Use Cases/Students

LogMark for Students

Capture lecture insights and study connections faster than you can context-switch.

The Problem

You're in a lecture and the professor connects two concepts you studied separately. That connection is worth more than a page of notes, but you're trying to follow the next point. Switching to a note app means missing what comes next. Writing it in your notebook means it's stranded from your digital notes.

Later, during revision, you can't remember why the connection felt important. The insight that would have tied two weeks of material together is gone.

Why LogMark

A hotkey capture takes under a second. You don't leave your laptop screen or lose your place in slides. Route to courses or topics for automatic organization. Entries are searchable by tag, so cross-course connections surface naturally.

Workflows

Insight capture during lectures

+algorithms i: Dijkstra's is just BFS with a priority queue -- same pattern, different data structure
+databases i: the normalization trade-off feels similar to the DRY principle in programming

Study task capture

+algorithms t: re-derive the time complexity proof for heapsort by friday
+databases t: practice SQL joins before the quiz

Connection capture

i: the caching discussion in databases maps directly to the memory hierarchy from computer architecture #cross-course

Block capture during problem sets

+algorithms b: dynamic programming on strings -- can't see how to set up the subproblem structure

Decision capture

d: focusing thesis on graph algorithms -- more relevant to my internship and better advisor match

Notation Guide

+algorithms, +databases, +thesis - Course or project routing
+cs-fundamentals, +mathematics - Domain routing for knowledge areas
#exam-prep, #cross-course, #review-needed - Cross-cutting tags
t:, b:, d:, i: - Quick entry types

Example Week

Monday lecture
Professor makes an aside that connects to last week's reading.
+algorithms i: amortized analysis explains why dynamic arrays are O(1) average despite occasional O(n) resizes
Tuesday study session
Stuck on a problem set.
+algorithms b: can't figure out the recurrence relation for this DP problem -- T(n) doesn't decompose cleanly
Wednesday
Reading sparks a connection.
i: the CAP theorem trade-off in databases is the same structure as the speed/accuracy trade-off in ML #cross-course
Thursday lab
Capture tasks for the weekend.
+databases t: finish the ER diagram for the group project by sunday +algorithms t: review all DP patterns before tuesday's midterm #exam-prep
Friday office hours
Understanding clicks.
+algorithms d: using the table-filling approach for DP instead of memoization -- easier to reason about for exams