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Samiya Akhtar

PROFILE

Samiya Akhtar

Samiya Akhtar developed core features for tenstorrent/tt-mlir and tt-forge-fe, focusing on StableHLO operation support and automated documentation. She implemented end-to-end builder APIs for Slice and Reduce operations in tt-mlir, aligning with StableHLO specifications and ensuring robust test coverage for tensor manipulation and multi-shard support using Python and MLIR. In tt-forge-fe, she built an automated documentation pipeline leveraging Python AST parsing to discover and document 78 operations, generating PyTorch-style pages that stay synchronized with code changes. Her work improved maintainability, API discoverability, and onboarding efficiency, demonstrating depth in Python development, testing, and documentation generation.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

3Total
Bugs
0
Commits
3
Features
3
Lines of code
4,807
Activity Months2

Work History

February 2026

1 Commits • 1 Features

Feb 1, 2026

February 2026: Implemented automatic operation discovery and PyTorch-style documentation generation for Forge. Key outcomes: automatic discovery of 78 operations from forge/forge/op/*.py using Python AST; generation of 78 operation pages plus a categorized index page; no manual entry required for new ops; improved docstrings, signatures, and parameter descriptions; fixed incorrect descriptions (e.g., Abs vs Sigmoid) and edge-case handling for missing docstrings. Impact: accelerates developer onboarding, improves API discoverability, reduces maintenance burden, and ensures docs stay in sync with code. Technologies: Python AST, docstring parsing, PyTorch-style docs, automated doc generation pipeline. Business value: faster API adoption, fewer support tickets, consistent docs across the codebase.

November 2025

2 Commits • 2 Features

Nov 1, 2025

Concise monthly summary for 2025-11 highlighting business value and technical achievements in StableHLO integration for tenstorrent/tt-mlir. Focused on delivering end-to-end op builders, golden references, and tests, with emphasis on reliability, maintainability, and performance readiness.

Activity

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Quality Metrics

Correctness100.0%
Maintainability93.4%
Architecture100.0%
Performance93.4%
AI Usage20.0%

Skills & Technologies

Programming Languages

Python

Technical Skills

MLIRMachine LearningPython DevelopmentPython programmingTensor ManipulationTensor OperationsTestingdocumentation generationsoftware development

Repositories Contributed To

2 repos

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

tenstorrent/tt-mlir

Nov 2025 Nov 2025
1 Month active

Languages Used

Python

Technical Skills

MLIRMachine LearningPython DevelopmentTensor ManipulationTensor OperationsTesting

tenstorrent/tt-forge-fe

Feb 2026 Feb 2026
1 Month active

Languages Used

Python

Technical Skills

Python programmingdocumentation generationsoftware development