Fix PDF deploy (rename) and add SCSS path to deploy trigger

Author: Keyu-He

.github/workflows/deploy.yml

@@ -7,11 +7,13 @@ on:
       - main
     paths:
       - "assets/**"
+      - "_sass/**"
       - "**.bib"
       - "**.html"
       - "**.js"
       - "**.liquid"
       - "**/*.md"
+      - "**.scss"
       - "**.yml"
       - "Gemfile"
       - "Gemfile.lock"
@@ -34,11 +36,13 @@ on:
       - main
     paths:
       - "assets/**"
+      - "_sass/**"
       - "**.bib"
       - "**.html"
       - "**.js"
       - "**.liquid"
       - "**/*.md"
+      - "**.scss"
       - "**.yml"
       - "Gemfile"
       - "Gemfile.lock"

_bibliography/papers.bib

@@ -9,7 +9,7 @@ @article{keyu2026recap
   journal={ACL Demo},
   abstract={Understanding how developers interact with AI coding assistants requires more than chat logs or git histories in isolation; it requires reconstructing the full context: which prompt led to which edit, what the developer tried and discarded, and how their strategy evolved over time. We present RECAP (Replay and Examine Captured AI Programming), an open-source platform that (1) passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer's workflow, (2) merges them into a unified timeline for interactive session replay, and (3) provides extensible analysis modules for behavioral classification and AI reliance measurement. Deployed in a university software engineering course, RECAP captured 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. We demonstrate how the platform's linked data and replay capabilities enable analyses of developer-AI interaction patterns that no single data source could support.},
   selected={true},
-  pdf={_Arxiv__ACL_Demo_Feb_2026__RECAP.pdf},
+  pdf={Arxiv_ACL_Demo_Feb_2026_RECAP.pdf},
   preview={RECAP.png}
 }
 

assets/json/resume.json

@@ -60,7 +60,7 @@
       "publisher": "ACL 2026 Demo",
       "releaseDate": "2026-07",
       "summary": "RECAP captures AI chat and code edits in VS Code, merges them into replayable timelines, and provides analysis modules for studying developer-AI interaction patterns.",
-      "url": "../assets/pdf/_Arxiv__ACL_Demo_Feb_2026__RECAP.pdf"
+      "url": "../assets/pdf/Arxiv_ACL_Demo_Feb_2026_RECAP.pdf"
     },
     {
       "name": "Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations",