Abel Dantas
I'm a software engineer focused on distributed systems and gamification.
Over the past decade, I've founded organizations and led technical teams. I was founding president of CTVC, Portugal's first tech cooperative, an organization I remain part of today. I also served as CTO of a blockchain gaming company for six years. Currently, I'm leading XARP as CEO, building a gamified platform for digital wardrobes and AI-powered try-ons centered on privacy and user control.
I'm pursuing a PhD at University of Porto. My research is on Distributed Ledgers with BFT-CRDTs. I'm also a visiting lecturer at FEUP and IPVC, teaching software engineering courses.
Ventures & Projects
Xarp
Founder & CEO (Nov 2024–Present)
An XR company building a privacy-first digital wardrobe platform that works across retailers, with AI-powered gamified recommendations and virtual try-ons that keep user data local, allowing retailers to offer personalized omnichannel experiences without compliance headaches. Working in partnership with INESC TEC to develop smart mirror (in-store devices) infrastructure.
CTVC
Chair of the Supervisory Board (2025–Present)
President (2021–2025)
Founded a tech cooperative bringing together specialists in a collaborative structure. We function as a platform for R&D collaboration and professional development.
FYX Gaming (formerly Kronoverse)
Co-Founder & CTO (2018–2024)
Helped shape on-chain gaming. Worked on CryptoFights and the Kronoverse platform, building non-custodial systems that hit 10M+ daily mainnet transactions and helped secure $6M in funding. This hands-on experience with distributed systems and high-throughput blockchain architectures directly informed my research on Distributed Ledgers with BFT-CRDTs.
Research & Publications
Peer-Reviewed Papers
Aquascan: Graph-Based Learning for Distributed Marine Sensing (2026, forthcoming)
Abel Dantas
To be presented at OMAE 2026 (International Conference on Ocean, Offshore and Arctic Engineering), June 2026
This paper proposes distributed networks of low-cost drifting sensors and presents a comparative study of heterogeneous graph neural networks (GNNs) versus Kalman filters for predicting marine entity trajectories with intermittent observations. Using the Aquascan simulation framework, experiments show GNNs significantly outperform Kalman filters, maintaining over 95% AUC across all prediction horizons while Kalman filters degrade from 97% to 69%. GNNs' superior performance stems from leveraging network topology and reasoning about non-detections to infer entity presence in coverage gaps.
CRDT-Based Game State Synchronization in Peer-to-Peer VR (2025)
Abel Dantas, Carlos Baquero
In Proceedings of the 12th Workshop on Principles and Practice of Consistency for Distributed Data (PaPoC)
arXiv | ACM DL
This paper presents the first exploration of Conflict-Free Replicated Data Types (CRDTs) within VR contexts, enabling low-latency collaboration in shared virtual environments.
Contact & Links
Viana do Castelo, Portugal