aws-samples/amazon-nova-samples
Dec 2025 – Jan 2026
2 Months active
Languages Used
MarkdownPythonYAML
Technical Skills
AWSAWS LambdaAWS SageMakerData ProcessingJupyterMachine Learning
Harsh Asnani
PROFILE
Harsh Asnani
During a two-month period, Haasnani contributed to the aws-samples/amazon-nova-samples repository by building and enhancing data preparation, evaluation, and local development workflows for legal-domain AI model fine-tuning. Haasnani developed a Jupyter notebook for scalable legal data processing and streamlined evaluation pipelines by centralizing configuration management using Python and AWS Lambda. The work included introducing a dedicated SFT evaluation function, cleaning up RFT code for maintainability, and automating evaluation pipelines. Additionally, Haasnani implemented a local data generation server with Flask, simplifying onboarding and testing. The engineering solutions emphasized reproducibility, reliability, and maintainability, demonstrating depth in backend and cloud development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
4
Lines of code
5,355
Activity Months2
Your Network
1563 peopleShared Repositories
77
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026Month: 2026-01 – Focused on enabling faster development and testing through a reproducible local data generation workflow. Delivered the Local Data Generation Server Setup (Flask) by removing the utils module initializer and adding clear instructions to run a local Flask server for data generation. This improves local testing reliability, reduces onboarding time for new contributors, and lays the groundwork for future data-generation features. Commit reference: dbcc88e1483a30877b894830af384511f3d304e9. Overall impact: streamlined setup, better developer velocity, and a more maintainable codebase.
1 Commits • 1 Features
Jan 1, 2026Month: 2026-01 – Focused on enabling faster development and testing through a reproducible local data generation workflow. Delivered the Local Data Generation Server Setup (Flask) by removing the utils module initializer and adding clear instructions to run a local Flask server for data generation. This improves local testing reliability, reduces onboarding time for new contributors, and lays the groundwork for future data-generation features. Commit reference: dbcc88e1483a30877b894830af384511f3d304e9. Overall impact: streamlined setup, better developer velocity, and a more maintainable codebase.
January 2026
December 2025
6 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary focusing on key features delivered, major fixes, impact, and technical competencies for the aws-samples/amazon-nova-samples project. The month emphasizes end-to-end enhancements to Nova 2.0 data preparation, evaluation workflows, and SFT/RFT evaluation tooling, with a focus on reliability, reproducibility, and business value.
December 2025
6 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary focusing on key features delivered, major fixes, impact, and technical competencies for the aws-samples/amazon-nova-samples project. The month emphasizes end-to-end enhancements to Nova 2.0 data preparation, evaluation workflows, and SFT/RFT evaluation tooling, with a focus on reliability, reproducibility, and business value.
Activity
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Quality Metrics
Correctness88.6%
Maintainability88.6%
Architecture85.8%
Performance88.6%
AI Usage37.2%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
AWSAWS LambdaAWS SageMakerBackend DevelopmentData GenerationData ProcessingFlaskJupyterMachine LearningPythonPython programmingSageMakerYAML configurationcloud computingconfiguration management
Repositories Contributed To
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