Graphic art, with its bold visual language and global appeal, has become more accessible than ever thanks to curated online...

Bret Victor’s personal research library organized alphabetically by author, covering foundational work in programming languages...

This systematic literature review examines the role of large language models (LLMs) in UI/UX design, synthesizing findings from 38 peer-reviewed studies published between 2022 and 2025. We identify key LLMs in use, including GPT-4, Gemini, and PaLM, and map their integration across the design lifecycle, from ideation to evaluation. Common practices include prompt engineering, human-in-the-loop workflows, and multimodal input. While LLMs are reshaping design processes, challenges such as hallucination, prompt instability, and limited explainability persist. Our findings highlight LLMs as emerging collaborators in design, and we propose directions for the ethical, inclusive, and effective integration of these technologies.

Generative UI is transforming interface design by facilitating AI-driven collaborative workflows between designers and computational systems. This study establishes a working definition of Generative UI through a multi-method qualitative approach, integrating insights from a systematic literature review of 127 publications, expert interviews with 18 participants, and analyses of 12 case studies. Our findings identify five core themes that position Generative UI as an iterative and co-creative process. We highlight emerging design models, including hybrid creation, curation-based workflows, and AI-assisted refinement strategies. Additionally, we examine ethical challenges, evaluation criteria, and interaction models that shape the field. By proposing a conceptual foundation, this study advances both theoretical discourse and practical implementation, guiding future HCI research toward responsible and effective generative UI design practices.

As artificial intelligence (AI) continues to evolve from a back-end computational tool into an interactive, generative collaborator, its integration into early-stage design processes demands a rethinking of traditional workflows in human-centered design. This paper explores the emergent paradigm of human-AI co-creation, where AI is not merely used for automation or efficiency gains, but actively participates in ideation, visual conceptualization, and decision-making. Specifically, we investigate the use of large language models (LLMs) like GPT-4 and multimodal diffusion models such as Stable Diffusion as creative agents that engage designers in iterative cycles of proposal, critique, and revision.

Generative AI (GenAI) models excel in their ability to recognize patterns in existing data and generate new and unexpected content. Recent advances have motivated applications of GenAI tools (e.g., Stable Diffusion, ChatGPT) to professional practice across industries, including product design. While these generative capabilities may seem enticing on the surface, certain barriers limit their practical application for real-world use in industry settings. In this position paper, we articulate and situate these barriers within two phases of the product design process, namely "getting the right design" and "getting the design right," and propose a research agenda to stimulate discussions around opportunities for realizing the full potential of GenAI tools in product design.

Front-end development constitutes a substantial portion of software engineering, yet converting design mockups into production-ready User Interface (UI) code remains tedious and costly. While recent work has explored automating this process with Multimodal Large Language Models (MLLMs), existing approaches typically rely solely on design images. As a result, they must infer complex UI details from images alone, often leading to degraded results. In real-world development workflows, however, design mockups are usually delivered as Figma files, a widely used tool for front-end design, that embed rich multimodal information (e.g., metadata and assets) essential for generating high-quality UI. To bridge this gap, we introduce Figma2Code, a new task that advances design-to-code into a multimodal setting and aims to automate design-to-code in the wild. Specifically, we collect paired design images and their corresponding metadata files from the Figma community. We then apply a series of processing operations, including rule-based filtering, human- and MLLM-based annotation and screening, and metadata refinement. This process yields 3,055 samples, from which designers curate a balanced dataset of 213 high-quality cases. Using this dataset, we benchmark ten state-of-the-art open-source and proprietary MLLMs. Our results show that while proprietary models achieve superior visual fidelity, they remain limited in layout responsiveness and code maintainability. Further experiments across modalities and ablation studies corroborate this limitation, partly due to models' tendency to directly map primitive visual attributes from Figma metadata.

Large language models (LLMs) promise to accelerate UI design, yet current tools struggle with two fundamentals: externalizing designers' intent and controlling iterative change. We introduce SPEC, a structured, parameterized, hierarchical intermediate representation that exposes UI elements as controllable parameters. Building on SPEC, we present SpecifyUI, an interactive system that extracts SPEC from UI references via region segmentation and vision-language models, composes UIs across multiple sources, and supports targeted edits at global, regional, and component levels. A multi-agent generator renders SPEC into high-fidelity designs, closing the loop between intent expression and controllable generation. Quantitative experiments show SPEC-based generation more faithfully captures reference intent than prompt-based baselines. In a user study with 16 professional designers, SpecifyUI significantly outperformed Stitch on intent alignment, design quality, controllability, and overall experience in human-AI co-creation. Our results position SPEC as a specification-driven paradigm that shifts LLM-assisted design from one-shot prompting to iterative, collaborative workflows.

A striking artist-led creative studio showcasing bold visual direction and modern design aesthetics. Features carefully curated typography, sophisticated layout composition, and premium creative direction.

A striking landing page for a craft beer brand in Bali. Combines warm tropical aesthetics with premium craft positioning, featuring bold typography, modular content sections, and seamless product showcase design.

AI-native design tool with first-class agent support. Features artboards, layers, auto-layout, and 66 integrated MCP tools for AI-powered design workflows. Free to use.

Professional vector animation software for creating SVG and Lottie animations. Designed for lightweight, high-performance animated icons, logos, banners, and illustrations.

An in-depth interview with concept artist Rhys Yorke about designing screen graphics and UI for The Expanse TV series. Explores how interface design can establish distinct visual identities for different factions and support storytelling without drawing attention to itself.

An in-depth analysis of typography and design in Ridley Scott's 1979 film Alien. Examines how typeface choices, including customized Futura and Helvetica variants, establish the Nostromo's utilitarian aesthetic and contribute to worldbuilding and narrative tension.

Text-to-image models now generate graphic design at production scale, yet their supervision still comes primarily from photo-style preference datasets with a single overall verdict per comparison. Designers evaluate designs along several distinct axes (e.g., typography, layout, color harmony) that a single preference label collapses. We release \emph{TASTE} \textit{(Typography, Aesthetics, Spatial, Tone, Etc.)}, a multi-dimensional preference dataset in which two disjoint cohorts of five professional designers each ranked outputs from four current text-to-image models across nine criteria along with per-image hallucination flags. We pair the dataset with two contributions. First, a criterion-agnostic signal-validation framework based on Kendall's $τ$, majority-vote probability, and Condorcet cycles against exact iid-uniform nulls; the analysis reveals significant but moderate designer agreement, with every TASTE criterion rejecting the random-rater null. Second, we benchmark preference models on TASTE and find that off-the-shelf VLM judges and dedicated T2I scorers fail to reach majority agreement with the designer panel, while a small MLP head trained directly on TASTE substantially narrows the gap to the single-rater ceiling, setting a baseline for future TASTE-trained preference models.

A design vocabulary and CLI tool for AI coding agents. Features 23 design commands, anti-pattern detection with 44 rules, and integration with Claude Code, Cursor, GitHub Copilot, and other AI platforms. Eliminates generic design outputs and maintains consistent design standards.

A resource on understanding psychology in product design. By Wouter de Bres, focusing on how to understand both the minds you design for and the mind you design with.

Ditther is a versatile browser-based image manipulation tool designed for creative professionals and designers. It specializes...

A stunning example of creative web design using 3D graphics. The bookshop creates an immersive environment with a carefully...

A thoughtfully designed personal portfolio featuring web design and development work. Uses clean typography, minimalist...

MP Orbital Ignition is a web-based application that showcases a highly polished cinematic loading experience. The interface...

Independent type foundry specializing in custom typeface design. Offers a curated selection of contemporary fonts with focus on quality typography and craftsmanship.

This interactive resource from En Dash explores two fundamental UI patterns and when each serves users best. Key Concepts Modal...

Uncamera is a mobile camera application that takes a deliberate stance against social media optimization. The app emphasizes...

A curated collection of creative 404 error page designs. Inspiration gallery showcasing innovative approaches to transforming user frustration into delightful experiences.

A minimalist coding portfolio featuring interactive canvas-based visual effects and a modern, clean aesthetic. Showcases engineering work with thoughtful interaction design.

Fauna Robotics builds capable, safe, and fun humanoid robots. Sprout is a modular platform designed with home safety as a priority, featuring compliant locomotion and whole-body teleoperation. Well-crafted product landing page with warm, approachable visual branding.

Award-winning design case study by O0 Studio. Transformed Fauna's Sprout humanoid robot into an approachable companion through playful branding, warm aesthetics, and character-driven visual language. Featured in Awwwards, Visuelle, and The Brand Identity.

An exploration of how Michelangelo's historic lettering from Vatican archives influenced modern typography and is featured in Microsoft Word fonts.

Curated library of 261,740 hand-drawn SVG icons with intelligent search. Find icons by group, synonym, or concept. Free to use, currentColor compatible, no attribution required.