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 modeling and honest chain growth analysis. He engineered reusable Jupyter Notebooks and Python modules to simulate and visualize stake distributions, including Pareto and uniform scenarios, and implemented robust inference models for total stake estimation. His work included optimizing convergence simulations for fault-tolerance analysis, validating model behavior across multiple epochs and parameter settings. By enhancing data visualization with Matplotlib and improving documentation, Dzmitry enabled rapid experimentation and knowledge transfer, providing a solid foundation for governance, risk assessment, and ongoing research in blockchain systems.
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 people
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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