This semester I was pushing three things forward at the same time: my graduation project, a competition, and major assignments for two courses. At the start of the semester I was full of confidence; by midterm I nearly fell apart—when I reviewed it, I found that what crushed me was not the number of tasks, but the "switching cost."
First half of the semester: one conversation thread per track
I used one WorkBuddy conversation for the graduation project, opened another for the competition, and another for coursework. Every time I switched back to the graduation project, it took twenty minutes to set the background again; the same batch of literature was fed into all three tracks separately; the data needed for the competition already existed in the graduation project's knowledge, but I couldn't remember which conversation it was in.
Second half of the semester: the three tracks share one library
After switching to MuchSci, the structure changed: the three tracks share the same knowledge base. The literature and framework for the graduation project track, the materials for the competition track, and the assignment archives for the course track all interconnect in the library—experimental data to be cited in the competition can be taken directly from the graduation project track; the background for the coursework had already been written once in the competition draft. When wrapping up, each of the three tracks produced its own draft without holding the others back.
The conclusion of the comparison is clear: WorkBuddy is an excellent single-track player, and it can perform on every track; but the bottleneck of running things in parallel is never single-track combat power—it's logistics: whether context, materials, and progress can follow you when you switch. MuchSci's "one library, multiple threads" exactly fills this gap.
The way it works is shown in the figure: graduation project track, competition track,