
Aleksandar Jankovic enhanced the tenstorrent/tt-llk and tt-metal repositories by building robust test infrastructure and performance validation systems. He unified runtime configuration using C++ and Python, migrating compile-time parameters to runtime for greater flexibility and maintainability. His work included refactoring test frameworks, implementing code coverage tooling, and optimizing CI/CD pipelines to reduce flakiness and accelerate feedback loops. By introducing deterministic test execution ordering and reproducibility features, Aleksandar improved bug isolation and hardware-state debugging. He also addressed stability issues in multi-core and hardware-integrated environments, demonstrating depth in debugging, build system management, and low-level programming to deliver reliable, efficient workflows.
April 2026 — Tenstorrent/tt-metal: Delivered deterministic test execution ordering and reproducibility, alongside targeted Quasar/Blackhole stability fixes. Implemented CLI-driven test order recording and replay, added a processing script, and stabilized test execution by reducing concurrent pytest runners. These workstreams improve bug reproducibility, CI reliability, and hardware-state debugging capabilities.
April 2026 — Tenstorrent/tt-metal: Delivered deterministic test execution ordering and reproducibility, alongside targeted Quasar/Blackhole stability fixes. Implemented CLI-driven test order recording and replay, added a processing script, and stabilized test execution by reducing concurrent pytest runners. These workstreams improve bug reproducibility, CI reliability, and hardware-state debugging capabilities.
March 2026 monthly summary: Delivered performance-focused infra improvements and stability fixes across the tt-metal and LLK test infrastructure, enabling faster, more reliable performance validation, and clearer developer feedback loops. The work spanned refactoring, build unification, and CI reliability improvements, resulting in measurable reductions in test times and improved determinism of performance results.
March 2026 monthly summary: Delivered performance-focused infra improvements and stability fixes across the tt-metal and LLK test infrastructure, enabling faster, more reliable performance validation, and clearer developer feedback loops. The work spanned refactoring, build unification, and CI reliability improvements, resulting in measurable reductions in test times and improved determinism of performance results.
February 2026: Delivered a Runtime Parameterization Overhaul in tenstorrent/tt-llk and stabilized the nightly test infrastructure, delivering measurable business and engineering impact. Key changes include migrating runtime configurations to RuntimeArgs and generate_input_dim, moving stimuli addresses into runtime structures, and standardizing input dimensions to improve flexibility, maintainability, and runtime efficiency. Implemented test infrastructure refinements to reduce nightly flakiness and runtime, including sanitization of matmul variants, selective coverage adjustments, and a dummy golden generator for compilation mode, resulting in substantial reductions in CI time and more reliable nightly runs. The work accelerated feedback loops, enabled broader architecture coverage with far fewer compiled variants, and demonstrated strong automation and performance-focused engineering. Technologies/skills demonstrated include runtime parameter design, refactoring for runtime configurability, CI/test automation, coverage tooling, and performance optimization.
February 2026: Delivered a Runtime Parameterization Overhaul in tenstorrent/tt-llk and stabilized the nightly test infrastructure, delivering measurable business and engineering impact. Key changes include migrating runtime configurations to RuntimeArgs and generate_input_dim, moving stimuli addresses into runtime structures, and standardizing input dimensions to improve flexibility, maintainability, and runtime efficiency. Implemented test infrastructure refinements to reduce nightly flakiness and runtime, including sanitization of matmul variants, selective coverage adjustments, and a dummy golden generator for compilation mode, resulting in substantial reductions in CI time and more reliable nightly runs. The work accelerated feedback loops, enabled broader architecture coverage with far fewer compiled variants, and demonstrated strong automation and performance-focused engineering. Technologies/skills demonstrated include runtime parameter design, refactoring for runtime configurability, CI/test automation, coverage tooling, and performance optimization.
January 2026 monthly summary for tt-llk focusing on test infrastructure improvements and performance optimizations. Highlights include stabilizing the nightly testing workflow, adding a coverage reporting script for easier test configuration, and implementing perf infrastructure fixes that speed up test execution and improve debugging capabilities. Deliverables are tied to two main commits that enhanced reliability, visibility, and efficiency in the testing pipeline.
January 2026 monthly summary for tt-llk focusing on test infrastructure improvements and performance optimizations. Highlights include stabilizing the nightly testing workflow, adding a coverage reporting script for easier test configuration, and implementing perf infrastructure fixes that speed up test execution and improve debugging capabilities. Deliverables are tied to two main commits that enhanced reliability, visibility, and efficiency in the testing pipeline.
December 2025 (tt-llk): Delivered code coverage tooling and a major testing infrastructure refactor to enable measurable coverage metrics, improving test quality and developer feedback. Hardened the test framework against infra updates, stabilizing CI and reducing flaky tests. These changes accelerate safe feature delivery and reduce time to detect regressions, delivering clear business value through higher quality releases.
December 2025 (tt-llk): Delivered code coverage tooling and a major testing infrastructure refactor to enable measurable coverage metrics, improving test quality and developer feedback. Hardened the test framework against infra updates, stabilizing CI and reducing flaky tests. These changes accelerate safe feature delivery and reduce time to detect regressions, delivering clear business value through higher quality releases.

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