CLOVER
Executing a comprehensive rebranding initiative aimed at strengthening our lab’s identity and fostering community growth.
the challenge.
Develop a GitHub Copilot clone that caters to beginner Computer Scientists. Give them useful, inline assistance without encouraging overreliance. Log every interaction within the VS Code extension, and deliver timely interventions so that students understand code before accepting the suggestions!
the results.
We delivered a working VS Code extension that provides in-editor help and integrates Supabase authentication (email and GitHub) with our backend. The system logs every suggestion and whether the student ultimately got it right or wrong, as well as key lifecycle events like login, logout, and signup, creating a clean dataset for analysis. I built dashboards to surface these patterns and we completed testing and documentation; the project is now being refined with the Temple HCI Lab for research use.
the process.
Define the core flow. We scoped Clover around a simple loop—authenticate, prompt, respond, reflect, and iterate—ensuring each step reinforces understanding instead of shortcutting learning.
Design the experience. I mapped the user journey inside a VS Code extension, focusing on low-friction prompting, readable explanations, and clear affordances for follow-up questions without breaking coding flow.
Build the extension. Clover was implemented as a custom VS Code extension, integrating the editor context with a lightweight UI layer to keep interactions fast and non-disruptive.
Generate guided responses. Using an LLM, Clover produces step-by-step explanations, conceptual breakdowns, and hints tailored to the user’s prompt, prioritizing comprehension over direct solutions.
Enable iteration. Users refine prompts, ask follow-ups, and apply guidance directly in their workflow, reinforcing an iterative learning loop that mirrors real-world problem solving.
the conclusion.
This collaboration transformed a one-person presence into a cohesive, student-led brand supported by a simple, repeatable operating model. With clear messaging, reusable assets, and defined roles, the lab can recruit, communicate, and measure impact without rebuilding each cycle. The AMA partnership reinforced consistency and accountability, while lightweight metrics keep iteration grounded in results. Most importantly, the system is built to outlast any individual, easy to hand off, easy to scale, and aligned with the lab’s community-first mission.
CLOVER
Executing a comprehensive rebranding initiative aimed at strengthening our lab’s identity and fostering community growth.
the challenge.
Develop a GitHub Copilot clone that caters to beginner Computer Scientists. Give them useful, inline assistance without encouraging overreliance. Log every interaction within the VS Code extension, and deliver timely interventions so that students understand code before accepting the suggestions!
the results.
We delivered a working VS Code extension that provides in-editor help and integrates Supabase authentication (email and GitHub) with our backend. The system logs every suggestion and whether the student ultimately got it right or wrong, as well as key lifecycle events like login, logout, and signup, creating a clean dataset for analysis. I built dashboards to surface these patterns and we completed testing and documentation; the project is now being refined with the Temple HCI Lab for research use.
the process.
Define the core flow. We scoped Clover around a simple loop—authenticate, prompt, respond, reflect, and iterate—ensuring each step reinforces understanding instead of shortcutting learning.
Design the experience. I mapped the user journey inside a VS Code extension, focusing on low-friction prompting, readable explanations, and clear affordances for follow-up questions without breaking coding flow.
Build the extension. Clover was implemented as a custom VS Code extension, integrating the editor context with a lightweight UI layer to keep interactions fast and non-disruptive.
Generate guided responses. Using an LLM, Clover produces step-by-step explanations, conceptual breakdowns, and hints tailored to the user’s prompt, prioritizing comprehension over direct solutions.
Enable iteration. Users refine prompts, ask follow-ups, and apply guidance directly in their workflow, reinforcing an iterative learning loop that mirrors real-world problem solving.
the conclusion.
This collaboration transformed a one-person presence into a cohesive, student-led brand supported by a simple, repeatable operating model. With clear messaging, reusable assets, and defined roles, the lab can recruit, communicate, and measure impact without rebuilding each cycle. The AMA partnership reinforced consistency and accountability, while lightweight metrics keep iteration grounded in results. Most importantly, the system is built to outlast any individual, easy to hand off, easy to scale, and aligned with the lab’s community-first mission.
CLOVER
Executing a comprehensive rebranding initiative aimed at strengthening our lab’s identity and fostering community growth.
the challenge.
Develop a GitHub Copilot clone that caters to beginner Computer Scientists. Give them useful, inline assistance without encouraging overreliance. Log every interaction within the VS Code extension, and deliver timely interventions so that students understand code before accepting the suggestions!
the results.
We delivered a working VS Code extension that provides in-editor help and integrates Supabase authentication (email and GitHub) with our backend. The system logs every suggestion and whether the student ultimately got it right or wrong, as well as key lifecycle events like login, logout, and signup, creating a clean dataset for analysis. I built dashboards to surface these patterns and we completed testing and documentation; the project is now being refined with the Temple HCI Lab for research use.
Define the core flow. We scoped Clover around a simple loop—authenticate, prompt, respond, reflect, and iterate—ensuring each step reinforces understanding instead of shortcutting learning.
Design the experience. I mapped the user journey inside a VS Code extension, focusing on low-friction prompting, readable explanations, and clear affordances for follow-up questions without breaking coding flow.
Build the extension. Clover was implemented as a custom VS Code extension, integrating the editor context with a lightweight UI layer to keep interactions fast and non-disruptive.
Generate guided responses. Using an LLM, Clover produces step-by-step explanations, conceptual breakdowns, and hints tailored to the user’s prompt, prioritizing comprehension over direct solutions.
Enable iteration. Users refine prompts, ask follow-ups, and apply guidance directly in their workflow, reinforcing an iterative learning loop that mirrors real-world problem solving.
the process.
This collaboration transformed a one-person presence into a cohesive, student-led brand supported by a simple, repeatable operating model. With clear messaging, reusable assets, and defined roles, the lab can recruit, communicate, and measure impact without rebuilding each cycle. The AMA partnership reinforced consistency and accountability, while lightweight metrics keep iteration grounded in results. Most importantly, the system is built to outlast any individual, easy to hand off, easy to scale, and aligned with the lab’s community-first mission.
the conclusion.