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Swiggy Improves Search Autocomplete Using Real Time Machine Learning Ranking
Swiggy detailed real-time machine-learning ranking system for autocomplete built on OpenSearch. The architecture separates candidate generation and ranking, uses feature stores for real time signals, and applies learning to rank models for improved relevance. It replaces heuristic ranking while maintaining strict latency constraints and enabling continuous model updates from user behavior signals.
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DoorDash Builds DashCLIP to Align Images, Text, and Queries for Semantic Search Using 32M Labels
DoorDash has launched a multimodal machine learning system that aligns product images, text, and user queries in a shared embedding space. Trained on 32 million labeled query-product pairs using contrastive learning, the system improves semantic search, product ranking, and advertising relevance. Embeddings also support other machine learning tasks across the marketplace.
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Uber Moves from Static Limits to Priority-Aware Load Control for Distributed Storage
Uber engineers detailed how they evolved their storage platform from static rate limiting to a priority-aware load management system. The approach protects Docstore and Schemaless, Uber’s MySQL-based distributed databases, by colocating control with storage, prioritizing critical traffic, and dynamically shedding load under overload conditions.
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LinkedIn’s Migration Journey to Serve Billions of Users by Nishant Lakshmikanth at QCon SF
Engineering Manager Nishant Lakshmikanth showcased LinkedIn's transformation at QCon SF 2025, detailing a shift from legacy batch-based systems to a real-time architecture. By decoupling recommendations and leveraging dynamic scoring techniques, LinkedIn achieved a 90% reduction in offline costs, enhanced session-level freshness, and improved member engagement while future-proofing its platform.
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Instagram Improves Engagement by Reducing Notification Fatigue with New Ranking Framework
Meta has introduced a diversity-aware ranking framework for Instagram notifications. The system applies multiplicative penalties to reduce repetitive alerts from the same creators or product surfaces, improving engagement while maintaining relevance and introducing content variety.
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Getting Rid of Annual Performance Reviews
Professional services firm, which employs hundreds of thousands of workers in cities around the globe, has been quietly preparing for the “massive revolution” of getting rid of annual performance reviews and rankings, in its internal operations.