Discover the hidden structure behind every great UI. Master how to use conceptual models to design systems that users instantly understand and grow to trust.
Mental Models and User Experience Design
What users believe they know about a user interface impacts how they use it. Mismatched mental models are common, especially with designs that try something new.
What are Mental Models? — updated 2026
Harness the power of understanding and predicting user behavior with mental models. Simplify systems and enhance decision-making in your designs.
Structural Analogies of User Interfaces with Belidor
Alex Russell | Web Components and Model Driven Views | Fronteers 2011
There's a lot of tension between today's markup and the semantics we're trying to express in our apps. HTML5 adds a few new types to help describe common cases, but what about when there's no allegory in markup for what you're building? What we need now is infrastructure, not guilt about being "non-semantic". This talk explores new standards-track work in WebKit that's going to enable say-what-you-mean development in completely new ways.
Exploring Notion's Data Model: A Block-Based Architecture | Notion
Notion’s data model enables the product’s most foundational component: blocks. Through blocks, we allow users more flexibility over their information.
Introducing Model Council
Today we are launching Model Council, a multi-model research feature that brings several models together for one answer.
Inside Design Tokens: Modeling & Communication
Design system architecture with focus on design tokens, that form the vocabulary of your visual design language, helping to streamline the communication amongst stakeholders. Modeling the system with boundaries to ensure clear purpose and predictability for design tokens and stability guarantees wit
Conceptual mathematics: a first introduction to categories
Conceptual mathematics by F. W. Lawvere, 2009, Cambridge University Press edition, in English - 2nd ed.
Systems design 3: LLMs and the semantic revolution
Long ago in the 1990s when I was in high school, my chemistry+physics teacher pulled me aside. "Avery, you know how the Internet works, righ...
Foundation Models | Apple Developer Documentation
Perform tasks with models that specialize in language understanding, structured output, and tool calling.
Foundation Models | Apple Developer Documentation
Perform tasks with models that specialize in language understanding, structured output, and tool calling.
Business Model Pattern List |
Discover powerful business model patterns by iconic firms and learn how you can leverage them for new insights for your own business model innovation initiatives.
A categorical manifesto
This paper tries to explain why and how category theory is useful in computing science, by giving guidelines for applying seven basic categorical concepts: category, functor, natural transformation, limit, adjoint, colimit and comma category. Some examples, intuition, and references are given for each concept, but completeness is not attempted. Some additional categorical concepts and some suggestions for further research are also mentioned. The paper concludes with some philosophical discussion.
Crafting a good (reasoning) model
A recent talk I gave on model training, reasoning, and the next frontier.
Systems programming the model
This paper examines the status of the language model object in generative AI, arguing that what we call a ‘model’ is inseparable from the systems deploying it. I first theorize how these objects emerge from systems-level interactions between trained artifacts, prompting mechanisms, and sampling methods, drawing on the philosophy of digital objects as well as software studies to show how models gain their objective character. Such interactions converge on programming, not prompting, language models, and I illustrate how critical code studies can therefore track these dynamics. In an overview of language model programming approaches, I discuss how prompt and program converge, demonstrating how this confluence tends toward the production of new feedback loops wherein models become models of and for themselves. Understanding these feedback loops is essential in view of recent efforts to infrastructuralize AI, in which multiple models cascade into compound systems that abstract toward a unified model of models. Thus the need, I argue, for a systems-level view that can address this new order of abstraction and complexity by identifying where and how the model emerges from the system.
