Augmented Reality Design Uses

🔬 Every pixel since the first CCD does one thing: detect OR emit light. ETH Zürich just ended this 6 decade-old constraint. In a 10µm element. 👇 Current pixels control only one property of light at a time time Cameras measure intensity. Displays emit it. Spatial light modulators shift the phase. etc... But no single element has ever handled amplitude, phase, and polarization simultaneously, nor enable both sensing and generation at the same time. This new type of pixel can do it all. ❓ Why does this matter? Light carries information in all three parameters. If your pixel ignores two of them, you're working with a fraction of what the light field is actually telling you. This is the fundamental bottleneck in adaptive optics, polarization imaging, and holographic displays; they all need separate bulky components for each function. 🔧 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 The Norris group at ETH Zurich uses surface plasmon polariton waves, coherent electromagnetic waves propagating along metallic surfaces, as intermediaries. When these plasmons hit precisely designed wavy microstructures, they scatter into arbitrary optical wavefronts. Run it in reverse, and incoming light couples back into the plasmons, fully characterizing the field. What is impressive is that designing it is actually quite simple, the design requires no electromagnetic simulation. The inverse Fourier transform of the wavefront you want gives you the surface profile to fabricate. ~1 day from concept to working device. 🎯 𝗞𝗲𝘆 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 🔹 Full control over amplitude, phase AND polarization, in both sensing and generation 🔹 >40% power efficiency across 500–700 nm 🔹 Complete Stokes polarimetry in a single 10×10 µm² element 🔹 Vortex beam generation up to topological charge q = +5 🔹 Works in silver (plasmonic) AND silicon nitride, meaning photonic chip integration is already on the table The paper explicitly targets adaptive optics, holographic AR displays, optical communications, and quantum information processing. Personally, for optical manipulation, it would be great to have a single element that simultaneously maps amplitude, phase, and polarization of your beam. ❓ And you what would you use it for? 🔗 Paper in first comment. #Photonics #Optics #Microscopy #AdaptiveOptics #Nanophotonics #Plasmonics #QuantumPhotonics #DeepTech #Semiconductors #OpticalEngineering

I never truly understood the power of UX—until I saw this. This railing is part of the panoramic terrace at the Castel Sant'Elmo in Naples, Italy, and offers a breathtaking and inclusive view. What's almost as moving as the panorama itself is the the braille etched into the railing. Someone thought: How can we make this view accessible to someone who can’t see it? Not just functional. Not just compliant. But empathetic. A powerful gesture that says: You belong here, too. If like me, you understood UX as a concept, but you didn't really get it, this is it. It's neither the pixels nor the wireframes, but rather the intentional design of an experience that includes, uplifts, and connects. In product design, they talk about delight, friction, accessibility but this is a reminder that experience design is an act of care. Even the smallest touch, when rooted in empathy, can make someone feel seen. If you build products, lead teams, or shape experiences, you've seen this before, but let it again be your north star. It makes one helluva difference.

🤦🏻 “How We Run Design Critiques at Figma” (https://lnkd.in/eERQmRnY), an honest case study by Noah Levin with helpful techniques and templates to run more effective design critiques ↓ 🚫 Most critiques are an avalanche of unstructured opinions. ✅ Good critiques are inspiring, and give you a plan of action. ✅ Critiques work best with 2–6 people in the room. ✅ Explain the problem before showing any work. ✅ Reiterate previous findings, decisions and research. ✅ Explain how far you are: 30%, 60% or 90% done. ✅ Explain what kind of feedback you are looking for. ✅ No Keynote/Powerpoint: Figma link + Observation mode. ✅ Assign a note-taker to capture key points (Google Doc). ✅ Show what you want to show: feedback is shaped by that. 🚀 Critique formats: 🎡 Round-the-room: everyone voices their feedback (2min / person). 🍿 Popcorn: freeform comments for flowing conversation. 🥁 Jams: for early explorations with brainstorms, group sketching. 🫱🏻🫲🏾 Pair design: for deep collaboration on a problem (small groups). 🤫 Silent critiques: for a large volume of written, structured feedback. 📋 Paper print-out: for complex flows and reviewing more at once. 📣 FYI critiques: for sharing context and invite feedback later. Design critiques are about applying critical thinking. It’s about how well a current iteration of design does what it’s trying to do. However, designers alone often don’t have the full picture. Don’t necessarily reserve critiques to design teams only: invite developers and stakeholders and PMs for early feedback. Don’t ask what people think — ask how well the design tackles a specific problem. And probably the most important thing is to enable a flowing conversations. Invite everyone to ask, to doubt, to scrutinize, but stay on point and gather structured feedback: that’s when good critiques emerge. Useful resources: Practical Design Critique Guide, by Darrin Henein https://lnkd.in/ey_cGKuc Mastering Design Critiques, by Jonny Czar https://lnkd.in/e_BYwNwf Anti-Behavior in Design Critiques, and How To Handle Them, by Ben Crothers https://lnkd.in/e4UrpsPs --- ⛵ Figma and Miro Templates Design Critique Meetings Guide (Figma), by Overflow https://lnkd.in/dE85MUAK Design Critique Template (Figma), by Janus Tiu https://lnkd.in/dCYp2MSY Design Critique Meeting (Figma), by Rodrigo Javier Peña https://lnkd.in/dP_8pCug Design Critique Playground Template (Miro), by Miroslava Jovicic https://lnkd.in/eryJShRd #ux #design

A surgeon in Berlin just operated while looking straight through a patient's spine. Not with X-rays. With holograms floating above the body. Helios Berlin-Buch is the first German hospital where surgeons wear AR glasses during spinal surgery. They see organs, bones, and blood vessels in 3D - while operating. What this means: → 40% more accurate implant positioning → Significantly shorter surgery times → Faster recovery for patients Think about that. 1.9 million spine surgeries happen globally each year. 1.9 million people facing potential paralysis. 1.9 million families holding their breath. Now imagine those same surgeries with: • Millimeter precision guided by AI • Surgeons seeing through tissue in real-time • Gesture controls keeping hands sterile The technology that once were for a privileged few? Now spreading to major hospital globally. Here's what changes everything: A spinal implant off by 2mm can mean permanent nerve damage. With AR, surgeons place it perfectly. First time. Every time. By 2025, 20% of surgeons will operate with this superhuman vision. That's 380,000 spine surgeries made safer. 380,000 people with better chances of walking. 380,000 families getting good news. This isn't just better surgery. It's a whole change in healthcare to improve and use the latest technology. The solutions are getting cheaper and more accessible, but still more funding is needed to support doctor's training with AR/VR and the otherwise complicated operations. Follow me, Dr. Martha Boeckenfeld, for more breakthroughs saving lives. ♻️ Share if you believe every surgeon should have superhuman vision. #MedTech #Innovation #FutureOfHealthcare

Spatial looks simple, but the moment you scale it, everything you know about data engineering starts to fall apart. Why? Partitioning doesn’t work: Hashing or range splitting won’t save you when geometry clusters unevenly. You need spatial indexes, space-filling curves, quadtrees, not random WKT hashing. Data transfer gets expensive fast: Geometries and rasters are heavy. Move them across your network enough times and your cloud bill tells the story. Geometry is computationally brutal: A polygon with 4 vertices is easy. A parcel or coastline with 40,000 isn’t. Spatial joins aren’t SQL joins. They combine indexing, geometry complexity, boundary rules, and massive compute. Rasters and climate data are a different world (no pun intended): Chunking, resampling, tiling, reprojection...one bad zonal stats query can stall a cluster. M/Z coordinates complicate mobility and trajectory data: Time in the M dimension, elevation in Z, your warehouse won’t handle it natively. Spatial really needs multi-hop: Bronze raw → Silver normalized + indexed → Gold analysis-ready. Skip a hop and your downstream systems pay the price. So what should data engineers do about this? If you touch climate, mobility, insurance, supply chain, telecom, logistics, agriculture, real estate, risk, or public sector data, spatial is already part of your stack whether you planned for it or not. Here’s what actually works: 1. Use engines designed for spatial Frameworks like Apache Sedona and Wherobots solve the problems your warehouse can’t: spatial partitioning optimized spatial joins distributed raster ops M/Z-aware trajectory handling compute pushed to the data 2. Adopt cloud-native spatial formats GeoParquet, COGs, Zarr/NetCDF, PMTiles, Iceberg - these remove most of the bottlenecks. 3. Build a spatially aware medallion architecture Bronze: ingest raw shapes Silver: reproject, simplify, index Gold: analysis-ready vector + raster layers 4. Watch for the triggers If you’re doing >10M feature joins, raster work, mobility data, ML/LLM spatial features, or paying huge transfer costs you need a spatial engine. Spatial isn’t niche anymore. The teams that treat it as a first-class data engineering problem will ship faster, spend less, and unlock capabilities most companies can’t touch. 🌎 I'm Matt and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 10k+ others learning from my newsletter → forrest.nyc

AI answers start with reasoning. Before an AI agent produces a response, it often goes through structured thinking processes to analyze the problem, explore options, and determine the best path to a solution. Modern AI systems rely on different reasoning methods to handle complex tasks more reliably. - Chain-of-Thought The model breaks problems into step-by-step reasoning before producing the final answer. This method helps with math, coding, and structured analytical tasks. - ReAct (Reason + Act) ReAct combines reasoning with tool usage. The agent observes information, chooses tools, executes actions, and updates context before generating the final response. - nTree-of-Thought Instead of following a single reasoning path, the model explores multiple possible solution branches and evaluates which one produces the best outcome. - Self-Consistency The system generates multiple reasoning attempts for the same problem and selects the most consistent answer across those attempts. - Plan-and-Execute The agent first creates a structured plan and then executes each step sequentially to complete complex tasks. - Reflexion The model evaluates its own outputs, learns from mistakes, and adjusts its reasoning before retrying a solution. - MRKL (Modular Reasoning) This approach routes problems to specialized tools or models, combining outputs from different components to produce the final result. - Program-of-Thought Instead of only reasoning in text, the model generates code to solve logical or analytical problems and executes the program to derive the answer. AI is moving beyond simple text prediction. Modern systems combine reasoning strategies, tool usage, and iterative learning to solve increasingly complex problems. Which reasoning method do you think will become the standard for future AI agents?

Having worked in the augmented reality space during my time at InvenSense (before the acquisition by TDK), I’m particularly impressed by this breakthrough—TDK has developed the world’s first full-color laser control device for 4K smart glasses, using lithium niobate thin film. This advancement could be the game-changer needed to accelerate adoption by making AR/VR glasses as light and sleek as regular eyewear. This is why we’re equally excited about our portfolio company, VueReal Inc., which is pushing the boundaries in microdisplay technology for AR/VR applications. Together, these innovations are setting the stage for a new era in immersive experiences, making them more accessible and practical. Looking forward to seeing the potential of TDK’s technology unfold, and how it can shape the future of augmented and virtual reality. 🔗 You can read the full article here: https://lnkd.in/g2n5y2zV #AR #VR #DeepTech #Innovation #TDKVentures #ImpactScalers #VC #CVC

Wow! Finally, we have an AR breakthrough that is saving lives. ❤️🧠 But doctors don’t want to use it. Here’s why: It finally works! Augmented reality (AR) that overlays 2D MRI scans onto a patient’s body in real time. Imagine “seeing through” skin to navigate tumors, blood vessels, or fractures with millimeter precision. Yet, many surgeons hesitate - not because the tech isn’t groundbreaking, but because the stakes of imperfection are catastrophic. 👩⚕️ Last week, I spoke with a neurosurgeon who admitted, “I stayed up all night thinking: What if the AR is off by one pixel? A tremor, a misalignment, and suddenly I’m responsible for irreversible harm. Do I blame myself, or the software? Neither answer feels right.” This isn’t just about technology - it’s about accountability. Give it a thought yourself: 1️⃣ Precision ≠ Perfection: A single pixel error isn’t just a glitch - it’s a lawsuit waiting to happen. How accurate must AI be before we trust it with our lives? 2️⃣ Responsibility: AR must become the surgeon’s tool, not the other way around. Doctors need to learn to operate with AI, knowing that they are making the final cut. 3️⃣ XAI (explainable AI): Doctors need transparency, not promises. Can the software explain why it aligns scans a certain way? Can it learn from surgeon feedback? 👉 What’s one non-negotiable thing that must be required from AR in healthcare?

One of the first killer applications of XR is emerging around something very human, bringing families together when they cannot be in the same place. As the holidays arrive, we especially become more aware of distance. XR offers a way to collapse that distance by making presence feel shared again, even when travel is impossible. The technology is already coming together to make this a reality. The big headset players are all working on ways to create hyper-realistic digital twins of their users, including Apple’s Personas and Meta’s Codec Avatars. And we are also seeing headset-free solutions like Google Beam’s 3D communication platform changing what it means to talk to someone at a distance. When a person shows up in your space as a lifelike avatar, the moment stops feeling like a video call and starts feeling like you are sharing the same room. This makes the experience feel like they are visiting rather than dialing in, and immediately enables all participants to be more present with one another. XR does more than shrink distance. It also changes how we experience time. With spatial video and volumetric capture, moments stop living in the past and start feeling like places you can return to. As AI becomes part of those recordings, the past begins to feel closer to the present. Companies like 2wai offer an early look at how people may one day see and speak with loved ones who are no longer here. This changes how people grieve, remember, and stay connected across generations. The holidays are the one time of year when people really try to show up for each other. When distance or time gets in the way, XR can offer a new way to stay close. That is when the technology feels less like hardware and more like something meaningful. #XR #virtualreality #VR #augmentedreality #AR #mixedreality #holidays #spatialcomputing

Augmented Reality helps employees learn by seeing digital instructions while they work. It makes training more practical, safer, and easier to update when procedures or equipment change. This means that: - Workers can follow instructions directly in the place where the task happens. - Practice becomes safer because people can learn before using real equipment. - Training can cost less when fewer physical tools or machines are needed. - Updates are easier when safety rules or procedures change. - Managers can see where people need more support or practice. - Training results can be connected to company systems and work standards. #AugmentedReality #WorkplaceTraining