tenstorrent/tt-metal
Sep 2025 – Oct 2025
2 Months active
Languages Used
PythonC++
Technical Skills
Machine LearningPython ProgrammingTensor OperationsC++ developmentCompiler error resolutionDebugging
Maxim Artemov
PROFILE
Maxim Artemov
Over a two-month period, contributed to the tenstorrent/tt-metal repository by delivering both a key feature and a critical bug fix. Refactored the ttnn tutorial to remove its dependency on PyTorch by replacing torch.rand with ttnn.rand, thereby improving library independence and tutorial portability. Addressed a GCC 12 build error by updating a range-for loop to use a const reference, which stabilized builds and enhanced CI reliability across compilers. Demonstrated proficiency in C++ development, Python programming, and debugging, with a focus on dependency management, code maintainability, and cross-compiler compatibility to support smoother downstream integration and onboarding.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
4,188
Activity Months2
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Work History
October 2025
1 Commits
Oct 1, 2025Monthly summary for 2025-10: Focused on stabilizing builds for GCC 12 compatibility in tenstorrent/tt-metal. The key delivery is a build-stability fix that uses a const reference in a range-for loop to prevent element copying, addressing a GCC 12 build error. This work improves CI reliability and cross-compiler parity, reducing risk in downstream integrations and enabling smoother shipping. Technologies demonstrated include C++ range-for semantics, const-correctness, debugging, and patch-level collaboration (commit c0f306ffd9f7282bf580b6217d0fdeaad39d3ea1).
1 Commits
Oct 1, 2025Monthly summary for 2025-10: Focused on stabilizing builds for GCC 12 compatibility in tenstorrent/tt-metal. The key delivery is a build-stability fix that uses a const reference in a range-for loop to prevent element copying, addressing a GCC 12 build error. This work improves CI reliability and cross-compiler parity, reducing risk in downstream integrations and enabling smoother shipping. Technologies demonstrated include C++ range-for semantics, const-correctness, debugging, and patch-level collaboration (commit c0f306ffd9f7282bf580b6217d0fdeaad39d3ea1).
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 20252025-09 TT-Metal: Key feature delivery focused on decoupling PyTorch dependency in the ttnn tutorial by replacing torch.rand with ttnn.rand, improving library independence and compatibility. Feature delivered: Tutorial: Remove PyTorch dependency by replacing torch.rand with ttnn.rand. Commits: 0c4639ac259782d46f22e5d89718dbe295d17116; 9a88b2460d98adcc4430ce789e7c85e4226d6d0d. Major bugs fixed: none reported this month. Impact: improves library independence, reduces external dependency risk, enhances onboarding and reproducibility for tutorials. Technologies/skills demonstrated: Python refactor, dependency management, code maintainability, version control hygiene, issue/PR traceability (#24292).
September 2025
2 Commits • 1 Features
Sep 1, 20252025-09 TT-Metal: Key feature delivery focused on decoupling PyTorch dependency in the ttnn tutorial by replacing torch.rand with ttnn.rand, improving library independence and compatibility. Feature delivered: Tutorial: Remove PyTorch dependency by replacing torch.rand with ttnn.rand. Commits: 0c4639ac259782d46f22e5d89718dbe295d17116; 9a88b2460d98adcc4430ce789e7c85e4226d6d0d. Major bugs fixed: none reported this month. Impact: improves library independence, reduces external dependency risk, enhances onboarding and reproducibility for tutorials. Technologies/skills demonstrated: Python refactor, dependency management, code maintainability, version control hygiene, issue/PR traceability (#24292).
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ developmentCompiler error resolutionDebuggingMachine LearningPython ProgrammingTensor Operations
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