newton-physics/newton
Jan 2026 – May 2026
4 Months active
Languages Used
Python
Technical Skills
Physics SimulationPythonUnit TestingGPU programmingsimulation developmentsoftware architecture
Vassilios Tsounis
PROFILE
Vassilios Tsounis
Contributed to the newton-physics/newton repository by developing advanced simulation features and architectural improvements over four months. Delivered granular joint constraint customization and scalable multi-world entity management, enhancing simulation realism and maintainability. Integrated the Kamino Physics Solver for constrained rigid multi-body systems, addressing both visualization accuracy and test reliability. Focused on performance optimizations for rigid body and joint handling, including data structure enhancements and collision detection refinements. Improved onboarding by reorganizing example directories for easier access. Worked primarily in Python, leveraging skills in GPU programming, numerical methods, and physics simulation to deliver robust, well-tested solutions that support future extensibility and collaboration.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
10Total
Bugs
2
Commits
10
Features
5
Lines of code
242,453
Activity Months4
Your Network
55 peopleWork History
May 2026
2 Commits • 2 Features
May 1, 2026May 2026 monthly summary for the newton repo focused on Kamino physics engine improvements and example accessibility. Delivered performance optimizations and stability fixes that increase runtime efficiency for rigid body and joint handling, plus a directory reorganization that improves discoverability and onboarding of Kamino examples. The work strengthens simulation reliability, accelerates feature iteration, and reduces onboarding time for new users and engineers.
2 Commits • 2 Features
May 1, 2026May 2026 monthly summary for the newton repo focused on Kamino physics engine improvements and example accessibility. Delivered performance optimizations and stability fixes that increase runtime efficiency for rigid body and joint handling, plus a directory reorganization that improves discoverability and onboarding of Kamino examples. The work strengthens simulation reliability, accelerates feature iteration, and reduces onboarding time for new users and engineers.
May 2026
March 2026
6 Commits • 1 Features
Mar 1, 2026March 2026: Kamino Physics Solver integration progressed the physics platform with targeted reliability fixes and visualization improvements. Delivered a beta-ready solver for constrained rigid multi-body systems, stabilized unit tests in VSCode, and corrected visualization offsets for single-world scenarios. These efforts collectively enhanced system realism, test reliability, and readiness for broader simulations.
March 2026
6 Commits • 1 Features
Mar 1, 2026March 2026: Kamino Physics Solver integration progressed the physics platform with targeted reliability fixes and visualization improvements. Delivered a beta-ready solver for constrained rigid multi-body systems, stabilized unit tests in VSCode, and corrected visualization offsets for single-world scenarios. These efforts collectively enhanced system realism, test reliability, and readiness for broader simulations.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for newton-physics/newton focusing on business value and technical progress. Key features delivered: - Per-world entity start-index management for Model and ModelBuilder implemented, enabling better organization and multi-world entity tracking across simulations. Major bugs fixed: - No major bugs fixed in this period. Focus was on architecture and feature delivery for cross-world indexing. Overall impact and accomplishments: - Established a scalable multi-world indexing capability that improves entity management, debugging, and future feature work for cross-world simulations, aligning with architecture goals and the referenced issue #1438. Technologies/skills demonstrated: - Software design for multi-world architecture, API surface tuning of Model/ModelBuilder, and traceable commits (e.g., ce0f69a8608c78d6dc58ef8f83e5bf50348c4384) to support maintainability and collaboration.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for newton-physics/newton focusing on business value and technical progress. Key features delivered: - Per-world entity start-index management for Model and ModelBuilder implemented, enabling better organization and multi-world entity tracking across simulations. Major bugs fixed: - No major bugs fixed in this period. Focus was on architecture and feature delivery for cross-world indexing. Overall impact and accomplishments: - Established a scalable multi-world indexing capability that improves entity management, debugging, and future feature work for cross-world simulations, aligning with architecture goals and the referenced issue #1438. Technologies/skills demonstrated: - Software design for multi-world architecture, API surface tuning of Model/ModelBuilder, and traceable commits (e.g., ce0f69a8608c78d6dc58ef8f83e5bf50348c4384) to support maintainability and collaboration.
February 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 — Delivered Joint Constraint Attribute Frequency Customization in the newton-physics/newton physics framework, enabling granular control over joint behavior and more realistic simulations. The feature is backed by comprehensive tests to validate correctness and regression safety. No major bugs were fixed this month; primary focus was feature delivery, test coverage, and code quality. Business value includes easier client integration, more accurate simulations, and faster tuning of joint behavior.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 — Delivered Joint Constraint Attribute Frequency Customization in the newton-physics/newton physics framework, enabling granular control over joint behavior and more realistic simulations. The feature is backed by comprehensive tests to validate correctness and regression safety. No major bugs were fixed this month; primary focus was feature delivery, test coverage, and code quality. Business value includes easier client integration, more accurate simulations, and faster tuning of joint behavior.
Activity
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Quality Metrics
Correctness94.0%
Maintainability84.0%
Architecture90.0%
Performance86.0%
AI Usage48.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
3D visualizationData StructuresGPU ProgrammingGPU programmingNumerical MethodsPhysics SimulationPythonSoftware DevelopmentUnit Testingback end developmentdebuggingfront end developmentphysics modelingsimulation developmentsoftware architecture
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