A few hours ago, publisher Bethesda Softworks revealed that id Software's DOOM: The Dark Ages has already reached 3 million players, making it the biggest launch ever in the franchise. For comparison, it was seven times quicker than the previous entry, DOOM Eternal, to get to this milestone. However, it should be noted that Eternal did not launch on Game Pass right away like The Dark Ages. Microsoft's acquisition of Bethesda was completed in March 2021, while DOOM Eternal was released about a year before. It's always hard to make comparisons between games in the same franchise when the new one is available on Microsoft's subscription service, because it inevitably skews data. Still, it does seem like the reception has been positive overall. DOOM: The Dark Ages is currently sitting at an 86% average user review score on Steam. Here on Wccftech, resident DOOM expert Chris Wray also gave it an 8.5/10 in his review: So, to put it all quite simply, DOOM: The Dark Ages is a good game—no, it's a very good game. I would recommend it to fans of the genre and people who want to have fun without the risk of anything transmissible via bodily fluids. Unless we're talking about demon blood and guts, there's lots of that. DOOM: The Dark Ages is yet another success from Id Software, and long may they continue. DOOM: The Dark Ages is a rip-tearing fun ride that reigns in some of the excesses of DOOM Eternal, bringing the gameplay closer to DOOM (2016), while adding meat to the story in a long, meaty, campaign across over twenty stages. Put simply, if you liked DOOM and DOOM Eternal, you will like this, even if there are a few minor missteps. In other news, NVIDIA has confirmed that the game will receive its path tracing update at some point in June. The patch will also add denoising through NVIDIA's DLSS Ray Reconstruction. Meanwhile, in an interview with Digital Foundry, id Software's Billy Khan explained why DOOM: The Dark Ages wouldn't have been possible without ray tracing. How are you enjoying this new entry? Let us know below! Products mentioned DOOM: The Dark Ages USD 69.99 Buy from Amazon Deal of the Day

Principal Software Engineer, ServicesRiot GamesShanghai, China2 hours agoApplyRiot Games was established in 2006 by entrepreneurial gamers who believe that player-focused game development can result in great games. In 2009, Riot released its debut title League of Legends to critical and player acclaim. As the most played video game in the world, over 100 million play every month. Players form the foundation of our community and it’s for them that we continue to evolve and improve the League of Legends experience.We’re looking for humble but ambitious, razor-sharp professionals who can teach us a thing or two. We promise to return the favor. Like us, you take play seriously; you’re passionate about games. We embrace those who see things differently, aren’t afraid to experiment, and who have a healthy disregard for constraints.That's where you come in.Riot的工程师不仅在特定技术领域拥有深厚的知识，同时也珍视在多元领域工作的机会。作为一名资深软件工程师，你将深入参与以跨团队目标为重点的项目，推动全局一致和标准化。你将主导多人游戏玩法功能开发、执行引擎修改，并为其他工程师提供卓越工程解决方案的清晰案例。你汇报对象是游戏制作人，所在团队目前处于项目早期阶段，正快速迭代并验证核心玩法。这个阶段决定了你会有足够的空间去定义技术路线、设定标准并构建技术中台能力。你的工作：构建高并发全球服务框架：设计并实现支持上万并发、全球在线的游戏后端服务框架，为多人实时玩法提供底层支撑。搭建实时玩法基础设施：打造并优化玩法逻辑同步、房间服务、匹配、会话管理等核心基础设施，保障玩家实时互动体验。提升架构的可扩展性与安全性：推进服务器架构的可扩展性、安全性与稳定性，提升系统整体韧性。建设卓越工程文化：指导团队成员，营造高效协同的工程文化，助力团队共同成长。构建现代云原生服务栈：应用云原生工具（如 AWS、K8s、Docker），构建现代化的后端服务技术栈。推动创意落地体验：与策划、美术等团队密切配合，拆解创意点子，将其真正转化为玩家可体验的内容。我们希望你具备：后端开发经验：10 年以上大型在线游戏或实时服务产品开发经验。项目主程经历：5年以上主程经验，至少一次完整从早期研发到上线的项目主程经验，能独立承担技术框架设计、需求讨论、系统设计与上线运维。分布式系统能力：具备扎实的分布式系统设计功底，能够设计高可用、高扩展性的后台架构。编程技能：精通至少一种主流后端编程语言（C++ / Java / Go）。云技术与自动化：熟悉 AWS/GCP 等云计算平台，了解 CI/CD 流水线及自动化运维工具。沟通与协作：能够以清晰、易懂的方式解释复杂技术方案，有效推动跨团队协作。额外加分项：具备10年以上手游服务端开发经验，特别是高并发、高并行玩法方向。有“万人同服”移动端服务端架构主导经验。熟悉 LOL 世界观或参与过 Riot IP 项目。熟悉客户端/服务端双端联调与性能调优。熟练应用 Docker、K8s、Jenkins、Prometheus 等现代运维工具。对玩法设计有热情，善于与设计同学探讨功能如何实现 live ops 运营。英文听说读写能力优秀，能够参与全球化或跨区沟通。 Create Your Profile — Game companies can contact you with their relevant job openings. Apply

As Unity’s Scriptable Render Pipeline (SRP)’s available feature set continues to grow, so does the amount of shader variants being processed and compiled at build time. Alongside ongoing support for additional graphics APIs and an ever-growing selection of target platforms, the SRP’s improvements continue to expand.Shaders are compiled and cached after an initial (“clean”) build, thus accelerating further incremental (“warm”) builds. While clean builds usually take the longest, lengthy warm build times can be a common pain point during project development and iteration.To address this problem, Unity’s Shader Management team has been hard at work to provide meaningful and scalable solutions. This has resulted in significantly reduced shader build times and runtime memory usage for projects created using Unity 2021 LTS and later versions.To read more about these new optimizations, including affected versions, backports, and figures from our internal testing, skip directly to the sections covering shader variantprefiltering and dynamic shader loading. At the end of this blog post, we also address our future plans to further refine shader variant management as a whole – across project authoring, build, and runtime.Before delving into the exciting improvements made to Unity’s shader system, let’s also take the opportunity to quickly review the concepts of conditional shader compilation, shader variants, and shader variant stripping.Conditional shader features enable developers and artists to conveniently control and alter a shader’s functionality using scripts, material settings, as well as project and graphics settings. Such conditional features serve to simplify project authoring, allowing projects to efficiently scale by minimizing the number of shaders you’ll have to author and maintain.Conditional shader features can be implemented in different ways:Static (compile-time) branchingShader variants compilationDynamic (runtime) branchingWhile static branching avoids branching-related shader execution overhead at runtime, it’s evaluated and locked at compilation time and does not provide runtime control. Shader variant compilation, meanwhile, is a form of static branching that provides additional runtime control. This works by compiling a unique shader program (variant) for every possible combination of static branches, in order to maintain optimal GPU performance at runtime.Such variants are created by conditionally declaring and evaluating shader functionality through shader_feature and multi_compile shader keywords. The correct shader variants are loaded at run time based on active keywords and runtime settings. Declaring and evaluating additional shader keywords can lead to an increase in build time, file size, and runtime memory usage.At the same time, dynamic (uniform-based) branching entirely avoids the overhead of shader variant compilation, resulting in faster builds and both reduced file size and memory usage. This can bring forth smoother and faster iteration during development.On the other hand, dynamic branching can have a strong impact on shader execution performance based on the shader’s complexity and the target device. Asymmetric branches, where one side of the branch is much more complex than the other, can negatively impact performance. This is because the execution of a simpler path can still incur the performance penalties of the more complex path.When introducing conditional shader features in your own shaders, these approaches and trade-offs should be kept in mind. For more detailed information, see the shader conditionals, shader branching, and shader variants documentation.To mitigate the increase in shader processing and compilation time, shader variant stripping is utilized. It aims to exclude unnecessary shader variants from compilation based on factors such as:Materials included and keywords enabledProject and Render Pipeline settingsScriptable strippingWhen enumerating shader variants, the Editor will automatically filter out any keywords declared with shader_feature that are not enabled by materials referenced and included in the build. As a result, these keywords will not generate any additional variants.For example, if the Clear Coat material property is not enabled by any material using the Complex Lit URP Shader, all shader variants that implement the Clear Coat functionality will safely be stripped at build time.In the meantime, multi_compile keywords prompt developers and players to freely control the shader’s functionality at runtime based on available Player settings and scripts. The flip side is that such keywords cannot automatically be stripped by the Editor to the same degree as shader_feature keywords. That’s why they generally produce a larger number of variants.Scriptable stripping is a C# API that lets you exclude shader variants from compilation during build time via keywords and combinations not required at runtime. The render pipelines utilize scriptable stripping in order to strip unnecessary variants according to the project’s Render Pipeline settings and Quality Assets included in the build. Low quality High quality Variant multiplier Main Light/Cast Shadows: Off On 2x Main Light/Cast Shadows: On On 1x Main Light/Cast Shadows: Off Off 1xIn order to maximize the effects of the Editor’s shader variant stripping, we recommend disabling all graphics-related features and Render Pipeline settings not utilized at runtime. Please refer to the official documentation for more on shader variant stripping.Shader variant stripping greatly reduces the amount of compiled shader variants, based on factors like the Render Pipeline Quality Assets in the build. However, stripping is currently performed at the end of the shader processing stage. Simply enumerating all the possible variants can still take a long time, regardless of compilation.In order to reduce the shader variant processing (and project build) times, we are now introducing a significant optimization to the engine’s built-in shader variant stripping. With shader variant prefiltering, both clean and warm build times are significantly reduced.The optimization works by introducing the early exclusion of multi_compile keywords, according to Prefiltering Attributes driven by Render Pipeline settings. This decreases the amount of variants being enumerated for potential stripping and compilation, which in turn, reduces shader processing time – with warm build times reduced byup to 90% in the most drastic examples.Shader variant prefiltering first landed in 2023.1.0a14, and has been backported to 2022.2.0b15 and 2021.3.15f1.Variant prefiltering also helps cut down initial/clean build times by applying the same principle.Historically, the Unity runtime would front-load all shader objects from disk to CPU memory during scene and resource load. In most cases, a built project and scene includes many more shader variants than needed at any given moment during the application’s runtime. For projects using a large amount of shaders, this often results in high shader memory usage at runtime.Dynamic shader loading addresses the issue by providing refined user control over shader loading behavior and memory usage. This optimization facilitates the streaming of shader data chunks into memory, as well as the eviction of shader data that is no longer needed at runtime, based on a user controlled memory budget. This allows you to significantly reduce shader memory usage on platforms with limited memory budgets.New Shader Variant Loading Settings are now accessible from the Editor’s Player Settings. Use them to override the maximum number of shader chunks loaded and per-shader chunk size (MB).With the following C# API now available, you can override the Shader Variant Loading Settings using Editor scripts, such as:PlayerSettings.SetDefaultShaderChunkCount and PlayerSettings.SetDefaultShaderChunkSizeInMB to override the project’s default shader loading settingsPlayerSettings.SetShaderChunkCountForPlatform and PlayerSettings.SetShaderChunkSizeInMBForPlatformto override these settings on a per-platform basisYou can also override the maximum amount of loaded shader chunks at runtime using the C# API via Shader.maximumChunksOverride. This enables you to override the shader memory budget based on factors such as the total available system and graphics memory queried at runtime.Dynamic shader loading landed in 2023.1.0a11 and has been backported to 2022.2.0b10, 2022.1.21f1,and 2021.3.12f. In the case of the Universal Render Pipeline (URP)’s Boat Attack, we observed a78.8% reduction in runtime memory usage for shaders, from 315 MiB (default) to 66.8 MiB (dynamic loading). You can read more about this optimization in the official announcement.Beyond the critical changes mentioned above, we are working to enhance the Universal Render Pipeline’s shader variant generation and stripping. We’re also investigating additional improvements to Unity’s shader variant management at large. The ultimate goal is to facilitate the engine’s increasing feature set, while ensuring minimal shader build and runtime overhead.Some of our ongoing investigations involve the deduplication of shader resources across similar variants, as well as overall improvements to the shader keywords and Shader Variant Collection APIs. The aim is to provide more flexibility and control over shader variant processing and runtime performance.Looking ahead, we are also exploring the possibility of in-Editor tooling for shader variant tracing and analysis to provide the following details on shader variant usage:Which shaders and keywords produce the most variants?Which variants are compiled but unused at runtime?Which variants are stripped but requested at runtime?Your feedback has been instrumental so far as it helps us prioritize the most meaningful solutions. Please check out our public roadmap to vote on the features that best suit your needs. If there are additional changes you’d like to see, feel free to submit a feature request, or contact the team directly in this shader forum.

In Brief Posted: 11:44 AM PDT · May 20, 2025 Image Credits:Bloomberg / Getty Images Intel is reportedly exploring a sale for its networking and edge unit Intel CEO Lip-Bu Tan continues to consider ways to help semiconductor giant refocus on its core business. Intel is allegedly considering selling its networking and edge unit, according to reporting to Reuters, as Tan looks to shed business divisions that aren’t considered critical. This unit builds makes chips for telecom equipment and was responsible for $5.8 billion of revenue in 2024. Intel has started engaging with potential buyers but has not launched a formal sales process, Reuters reported. If Intel does end up pursuing a sale, it wouldn’t be surprising. Tan has made it clear that he wants the company to refocus on its core business units — PCs and data center chips. In March, Tan told Intel’s customers that the company would spin off its non-core assets at the company’s Intel Vision conference. Intel declined to comment. Topics

After over four years of fighting, including in the courts, Epic Games’ Fortnite is back on Apple’s U.S. App Store. Disagreements with Epic and Apple started with when the former wanted to take payments directly from its players, bypassing Apple and its 30% cut on purchases. After years of legal back and forth, a decision earlier this month sided with Epic, which seemed to signal Fortnite’s imminent return on iOS. But Apple didn’t immediately comply, holding up Fortnite’s reinstation and looking to appeal the court’s decision. “Although Apple’s contracts may permit it to reject an app for lawful reasons, the Injunction provides that Apple may no longer reject an app—including Fortnite—because its developer chooses to include an external purchase link,” Epic noted at the time. “Likewise, if the Injunction is to have any teeth, Apple cannot reject an app on the ground that its developer has sought to enforce the Injunction’s prohibitions.” Apple has now budged, and Fortnite is available for download on its store again. The fight between these companies will have major ramifications across the industry, as it opens the door for publishers to work around the cut that digital store owners usually take from purchases. GB Daily Stay in the know! Get the latest news in your inbox daily Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Google unveiled major AI advancements at I/O 2025, including Gemini 2.5 with Deep Think, AI Mode in Search, Veo 3 for video with audio, and a $249 Ultra plan aimed at power users and enterprises.Read More

Sakana AI a le vent en poupe. Huit mois après avoir levé 100 millions de dollars, cette start-up nippone fondée par deux anciens chercheurs de...

Table of Contents Table of Contents Table of Contents Skullcandy vs. Bose: Sound quality Skullcandy vs. Bose: Noise cancellation Skullcandy vs. Bose: Comfort and design Skullcandy vs. Bose: Battery life Skullcandy vs. Bose: Companion apps and extra features Our winner: Get the Bose earbuds on sale How We Tested Frequently Asked Questions The noise-cancelling earbuds market is not just crowded — it's packed. If a brand wants to stand out with a new release, it faces the difficult task of bringing something new to the table that can outshine the products of the major brands like Sony, Apple, and, of course, Bose. Skullcandy's new earbuds, the Method 360 ANC earbuds, are attempting to do just that by partnering with one of those major brands — Bose. The earbuds come with Bose-powered sound at the very impressive introductory price of $99.99 (Skullcandy will be raising the price to $129.99 at an unspecified date in the future). The cheapest Bose earbuds, on the other hand, are the QuietComfort earbuds, which retail for $179. Though they are considerably more expensive, they do often go on sale for $149 and have reached the record-low price of $129, which made us especially curious to put them head to head with the Skullcandy earbuds.Below, you'll find our breakdown of how the Bose QuietComfort earbuds compare to the Skullcandy Method 360 ANCs. Skullcandy vs. Bose: Sound qualityIn the press release for the Method 360 earbuds, Skullcandy called them its "most advanced audio experience to date." In listening to everything from indie rock, video game soundtracks, and podcasts, I can see why. The Skullcandy earbuds had a balance that matched up easily to other impressive budget earbuds I've tested. Whether I was listening to the Final Fantasy VII soundtrack or a live Daft Punk performance, these earbuds punched above a $100 price point. How does Bose-powered sound compare to the real deal? Credit: Bethany Allard / Mashable However, when listening to them side by side with the Bose earbuds, the Skullcandy earbuds felt muffled and muddier (though I wouldn't describe them as muddy on their own).When listening to Japanese Breakfast's "Savage Good Boy," I could still pick out different parts of the song's instrumentation, but I had to listen harder to pick up the piano past the guitar and vocals than I did with the Bose earbuds. While listening to the "Hush (Still Woozy remix)" by The Marías, the layers of the song were present with the Skullcandy earbuds, but shined with the Boses. Ultimately, the Skullcandys provided a solid listening experience, but the one the Bose earbuds provided was undeniably better. At the same volume, the QuietComfort earbuds sounded warmer, fuller, and generally more powerful. (I say this as someone who doesn't necessarily love bumping the volume super high.) Bose offers five total EQ presets, plus a custom setting. Credit: Screenshot: Bose Compared to Skullcandy's three presets and custom equalizer. Credit: Screenshot: Skullcandy For both, I found their standard mix to be on the bass-heavy side. However, Bose offers five EQ presets and a custom equalizer, compared to Skullcandy's three presets with a custom equalizer.Winner: Bose QuietComfortSkullcandy vs. Bose: Noise cancellationSkullcandy uses four mics to help block out sound, compared to the QuietComforts' three mics.That said, Bose's earbuds literally have the words quiet and comfort in their name. Not to mention, the brand's been a leader in the noise-cancelling space for quite some time. Whether I was working at home with my partner on the other side of the wall, in a cafe, library, or taking a walk, I generally found the Bose earbuds more adept at muting and outright blocking out sounds. That especially came through when I used just the noise cancellation without music to help it.However, the Skullcandy earbuds do offer adjustable ANC via the companion app, which isn't currently available on any pair of Bose earbuds. For their price point, it's not a feature I expected, but one I was pleasantly surprised to encounter. At its highest setting, the noise cancellation only had the slightest hiss. While Bose essentially had none at all in my experience, I was still surprised that the Skullcandy's noise cancellation was comparable at all, considering the price discrepancy. The Skullcandy companion app allows you to adjust the ANC on a sliding scale. Credit: Screenshot: Skullcandy And still, it's worth saying: the Skullcandy earbuds' ANC is very impressive for the price point. While working at the library, I could barely hear the murmurs of a conversation across the room that I could make out word for word without wearing the earbuds. On the Bose buds, the conversation disappeared altogether, giving them the slightest edge. Both earbuds allow you to toggle the ANC on and off and activate a transparency mode. With this last setting, the Bose stood out more effortlessly, amplifying the world around me just enough without overly emphasizing sounds or the room tone. The Skullcandy earbuds, on the other hand, exhibited a stronger background hiss in this mode. It didn't make it unusable by any means, but definitely less pleasant than that of the Bose earbuds.Winner: Bose QuietComfort, by a hair Skullcandy vs. Bose: Comfort and designFor how they fit into the ear, the designs are quite similar on these earbuds, which makes them wear very similarly. In my ears, I found the Bose earbuds to be slightly more comfortable, but both pairs come with three sizes of stability bands and three sizes of ear tips. At the end of the day, fit can be very personal, especially with earbuds. The earbuds have a similar shape, and both come equipped with a similar type of ear tip and stability band. Credit: Bethany Allard / Mashable As for the earbuds themselves, the Skullcandy earbuds actually resembled the Bose QuietComfort Ultra earbuds more, with a slightly elongated stem and exterior touch controls with the perfect level of sensitivity. As someone who far prefers tactile on-ear controls, I was surprised by how well these earbuds distinguished the different types of presses. I think because the earbud has a slight groove where the touch controls are, it's easy to identify where to touch, and to not brush against the surface and activate the controls accidentally.On the other hand, the regular QC earbuds have a flatter, more rounded touch control surface, which, in my testing of these earbuds last fall, I found to err on the side of oversensitivity. While I didn't experience that issue much this time around, it's worth noting that I tested them initially during a time of year I was wearing more hats and hoods. The Skullcandy earbuds perform like the Bose QuietComfort earbuds (pictured left) but look more like the QuietComfort Ultra earbuds (pictured right). Credit: Bethany Allard / Mashable The Skullcandy earbuds might've taken the win in this section, if not for one glaring feature — their case. Unlike most other earbuds cases that merely snap open and closed, this one has a sliding mechanism with an O-ring and a slot for one earbud on either side of the interior case. If the intention was to create something outside of the box, Skullcandy did a great job, but it sacrificed some functionality in the process. This case is super bulky. Credit: Bethany Allard / Mashable As you can see, the case is huge compared to the earbuds, meaning if you're not using the O-ring, it is one of the least travel-friendly pairs of earbuds out there. In their design, Skullcandy seemed to assume that's how most people would use the case, as they placed the earbuds on the opposite side of the case. In other words, when looking at it head-on, the right earbud goes on the left side, and the left earbud goes on the right side. If you have the case clipped onto your belt with the front facing outward, this configuration makes perfect sense. In pretty much every other use case, it makes none.The Bose case, on the other hand, is relatively compact and has a clear spot for each earbud. The Bose earbuds don't have a clip-on ring, but feel marginally more portable. Credit: Bethany Allard / Mashable Both earbuds are IPX4 water-resistant, so they should withstand sweating during workouts just fine.Our winner: TieSkullcandy vs. Bose: Battery lifeThere's not much to say here other than both of these earbuds have excellent battery life. With ANC on, Skullcandy estimates you can get roughly nine hours per charge, with 23 hours of battery life in the case. Bose estimates 8.5 hours, with 31.5 hours in the case. Numbers-wise, Bose is the winner here, but in two work weeks of testing, I only needed to charge up the Skullcandy earbuds once. The Bose app has a feature that lets you easily see the hours breakdown of your remaining battery life, but it's not something I checked on super regularly during testing. Related Stories The Bose app makes it easy to tell how much playtime you have left. Credit: Screenshot: Bose For day-to-day use, I found these earbuds performed very similar in the battery life department.Our winner: TieSkullcandy vs. Bose: Companion apps and extra featuresWhen testing these earbuds side by side, I noticed right away that the Bose-powered element doesn't make itself hidden. Skullcandy adopted the Bose QuietComfort's power-on sound, voice assistant, and tiled app interface. The Skullcandy app. Credit: Screenshot: Skullcandy Versus the Bose app. Credit: Screenshot: Bose While the Bose app has a few more features, including a battery prediction, smart calls, and a voice assistant section, there's also quite a bit of overlap. Both have equalizer settings, touch control customization, low latency modes, and even a remote selfie section.Numbers-wise, Bose simply has more features you can mess around with to truly tailor your earbud experience to what suits you best. Features-wise, Skullcandy has a slight edge with adjustable ANC, but Bose's better ANC overall, alongside the greater customization, its battery prediction screen, and more varied EQ presets help it stand out ahead of the cheaper earbuds.Our winner: Bose QuietComfortOur winner: Get the Bose earbuds on saleThe fact that a pair of Skullcandy earbuds can stand up to Bose earbuds at all is a testament to what the brand has done with the Method 360 ANCs. While overall, I wouldn't call them exactly the same, I would say they're similar enough that you won't be upset to grab them at $100. If they stayed at their introductory price forever, they'd be easy to recommend as the winner of this matchup.However, they will be jumping up to $129.99. Combined with the fact that the Bose earbuds get marked down to $149 frequently enough that these earbuds will sit just $20 apart with some regularity, it's hard not to suggest just springing the extra $20 to get slightly better earbuds with slightly more features. Get the Skullcandy and Bose earbuds Bose QuietComfort earbuds $179.00 at Amazon Skullcandy Method 360 ANC $99.99 at Amazon Still can't decide? Check out our full reviews of the Bose QuietComfort earbuds and the Skullcandy Method 360 ANC earbuds. If you have a bigger budget, we recommend checking out our review of the Bose QuietComfort Ultra earbuds.

AMD has aggressively pushed its workstation hardware to ever-higher performance levels for years, and the company hopes to do it all over again with new Zen 5-based Threadripper processors for workstations and home desktops. Dropped during Computex 2025, these new chips augment AMD’s existing Zen 5-based Epyc portfolio by providing more performance and lower power ratings. A new Radeon AI Pro R9700 workstation graphics card will launch alongside these, the first based on AMD’s new RDNA 4 graphics architecture.Epyc and Threadripper Receive the Zen 5 UpgradeWith the workstation and server markets hot in recent years, AMD didn’t hesitate to update its Epyc product line with models based on its new Zen 5 microarchitecture. The first of these was announced shortly after the Zen 5 made its debut, and several Zen 5-based Epyc processors shipped out last fall. However, AMD’s Threadripper line didn't get an update before this, and now will also receive the Zen 5 boon.(Credit: AMD)AMD's new Threadripper processors aren’t intended to replace any of its Epyc CPUs; instead, these new chips augment the line with different SKUs featuring improved specs. For example, the top new CPU AMD announced is the Ryzen Threadripper Pro 9995WX, which has 96 CPU cores, 192 threads, a max boost clock of 5.4GHz, and a 350-watt (W) thermal design power (TDP) rating.(Credit: AMD)Compared with the current top Epyc processors, like the AMD Epyc 9965, the Threadripper Pro 9995WX lags. This difference is because the Epyc 9965 has twice as many CPU cores, double the number of threads, and an unsurprisingly higher TDP of 500W. The Threadripper Pro 9995WX compares quite favorably with the AMD Epyc 9655. This CPU also has 96 cores and 192 threads, just like the Threadripper Pro 9995WX, but it has a lower boost clock of 4.5GHz and a 400W TDP.On paper, at least this shows that the Threadripper Pro 999WX is a more alluring option over the Epyc 9965. The other new Threadripper Pro processors are all similar and tend to provide better overall specs than their closest Epyc counterparts.Recommended by Our EditorsThreadripper Consumer Returns With Zen 5AMD also announced a series of Threadripper processors for home PCs. The most significant difference is that these processors do not support AMD Pro technologies, which are meant to provide greater security and stability features for AMD processors. Otherwise, their specs match those of models in the Threadripper Pro line.(Credit: AMD)These processors are likely best suited for someone with heavy compute needs who doesn’t require the added security or stability that the AMD Pro technologies provide. If you do a lot of content creation or programming work from home or as a hobby, you sound like the target audience for the consumer-grade Threadrippers.RDNA 4 Revs Up the AMD Radeon AI Pro R9700AMD won't wait any longer to bring its new RDNA 4 architecture to workstations and servers either. The new AMD Radeon AI Pro R9700 is based on the RDNA 4 architecture and likely uses the same GPU core as AMD’s Radeon RX 9070 XT. AMD hasn’t confirmed this, but every generation we see the top AMD graphics chip end up in the top AMD workstation card, so it’s a safe bet.(Credit: AMD)This would give the card 64 compute units, 4,096 stream processors, and 128 AI accelerators. AMD confirmed the R9700 would indeed have 128 AI accelerators, which makes using the same GPU as the RX 9070 XT all the more likely. It would also mean the card has a 256-bit wide memory interface. AMD didn’t report the clock speed that the R9700 would operate at, but it has 32GB of GDDR6 and a 300W TDP.(Credit: AMD)According to AMD, leveraging that large 32GB memory pool, the R9700 can outperform Nvidia’s GeForce RTX 5080 in several AI-related workloads. Of course, performance will vary between applications, but AMD’s reported performance numbers are impressive if true.Coming Soon, But No Pricing for Now(Credit: AMD)AMD intends to ship its Threadripper Pro, Threadripper, and Radeon AI Pro R9700 products in July 2025. At this time, we don’t have any idea how much these products will cost. The new Threadripper parts will likely be priced below their Threadripper Pro counterparts as they have fewer features, but their prices could still land just about anywhere.

Reading Time: 7 minutes The way brands look at data has changed over the past few years. While in the last decade, brands may have focused on data collection (the more data, the merrier), over the passage of time, marketers are dawning upon the realization that unless data is actionable, it’s not really usable. Marketers have struggled for years to draw actionable insight from fragmented customer data that exists in silos across countless tools.  Rather than adding yet another tool to their tech stack to mitigate this silo problem, marketers are re-evaluating their entire tech stack. As a result, forward-thinking brands realize that to unlock truly personalized, real-time customer experiences requires tapping into the single source of truth: their data warehouse. Let’s explore why this shift is happening and how a CDEP like MoEngage is the perfect partner in this new paradigm. From Passive to Active: The Evolution of The Modern Data Warehouses Data warehouses aren’t new. Traditionally, many businesses relied on on-premise warehouses, which were powerful but often rigid, expensive to scale, and sometimes siloed.  The game-changer has been the advent of cloud data warehouses (such as Snowflake, Google BigQuery, Databricks, Amazon Redshift, and Azure Synapse). Cloud data warehouses offer consumer brands the following advantages: Scalability: Easily handle vast and growing datasets without massive upfront infrastructure investment. Flexibility: Integrate structured and semi-structured data from teams like sales, customer support, finance, marketing, etc. Performance: Process complex queries rapidly, enabling faster insights. Accessibility: Democratize data access for various teams (with appropriate governance). Gone are the days when data warehouses were merely passive repositories of data. Today, they often contain the most complete, up-to-date, and trustworthy business view. Benefits of the Warehouse-First Approach for Campaigns Real-time data: Data warehouses often provide real-time data, enabling marketers to understand the pulse of the customer and make decisions based on timely and accurate information rather than the slightly-dated information that ends up in a CDP (customer data platform). Real-time analytics: A warehouse-first approach helps marketers to understand the current state of the business (as in ‘what is happening right now’. This real-time analytics also helps them spot deviations in key performance indicators (KPIs), detect problems proactively, and make faster decisions. Real-time segmentation: Traditional segmentation relies on slightly stale data (hours or days old). By automatically moving customers into or out of segments based on their latest actions/changes in attributes, marketers can personalize the customer experience in real time, thereby becoming more relevant and timely. Data consistency and quality: Loading data into a warehouse involves data cleansing, validation, standardization, and transformation, which improves the quality of data in the warehouse. Speed, scalability, and cost-effectiveness: Data warehouses offer scalable storage and processing power, allowing businesses to adapt to their evolving needs rapidly. Compared to storing and processing data within multiple tools, data warehouses are a more cost-effective way for businesses to manage massive amounts of diverse data. Faster time-to-value (Potentially): Once set up, activating new data points or segments can be quicker if the data already resides in the warehouse. This also means that campaigns can be launched faster vs. when data needs to be copied into a CDP before being sent to an email platform or an SMS automation tool. Improved data governance: By consolidating all data into a central repository, data warehouses ensure that data governance policies, standards, and procedures are standardized across the board.   Benefits of a Warehouse-First Approach For B2C Consumer Brands CDEP and the Warehouse-First Approach Forward-thinking consumer brands have realized that customer data management and customer engagement cannot exist in silos.  This is how a data-driven marketer is thinking in 2025: I have all the information about my customer consolidated in a data warehouse. The data combines customers’ past purchase history, product interactions, customer support history, marketing-related data, and many other pivotal data points. I want to use this data warehouse information directly to drive contextual, relevant, and personalized experiences through my campaigns in real-time. I don’t want to spend time copying the data into another tool and then into a marketing automation or a customer engagement platform. Any data latency will prevent me from providing real-time personalized experiences, and will negatively impact my engagement, retention, and monetization campaigns.   For consumer brands that need to respond to the pulse of the customer in real-time, the martech stack needs to be warehouse-native.  This is where a CDEP (Customer Data and Engagement Platform) fits in like a glove. What is a CDEP? A customer data and engagement platform (CDEP) is an integrated, all-in-one platform that combines robust data management capabilities with best-in-class engagement, AI, and analytical capabilities.  A CDEP can: Unify data to build a single customer profile with real-time data ingestion Segment your customers based on behavior, demographics, or propensity Hyper-personalize communications in real-time, at scale Automate campaigns via advanced AI functionalities Analyze campaign performance and customer behavior to unlock deep customer insights What Makes MoEngage the Perfect Warehouse-Native CDEP? A CDEP helps consumer brands leverage warehouse as the central system without creating new data silos.  A warehouse-native CDEP like MoEngage minimizes data duplication and data movement, leverages the warehouse’s processing power, enhances security, and can lower costs in the long run. To evaluate why a CDEP (Customer Data and Engagement Platform) like MoEngage shines in a warehouse-first marketing approach, we need to understand these three pillars: 1) Capabilities of a CDEP Data Ingestion, Unification, and Management Capabilities Instead of extensive data storage, a warehouse-native CDEP connects directly to your data warehouse. It allows marketers to build segments using rich data in situ within the warehouse environment, leveraging the warehouse’s processing power and data freshness. Side note: A CDEP can also double down as a data management platform if you do not have a data warehouse or do not wish to connect it with your warehouse.  Data Ingestion: A CDEP comes equipped with flexible and robust data management capabilities that can capture a complete view of customer interactions. Sources of data ingestion include event streaming, batch data uploads, platform integrations (via connectors and APIs), and data warehouse integrations (Snowflake, BigQuery, RedShift, etc). Data Storage: A CDEP would leverage your existing data warehouse as the primary, persistent storage location for comprehensive customer data. Data Unification: CDEPs can unify data from various sources into a 360-degree customer view for each individual, resolving identity across devices and channels. CDEPs do this by ingesting and recognizing identifiers (email, phone, user ID, device ID etc) and applying deterministic matching rules to stitch identifiers and associated data together. Data Governance, Privacy & Security: CDEPs have best-in-class data governance, privacy and security features like Data Access Management, Data Encryption, PII Masking and Tokenization, and Data Retention Policies. These measures ensure that data remains protected at all costs. Integrated Engagement Capabilities A CDEP seamlessly combines data processing with built-in omnichannel campaign orchestration and delivery across multiple channels. These best-in-class engagement capabilities are further augmented with AI capabilities that can help consumer brands improve the customer experience. Omnichannel Messaging Capabilities With over 11 channels, including email, SMS, push notification, WhatsApp, etc., a CDEP like MoEngage can help your brand build and optimize personalized customer journeys across channels and devices. AI Capabilities Generative and predictive AI capabilities offered by a CDEP can easily learn from the warehouse data and use AI agents to suggest the ideal customer journeys, decisions that help brands optimize campaigns, and generate segments. Segmentational Capabilities A CDEP can generate actionable segments in real time based on demographics and attributes, behavioral and transactional data, and propensity. A CDEP uses fresh and comprehensive data in the warehouse, allowing marketers to build customer segments based on demographics, attributes, behavior, transactions, or propensity scores. Analytical Capabilities After unifying data from different sources, a CDEP can help marketers make sense of the data through the power of its analytical capabilities. Be it identifying trends and patterns in customer behavior, segmenting the audience, predicting the likelihood of certain actions in the future, or informing future strategy, CDEP’s robust analytical capabilities can help marketers with actionable insights in real time. Reporting and Dashboards: Built-in data visualization capabilities can help marketing, growth, and product teams across consumer brands track key business metrics like campaign engagement rates, conversion rates, churn rates, DAUs, and MAUs. Behavioral Analytics: Features like funnel, RFM, and cohort analysis can help consumer brands analyze behavioral trends of customers. 2) Scalability, Reliability, and Elasticity Reliability offered by a CDEP means that the platform is consistently available without any downtimes or disruptions. A reliable CDEP ensures high availability, fast response times, and stable performance for brands like yours. A reliable CDEP is built on elastic infrastructure, which means that the platform’s capabilities will be available to you even during periods of extensive and high usage by other customers. 3) Customer Support  If you’re planning to migrate to a warehouse-native CDEP, you might have some concerns about the amount of effort, developer bandwidth, friction, and migration costs that you could incur while making the switch. MoUpgrade program by MoEngage accelerates migration timelines and delivers faster time-to-value (TTV) in weeks instead of months or years.  The program allows for a smooth migration to a warehouse-native CDEP without any disruptions to ongoing campaigns, data collection cadences, or your brand’s existing data pipelines. Conclusion: This shift towards warehouse-first marketing marks a pivotal change, moving beyond fragmented data collection to leveraging the data warehouse as the single source of truth. A warehouse-native CDEP like MoEngage emerges as the ideal solution. By integrating data management, engagement, and analytics directly with the warehouse, CDEPs empower brands to deliver truly contextual, real-time experiences, driving better results and faster time-to-value in 2025 and beyond. Talk to our team to understand how a CDEP can help your brand better harness the power of your data warehouse.   The post Warehouse-First Marketing: Why Brands in 2025 Are Choosing a CDEP appeared first on MoEngage.