CurveFS lazily fetches remote files on first access, introducing latency from network I/O and local writes. I extended the warmup subsystem with three new interfaces: preserving existing job file lists on subsequent adds, canceling in-flight jobs and their downstream tasks, and listing all active warmup jobs per filesystem. These additions give operators direct observability and control over the pre-fetch lifecycle.

The chart above shows off-role parity rising sharply in the targeted region immediately after rollout. The core problem was that the existing algorithm permitted asymmetric role confidence between opponents, pairing one player's off-role against another's main position and creating a perceivable disadvantage.

I redesigned the matching logic to enforce parity by grouping players into role-confidence tiers before pairing them. To manage the queue-time tradeoff, I ran offline simulations against 2M+ historical matches using Python and Databricks, iterating until the parity gain justified the latency cost. The service ran on Java and Spring.

The feature surfaces position scarcity to players during champion select. What appeared to be a straightforward UI change required careful distributed systems work: the scarcity signal is derived from state spread across parties, lobbies, and matchmaking services on separate servers, so inconsistent views between lobby members would undermine the feature's usefulness.

I stored the shared state in a Redis-backed cache with periodic source refresh, ensuring all clients in a lobby see a consistent snapshot. The client layer used Ember.js and C++; the backend used Java and Spring.

The platform has two components: a React + Node.js dashboard for visualizing historical metrics and configuring targets, and a monitoring daemon containerized with Docker and orchestrated as a k8s Job. The daemon runs continuously, collecting Lighthouse scores and Web Vitals across hundreds of Houzz pages and persisting results to MongoDB for trend analysis.

I extended the existing C++ and C# wizard to support Linux sandbox creation alongside the existing Windows flow. The feature automates VM import, package installation, and environment hardening via Shell Script, replacing what had been a manual process. Adding Linux coverage meaningfully expanded the malware detection surface; Linux VMs now account for over 34% of active sandboxes.

I owned the preprocessing stage of the malware analysis pipeline, where incoming file samples are triaged before scanning. One key addition was EGG-format decompression support, a Korean archive format that the system had not previously handled. I refactored an open-source C++ EGG library and integrated it into the existing pipeline without disrupting the surrounding flow.