After nearly three years of fan theories, trailer breakdowns, and unjustified hate, James Gunn’s Superman film is almost here. While the general public will get to gander at the latest attempt at telling Clark Kent’s story on July 11, film critics an

Enjoy seeing how the VFX in The Snow Sister were created. As always, the best VFX are the ones you can't spot! Source

3D Artist Nick Carver, known for his outstanding stylized artwork, unveiled a new whimsical scene, showing fascinating procedural ivy.The artist stayed true to his signature style, with dreamlike colors and charming hand-painted aesthetics, featuring richly detailed set dressing and high-quality animation.Earlier, Nick Carver showcased this splendid character study, a peaceful 3D scene with a calm river, and more:Follow the artist on X/Twitter and don't forget to join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more.

Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerial (or satellite) image. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-View (BEV) but are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the vertical (height) dimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoF (x, y, and yaw) pose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models

You’ve drawn fifty versions of the same screen — and you still hate every one of them. Begrudgingly, you pick three, show them to your product manager, and hear: “Looks cool, but the idea doesn’t work.” Sound familiar? In this article, I’ll unpack why designers fall into detail work at the wrong moment, examining both process pitfalls and the underlying psychological reasons, as understanding these traps is the first step to overcoming them. I’ll also share tactics I use to climb out of that trap. Reason #1 You’re Afraid To Show Rough Work We designers worship detail. We’re taught that true craft equals razor‑sharp typography, perfect grids, and pixel precision. So the minute a task arrives, we pop open Figma and start polishing long before polish is needed. I’ve skipped the sketch phase more times than I care to admit. I told myself it would be faster, yet I always ended up spending hours producing a tidy mock‑up when a scribbled thumbnail would have sparked a five‑minute chat with my product manager. Rough sketches felt “unprofessional,” so I hid them. The cost? Lost time, wasted energy — and, by the third redo, teammates were quietly wondering if I even understood the brief. The real problem here is the habit: we open Figma and start perfecting the UI before we’ve even solved the problem. So why do we hide these rough sketches? It’s not just a bad habit or plain silly. There are solid psychological reasons behind it. We often just call it perfectionism, but it’s deeper than wanting things neat. Digging into the psychology (like the research by Hewitt and Flett) shows there are a couple of flavors driving this: Socially prescribed perfectionismIt’s that nagging feeling that everyone else expects perfect work from you, which makes showing anything rough feel like walking into the lion’s den. Self-oriented perfectionismWhere you’re the one setting impossibly high standards for yourself, leading to brutal self-criticism if anything looks slightly off. Either way, the result’s the same: showing unfinished work feels wrong, and you miss out on that vital early feedback. Back to the design side, remember that clients rarely see architects’ first pencil sketches, but these sketches still exist; they guide structural choices before the 3D render. Treat your thumbnails the same way — artifacts meant to collapse uncertainty, not portfolio pieces. Once stakeholders see the upside, roughness becomes a badge of speed, not sloppiness. So, the key is to consciously make that shift: Treat early sketches as disposable tools for thinking and actively share them to get feedback faster. Reason #2: You Fix The Symptom, Not The Cause Before tackling any task, we need to understand what business outcome we’re aiming for. Product managers might come to us asking to enlarge the payment button in the shopping cart because users aren’t noticing it. The suggested solution itself isn’t necessarily bad, but before redesigning the button, we should ask, “What data suggests they aren’t noticing it?” Don’t get me wrong, I’m not saying you shouldn’t trust your product manager. On the contrary, these questions help ensure you’re on the same page and working with the same data. From my experience, here are several reasons why users might not be clicking that coveted button: Users don’t understand that this step is for payment. They understand it’s about payment but expect order confirmation first. Due to incorrect translation, users don’t understand what the button means. Lack of trust signals (no security icons, unclear seller information). Unexpected additional costs (hidden fees, shipping) that appear at this stage. Technical issues (inactive button, page freezing). Now, imagine you simply did what the manager suggested. Would you have solved the problem? Hardly. Moreover, the responsibility for the unresolved issue would fall on you, as the interface solution lies within the design domain. The product manager actually did their job correctly by identifying a problem: suspiciously, few users are clicking the button. Psychologically, taking on this bigger role isn’t easy. It means overcoming the fear of making mistakes and the discomfort of exploring unclear problems rather than just doing tasks. This shift means seeing ourselves as partners who create value — even if it means fighting a hesitation to question product managers (which might come from a fear of speaking up or a desire to avoid challenging authority) — and understanding that using our product logic expertise proactively is crucial for modern designers. There’s another critical reason why we, designers, need to be a bit like product managers: the rise of AI. I deliberately used a simple example about enlarging a button, but I’m confident that in the near future, AI will easily handle routine design tasks. This worries me, but at the same time, I’m already gladly stepping into the product manager’s territory: understanding product and business metrics, formulating hypotheses, conducting research, and so on. It might sound like I’m taking work away from PMs, but believe me, they undoubtedly have enough on their plates and are usually more than happy to delegate some responsibilities to designers. Reason #3: You’re Solving The Wrong Problem Before solving anything, ask whether the problem even deserves your attention. During a major home‑screen redesign, our goal was to drive more users into paid services. The initial hypothesis — making service buttons bigger and brighter might help returning users — seemed reasonable enough to test. However, even when A/B tests (a method of comparing two versions of a design to determine which performs better) showed minimal impact, we continued to tweak those buttons. Only later did it click: the home screen isn’t the place to sell; visitors open the app to start, not to buy. We removed that promo block, and nothing broke. Contextual entry points deeper into the journey performed brilliantly. Lesson learned: Without the right context, any visual tweak is lipstick on a pig. Why did we get stuck polishing buttons instead of stopping sooner? It’s easy to get tunnel vision. Psychologically, it’s likely the good old sunk cost fallacy kicking in: we’d already invested time in the buttons, so stopping felt like wasting that effort, even though the data wasn’t promising. It’s just easier to keep fiddling with something familiar than to admit we need a new plan. Perhaps the simple question I should have asked myself when results stalled was: “Are we optimizing the right thing or just polishing something that fundamentally doesn’t fit the user’s primary goal here?” That alone might have saved hours. Reason #4: You’re Drowning In Unactionable Feedback We all discuss our work with colleagues. But here’s a crucial point: what kind of question do you pose to kick off that discussion? If your go-to is “What do you think?” well, that question might lead you down a rabbit hole of personal opinions rather than actionable insights. While experienced colleagues will cut through the noise, others, unsure what to evaluate, might comment on anything and everything — fonts, button colors, even when you desperately need to discuss a user flow. What matters here are two things: The question you ask, The context you give. That means clearly stating the problem, what you’ve learned, and how your idea aims to fix it. For instance: “The problem is our payment conversion rate has dropped by X%. I’ve interviewed users and found they abandon payment because they don’t understand how the total amount is calculated. My solution is to show a detailed cost breakdown. Do you think this actually solves the problem for them?” Here, you’ve stated the problem (conversion drop), shared your insight (user confusion), explained your solution (cost breakdown), and asked a direct question. It’s even better if you prepare a list of specific sub-questions. For instance: “Are all items in the cost breakdown clear?” or “Does the placement of this breakdown feel intuitive within the payment flow?” Another good habit is to keep your rough sketches and previous iterations handy. Some of your colleagues’ suggestions might be things you’ve already tried. It’s great if you can discuss them immediately to either revisit those ideas or definitively set them aside. I’m not a psychologist, but experience tells me that, psychologically, the reluctance to be this specific often stems from a fear of our solution being rejected. We tend to internalize feedback: a seemingly innocent comment like, “Have you considered other ways to organize this section?” or “Perhaps explore a different structure for this part?” can instantly morph in our minds into “You completely messed up the structure. You’re a bad designer.” Imposter syndrome, in all its glory. So, to wrap up this point, here are two recommendations: Prepare for every design discussion.A couple of focused questions will yield far more valuable input than a vague “So, what do you think?”. Actively work on separating feedback on your design from your self-worth.If a mistake is pointed out, acknowledge it, learn from it, and you’ll be less likely to repeat it. This is often easier said than done. For me, it took years of working with a psychotherapist. If you struggle with this, I sincerely wish you strength in overcoming it. Reason #5 You’re Just Tired Sometimes, the issue isn’t strategic at all — it’s fatigue. Fussing over icon corners can feel like a cozy bunker when your brain is fried. There’s a name for this: decision fatigue. Basically, your brain’s battery for hard thinking is low, so it hides out in the easy, comfy zone of pixel-pushing. A striking example comes from a New York Times article titled “Do You Suffer From Decision Fatigue?.” It described how judges deciding on release requests were far more likely to grant release early in the day (about 70% of cases) compared to late in the day (less than 10%) simply because their decision-making energy was depleted. Luckily, designers rarely hold someone’s freedom in their hands, but the example dramatically shows how fatigue can impact our judgment and productivity. What helps here: Swap tasks.Trade tickets with another designer; novelty resets your focus. Talk to another designer.If NDA permits, ask peers outside the team for a sanity check. Step away.Even a ten‑minute walk can do more than a double‑shot espresso. By the way, I came up with these ideas while walking around my office. I was lucky to work near a river, and those short walks quickly turned into a helpful habit. And one more trick that helps me snap out of detail mode early: if I catch myself making around 20 little tweaks — changing font weight, color, border radius — I just stop. Over time, it turned into a habit. I have a similar one with Instagram: by the third reel, my brain quietly asks, “Wait, weren’t we working?” Funny how that kind of nudge saves a ton of time. Four Steps I Use to Avoid Drowning In Detail Knowing these potential traps, here’s the practical process I use to stay on track: 1. Define the Core Problem & Business Goal Before anything, dig deep: what’s the actual problem we’re solving, not just the requested task or a surface-level symptom? Ask ‘why’ repeatedly. What user pain or business need are we addressing? Then, state the clear business goal: “What metric am I moving, and do we have data to prove this is the right lever?” If retention is the goal, decide whether push reminders, gamification, or personalised content is the best route. The wrong lever, or tackling a symptom instead of the cause, dooms everything downstream. 2. Choose the Mechanic (Solution Principle) Once the core problem and goal are clear, lock the solution principle or ‘mechanic’ first. Going with a game layer? Decide if it’s leaderboards, streaks, or badges. Write it down. Then move on. No UI yet. This keeps the focus high-level before diving into pixels. 3. Wireframe the Flow & Get Focused Feedback Now open Figma. Map screens, layout, and transitions. Boxes and arrows are enough. Keep the fidelity low so the discussion stays on the flow, not colour. Crucially, when you share these early wires, ask specific questions and provide clear context (as discussed in ‘Reason #4’) to get actionable feedback, not just vague opinions. 4. Polish the Visuals (Mindfully) I only let myself tweak grids, type scales, and shadows after the flow is validated. If progress stalls, or before a major polish effort, I surface the work in a design critique — again using targeted questions and clear context — instead of hiding in version 47. This ensures detailing serves the now-validated solution. Even for something as small as a single button, running these four checkpoints takes about ten minutes and saves hours of decorative dithering. Wrapping Up Next time you feel the pull to vanish into mock‑ups before the problem is nailed down, pause and ask what you might be avoiding. Yes, that can expose an uncomfortable truth. But pausing to ask what you might be avoiding — maybe the fuzzy core problem, or just asking for tough feedback — gives you the power to face the real issue head-on. It keeps the project focused on solving the right problem, not just perfecting a flawed solution. Attention to detail is a superpower when used at the right moment. Obsessing over pixels too soon, though, is a bad habit and a warning light telling us the process needs a rethink.

IntroductionHi, my name is Pankaj Kholiya, and I am a Senior 3D Character Artist. I've been working in the game industry for the past 8 years. I worked on titles like Call of Duty: Black Ops 6, That Christmas, Ghost of Tsushima Director's Cut, Star Wars: Outlaws, Alan Wake 2, Street Fighter 6, and many more. Currently, I'm working as a freelancer for the gaming and cinematics industry.Since my last interview, I made a few personal works, was a part of a Netflix movie, That Christmas, and worked with Platige on Star Wars: Outlaws and Call of Duty: Black Ops 6 cinematic.The Breakdancing Clothing ProjectIt all started when I witnessed a dance battle that a friend organized. It was like watching Step Up live. There, I got the inspiration to create a break dancer. I started by gathering different references from the internet. I found one particular image on Pinterest and decided to recreate it in 3D.At first, the idea was to create the outfit in one pose, but along the way, I also decided to create a dancing version of the character and explore Unreal Engine. Here is the ref I used for the dancing version:Getting StartedFor the upcoming talents, I'll try to describe my process in a few points. Even before starting Marvelous Designer, I made sure to have my base character ready for animation and simulation. This time, I decided to use the MetaHuman creator for the base due to its high-quality textures and materials. My primary focus was on the clothing, so using MetaHuman saved a lot of time.After I was satisfied with how my MetaHuman looked, I took it to Mixamo to get some animations. I was really impressed by how good the animations worked on the MetaHuman. Once I had the animations, I took the animation into Marvelous Designer and simulated the clothes.For the posed character, I adjusted the rig to match the pose like the reference and used the same method as in this tutorial to pose the character:ClothingFor this particular project, I didn't focus on the topology as it was just for a single render. I just packed the UVs in Marvelous Designer, exported the quad mesh from Marvelous Designer, subdivided it a few times, and started working on the detailing part in ZBrush.For the texture, I used the low-division mesh from the ZBrush file, as I already had the UVs on it. I then baked the normal and other maps on it and took it to Substance 3D Painter.AnimationThere are multiple ways to animate the metahuman character. For this project, I've used Mixamo. I imported my character into Mixamo, selected the animation I liked, and exported it. After that, I just imported it to Marvelous Designer and hit the simulation button. You can check my previous breakdown for the Mixamo pipeline.Once happy with the result, I exported the simulated cloth as an Alembic to Unreal Engine. Tutorial for importing clothes into Unreal Engine:Lighting & RenderingThe main target was to match the lighting closely to the reference. This was my first project in Unreal Engine, so I wanted to explore the lighting and see how far I could go with it. Being new to the Unreal Engine, I went through a lot of tutorials. Here are the lights I've used for the posed version:For the dancing version, I've created a stage like the ref from the Step Up movie: Some tips I found useful for the rendering are in the video below:ConclusionAt first, I had a clear direction for this project and was confident in my skills to tackle the art aspect of it. But things changed when I dived into Unreal Engine for my presentation. More than half the time on this project went into learning and getting used to Unreal Engine. I don't regret a single second I invested in Unreal, as it was a new experience. It took around 15 days to wrap this one up.The lesson I learned is that upgrading your knowledge and learning new things will help you grow as an artist in the long run. Approaching how you make an artwork has changed a lot ever since I started 3D, and adapting to the changing art environment is a good thing. Here are some recommendations if you are interested in learning Unreal Engine.Pankaj Kholiya, Senior 3D Character ArtistInterview conducted by Amber Rutherford

ChatGPT is rapidly changing the world. The process is already happening, and it’s only going to accelerate as the technology improves, as more people gain access to it, and as more learn how to use it. What’s shocking is just how many tasks ChatGPT is already capable of managing for you. While the naysayers may still look down their noses at the potential of AI assistants, I’ve been using it to handle all kinds of menial tasks for me. Here are my favorite examples. Further reading: This tiny ChatGPT feature helps me tackle my days more productively Write your emails for you Dave Parrack / Foundry We’ve all been faced with the tricky task of writing an email—whether personal or professional—but not knowing quite how to word it. ChatGPT can do the heavy lifting for you, penning the (hopefully) perfect email based on whatever information you feed it. Let’s assume the email you need to write is of a professional nature, and wording it poorly could negatively affect your career. By directing ChatGPT to write the email with a particular structure, content, and tone of voice, you can give yourself a huge head start. A winning tip for this is to never accept ChatGPT’s first attempt. Always read through it and look for areas of improvement, then request tweaks to ensure you get the best possible email. You can (and should) also rewrite the email in your own voice. Learn more about how ChatGPT coached my colleague to write better emails. Generate itineraries and schedules Dave Parrack / Foundry If you’re going on a trip but you’re the type of person who hates planning trips, then you should utilize ChatGPT’s ability to generate trip itineraries. The results can be customized to the nth degree depending on how much detail and instruction you’re willing to provide. As someone who likes to get away at least once a year but also wants to make the most of every trip, leaning on ChatGPT for an itinerary is essential for me. I’ll provide the location and the kinds of things I want to see and do, then let it handle the rest. Instead of spending days researching everything myself, ChatGPT does 80 percent of it for me. As with all of these tasks, you don’t need to accept ChatGPT’s first effort. Use different prompts to force the AI chatbot to shape the itinerary closer to what you want. You’d be surprised at how many cool ideas you’ll encounter this way—simply nix the ones you don’t like. Break down difficult concepts Dave Parrack / Foundry One of the best tasks to assign to ChatGPT is the explanation of difficult concepts. Ask ChatGPT to explain any concept you can think of and it will deliver more often than not. You can tailor the level of explanation you need, and even have it include visual elements. Let’s say, for example, that a higher-up at work regularly lectures everyone about the importance of networking. But maybe they never go into detail about what they mean, just constantly pushing the why without explaining the what. Well, just ask ChatGPT to explain networking! Okay, most of us know what “networking” is and the concept isn’t very hard to grasp. But you can do this with anything. Ask ChatGPT to explain augmented reality, multi-threaded processing, blockchain, large language models, what have you. It will provide you with a clear and simple breakdown, maybe even with analogies and images. Analyze and make tough decisions Dave Parrack / Foundry We all face tough decisions every so often. The next time you find yourself wrestling with a particularly tough one—and you just can’t decide one way or the other—try asking ChatGPT for guidance and advice. It may sound strange to trust any kind of decision to artificial intelligence, let alone an important one that has you stumped, but doing so actually makes a lot of sense. While human judgment can be clouded by emotions, AI can set that aside and prioritize logic. It should go without saying: you don’t have to accept ChatGPT’s answers. Use the AI to weigh the pros and cons, to help you understand what’s most important to you, and to suggest a direction. Who knows? If you find yourself not liking the answer given, that in itself might clarify what you actually want—and the right answer for you. This is the kind of stuff ChatGPT can do to improve your life. Plan complex projects and strategies Dave Parrack / Foundry Most jobs come with some level of project planning and management. Even I, as a freelance writer, need to plan tasks to get projects completed on time. And that’s where ChatGPT can prove invaluable, breaking projects up into smaller, more manageable parts. ChatGPT needs to know the nature of the project, the end goal, any constraints you may have, and what you have done so far. With that information, it can then break the project up with a step-by-step plan, and break it down further into phases (if required). If ChatGPT doesn’t initially split your project up in a way that suits you, try again. Change up the prompts and make the AI chatbot tune in to exactly what you’re looking for. It takes a bit of back and forth, but it can shorten your planning time from hours to mere minutes. Compile research notes Dave Parrack / Foundry If you need to research a given topic of interest, ChatGPT can save you the hassle of compiling that research. For example, ahead of a trip to Croatia, I wanted to know more about the Croatian War of Independence, so I asked ChatGPT to provide me with a brief summary of the conflict with bullet points to help me understand how it happened. After absorbing all that information, I asked ChatGPT to add a timeline of the major events, further helping me to understand how the conflict played out. ChatGPT then offered to provide me with battle maps and/or summaries, plus profiles of the main players. You can go even deeper with ChatGPT’s Deep Research feature, which is now available to free users, up to 5 Deep Research tasks per month. With Deep Research, ChatGPT conducts multi-step research to generate comprehensive reports (with citations!) based on large amounts of information across the internet. A Deep Research task can take up to 30 minutes to complete, but it’ll save you hours or even days. Summarize articles, meetings, and more Dave Parrack / Foundry There are only so many hours in the day, yet so many new articles published on the web day in and day out. When you come across extra-long reads, it can be helpful to run them through ChatGPT for a quick summary. Then, if the summary is lacking in any way, you can go back and plow through the article proper. As an example, I ran one of my own PCWorld articles (where I compared Bluesky and Threads as alternatives to X) through ChatGPT, which provided a brief summary of my points and broke down the best X alternative based on my reasons given. Interestingly, it also pulled elements from other articles. (Hmph.) If you don’t want that, you can tell ChatGPT to limit its summary to the contents of the link. This is a great trick to use for other long-form, text-heavy content that you just don’t have the time to crunch through. Think transcripts for interviews, lectures, videos, and Zoom meetings. The only caveat is to never share private details with ChatGPT, like company-specific data that’s protected by NDAs and the like. Create Q&A flashcards for learning Dave Parrack / Foundry Flashcards can be extremely useful for drilling a lot of information into your brain, such as when studying for an exam, onboarding in a new role, prepping for an interview, etc. And with ChatGPT, you no longer have to painstakingly create those flashcards yourself. All you have to do is tell the AI the details of what you’re studying. You can specify the format (such as Q&A or multiple choice), as well as various other elements. You can also choose to keep things broad or target specific sub-topics or concepts you want to focus on. You can even upload your own notes for ChatGPT to reference. You can also use Google’s NotebookLM app in a similar way. Provide interview practice Dave Parrack / Foundry Whether you’re a first-time jobseeker or have plenty of experience under your belt, it’s always a good idea to practice for your interviews when making career moves. Years ago, you might’ve had to ask a friend or family member to act as your mock interviewer. These days, ChatGPT can do it for you—and do it more effectively. Inform ChatGPT of the job title, industry, and level of position you’re interviewing for, what kind of interview it’ll be (e.g., screener, technical assessment, group/panel, one-on-one with CEO), and anything else you want it to take into consideration. ChatGPT will then conduct a mock interview with you, providing feedback along the way. When I tried this out myself, I was shocked by how capable ChatGPT can be at pretending to be a human in this context. And the feedback it provides for each answer you give is invaluable for knocking off your rough edges and improving your chances of success when you’re interviewed by a real hiring manager. Further reading: Non-gimmicky AI apps I actually use every day

Donald Trump’s military parade in Washington this weekend — a show of force in the capital that just happens to take place on the president’s birthday — smacks of authoritarian Dear Leader-style politics (even though Trump actually got the idea after attending the 2017 Bastille Day parade in Paris).Yet as disconcerting as the imagery of tanks rolling down Constitution Avenue will be, it’s not even close to Trump’s most insidious assault on the US military’s historic and democratically essential nonpartisan ethos.In fact, it’s not even the most worrying thing he’s done this week.On Tuesday, the president gave a speech at Fort Bragg, an Army base home to Special Operations Command. While presidential speeches to soldiers are not uncommon — rows of uniformed troops make a great backdrop for a foreign policy speech — they generally avoid overt partisan attacks and campaign-style rhetoric. The soldiers, for their part, are expected to be studiously neutral, laughing at jokes and such, but remaining fully impassive during any policy conversation.That’s not what happened at Fort Bragg. Trump’s speech was a partisan tirade that targeted “radical left” opponents ranging from Joe Biden to Los Angeles Mayor Karen Bass. He celebrated his deployment of Marines to Los Angeles, proposed jailing people for burning the American flag, and called on soldiers to be “aggressive” toward the protesters they encountered.The soldiers, for their part, cheered Trump and booed his enemies — as they were seemingly expected to. Reporters at Military.com, a military news service, uncovered internal communications from 82nd Airborne leadership suggesting that the crowd was screened for their political opinions.“If soldiers have political views that are in opposition to the current administration and they don’t want to be in the audience then they need to speak with their leadership and get swapped out,” one note read.To call this unusual is an understatement. I spoke with four different experts on civil-military relations, two of whom teach at the Naval War College, about the speech and its implications. To a person, they said it was a step towards politicizing the military with no real precedent in modern American history.“That is, I think, a really big red flag because it means the military’s professional ethic is breaking down internally,” says Risa Brooks, a professor at Marquette University. “Its capacity to maintain that firewall against civilian politicization may be faltering.”This may sound alarmist — like an overreading of a one-off incident — but it’s part of a bigger pattern. The totality of Trump administration policies, ranging from the parade in Washington to the LA troop deployment to Secretary of Defense Pete Hegseth’s firing of high-ranking women and officers of color, suggests a concerted effort to erode the military’s professional ethos and turn it into an institution subservient to the Trump administration’s whims. This is a signal policy aim of would-be dictators, who wish to head off the risk of a coup and ensure the armed forces’ political reliability if they are needed to repress dissent in a crisis.Steve Saideman, a professor at Carleton University, put together a list of eight different signs that a military is being politicized in this fashion. The Trump administration has exhibited six out of the eight.“The biggest theme is that we are seeing a number of checks on the executive fail at the same time — and that’s what’s making individual events seem more alarming than they might otherwise,” says Jessica Blankshain, a professor at the Naval War College (speaking not for the military but in a personal capacity).That Trump is trying to politicize the military does not mean he has succeeded. There are several signs, including Trump’s handpicked chair of the Joint Chiefs repudiating the president’s claims of a migrant invasion during congressional testimony, that the US military is resisting Trump’s politicization.But the events in Fort Bragg and Washington suggest that we are in the midst of a quiet crisis in civil-military relations in the United States — one whose implications for American democracy’s future could well be profound.The Trump crisis in civil-military relations, explainedA military is, by sheer fact of its existence, a threat to any civilian government. If you have an institution that controls the overwhelming bulk of weaponry in a society, it always has the physical capacity to seize control of the government at gunpoint. A key question for any government is how to convince the armed forces that they cannot or should not take power for themselves.Democracies typically do this through a process called “professionalization.” Soldiers are rigorously taught to think of themselves as a class of public servants, people trained to perform a specific job within defined parameters. Their ultimate loyalty is not to their generals or even individual presidents, but rather to the people and the constitutional order.Samuel Huntington, the late Harvard political scientist, is the canonical theorist of a professional military. In his book The Soldier and the State, he described optimal professionalization as a system of “objective control”: one in which the military retains autonomy in how they fight and plan for wars while deferring to politicians on whether and why to fight in the first place. In effect, they stay out of the politicians’ affairs while the politicians stay out of theirs.The idea of such a system is to emphasize to the military that they are professionals: Their responsibility isn’t deciding when to use force, but only to conduct operations as effectively as possible once ordered to engage in them. There is thus a strict firewall between military affairs, on the one hand, and policy-political affairs on the other.Typically, the chief worry is that the military breaches this bargain: that, for example, a general starts speaking out against elected officials’ policies in ways that undermine civilian control. This is not a hypothetical fear in the United States, with the most famous such example being Gen. Douglas MacArthur’s insubordination during the Korean War. Thankfully, not even MacArthur attempted the worst-case version of military overstep — a coup.But in backsliding democracies like the modern United States, where the chief executive is attempting an anti-democratic power grab, the military poses a very different kind of threat to democracy — in fact, something akin to the exact opposite of the typical scenario.In such cases, the issue isn’t the military inserting itself into politics but rather the civilians dragging them into it in ways that upset the democratic political order. The worst-case scenario is that the military acts on presidential directives to use force against domestic dissenters, destroying democracy not by ignoring civilian orders, but by following them.There are two ways to arrive at such a worst-case scenario, both of which are in evidence in the early days of Trump 2.0.First is politicization: an intentional attack on the constraints against partisan activity inside the professional ranks.Many of Pete Hegseth’s major moves as secretary of defense fit this bill, including his decisions to fire nonwhite and female generals seen as politically unreliable and his effort to undermine the independence of the military’s lawyers. The breaches in protocol at Fort Bragg are both consequences and causes of politicization: They could only happen in an environment of loosened constraint, and they might encourage more overt political action if gone unpunished.The second pathway to breakdown is the weaponization of professionalism against itself. Here, Trump exploits the military’s deference to politicians by ordering it to engage in undemocratic (and even questionably legal) activities. In practice, this looks a lot like the LA deployments, and, more specifically, the lack of any visible military pushback. While the military readily agreeing to deployments is normally a good sign — that civilian control is holding — these aren’t normal times. And this isn’t a normal deployment, but rather one that comes uncomfortably close to the military being ordered to assist in repressing overwhelmingly peaceful demonstrations against executive abuses of power.“It’s really been pretty uncommon to use the military for law enforcement,” says David Burbach, another Naval War College professor (also speaking personally). “This is really bringing the military into frontline law enforcement when. … these are really not huge disturbances.”This, then, is the crisis: an incremental and slow-rolling effort by the Trump administration to erode the norms and procedures designed to prevent the military from being used as a tool of domestic repression. Is it time to panic?Among the experts I spoke with, there was consensus that the military’s professional and nonpartisan ethos was weakening. This isn’t just because of Trump, but his terms — the first to a degree, and now the second acutely — are major stressors.Yet there was no consensus on just how much military nonpartisanship has eroded — that is, how close we are to a moment when the US military might be willing to follow obviously authoritarian orders.For all its faults, the US military’s professional ethos is a really important part of its identity and self-conception. While few soldiers may actually read Sam Huntington or similar scholars, the general idea that they serve the people and the republic is a bedrock principle among the ranks. There is a reason why the United States has never, in over 250 years of governance, experienced a military coup — or even come particularly close to one.In theory, this ethos should also galvanize resistance to Trump’s efforts at politicization. Soldiers are not unthinking automatons: While they are trained to follow commands, they are explicitly obligated to refuse illegal orders, even coming from the president. The more aggressive Trump’s efforts to use the military as a tool of repression gets, the more likely there is to be resistance.Or, at least theoretically.The truth is that we don’t really know how the US military will respond to a situation like this. Like so many of Trump’s second-term policies, their efforts to bend the military to their will are unprecedented — actions with no real parallel in the modern history of the American military. Experts can only make informed guesses, based on their sense of US military culture as well as comparisons to historical and foreign cases.For this reason, there are probably only two things we can say with confidence.First, what we’ve seen so far is not yet sufficient evidence to declare that the military is in Trump’s thrall. The signs of decay are too limited to ground any conclusions that the longstanding professional norm is entirely gone.“We have seen a few things that are potentially alarming about erosion of the military’s non-partisan norm. But not in a way that’s definitive at this point,” Blankshain says.Second, the stressors on this tradition are going to keep piling on. Trump’s record makes it exceptionally clear that he wants the military to serve him personally — and that he, and Hegseth, will keep working to make it so. This means we really are in the midst of a quiet crisis, and will likely remain so for the foreseeable future.“The fact that he’s getting the troops to cheer for booing Democratic leaders at a time when there’s actually [a deployment to] a blue city and a blue state…he is ordering the troops to take a side,” Saideman says. “There may not be a coherent plan behind this. But there are a lot of things going on that are all in the same direction.”See More: Politics

CGI 3D Animated Short Film: Nodoubt Animated Short Film by Lin Lin at Ringling College of Art and Design. Featured on CGMeetup Gallery http://www.cgmeetup.com/gallery Motivated by a poster in her hospital room, Ave Lee, embarks on a journey of recovery. She finds her inner warrior to overcome the mountain of doubt that confronts her. Directed by Lin Lin https://doubles.portfoliobox.net/&amp ;#13; 
 SUBSCRIBE to CGMeetup for more inspiring content! http://bit.ly/Sub2CGMeetup&amp ;#13; Watch More CGI & VFX Animated Short Films: https://www.youtube.com/playlist?list=PLc6NCp8iAPDa4dBRHY4E5uvuqNcYe8AXX&amp ;#13; VFX Breakdowns, Making of & Behind the Scene: https://www.youtube.com/playlist?list=PLc6NCp8iAPDYMZcYBcEBQRoGidvdi0iPN 
 
 FOLLOW US:
 Website: http://www.cgmeetup.com 
 YouTube https://www.youtube.com/user/CGMeetUp&amp ;#13; Facebook https://www.facebook.com/CGMeetup&amp ;#13; Twitter https://twitter.com/cgmeetup&amp ;#13; Google+ https://plus.google.com/+Cgmeetup&amp ;#13; Dailymotion http://www.dailymotion.com/user/CGMeetup&amp ;#13; Pinterest https://www.pinterest.com/cgmeetup/&amp ;#13; Instagram https://instagram.com/cgmeetup/&amp ;#13; 
 WANT TO GET FEATURED?
 All CGI artists, studios or schools who would like their work featured or
 published on CGMeetup please apply below. We’re looking for short films, commercial spots, breakdowns, showreels etc.
 Submit here → https://www.cgmeetup.com/gallery&amp ;#13; Contact us here → info@cgmeetup.com or info@cgmeetup.net
 
 Please Note: All videos are uploaded after written copyright permission from respected Artists, Studios or Schools Or part of the Creative Commons license.
 https://creativecommons.org/licenses/by/4.0/legalcode&amp ;#13; For more details or to dispute please contact us at info@cgmeetup.com or info@cgmeetup.net
 
 ABOUT CGMeetup:
 CGMeetup is the #1 inspiration resource for all CGI, VFX, 3D and Digital artists. We feature a wide variety of CGI content including behind-the-
 scenes, vfx breakdowns, short films & showreels.
 
 CGMeetup also serves as networking resource for CGI Professionals.
 Professionals use CGMeetup to exchange ideas, knowledge & job
 opportunities.
 
 CGI Animated Short Film: "Nodoubt" by Lin Lin | CGMeetup 
 https://youtu.be/HgQwWdgknqg&amp ;#13; 
 CGMeetup
 https://www.youtube.com/CGMeetup 
 
 #animation #animated #3d #cgi #shortfilm #short #animatedshortfilm