{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/0cd99f26a7a44051b5c202e6cfc240a9\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/0cd99f26a7a44051b5c202e6cfc240a9-da1fb20a159afb4b.gif","duration":227.771,"title":"Building a High-Frequency Trading Terminal with AI Fraud Detection (Spring WebFlux + Redis + Python)","description":"In this video, I present my project, BidStream, which is a high-frequency trading terminal and auction platform designed to handle large traffic spikes while preventing race conditions and maintaining performance. I utilize Spring WebFlux, Redis, and Lua, along with server-sent events for real-time UI updates, and demonstrate how a RateLimiter effectively mitigates simulated DDoS attacks. The architecture ensures atomic operations to eliminate double spending, and I showcase a live telemetry graph tracking throughput and latency. Additionally, I explain the integration of an AI fraud detection microservice that identifies and manages bot activity. I encourage viewers to explore the demo and provide feedback on the system's performance."}