Why I Use Obsidian and Excalidraw for Academic Notes

Thu, 24 Sep 2026 00:00:00 +0000

I take many of my academic notes by hand. Diagrams, equations, and quick annotations are often easier to draw than type, but handwritten pages are hard to search and connect to related material later.

I use Obsidian, a notes app, with Excalidraw, a drawing tool that lets me write and sketch inside it. Saving a note automatically creates an SVG image of the drawing. My Notes Transcription Pipeline reads those images and turns the handwriting into text that can be searched alongside my other course materials.

OneNote and GoodNotes made writing feel natural, but their file-type limits got in the way of my processing workflow. This setup keeps my handwritten work alongside lecture slides, textbooks, and other course materials, with links between related notes.

Customization was another major reason to choose this setup. I can edit the source code for Obsidian plugins, which let me add an eraser button and translate a pen-tool menu. When the tablet’s gesture bar covered the zoom and undo controls, I changed the interface styles to move them within reach. Being able to fix those details myself was an advantage over my experience with OneNote.

I keep large reference files out of the notes sync so the tablet only needs to download the working notes and drawings. Some plugin settings and customizations still need to be copied or enabled by hand.

A lecture can leave me with two versions of a drawing: the notes I wrote in class, and a later copy with the lecture slides pasted beside them. Once transcribed, the search tools can recognize some of those pairs and avoid showing both copies when the more complete version is available. Both originals remain editable.

Matching whole notes by subject matter proved too broad: separate lectures on similar topics could look like duplicate versions. Comparing short sequences of words in the handwritten portions gave a cleaner distinction in the tested microeconomics notes. The useful signal was whether the same writing appeared in both versions.

That connection is what makes the setup useful to me. I can keep writing by hand while making the notes easier to find and revisit later. The transcription tools make the saved drawings searchable, and the sync setup occasionally needs attention.

Updated October 15, 2026, to include the related-note matching added after this post was first written.

For the sync setup, transcription steps, and current matching limits, see the , , and project pages.

The Resume Step That Became Faster When I Removed the AI

Wed, 09 Sep 2026 00:00:00 +0000

The first structured import of my resume took over 90 minutes and still mixed up parts of the record. Replacing the model that organized the extracted text with ordinary parsing code brought that step down to about 1.5 seconds in the recorded run. The larger improvement was having explicit rules for where each fact belonged.

The is meant to help tailor applications without losing track of the experience behind them. It keeps a master record, uses a job description to guide the emphasis of a draft, and renders the result as a PDF. Before any of that can work, the master has to represent the source resume faithfully.

An early version let the model write the master as free-form text. My source resume groups several roles under one employer, with a date range attached to the employer rather than every individual role. The model filled some missing date positions with locations. The result looked organized, but the meaning of a field had quietly changed.

Giving every fact a named field helped. Employer, role, location, and dates no longer had to compete for the same space in a heading. Yet asking the local model to populate that structure introduced another set of problems: malformed output, joined lines that failed literal checks, and content that still landed in the wrong place. Fixing the formatting was not enough to establish that the record was right.

The source had more regularity than the model-based approach was using. Section headings, date patterns, and the arrangement of roles supplied practical rules for a parser. The revised importer uses those rules to turn extracted PDF text into a structured master without a model call. That is the step behind the 1.5-second result; it is not the time to tailor and render a complete application, nor a benchmark across arbitrary resume layouts.

Language generation still has a useful job later. A model can suggest which experience to emphasize and how to phrase a bullet for a particular opportunity. The surrounding code copies fixed fields from the master instead of asking the model to reproduce them. Bullet wording still needs review: preserving a date or a number does not establish that every implication of a rewritten sentence is faithful.

This small project shares a concern with the academic tools: preserve a dependable source record, then make the interpretation easy to inspect. Here, the most effective change was recognizing which part of the job already had enough structure for code to do it directly.

Retrospective account prepared October 16, 2026, covering the September 9 milestone. The and document the sequence. The project page includes later developments.

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Why I Use Obsidian and Excalidraw for Academic Notes

The Resume Step That Became Faster When I Removed the AI