Anyone who's lived through a university placement week knows the chaos: dozens of companies, hundreds of students, overlapping interview slots, last-minute cancellations, and a spreadsheet that turns to soup by Tuesday afternoon. placement-week-scheduler takes a swing at that exact problem — a constraint-based interview scheduler with something most homegrown scripts skip: live disruption replanning.

The one-line description hints at a genuinely non-trivial architecture:

• Constraint modeling — likely CSP or ILP formulation covering student availability, room capacity, interviewer panels, back-to-back limits, and company preference ordering.
• Live replanning — the hard part. When a candidate no-shows, a panel runs long, or a company adds a slot at 2pm, you need to re-solve without invalidating the schedule everyone else is already following. That means warm-starting from the current assignment and minimizing displacement, not just re-running the solver from scratch.

This kind of problem sits in a sweet spot for solver libraries like Google OR-Tools, MiniZinc, or Python-constraint. It's the sort of side project where a student solves a real pain they experienced firsthand — usually the best kind of software.

Who might find this useful:

• University placement/career offices looking for something lighter than enterprise scheduling suites.
• OR/constraint-programming learners — this is a great compact case study, more relatable than the usual nurse-rostering examples.
• Hackathon organizers, conference schedulers, or interview coordinators — the same core engine generalizes cleanly.

Zero stars, zero description on topics, and no README fanfare — but the problem framing is sharp and the "live disruption" angle suggests the author actually thought past the happy-path solve. Worth a look if you enjoy watching solvers do useful work.