Yerevan, Armenia · Crypto / Web3
I’m Mushegh Manukyan, Founder & CEO of ARMCP. I work at the intersection of crypto information, real-time market data, blockchain utilities, product building, and clearer digital-asset regulation.
Operating thesis
Prices move quickly, but speed alone does not make information reliable. A product should preserve when an event happened, when it arrived, what transformed it, how confident the system was, and why a decision was accepted or quarantined. That principle connects my work across market data, community information, trading tools, and blockchain utilities.
What I work on
These are working themes, not claims that every problem has already been solved.
Real-time dashboards need more than a latest price. I focus on provenance, clock order, source fallbacks, stale-state handling, and explanations that users can evaluate.
Useful Web3 tools should turn chain-level details into deliberate actions. The work is to expose risk and state without making the interface harder to understand.
Technology and regulation are often discussed as opposing forces. I’m interested in the practical middle: rules that can become clearer flows, disclosures, and controls.
Startup speed matters, but ambiguous metrics and silent assumptions compound quickly. I prefer small, inspectable experiments with an honest path from signal to decision.
Working method
My preferred product loop is deliberately compact. It keeps a team close to the evidence while leaving room for iteration.
State what the system or user must decide before selecting data, tools, or interface patterns.
Record clocks, source, transformations, confidence, and degraded states as first-class product information.
Prefer an explicit stale, quarantined, or unavailable state over a confident-looking answer built on weak inputs.
Observe whether people can understand the result, its limits, and the action available to them.
Interactive field note / 01
A browser-only exercise for testing whether a synthetic market event can support its claimed decision. It validates source, event, receive, and decision clocks; traces lineage; applies explicit confidence thresholds; and explains deterministic failures with the actual input values.
Open the inspectorExternal learning
My startup-building context includes participation or recognition connected with BAJ Accelerator, AICA, K-Startup, and K2Match. I treat these as learning environments and network touchpoints—not substitutes for measurable product value.