
During January 2025, work focused on the google-ai-edge/mediapipe-samples repository, where a model selection and dynamic inference model loading feature was developed for Android using Kotlin and Jetpack Compose. This involved designing a user interface that allows users to choose between different LLM inference models, followed by refactoring the InferenceModel component to support dynamic loading of the selected model at runtime. A loading screen was also introduced after model selection to enhance user experience and perceived performance. The changes enable rapid experimentation with various models, streamline onboarding, and improve code modularity, demonstrating skills in mobile development and asynchronous UI flows.
January 2025 - google-ai-edge/mediapipe-samples: Key feature delivered was Model Selection and Dynamic Inference Model Loading. A model selection screen was introduced to pick between different LLM inference models; InferenceModel refactored to load the selected model dynamically; a loading screen after model selection was added to improve UX. This work is captured in commit 8e903821a0332b8ba5c776b1fd020d048781ee4d: 'Add model selection screen'. Major bugs fixed: None reported. Overall impact and accomplishments: Enables rapid experimentation with different models, reduces time to validate backends, improves user onboarding and perceived performance, and strengthens the codebase with modular dynamic loading. Technologies/skills demonstrated: UI design, modular refactoring, dynamic loading patterns, asynchronous flows, and integration with LLM inference models.
January 2025 - google-ai-edge/mediapipe-samples: Key feature delivered was Model Selection and Dynamic Inference Model Loading. A model selection screen was introduced to pick between different LLM inference models; InferenceModel refactored to load the selected model dynamically; a loading screen after model selection was added to improve UX. This work is captured in commit 8e903821a0332b8ba5c776b1fd020d048781ee4d: 'Add model selection screen'. Major bugs fixed: None reported. Overall impact and accomplishments: Enables rapid experimentation with different models, reduces time to validate backends, improves user onboarding and perceived performance, and strengthens the codebase with modular dynamic loading. Technologies/skills demonstrated: UI design, modular refactoring, dynamic loading patterns, asynchronous flows, and integration with LLM inference models.

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