# sports Published articles for sports. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## EA is using genAI for commentator voiceover in NHL 27 DevFeed: [EA is using genAI for commentator voiceover in NHL 27]() Original publisher: [Read original article]() Author: Diego Argüello Published: 2026-09-10T17:18:45Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [genai](), [Artificial Intelligence](), [Sports](), [simulator]() Tags: [ai](), [gaming](), [genai](), [simulator](), [sports]() ### AI overview EA says NHL 27 uses AI voice technology to expand commentary recordings made with John Buccigross and Darren Pang, with their consent and active collaboration. The article also reports earlier claims about the technology and notes that AI-generated lines can require correction. ### Source excerpt EA says it's using the tech to expand on recording sessions with John Buccigross and Darren Pang's 'full consent and active collaboration.' ## Why Temporal is expanding its partnership with Crystal Palace to include Palace Women DevFeed: [Why Temporal is expanding its partnership with Crystal Palace to include Palace Women]() Original publisher: [Read original article]() Author: Clair Byrd Published: 2026-09-04T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Crystal](), [Sports](), [coding]() Tags: [announcements](), [crystal](), [football](), [partnership](), [software](), [sports](), [tech](), [uk]() ### AI overview Temporal is expanding its Crystal Palace sponsorship to include the women's team, linking broader support for women's football with efforts to improve access and opportunity in technology. ### Source excerpt Temporal is extending its Crystal Palace partnership to Palace Women, uniting to fight the access gap in football alongside the one in tech. ## How Dream11 Built a Real-Time AI Sports Streaming Companion DevFeed: [How Dream11 Built a Real-Time AI Sports Streaming Companion]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-06-15T09:51:14Z Content type: tutorial Language: en Sources: [Dream11 Engineering]() Topics: [AI Agent](), [Streaming](), [Artificial Intelligence](), [real-time](), [voice ai](), [Sports]() Tags: [agent](), [ai](), [ai-agent](), [creators](), [cricket](), [latency](), [real-time](), [sports](), [streaming](), [tech](), [voice](), [voice-ai]() ### AI overview This developer article describes Dream11's AI Companion, a multilingual voice AI agent that joins live creator streams as a co-host. It explains that the system watches matches, reacts to events, interacts with creators and audiences, hosts trivia, and supports live cricket commentary with approximately two-second voice latency. ### Source excerpt Written by Narayan Sharma, Rajesh Mohanty, Dhruv Nigam, Chetna Meena and Palash TatteThe Moment That Sparked Everything Picture this: a live cricket stream. A boundary flies to the fence. The creator reacts; screaming, laughing, and enjoying the moment. The audience piles on in the chat. And in the corner of the screen, a street-smart monkey named Googly Bhai mutters: "Bhai kya cover drive mara Kohli ne, ball field pe aise glide kar rahi hai jaise garam parathe pe makkan!" The audience erupts. Not because it was scripted. Not because a human typed it. But because AI watched the same ball, processed the same moment, and reacted, with personality, with timing, and with an opinion, in under two seconds. That's the Dream11 AI Companion. And building it was anything but simple. 🏆 World's first real-time AI sports streaming companion ⚡ ~2 second end-to-end voice latency 🗣 70+ languages supported natively by S2S model; primary: Hindi, English, Hinglish 🔌 Zero extra setup for streamers -- integrates directly via OBS + Creator Console What We Set Out to Build With 250M+ users, Dream11 has been on a journey to become everyone's sports entertainment destination. And entertainment lives where the fans are; inside live streams, creator channels, match-day conversations. The question we asked ourselves: What if a creator never had to stream alone again? Or if they were unavailable, could we ensure they had a back up? The answer was an AI Companion: A real-time, multilingual voice AI agent that joins live creator streams as a co-host. It watches the match. It reacts to what's happening. It banters with creators and audiences. It hosts trivia. It roasts players. It debates cricket. Meet the Characters Before we get into architecture, meet the two AI characters at the heart of this product. Bittu Bindas 🐻 A bold, banterous bear. Speaks Hindi (in Mumbaikar style). Playful, opinionated, and absolutely no-filter. Bittu doesn't overthink. He just says what every fan in the room is think ## From Static Catalogue to Smart Discovery: Engineering Lightning-Fast Search at DreamSetGo DevFeed: [From Static Catalogue to Smart Discovery: Engineering Lightning-Fast Search at DreamSetGo]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-03-09T13:50:30Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [PostgreSQL](), [Microservice](), [systems](), [elasticsearch]() Tags: [architecture](), [dream-sports](), [elasticsearch](), [engineering](), [open](), [performance](), [postgresql](), [search](), [search-engines](), [sports](), [tech](), [travel]() ### AI overview DreamSetGo's tech team explains how its growing sports-travel catalogue created a need for faster, more intuitive product discovery. The article describes rebuilding the catalogue as a microservice, considering PostgreSQL with Elasticsearch, and choosing PostgreSQL based on architectural and operational trade-offs. ### Source excerpt By the DreamSetGo Tech Team If you're excited about building high-performance systems that power premium sports travel experiences, explore our open roles at DreamSetGo. Why Search Became a Product Imperative Over the past couple of years, DreamSetGo's catalogue didn't just grow, it accelerated. We were introducing access to new tournaments, adding destinations across continents, and were expanding into different hospitality formats. What began as a few structured products quickly evolved into a layered catalogue of packages and experiences. And that's when we started noticing something subtle but important. Adding products was straightforward. Making them discoverable wasn't. Every new experience meant longer descriptions and richer metadata. On paper, this was definitely progress. But from a user perspective, it introduced friction. If a traveller already knew exactly where to click, they could navigate their way through the catalogue. But if they came in with intent like: "Wimbledon finals hospitality" "Hot air balloons in Cappadocia" "Scuba diving in the Andamans" ...there was no fast, intuitive way to show them the most relevant experience. Once we acknowledged this gap, the next step became clear: we needed a search system that was scalable, resilient, and fast enough to keep up with our growth. This blog takes you through how we designed that search, from architectural trade-offs to production optimisations, and why PostgreSQL turned out to be the best choice for this. The Obvious Architecture (That We Didn't Choose) We started revamping our architecture six months back and rebuilt our travel product catalogue as a dedicated microservice, one that would eventually power search, and filter it by date, price and more. At that point, we had roughly 1,000 products. But we weren't building for 1,000. We had a clear vision of significantly expanding the catalogue within the near future. Like most teams, our first instinct was predictable: PostgreSQL -> Source of Truth ## From VHS to Interactive Streams: Customization at Scale with the NFL Sunday Ticket on YouTube DevFeed: [From VHS to Interactive Streams: Customization at Scale with the NFL Sunday Ticket on YouTube]() Original publisher: [Read original article]() Author: Shawn DeVries Published: 2026-01-23T23:05:00Z Content type: article Language: en Sources: [8th Light Insights]() Topics: [Streaming](), [personalization](), [Sports](), [real-time](), [Playback](), [ui](), [parallel](), [Mobile]() Tags: [customization](), [interactive](), [mobile](), [parallel](), [personalization](), [platform-innovation](), [playback](), [real-time](), [scale](), [sports](), [streams](), [ui]() ### AI overview This article examines how NFL Sunday Ticket on YouTube moved live sports viewing from a fixed broadcast toward personalized, interactive streams. It describes custom Multiview layouts, fantasy integrations, spoiler controls, playback options, and interfaces that work across TVs, mobile devices, and tablets. ### Source excerpt It's the mid-90s, it's Sunday morning, and you're about to set your VCRs to record on your NFL game of choice You'd capture everything, commercials, dead time, halftime filler, and hope the tape didn't cut out in overtime. It was static. One-size-fits-all. Then TiVo arrived. That pause button changed the game. Suddenly, fans could freeze a field goal, run to grab game day food, and unpause just in time for the kick. Control entered the living room. Fast forward to NFL Sunday Ticket on YouTube. Fans are now building their own broadcast in real time. The cultural shift is clear: live sports have moved from passive consumption to personalized control over the gameday experience. Raising the Stakes For nearly three decades, NFL Sunday Ticket lived only on DirecTV. It was a premium cable-era product; expensive, exclusive, and tied to satellite hardware. Fans outside their home market had little choice but to buy into the bundle if they wanted every game. That era is over. When YouTube secured the Sunday Ticket package, it leapt from the satellite dish into the streaming stack. And with that leap came a redefinition of expectations: reliability is still required, but now the baseline includes interactivity and personalization at a massive scale. Inside Sunday Ticket's interactive layer: Custom Multiview Fans can build their own layout with two, three, or four games running in parallel. Local broadcasts integrate seamlessly for YouTube TV base subscribers. Multiview works across TVs, mobile, and tablets, keeping the experience consistent. Fantasy View Integration Users can link NFL Fantasy and Yahoo Fantasy accounts. This allows fantasy stats and team updates surface directly in the stream. Spoiler Mode & Playback Features Hide scores until you're ready to see them. Advanced playback controls such as broadcast delay toggle All the features create more ways to personalize the live feed. Improved UI & Side-by-Side Experience Dual-column layouts show live action next to stats ## 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 ## How AI is Changing the Way FanCode Builds, Tests and Ships DevFeed: [How AI is Changing the Way FanCode Builds, Tests and Ships]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-21T12:17:03Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [Artificial Intelligence](), [Development](), [pull-requests](), [Testing](), [Latency]() Tags: [ai](), [deployment](), [development](), [engineering](), [latency](), [pull-requests](), [software-engineering](), [sports](), [tech](), [testing](), [velocity]() ### AI overview FanCode describes how it has integrated AI into engineers' daily development workflows, including building, reviewing, testing, and shipping software for live sports. The article links this work to faster delivery and more reliable systems under load, while describing review delays and other growing pains in a lean engineering team. ### Source excerpt By Guruwinder Rishi and Pramod Kumar At FanCode, engineers work on problems that show up live -- in front of millions of fans -- and shape how sports are experienced in real time. If building, reviewing, testing, and shipping at scale excites you, we'd love to talk. Check out open roles at FanCode. At FanCode, engineering efficiency isn't an internal KPI. It's the difference between a fan watching a last-over finish without interruption and a stream dropping at the worst possible moment. When you're building for live sports, the margin for error is near zero. We stream 10,000+ live games every year, across sports, formats, and devices -- often with millions of fans watching at the same time. At that scale, every commit, every review delay, and every flaky test eventually shows up somewhere: in latency, stability, or how confident we feel pushing a change while a match is live. Behind the scale we operate is a lean 50 member engineering team across backend, frontend, and testing -- and that's by design. The problem we've been solving for years isn't scale through headcount. It's scale through clarity, velocity, and trust in our systems, while still giving engineers the space to learn, iterate, and do some fantastic work. We've been using AI across FanCode for a while now. But what's changed over the last year is how deeply we've started integrating it into the everyday lives of engineers. This blog outlines the changes we've made to our development workflows, how we used AI, and the results we saw in terms of faster delivery and more reliable systems under load. With Scale, the Cracks That Started to Show As FanCode scaled, we began facing the classic growing pains of a fast-moving, high-output tech team. Inefficiencies that were manageable earlier started compounding quickly, and a few patterns became impossible to ignore. I. The Review Bottleneck: Pull requests routinely took 2-3 days to move from "raised" to "merged". On their own, such delays didn't seem alarming. Bu ## A Day in the Life of Ayush Sharma, DevOps Engineer at FanCode DevFeed: [A Day in the Life of Ayush Sharma, DevOps Engineer at FanCode]() Original publisher: [Read original article]() Author: Dream Blog Published: 2025-12-24T07:59:51Z Content type: opinion Language: en Sources: [Dream11 Engineering]() Topics: [DevOps](), [Cloud](), [Linux](), [observability](), [dashboards](), [Latency](), [Security](), [Shell]() Tags: [culture](), [dashboards](), [devops](), [employee-experience](), [engineering](), [latency](), [linux](), [observability](), [security](), [sports]() ### AI overview A personal account of Ayush Sharma's work as an SDE-3 in DevOps at FanCode. The article describes his background, his transition from Linux and shell scripting to cloud-focused work, and his daily routines for monitoring systems, supporting teams, maintaining security, and helping keep streaming experiences reliable. ### Source excerpt If you're excited about building reliable, real-time experiences for millions of fans, we're hiring across FanCode. Explore open roles and come build with us. I grew up in a defence family in the same state that gave the world MS Dhoni. Sports wasn't a hobby; it was the air we breathed -- basketball on rough courts, kabaddi on dusty grounds, football and cricket wherever there was space. That mix of discipline and play shaped the way I work even today. Off the field, I was the kid who buried himself in esports matches and gadgets. Scrims taught me the value of low latency, clear comms, and decision-making under pressure. I didn't know it back then, but those instincts would later define my DevOps career. Before cloud was my day job, Linux was my playground. I fixed permissions on messy shared hosts, learned how logs speak when something's wrong, and wrote small shell scripts to make life easier. In college, I even stitched together a tiny "cloud" using a bunch of friends' laptops -- half experiment, half curiosity. Seven years later -- five of which I've spent at FanCode -- I'm here as an SDE-3 in DevOps, driven by one promise: make speed feel safe. Inside the company, my end users are the people who design, build, test, analyse, ship and secure our products. Outside, our fans just expect the stream to work. And my job is to make that seamless. How I Run My DayStart: Steadying the Field I like to ease into the day with a simple ritual: coffee in hand and a quick health sweep of the system. Before diving into any work, I read through the on-call handover to understand what unfolded overnight, not just what alerted, but how the system behaved. From there, I open our observability board and look at alerts and our key dashboards for patterns. These early clues help me understand if some team or process is blocked, or if a fan is facing an issue and address that immediately. I also go through requests from other teams -- tickets, a product experiment that needs support, a pip ## Dream UTT Juniors: Building India's Next Generation of Table Tennis Champions DevFeed: [Dream UTT Juniors: Building India's Next Generation of Table Tennis Champions]() Original publisher: [Read original article]() Author: Dream Blog Published: 2025-12-13T07:05:51Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [Sports]() Tags: [2025](), [collaboration](), [community](), [competition](), [dream-sports](), [india](), [platforms](), [sports](), [table-tennis](), [visibility](), [youth-sports-development]() ### AI overview The article describes Dream UTT Juniors, a collaboration between Dream Sports Foundation and Ultimate Table Tennis to give India's U-15 table-tennis players experience competing in a professional-league environment. It explains how sixteen players from the Dream Sports Championship: Table Tennis were drafted into UTT franchises and reports that the league was streamed on FanCode. ### Source excerpt At Dream Sports Foundation (DSF), our goal has always been simple: to make sports better by strengthening India's sporting ecosystem and creating opportunities that truly matter. From cricket and football to boxing and weightlifting, we've worked across sports, supporting their growth through high-impact programmes and partnerships that enable athletes to perform at their best. As we continued to expand our focus, we also began exploring Olympic and high-potential sports where structured, high-quality competitions could make a meaningful difference. That's how table tennis found its way into our plans -- a sport with immense potential in India, yet limited access to professional-level platforms for young athletes. The idea was to bridge that gap, to create an opportunity that allows young players to experience the intensity, standards, and spirit of a professional league early in their journey. The outcome was Dream UTT Juniors -- a collaboration between DSF and Ultimate Table Tennis (UTT). How Dream UTT Juniors Came to Life Sometimes, all it takes is one question to spark something extraordinary. For us, it began with a simple idea shared between DSF and UTT: "What if India's U-15 players could experience the thrill of a professional league, just like seasoned players?" That question set everything in motion. What started as a passing thought quickly turned into a shared mission -- to create a platform where India's most promising U-15 talent could play and compete like professionals. By early 2025, DSF's own Dream Sports Championship: Table Tennis, held in association with the Table Tennis Federation of India (TTFI), had already taken place -- bringing together remarkable young talent from across the country in a first-of-its-kind competition. The top sixteen players from that tournament were then drafted into the UTT franchises (U Mumba TT, Stanley's Chennai Lions, PBG Pune Jaguars, Kolkata Thunder Blades, Jaipur Patriots, Dempo Goa Challengers, Dabang Delhi T.T.C., ## Building Real-Time Live Sports Viewer Analytics with Tinybird and AWS DevFeed: [Building Real-Time Live Sports Viewer Analytics with Tinybird and AWS]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2024-12-03T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [real-time](), [Amazon Web Services](), [Sports]() Tags: [architecture](), [aws](), [real-time](), [scalable-analytics-architecture](), [sports]() ### AI overview The article describes building real-time live sports viewer analytics with Tinybird and AWS, including an architecture intended to support millions of viewers without crashing. ### Source excerpt Showing real-time stats to millions of viewers without crashing? It's possible. Here's the architecture that won't drain your budget. ## How a sports betting platform used CockroachDB for multi-state compliance DevFeed: [How a sports betting platform used CockroachDB for multi-state compliance]() Original publisher: [Read original article]() Author: Michelle Gienow Published: 2024-02-09T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Sports](), [Databases](), [real-time](), [Amazon Web Services](), [gateway](), [PostgreSQL]() Tags: [aws-outposts](), [cockroachdb](), [compliance](), [consistency](), [database](), [gateway](), [postgresql](), [sports](), [switching]() ### AI overview This article describes how a North America-based sports betting platform designed a multi-state architecture to meet performance, consistency, and data-locality requirements. It explains how CockroachDB, AWS Outposts, database gateway nodes, and geo-partitioning supported compliance while reducing the complexity of operating separate databases in each state. ### Source excerpt The challenges facing sports betting platforms are rigorous: they have to be fast and consistent in a way that's similar to high-frequency trading platforms. At the same time, they have a variety of data-locality requirements to comply with depending on which states or countries they serve. In this blog, we'll look at a real-world example of a North America-based sports betting platform that designed a gaming and sports betting architecture that not only meets the performance, consistency, and compliance requirements of the industry; but also enabled the business to swiftly enter new regional markets without requiring a massive engineering effort. ## Why Playing Team Sports Makes Me a Better Developer DevFeed: [Why Playing Team Sports Makes Me a Better Developer]() Original publisher: [Read original article]() Author: Eevis Panula Published: 2023-01-03T08:57:50.675000Z Content type: opinion Language: en Sources: [Eevis Blog]() Topics: [Sports](), [Job]() Tags: [blog-post](), [coaching](), [developer](), [fairness](), [sports](), [team](), [work]() ### AI overview The author reflects on how years of team sports, coaching, and organizational involvement have influenced their work as a developer. The article focuses on teamwork, sportsmanship, collaboration, and learning from diverse teammates, while noting that these benefits may also come from other activities. ### Source excerpt I've played team sports since I was something like ten. First, it was floorball - this fantastic indoor sport, where you have a stick, a ball, and goals. Something similar to hockey, but less violent. And after all, completely different. Then I moved to another city and soon realized that it's not as fun as it used to be with the team I had played with for 10 years. I switched to ultimate frisbee, which is something I still play. I also did a tour in the roller derby world, loved the sport but got a brain injury. I realized that if I want to be able to do my job, I need to let this awesome thing go. I'm sometimes still sad about it. So, I have a history (and hopefully future) with team sports. I've done some coaching during the years and participated in different boards - be it a club or national level organization. So I could say I've gotten a lot from this and also learned a ton. One day, while I was doing some ladder drills (they're for agility on the field), I started thinking if there has been anything I've used from my sports background in my day-to-day job. After pondering for a while, I realized that yes, there is. In this blog post, I will share some of those thoughts. Many other activities may provide just the same benefits. However, my background is in team sports, so that's why my angle is from the perspective of team sports. Team Spirit (of the Game) If you're not familiar with ultimate frisbee, you might wonder what the heck the title of this section means. Let me explain. In ultimate frisbee, there is this thing called "Spirit of the Game," which has to do with, for example, fairness of the game and sportsmanship. It's something that makes the sport possible to play without any referees (yup, you read it right!). During the years, being part of a team has indeed taught me to be a better team member. Especially being part of teams that comply with such philosophy as the Spirit of the Game. I know how to work in a group, and also that different people h ## Capture the Flag events and eSports DevFeed: [Capture the Flag events and eSports]() Original publisher: [Read original article]() Author: Jonathan Marcil Published: 2019-09-11T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Sports]() Tags: [competition](), [events](), [games](), [lan](), [sports](), [strategy](), [team]() ### AI overview The article compares Capture the Flag events with eSports and argues that CTFs are intrinsically an eSport with strong educational value. It describes eSports audiences, broadcasts, competition, professional gaming, and amateur LAN and online events. ### Source excerpt A breakdown of CTFs and eSports ## What Made Maddy Run DevFeed: [What Made Maddy Run]() Original publisher: [Read original article]() Author: Graham King Published: 2017-11-11T21:58:29Z Content type: opinion Language: en Sources: [Graham King]() Topics: [Sports](), [digital](), [context]() Tags: [behaviour](), [book](), [case-studies](), [case-study](), [digital](), [running](), [sports]() ### AI overview A review of Kate Fagan's book What Made Maddy Run, which reconstructs Madison Holleran's suicide through digital media and examines the pressures she faced as a student-athlete transitioning from high school to the University of Pennsylvania. ### Source excerpt Unveiling the hidden struggles behind a perfect facade.