tenstorrent/tt-inference-server
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
API developmentbackend developmentdatabase managementunit testing
Milan Cupac
PROFILE
Milan Cupac
In January 2026, Mark Cupac developed a SQLite-backed persistence layer for job state management in the tt-inference-server repository. He designed and implemented a database-driven job lifecycle, enabling job creation, cancellation, and result path retrieval to persist reliably across server restarts. Using Python, Mark refactored the backend to standardize field naming and integrated settings-driven database paths, while also updating and expanding unit tests to reflect the new schema. His work improved the reliability and recoverability of long-running inference jobs, enhanced observability, and streamlined troubleshooting, demonstrating depth in API development, backend engineering, and database management within a production environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
912
Activity Months1
Your Network
379 peopleWork History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a SQLite-backed persistence layer for job state in the tt-inference-server, enabling durable job data across restarts and more robust cancellation handling. Implemented a DB-backed job lifecycle (create, cancel, get result_path), updated field naming to result_path, and wired tests and settings accordingly. The changes improve reliability, traceability, and recoverability of long-running inference jobs, with maintainability gains from refactors and CI-ready code.
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a SQLite-backed persistence layer for job state in the tt-inference-server, enabling durable job data across restarts and more robust cancellation handling. Implemented a DB-backed job lifecycle (create, cancel, get result_path), updated field naming to result_path, and wired tests and settings accordingly. The changes improve reliability, traceability, and recoverability of long-running inference jobs, with maintainability gains from refactors and CI-ready code.
January 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API developmentbackend developmentdatabase managementunit testing
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