The liquidity doesn’t come from nowhere - it comes directly from $BKRK. That’s a deliberate engineering choice. I’m building the system so that the main $BKRK liquidity acts as the source that bootstraps new tokens on the platform. When a token is launched, it can immediately trade because that liquidity is already there, coming from the core. That’s what allows volume to exist from the start. From that activity, 10% of the volume generated by each token flows back into $BKRK, reinforcing the same pool that made trading possible in the first place. It’s a closed loop by design: $BKRK provides liquidity, liquidity enables trading, trading creates volume, and part of that volume strengthens $BKRK again. That’s the engineering process I’m working on.

I’m currently working on this system, and the core idea behind it is simple. When a token is launched on the platform, the protocol provides the liquidity. That liquidity is what makes trading possible in the first place. Without it, there are no buys, no sells, no volume. By putting liquidity in and locking it, the system takes on that role from the start. Because trading happens thanks to that liquidity, value is naturally generated through volume and fees. The 10% exists because of that process — it’s the system’s share for enabling and sustaining the market. I’m actively building and refining this logic so that every launched token contributes back to $BKRK, strengthening the core that powers the ecosystem.

Over the last 12 hours, I’ve been working on the core system architecture and the fee-routing logic that powers BlackRock. The design operates at the protocol level by default. When a token is created, volume and fees are tracked continuously by the system, and 10% of the generated value is programmatically calculated and routed into $BKRK liquidity. This flow runs without manual control, admin keys, or external intervention. The routing, accounting, and execution logic are embedded into the system itself, so every token feeds back into BlackRock automatically as real activity happens. The current focus is validating execution timing, edge cases, and consistency under live volume to ensure deterministic behavior.