logos-co/nomos-pocs
Jun 2025 – Jun 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
BlockchainData AnalysisData ScienceData SimulationData VisualizationJupyter Notebook
kashepavadan
PROFILE
Kashepavadan
During June 2025, Dzmitry Kashepava developed core simulation frameworks for the logos-co/nomos-pocs repository, focusing on stake concentration and honest chain growth modeling. He engineered reusable Jupyter Notebooks and Python modules to simulate stake distributions and analyze fault-tolerant blockchain growth across multiple epochs. His work included refactoring simulation code, optimizing performance for rapid experimentation, and enhancing data visualization using Matplotlib and NumPy. By validating model convergence and documenting workflows, Dzmitry enabled robust governance and risk assessment for blockchain protocols. The depth of his contributions provided a solid foundation for ongoing experimentation and knowledge transfer within the scientific computing domain.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
2
Lines of code
5,848
Activity Months1
Your Network
8 peopleShared Repositories
8
Work History
June 2025
11 Commits • 2 Features
Jun 1, 2025During June 2025, the nomos-pocs repository advanced two core modeling streams: stake concentration and stake-inference modeling, and honest chain growth with fault-tolerance analysis. Work focused on delivering a robust, reusable simulation framework, enhanced visualization, and validated models across multiple epochs and parameter settings. The results inform governance and risk assessment and provide a solid foundation for ongoing experimentation.
11 Commits • 2 Features
Jun 1, 2025During June 2025, the nomos-pocs repository advanced two core modeling streams: stake concentration and stake-inference modeling, and honest chain growth with fault-tolerance analysis. Work focused on delivering a robust, reusable simulation framework, enhanced visualization, and validated models across multiple epochs and parameter settings. The results inform governance and risk assessment and provide a solid foundation for ongoing experimentation.
June 2025
Activity
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Quality Metrics
Correctness81.8%
Maintainability81.8%
Architecture78.2%
Performance72.8%
AI Usage23.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
BlockchainData AnalysisData ScienceData SimulationData VisualizationJupyter NotebookMachine LearningMathematical ModelingMatplotlibNumPyNumerical AnalysisNumerical SimulationPythonScientific ComputingSimulation
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