Kotlin, Compose, and mobile background services.
Treat it as a process gap, not a grudge. Find out where and when decisions are made, get your team's constraints in front of the deciders before the meeting through short written proposals with a comment window both time zones can reach, and escalate a specific costly decision, not the people who made it.
Bitmask DP keeps the cheapest cost for every subset of workers already used. The number of set bits says which task comes next, so there are 2^n states with n moves each, O(2^n * n) time and 2^n memory. At n = 20 that is about a million states, where trying all 20! orders is hopeless.
Plain Dijkstra minimises cost alone, so it can return a cheap route with too many stops, and settling each city once discards a dearer route that still has stops to spare. Make the state city plus edges used, or run Bellman-Ford for exactly K + 1 rounds, each reading only the previous round's distances.
For one grid, number of islands is a flood fill: each unvisited land cell starts a BFS or DFS, O(rows x cols). For land arriving cell by cell, rerunning that is too slow, so use union-find. Each new cell adds one island, and each union with a neighbour in a different set removes one, at near-constant amortised cost.
Evaluate feature flags in an SDK from a cached ruleset, assign users by hashing a per-experiment salt with the user id so assignment is sticky, log exposure where the arms first diverge, gate readouts on an automated SRM check, and make the kill switch a top-precedence rule that works with the control plane down.
A notification system at 50 million a day is sized by bursts and provider limits, not its average of about 580 a second. Accept with an idempotency key, apply preferences once, fan out to per-channel queues with transactional and bulk lanes, rate-limit each provider, and never fail over blindly on a timeout.