intel/AI-PC-Samples
Jul 2025 – Jul 2025
1 Month active
Languages Used
HTMLJavaScriptShell
Technical Skills
HTMLJavaScriptMachine LearningONNX Runtime WebTransformers.jsWebGPU
Rakshith
PROFILE
Rakshith
Rakshith Krishnappa developed and delivered comprehensive WebNN workshop content and sample applications for the intel/AI-PC-Samples repository, focusing on improving onboarding and project clarity. He introduced new directories to organize WebNN applications and samples, updated the README to document these resources, and removed outdated checkpoint files to streamline the codebase. His work leveraged JavaScript, HTML, and WebNN, integrating modern machine learning tools such as ONNX Runtime Web and Transformers.js. While the scope was limited to a single feature over one month, the contributions provided foundational structure for future WebNN demonstrations and improved the repository’s usability for both new and existing developers.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
8,549
Activity Months1
Your Network
1974 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for intel/AI-PC-Samples: Delivered WebNN Workshop content and samples, introduced new directories for WebNN applications and samples, updated the README to reflect additions, and removed obsolete checkpoint files. No other major bugs documented this month. Overall, the work enhances WebNN onboarding, clarifies project structure, and reduces repository clutter, laying groundwork for broader WebNN demonstrations.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for intel/AI-PC-Samples: Delivered WebNN Workshop content and samples, introduced new directories for WebNN applications and samples, updated the README to reflect additions, and removed obsolete checkpoint files. No other major bugs documented this month. Overall, the work enhances WebNN onboarding, clarifies project structure, and reduces repository clutter, laying groundwork for broader WebNN demonstrations.
July 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
HTMLJavaScriptShell
Technical Skills
HTMLJavaScriptMachine LearningONNX Runtime WebTransformers.jsWebGPUWebNN
Repositories Contributed To
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