Interviews Daredevil – Born Again: Gong Myung Lee – Production VFX Supervisor By Vincent Frei - 15/05/2025 With over two decades of experience in visual effects, Gong Myung Lee started her career in 2003 and went on to work at renowned studios like Mr. X and Method Studios. She became part of the Marvel Studios team in 2022. Her credits include The Finest Hours, Deadpool 2, Triple Frontier, and The Changeling. What is your background? I started in VFX as a Lighting Technical Director and Compositor, which gave me a deep appreciation for how images work—light, depth, and how all the parts come together. From there, I moved into CG supervision and eventually into VFX supervision, working across features, commercials, and episodic projects. I’ve worked both on the vendor side and in production. At facilities, I built and led teams and tackled creative challenges from inside the post pipeline. Shifting to the production side let me be involved earlier—helping shape decisions on set and ensuring VFX supports the storytelling from day one. How did you get involved on this show? I’d always hoped to work with Marvel on the production side, so when Jen Underdahl called me about this opportunity, I said yes immediately. I’ve been a fan of Daredevil for years—his internal conflict, his vulnerability, and the constant pull between justice and vengeance. And the fact that it was shooting entirely in New York made it even more exciting. Daredevil: Born Again has a distinct visual style. How did you collaborate with the showrunner and directors to ensure that the VFX seamlessly fit within their creative vision? From the start, it was clear this wasn’t going to be a typical superhero show. No lasers, no flying, no giant CG creatures. Daredevil is a street-level hero—he gets hurt, he bleeds, and he wrestles with morality and faith. The VFX had to reflect that same grounded humanity. Everything needed to feel physical, real, and restrained. We worked closely with showrunner Dario Scardapane, directors Justin Benson and Aaron Moorhead, and Marvel creative executive Sana Amanat to define a visual language built around subtlety. The VFX weren’t there to impress—they were there to disappear into the world. We also pulled from key comic book imagery that Sana wanted to preserve—Daredevil crouched on a rooftop, his mid-air baton throws, the skyline behind him. Those became touchpoints for our design language. Being involved from preproduction through delivery meant we could plan closely with every department—art, stunts, SFX, camera—so that VFX could step in where needed. We captured as much in camera as possible, using VFX for what couldn’t be achieved safely or practically. That ranged from invisible stitches to CG weapons, digi-doubles, and full digital builds. Because we had multiple directors and DPs, VFX became a continuity backbone—carrying consistent design and tone across episodes. That applied to everything from the sensory visuals and BB Report graphics, to how Daredevil’s baton behaved from one fight to the next. We also tracked the emotional and visual arc of the show. The intensity of violence escalates as the story deepens. In Episode 102, when Daredevil breaks a cop’s arm, we held back—the blood simply soaks through the sweater, staying grounded in realism. By Episode 109, when Fisk crushes Gallo’s head, the moment is fully on camera. That evolution was intentional. To make it all sit inside the frame, we were rigorous with data acquisition. Every set was LiDAR scanned with photogrammetry, and effects-heavy scenes were supported with HDRIs, cyberscans, clean plates, and lens grids. The goal wasn’t just coverage—it was making sure our work belonged inside the photography. Were there specific requests from the directors regarding the way VFX should enhance the realism and grittiness of the series, particularly in terms of lighting, camera movement, or fight choreography? Absolutely. From the get-go, directors Justin Benson and Aaron Moorhead established a clear visual rulebook. Camera language was closely tied to character: Fisk’s scenes leaned into locked-off frames and symmetry, while Murdock’s were more handheld and reactive. Zooms were preferred over push-ins, and dutch angles were generally avoided. Even when VFX took over a shot—like a CG camera move—we stuck to those same principles to preserve the tone. Our DP, Hillary Spera, shot on Alexa 35s using custom-tweaked anamorphic lenses, which created beautiful lens flares and optical distortions. We mapped those lenses and captured a full library of flare elements so that VFX could match them precisely. That look carried through to DI and helped define the show’s distinct texture. Atmosphere—especially fog and wetdowns—was critical to the show’s look. It shaped lighting and grounded the environments. But in shots requiring CG or 2D elements, we often recreated matching FX atmosphere in post to ensure seamless interaction and integration into the plate. Action followed the same grounded approach. Our Action Director and Supervising Stunt Coordinator, Philip Silvera, choreographed fights based on real-world combat logic, but with emotional weight built into the pacing—breath, exhaustion, impact. For digital takeovers, we started with mocap of stunt performances, then refined using video reference to match the actors’ specific movement and intention. We didn’t shy away from gore either. The directive was clear: keep it grounded, but don’t pull punches. We referenced surgical and forensic material to guide the look of blood hits, wounds, and trauma. Practical prosthetics and squibs formed the base, with VFX adding layers—muscle tear, splatter, and dynamic blood flow—to heighten realism without drifting into stylization. Photo credit: Giovanni Rufino How did you choose the various vendors and split the work amongst them? VFX is a team sport—and like any good team, you need to know your players and play to their strengths. Every facility brings something unique, and a big part of our early conversations was about assigning work based on where each vendor excels. It was about setting everyone up for success. The best VFX comes from the artist’s eye and hand. That final 5%—the nuance and polish—is where the work really comes alive. So I looked for teams who not only had the technical chops but understood the tone of the show and cared deeply about the craft. When that alignment happens, the collaboration just works. We planned the vendor split early, ensuring shared assets would work across facilities and that pipelines were compatible. The Third Floor handled previs, techviz, and postviz. LolaVFX supported early look development. RISE took on some of the most technically complex sequences—like the opening oner at Josie’s, digi-double transitions, and full environment builds. Folks VFX delivered rooftop environments, the finale apartment fight, and key continuity-heavy scenes. Ghost VFX handled the subway fight and Daredevil’s tunnel run, while Phosphene developed the sensory language and crafted bespoke moments like the blackout sequence and car comps. Soho VFX and Powerhouse VFX focused on fight choreography, weapon augmentation, and full-CG sequences. Cantina Creative created all in-world monitor graphics and interfaces. Anibrain, SDFX, and Base FX tackled difficult cleanups, split comps, and continuity patches. Dark Red Studios handled the BB Report design. Scanable handled all LiDAR and cyberscans across our sets and cast—crucial for digi-double and environment work. And we also had an internal team dedicated to the more subtle sensory shots. It was a true team effort, and the final result reflects the strength and dedication each vendor brought to the table. Photo credit: Giovanni Rufino The opening sequence is an incredibly complex oner action scene. Can you walk us through the planning and VFX techniques used to stitch everything together seamlessly? This sequence was a massive effort across all departments, and for VFX, it was one of the most complex and rewarding challenges of the season. The scene begins with Daredevil and Bullseye crashing through the window of Josie’s Bar, moves into the back room, up through a stairway, and eventually emerges onto a rooftop. None of those locations existed together physically, so stitching them into one continuous action required close collaboration between stunts, camera, SFX, and VFX from day one. Our stunt team, led by Philip Silvera, provided a detailed stuntviz that broke the choreography into stitchable beats, which became our foundation. Directors Justin Benson and Aaron Moorhead wanted the camera to feel intentional and fluid—no fast whips or chaotic edits—so every transition had to be precise. There was nowhere to hide a bad seam. RISE led the VFX for the sequence, using a blend of techniques—2D wipes with foreground elements, bluescreen set extensions, FX smoke, and CG transitions—but the real connective tissue was in 3D. We LiDAR-scanned all key locations and rebuilt them in CG, allowing us to bridge multiple stages, extend environments, and support digi-double and FX work with accuracy. Stitching those spaces and action beats required careful spatial continuity and character animation. We used motion capture as the base for digital double work, then enhanced it with keyframed animation to preserve choreography and hit those iconic silhouettes that are signature to Daredevil and Bullseye. The camera tilt moment—where we drop down to see Foggy on the street and then tilt back up to Daredevil and Bullseye—was a digital blend using a 2.5D street projection and a full-CG building facade. That allowed us to link two separate physical locations with one fluid camera move. SFX, supervised by our SFX Coordinator Roy Savoy, handled all the glass breaks and practical sparks inside Josie’s, while Props took care of the breakaway items. Together, they gave us strong in-camera interaction points that VFX could build on. We layered in additional CG debris, neon signage, and more sparks to push the action further. The smoke and atmosphere—inside the bar and on the rooftop—were fully CG to allow precise interaction with digital characters, weapons, gore, and FX. Everything—smoke, blood hits, atmosphere, digi-double transitions—had to feel cohesive and continuous. The real achievement was making the stitching and technical complexity disappear. Given the mix of practical stunts and digital enhancements, what were the biggest challenges in maintaining continuity and realism throughout this oner? One of the biggest challenges was maintaining character integrity—especially for Daredevil. Charlie brings such a specific physicality to the role that even under smoke or in silhouette, his movement reads clearly. Every digital double had to mirror that language—his timing, stance, and how he moves through space—so the audience never questioned the switch. RISE used stunt mocap as the foundation for the digital doubles, then layered in character-specific adjustments to preserve that continuity. At the same time, we were building out full-CG environments to bridge shots, which meant matching textures, lighting, geometry, and camera lensing so that transitions were seamless. Weapons were another layer. Daredevil’s baton, Bullseye’s firearms and knives—all were CG. In one sequence, Daredevil is stabbed by 17 blades, some of which he pulls out to fight back. We had to track each knife’s position, contact shadow, suit tear, and blood drip from shot to shot, which required full-body rotomation and progressive damage built into the suit asset. That also extended to the environment—any damage had to be tracked through the sequence. Grounding the action was essential. Even when Daredevil lands from a rooftop with his grappling hook, it had to feel weighty. We added dust bursts, imperfect footfalls, and a bit of camera vibration to keep it visceral. In a sequence this seamless, every movement, every transition, every detail had to hold up. The illusion depended on total cohesion. How did you approach the digital recreation of New York and Hell’s Kitchen to match the series’ darker, more grounded aesthetic? New York is more than a backdrop—it’s part of Daredevil’s identity. So our approach to the digital builds had to feel authentic, responsive, and emotionally aligned with the show’s tone. We LiDAR-scanned full city blocks, rooftops, and adjacent buildings, and captured tiled plates and HDRIs throughout the day to map real lighting conditions. This gave us a strong base to work from, while still allowing flexibility for framing and layout. Folks VFX handled Matt’s rooftop in Episode 103, while RISE built the Josie’s rooftop and surrounding Hell’s Kitchen. Both spaces had to feel true to the city—textured and believable. Even slight changes in building height or placement could shift the tone of the scene, so we were careful to maintain a balance between realism and composition. Atmosphere was a defining element. We added fog, steam, wetdowns, reflections, and other city-layered effects—like flickering windows and distant traffic—to give each environment weight and depth. These elements weren’t just aesthetic—they helped shape the lighting and made our digital spaces feel physically inhabited and consistent with the show’s tone. Were there any key locations that had to be fully CG or heavily augmented with VFX? If so, what was the process of integrating them into the live-action footage? Yes—several locations had to be fully CG or significantly augmented. Matt’s apartment rooftop in Williamsburg, the Hell’s Kitchen rooftop at Josie’s, the fire escape sequence, Hector’s subway fight, Daredevil’s tunnel run, and the sensory bank vault scene all required extensive digital builds. We began by scanning or photographing real-world references, scouted in collaboration with our creative team, Production Designer and DP. Using LiDAR and photogrammetry, we rebuilt those environments in 3D, preserving scale and lighting using on-set HDRIs. Foreground and midground buildings were modeled in high detail, and wherever windows were visible, we added interior structure and depth to maintain a sense of life beyond the facade. In some cases, city regulations or safety concerns limited our ability to fully scan practical locations. For sequences like the subway platform and the tunnel Daredevil runs through in Episode 106, we used partial scans and reference photography to reconstruct and extend those environments in CG—including the train asset and the tunnel’s structural continuity. In Episode 105, the bank vault sensory sequence was shot on stage with only the real vault door present. Everything else—the spatial layout, dimensionality, and lighting—was designed in CG to reflect the show’s established visual language for Daredevil’s heightened perception. Not exactly an environment extension, but for the “blackout” sequences in the finale, we had to heavily modify our plates to feel like a true city-wide outage. That involved blending day-for-night captures with digital relighting in comp. In Times Square, we replaced the practical LED content with FISK delivering his New Year’s Eve speech. And to build tension, we expanded Fisk’s motorcade in Red Hook using CG mocaped taskforce units and Humvees. Whether it was a full CG build or a heavy plate modification, every element was grounded back into the photography through precise matchmove, consistent lighting cues, and environmental reference. Marvel projects often have a mix of on-location and green-screen work. What techniques did you use to ensure a seamless blend between real and digital environments? The key was always grounding the visual effects in something tangible. Whether we were working on a full bluescreen stage or extending a rooftop, the more real-world reference we could gather, the more seamlessly the VFX would sit in the frame. On Daredevil: Born Again, we captured practical plates and tiled reference footage wherever possible—especially throughout New York. That gave us a photographic base to match to. From there, we built every VFX setup on solid acquisition: LiDAR scans, high-res photogrammetry, HDRIs for lighting, and texture capture from all angles. We avoided relying entirely on CG whenever we could. Even in the most complex shots, we tried to include a practical element—whether it was a foreground prop, atmospheric haze, wet-downs or interactive lighting on set. Those physical components gave us something real to build off and helped the audience feel rooted in the scene. For environments, 2.5D extensions were backed by real plates, and full-CG builds were constructed from real-world geometry and surface data. Even on bluescreen stages, we lit to match the look and feel of the corresponding practical scenes to preserve continuity. We also worked closely with DI to carry through the grade and environmental treatment. That final layer ensured the shot didn’t just blend technically—but emotionally felt like it belonged to the same world. Daredevil is known for its brutal, grounded action sequences. How did VFX help enhance the stunt work while keeping it as practical as possible? Our approach was always practical-first. If it could be done in camera—blood hits, breakaways, physical impacts—we shot it that way. Then VFX would step in to enhance what was already there and bring the moment to full fidelity. For example, blood on faces and inside mouths was done practically using SFX makeup. But for continuity or when choreography called for additional impact, we added digital blood sprays, squibs, or drips—carefully tracked to the performance so they felt embedded, not layered on. Our stunt team gave us strong reference across all key fight beats. We combined that with mocap when needed, especially in sequences that transitioned into digital doubles. Having that real-world base allowed us to maintain physicality and character intention throughout. We also captured a library of flares and lens artifacts from our camera package, giving VFX the right visual language to match the look and feel of the photography—especially during close-quarters combat, where blood, smoke, and light moved dynamically through frame. It helped reinforce the tactile, in-camera aesthetic, even in fully augmented shots. Were there any particular fight sequences that required extensive digital double work, and how did you ensure they remained indistinguishable from the real actors? There were definitely moments where digi-doubles became essential—sometimes for safety, sometimes to maintain camera continuity, and sometimes because the physical environment couldn’t support the choreography as scripted. Sequences like Daredevil’s rooftop runs, tunnel chases, fire escape landings, grappling hook jumps, transitions within the oner, and background extensions to enhance Fisk’s task force all required digital double work at varying levels of complexity. In every action scene, our approach was to ground the work in real-world performance. We captured motion for all key stunts and fight beats, and did detailed cyberscans of the main cast and their stunt doubles. That gave us accurate silhouettes, proportions, and suit detail—everything we needed to maintain visual continuity between live-action and CG. While we didn’t use facial capture rigs or volume setups, Charlie Cox gave us highly expressive reference performances. His focused takes gave us insight into the intention behind each movement, which we either projected in 2.5D or matched in animation for the digi-double moments. What really sold it was the integration. We were meticulous about matching HDRI lighting, lensing, atmosphere, and even subtle depth-of-field cues. Every digital element had to feel like it was captured on the same lens, in the same space, at the same time. That’s what kept the illusion seamless—even in full-CG moments. Were there any memorable moments or scenes from the series that you found particularly rewarding or challenging to work on from a visual effects standpoint? One that stands out is the rooftop environment for Matt’s apartment. It was our first major VFX presentation to the directors and creatives, and it set the tone for what our digital New York would become. It needed to feel cinematic, atmospheric, and emotionally grounded. We scanned and photographed rooftops around Williamsburg, then composited those elements into a custom layout tailored for our scenes. But even subtle changes—like shifting a building’s height or shape—could disrupt the familiarity of the skyline. It’s like adjusting features on a face: subtle, but if it’s wrong, you feel it. Getting that balance right, while still supporting our camera moves and blocking, was incredibly rewarding. Another favorite was developing Daredevil’s sensory language, which we crafted in close collaboration with Phosphene. It evolved throughout the season, but the most ambitious moment was what we called the “Grande Sensory” shot—when Daredevil tunes into a specific sound and his world expands perceptually. We shot it using a custom three-camera rig: a spherical zoom lens in the center, flanked by wide primes. As we pushed in while zooming out—a zolly—we stitched the feeds together at the widest focal length into an approximately 230° field of view, with extra image gathering on the sides. Simultaneously, the aspect ratio expands. Once Daredevil locks onto the target sound, we “unstitch” back to the center camera and compress the aspect ratio as we zoom in for a clean finish. The transitions are subtle but emotionally impactful—designed to be felt, not noticed. We also had the “petite sensory” moments—simpler, analog-feeling visual cues that suggest Daredevil is focusing in. These used optical lens tricks, subtle distortions, and flares—many based on practical elements we shot—so even the heightened perception moments felt grounded in the show’s visual language. And finally, the Gallo head-crush—one of the season’s most graphic shots. The practical prosthetic was impressive, but once pressure was applied, it deformed in a way that didn’t mimic human anatomy. We scanned both the actor and prosthetic for a partial digital takeover—replacing areas with CG muscle, bone, blood, and facial details. Folks VFX handled this sequence and absolutely nailed it, especially the vascular bursts in the eyes and the blink just before the fade. Looking back on the project, what aspects of the visual effects are you most proud of? There’s a lot to be proud of, but what stands out most to me is the consistency of tone and craft—how the visual effects supported the grounded, emotional feel of the show without ever pulling focus. Even in sequences that were technically complex—like the rooftop builds, sensory moments, or the opening oner—we always came back to story. The VFX weren’t about spectacle. They were about precision, intention, and integration. I’m also proud of the continuity we maintained across multiple directors and DPs. Visual effects became a steady thread—carrying design language, violence levels, atmosphere, and choreography from one episode to the next. Most of all, I’m proud that the work feels cohesive. Whether it’s a subtle stitch or a full CG shot, it never distracts. It feels lived in—part of the world, not outside of it. How long have you worked on this show? I was on from November 2022 through November 2024, so a full two years. Being involved early really helped—we had time to plan things properly, make smart choices, and stay ahead instead of playing catch-up. What’s the VFX shot count? We delivered 1,380 final shots in the official turnover cut. What is your next project? We’re already shooting Season 2 of Daredevil: Born Again, which has been exciting. Coming back with the same core team means there’s already a rhythm in place—we know how to work together, and that gives us room to push things further. We’re building on what we set up in Season 1, keeping the tone grounded, but also exploring new visual ideas and storytelling challenges. It’s great to be back in this world, with a chance to evolve it in new ways. What are the four movies that gave you the passion for cinema? There are many, but ones that come to mind immediately are The Godfather I & II, The Shining, In the Mood for Love, and Dune: Part One & Part Two. A big thanks for your time. WANT TO KNOW MORE?RISE: Dedicated page about Daredevil: Born Again on RISE website. © Vincent Frei – The Art of VFX – 2025

Bandai Namco's cancelled MMORPG Blue Protocol will release outside of Japan this year Coming to both PC and mobile. Image credit: Bokura News by Ed Nightingale Deputy News Editor Published on May 15, 2025 Bandai Namco's cancelled anime MMORPG Blue Protocol will release on PC and mobile this year outside of Japan, in Europe, North America and Latin America. In August last year, the publisher stated it would shut down the Japanese servers for Blue Protocol and cancelled its western release. Then, a few months later, the Tencent-funded developer Bokura picked up the project, carried over some of the original development team, and gave it a new name, Blue Protocol: Star Resonance. Now the free-to-play game is confirmed for release outside of Japan for the first time and will be available on PC (Steam, Epic Games Store) and on mobile (iOS, Google). Blue Protocol: Star Resonance | Announcement TrailerWatch on YouTube The game will also support cross-platform play between the two platforms, with synchronised progression. The MMORPG released in Japan in June 2023, with a proposed western release managed not by Bandai Namco but by Amazon Games. The game was intended to follow the likes of Lost Ark and New World as Amazon Games' next big MMO. However, the game received a mixed reaction from Japanese players. Its shutdown last year brought an end to hopes it would finally see a release outside of the country. A statement from the developers at the time said the "difficult decision" had been made to end service as "it will not be possible to provide a service that satisfies all of you". Bandai Namco reportedly lost over a hundred staff in the last year, with the cancellation of Blue Protocol assumed to have caused job losses. Now the game is making a return, promising a dynamic combat system, an anime-styled open world, deep character customisation, and plenty of options for socialising through trading, guilds, and community events. I went hands-on with Blue Protocol in 2023 and although I enjoyed it, I had concerns on how it would stand out from other games in the genre. At least now we'll be able to see for ourselves here in the west.

Documents reveal team tested options including building over station’s entire listed trainshed New image of Acme's proposals for the station's main entrance 1/22 show caption The City of London has published the planning application for Network Rail’s proposed redevelopment of Liverpool Station, revealing new images of how the scheme could look when built. The £1bn Acme designed scheme was submitted at the beginning of April and has been validated by Square Mile planners in the space of just six weeks.  A highly controversial previous version designed by Herzog & de Meuron for Network Rail and its former development partner Sellar, which has since been dropped, took more than six months to appear on the City’s planning portal. More than 20 previously unseen images of the new proposals, set to be one of the largest schemes in London, have been unveiled along with new details about how the scheme would be built. Acme’s plans would see a 21-storey office block built above the 1980s extension to the grade II-listed station’s train shed and a set of new vaulted gothic entrances built to replace the building’s existing gateways. These entrances would be faced predominately with yellow stock bricks, the same type used for the original 1875 station building and its 20th century extension, with bricks from parts of the extension set to be demolished to be reused in the new entrances. Acme has also proposed incorporating amber-tinted glass bricks, which will be “speckled” in the upper parts of the 18m-high entrance vaults and concentrated at the top of the concave areas of the arches between the ribs. Network Rail said the glass bricks will “serve as one of the contemporary subversions of an otherwise historic typology”, adding a “crystalline light scatter of the material [to] mark the stations thresholds as spaces of architectural interest”. The application documents also reveal Network Rail had considered building over the entire listed train shed roof prior to opting for a limited development over the 1980s concourse area. Options tested included a single block facing Exchange Square at the northern end of the train shed, three blocks spaced over the length of the train shed, elongated blocks running along either side and a large block containing multiple light wells which would sprawled over the full extent of the station. Options for the over-station development tested by Network Rail prior to the selection of the current proposal for a building above the concourse A further option to build a tower scheme over the existing Metropolitan Arcade opposite the main station building on Liverpool Street was also considered but was ruled out due to ownership issues and below ground constraints of the Circle and Elizabeth Lines. Network Rail initially favoured an over station development facing Exchange Square but concluded this was scrapped because of its impact on train services, its engineering complexity and difficulty in creating viable entrances. The preferred development above the concourse was identified as the most viable, although it will not include building above the grade II*-listed former Great Eastern Hotel which had been one of the most controversial aspects of Herzog & de Meuron’s proposals for the site. The plans confirm Network Rail’s pledge last year to take a more “heritage-led” approach to the redevelopment compared to the previous scheme, which had proposed interventions in a strikingly different design to the 19th century station. That scheme was abandoned last year with Network Rail’s development partner Sellar dropped after the application amassed more than 2,000 objections from members of the public and criticism from heritage groups including Historic England. A selection of Acme’s early design concepts for the station entrances Network Rail’s property arm, Network Rail Property, is now leading the redevelopment and has sought closer collaboration with heritage groups on the design, although the Victorian Society, which led the campaign against the previous proposals, is still objecting to the new designs and has described the planned over-station office tower as “perverse”. The office component is being used to fund improvements to the rest of the station, which is currently the UK’s busiest with around 118 million people a year crossing its concourse with annual passenger numbers expected to hit 158 million by 2041. Network Rail said the redevelopment, which will significantly enlarge the building’s concourse, will enable the station to serve more than 200 million passengers a year. It also aims to turn the station into a “destination in its own right” with new retail, leisure and workspace, aligning with the City of London’s Destination City ambition to diversify its economy. The project team includes Aecom on engineering and transport, Certo as project manager, Newmark, previously known as Gerald Eve, on planning, Gleeds as cost manager, Donald Insall Associates on heritage and townscape, GIA on daylight and sunlight and SLA as landscape architect.

Lindsay SalazarEvery item on this page was hand-picked by a House Beautiful editor. We may earn commission on some of the items you choose to buy.From marble and concrete to zellige and subway, there are limitless possibilities when it comes to tile. While the material’s popularity has waxed and waned over the years, we’re currently entering a tile renaissance. No longer limited to just your floors or shower stalls, tile is covering everything from side tables to skylights, drenching bathrooms and kitchens in a glossy new light. “Clients aren’t defaulting to the standard white subway tile anymore,” says interior designer Sarah Stacey. “There’s a lot more interest in making intentional, expressive choices with tile now, whether it’s through color, pattern, or texture. People are much more willing to take creative risks than they were even a few years ago.” Interestingly enough, the biggest tile trend of the season is being anti-trend. Personality and joy-driven interiors are being prioritized instead of property value and neutral finishes. “Clients are less interested in safe, resale-driven choices and more open to statement moments, ” says interior designer Michelle Murphy of DEMI RYAN. “They want spaces that reflect them, not just trends—a dramatic shower wall, patterned floors, or jewel-box powder rooms.” Whether your aesthetic leans more towards color-drenching or pattern mixing, there’s a creative use of tile just begging to be a part of your next renovation or remodel. Below, 16 top experts reveal the tile trends they’re most excited about right now—alongside images showing how they used them to stunning advantage.See more current design trends we’re obsessed with:1Scalloped EdgesTrevor Tondro“I'm loving scalloped designs! This spring, we are seeing lots of scalloped edges in dinnerware, furniture, soft goods, and now in tile! We are thrilled to launch our Ashton Meadows collection with Studio McGee, which features a scalloped edge that is tailored yet whimsical. ” —DeeDee Gundberg, Chief Designer, Ann Sacks2Retro RevivalCourtesy of Tile of Spain“From kaleidoscopic patterns to '70s palettes, there is a marked nod towards a retro revival lately. A true hint of nostalgia can be seen with zesty colourways taking centre stage, while striking patterns create visual focal points, allowing interiors to celebrate bygone design eras in a new, contemporary fashion.” —María D Arráez, Director of Tile of Spain UKAdvertisement - Continue Reading Below3Color-DrenchingCourtesy of Ceramics of Italy“After attending Coverings 2025—the largest international tile and stone exhibition in North America—we’ve noticed a palpable shift towards playful and expressive tile designs. Italian ceramic manufacturers are leading the charge with small-format tiles featuring whimsical patterns and vibrant colors, ideal for both walls and floors in the bathroom. Notably, the trend of “color drenching,” and in the tile world, “mineral drenching," is gaining traction, where entire spaces are enveloped in rich tones and textures reminiscent of marble, onyx, or travertine, to fully immerse a space.”—Brittney Harmond, representative of Ceramics of Italy 4Natural MaterialsCourtesy of Vitale Design Group“The revival of earthly natural stones in 2025 truly brings sophistication and romance to the bathroom. This trend creates the perfect ambiance and enhances the mood through the use of warm tones in travertine, limestone, and bold vein marbles. Our second favorite trend is Zellige tiles. Zellige tiles are handmade Moroccan tiles that are multi-tonal, and the hand-glazing allows the colors to truly feel alive. These tiles bring timeless elegance & versatility to any space.”—Alexis Vitale, Vitale Design GroupAdvertisement - Continue Reading Below5Custom ConfigurationsJoseph Bradshaw“Mosaics offer such exciting possibilities, and we’re seeing a significant rise in the use of custom mosaics, especially. Designers are leveraging the versatility of mosaic tile to craft completely personalized designs, whether that’s a fully branded tile wall or an ancient Greece-inspired residential bathroom floor.” — Jamie Chappell, VP of Brand and Marketing at Fireclay Tile63D MotifsL'ANTIC COLONIAL“The 3D relief tile trend is particularly captivating. Tiles with dimensional surfaces play with light and shadow, adding depth and dynamism to spaces. This tactile quality not only enhances the visual appeal but also contributes to the overall sensory experience of the bathroom, aligning with the broader trend of creating spa-like, restorative environments at home.” —Jordana Moche, NYC Managing Director at PorcelanosaAdvertisement - Continue Reading Below7Rare PairingsLauren Miller“I like the mash-up of the unexpected—it used to be that limestone and marble weren’t really materials you mixed, but I like the recent departure from matching to something a little more freeform. I also like the exploration of deeper tones like oxblood marbles and green onyx.” —Sam Sacks, Sam Sacks Design8Sidestep ShinyGieves Anderson“My favorite trend is tile having a more natural and organic look, rather than the crisp modern look that has been popular. This is why I am using more ceramic tile and opting for more matte and honed pieces rather than the polished style we’ve been using for so long now. 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Today, MarkTechPost had the pleasure of interviewing Joey Conway from NVIDIA to discuss their exciting work on open-source large language models, including Llama Nemotron Ultra & Parakeet. Highlights from the interview: NVIDIA’s Open Source Powerhouse: Discover how NVIDIA is pushing the boundaries of open-source AI with the release of cutting-edge models like Llama Nemotron Ultra and Parakeet TDT. Llama Nemotron Ultra: Smaller Size, Giant Performance: Learn how NVIDIA achieved on-par performance with models twice the size, enabling deployment on a single GPU node. Explore their innovative FFN fusion technique for significant speedups. Reasoning on Demand: Uncover the unique “reasoning on/off” feature in Llama Nemotron Ultra, offering unprecedented control for production deployments and cost optimization. Revolutionary Speech Recognition with Parakeet TDT: Dive into NVIDIA’s state-of-the-art ASR model that transcribes one hour of audio in one second with only a 6% word error rate – 50 times faster than other open-source alternatives! The “How”: Architectural Innovations: Get insights into the advanced architectures and optimizations behind these models, including FFN fusion, limited context attention, and the Token Duration Transducer (TDT)  Democratizing AI with Open Data: Learn about NVIDIA’s commitment to the open-source community through the release of model weights and massive, high-quality datasets for both language and speech. Future Directions: Get a sneak peek into NVIDIA’s plans for multilingual support, even smaller edge-optimized models, and advancements in real-time streaming for speech recognition. Production-Ready AI: Understand how these models are designed with real-world deployment challenges in mind, focusing on accuracy, efficiency, and cost-effectiveness. Jean-Marc Mommessin: Joey, welcome to Marketechpost! We’re thrilled to have you here and to delve into the impressive open-source models NVIDIA has been releasing. To start, could you please introduce yourself and your role at NVIDIA? Joey Conway: Hi Jean-Marc, it’s great to be here. I’m Joey Conway, and I work in product management for some of the deep learning software at NVIDIA. Our team focuses on large language models like Nemotron and Llama Nemotron, as well as text-to-speech models such as Parakeet. Jean-Marc Mommessin: Wonderful. And you’ve been at NVIDIA for over seven years now, witnessing significant waves of innovation in AI. Let’s talk about your recent release, Llama Nemotron Ultra, a 253 billion parameter model. From what we’ve seen, it delivers performance on par with models like Llama 405B and DeepSeek R1, which are about twice its size. Remarkably, it can run on a single 8x H100 node. What else can you tell us about Llama Nemotron Ultra and what makes it so impressive? Joey Conway: We’re big believers in the open-source community and the fantastic work being done there. With Llama Nemotron, our goal was to build upon the existing foundations, particularly Llama, for which we greatly appreciate Meta’s contributions. We also observed significant progress in reasoning within the open community earlier this year. Inspired by this, we wanted to contribute and see how we could enhance Llama, especially for enterprise use cases. Our focus was primarily on improving reasoning capabilities and agentic tasks like tool calling and chat. We aimed to take the strengths of the open-source community, enhance them, and then contribute those improvements back. Jean-Marc Mommessin: Did you identify specific gaps in existing models that you aimed to address? You mentioned reasoning, but could you provide an example or two of enterprise agentic tasks where you felt there were shortcomings that Llama Nemotron Ultra overcomes? Joey Conway : Yes, I think looking back to the beginning of the year, a key challenge in enterprise deployments was handling complex queries requiring significant thought and reflection. These could be multi-step processes or involve substantial calculations and the use of external tools. At that time, there weren’t many strong open-weight models capable of robust reasoning. The progress we’ve seen in the last few months in this area is very encouraging. Another critical aspect for enterprises is the ability to accurately call APIs and closely follow instructions in user queries. We wanted to ensure that while we focused on improving reasoning, we didn’t compromise these essential production-level capabilities. Furthermore, we often noticed that when both reasoning and instruction following were well-addressed, they typically resided in separate models. Our aim was to simplify this by creating a single model that excels in both. This was the landscape we observed when we started this project around January and February. Jean-Marc Mommessin: That makes perfect sense and aligns with what we’re seeing in the industry as well. Now, let’s dive into the “how.” Your paper mentions FFN fusion as a key optimization. Could you elaborate on this technique, starting with a high-level explanation? Joey Conway: Absolutely. Our focus on optimization stemmed from the realization that deploying state-of-the-art models often requires a significant deployment footprint. We wanted to optimize this to fit within more common GPU setups. We explored various techniques, including our Puzzle neural architecture search. For dense transformer models, particularly those in the Llama family, we discovered a way to reduce or eliminate redundant attention layers. This process aligned the feed-forward network (FFN) layers in a sequence, allowing us to explore fusion methods. Our fundamental goal on the GPU is to maximize parallel execution. Fusing these aligned FFN layers enables greater parallel computation than was previously possible. By removing redundant layers, we found opportunities to essentially merge or fuse the remaining ones. This is a key example of how we tackle the challenges of running these models at scale. Importantly, this technique often yields greater improvements with larger models, which was beneficial for our Ultra model based on Meta’s Llama 3.1 -405B. Jean-Marc Mommessin: And this FFN fusion significantly improves the model’s throughput, achieving notable speedups. If I recall correctly, it’s in the range of 3 to 5x for the Ultra model? Joey Conway: That’s right, the speedups for the Ultra model are in that range. Additionally, by reducing the model’s size in terms of weights, we also lowered its memory footprint. This allowed us to utilize a larger KV cache. For Llama Nemotron Ultra, we could fit it onto a 8x H100 80GB setup, which is quite significant as it fits within common node configurations. So, FFN fusion provided both a substantial compute speedup and a reduction in memory usage, enabling us to handle larger context lengths. These are very exciting outcomes for us. Jean-Marc Mommessin: Let’s switch gears to data curation. AI data is crucial, and your training pipeline seems very sophisticated. You touched on “instruction following” earlier. Could you elaborate on your data curation process and how you ensured high-quality data, especially considering you leveraged other models in the process? Image source: NVIDIA Joey Conway: Transparency and openness were key in our approach. We wanted to share as much as possible about our data, techniques, and tooling so the community could understand and even use it themselves. Our primary goal with data curation was to improve accuracy across several key domains, including reasoning tasks like math and coding, as well as non-reasoning tasks like tool calling, instruction following, and chat. Our strategy involved curating specific datasets to enhance performance in these areas. Within our supervised fine-tuning process, we differentiated between “reasoning on” and “reasoning off” scenarios. For example, in math and coding, we curated data for simple questions that don’t require complex reasoning, as well as more intricate problems that do. This helps the model learn when and how to apply reasoning. A key part of this process was leveraging high-quality models from the community as “experts” in specific domains. For instance, we used DeepSeek R-1 extensively for reasoning-intensive math and coding tasks. For non-reasoning tasks like basic math, coding, chat, and tool calling, we utilized models like Llama and Qwen. Our aim was to blend the best capabilities of these community models into a single model. We’ve also made this curated dataset publicly available on Hugging Face, with around 30 million question-answer pairs. This allows the community to explore, use, and build upon our work. We were also excited to see our partner ServiceNow recently announce their apprehend Nemotron model, which was trained using our dataset to enhance their own reasoning capabilities. Jean-Marc Mommessin: That’s fantastic that you’re sharing the dataset. Given that you used other models to generate some of this data, what kind of quality checks did you implement to ensure the reliability of the training pairs? Joey Conway: Data quality was absolutely paramount. Since we were generating a significant portion of the data using other models, we implemented a rigorous multi-layered quality assurance process. First, for each expert model used to generate data in a specific domain, we would generate multiple candidate responses for the same prompt. Then, we employed a separate set of “critic” models to evaluate these candidates based on correctness, coherence, and adherence to the prompt. Second, we implemented a scoring mechanism. Each generated question-answer pair received a quality score based on the critic model’s evaluation. We set a high threshold, and any pair that didn’t meet this standard was discarded. Third, human review was integrated at various stages. Our team of data scientists and engineers manually inspected samples of the generated data to identify any systematic errors, biases, or instances of hallucination. This human oversight was crucial for catching nuances that automated systems might miss. Fourth, we focused on the diversity of the generated data. We wanted to ensure we weren’t just getting variations of the same types of questions and answers. We implemented strategies to encourage the expert models to generate a broad range of examples within each domain. Finally, after training Llama Nemotron Ultra on this curated data, we conducted extensive evaluations against benchmark datasets and in real-world use cases. This feedback loop helped us further refine our data generation and filtering techniques. So, it was a comprehensive approach involving expert generation, automated criticism and scoring, human review, diversity checks, and rigorous downstream evaluation to ensure the high quality of our training data. Jean-Marc Mommessin: The quality of the synthetic data is so important. Could you elaborate on the stages you take to ensure high accuracy when generating this data? Joey Conway: Absolutely. When doing synthetic data generation, there are a few key stages to ensure high accuracy. The first is the prompts – the seed data and how we prompt the model. The second is the quality of the responses. On the prompting side, we focus on prompting models where we believe they excel. For example, we might use Llama for chat-related prompts but avoid using a non-reasoning model for math. It’s crucial to align the prompts with the core strengths of the model. For vetting the responses, we invest time in both human manual review and automated methods. Going forward, we anticipate increasing our use of verifiers and reward models, similar to what we’ve done on the Reinforcement Learning (RL) side. The reason we’ve open-sourced much of this is that there’s a lot of nuance involved, and we wanted the community to engage with these challenges. Enterprises like ServiceNow have specific goals, and some of our data might be more or less useful to them. By making it available, they can vet it themselves. We also provide tools like classifier models to help categorize content, such as news or sports, allowing users to make informed decisions about the data blends they use for training. Jean-Marc Mommessin: Perfect. Is there anything else you’d like to highlight regarding this pipeline? Joey Conway: Yes, I’d like to touch on the Reinforcement Learning (RL) aspect. Following the supervised fine-tuning stage, where we enhanced core skills, we’ve just begun to explore the potential of RL with Nemotron. We believe this will be a significant area of future development. What’s exciting about RL is that its effectiveness is largely tied to the available compute time. The more time we invest, the better the model becomes at specific tasks. In our RL stages, we’ve developed methods to automate the process of asking the model a question, grading its answer, and providing feedback to allow it to learn and improve. You can see on the slide the domains where we’ve applied this: scientific reasoning, instruction following, and chat. If you look at the leaderboards, you’ll see that even with new models emerging, we’ve maintained a strong position in these areas, largely due to the effectiveness of RL in achieving top-tier accuracy. We’re optimistic that we’ll see more of this in the community, with more discussion and publication of techniques and data. We’ve started sharing some of our work in this area and will have much more to come in the next three to six months. Jean-Marc Mommessin: You mentioned RL and instruction following, which ties back to the beginning of our conversation. It seems like you’ve come full circle here. Joey Conway: Exactly. The exciting aspect here is automating the feedback loop wherever possible. For chat, we published a fine-tuned reward model last fall. Those who followed our work might recall that our Llama Nemotron model topped the chat leaderboards then. This was because the reward model provides an automated way to teach the original model whether its responses are good or bad. It essentially grades responses based on helpfulness, conciseness, verbosity, groundedness, and similar factors. This granular feedback per generated response allows the model to improve significantly, often more so than through supervised fine-tuning alone, which typically involves a few passes without a continuous feedback loop. Similarly, for instruction following, we use a verifier and a dataset to teach the model whether it followed instructions well or needs to try again. We’re eager to expand this approach to more domains. We’ve already published datasets related to coding and math since the release of this model a few weeks ago, and these have become popular on Hugging Face. I anticipate significant growth in this area within the community. Jean-Marc Mommessin: Alright, so one of the big innovations here, and you touched upon it, but I want to emphasize it, is the ability to toggle reasoning on and off via the system prompt. This is quite unique, and I’m sure many will follow suit. Could you expand on the idea behind this, how you see it applying to agents and beyond, its value, and the key challenges in implementing it? Joey Conway: The reasoning on and off capability was a core goal from the outset. We observed that models in the community often excelled in either reasoning or non-reasoning tasks, and we wanted to simplify deployment by having a single model that could handle both. We had to determine the best way to teach the model when to reason and when not to, while also providing enterprises with explicit control, as they often have deeper domain knowledge than we do. The motivation behind this is that reasoning generates significantly more tokens, which can lead to higher latency and cost. While crucial for solving complex problems, it’s not always necessary. We wanted to give enterprises the control to balance accuracy with latency and cost, allowing them to decide when to employ reasoning and when to opt for faster, less computationally intensive responses. Initially, we weren’t sure how to achieve this, as it hadn’t been widely implemented in the community. Our approach in the supervised fine-tuning stage was to explicitly teach the model by presenting the same question with two different answers: one with detailed reasoning and one without. This essentially doubled our dataset for this specific purpose. However, the outcome is a single model where users can simply include “use detailed thinking on” or “use detailed thinking off” in the prompt to control the model’s reasoning process. On the training side, this required more effort to teach the model this distinction. What we have today is essentially a v1, and I expect others will follow this approach. We’re also excited about future developments, such as time or token limits for reasoning and more granular controls. I’m optimistic that we’ll see further breakthroughs in this area within the next six to nine months, as the problem-solving power of reasoning is significant, but it comes with trade-offs that the community will continue to refine. Jean-Marc Mommessin: We all know that the real test comes in production. Production environments are sensitive to latency, cost, and while accuracy and reasoning are vital, excessive reasoning can lead to scalability issues and increased latency. The flexibility you’ve introduced is fantastic, and I can see numerous production use cases that will greatly benefit from the ability to control reasoning on a per-query basis. So, when you were developing this model, you aimed to balance accuracy and efficiency. Could you share some insights into how you made these trade-offs, the timeline for building the model and the team involved, and how you determined the optimal compromise between these two critical factors? Joey Conway: Balancing accuracy and efficiency is always a challenge. Our initial goal was to achieve both, which is a difficult undertaking. We started with the “Super” model, which was the most recent Llama 3.1 70B release from Meta, as our baseline for accuracy. We weren’t sure if we could simultaneously improve accuracy and reduce the model size. We found that through our training techniques and distillation process, we could indeed boost accuracy. We even released an initial checkpoint reflecting this. However, we wanted to go further by incorporating strong reasoning capabilities, aiming for state-of-the-art reasoning scores. This is where the SFT and RL stages came in, which required significant time for synthetic data generation since this type of data didn’t exist. During training, we carefully considered the number of epochs for each skill and continuously measured accuracy. Our goal was to improve performance across all six key areas rather than excelling in just a couple. This balancing act took more time as we experimented to find the right combinations. However, we felt it was crucial to ensure world-class performance in these six enterprise-relevant scenarios, including chat and instruction following. For areas like MMLU, we focused on maintaining performance and preventing regression rather than actively trying to improve scores. So, there were definitely priorities and trade-offs involved. Ultimately, we believe these were the right focus areas for our enterprise customers. Jean-Marc Mommessin: You are releasing this model family as part of the open-source community. We’ve discussed the gaps you aimed to address and the unique reasoning on/off feature for production scalability. Could you share your thoughts on how NVIDIA and your team view the role of these models within the broader open-source and LLM ecosystem, especially given your work building upon the Llama base? Joey Conway: NVIDIA has a long history of contributing models to the open-source community. What excites us about Llama is its strong traction with enterprise customers. While NVIDIA Research publishes extensively across various domains, our goal with Llama Nemotron was to build upon Llama’s momentum in enterprise adoption by focusing narrowly on specific areas. The base Llama models already cover many things exceptionally well, so we saw an opportunity to build on top of that and be very targeted in our enhancements. The recent LlamaCon event and Meta’s announcements sound very promising, and we’re excited about Llama 4 and the ongoing work there. Moving forward, we anticipate continuing to identify specific areas where we can add significant value, while Meta continues to build excellent general-purpose models suitable for enterprise production. From our perspective, reasoning will likely remain a key focus, and we’re also excited about Meta’s advancements in this area. Tool calling, instruction following, and chat are also areas we’ll continue to develop. One area we’re particularly interested in exploring is multilingual capabilities. For large enterprises, supporting multiple languages is crucial. While many models handle individual languages well, we aim to focus on a few key languages and ensure world-class accuracy for reasoning, tool calling, and chat within those. This is likely the next major area of expansion for us, beyond the exciting developments in model architectures like Llama 4’s new MoE architecture, which we’re also keen to explore for potential distillation and optimization for NVIDIA GPUs. So, there’s a lot of exciting work ahead. Jean-Marc Mommessin: When you say multilingual, are you thinking of supporting a broad range, like 50 languages, or a more focused set, perhaps around 5 or 10 initially, given the benchmark challenges you mentioned? Joey Conway: We’ll probably start with a more focused set, perhaps around 5 to 10 languages. The challenge is that the community currently lacks comprehensive benchmarks for tasks like reasoning or tool calling across a wide variety of languages. As we develop these multilingual models, we’re also having to create evaluation data simultaneously, which takes time. If those benchmarks were readily available, the process would be smoother. However, we see this as an exciting challenge. Our initial focus will likely be on a smaller set of languages where we can establish strong performance, given the current limitations in community-wide benchmarks. Jean-Marc Mommessin: Let’s shift gears and talk about another state-of-the-art open-source model you recently released: Parakeet TDT 0.6 B parameters, V2. This model has set a new standard for automatic speech recognition (ASR), transcribing one hour of audio in just one second. That’s 50 times faster than other open-source ASR models, and remarkably, it achieves only a 6% word error rate. This is truly impressive. What else would you like to highlight about this model before we discuss the “how” behind its incredible performance? Joey Conway: It’s worth noting that NVIDIA has been working on ASR models for a long time, even before I joined. We’ve also released many open models in this space over the years. The teams working on this are exceptional, and they consistently strive to balance accuracy with latency and throughput. Parakeet V2 is the latest in this line of high-performance models from NVIDIA. Jean-Marc Mommessin: It sounds like the advancements will keep coming. So, let’s delve into how you achieved this remarkable performance with Parakeet TDT. What kind of architecture did you use? I understand it’s based on a Fast Conformer architecture with specific optimizations like 8x depth-wise separable convolutional downsampling and limited context attention. Could you explain how you arrived at this approach and whether these optimizations primarily enhance speed and throughput or if they also contribute to accuracy and the ability to process long audio segments like a full hour in one shot? Joey Conway: Yes, we’ve explored various architectures for ASR over the years, and the Conformer architecture, originally from Google, has shown great promise. Our goal with Parakeet TDT was to take the Conformer architecture and make it significantly more efficient and faster without sacrificing quality. We’ve implemented several key optimizations.  First, as you mentioned, the depth-wise separable convolution downsampling. At the input stage, we significantly downsample the audio, which reduces the computational cost and memory requirements for processing. Second is the limited context attention. By focusing on smaller, overlapping chunks of audio, we can maintain accuracy while achieving a speedup in processing. Third, on the encoder side, we also utilize a sliding window attention technique, which allows us to process longer audio files without having to split them into shorter segments. This is crucial for handling long-form audio like a full hour in a single pass. Beyond the Conformer architecture, Parakeet TDT incorporates a Token and Duration Transducer (TDT). Traditional Recurrent Neural Network (RNN) transducer technology processes audio frame by frame. What we’ve done with TDT is enable the model to predict both the tokens and the expected duration of those tokens. This allows it to make decisions to skip over redundant frames, significantly speeding up the transcription process. This TDT innovation alone contributes to around a 1.5 to 2x speedup. So, there’s a combination of architectural choices and specific optimizations that contribute to Parakeet TDT’s impressive speed and accuracy. Jean-Marc Mommessin: I want to go back to one or two of those. Those are amazing, frankly. The speed increase is remarkable. Joey Conway: Yes, and we have another technique called a label looping algorithm. Essentially, when we’re doing batch inference, this algorithm allows us to advance the tokens independently for different samples. This separation of the workflow enables us to sweep and loop over frames and labels more efficiently, significantly speeding up the decoding process. Lastly, on the decoder side, we’ve moved some of the computation into CUDA graphs, which is a more efficient way to run many small kernels. This optimization alone provided around a 3x speed boost. So, as you can see with TDT models, we’ve been able to achieve speeds comparable to Connectionist Temporal Classification (CTC) decoders, which are also known for their speed, while maintaining high accuracy. Our overall theme is always to balance speed improvements with maintaining or even enhancing accuracy. Techniques like CTC decoders have been around for a while and are fast but might not be as accurate. It really depends on the use case, but we’re always striving for that balance. Jean-Marc Mommessin: Can we revisit the limited context attention? Do you see this technique having broader applications in other areas down the line? Joey Conway: Yes, I believe so. Patterns like the sliding window attention are already used in other areas, such as LLMs. Our research teams are constantly experimenting, looking at successful techniques from different domains, and trying to apply them in new ways. Interestingly, some of the researchers who worked on Parakeet TDT also work on Llama Nemotron, so there’s a cross-pollination of ideas. I do expect that some of these techniques will find broader applications going forward. We also anticipate further improvements to TDT and the Conformer architecture, as we’ve been working on them for several years now. I don’t see these core technologies going away anytime soon; we’ll likely continue to refine them. Jean-Marc Mommessin: Leaving the TDT aside, do you see other potential applications for the Token and Duration Transducer concept in other domains? Joey Conway: That’s a good question. I’m not immediately seeing a direct application of the TDT concept outside of ASR. Its history is rooted in RNNs and RNN transducers, which have primarily been used in speech recognition. However, some of the underlying techniques we’ve applied to it, like using CUDA graphs for optimizing kernel execution, are general techniques that we use whenever we identify bottlenecks in a model’s pipeline. So, while the TDT itself might be domain-specific, some of the optimization strategies we’ve employed could certainly translate to other areas, including large language models. Jean-Marc Mommessin: let’s talk about data. AI data is always a key topic. How do you ensure that the data used to train Parakeet TDT is diverse enough to handle various accents, dialects, vocal ranges, pitches, and noisy background conditions, which often negatively impact ASR performance? Joey Conway: You’re absolutely right. As humans, we naturally filter out accents and background noise to understand speech. However, deep learning models are only as good as the data they’re trained on. Early on, limited data for specific accents or languages resulted in poor performance for those variations. What might have initially seemed like edge cases have become increasingly common, highlighting the need for more representative data. We’ve invested significant effort in curating our datasets to reflect this real-world diversity. We use techniques like classifiers to analyze our data and understand the distributions of accents, dialects, and acoustic conditions. We’ve worked with customers like YUM! Brands, who have drive-through use cases with significant highway noise, illustrating the importance of training the model to handle such challenging environments. Ensuring the right blend and distribution of these conditions in our training data is crucial for the model’s robustness. I’m also excited to announce that we plan to open-source a substantial speech dataset, around 100,000 hours, where we’ve meticulously performed this kind of curation. This dataset will include variations in sound levels, signal-to-noise ratios, background noise types, and even telephone audio formats relevant for call centers. Our goal is to provide the community with high-quality, diverse data that enables models to perform well across a wide range of real-world scenarios. Jean-Marc Mommessin: That’s fantastic news about the open-sourcing of the speech dataset! My final question regarding the Parakeet family: you currently have the 600 million and 1.1 billion parameter models. How do you envision future development for this family? What are the potential directions? Joey Conway: We’re considering development along two main dimensions: model size and the number of supported languages. In terms of size, we’ve released models at the smaller and mid-range to demonstrate the potential, similar to our approach with Llama Nemotron Super. We plan to explore larger models, potentially around 2 billion parameters, which we anticipate will handle even more languages and dialects. On the smaller end, we’re even considering models down to around 50 million parameters. The motivation here is to address use cases at the edge where a smaller footprint is necessary, such as enabling real-time audio processing for robots in noisy environments. We’ll be exploring the right trade-offs for such applications. Technologically, we plan to work on streaming capabilities for TDT. Currently, much of the processing is done in an offline batch mode, but we want to enable real-time, live transcription. And as mentioned, we’re excited about releasing the large, curated speech dataset. Finally, for those looking to deploy these models in production, we recommend exploring techniques like word boosting, which allows for customization of text normalization to include domain-specific terms and acronyms. We aim to provide a wide range of options for users to get started and tailor the models to their specific needs. Jean-Marc Mommessin: I’m very familiar with the NVIDIA Orin platform. Would these Parakeet models currently run on NVIDIA Orin? Joey Conway: Yes, I believe the 0.6 billion parameter model likely would run on Orin. I would need to double-check the exact specifications, but I’m quite confident it’s feasible. Jean-Marc Mommessin: Orin packs a significant punch. I especially love the robotics use case you mentioned. While there’s been a lot of focus on robot vision, the ability to hear and understand quickly is equally crucial, especially for safety. A model that’s 50 times faster and highly accurate in understanding another modality seems like a perfect fit for robotics. Joey Conway: Yes, and the slight hesitation I had earlier was due to the understanding that in robotics, there are often multiple models running simultaneously, including vision models. So, resource allocation is a consideration. However, our push towards smaller, more efficient models is precisely to address these kinds of multi-modal edge computing scenarios. The low latency and real-time processing capabilities of Parakeet are indeed very beneficial for enabling robots to react quickly and safely to auditory cues. Jean-Marc Mommessin: Anything else you’d like to add as a final thought on the Llama Nemotron Ultra and Parakeet families? They’re both open-source, fast, high-throughput, cost-efficient, and run on smaller footprints – are these the key takeaways? Joey Conway: Yes, that’s a great summary. Those were the core objectives we set out to achieve. We aimed for state-of-the-art accuracy, optimized footprints for efficient GPU utilization in terms of latency and throughput, and a commitment to open-sourcing everything to empower the community. We’ve strived to be as community-friendly as possible by releasing datasets, using permissive licenses, and making it easy for people to experiment. We’re eager to see the community’s feedback and the innovative applications they build upon our work. We’re also looking forward to learning from their experiences. Jean-Marc Mommessin: Where are all these models and datasets available? Joey Conway: Everything we’ve published is on Hugging Face – the models and the datasets. The software stack to run them comes from NVIDIA and is available on NGC, our content repository. Much of the underlying software is also open-source and can be found on GitHub. We also provide pip wheels for easier installation. The Nemo framework is the central hub for much of this software stack, whether you want to run the models or fine-tune them. We’ve tried to make it as user-friendly as possible. We use the same software internally to build the models, so it should be relatively straightforward for others to pick up and deploy as well. Jean-Marc Mommessin: Well, Joey, this has been fantastic. I’m continually impressed by NVIDIA’s commitment to giving back to the community with state-of-the-art models that will undoubtedly find their way into production. Thank you so much for your time and insights. I look forward to our next conversation. Joey Conway: Thank you, Jean-Marc. It was my pleasure, and we appreciate the opportunity.  Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/NVIDIA AI Releases HOVER: A Breakthrough AI for Versatile Humanoid Control in RoboticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Speech-to-Speech Foundation Models Pave the Way for Seamless Multilingual InteractionsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Lowe’s Revolutionizes Retail with AI: From Personalized Shopping to Proactive Customer AssistanceJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Google DeepMind’s Gemini Robotics: Unleashing Embodied AI with Zero-Shot Control and Enhanced Spatial Reasoning

How co-development companies are navigating a turbulent industry in 2025 GamesIndustry.biz catches up with some of the top companies working in the co-dev space to see how they are faring given current industry headwinds Image credit: Bethesda Softworks Feature by Alex Calvin Contributor Published on May 15, 2025 In one form or another, co-development companies have nearly always been around in the games industry. For as long as people have been making games, there have been companies and developers who have made a living helping out other studios with their projects. For the most part, these companies had a low profile, sitting in the shadows of the developers they were working for; but with the growing scale of modern AAA projects, co-development firms are more visible than ever. "There's a growing awareness and desire among studios worldwide to leverage distributed development and external services," says Jake DiGennaro, the chief revenue officer at Virtuos, which, along with Bethesda, co-developed the recently released The Elder Scrolls 4: Oblivion Remastered. "Some reputable studios that have shipped their own IP are even embracing a co-op model, working on original IP development while offering co-development services to others. This approach has long been a tradition in Japan and is now becoming mainstream in North America and Europe." Alejandro Garcia-Tunon, the head of co-dev at Side, adds: "Co-development definitely remains a key part of the game development ecosystem – if anything, more so now than ever before. An increased number of development specialities, as well as scopes that are well beyond what the game industry has seen in the past, have increased costs. "Having a full-time team of experts in each field you'd need to cover just wouldn't make sense, so bringing in an expert is a great way for studios and publishers to tap into that talent without blowing up their budgets." Helping hand The work that co-development companies are being brought in to assist with varies wildly. Sometimes it's individual disciplines (art and animation are the most popular, according to research from the External Development Summit), but for others it is entire segments of a project. "What we are finding is that the balance of the co-development work is changing from coding or feature-focused 'team augmentation' to fully rounded content teams that can take on a whole section of games or even whole games, like we did with the delivery of Lego Horizon Adventures," explains Ashley Liu, the managing director of Keywords Studios' Create arm. Garcia-Tunon adds: "It runs the gamut from individual specialities, like AI, combat, physics, networking, and UI, to taking on entire chunks of a game like a full biome or boss. Sometimes we will cover different aspects even within a single project. Image credit: Konami "When we worked on Silent Hill 2 Remake with Bloober Team, we worked on two boss fights in very different ways. For the fight with Eddie's character, we were creative consultants, advising how we could lift the level from the previous version and make it more of a pivotal moment in the game. With Maria, the final boss fight, Bloober Team already had a vision in mind which we were able to help execute from a technical perspective." Despite co-development companies becoming a bigger part of the industry, the sector isn't immune to the broader economics of the games market. For a variety of reasons, publishers have chosen to be more conservative in funding projects, and fewer games in development means less work for those whose role it is to help out. "Demand grew very strongly over the past decade as games became larger and more complex – developers required additional support," explains Liu. "In the last 18 months, the volume of work reversed due to the overall contraction of the industry, but projects are starting to come back as the industry realises there's been an overcorrection. Players expecting more engaging content will drive some renewed demand." The turbulence in the market, however, does present something of an opportunity for co-development companies. In an uncertain world where publishers are being more risk-averse, partners that provide a great deal of flexibility can be useful. "There are opportunities for co-development, though they may not look like what we've seen in the past," Liu says. "Specifically, as our clients restructure their businesses, there are now more opportunities for partners to fill resource gaps. "There are also opportunities with clients that perhaps would like us to take over their older titles, especially as their best teams move on to new projects." "The headwinds that we face collectively as an industry have reached a breaking point where we can't continue to make games the way we did 20 years ago" Jake DiGennaro, Virtuos Garcia-Tunon adds: "The last couple of years have been an anomaly, no question, but the uncertainty has been really challenging. The co-dev space has historically been growing year over year because the entire industry has been growing – more games equals more people needing help to make games. Our opportunities have come from our ability to quickly adapt to fit client demands." Some co-development companies view themselves as somewhat indispensable. The fact that games now require so many people, so many hours of work, and so many disciplines means that it is tough to make them without external help. "The headwinds that we face collectively as an industry have reached a breaking point where we can't continue to make games the way we did 20 years ago and expect not to run into a wall eventually," DiGennaro believes. "With the rising costs of production, the economics of creating a successful game have become increasingly difficult. "We have to acknowledge the fact that there's something about the way we make games that needs a paradigm shift. While factors like discoverability, delivery, and distribution all play important roles, the process of how we build games is a critical part of the problem," DiGennaro continues. "More than seeing this as an opportunity for co-development companies, I think it's an opportunity to reshape the collective mentality and methodologies of our industry towards increased sustainability and reduced volatility." As always, big games companies are very anxious about their IP and will often go to great lengths to protect it. Before its official reveal, screenshots and marketing materials for The Elder Scrolls 4: Oblivion Remastered were leaked from Virtuos' backend on its Wordpress. "We have very strict processes and information controls at Side that have been honed over decades of work and thousands of games," Garcia-Tunon says. "But our business relies on our reputation – successful projects build great long-term relationships and recommendations from previous partners are worth their weight in gold." Image credit: Keywords Keywords' Liu adds: "Ensuring there are no leaks has always been a challenge in game development, especially for projects that take years to complete with hundreds of people working on it at different times. As with any studio, team composition changes throughout the project lifecycle, making leakages an ongoing area to monitor. "We can leverage experienced global IT and InfoSec departments to protect our client's IP and data. They oversee hundreds of projects, thousands of configurations, and have successfully dealt with a vast number of incidents, allowing us to bring a level of protection surpassing many of our competitors," Liu continues. "In the unlikely event of a data breach, we inform the client first and foremost and together we find a way to resolve the situation. Sometimes, because we are open and honest, the clients trust us more." What comes next Looking to the future, it's clear that co-development isn't going anywhere anytime soon. Modern video games are, by and large, too big and require too much specialist expertise to be made by one company, at least within a reasonable timeframe. Co-dev firms are here to stay. "Fundamentally, I think distributed co-development is how games will be – and should be – built moving forward," DiGennaro says. "It has the potential to make a profound impact in the sustainability of this industry." Liu adds: "Despite the layoffs and project cancellations, this industry will grow. We'll work with partners to release more creative content and better experiences, leveraging new technology to create games and mechanics that have never been seen before. We'll reach new artistic heights that truly engage players." But, as with the rest of the industry, change may be afoot in the near future. New technologies do look like they could have an impact on how games are made. "Workflows are already evolving to include developing technologies like AI," Garcia-Tunon admits. "That said, my outlook is positive – as long as we have humans playing games, we'll need humans to develop and discuss what games should look like."

Christine Farro has cut back on the presents she sends her grandchildren on their birthdays, and she’s put off taking two cats and a dog for their shots. All her clothes come from thrift stores and most of her vegetables come from her garden. At 73, she has cut her costs as much as she can to live on a tight budget.But it’s about to get far tighter.As the Trump administration resumes collections on defaulted student loans, a surprising population has been caught in the crosshairs: Hundreds of thousands of older Americans whose decades-old debts now put them at risk of having their Social Security checks garnished.“I worked ridiculous hours. I worked weekends and nights. But I could never pay it off,” says Farro, a retired child welfare worker in Santa Ynez, California.Like millions of debtors with federal student loans, Farro had her payments and interest paused by the government five years ago when the pandemic thrust many into financial hardship. That grace period ended in 2023 and, earlier this month, the Department of Education said it would restart “involuntary collections” by garnishing paychecks, tax refunds and Social Security retirement and disability benefits. Farro previously had her Social Security garnished and expects it to restart.Farro’s loans date back 40 years. She was a single mother when she got a bachelor’s degree in developmental psychology and when she discovered she couldn’t earn enough to pay off her loans, she went back to school and got a master’s degree. Her salary never caught up. Things only got worse.Around 2008, when she consolidated her loans, she was paying $1,000 a month, but years of missed payments and piled-on interest meant she was barely putting a dent in a bill that had ballooned to $250,000. When she sought help to resolve her debt, she says the loan company had just one suggestion.“They said, ‘Move to a cheaper state,'” says Farro, who rents a 400-square-foot casita from a friend. “I realized I was living in a different reality than they were.”Student loan debt among older people has grown at a staggering rate, in part due to rising tuitions that have forced more people to borrow greater sums. People 60 and older hold an estimated $125 billion in student loans, according to the National Consumer Law Center, a six-fold increase from 20 years ago. That has led Social Security beneficiaries who have had their payments garnished to balloon by 3,000% over the same period, according to the Consumer Financial Protection Bureau.An estimated 452,000 people aged 62 and older had student loans in default, according to a January report from CFPB.Debbie McIntyre, a 62-year-old adult education teacher in Georgetown, Kentucky, is among them. She dreams of retiring and writing more historical fiction, and of boarding a plane for the first time since high school. But her husband has been out of work on disability for two decades and they’ve used credit cards to get by on his meager benefits and her paycheck. Their rent will be hiked $300 when their lease renews. McIntyre doesn’t know what to do if her paycheck is garnished.She floats the idea of bankruptcy, but that won’t automatically clear her loans, which are held to a different standard than other debt. She figures if she picks up extra jobs babysitting or tutoring, she could put $50 toward her loans here and there. But she sees no real solution.“I don’t know what more I can do,” says McIntyre, who is too afraid to check what her loan balance is. “I’ll never get out of this hole.”Braxton Brewington of the Debt Collective debtors union says it’s striking how many older people dial into the organization’s calls and attend its protests. Many of them, he says, should have had their debts cancelled but fell victim to a system “riddled with flaws and illegalities and flukes.” Many whose educations have left them in late-life debt have, in fact, paid back the principal on their loans, sometimes several times over, but still owe more due to interest and fees.For those who are subject to garnishment, Brewington says, the results can be devastating.“We hear from people who skip meals. We know people who dilute their medication or cut their pills in half. People take drastic measures like pulling all their savings out or dissolving their 401ks,” he says. “We know folks that have been driven into homelessness.”Collections on defaulted loans may have restarted no matter who was president, though the Biden administration had sought to limit the amount of income that could be garnished. Federal law protects just $750 of Social Security benefits from garnishment, an amount that would put a debtor far below the poverty line.“We’re basically providing people with federal benefits with one hand and taking them away with another,” says Sarah Sattelmeyer of the New America think tank.Linda Hilton, a 76-year-old retired office worker from Apache Junction, Arizona, went through garnishment before COVID and says she will survive it again. But flights to see her children, occasional meals at a restaurant and other pleasures of retired life may disappear.“It’s going to mean restrictions,” says Hilton. “There won’t be any travel. There won’t be any frills.”Some debtors have already received notice about collections. Many more are living in fear. President Donald Trump has signed an executive order calling for the Department of Education’s dismantling and, for those seeking answers about their loans, mass layoffs have complicated getting calls answered.While Education Secretary Linda McMahon says restarting collections is a necessary step for debtors “both for the sake of their own financial health and our nation’s economic outlook,” even some of Trump’s most fervent supporters are questioning a move that will make their lives harder.Randall Countryman, 55, of Bonita, California, says a Biden administration proposal to forgive some student debt didn’t strike him as fair, but he’s not sure Trump’s approach is either. He supported Trump but wishes the government made case-by-case decisions on debtors. Countryman thinks Americans don’t realize how many older people are affected by policies on student loans, often thought to be the turf of the young, and how difficult it can be for them to repay.“What’s a young person’s problem today,” he says, “is an old person’s problem tomorrow.”Countryman started working on a degree while in prison, then continued it at the University of Phoenix when he was released. He started growing nervous as he racked up loan debt and never finished his degree. He’s worked a host of different jobs, but finding work has often been complicated by his criminal record.He lives off his wife’s Social Security check and the kindness of his mother-in-law. He doesn’t know how they’d get by if the government demands repayment.“I kind of wish I never went to school in the first place,” he says. Matt Sedensky can be reached at msedensky@ap.org and https://x.com/sedensky —Matt Sedensky, AP National Writer

There’s now data to back up the theory that stressful events like the loss of a loved one can change your heart’s physiology and contribute to atrial fibrillation, heart attacks, and stroke, among others. Researchers followed nearly 200,000 U.S. adults over five years and report that such instances were “high and unchanged” from 2016 to 2020, according to a research article in the Journal of the American Heart Association.Read More: Why Are We Addicted to Love?Stress Can Change Your Heart's ShapeThe term “broken heart syndrome” was coined in 1990, when a Japanese researcher noticed that stress can cause parts of the heart to temporarily enlarge and affect the ability of the organ to pump blood. Scientists suspect that the temporary ballooning is a reaction to a surge of stress hormones caused by a difficult emotional event. Although change tends to be short-term, it can still be fatal. It can be misdiagnosed as a heart attack, because even though the physical changes to the heart are different, the symptoms a patient experiences are similar.Over the course of the study — one of the largest ever of its kind — the death rate from the syndrome held steady, at 6.5 percent. Although more women than men experienced the syndrome, it was twice as fatal in men, with 11.2 percent of cases proving fatal, compared to 5.5 percent in women.Who Gets Broken Heart Syndrome“The continued high death rate is alarming, suggesting that more research be done for better treatment and finding new therapeutic approaches to this condition,” M. Reza Movahed, a cardiologist at the University of Arizona’s Sarver Heart Center in Tucson and an author of the study, said in a press release.Besides gender, age also appears to play a significant role in determining who gets affected by the syndrome. People older than 61 had the highest incident rates, but the data showed a substantial jump between the next two groups. Adults 46 to 60 were 2.6 to 3.25 times more likely to experience it compared to those who were 31 to 45 during the study.The data showed a slight variation of incidence by race. Socioeconomic factors like household income and insurance status may also play a significant role.Identifying Symptoms of the SyndromeThe study could raise awareness of the syndrome among both doctors and patients. For doctors, knowing that the symptoms resemble that of a heart attack can aide in diagnosis. If tests show that there are no physiological signs of a heart attack underway, they could still ensure the patient gets treated. Also knowing that the syndrome impacts many people under 60 could help diagnosis.For patients, recognizing that stress can cause these symptoms could help guide them to ways that could alleviate it. And recognizing that the symptoms are very much real, rather in their heads, could also prompt them to seek treatment.This article is not offering medical advice and should be used for informational purposes only.Read More: What Does the Heart Do?Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:Journal of the American Heart Association. High Mortality and Complications in Patients Admitted With Takotsubo Cardiomyopathy With More Than Double Mortality in Men Without Improvement in Outcome Over the YearsAmerican Heart Association. Is Broken Heart Syndrome Real? Before joining Discover Magazine, Paul Smaglik spent over 20 years as a science journalist, specializing in U.S. life science policy and global scientific career issues. He began his career in newspapers, but switched to scientific magazines. His work has appeared in publications including Science News, Science, Nature, and Scientific American.

Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice BiennaleSave this picture!© Federico CairoliThe proposal by Argentinian architects Marco Zampieron and Juan Manuel Pachué for the Argentinian Pavilion at the 19th Venice Architecture Biennale 2025 is clear from the outset: upon entering Siestario, visitors are immersed in a space of dim lighting and evocative sound, and immediately encounter—at the center of the room, stretched across its width, and acting as the undeniable protagonist—a large, inflated pink bag that instinctively invites repose. This is a silobag, an object commonly used in the Argentinian countryside to store grain and a potent symbol of the country’s export-driven economy. In this setting, the silobag becomes more than a spatial intervention; it also introduces a temporal dimension: an invitation to pause and reflect amid the relentless rhythm of the Biennale. Save this picture!Under the motto “Intelligens. Natural. Artificial. Collective”, general curator and Italian architect Carlo Ratti invites participants to explore and debate the many meanings of these terms, setting the stage for a critical rethinking of contemporary architecture. Within this framework, both national pavilions and individual projects and installations engage with the tensions and possibilities that emerge between the natural, the artificial, and the collective—each from their own distinct perspective.Save this picture!Marco Zampieron and Juan Manuel Pachué — Rosario-based architects and co-founders of Cooperativa — were selected through a public competition organized by the Argentine Foreign Ministry, in partnership with the Chamber of Urban Developers (CEDU), with their winning project to represent Argentina in this edition of the Biennale. Related Article Bahrain Wins the Golden Lion for Best National Participation at the 2025 Venice Architecture Biennale Save this picture!With the support of CEDU and Base Proyectos, ArchDaily was present at the inauguration of the Argentine Pavilion on May 9, at the extraordinary Arsenale in Venice. During the first days of the event, we had the opportunity to speak with the authors of Siestario about their experience participating in the Biennale, the creative process of the project, and other aspects related to their practice and understanding of architecture.Save this picture!AD (Paula Pintos): What was your motivation to participate in the competition to represent Argentina at the Venice Architecture Biennale 2025?Marco Zampieron & Juan Manuel Pachué (MZ & JP): As Cooperativa, our group is deeply engaged with competitions. We participate regularly, advocate for them as a valuable tool, and truly enjoy the process they entail. This one, in particular, stood out because, while it’s a national competition, it offers a strong international platform: it’s about representing the country on one of the most important global stages for architecture. Our studio operates as a collaborative collective that expands or contracts depending on the nature of each project. At its core, our practice revolves around competitions. We see them as a way to access commissions without requiring an extensive portfolio, a strong reputation, or an established body of work. That’s why we especially value competitions that are anonymous and transparent — they open the door to meaningful opportunities, such as building a public-scale pavilion with high visibility, through a more horizontal and equitable framework.Save this picture!AD: How does Siestario relate to the theme proposed by the Venice Biennale 2025 — Intelligens. Natural. Artificial. Collective — and what possible interpretations did you envision based on that premise?MZ & JP: These mottos or curatorial texts often tend to be quite ambiguous — and we see that as something positive, because it opens the door to multiple interpretations. With so many people participating in the Biennale through diverse projects, it’s valuable for the thematic framework to be broad: it allows for a wide range of perspectives and approaches. From there, the question of how to represent Argentina naturally arises. Where do you anchor that representation? In our proposal, what comes through is our way of thinking about architecture, our understanding of the Biennale, and our perspective on the collective.Not only were the notions of intelligence, nature, and collectivity present, but there was also something that moved away from the idea of one place, one solution—a concept that truly captivated us. We find the idea of artificial intelligence somewhat challenging... perhaps we’re a bit romantic in that sense; there’s a certain resistance on our part. When we look at some international pavilions, we see a strong emphasis on technology and technical prowess. And while the artificial and the natural are in constant dialogue, what interests us is thinking about the artificial through the lens of local production—through cultural construction. Not as a race toward technological advancement, but as a way of drawing from the depth of knowledge that already exists within our own culture.It is also important that these competitions are resolved quickly, and often one starts from previous ideas or works. In our case, the collective already had a background, a sensitive research on the territory linked to art and certain key elements. This allowed us to connect with the Biennale's themes more deeply in a short time. We see this in other pavilions as well: many have a foundation of prior work and research. In our architecture — and also in art and other disciplines with which we engage — each person brings a background, certain interests, preferences, and tastes. From the place one comes from, what one observes, all of that forms a layer of information from which we project. We try to reflect on this to make it as conscious as possible, so that we can create something that feels close and authentic to us.Save this picture!Representing Argentina also forces us to ask ourselves what we want to say, how we want to speak about who we are. We are interested in a certain metaphor, a certain poetics. We think about the conditions the country is currently going through, both economically and politically, and this image of Argentina resting upon the Argentine countryside emerges, along with the cost of this rest. The proposal of Siestario suggests that the visitor to the Biennale, weary from seeing so much, could climb uphill to a silobag — that iconic symbol of Argentina's agricultural production — and rest, take a nap. But of course, that silobag is the one that stores soybeans, and soybeans, with all their symbolic, productive, and also destructive weight: they contaminate water, devastate forests, and affect local communities. So, Siestario doesn’t just offer a break; it also questions what that rest truly means. There’s a critique, a political stance, a reading on the artificial, on comfort, on the cost of being at ease. All of this emerges when the concepts are linked: the natural, the artificial, the collective. And here, the siesta also appears as a gesture, as a metaphor, as a critique, as a refuge.Save this picture!AD: What motivated you to use the silobag as the central element of the project? How was the process of re-signifying this object within the exhibition context of the Biennale? MZ & JP: The relationship with the silobag is not new to us; in fact, we had already worked with this element in previous projects, documenting and traveling through different towns. This prior research allowed us to see the silobag not just as a functional element, but also as an object loaded with meaning. The process of re-signifying it here at the Biennale involves decontextualizing it. We wanted to change its scale, give it a new role, and use it as a place for a nap, a space for rest. By doing so, the silobag transforms, ceasing to be just an agricultural object and becoming something more playful, connected to the body, fatigue, and the intimacy of the siesta. It’s a simple gesture, but a profound one: it proposes a space for people to recline and feel comfortable in it.Furthermore, the silobag becomes an object that, although it has strong rural and economic connotations, is reinterpreted within the exhibition context, adding a layer of reflection on the everyday and the collective. The intimacy of the siesta becomes social, as the act of resting in a shared space like this creates a collective moment. In the pavilion, we also worked with the atmosphere: the environment is designed to darken, with sounds and lights that induce a sense of relaxation, contributing to creating the right space for that collective rest.Save this picture!AD: What were you hoping to convey through this installation, and how did you expect visitors to experience or interpret the act of "pausing" within the exhibit's journey?MZ & JP: The proposal for this installation revolves around the idea of "stopping." Stopping to think, to look at what you’re doing, to know yourself, to decide. Ultimately, it’s an invitation to stop, to rest, to sleep. We aim for visitors to find that pause within themselves, to disconnect from the fast pace of their surroundings. Furthermore, this act of "pausing" becomes something desirable, almost like an object of desire: the urge to try it, to climb onto the silobag. We wanted to create a physical and experiential experience, not just a contemplative one. Walking through the pavilion becomes an invitation to interact with the space. It even allows walking on the silobag, changing heights, lying down, and resting.We know that at the Biennale, visitors tend to walk a lot and want to see as much as possible. However, this space demands that you stop, that you dedicate time to the experience. It’s not a place to just pass through quickly; it’s a space that asks you to stay for at least 15 or 20 minutes, to climb onto the silobag, to experience the physical effort of doing so, because it’s not a comfortable chair — it’s an experience that requires an investment of time and attention.The curation and design of the rest of the space, along with the projections, are intended so that the installation partially disappears as one moves through it, creating a visual and spatial effect that forces visitors to interact with the place, to stay alert. The project takes a risk; we know that, in the frenetic pace of the Biennale, some may not pay attention to this proposal. But we believe that, to truly enjoy it, it’s necessary to take that time, lie down, listen to the sounds, and be present.Save this picture!AD: How did you approach the relationship between architecture, space, and time within the Argentinian Pavilion?MZ & JP: Our approach has always been to give prominence to the public and collective. In the projects we develop, we try to make the ground floors open and accessible spaces where people can enter spontaneously, without the design being entirely directed toward exclusive use. We are interested in simplicity and clarity, in being radical in the sense of the purity of the idea. This pavilion, for example, uses a single element to express and resolve multiple layers of meaning. The design process seeks to simplify and refine complex ideas, ensuring that the result is direct and clear.In relation to time, we are interested in how architecture, over time, ages and transforms. In Venice, for example, one can see how the city and architecture are not static, how the passage of time causes materials to change, deteriorate, and feel lived in. This concept is present not only in the built architecture but also in the thinking and creation process. For us, time is not just a factor that affects buildings, but a constant in the design process. We value the time spent on physical models, on hand drawing, on processes that endure and allow us to generate something with more depth.Save this picture!Save this picture!AD: There is something disruptive about Siestario: by not displaying so much information or explicit context, the space becomes entirely an experience. What place did you give to the sensory aspect compared to the more direct information exhibition?MZ & JP: This approach is carried over to all our projects, not just the pavilion. We are interested in making spaces sensory experiences, where people not only see but also feel. We seek the space to invite reflection, but also for the enjoyment of the body, for people to engage directly with the environment. In the pavilion, in particular, we wanted the images to function as thresholds, leading you to other places, to the world of the dreamlike. This project, in particular, emphasizes the idea of pause, of stopping to reflect and rest, which connects with our broader vision of how we understand architecture: not just as something built, but as a lived experience.What we aim to create are silent spaces, ones that impose nothing, but at the same time offer power in their simplicity. This is something noticeable in other projects we've done, where the intervention is almost invisible, yet has a significant impact on people's experience, providing a space for them to claim it and inhabit it in their own way.Save this picture!AD: How was the curatorial process in constructing the space through the projections on the sides of the space?MZ & JP:  The curatorial proposal was built with the aim of integrating architecture, art, and landscape, bringing together the different actors. From the outset, we decided to organize the curation of images into three groups: one linked to architecture, another to art, and another to the very recording of the silobag in the field. This division not only provided the visitor with information about the origin and context of the project but also sought to establish a dialogue between architecture and art, something that is fundamental for us. Through the arts, we wanted to tell the story of our landscape and our territory, and we feel that artists have a special sensitivity to read and construct that landscape in a different way.The curatorial line was also guided by an idea that particularly interested us: that from naps emerge dreams, desires, and aspirations of a country. We wanted to propose a positive perspective within a critical reading, showcasing an Argentina that also produces art and architecture. The selected images engage in dialogue with the territory, and they do not respond to a specific temporal cut but rather bring together works from different moments, generating intersections across diverse times. We were interested in showing how these works relate to one another and how, through them, the variable of time appears in architectural production, in the territory, and in the contexts in which these works are being built.Save this picture!Additionally, there was significant archival work: we incorporated original drawings and unpublished material to add historical value. The selection included examples from across the country, even works that are not always associated with formal architecture, such as recreational or leisure spaces. We aimed to represent not only the major cities but also other realities, histories, and geographies. We also included maps and references to historical processes of urbanization and territorial defense, combining them with contemporary works to build the narrative.Save this picture!Save this picture!AD: What does it mean for you to represent Argentina at an event like the Venice Biennale, and how do you experience the responsibility and opportunity that this entails?MZ & JP: Representing Argentina at the Venice Biennale carries significant weight in the national architectural sphere. Winning the competition gave us a certain level of acceptance within the architecture community in Argentina. It’s difficult to fully reflect on the impact of this, as we are still in the process, but we do notice there is recognition. Our motivation was, above all, a genuine interest in participating in the competition, but upon taking on this responsibility, we realized the importance of representing our country at such a prominent event. For our careers, our studio, and our aspirations, this opportunity is significant. Being part of the Venice Biennale is something that makes us feel good because we know that what we do is part of a system that, although imperfect, allows us to reach places like this. It’s an important point in our trajectory and an achievement that fills us with satisfaction.Save this picture! Image gallerySee allShow less About this authorPaula PintosAuthor••• Cite: Pintos, Paula. "Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice Biennale" [El Pabellón Argentino en la Bienal de Venecia 2025: La siesta como experiencia en un espacio cargado de simbolismos] 15 May 2025. ArchDaily. Accessed . <https://www.archdaily.com/1030139/rest-as-an-experience-in-a-space-rich-with-symbolism-insights-from-the-argentinian-pavilion-at-the-2025-venice-biennale&gt ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream

How is the discipline of sports analytics evolving to deliver marginal gains to the performance of players and teams? Rugby football is no stranger to analytics. But what difference does it make at an elite competitive level? Dan Biggar (pictured above) is a Welsh professional rugby player, primarily known for his role as a fly-half – a position that is known as the creative brain of a rugby team, like the number 10 position in football. Biggar is currently playing for French Top 14 side Toulon, and has twice been selected to tour with the British and Irish Lions. He earned his first cap for Wales in 2008 and has been a mainstay in the national team, representing his country in Rugby World Cups and Six Nations Championships. With 112 caps, he is the most-capped fly-half for Wales and has scored over 600 points in his career. Over his international career, he has won three Six Nations titles and a Grand Slam with Wales, and was captain for the 2022 Six Nations campaign. What does Biggar think of the role of analytics in rugby? Compared with, for example, more traditional aspects of team sports, like the culture of a specific group of players at a particular moment in time? “I’m always really intrigued by the word ‘culture’ because you can’t coach it. As soon as you start coaching culture, it becomes forced, and some people, especially in a team environment, may not agree, or it’s difficult to get,” he said. Wales bucked the culture of being the underdogs, but we only did that because of the hard work we put in Dan Biggar, professional rugby player “And culture is very difficult to measure. How do you measure the All Blacks’ culture against England’s culture, or Wales’s culture, or Scotland’s culture? It’s just something that you create without knowing you’re creating it,” added Biggar. “The culture of Welsh rugby is always wanting to be, or always thinking of ourselves as, the underdogs. Wanting to punch above your weight. Now, one thing I will say on that is, when we had our most successful years, 2011 to 2021, we bucked every cultural trend of Wales because, during those 10 years, we were not underdogs very often. We weren’t expected to punch above our weight. We were the guys who were the standard bearers in a sense, certainly in the northern hemisphere,” he recalled. “It’s a fascinating discussion, which probably can’t be measured. But what can be measured is how hard you work, how many metres you’ve run, how many minutes you’ve spent on the training pitch, and so on. If you combine those numbers and put them into, say, a Welsh culture of being hard-working underdogs, that’s where you’d find an overlap between the two. “So, we bucked the culture of being the underdogs, but we only did that because of the hard work we put in. If you have a hard-working culture with the stats to back it up, you have an overlap. But if the stats say you are way off it compared with other teams, you don’t have a hard-working culture.” He also says you can hold team members to account with data, and vice versa. “I used to act on what I felt, and I think that has stood me in good stead in general. But then, when you go back and look at the data, or the video analysis, or whatever, you can think that probably wasn’t the right way to handle this or that situation. Wales had a culture where we could hold each other to account. And if we had a heated disagreement on the field, in the moment, we were brilliant as a group by the time we’d got off the pitch or showered or had dinner together in the hotel afterwards – it was gone,” said Biggar. “It’s always helpful if you’ve got something to back [your theory] up, whether that’s a video analysis, a stats pack, data analysis, whatever it is” Dan Biggar, professional rugby player “We could then look back and say, ‘What do you think here? What do you think the stats say here? If we did it that way, we’d have been better off. Or if we did it the other way, we would have got more success out of it’, you know? “It’s always helpful if you’ve got something to back it up, whether that’s a video analysis, a stats pack, data analysis, whatever it is. If you’ve got something to make your point more valid, and you do it in the right way, you’re generally going to get a lot more out of your players, employees, workers, whatever.” On the question of whether data analytics is landing with professional rugby players better than it might have done 10 to 15 years ago, Biggar says 100% yes. “The game has changed dramatically in the last 25 years, but the game has changed even from five to 10 years ago. And I think nowadays players are very much more wanting to look at certain examples on laptops or get provided with stats and analysis pieces,” he said. And that is especially true of his position, he says, which is a playmaker position. “[Others can] go through the whole game and just know their role, just know their positions and what they need to do individually. Probably positions like myself, I need to know, ‘What does Dan Biggar need?’ But also, ‘What does the number 12 for Australia do? What does the number 6 for Argentina do? And how often do Ireland lose playing into the wind?’ When you are in a position of real leadership, you probably need to know more about the data and the analytics.” Biggar was speaking at a recent media event where the possible data analytics generated by Sportable’s “smart ball” were being demonstrated. Sage, which provides accounting, financial, HR and payroll technology for small and medium-sized businesses, set up the event alongside Sportable, a global data collection and analytics platform. Sage has been sponsoring Six Nations Rugby since October 2022, bringing fans of the game real-time data and analytics captured through the smart ball technology and advanced predictive analytics. Sage has been collaborating with Sportable, which specialises in technology that tracks balls and players across various sports, including rugby, Australian rules football, American football and soccer. Its hardware is underpinned by artificial intelligence (AI) and machine learning (ML), which turn thousands of data points into real-time, automated insights on how players and the ball move during a game and in training.  The Gilbert x Sportable Smart Ball, which has been used in elite professional-level rugby games since 2022, works with wireless beacons around the field to show live game data, such as how far the ball is kicked or passed, and how much territory a team gains as they attack. The technology helps players, coaches and teams use these insights to make performance improvement, tactical and recruitment decisions, while broadcasters and fans benefit from immediate game analysis.Raphael Brandon, director of performance science at Sportable, spoke at the Sage event about the novelty of the use of the analytics generated by the ball. “The chip in the ball provides new sets of data that cannot be got manually. For example, Arsenal coaches are able to put objectivity on many things, using the smart ball data, against which they are judging players and looking for improvement” Raphael Brandon, Sportable “The chip in the ball provides new sets of data that cannot be got manually. For example, Arsenal coaches, who use our system, are able to put objectivity on many things, using the smart ball data, against which they are judging players and looking for improvement,” he said. “It can be as simple as, ‘We want our midfield player to move the ball faster’. The chip means they can put a number on that. You can put a level of specificity on player technical performance, say comparing the tempo of the first team and the under-18s,” added Brandon. “Also, the automation of the game analysis [with the smart ball analysis] is novel. Otherwise, you either have a person doing manual analysis or semi-automated computer vision from a camera, which can’t track the movement of the ball sufficiently well. The smart ball makes the analysis fully automated. And that means the democratisation of advanced sports analytics down the levels. So, not just Six Nations or the Premiership, but, say, Dutch Division Two, or youth or many women’s sports where advanced analytics is not affordable.” Read more about data analytics in sport Football players are seeking to gain more control of their performance data through ‘stop processing’ requests, in an attempt to strike a better balance between firms’ commercial interests and the data rights of footballers. Spain’s La Liga has turned to Databricks’ Lakehouse Platform to analyse footballer performance and engage with fans. Leicester City Football Club was the surprise winner of the 2016 season’s English Premier League, thanks, in part, to data analytics.