core group of people began digging deeper, asking sharper questions, and even proposing their own tweaks to the methodology. That was the moment we knew: Research2Earn could be more than a mechanic — it could be a mindset.

They didn’t earn prizes or collectibles — they earned trust. Yes, they received $HAI for accurate contributions, but what truly defined the experience was their transformation. Some became essential to Hacken’s core research operations. Others helped shape what would eventually become DYOR Alpha Intelligence — a subcommunity we’ll speak more about later. But it all started here.

The MVP Phase: The First Steps into a New Kind of DYOR

The MVP proved something critical: that research can be decentralized without being chaotic. By combining human insight, structured tasks, and layered validation, Trust Army became more than an experiment. It became a live system of education, evaluation, and contribution.

Every report became part of a growing database. Fragments from different researchers were validated, cross-matched, and aggregated into one coherent truth. We didn’t chase volume — we chased alignment. Less noise. More signal.

The Academy wasn’t about memorizing — it was about learning through doing. From first form to final chapter, participants became researchers. Not analysts. Not speculators. But value-generating nodes in a smarter Web3.

The First Wins: Discovering the Real Value of Community-Powered Research

The biggest win wasn’t the metrics — it was the reaction.

Founders started reaching out. Projects wanted access. API requests started to come in even before the system was finalized. Trust Army wasn’t just “good for onboarding” — it became a trusted funnel to identify high-signal humans. The kind who can spot fraud. Or find critical bugs. Or just ask the question nobody else thought to ask.

We realized that what we were really building is more than just another research machine. It was a reputation layer. A future leaderboard. A validation funnel soon to be upgraded by AI. A source of Digital Profiles that would finally show who’s real in Web3 — and what they’re really good at.

This isn’t finished. The full Trust Army system is still evolving. But even now, in its partial form, it’s already proven one thing:

The next generation of Web3 contributors won’t be lurkers. They’ll be researchers. And they’ll be ready.