Spatial Computing | Techlist.io

googleMar 25, 20263 min readCurated summary

Vibe Coding XR: Accelerating AI + XR prototyping with XR Blocks and Gemini

Vibe Coding XR combines Gemini’s natural-language coding capabilities with the open-source XR Blocks framework to rapidly create interactive, physics-aware WebXR applications. Users can describe an experience—such as a dandelion, physics lab, or educational visualization—and receive a working Android XR prototype in under 60 seconds. The workflow supports both desktop simulation and deployment to Android XR headsets, making spatial prototyping faster and more accessible. ## Bridging AI Prototyping and XR - Traditional XR development requires fragmented perception systems, game engines, and low-level sensor integrations. - Vibe-coded prototypes let developers quickly evaluate 3D interfaces, spatial interactions, and visualizations before investing in full production. - The workflow is designed for both experienced developers and creators without prior XR expertise. - Gemini translates natural-language prompts into functional XR applications with scene setup, perception, interaction, and physics logic. ## The Vibe Coding XR Workflow - Users open the XR Blocks Gem in Chrome on an Android XR headset or desktop. - They provide a prompt by typing or using voice, such as “Create a beautiful dandelion.” - Gemini plans and implements the experience using XR Blocks examples and templates. - On Android XR, users can enter the experience with a pinch gesture and interact naturally—for example, pinching to blow away an animated dandelion. - Applications can be published through a shareable public link. - Desktop Chrome provides a simulated-reality environment for testing before deployment, while Android XR enables advanced features such as hand tracking, depth sensing, and physics. ## Technical Foundation - XR Blocks is built on WebXR, three.js, and LiteRT.js. - Its engine coordinates: - Environmental perception - XR interaction - Spatial computing - AI integration - Gemini receives a specialized system prompt containing: - XR design guidelines for room-scale environments, spatial layout, scale, and interaction distances - Package-management rules and recommended styles - Curated source code, templates, and working samples - Grounding Gemini in valid XR Blocks APIs reduces hallucinated code and encourages consistent implementation patterns. ## Educational and Interactive Applications - **Math tutor:** Visualizes Euler’s theorem using tetrahedra, cubes, and octahedra, with pinch-based highlighting of vertices, edges, and faces. - **Physics lab:** Lets users pick up and place labeled weights on a balance scale to learn about equilibrium. - **Immersive chemistry:** Simulates methane, ethylene, and acetylene combustion with educational cards and volumetric effects, offering a safer mixed-reality alternative to physical experiments. - **Schrödinger’s cat:** Uses pinch and proximity interactions to demonstrate superposition, revealing alive and dead versions of a cat before collapsing the state into one outcome. - **XR sports:** Generates interactive experiences such as hand-based volleyball, including textured balls, environmental collision, and adjustable launch behavior. ## Practical Value - Creators can test spatial ideas in minutes rather than building complete XR pipelines first. - The same prototype can be evaluated on desktop and then experienced with body and hand interactions on Android XR. - The approach is especially useful for education, interaction design, scientific visualization, and early-stage product exploration. Vibe Coding XR is best viewed as a rapid experimentation layer rather than a replacement for production XR engineering. By combining Gemini’s reasoning with XR Blocks’ specialized runtime and templates, it significantly lowers the barrier to creating and validating intelligent spatial experiences.

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Spatial Computing Gemini Vibe Coding Webxr+4

awsJan 20, 2026Original article

Announcing Amazon EC2 G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs (opens in new tab)

Amazon has announced the general availability of EC2 G7e instances, a new hardware tier powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs designed for generative AI and high-end graphics. These instances deliver up to 2.3 times the inference performance of their G6e predecessors while providing significant upgrades to memory and bandwidth. This launch aims to provide a cost-effective solution for running medium-sized AI models and complex spatial computing workloads at scale. **Blackwell GPU and Memory Advancements** * The G7e instances feature NVIDIA RTX PRO 6000 Blackwell GPUs, which provide twice the memory and 1.85 times the memory bandwidth of the G6e generation. * Each GPU provides 96 GB of memory, allowing users to run medium-sized models—such as those with up to 70 billion parameters—on a single GPU using FP8 precision. * The architecture is optimized for both spatial computing and scientific workloads, offering the highest graphics performance currently available in the EC2 portfolio. **High-Speed Connectivity and Multi-GPU Scaling** * To support large-scale models, G7e instances utilize NVIDIA GPUDirect P2P, enabling direct communication between GPUs over PCIe interconnects with minimal latency. * These instances offer four times the inter-GPU bandwidth compared to the L40s GPUs found in G6e instances, facilitating more efficient data transfer in multi-GPU configurations. * Total GPU memory can scale up to 768 GB within a single node, supporting massive inference tasks across eight interconnected GPUs. **Networking and Storage Performance** * G7e instances provide up to 1,600 Gbps of network bandwidth, a four-fold increase over previous generations, making them suitable for small-scale multi-node clusters. * Support for NVIDIA GPUDirect Remote Direct Memory Access (RDMA) via Elastic Fabric Adapter (EFA) reduces latency for remote GPU-to-GPU communication. * The instances support GPUDirect Storage with Amazon FSx for Lustre, achieving throughput speeds up to 1.2 Tbps to ensure rapid model loading and data processing. **System Specifications and Configurations** * Under the hood, G7e instances are powered by Intel Emerald Rapids processors and support up to 192 vCPUs and 2,048 GiB of system memory. * Local storage options include up to 15.2 TB of NVMe SSD capacity to handle high-speed data caching and local processing. * The instance family ranges from the g7e.2xlarge (1 GPU, 8 vCPUs) to the g7e.48xlarge (8 GPUs, 192 vCPUs). For developers ready to transition to Blackwell-based architecture, these instances are accessible through AWS Deep Learning AMIs (DLAMI). They represent a major step forward for organizations needing to balance the high memory requirements of modern LLMs with the cost efficiencies of the G-series instance family.

Spatial Computing AI Generative AI Amazon Ec2+5

figmaMay 18, 20253 min readCurated summary

Andrew “Boz” Bosworth's 10 Rules for Navigating the Next Design Paradigm | Figma Blog

Andrew “Boz” Bosworth argues that designers entering the next era of AI and spatial computing must challenge inherited assumptions rather than merely improve existing interfaces. His approach begins with real human problems, favors experimentation and intuition, and treats interactions as complete systems. The goal is to create technology that understands users and ultimately becomes nearly invisible. ## Start with real human problems - Identify a specific person with a genuine problem. - Let users determine whether a product is useful through their behavior. - Avoid prioritizing abstract ideas over practical human needs. ## Question inherited design assumptions - Recognize that familiar constraints and interaction patterns are man-made paradigms. - Ask whether the current approach is actually appropriate. - Treat seemingly fixed limitations as potentially changeable. ## Reimagine the interaction paradigm - Current computing often forces users to translate simple intentions into complicated sequences of apps and services. - After decades of desktop and mobile conventions, designers should reconsider the entire model of interaction. - New technology should simplify intentions rather than expose underlying complexity. ## Distinguish invention from optimization - “Zero-to-one” invention happens without established customers or constraints. - Optimization improves and refines an existing product with audience feedback. - AI and spatial interfaces provide greenfield opportunities where old assumptions may not apply. ## Use taste and intuition to choose the right direction - Product development resembles climbing through a difficult problem space. - Intuition helps teams choose promising “terrain” before investing in execution. - Good judgment does not remove the hard work, but it improves the odds of pursuing the right problem. ## Prototype aggressively - Build rough, unconventional prototypes to test whether an idea is worth pursuing. - Meta has used crude physical setups, including tracked hats and mesh-walled rooms, to explore spatial computing. - Direct experimentation reveals possibilities that discussion alone cannot. ## Design the complete system - Spatial products cannot be designed by changing one isolated component. - Gestures, visual/audio/haptic feedback, and resulting functionality must evolve together. - Iteration should happen across the whole interaction system. ## Give tools an appropriate theory of mind - Future tools should understand users’ intentions and goals. - They need enough agency to assist meaningfully, but not so much that they become intrusive or unpredictable. - Effective assistance depends on balancing automation with user control. ## Treat products and people as works in progress - Products should be viewed as successive versions rather than finished objects. - Bugs and improvements are a normal part of continued development. - Designers should apply the same iterative mindset to their own growth. ## Make interfaces disappear - Interfaces should be as seamless and minimal as possible. - Their value lies in enabling an experience, not in drawing attention to themselves. - The ideal interaction removes unnecessary friction between a person and their intention. Bosworth’s practical recommendation is to stay close to human needs, challenge conventional assumptions, prototype early, and design the entire experience. In emerging fields such as AI and spatial computing, success depends less on polishing familiar interfaces than on inventing simpler, more natural ways for people to accomplish things.

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Spatial Computing AI Human Computer Interaction Interface Design+4

figmaNov 13, 20242 min readCurated summary

8 Ways to Craft an Unforgettable Config Talk | Figma Blog

The post presents memorable Config talks as a source of inspiration and a practical rubric for future speakers. Its central lesson is that standout presentations challenge conventional thinking, tell a personal story, and show genuine care for the people affected by the work. The excerpt focuses on taking creative risks rather than blindly following industry best practices. ## Taking Risks and Rejecting Convention - Josh Wardle’s talk, **“Opting for the opposite,”** explains how he created Wordle by deliberately ignoring common growth tactics. - Wordle: - Could be played only once per day. - Did not link back to the game when shared. - Was built as a personal gift for Wardle’s partner, not as a viral product. - The talk resonated with Config speakers because it demonstrates that: - Simplicity can outperform metrics-driven optimization. - Conventional wisdom is not always appropriate for a specific audience. - Building with deep care for users can matter more than maximizing engagement. - The broader message is to show how a meaningful product decision emerged from a willingness to do things differently. ## Creating an Inspirational Presentation - The article frames memorable talks as more than informational sessions: they should change assumptions and leave audiences thinking differently. - Other referenced Config sessions include: - **“An Infinite Canvas,”** by Linda Dong and Mike Stern, about spatial-computing design. - A discussion between George Kedenburg III and Humane co-founder Imran Chaudhri about the development and vision of the Ai Pin. - These examples suggest that strong talks combine: - A distinctive perspective. - A compelling story about the work. - Clear attention to users and real-world impact. When preparing a Config talk—or any presentation—focus on a genuine insight or unconventional decision, explain the reasoning behind it, and connect it to the people you are building for.

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Spatial Computing Figma Product Design Web Development+2

figmaFeb 25, 20242 min readCurated summary

David Hoang on how AI brings design and development together | Figma Blog

AI is reshaping creative tools by making interfaces more dynamic, multimodal, and less fully controlled by designers. David Hoang argues that design and engineering are converging as AI augments both disciplines, enabling more people to move from learning and prototyping to shipping real products. Replit’s focus is “Artificial Developer Intelligence” (ADI), aimed at increasing autonomy, productivity, and collaboration rather than pursuing general intelligence. ## Dynamic Interfaces and a New Design Paradigm - Like the rise of mobile, AI is resetting the field and giving designers an opportunity to rethink how people interact with technology. - Interfaces will increasingly adapt across modalities and form factors instead of presenting a fixed, fully designed experience. - Designers must relinquish some control over presentation while shaping how systems behave and respond. - AI, spatial computing—including Apple Vision Pro—and other emerging technologies are converging to create new interaction models. ## Design and Engineering Converge - AI can augment both designers and engineers, making the boundaries between the disciplines less distinct. - Product development is evolving toward a tightly integrated design-and-engineering practice. - Replit frames this strategy as Artificial Developer Intelligence, or ADI, rather than Artificial General Intelligence. - ADI is intended to give people greater autonomy and productivity through collaboration between humans and AI. ## Replit’s Vision for Artificial Developer Intelligence - Future ADI agents could: - Generate code and complete code automatically. - Build complex software architectures. - Orchestrate advanced tools deployed on Replit. - Understand how teams work and improve organizational collaboration. - Hoang describes Replit as a potential “technical co-founder” for people with ideas but limited technical expertise. - The goal is to accelerate the path from learning to coding, launching a business, and scaling an idea. ## Lowering the Barrier to Building Software - AI-powered tools can enable nontechnical users to create technically sophisticated applications. - A Replit hackathon example showed a nontechnical product manager producing work that surpassed projects built by teams of engineers. - Prompting becomes an important skill because translating goals into effective instructions resembles the product manager’s role in defining what should be built. AI’s practical impact may be greatest when it helps people combine product judgment, design, and engineering execution. Rather than replacing creative or technical roles, tools like ADI are positioned to expand who can build software and shorten the distance between an idea and a working product.

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Spatial Computing AI Prompt Engineering Code Generation+4