Michael M. Santiago/Getty Images; Getty Images; BIWall Street banks are proving that generative AI is here to stay, and the tech is not just a fad.Business Insider has reported on how some of finance's biggest banks are approaching generative AI.See how giants like Goldman Sachs and JPMorgan are weaving the tech into the fabric of their firms.Wall Street bank leaders say generative AI is here to stay, and they're weaving the technology throughout the fabric of their banks to make sure.From trading to payments to marketing, it's hard to find a corner of the banking industry that isn't claiming to use AI.In fact, the technology's impact, made mainstream by OpenAI's ChatGPT in late 2022, is becoming cultural. Generative AI is changing what it takes to be a software developer and how to stand out as a junior banker, especially as banks mull over how to roll out autonomous AI agents. The technology is even changing roles in the c-suite. But it's also presented new challenges — bank leaders say they are struggling to keep up with AI-powered cyberattacks.From supercharging productivity via AI-boosted search engines to figuring out the best way banks can realize a return on their AI investments, here's what we know about how Wall Street banks are embracing AI.JPMorgan ChaseJPMorgan CEO Jamie DimonTom Williams/CQ-Roll Call, Inc via Getty ImagesJPMorgan CEO Jamie Dimon is a "tremendous" user of the bank's generative AI suite. We have the story of how he and other bank executives use AI.JPMorgan's AI rollout: Jamie Dimon's a 'tremendous' user and it's caused some 'healthy competition' among teamsJPMorgan is making a big bet on AI. Here's how its private bankers are using it.Dimon also laid out his vision for how America's largest bank will win the AI battle against fintechs through data. Meet the leaders of that mission.Jamie Dimon says he's out to win the AI arms race. See who he's put in charge of this critical mission.JPMorgan's Lori Beer just added 2 new tech leaders to her ranks. Meet the team behind the bank's AI future.Mary Erdoes, the boss of JPM's asset- and wealth-management business, used these slides to outline how she wants to get her people ready for the "AI of the future."Here's how JPM boss Mary Erdoes wants to use AI to eliminate "no joy" workIt's not just JPMorgan's in-house tech teams that have been gearing up for an AI future. Cloud partners, like AWS, also play an important role.AWS is helping financial giants like JPMorgan and Bridgewater with their AI ambitionsGoldman SachsGoldman Sachs' David SolomonMichael KovacIs Goldman in its AI era? These real-world stories about employees using AI (in some cases daily) make it seem so. Take a look at how AI is being put to the test across the bank and seniority levels, from C-suites to analysts.7 Goldman Sachs insiders explain how the bank's new AI sidekick is helping them crush it at workGoldman is assembling a growing arsenal of AI tools. Here's everything we know about 5.AI is doing 95% of the work on an IPO prospectus, Goldman Sachs CEO saysGoldman's top partners and CEO David Solomon are eager to see AI rev up their businesses. From realizing internal productivity gains to capturing more business as clients look to raise money in anticipation of AI development and acquisitions, here's what the top echelon is expecting.How AI will shake up Goldman Sachs, according to top partners and CEO David SolomonInside Goldman Sachs' plans for AI, from helping non-tech workers do more with software to streamlining how code is documentedThere is no AI without data, and there is no data strategy at Goldman without its chief data officer, Neema Raphael. Raphael gave BI an inside look at how his roughly 500-person team melds with the rest of the bank to get the most out of its data.Meet Neema Raphael, the data whiz key to Goldman's AI ambitions who's overseeing the bank's army of engineers and scientistsAI's impact has ripple effects that go far beyond technology. Goldman's chief information officer, Marco Argenti, predicts that cultural change will be critical to getting the bank to 100% adoption.Goldman Sachs CIO Marco Argenti says Wall Street's 'next big wave' in AI will come down to culture, not just technologyMany dollars are being spent on Wall Street's AI ambitions. But how do you measure the return on the investment? Argenti offers some tips on the calculus that can help firms prioritize where to invest.As AI usage ramps up on Wall Street, Goldman Sachs CIO details how the bank analyzes return on investment for the techMorgan StanleyMorgan Stanley CEO Ted PickJeenah Moon / ReutersMorgan Stanley wants to turn employees' AI ideas into a reality. Here's an exclusive look at that process.Morgan Stanley has 30 AI projects in the pipeline. Here's how the bank sources employees' ideas for inspiration.See how AI is transforming Morgan Stanley's wealth division and the jobs of its 16,000 financial advisors.Morgan Stanley is betting on AI to free up advisors' time to be 'more human.' Nearly 100% of advisor teams use it, and here's how.Thanks to its partnership with ChatGPT-maker OpenAI, Morgan Stanley has ramped up its AI efforts. The exec in charge of tech partnerships and firmwide innovation opened up about how it all started.Morgan Stanley's new innovation head lays out his plan for more OpenAI-type partnershipsA Morgan Stanley exec breaks down how the bank courts tech partners like OpenAICitiCiti's Jane FraserNICHOLAS KAMM/Getty ImagesMeet the new exec in charge of giving an AI facelift to Citi's lagging wealth business.Citi snags AI leader from Morgan Stanley to help turn around its wealth techCiti's top tech executive, Shadman Zafar, outlined the bank's four-phased AI strategy and how it will "change how we work for decades to come."A top Citi tech exec breaks down the 4 phases of the bank's AI strategy that will impact everyone from operations to wealth and productBank of AmericaBank of America's Brian MoynihanJohn Lamparski/Getty ImagesBank of America's chief experience officer, Rob Pascal, details how the bank's internal-facing AI assistant helps bankers collect, record, and review client data. Here are all the ways it's helping employees be more effective and efficient.How Bank of America is using an AI-powered tool to help its bankers prep for client meetings more efficientlyAI hits the investment bankWall Street investment banks prepare for an AI future.Momo Takahashi/BIInvestment bankers are hopeful that corporate America's obsession with AI could kick off a new era of mergers, acquisitions, and IPOs. From execs stepping into recently created roles to accommodate the sector to industry veterans launching their own AI-focused M&A-advisory firm, meet 11 investment bankers poised to lead Wall Street's AI revolution.Wall Street is gearing up for an AI shopping spree. Meet 11 bankers poised to come out on top.We spoke with four of those AI bankers about why 2025 is going to be all about AI pickaxes and shovels rather than pure-play AI deals.The deals 4 tech bankers think the 'AI arms race' could drive in 2025AI could save junior bankers time by automating tedious tasks known all too well by Wall Street's youngest ranks. But it can also make it harder to break into the industry by shifting the skills required for entry.How AI will change the job of the junior banker and raise the bar to entryA former Goldman Sachs managing director built an AI-powered networking tool to spur dealmaking. The budding startup, Louisa AI, already has a few clients, including Goldman Sachs, Insight Partners, and a global exchange.See the pitch deck that helped a former Goldman Sachs exec raise $5 million for an AI-powered networking startup that feeds deal ideas to bankers.Here's how former investment bankers left their Wall Street jobs to build an AI startup to solve junior bankers' woes.See the pitch deck that helped former investment bankers raise a $7 million seed round for their generative 'AI consigliere' to bankers and analystsRead the original article on Business Insider

Ecovacs Deebot X9 Pro Omni robot vacuum review: superior mopping skills MSRP $1,600.00 Score Details “The Ecovacs Deebot X9 Pro Omni features one of the best mopping systems ever used in a robot vacuum.” Pros Innovative mopping system Retractable brushes and mops Self-cleaning dock Anti-tangle vacuuming Cons Expensive Too tall to fit under furniture Ecovacs launched the Deebot X8 Pro Omni earlier this year. Featuring a unique roller mop system, it offered one of the best mopping performances we’d ever seen in a robot vacuum. Now, a few months later, the company has already launched its successor — the Ecovacs Deebot X9 Pro Omni. It takes everything we love about the X8 and makes it better, including a new mop-lifting system, enhanced vacuuming system, and a fantastic anti-tangle design that eliminates hair and string from clogging up its brushes. Recommended Videos If you’re looking for a robot vacuum to thoroughly clean hard floors, you can’t go wrong with the Ecovacs Deebot X9 Pro Omni. It’s not perfect, but aside from a few minor complaints, the expensive X9 Pro Omni should make a premium addition to any smart home. One of the best mopping systems ever Jon Bitner / Digital Trends I’ve tested dozens of robot vacuums over the years, and I can say without a doubt that the Ecovacs Deebot X9 Pro Omni offers one of the best mopping experiences on the market. Known as “OZMO Roller Mopping Technology,” it’s essentially a giant roller brush on the bottom of the robot. A series of sprayers are constantly rewetting the mop to ensure a consistent clean across your floors, while a scraper is used to pull dirty water off its surface. The end result is remarkably shiny floors. Combined with its ability to apply 3,700 Pa of pressure and roll at speeds up to 220 rpm, it eliminates just about any stain or spill it encounters. Ecovacs also outfitted the X9 Pro Omni with some cool bonus features. The first is a triple-lift system, allowing the mop, main brush, and side brush to retract depending on the task at hand. When a spill is detected, both mops will retract, ensuring they stay clean. When carpet is detected, the mop will retract, ensuring your floors stay dry. Better yet, the robot offers a “Carpet First” function — meaning it’ll zip around your home and vacuum with a dry mop before starting the wetting and mopping process. When all of this is taken together, the end result is a robot vacuum perfect for cleaning hard floors. It’s also great that it can traverse carpets with dry mops — though it would be even better if it could automatically detach its mop and leave it in the base station when vacuuming. It’s a feature some competitors are starting to use to great effect, as it ensures your mops aren’t touching carpet at all. Vacuuming and self-cleaning Jon Bitner / Digital Trends Oddly enough, the X9 Pro Omni offers less suction than the older X8 Pro Omni. The previous robot packed in 18,000 Pa — the X9 Pro Omni shifts it down to just 16,600. However, its vacuuming performance remains about the same (if not better), thanks to the inclusion of BLAST technology. BLAST is shorthand for “Boosted Large-Airflow Suction Technology,” and it works via a combination of suction and maximized airflow. The idea revolves around using additional airflow to knock loose dust particles and other debris, which can then be suctioned up without requiring as much force. In practice, we’ve found the system to work incredibly well. Along with handling dust and small particles, it picks up clumps of dirt and larger objects without much trouble. It also leaves fantastic lines on plush carpet — giving your home a “just cleaned” appearance most shoppers will appreciate. The side brush doesn’t swing out like some competitors, but it still did a great job of tackling corners and tight spaces, thanks to its impressive navigation skills. Best of all, you can use onboard AI to allow the robot to determine all the best settings for a superior cleaning. There are dozens of ways to manually customize its performance via the mobile app, but I’ve found letting the robot do its own thing often results in the best clean. Jon Bitner / Digital Trends Once a cleaning cycle is complete, the robot heads back to its dock to recharge and clean itself. The docking station washes the mop with hot water, dries it with hot air, and can even dispense a cleaning solution into the robot’s water tank for a better mopping experience. The onboard dustbin will also be siphoned off into a larger dustbag in the docking station, allowing you to go 90 days before needing a replacement. Finally, the docking station will clean itself, washing away any gunk removed from the robot during the cleaning cycle. Pretty much all of the maintenance tasks are taken care of for you. You’ll just need to monitor the clean and dirty water tanks, and perform a more thorough washing tray cleaning every 150 days. As far as robot vacuums are concerned, that’s a huge win for autonomy. Verdict There’s a lot to love about the Ecovacs Deebot X9 Pro Omni, and it’s an easy recommendation for most shoppers. If your home is largely composed of hard floors, it’s an even easier choice. However, there are a few limitations to consider. For one, it can’t leave its mop behind in its docking station. Yes, it will raise it while traveling on carpet, but most shoppers will likely find the auto-detachable mops of the Roborock Saros 10 to be a more elegant solution. The robot is also quite large. At 3.8 inches tall, it’s almost a full inch taller than some competitors. So while it’s small enough to clean under most furniture, smaller nooks and crannies will be too tight for the X9 Pro Omni to fit underneath. These are minor complaints on an otherwise stellar device. The Ecovacs Deebot X9 Pro Omni proves the brand has what it takes to stick with the leaders of the segment, offering a superior mopping experience matched by few rivals. Add in the effective BLAST vacuuming system, self-cleaning dock, and impressive onboard AI, and you’ve got one of the best robot vacuums of 2025.

Image Credit: Chat GPT o3I’ve been using AI coding assistants like Claude Code for a while now, and I’m here to say (with all due respect to people who have substance abuse issues), I may be an addict. And boy is this is an expensive habit.Its “almost there” quality — the feeling we’re just one prompt away from the perfect solution — is what makes it so addicting. Vibe coding operates on the principle of variable-ratio reinforcement, a powerful form of operant conditioning where rewards come unpredictably. Unlike fixed rewards, this intermittent success pattern (“the code works! it’s brilliant! it just broke! wtf!”), triggers stronger dopamine responses in our brain’s reward pathways, similar to gambling behaviors.What makes this especially effective with AI is the minimal effort required for potentially significant rewards — creating what neuroscientists call an “effort discounting” advantage. Combined with our innate completion bias — the drive to finish tasks we’ve started — this creates a compelling psychological loop that keeps us prompting.I don’t smoke, but don’t these bar graphs look like ciagrettes?Since Claude Code has been released, I have probably spent over $1,000 vibe coding various projects into reality (some of which I hope to announce soon, don’t worry).But lets talk about the expense too, because I think there’s something bad there as well: coding agents, and especially Claude 3.7 (the backend of Claude Code), tend to write too much code, a phenomenon that ends up costing users more than it should.Where an experienced developer might solve a problem with a few elegant lines with a thoughtful functional method, these AI systems often produce verbose, over-engineered solutions that tackle problems incrementally rather than addressing them at their core.My initial reaction was to attribute this to the relative immaturity of LLMs and their limitations when reasoning about abstract logic problems. Since these models are primarily trained to predict and generate text based on patterns they’ve seen before, it makes sense that they might struggle with the deeper architectural thinking that leads to elegant, minimal solutions.My human code on the left, Claude Code on the right implementing the same algorithmAnd indeed, the highly complex tasks I’ve handed to them have largely resulted in failure: implementing a minimax algorithm in a novel card game, crafting thoughtful animations in CSS, completely refactoring a codebase. The LLMs routinely get lost in the sauce when it comes to thinking through the high level principles required to solve difficult problems with computer science.In the example above, my human implemented version of minimax from 2018 totals 400 lines of code, whereas Claude Code’s version comes in at 627 lines. The LLM version also requires almost a dozen other library files. Granted, this version is in TypeScript and has a ton of extra bells and whistles, some of which I explicitly asked for, but the real problem is: it doesn’t actually work. Furthermore, using the LLM to debug it requires sending the bloated code back and forth to the API every time I want to holistically debug it.In an effort to impress the user and over-deliver, LLMs end up creating a rat’s nest of ultra-defensive code littered with debugging statements, neurotic comments and barely-useful helper funcitions. If you’ve ever worked in a highly functional production codebase, this is enough to drive you insane.I think everyone who spends any time vibe coding eventually discovers something like this and realizes that it’s much more worthwhile to work with a plan composed of discrete tasks that could be explained to a junior level developer vs. a feature-level project handed off to a staff engineer.There’s also the likelihood that the vast majority of code that LLMs have been trained on tends to be inelegant and overly verbose. Lord knows there’s a lot of AbstractJavaFinalSerializedFactory code out there.But I’m beginning to think the problem runs deeper, and it has to do with the economics of AI assistance.The economic incentive problemMany AI coding assistants, including Claude Code, charge based on token count — essentially the amount of text processed and generated. This creates what economists would call a “perverse incentive” — an incentive that produces behavior contrary to what’s actually desired.Let’s break down how this works:The AI generates verbose, procedural code for a given taskThis code becomes part of the context when you ask for further changes or additions (this is key)The AI now has to read (and you pay for) this verbose code in every subsequent interactionMore tokens processed = more revenue for the company behind the AIThe LLM developers have no incentive to “fix” the verbose code problem because doing so will meaningfully impact their bottom lineAs Upton Sinclair famously noted: “It is difficult to get a man to understand something when his salary depends on his not understanding it.” Similarly, it might be difficult for AI companies to prioritize code conciseness when their revenue depends on token count.The broader implicationsThis pattern points to a more general concern in AI development: the alignment between how systems are monetized and how well they serve user needs. When charging by token count, there’s naturally less incentive to optimize for elegant, minimal solutions.Even “all you can eat” subscription plans (e.g. Claude’s “Max” subscription) don’t fully resolve this tension, as they typically come with usage caps or other limitations that maintain the underlying incentive structure.System instructions and verbosity trade-offsThe perverse incentives in AI code generation point to a more fundamental issue that extends beyond coding assistants. When she was reading a draft of this, Louise pointed out some recent research from Giskard AI’s Phare benchmark that reveals a troubling pattern that mirrors our coding dilemma: demanding shorter responses jeopardizes the accuracy of the answers.According to their findings, instructions emphasizing conciseness (like “answer this question briefly”) significantly degraded factual reliability across most models tested — in some cases causing a 20% drop in hallucination resistance. When forced to be concise, models face an impossible choice between fabricating short but inaccurate answers or appearing unhelpful by rejecting the question entirely. The data shows models consistently prioritize brevity over accuracy when given these constraints.There’s clearly something going on where the more verbose the LLM is, the better it does. This actually makes sense given the discovery that chain-of-thought reasoning improves accuracy, but this issue has begun to feel like a real tradeoff when it comes to these almost-magical systems.We see this exact tension in code generation every day. When we optimize for conciseness and ask for the problems to be solved in fewer setps, we often sacrifice quality. The difference is that in coding, the sacrifice manifests as over-engineered verbosity — the model produces more tokens to cover all possible edge cases rather than thinking deeply about the elegant core solution or a root cause problem. In both cases, economic incentives (token optimization) work against quality outcomes (factual accuracy or elegant code).Just as Phare’s research suggests that seemingly innocent prompts like “be concise” can sabotage a model’s ability to debunk misinformation, our experience shows that standard prompting approaches can yield bloated, inefficient code. In both domains, the fundamental misalignment between token economics and quality outputs creates a persistent tension that users must actively manage.Some tricks to manage these perverse incentivesWhile we wait for AI companies to better align their incentives with our need for elegant code, I’ve developed several strategies to counteract verbose code generation:1. Force planning before implementationI harass the LLM to write a detailed plan before generating any code. This forces the model to think through the architecture and approach, rather than diving straight into implementation details. Often, I find that a well-articulated plan leads to more concise code, as the model has already resolved the logical structure of the solution before writing a single line.2. Explicit permission protocolI’ve implemented a strict “ask before generating” protocol in my workflow. My personal CLAUDE.md file explicitly instructs Claude to request permission before writing any code. Infuriatingly, Claude Code regularly ignores this, likely due to its massive system prompt that talks so much about writing code it overrides my preferences. Enforcing this boundary and repeatedly belaboring it (“remember, don’t write any code”) helps prevent the automatic generation of unwanted, verbose solutions.3. Git-based experimentation with ruthless pruningVersion control becomes essential when working with AI-generated code. I frequently benchmark code in git when I arrive at an “ok it works as intended” moment. Creating experimental branches is also very helpful. Most importantly, I’m ready to throw out branches entirely when fixing them would require more work than starting from scratch. This willingness to abandon sunk costs is surprisingly important — it helps me work through problems and figure out the AI’s hangups while preventing the accumulation of bandaid solutions on top of fundamentally flawed approaches.4. Use a cheaper modelSometimes the simplest solution works best: using a smaller, cheaper model often results in more direct solutions. These models tend to generate less verbose code simply because they have limited context windows and processing capacity. While they might not handle extremely complex problems as well, for many day-to-day coding tasks, their constraints can actually produce more elegant solutions. For example, Claude 3.5 Haiku is currently 26% the price of Claude 3.7 ($0.80 per token vs. $3). Also, Claude 3.7 seems to overengineer more frequently than Claude 3.5.Moving toward better alignmentWhat might a better approach look like?LLM coding agents could evaluated and incentivized based on code quality metrics rather than just token counts. The challenge here is that this kind of metric is quite subjective.Companies could offer pricing models that reward efficiency rather than verbosity (I have no idea how this would work, this was Claude’s dumb idea)LLMs training should incorporate feedback mechanisms that specifically promote concise, elegant solutions via RLHF (e.g. showing developers multiple versions of the same code and having them pick the opitmal one, perhaps this is already happening)Companies realize that overly verbose code generation is not good for their bottom line (e.g. Sam Altman admitted that users saying “please” and “thank you” to ChatGPT is costing them millions of dollars)This isn’t just about getting better AI — it’s about making sure that the economic incentives driving AI development align with what we actually value as developers: clean, maintainable, elegant code that solves problems at their root.Until then, don’t forget: brevity is the soul of wit, and machines have no soul.Thanks to Louise Macfadyen, Justin Kazmark and Bethany Crystal for reading and suggesting edits to a draft of this.— -PS: Yes, I used Claude to help write this post critiquing AI verbosity. There’s a delicious irony here: these systems will happily help you articulate why they might be ripping you off. Their willingness to steelman arguments against their own economic interests shows that the perverse incentives aren’t embedded in the models themselves, but in the business decisions surrounding them. In other words, don’t blame the AI — blame the humans optimizing the revenue models. The machines are just doing what they’re told, even when that includes explaining how they’re being told to do too much.The perverse incentives of Vibe Coding was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Little Kitty, Big City packs a lot to do in its meowtropolis (sorry, that’s my first and only cat pun), all of which is as cute and fun as you’d expect for a game about a lost kitty in a big ‘ol city. But this city is actually more bite-sized than you might anticipate by the game’s name, offering a brief but enjoyable romp that doesn’t overstay its welcome. If you’d like to know just how long you’ll be spending with this little black kitty, here’s what you need to know.Suggested ReadingNintendo Switch 2 Could Launch With Almost No Reviews Share SubtitlesOffEnglishSuggested ReadingNintendo Switch 2 Could Launch With Almost No Reviews Share SubtitlesOffEnglishNintendo Switch 2 Could Launch With Almost No ReviewsLittle Kitty, Big City?Little Kitty, Big City is a short game, especially if you overlook any unnecessary side-quests. If your main goal is to just get our titular kitty back home, you can achieve that in only 2-3 hours. But skipping all of the optional content in the game would be missing some of the most charming parts of this cat’s journey. There are tons of animal friends to find and assist with adorable tasks, as well as a variety of quirky trophies and achievements that require you to do silly things around the city, such as leaving paw prints on wet concrete, knocking paint on a car, or making a human slip on a banana peel.Speaking of trophies and achievements, you’ll need to collect a whole lot of stuff on the road to 100-percent completion. You’ll have to find 200 shinies (currency), collect every hat, knock over every bird nest, recycle 100 items in bins around the city, and complete multiple other similar tasks. Knocking out all the side-quests and achievement/trophy tasks in Little Kitty, Big City can push your total playtime to around 5 hours or so. But rest assured that, even once you’re finished with all of that, there’s still some fun to be had running around the city tripping humans and stealing their phones. Being a cat never gets old.Little Kitty, Big City is available now on PS5, Xbox Series X/S, Nintendo Switch, and Windows PCs..

Senior Lighting / Compositing Artist - Temp (SFD / Cinematics)Blizzard EntertainmentIrvine California 92618 United States of America1 hour agoApplyTeam Name:CinematicsJob Title:Senior Lighting / Compositing Artist - Temp (SFD / Cinematics)Requisition ID:R025349Job Description:We pride ourselves in forging amazing worlds and experiences for our players to dive into here at Blizzard. To accomplish these epic goals, we need committed, passionate, and creative people with amazing talent. We value a tight-knit team that celebrates artistic passion and pushing the creative bounds. If you dream of pushing your talents to help us build the next exciting Blizzard experience, then this could be the position for you.The Blizzard Entertainment Animation team is seeking an experienced senior lighting /compositing artist to jump into working on Blizzard’s imaginative worlds. We are looking for someone with the experience and passion to make our shots epic (Both pre-rendered and real time contents).This role is anticipated to be a hybrid work position, with some work on-site and some work-from-home. The potential home studio for this role is Irvine, CA. However, we understand each person’s circumstances may be unique and will work with you to explore other possible options, including remote work arrangements.We have two temporary roles open. One is roughly a 5-month term, and the other is roughly a 7-month term.What You Bring to the TablePriorities can often change in a fast-paced environment like ours, so this role includes, but is not limited to, the following responsibilities:Design and implement lighting of characters and environments for shots that meet and enhance the given art direction.Maintain the overall look, color balance, and quality for assigned shots and sequences to create uniformity in all finished work.Work with the FX department for integrating and developing FX.Work closely with VFX supervisors, Art Directors, Producers, Lighting/Compositing Supervisor and Leads to achieve the final look.Accountable for achieving productivity and quota targets.Ultimately responsible for completing and quality control all lighting and compositing of shots.Minimum RequirementsExperienceA minimum of 7 years’ experience in the game, TV, feature film or commercial production environment.Previous experience working with Directors, Lighting Supervisors, and/or Leads to develop, define, and maintain lighting and compositing setups.Knowledge & SkillsA demo reel displaying a firm artistic understanding of light and composition.Have a strong knowledge of Katana, Nuke, Maya, Linux, Renderman-RIS, Redshift and a decent understanding of modern game engines. Can easily adapt to new pipelines and proprietary lighting tools.Able to plan and implement complex 3D lighting scenes, including working with layers and passes.Able to determine the best approach to tackle lighting and compositing challenges and develop the final overall look.Key AttributesAble to perform tasks associated with the compositing and lighting process, including 2D tracking, layering of elements, color grading, roto work, projection of elements, and extensive 3D compositing.Comfortable working in an “iteration intensive” shot pipeline.Extra PointsExperience in working in a full 3D Animated Pipeline.Experience in working in a real-time Game Engine Pipeline.Required Application MaterialsResumeCover Letter (optional)Link to a portfolio/reel. Provide a demo reel and description of your work per shot.Your PlatformBest known for iconic video game universes including Warcraft®, Overwatch®, Diablo®, and StarCraft®, Blizzard Entertainment, Inc. (www.blizzard.com), a division of Activision Blizzard, which was acquired by Microsoft (NASDAQ: MSFT), is a premier developer and publisher of entertainment experiences. Blizzard Entertainment has created some of the industry’s most critically acclaimed and genre-defining games over the last 30 years, with a track record that includes multiple Game of the Year awards. Blizzard Entertainment engages tens of millions of players around the world with titles available on PC via Battle.net®, Xbox, PlayStation, Nintendo Switch, iOS, and Android.Our WorldActivision Blizzard, Inc., is one of the world's largest and most successful interactive entertainment companies and is at the intersection of media, technology and entertainment. We are home to some of the most beloved entertainment franchises including Call of Duty ®, World of Warcraft® , Overwatch ®, Diablo ®, Candy Crush ™ and Bubble Witch ™. Our combined entertainment network delights hundreds of millions of monthly active users in 196 countries, making us the largest gaming network on the planet!Our ability to build immersive and innovate worlds is only enhanced by diverse teams working in an inclusive environment. We aspire to have a culture where everyone can thrive in order to connect and engage the world through epic entertainment. We provide a suite of benefits that promote physical, emotional and financial well-being for ‘Every World’ - we’ve got our employees covered!The videogame industry and therefore our business is fast-paced and will continue to evolve. As such, the duties and responsibilities of this role may be changed as directed by the Company at any time to promote and support our business and relationships with industry partners.We love hearing from anyone who is enthusiastic about changing the games industry. Not sure you meet all qualifications? Let us decide! Research shows that women and members of other under-represented groups tend to not apply to jobs when they think they may not meet every qualification, when, in fact, they often do! We are committed to creating a diverse and inclusive environment and strongly encourage you to apply.We are committed to working with and providing reasonable assistance to individuals with physical and mental disabilities. If you are a disabled individual requiring an accommodation to apply for an open position, please email your request to accommodationrequests@activisionblizzard.com. General employment questions cannot be accepted or processed here. Thank you for your interest.We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status, among other characteristics.RewardsWe provide a suite of benefits that promote physical, emotional and financial well-being for ‘Every World’ - we’ve got our employees covered! Subject to eligibility requirements, the Company offers comprehensive benefits including:Medical, dental, vision, health savings account or health reimbursement account, healthcare spending accounts, dependent care spending accounts, life and AD&D insurance, disability insurance;401(k) with Company match, tuition reimbursement, charitable donation matching;Paid holidays and vacation, paid sick time, floating holidays, compassion and bereavement leaves, parental leave;Mental health & wellbeing programs, fitness programs, free and discounted games, and a variety of other voluntary benefit programs like supplemental life & disability, legal service, ID protection, rental insurance, and others;If the Company requires that you move geographic locations for the job, then you may also be eligible for relocation assistance.Eligibility to participate in these benefits may vary for part time and temporary full-time employees and interns with the Company. You can learn more by visiting https://www.benefitsforeveryworld.com/ .In the U.S., the standard base pay range for this role is $44.67 - $82.60 Hourly. These values reflect the expected base pay range of new hires across all U.S. locations. Ultimately, your specific range and offer will be based on several factors, including relevant experience, performance, and work location. Your Talent Professional can share this role’s range details for your local geography during the hiring process. In addition to a competitive base pay, employees in this role may be eligible for incentive compensation. Incentive compensation is not guaranteed. While we strive to provide competitive offers to successful candidates, new hire compensation is negotiable. Create Your Profile — Game companies can contact you with their relevant job openings. Apply

ZDNET's key takeaways The RingConn Gen 2 Air is a $200 smart ring, one of the first subscription-free and affordable smart rings I've tested. The battery life can survive up to eight or nine days of use, and the sleep data collection rivals Oura. The AI health partner feature is lackluster, and it's essential to view the brand's privacy policy, given the affordable nature of a health wearable. more buying choices Smart rings are rising in popularity, but the big names -- the Ouras, Samsungs, and Ultrahumans -- can run $300 to $400. That's not counting the subscription fee, which some customers pay for full access to all the data that the smart ring aggregates. With all these murmurs of an impending recession causing pennies to be pinched and wallets to be watched, who has the money for a $400 smart ring right now? Also: How we test smart rings at ZDNET in 2025I hadn't yet tested a high-quality and affordable smart ring from a notable brand, but the newest smart ring from RingConn, the RingConn Gen 2 Air, could change that. It's the first ring I've tested that retails for a price as low as $200, without any sale or special deal. I've worn RingConn's newest smart ring over the past few weeks to see how the brand's budget ring fares as a sleep and activity tracker. Keep reading to learn how it compares to the RingConn Gen 2, which costs $100 more, and the Oura Ring 4, which costs double.  details View at RingConn The smart ring looks and feels almost exactly the same as the RingConn Gen 2, with the main difference being the charging case and battery size. The Gen 2's charging case has enough power to boost the smart ring for over 150 days, and the ring has a battery life of up to 12 days. The Gen 2 Air, on the other hand, does not come with a charging case equipped with extra juice, and it advertises a 10-day battery life (though, in my testing, I got around eight days). Even though the Gen 2 Air's battery is lower than the Gen 2, both RingConn rings match or outcompete the battery life of the Oura Ring, the Ultrahuman Ring Air, and the Samsung Galaxy Ring. The Gen 2 Air is made of stainless steel, a more affordable material compared to the Gen 2's aerospace-grade titanium. Regardless of the material, the Gen 2 Air is comfortable to wear and feels unnoticeable around my finger. It comes in a silver and gold finish, and available sizes range from six to 14.Both rings are connected to the same app experience, so whether you're using the $300 ring or the $200 ring, you'll get your data and insights presented to you in exactly the same manner. Much like the Gen 2, the Gen 2 Air tracks sleep, stress, vitals, and activity. It takes all those factors and daily scores to deliver a Wellness Balance score. The Wellness Balance is displayed like a petal on a four-petal flower, and each biometric is ranked out of 100. At the start of the day, the activity petal is nonexistent, but the activity score increases as I exercise or walk. At the end of the day, I can view my aggregate Wellness Balance, and this score provides a holistic picture of my health. As I said in my spotlight of the RingConn Gen 2, the downside to this Wellness Balance is the absence of a readiness or recovery score that other wearables have calculated and provided to users at the start of their day to understand how their body bounced back from activity and how much activity or rest they should take on for the day. This recovery score provides insights into a user's overall health and resilience, and it's helpful for those who are following a training regime to know how to treat their bodies each day.I tested the RingConn Gen 2 Air hand in hand with my Oura Ring 4, and both devices gathered fairly similar health metrics. One night, Oura gave me an 87 Sleep Score, and RingConn gave me an 86. Oura said my resting heart rate overnight was 48 beats per minute, while RingConn said 49 beats per minute. Oura said my average heart rate was 55 beats per minute, and RingConn said it was 52. While not identical, these numbers are close. Activity data is a different story. One day, Oura said I walked 7,454 steps, while RingConn said I walked 5,706. This was on a moderately active day when I took a 25-minute walk. On an intensely active day, when I went to the gym for two hours and was out and about, Oura calculated that I walked 16,291 steps, and RingConn calculated that I walked 15,454 steps. Nina Raemont/ZDNETRingConn advertises an "AI health partner" as a function of its app experience. Every day, the AI partner tells you information, like how you slept or whether you hit your step count, and then asks questions based on your behaviors. These summaries aren't available on the main page where sleep, activity, and stress data are displayed -- they're only available on the AI tab. But unlike other AI assistants, you can't type out a personalized answer to these questions.Instead, you pick from the choices of responses. It didn't feel like AI to me because it wasn't as personalized, and the responses you could choose were simple; rather, it felt like a multiple-choice health summary. This is a classic case of a brand slapping AI onto a feature that isn't actually AI. What I like about RingConn smart rings is they will tell you exactly when the ring will die and notify the user to charge it ahead of time. This is a helpful feature I hope other smart ring brands adopt. Also: Oura Ring 3 vs Oura Ring 4: Opt for the older, discounted smart ring or the newest?One concern about a smart ring this affordable is whether the brand is keeping the user's data safe and protected. Health data is valuable, and in the wrong hands, it can be sent to advertisers or health insurance agencies. Smart ring brands pay a lot of money to keep such data safe, which is why they cost as much as they do. A RingConn representative did not immediately answer my questions about why the smart ring can be so cheap, if user data is secure and protected, or if it is being compromised for the sake of a lower-cost smart ring. I did, however, review RingConn's privacy policy which relays what user data is collected, used, and stored. I recommend looking over a health wearables privacy policy before purchasing a ring, so you understand how a company keeps your information safe and secure. ZDNET's buying advice So, should you buy this $200 smart ring? If you're looking for an alternative to some of the big names that won't cost you as much but will collect nearly the same level of health data, I'd suggest the RingConn Gen 2 Air.Also: The best smart rings: Expert tested and reviewedWith competitive battery life, data display that matches its $300 sibling, and data collection that gets close to the Oura Ring 4, the RingConn Gen 2 Air is one of the first $200 smart rings I've tested that I can confidently recommend for the budget-friendly and smart ring-curious shopper. Looking for the next best product? Get expert reviews and editor favorites with ZDNET Recommends.Featured reviews

Late Friday, Epic Games sent a letter to U.S. District Judge Yvonne Gonzalez Rogers accusing Apple of refusing to consider its Fortnite submission, calling it “Apple’s latest attempt to circumvent this Court’s Injunction and this Court’s authority.” As reported by Mark Gurman, Epic’s petition reads: Apple’s refusal to consider Epic’s Fortnite submission is Apple’s latest attempt to circumvent this Court’s Injunction and this Court’s authority. Epic therefore seeks an order enforcing the Injunction, finding Apple in civil contempt yet again, and requiring Apple to promptly accept any compliant Epic app, including Fortnite, for distribution on the U.S. storefront of the App Store. In the document, Epic also makes a point to mention Tim Cook’s testimony at trial, in which he said: It would be to the benefit of the users to have Fortnite back on the App Store. Epic also stresses that: Apple also expressly and repeatedly told both this Court and Epic that it would welcome Fortnite back to the App Store if Epic complied with all of Apple’s Guidelines. That is exactly what Epic did. A bit of context The request comes after Apple told Epic Games it would “not revisit” the decision to reinstate the Epic Games developer account “until after the U.S. litigation between the parties concludes”. Earlier today, Epic informed players that Apple had blocked its most recent Fortnite build, rendering the game unavailable worldwide on iOS until the ban is lifted. Apple countered the claims, stating that it merely asked Epic Sweden (the developer account handling the resubmission) to remove the U.S. storefront from the build so the game could remain live in other regions. What might come next Back in the very first hearing of this case, Judge Rogers said she was “inclined not to require Apple to allow Fortnite on the App Store”. A few months later, she denied Epic’s request for a preliminary injunction, effectively allowing Apple to continue banning the game. That matters now because Epic’s new motion asks the judge to go a step further and order Apple to green-light Fortnite anyway. To grant that, she would likely have to conclude that Apple’s continued refusal has crossed the line into contempt of court, despite her earlier statement that Apple wasn’t obligated to host the game. Epic’s request may be bolstered by Judge Rogers’ recent order demanding Apple comply with the court’s 2021 injunction. In the days since, Epic has tried to pressure Apple by live-tweeting the ongoing stalemate over the new app submission. But with that approach seemingly going nowhere, this new legal strategy may be Epic’s way of pressing its luck a little further. Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel

Stand aside, Intel and AMD: Qualcomm is once again heading to Computex with big ambitions to reshape the PC market with its own Arm-based chips. CEO Cristiano Amon is scheduled to deliver a keynote at the annual trade show, and we expect plenty of announcements, including new chips and more details about Qualcomm’s strategy around AI PCs. But you’ll need to stay up late if you plan on watching the keynote from the US. The event — which will be live streamed on YouTube from Taipei, Taiwan— is scheduled for Sunday, May 18, at 11 p.m. PT / Monday, May 19 at 2 a.m. ET (video embedded above).It’s been almost a year since Qualcomm began selling Windows Copilot+ laptops built with Snapdragon X processors. So it’ll be interested to hear what the company has learned since then, and how it plans on bolstering its Windows offerings when competition in the PC chip space is intensifying amid rumors Nvidia might expand into the market.Recommended by Our EditorsQualcomm’s PC processors stand out for using Arm architecture, a sharp contrast to the long-dominant x86 processors from Intel and AMD. Although Arm chips can lack software compatibility, the processors can offer impressive performance, including for AI workloads and GPU tasks. Qualcomm has even boasted that its Snapdragon X chips outperform rival processors from Intel, which has caused some bickering. But the company's foray into PCs also highlights growing adoption of Arm chips. Apple famously dumped Intel-based processors to pursue its own Arm-based chips for MacBooks, which have received rave reviews for their high performance and long battery life. On top of all this, reports suggest Nvidia might introduce its own Arm-based chips for Windows PCs at Computex. All of this sets the stage for Qualcomm’s keynote to highlight the escalating rivalry between Arm and x86 in the PC market. Viewers can also expect plenty of talk about generative AI. In a press release, Qualcomm noted that Amon will “share insights on how AI is transforming PC experiences for consumers, enterprises, and in industrial sectors.” This includes demonstrating “how on-device AI is poised to significantly enhance productivity, creativity, and much more.”

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google’s new AlphaEvolve shows what happens when an AI agent graduates from lab demo to production work, and you’ve got one of the most talented technology companies driving it. Built by Google’s DeepMind, the system autonomously rewrites critical code and already pays for itself inside Google. It shattered a 56-year-old record in matrix multiplication (the core of many machine learning workloads) and clawed back 0.7% of compute capacity across the company’s global data centers. Those headline feats matter, but the deeper lesson for enterprise tech leaders is how AlphaEvolve pulls them off. Its architecture – controller, fast-draft models, deep-thinking models, automated evaluators and versioned memory – illustrates the kind of production-grade plumbing that makes autonomous agents safe to deploy at scale. Google’s AI technology is arguably second to none. So the trick is figuring out how to learn from it, or even using it directly. Google says an Early Access Program is coming for academic partners and that “broader availability” is being explored, but details are thin. Until then, AlphaEvolve is a best-practice template: If you want agents that touch high-value workloads, you’ll need comparable orchestration, testing and guardrails. Consider just the data center win. Google won’t put a price tag on the reclaimed 0.7%, but its annual capex runs tens of billions of dollars. Even a rough estimate puts the savings in the hundreds of millions annually—enough, as independent developer Sam Witteveen noted on our recent podcast, to pay for training one of the flagship Gemini models, estimated to cost upwards of $191 million for a version like Gemini Ultra. VentureBeat was the first to report about the AlphaEvolve news earlier this week. Now we’ll go deeper: how the system works, where the engineering bar really sits and the concrete steps enterprises can take to build (or buy) something comparable. 1. Beyond simple scripts: The rise of the “agent operating system” AlphaEvolve runs on what is best described as an agent operating system – a distributed, asynchronous pipeline built for continuous improvement at scale. Its core pieces are a controller, a pair of large language models (Gemini Flash for breadth; Gemini Pro for depth), a versioned program-memory database and a fleet of evaluator workers, all tuned for high throughput rather than just low latency. A high-level overview of the AlphaEvolve agent structure. Source: AlphaEvolve paper. This architecture isn’t conceptually new, but the execution is. “It’s just an unbelievably good execution,” Witteveen says. The AlphaEvolve paper describes the orchestrator as an “evolutionary algorithm that gradually develops programs that improve the score on the automated evaluation metrics” (p. 3); in short, an “autonomous pipeline of LLMs whose task is to improve an algorithm by making direct changes to the code” (p. 1). Takeaway for enterprises: If your agent plans include unsupervised runs on high-value tasks, plan for similar infrastructure: job queues, a versioned memory store, service-mesh tracing and secure sandboxing for any code the agent produces.  2. The evaluator engine: driving progress with automated, objective feedback A key element of AlphaEvolve is its rigorous evaluation framework. Every iteration proposed by the pair of LLMs is accepted or rejected based on a user-supplied “evaluate” function that returns machine-gradable metrics. This evaluation system begins with ultrafast unit-test checks on each proposed code change – simple, automatic tests (similar to the unit tests developers already write) that verify the snippet still compiles and produces the right answers on a handful of micro-inputs – before passing the survivors on to heavier benchmarks and LLM-generated reviews. This runs in parallel, so the search stays fast and safe. In short: Let the models suggest fixes, then verify each one against tests you trust. AlphaEvolve also supports multi-objective optimization (optimizing latency and accuracy simultaneously), evolving programs that hit several metrics at once. Counter-intuitively, balancing multiple goals can improve a single target metric by encouraging more diverse solutions. Takeaway for enterprises: Production agents need deterministic scorekeepers. Whether that’s unit tests, full simulators, or canary traffic analysis. Automated evaluators are both your safety net and your growth engine. Before you launch an agentic project, ask: “Do we have a metric the agent can score itself against?” 3. Smart model use, iterative code refinement AlphaEvolve tackles every coding problem with a two-model rhythm. First, Gemini Flash fires off quick drafts, giving the system a broad set of ideas to explore. Then Gemini Pro studies those drafts in more depth and returns a smaller set of stronger candidates. Feeding both models is a lightweight “prompt builder,” a helper script that assembles the question each model sees. It blends three kinds of context: earlier code attempts saved in a project database, any guardrails or rules the engineering team has written and relevant external material such as research papers or developer notes. With that richer backdrop, Gemini Flash can roam widely while Gemini Pro zeroes in on quality. Unlike many agent demos that tweak one function at a time, AlphaEvolve edits entire repositories. It describes each change as a standard diff block – the same patch format engineers push to GitHub – so it can touch dozens of files without losing track. Afterward, automated tests decide whether the patch sticks. Over repeated cycles, the agent’s memory of success and failure grows, so it proposes better patches and wastes less compute on dead ends. Takeaway for enterprises: Let cheaper, faster models handle brainstorming, then call on a more capable model to refine the best ideas. Preserve every trial in a searchable history, because that memory speeds up later work and can be reused across teams. Accordingly, vendors are rushing to provide developers with new tooling around things like memory. Products such as OpenMemory MCP, which provides a portable memory store, and the new long- and short-term memory APIs in LlamaIndex are making this kind of persistent context almost as easy to plug in as logging. OpenAI’s Codex-1 software-engineering agent, also released today, underscores the same pattern. It fires off parallel tasks inside a secure sandbox, runs unit tests and returns pull-request drafts—effectively a code-specific echo of AlphaEvolve’s broader search-and-evaluate loop. 4. Measure to manage: targeting agentic AI for demonstrable ROI AlphaEvolve’s tangible wins – reclaiming 0.7% of data center capacity, cutting Gemini training kernel runtime 23%, speeding FlashAttention 32%, and simplifying TPU design – share one trait: they target domains with airtight metrics. For data center scheduling, AlphaEvolve evolved a heuristic that was evaluated using a simulator of Google’s data centers based on historical workloads. For kernel optimization, the objective was to minimize actual runtime on TPU accelerators across a dataset of realistic kernel input shapes. Takeaway for enterprises: This clarity allows the agent to self-improve and demonstrate unambiguous value. 5. Laying the groundwork: essential prerequisites for enterprise agentic success While AlphaEvolve’s achievements are inspiring, Google’s paper is also clear about its scope and requirements. The primary limitation is the need for an automated evaluator; problems requiring manual experimentation or “wet-lab” feedback are currently out of scope for this specific approach. The system can consume significant compute – “on the order of 100 compute-hours to evaluate any new solution” (AlphaEvolve paper, page 8), necessitating parallelization and careful capacity planning. Before allocating significant budget to complex agentic systems, technical leaders must ask critical questions: Machine-gradable problem? Do we have a clear, automatable metric against which the agent can score its own performance? Compute capacity? Can we afford the potentially compute-heavy inner loop of generation, evaluation, and refinement, especially during the development and training phase? Codebase & memory readiness? Is your codebase structured for iterative, possibly diff-based, modifications? And can you implement the instrumented memory systems vital for an agent to learn from its evolutionary history? Takeaway for enterprises: The increasing focus on robust agent identity and access management, as seen with platforms like Frontegg, Auth0 and others, also points to the maturing infrastructure required to deploy agents that interact securely with multiple enterprise systems. The agentic future is engineered, not just summoned AlphaEvolve’s message for enterprise teams is manifold. First, your operating system around agents is now far more important than model intelligence. Google’s blueprint shows three pillars that can’t be skipped: Deterministic evaluators that give the agent an unambiguous score every time it makes a change. Long-running orchestration that can juggle fast “draft” models like Gemini Flash with slower, more rigorous models – whether that’s Google’s stack or a framework such as LangChain’s LangGraph. Persistent memory so each iteration builds on the last instead of relearning from scratch. Enterprises that already have logging, test harnesses and versioned code repositories are closer than they think. The next step is to wire those assets into a self-serve evaluation loop so multiple agent-generated solutions can compete, and only the highest-scoring patch ships.  As Cisco’s Anurag Dhingra, VP and GM of Enterprise Connectivity and Collaboration, told VentureBeat in an interview this week: “It’s happening, it is very, very real,” he said of enterprises using AI agents in manufacturing, warehouses, customer contact centers. “It is not something in the future. It is happening there today.” He warned that as these agents become more pervasive, doing “human-like work,” the strain on existing systems will be immense: “The network traffic is going to go through the roof,” Dhingra said. Your network, budget and competitive edge will likely feel that strain before the hype cycle settles. Start proving out a contained, metric-driven use case this quarter – then scale what works. Watch the video podcast I did with developer Sam Witteveen, where we go deep on production-grade agents, and how AlphaEvolve is showing the way: Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.