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1 year of experience
I’m originally trained in psychology, but in recent years I’ve become deeply interested in AI, web3, and blockchain. I’m especially interested in how agentic systems mirror parts of human cognition, from memory and reasoning to habits, incentives, and decision-making under constraints. Lately, I’ve been exploring and building at the intersection of health, intelligence, and technical systems.

Veliora is a multi-agent biomedical research application, built on Arc and powered by Circle, designed to make disease-focused repurposing analysis more structured, traceable, and economically viable. Today, early-stage biomedical research is often fragmented across literature review, target analysis, pathway interpretation, and manual candidate comparison. This process is slow, expensive, and difficult to audit—especially when teams need to determine whether a signal is genuinely promising or still too weak to advance. Veliora transforms this workflow into a funded, staged research system. A user submits a disease-focused question, funds the job in USDC, and specialized agents perform literature review, candidate screening, pathway analysis, evidence scoring, critical challenge, and report synthesis. Instead of forcing every run into a potentially misleading final answer, the system can return reportable candidates, early-stage hypotheses, pipeline-reviewed signals, or a rejected outcome, depending on evidence quality. What makes the project distinctive is the combination of multi-agent orchestration and a usage-based economic model. Payments are tied to actual task progression rather than flat subscription access, and the final output is a structured research brief with traceable provenance. Veliora is designed for researchers, evaluators, and scientific teams who need a more transparent and reliable way to prioritize evidence-backed signals while preserving reviewability and decision quality.
26 Apr 2026