EXCEEDS logo
Exceeds
Andrej Jakovljevic

PROFILE

Andrej Jakovljevic

Aleksandar Jakovljevic focused on stabilizing and enhancing core infrastructure in the tenstorrent/tt-metal and tenstorrent/tt-forge-models repositories. He improved tensor reshape reliability by adding validation for rank-1 shapes, preventing crashes and incorrect results in C++ tensor operations. Aleksandar refactored the Tensor library to introduce namespaced multi-device host tensor checks and expanded 2D convolution testing, using C++ and Python to boost maintainability and test coverage. In tenstorrent/tt-forge-models, he resolved dependency conflicts for Yolox and ONNX Runtime, streamlining Python packaging and build processes. His work addressed edge-case stability, improved CI reliability, and reduced maintenance overhead for distributed computing workflows.

Overall Statistics

Feature vs Bugs

25%Features

Repository Contributions

5Total
Bugs
3
Commits
5
Features
1
Lines of code
690
Activity Months3

Work History

October 2025

2 Commits

Oct 1, 2025

October 2025: Stabilized Yolox and ONNX Runtime dependency management for tt-forge-models to eliminate nightly build failures and improve environment reproducibility, enabling faster iterations and more reliable model tooling.

May 2025

2 Commits • 1 Features

May 1, 2025

For May 2025, focused on enhancing the Tensor library stability and test coverage in tenstorrent/tt-metal. Delivered internal refactor for tensor handling with a namespaced multi-device host tensor check, expanded 2D convolution testing, and performed build/config cleanups. The work reduces maintenance burden, improves reliability, and provides better performance visibility for future optimizations.

November 2024

1 Commits

Nov 1, 2024

November 2024: Focused stabilization of the tensor reshape path in tt-metal (tenstorrent/tt-metal) with a targeted bug fix for rank-1 shapes. Delivered an extra validation check in the reshape operation to handle degenerate shapes, preventing indexing errors, crashes, and incorrect results. This work improves reliability for edge-case inputs and strengthens production stability for Metal-backed tensor operations.

Activity

Loading activity data...

Quality Metrics

Correctness88.0%
Maintainability84.0%
Architecture76.0%
Performance76.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++PythonText

Technical Skills

BenchmarkingC++C++ developmentData MovementDependency ManagementDistributed computingPython PackagingPython scriptingTensor OperationsTensor manipulationTensor operationsUnit testing

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

tenstorrent/tt-metal

Nov 2024 May 2025
2 Months active

Languages Used

C++Python

Technical Skills

C++Data MovementTensor OperationsBenchmarkingC++ developmentDistributed computing

tenstorrent/tt-forge-models

Oct 2025 Oct 2025
1 Month active

Languages Used

PythonText

Technical Skills

Dependency ManagementPython Packaging

Generated by Exceeds AIThis report is designed for sharing and indexing