Why Researchers Should Use VS Code
VS Code is the tool that unified all my workflows. I write LaTeX, Python scripts, Markdown, and Jupyter Notebooks — all in the same editor, with the same shortcuts.
Before VS Code I was juggling: TeXstudio for LaTeX, Spyder or Jupyter Lab for Python, a separate Markdown editor for notes. VS Code replaces all of them.
Essential Extensions for Researchers
- LaTeX Workshop — live preview, auto-compile on save, SyncTeX (click PDF → jump to source)
- Python + Pylance — IntelliSense, variable explorer, automatic linting
- Jupyter — run Jupyter Notebooks natively inside VS Code
- Markdown All in One — preview, table of contents, list formatting
- GitLens — visual Git history, inline blame, branch management
- GitHub Copilot — AI autocomplete for code and text (free with academic email)
The LaTeX Workflow in VS Code
See the full tutorial video linked above. Key steps:
- Install a local TeX distribution (TeX Live on Linux / Mac, MiKTeX on Windows)
- Install the LaTeX Workshop extension
- Configure
latex-workshop.latex.recipesto match your build chain (pdflatex → biber → pdflatex × 2) - Enable SyncTeX for two-way navigation between source and PDF
Benefits over Overleaf: works offline, no file size limits, version control with Git, and GitHub Copilot works in the editor.
Free GPU Access in VS Code
The second video shows how to use VS Code to access free GPU resources for running AI models locally. This is useful for researchers who want to fine-tune models, run inference, or test HuggingFace pipelines without paying for cloud GPU time.
Python for Research
Combined with the Jupyter extension and GitHub Copilot, VS Code becomes a powerful research environment:
- Notebook cells for exploratory data analysis
- Python scripts for reproducible data pipelines
- Git integration to track every version of your analysis