# dream11 Published articles for dream11. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence DevFeed: [Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-22T06:40:39Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [foundation-models](), [Sports](), [Machine learning](), [Large language models (LLMs)]() Tags: [analysis](), [competition](), [context](), [dream11](), [fragmentation](), [incremental](), [llm](), [ml](), [models](), [notifications](), [personalisation](), [scale](), [sports](), [systems](), [tech]() ### AI overview Dream11 describes Lumos, a foundation model for personalisation that connects user behaviour, context, and changing interests across sports experiences. The article reports a 2.5% lift in ROC AUC and a 4.6% reduction in MAPE across key tasks, while replacing dozens of task-specific systems with a single scalable foundation. ### Source excerpt By Dhruv Nigam At Dream11, our mission to 'make every match more exciting' starts with a simple truth: every fan experiences sport differently. Some users show up for marquee matches, while others engage consistently across the season. Some enjoy deep analysis; others come for emotion, banter, and shared moments. Even how fans prefer to be spoken to -- through in-app communication or notifications -- varies, from playful and expressive to direct and informational. In sports, context changes everything. A quiet weekday feels very different from the eve of a knockout match, and behaviour shifts with formats, rivalries, and the stage of competition. Personalisation at Dream11 therefore goes beyond surface-level customisation -- it's about understanding fans in motion and how their interests evolve. We've long recognised this challenge, but understanding and acting on these signals across millions of users, each with their own patterns and preferences, is far from easy. Over time, it became clear that small, incremental ML enhancements wouldn't get us where we needed to go. To stay truly user-first, we needed a system that could connect behaviour, context, and past, present, and future moments, all at once. That realisation led us to a ground-up rethink of how we build models at Dream11, and eventually, to Lumos -- our foundation model for personalisation. Lumos helped deliver a 2.5% lift in ROC AUC (Area Under the Receiver Operating Characteristic Curve) and a 4.6% reduction in MAPE (mean absolute percentage error) across key tasks, significantly improving personalisation, while replacing dozens of task-specific systems with a single, scalable foundation.The Problem: When Task-Based Models Stop Scaling For a long time, our personalisation stack relied on 50+ small, specialised models, each designed to understand a narrow aspect of user behaviour. Some models focused on sports affinity, others on language preferences or communication style. While these were effective in iso ## From Code to Court: The Tech Behind India's Online Gaming Revolution DevFeed: [From Code to Court: The Tech Behind India's Online Gaming Revolution]() Original publisher: [Read original article]() Author: Shivam Anand Published: 2025-08-24T09:01:51Z Content type: article Language: en Sources: [Shivam Anand - DevOps & Cloud Engineering Blog]() Topics: [Code](), [real-time](), [Kafka](), [DevOps](), [Back end](), [Terraform](), [Node.js](), [React Native](), [Databases](), [Front end](), [Go Language](), [Java]() Tags: [aws](), [backend](), [blue-green-deployment](), [code](), [databases](), [devops-cloud-infrastructure](), [dream11](), [dream11-technology](), [engineering](), [fantasy-sports-app-tech](), [gaming](), [india](), [infrastructure](), [kafka](), [microservices-gaming](), [node-js](), [online-gaming-engineering](), [online-gaming-india](), [react-native](), [real-time](), [real-time-gaming-platform](), [scalable-infrastructure](), [streamverse](), [tech](), [winzo-tech]() ### AI overview This article examines the engineering behind Dream11 and WinZO Games, focusing on real-time activity, stream processing, scalable infrastructure, observability, deployments, and high-concurrency systems. It also discusses how legislative changes are affecting India's online gaming industry. ### Source excerpt Explore the engineering behind India's top online gaming platforms, Dream11 and WinZO. Learn how they handle millions of users, real-time updates, and scalable infrastructure, even as regulations evolve.