Adaptive Design through Artificial Intelligence

How to Build Adaptive UIs to democratize Enterprise Data. Imagine an interface that doesn’t just show data, but understands what users need and delivers it in the most meaningful form. That’s the value of an Adaptive User Interface, powered by an AI agent that turns natural language questions into clear, personalized visualizations. Instead of navigating complex dashboards, users can simply ask: “Which areas need the most improvement?” “Show me trends over the last quarter.” “Which categories improved the most?” Your system then identifies the right data, selects the best visualization, and presents it clearly. No manual configuration required. Here’s how to build that system. The Intelligence Behind Adaptive Charting: A Three-Step Agentic Workflow The core of an Adaptive UI is an AI agent that can reason and take action. You can implement this as a workflow within your backend service: 1. Code Generation for Analysis: First, build your agent to understand the user's question and the structure of their data. The agent should then generate and execute a script to perform the correct analysis. This step moves beyond simple Q&A and produces a structured data table containing the answer. 2. Expert Visualization Choice: This is the critical step that makes the UI "adaptive." Instead of defaulting to a table, train your agent to make an expert decision on the best way to visualize the resulting data. You can achieve this by creating a detailed *system prompt* that instructs the LLM to act as a visualization expert. This prompt should guide the LLM to choose between bar charts for comparisons, line charts for trends, etc., and then generate a structured **JSON configuration** that defines the entire chart. This JSON should specify the chart type, axes, colors, and even human-readable metadata like a title and key insights. 3. Dynamic Frontend Rendering: Finally, design your frontend to be a dynamic rendering engine, not a static dashboard. Create a component (e.g., in React) that can accept the JSON configuration from your agent. This component will read the spec and render the prescribed chart on the fly. If your agent decides a bar chart is best, the user sees a bar chart. If a line chart tells a clearer story, a line chart appears instantly. **The Result: An Empowered, Data-Driven User** By implementing this agentic workflow, you create a system that is far more valuable than a traditional BI tool. You democratize data access, eliminate the need for specialized training, and allow everyone, from executives to managers to get critical insights instantly. The interface adapts to the user's intent, not the other way around, leading to faster, better-informed decisions.

AI is no longer just decorating rooms. It’s redesigning how we live. AI can now rethink rooms, floors, and entire layouts—turning bold ideas into build-ready designs. Would you do floor like that? The data behind the shift: • 30–50% faster design cycles using generative layout tools • 100+ layout permutations generated from a single brief • Up to 20–30% improvement in space utilization • 10–25% energy savings when airflow, lighting, and thermal paths are simulated early • 40% fewer late-stage design changes thanks to digital testing What’s fundamentally different? AI treats floor plans like software systems: Pedestrian movement is simulated before construction Natural light and ventilation are optimized virtually Furniture, walls, and utilities are stress-tested digitally Cost, carbon footprint, and materials are optimized in parallel This enables: Smaller homes that feel larger Offices designed around productivity and wellbeing Buildings that adapt over time instead of aging poorly The biggest myth? AI replaces architects and designers. Reality: AI handles complexity and permutations. Humans focus on vision, culture, emotion, and identity. The future of architecture isn’t just smart. It’s generative, data-driven, and human-centric. #AI #Architecture #Design via @Visual Spaces Lab #PropTech #GenerativeAI #FutureOfLiving #SmartBuildings #Innovation

How do materials fail, and how can we design stronger, tougher, and more resilient ones? Published in #PNAS, our physics-aware AI model integrates advanced reasoning, rational thinking, and strategic planning capabilities models with the ability to write and execute code, perform atomistic simulations to solicit new physics data from “first principles”, and conduct visual analysis of graphed results and molecular mechanisms. By employing a multiagent strategy, these capabilities are combined into an intelligent system designed to solve complex scientific analysis and design tasks, as applied here to alloy design and discovery. This is significant because our model overcomes the limitations of traditional data-driven approaches by integrating diverse AI capabilities—reasoning, simulations, and multimodal analysis—into a collaborative system, enabling autonomous, adaptive, and efficient solutions to complex, multiobjective materials design problems that were previously slow, expert-dependent, and domain-specific. Wonderful work by my postdoc Alireza Ghafarollahi! Background: The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Our model overcomes these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of LLMs and the dynamic collaboration among AI agents with expertise in various domains, incl. knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. We demonstrate accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of alloys. Paper: https://lnkd.in/enusweMf Code: https://lnkd.in/eWv2eKwS MIT Schwarzman College of Computing MIT Civil and Environmental Engineering MIT Department of Mechanical Engineering (MechE) MIT Industrial Liaison Program MIT School of Engineering

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗗𝗲𝘀𝗶𝗴𝗻 𝗧𝗼𝗼𝗹𝘀? 𝗔𝗜 𝗪𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗔𝘀𝘀𝗶𝘀𝘁—𝗜𝘁 𝗪𝗶𝗹𝗹 𝗘𝘃𝗼𝗹𝘃𝗲 𝗪𝗶𝘁𝗵 𝗬𝗼𝘂 🤯🎨 Ever felt like design tools force 𝘺��𝘶 to adapt instead of the other way around? That might change soon. Patrick Hebron, a former Adobe, Nvidia, and Stability AI expert (and a fantastic colleague), just published a great essay on 𝗵𝗼𝘄 𝗔𝗜 𝗰𝗮𝗻 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗲 𝘁𝗵𝗲 𝘄𝗮𝘆 𝘄𝗲 𝗰𝗿𝗲𝗮𝘁𝗲. It’s not about AI replacing designers—it’s about AI helping designers 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻 𝘁𝗼𝗼𝗹𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗳𝗹𝘆. Imagine shaping an interface 𝘸𝘩𝘪𝘭𝘦 designing, rather than being stuck with pre-built software constraints. That vision really stuck with me. It’s not just "AI doing the work for you"—it’s 𝗔𝗜 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝘁𝗵𝗮𝘁 𝗳𝗶𝘁 𝘆𝗼𝘂𝗿 𝗳𝗹𝗼𝘄 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲. The video illustrates that quite well. One quote from the essay that really hits home: 👉 "𝘊𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘧𝘭𝘰𝘸 𝘤𝘢𝘯 𝘣𝘦 𝘨𝘭𝘰𝘳𝘪𝘰𝘶𝘴, 𝘣𝘶𝘵 𝘪𝘵 𝘪𝘴 𝘢𝘭𝘴𝘰 𝘲𝘶𝘪𝘵𝘦 𝘧𝘳𝘢𝘨𝘪𝘭𝘦. 𝘜𝘴𝘦𝘳𝘴 𝘭𝘰𝘴𝘦 𝘪𝘵 𝘸𝘩𝘦𝘯 𝘧𝘰𝘳𝘤𝘦𝘥 𝘵𝘰 𝘢𝘥𝘢𝘱𝘵 𝘵𝘩𝘦𝘪𝘳 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘵𝘰 𝘢 𝘵𝘰𝘰𝘭 𝘵𝘩𝘢𝘵 𝘧𝘦𝘦𝘭𝘴 𝘶𝘯𝘯𝘢𝘵𝘶𝘳𝘢𝘭 𝘰𝘳 𝘶𝘯𝘧𝘢𝘮𝘪𝘭𝘪𝘢𝘳. 𝘈𝘐-𝘦𝘯𝘢𝘣𝘭𝘦𝘥 𝘵𝘰𝘰𝘭𝘴 𝘰𝘧𝘧𝘦𝘳 𝘢𝘯 𝘢𝘭𝘵𝘦𝘳𝘯𝘢𝘵𝘪𝘷𝘦, 𝘢𝘭𝘭𝘰𝘸𝘪𝘯𝘨 𝘶𝘴𝘦𝘳𝘴 𝘵𝘰 𝘸𝘰𝘳𝘬 𝘢𝘵 𝘢 𝘩𝘪𝘨𝘩𝘦𝘳, 𝘤𝘰𝘯𝘤𝘦𝘱𝘵𝘶𝘢𝘭 𝘭𝘦𝘷𝘦𝘭 𝘸𝘩𝘪𝘭𝘦 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 𝘩𝘢𝘯𝘥𝘭𝘦 𝘵𝘩𝘦 𝘥𝘦𝘵𝘢𝘪𝘭𝘴 𝘣𝘦𝘩𝘪𝘯𝘥 𝘵𝘩𝘦 𝘴𝘤𝘦𝘯𝘦𝘴." The essay explores how AI-powered tools could break away from 𝘀𝘁𝗮𝘁𝗶𝗰 𝗺𝗲𝗻𝘂𝘀, 𝗿𝗶𝗴𝗶𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀. Instead, AI could dynamically generate 𝗰𝘂𝘀𝘁𝗼𝗺 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀, 𝗮𝗻𝗱 𝗲𝘃𝗲𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁. It’s a bold rethink of what "design software" even means. Patrick was part of the Designing with Artificial Intelligence conference I co-organized back in 2020—way before the AI hype wave. His depth of knowledge in this field is incredible, and this essay is packed with 𝗯𝗶𝗴 𝗶𝗱𝗲𝗮𝘀 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜, 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗸. 🔗 𝗛𝗶𝗴𝗵𝗹𝘆 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗿𝗲𝗮𝗱 (but set aside some time—it's not a quick skim!): https://lnkd.in/duy6Nhu8 #AI #UX #UXDesign #Design #DesignTools #FutureOfWork #UXLeadership #AIInnovation #Creativity Virtual Identity

I've been thinking about friction in design workflows lately. Not the core design part of early phase work but the other part. The hours spent in the initial phase through regulatory codes, context switching between research and modeling or even rebuilding massing studies just to test an idea. With time, I guess that we've accepted this friction as inevitable. As if gathering intelligence and designing must be completely different processes. But what if that separation has never been conceptual, but if it just has been a limitation of the tools we use? Snaptrude's latest release has me questioning this. They've brought AI directly into the earliest stages of design. As I always believe in, it's all about how we're using these frameworks. You describe what you're designing. Building type, site context, programmatic intent. The system generates a spatial layout that already understands adjacencies, proportions, functional relationships. Not as a template, but more as an initial point to start with. What got me wasn't the generation itself but probably the transition. Moving from an AI-generated concept into full design control is seamless. The built-in research layer changes things further. Codes, ADA requirements, space standards, cost benchmarks. All surfaced in context, exactly when you need them. I keep coming back to this: what does it mean when tools finally understand that gathering intelligence and making spatial decisions aren't separate activities? Maybe we're approaching a moment where the boundaries between phases start to dissolve. Where the friction that's defined early stage work for decades finally becomes optional. Maybe the question isn't even whether these AI frameworks can speed up design anymore.... Maybe it's whether we're ready to redesign how we work once that friction disappears? #architecture #design #artificialintelligence #computationaldesign #urbandesign

I used to spend hours drawing blueprints as an architect. Now AI is making this skill obsolete. The data behind the shift: → 30–50% faster design cycles using generative layout tools → 100+ layout permutations generated from a single brief → 20–30% improvement in space utilization → 10–25% energy savings when airflow, lighting, and thermal paths are simulated early → 40% fewer late-stage design changes thanks to digital testing What's fundamentally different? AI treats floor plans like software systems: → Pedestrian movement simulated before construction → Natural light and ventilation optimized virtually → Furniture, walls, and utilities stress-tested digitally → Cost, carbon footprint, and materials optimized in parallel This enables: → Smaller homes that feel larger → Offices designed around productivity and wellbeing → Buildings that adapt over time instead of aging poorly The biggest myth? AI replaces architects and designers. Reality: AI handles complexity and permutations. Humans focus on vision, culture, emotion, and identity. The future of architecture isn't just smart. It's generative, data-driven, and human-centric. ---- ♻️ Repost if your network needs to see this transformation ➕ Follow me (Basia Kubicka) for more AI insights 🔔 Subscribe to my newsletter for deep dives: https://air-scale.kit.com/ Opinions expressed are my own and do not represent the views, policies, or positions of my employer.

This isn’t a passing design trend, it’s a measurable shift in how homes are imagined and built. So how practical is it? The data speaks for itself. Here’s how AI is reshaping residential design: * AI-powered design tools shorten concept iterations by 60–80% * Early AI simulations reduce construction change orders by up to 30% * Smarter material optimization cuts waste by *10–20%, improving sustainability and cost efficiency * Advanced lighting and spatial simulations boost perceived space efficiency by as much as 25% * Personalized design enhances homeowner satisfaction and resale value - premium homes with distinctive architectural features often achieve 5–15% higher valuations Take these pebble stone stairs as an example. AI enabled designers to: * Optimize stone dimensions and placement for enhanced anti-slip safety * Simulate how light interacts with textured surfaces throughout the day * Balance refined aesthetics with long-term durability * Seamlessly integrate the staircase into the home’s overall spatial flow The real takeaway: AI doesn’t replace architects or designers, it empowers them. Humans bring vision, emotion, and taste. AI delivers speed, simulation, and optimization. Together, they create: * Smarter design decisions * Fewer expensive revisions * More sustainable construction * Truly personalized luxury homes AI isn’t just transforming software and chips anymore. It’s redefining how we design, build, and live Follow Iraj Janali and Janco for Engineering, HVAC and Leadership insights. ➕ Follow Iraj Janali & JANCO for insights on: 🔹 Leadership 🔹 Engineering 🔹 HVAC & industrial production 🔹 If you want to learn about business, follow JanLink | جانلینک 💙 #Engineering #AI #Architecture #DesignInnovation #LuxuryDesign #SmartHomes #PropTech #Futureliving #Sustainabledesign via@diycraftstvofficial

How to design for AI? Most design systems were built for static dashboards, web forms, and predictable UI flows. But #agentic AI changes the game. When your interface is mediating collaboration between humans and autonomous agents, old patterns break down. From our experience building world-class agentic solutions, the Soul of the Machine #design team wrote a guide that lays out 11 principles for building AI-native design systems - the kind you need when AI isn't just a backend feature but a primary user interface layer. A few things we cover: 1. Why 'clarity over density' matters more than ever when users are monitoring autonomous processes 2. How progressive disclosure keeps agent reasoning transparent without overwhelming people 3. What 'async-first' interaction patterns look like when AI tasks span hours, not seconds 4. Why trust indicators need to be earnable and revocable at every interaction 5. How to build flexible control surfaces that feel like a co-pilot, not an autopilot with an emergency override The core idea: AI-native design isn't about making traditional interfaces faster. It's about embracing fundamentally new characteristics - asynchronicity, variable-length outputs, layered transparency, and continuous refinement. Organizations that get this right will find their AI agents more trusted, more utilized, and ultimately more valuable. Read the full guide, and share your feedback: https://lnkd.in/e_KZGSee