Figma

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www.figma.com/blog/engineering

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Figma528 Design Systems246 User Experience140 Prototyping135 AI110 Figjam98 Product Design76 User Interface63 Generative AI47 Collaborative Design47 Accessibility41 React39+1265

figmaJul 29, 20242 min readCurated summary

HP Powers Up Design Handoff with Dev Mode | Figma Blog

HP built its Veneer design system to unify digital experiences across more than 100 product lines and many independent business units. By combining design language, reusable components, documentation, governance, and community feedback, HP reduced duplication and improved consistency. Figma’s Dev Mode further streamlined handoff, helping developers interpret designs faster and cutting development time by up to 50% on some projects. ## Building a multilayered design system - HP’s diverse products—including printers, laptops, and gaming systems—each have distinct brand and product requirements. - Teams previously worked in isolation, making it difficult to maintain a consistent digital experience. - Veneer evolved from a frontend component library into a broader system containing: - Design language and tokens - Shared components and patterns - Usage guidelines, code standards, and snippets - Governance and community collaboration - Its multilayered structure supports HP’s numerous sub-brands rather than forcing every product into one rigid system. ## Measuring adoption and impact - HP tracks both usage data and feedback from designers and developers. - Veneer’s iconography library includes 915 components used by 320 teams. - The library averages approximately 85,000 component inserts per week. - HP reported that Veneer saved projects 500% more time than the time invested in creating the system during 2023. - Some projects experienced a 50% reduction in development time. - Adoption remained challenging because designers were protective of their product-specific work and teams needed flexibility for different sub-brands. ## Dev Mode improves design handoff - Dev Mode gives developers direct access to design specifications in Figma, reducing meetings and repeated clarification between design and engineering. - **Compare changes** helps teams identify differences between design versions, especially when updating existing products. - **Ready for development** lets designers indicate which parts of a design are prepared for implementation, helping developers focus on the correct work. - **Variables** connect designs to primitive and semantic tokens, allowing HP’s system to scale across themes and modes. HP’s experience suggests that a successful enterprise design system must be flexible, measurable, and supported by strong collaboration. Pairing such a system with Dev Mode can make handoff clearer, reduce redundant work, and give both designers and developers more time to focus on higher-value work.

Figma Design Systems Design Tokens Dev Mode+2

figmaJul 16, 20242 min readCurated summary

An Update on our Make Designs Feature | Figma Blog

Figma temporarily disabled its Make Designs AI feature after discovering that some generated mockups closely resembled real applications, including Apple’s weather app. The issue came not from model training, but from insufficiently reviewed components and example screens in Figma’s custom design systems. Figma removed the problematic assets and planned stronger quality assurance before relaunching the feature, later bringing it back under the name First Draft with updates. ## How Make Designs Works - Combines an AI model, contextual design-system data, and a user prompt. - Uses generally available models such as OpenAI’s GPT-4o and Amazon Titan, without additional fine-tuning. - Relies on separate mobile and desktop design systems containing hundreds of components and example compositions. - The language model selects, arranges, parameterizes, and themes components based on the prompt. - Amazon Titan generates the images used in the resulting designs. ## What Went Wrong - Figma reviewed the design systems during development and private beta testing. - Shortly before Config 2024, new components and example screens were added without sufficient vetting. - Some assets resembled patterns from real-world applications. - A prompt for a weather app produced results that appeared notably similar to Apple’s first-party design. - The incident was identified after designer Andy Allen raised the concern, prompting an immediate investigation. ## Figma’s Response - The team traced the similarities to assets in the underlying design systems. - Problematic components and examples were removed. - Make Designs was rolled back and disabled. - Figma postponed relaunching the feature while developing a more robust QA process. ## Future Direction - Make Designs was originally called “First Draft” to emphasize that AI output is only a starting point. - Figma wants users eventually to connect the feature to their own company design systems. - This could reduce the time spent locating, assembling, and configuring components. - Figma maintains that designers remain essential for refining drafts into meaningful user experiences. - The feature was later re-enabled with updates and renamed First Draft. Figma’s experience highlights the need to carefully audit not only AI models but also the data, components, and examples supplied to them. AI-generated designs should be treated as starting points that require professional review and creative refinement.

AI Design Systems Generative AI User Interface+3

figmaJul 10, 20242 min readCurated summary

Crafting the Visual Identity for Config 2024 | Figma Blog

Figma’s Brand Studio created Config 2024’s visual identity as an immersive extension of the Figma brand. Inspired by the redesigned Figma canvas and Figma Slides, the team developed a flexible, shape-based system that could work across a massive physical and digital conference experience. The result balanced visual variety with practical UX needs for more than 10,000 in-person attendees and a larger virtual audience. ## Balancing Form and Function - The team drew inspiration from switching between different modes of making, such as creating, sharing, and presenting work. - They developed a visual language centered on shapes that could: - Shift and transform - Amplify creative ideas - Reveal unexpected perspectives - Represent collaboration and multiple modes of creation - Three large physical supergraphics formed the conference’s central installations. - Attendees used them as seating, photo backdrops, and meeting points. - The identity was designed not only for visual impact but also for usability. - It supported complex experiences such as registration and navigating popular talks. - The system helped guide attendees through the event while maintaining a cohesive look. - Simple primitive shapes served as the foundation for more complex graphic structures. ## Designing at Scale - Config required a large volume of assets across physical spaces, digital surfaces, signage, merchandise, and other touchpoints. - The team created a modular system that could remain consistent without becoming repetitive. - Its core shapes came directly from the Figma toolbar: - Rectangle - Circle - Polygon - These basic forms reflected the starting point of the design process in Figma and became components of larger supergraphics. - The team built a reusable component library in Figma to generate variations efficiently. - Each supergraphic included three variants, allowing shapes to be presented from different angles and creating visual variety across the conference. Figma’s approach demonstrates how a strong event identity can combine expressive branding with a practical, reusable design system. By treating simple interface primitives as flexible building blocks, the team created a conference experience that could scale across environments while remaining recognizably Figma.

Figma Design Systems User Experience Component Library+1

figmaJun 25, 20242 min readCurated summary

What is Good Design in the Age of AI? | Figma Blog

AI is making product creation faster and more accessible, increasing the importance of design as a differentiator. Figma argues that designers should not chase automation or trends, but apply enduring principles such as empathy, creativity, and solving real user needs. The future of good design will depend on experimentation, stronger design-to-code connections, pragmatism, and new forms of human–AI collaboration. ## AI Creates a New Design Inflection Point - Like the iPhone’s launch in 2007, AI is introducing a new medium that requires experimentation and the development of new interaction patterns. - Early mobile products often forced desktop experiences onto smaller screens; today, many AI products similarly rely on basic chatbots and templates. - Designers can unlock AI’s potential through: - Richer interactions - Intuitive gestures - Patterns designed specifically for AI - AI can generate code, designs, and complete applications from prompts, allowing teams to move rapidly from concept to creation. - As more people participate in product development, thoughtful design becomes increasingly important for products to stand out. ## Codifying the Fundamentals of Good Design - Figma’s AI feature for generating initial UI drafts needed to understand the mechanics of good design. - Because large language models are text-oriented, generating high-quality visual interfaces is more difficult than generating text or code. - A complete rulebook for design is impractical: - Good design contains too many contextual details to define exhaustively. - Extremely large prompts exceed technical token limits. - Figma instead focused on reducing design expertise to a small set of broadly applicable principles. - Teaching AI requires designers to make their intuitive knowledge explicit by creating rules that are: - Clear - Concrete - Practical - General enough to apply across many interfaces - The process resembles teaching design: instructors must break complex judgment into principles that others can understand and use. ## Design’s Continuing Role - AI should elevate design rather than simply automate it. - The most valuable design foundations remain relatively constant despite technological change. - Designers’ roles may evolve, but their understanding of users, creativity, and ability to solve meaningful problems remain essential. ## Practical Direction Teams should treat AI as a new design medium, not merely an automation tool. They should experiment with AI-native interaction patterns while grounding products in concise, teachable design principles and genuine user needs.

Figma AI Design Systems Large Language Models+2

figmaJun 25, 20243 min readCurated summary

Are we finally entering the age of androids? | Figma Blog

Humanoid robots are moving from science fiction into public spaces and workplaces, forcing people to confront both the promise and risks of embodied AI. Their humanlike form makes technology more intuitive and emotionally engaging, but it also encourages people to project intelligence, intention, and personality onto machines. The article argues that designers must shape this illusion carefully, using humanoid robots to foster connection and understanding rather than control or deception. ## Humanoids as Technology in Human Form - Ameca, created by Engineered Arts, performs at Las Vegas’s Sphere as an interactive entertainer. - It turns toward speakers, displays facial expressions, tells jokes, and responds conversationally. - Its appeal comes from combining AI with expressive robotics. - Apollo, developed by Apptronik and argodesign, represents a different model: - It is designed as a general-purpose laborer. - Its flat face, cameras, and LED mouth prioritize function over lifelike appearance. - Humanoid robots have deep cultural roots, appearing in Greek mythology, Taoist philosophy, and science fiction. - Their human form makes software easier to engage with through gestures, expressions, and face-to-face interaction—what Engineered Arts CEO Will Jackson describes as a heads-up alternative to screen-based technology and virtual reality. ## What the Illusion of Sentience Unlocks - Humans naturally anthropomorphize objects and search for faces, motives, and signs of life. - Madeline Gannon uses body language and animal behavior as inspiration for designing industrial robots with recognizable personalities. - Even simple geometric animations can appear intentional: the Heider and Simmel experiment showed that people assign motives to moving shapes. - Ameca intensifies this effect through: - Furrowed brows, smiles, and expressions of surprise - Celebrity impressions - Custom personalities created by a dedicated “Persona Architect” - The ability to switch behavioral modes depending on context - Engineered Arts deliberately avoids making Ameca appear fully human: - Its metallic body avoids realistic skin. - It has no defined race or gender. - Its artificiality makes the theatrical nature of the interaction more visible. - The technology underneath remains impersonal: AI interprets language and maps it to suitable facial expressions. Nevertheless, users can experience meaningful emotional moments, such as a shy attendee gaining confidence while speaking Japanese with Ameca. - Gannon argues that designers must make complex systems legible, much as everyday objects communicate how they should be used. - She sees design as a way to redirect technology toward curiosity, kindness, and care, creating relationships based on connection rather than control. ## Designing Androids for Work - Apollo does not claim to be sentient; its purpose is practical labor. - It is being developed to address worker shortages, including work in Mercedes-Benz factories and potentially space exploration. - Its humanoid shape is primarily a response to human-scale environments and tools. - Unlike Ameca, Apollo must appear approachable enough for workers to accept, while avoiding the uncanny valley. - The article frames this as a central design challenge: workplace robots need to communicate socially without misleading people about what they are. Humanoid robots are most valuable when their appearance and behavior clarify how people should interact with them. Whether used for entertainment or labor, designers should treat the illusion of intelligence as an ethical material—balancing emotional engagement with transparency and purposeful design.

AI Large Language Models Automation Human Computer Interaction+3

figmaJun 25, 20242 min readCurated summary

Can we reach beyond the echo chamber? | Figma Blog

The Browser Company’s Arc browser aims to rethink everyday browsing by drawing inspiration from outside the technology industry. Karla Mickens Cole and Nashilu Mouen argue that products become distinctive when they reflect many creative influences rather than copying existing conventions. Their approach to AI emphasizes subtle, useful experiences that blend naturally into users’ lives instead of adding AI merely as decoration. ## Designing Beyond the Tech Echo Chamber - The team deliberately looks to literature, film, art, and nature for inspiration. - Mouen cites writers such as Zadie Smith and Toni Morrison, asking how technology can be “in tech, without being of tech.” - The browser is treated as a large creative canvas with room for experimentation and play. - The team’s diverse perspectives contribute to a brand built from “many voices.” ## Reinventing Familiar Browser Conventions - Arc challenges established patterns, including the traditional placement of browser tabs. - The team’s recurring question is “Why not?”—a mindset that encourages them to reconsider assumptions. - Arc’s unboxing experience drew on: - Movie title sequences - The opening atmosphere of A24 films - The visual phenomenon of sunspots - These outside references help the product feel meaningfully different, not simply functionally new. ## Making AI Feel Natural - The Browser Company wants AI to solve practical problems and blend into ordinary browsing rather than overwhelm users with conspicuous AI branding. - Cole compares the desired approach to flowers: - They can appear unexpectedly and make an experience feel special. - They suggest care without being disruptive. - They reflect AI’s ongoing “seasons of growth.” - The team sees AI as something to plant thoughtfully within the product experience, not apply indiscriminately. ## Rapid Experimentation - The team prototypes quickly and evaluates which AI ideas genuinely improve the product. - Mouen notes that they had explored more than 30 applications in the previous month alone, indicating an experimental process in which some concepts will work and others will not. - Their view of AI is therefore practical and seasonal: its impact depends on the context and the moment. The article’s central recommendation is to build technology with influences beyond technology itself. For AI in particular, thoughtful integration, experimentation, and emotional subtlety may create more valuable experiences than adding obvious, standardized features.

AI User Experience Product Design Web Browsers+1

figmaJun 25, 20242 min readCurated summary

Welcome to The Prompt | Figma Blog

AI may transform design and building, but its ultimate impact remains unsettled. Figma’s *The Prompt* explores that uncertainty through essays and interviews with experts across design, engineering, product development, and the built environment. The collection argues that human judgment—especially the ability to ask thoughtful, well-framed questions—will remain central to making AI useful. ## Prompting as a Creative Discipline - Prompt engineering is described as the practice of getting better answers by asking better questions. - Like interviewing or editing a magazine, effective prompting requires: - Clear context - Thoughtful framing - Useful guidance - AI’s capabilities are treated as largely inert without human direction; people must coax useful results from the technology. - The act of questioning is presented as a fundamentally human and creative instinct. ## The Purpose of *The Prompt* - Created by Figma’s Story Studio and Brand Studio, the magazine launched at Config 2024. - It combines writing, interviews, and illustrations to examine how AI is changing creative and technical work. - Contributors come from both inside and outside Figma and work across: - Design - Engineering - Product development - Robotics - Manufacturing - Residential housing ## Questions About AI’s Future The magazine uses a range of prompts to investigate both immediate applications and larger societal questions, including: - What constitutes good design when AI can generate and automate more work? - Whether code becoming a commodity should be feared - How much data is actually necessary - How starting with imperfect or incomplete ideas can shape innovation - Whether AI development can move beyond technological echo chambers - If efficiency undermines creativity - How to build AI features that people both want and trust - The relationship between artificial design intelligence (ADI) and artificial general intelligence (AGI) - Whether automation can unlock the full potential of design systems - The role of robots in construction and housing - Whether society is entering an age of androids ## Practical and Long-Term Perspectives - Contributors examine ambitious challenges, such as applying AI to manufacturing and housing. - They also focus on what AI can deliver reliably today rather than only speculating about distant possibilities. - The goal is to make complex systems more understandable and usable while learning how to guide AI more effectively. Figma presents *The Prompt* as both a magazine and an experiment in inquiry: meaningful progress with AI depends not just on increasingly capable systems, but on humans asking clearer, more imaginative, and more responsible questions.

AI Design Systems Large Language Models Automation+4

figmaJun 25, 20243 min readCurated summary

Why Are We So Afraid Of Code As A Commodity? | Figma Blog

AI may commoditize code production, including language translation and design-to-code workflows, but that does not eliminate the need for engineers. The article argues that engineering’s lasting value lies in identifying the right problems, understanding users and constraints, and designing elegant, maintainable systems. AI should therefore be viewed less as a threat and more as a tool that expands creativity and shifts engineers toward higher-level decision-making. ## Code Generation Is Not the Same as Engineering - AI can increasingly: - Translate between programming languages, such as Python and C++. - Generate code more efficiently. - Convert designs into implementations using frameworks such as React, TypeScript, Kotlin, and Jetpack. - Design-to-code is comparable to translating between programming languages because modern design tools already represent designs in structured, code-like forms. - Producing code is only one part of engineering. Engineers must also: - Decide which problems are worth solving. - Choose appropriate solutions. - Create abstractions for reasoning about complex systems. - Balance correctness, simplicity, context, and constraints. - Framework-specific expertise becomes less valuable over time than first-principles reasoning about the common ideas underlying different platforms. ## The Art and Creativity of Engineering - AI is expected to automate rote work, potentially freeing engineers to focus on more creative activities. - There are often many viable ways to build a system; AI may expose additional approaches that engineers would not have considered. - Engineers remain responsible for evaluating tradeoffs among those options. - Technical implementation is presented as a creative discipline in which constraints can inspire better solutions and product decisions. ## Embracing Shifts in Engineering Roles - Engineering work begins before coding: - Teams discuss user needs. - They triage problems. - They align on what to build and how to approach it. - As AI handles more low-level implementation, coding will represent a smaller portion of an engineer’s responsibilities. - Engineers will spend more time prioritizing, aligning teams, interpreting context, and making product and system-level decisions. - The abstraction level of software development is rising as AI takes responsibility for increasingly lower-level parts of the technology stack. ## What AI Will Not Commoditize - AI still struggles to fully understand: - What users actually need. - The context surrounding a problem. - Conflicting constraints and product priorities. - How to compose intuitive, maintainable systems. - Engineers will continue to add value by reasoning from first principles and solving technical challenges from the ground up. - The central question is not whether AI automates design-to-code, but how engineers use that automation to work faster and explore better solutions. The practical recommendation is to embrace AI for repetitive implementation work while developing the higher-level skills that remain difficult to automate: problem selection, user understanding, system design, tradeoff analysis, and creative technical reasoning.

Generative AI React TypeScript Design To Code+2

figmaJun 25, 20242 min readCurated summary

Stack the deck with Figma Slides | Figma Blog

Figma Slides is introduced as an open-beta presentation tool combining Figma’s high-fidelity design capabilities with FigJam’s collaborative feedback features. It aims to help designers and non-designers create more engaging presentations without exporting work into separate tools. Figma positions it as a way to turn product reviews, sales decks, and pitches into clearer visual stories and collaborative conversations. ## Why Figma Built Slides - Presenters commonly struggle with: - Designing creative layouts - Finding effective visuals - Spending more than eight hours creating a deck - Figma users created approximately 3.5 million presentations in the previous year. - Designers often had to move Figma work into other presentation tools or distribute prototypes and pre-reads to non-designers. - Figma Slides brings presentation creation into the same environment as design and collaboration. ## High-Fidelity Design Features - Slides includes core presentation features such as: - Speaker notes - Slide transitions - Presentation-focused editing tools - Design mode provides access to Figma Design capabilities, including: - Auto Layout - Advanced properties - Precise alignment and snapping - Interactive components and states - Interactive components, such as hover states, can remain functional during presentations. ## Shared Libraries and Assets - Existing Figma text styles, color styles, and design assets are available directly in Slides. - Designers can reuse established libraries instead of recreating presentation elements. - Copy-and-paste workflows work across Figma products, reducing the need to export interface designs individually. ## Grid View for Structuring Narratives - Users can edit individual slides or switch to grid view for an overview of the entire deck. - Grid view organizes slides into rows, making it easier to: - Rearrange sections - Review the overall story - Refine the narrative arc - This bird’s-eye perspective is particularly useful for long presentations where restructuring becomes difficult. ## From Presentations to Conversations - Figma’s experience with FigJam revealed the limitations of one-way presentations. - FigJam became a regular tool for meetings such as team kickoffs, bug bashes, executive reviews, all-hands meetings, and Q&A sessions. - Figma Slides is intended to combine polished visual storytelling with collaborative participation, helping more people contribute to and influence the discussion. Figma Slides’ central promise is to make presentations both visually sophisticated and easier to build collaboratively, allowing teams to design, organize, and discuss their ideas in one shared space.

Figma User Experience Figjam Design Collaboration+3

figmaJun 25, 20242 min readCurated summary

Inside the Redesigned Figma, Where Your Work Takes Center Stage | Figma Blog

UI3 is Figma’s third major redesign, created to manage the complexity accumulated over nearly a decade of new features. Its central goal is to put users’ work—not the interface—at the center while preserving professional workflows and making Figma more approachable. The redesign also establishes a flexible foundation for products such as Dev Mode, Figma AI, and Figma Slides. ## Why Figma redesigned the interface - **Center users’ ideas:** New capabilities such as interactive components and AI risked making the interface feel crowded or distracting. - **Serve both beginners and experts:** Figma aimed to become more intuitive for newcomers without removing the power and familiar ergonomics professional designers rely on. - **Adapt to modern design practices:** Reusable components, generative AI, and higher-level abstractions require tools that provide building blocks rather than focusing only on individual pixels. - **Prepare for the future:** UI3 supports Figma’s broader ecosystem, including closer connections between design and code, Dev Mode, Figma AI, and the newly introduced Figma Slides. ## Clearing the stage for the canvas - The redesign began by maximizing canvas space and reducing visual distractions. - Figma experimented with extreme concepts, including interfaces that appeared only on hover and layouts where panels constantly appeared and disappeared. - These approaches made the workspace feel unstable, so the team moved toward a more consistent solution: - Resizable and collapsible panels - A slim toolbar positioned at the bottom of the canvas - The ability to hide the interface completely and reveal panels when needed - The new structure creates a roomier workspace and provides a consistent framework for switching between Figma products. ## Iterative design process - Figma explored hundreds of interface variations. - Designers built and used many prototypes internally to evaluate how each approach affected real workflows. - Features and controls were deliberately removed until the team identified what users genuinely missed, then selectively restored them. UI3’s practical recommendation is to give designers more control over the interface: keep tools accessible, but allow the canvas and the work itself to take priority.

Figma Design Systems User Experience Generative AI+2

figmaJun 25, 20243 min readCurated summary

Should robots be building our homes? | Figma Blog

Icon CEO Jason Ballard argues that robotics and AI could make housing faster, cheaper, more durable, and more sustainable. Icon’s robots 3D-print cement-based walls, while its Vitruvius AI system is intended to generate designs, budgets, schedules, and eventually robotic construction instructions. Ballard sees the same technologies eventually supporting construction beyond Earth, including on the Moon. ## From Housing Mission to Robotics - Ballard’s interest in construction grew from work with homeless shelters and sustainable building in Colorado. - Although he once planned to become an Episcopalian priest, he chose to pursue housing affordability, dignity, beauty, and comfort as his life’s mission. - A master’s degree in space resources also shaped his interest in using robots for construction in extreme environments. ## Why Icon Focused on 3D-Printed Walls - Icon believed advanced software and robotics could improve construction. - The company targeted walls because they are among the slowest, most complex, and labor- and material-intensive parts of building. - Its system extrudes layers of cement-based material reinforced with steel rods. - The printed wall replaces much of conventional construction, including framing, insulation, drywall, sheathing, finishes, and siding. ## Claimed Benefits of 3D-Printed Homes - Faster and potentially more affordable construction. - Walls rated to withstand fires for two hours and 57 minutes and winds up to 250 miles per hour. - Some residents in Icon’s 3D-printed neighborhood reportedly pay as little as $17 per month in energy costs. - Icon says its homes have performed strongly in testing for compressive strength, bending, energy efficiency, fire, flooding, hurricanes, and termites. - Ballard also emphasizes that the homes can be aesthetically appealing, not merely functional. - He predicts that conventional stick-frame construction could eventually become obsolete or even prohibited. ## Vitruvius and AI-Driven Architecture - Icon had long worked on automating architectural tasks but struggled with the complexity and computational demands involved. - Building projects must account for budgets, schedules, designs, and highly localized permitting requirements. - Building codes vary across thousands of jurisdictions, making them difficult to interpret and apply. - Icon began pursuing generative AI training roughly two years before the interview. - The company collected floor plans, building designs, permits, and related documents to create what Ballard describes as the world’s largest architectural dataset. - Vitruvius is intended to produce architectural designs and construction plans, then translate them into instructions for Icon’s robots. ## Construction on Earth and Beyond - Icon’s broader ambition is to use autonomous construction systems in challenging environments. - The company is collaborating with NASA on potential infrastructure for the Moon. - Ballard presents lunar construction as an extension of the same incremental process used to improve housing on Earth. Icon’s approach combines automated design with robotic construction rather than treating AI as a standalone design tool. If its performance and cost claims continue to hold up in real-world projects, the technology could offer a promising alternative to traditional construction, particularly where labor, materials, or access are limited.

AI Generative AI Automation Robotics+4

figmaJun 25, 20242 min readCurated summary

Config 2024 In Review | Figma Blog

Figma’s Config 2024 announcements focus on making design more exploratory, efficient, and connected to development. The company introduced Figma AI, a redesigned UI3 interface, Figma Slides, major Dev Mode improvements, and quality-of-life updates. Figma argues that as AI makes software creation easier, thoughtful design will become an even stronger differentiator. ## Figma AI: Faster exploration and production - **Visual Search** lets users find similar designs across accessible team files using a screenshot, selected frame, image, or sketch. - Improved **Asset Search** understands the context of queries, even when search terms do not match asset names. - New AI-powered efficiency features can: - Generate realistic images and copy - Rewrite, translate, or vary text - Automatically create prototype connections - Rename layers - **Make Designs**, available through the Actions panel, generates initial UI layouts and component options from text prompts. - Figma says these tools are built around practical user needs rather than AI hype, using large language models to reduce tedious work and help designers explore more possibilities. ## Other major Config announcements - **UI3** redesigns the Figma interface. - **Figma Slides** introduces a dedicated environment for building, collaborating on, and presenting presentations. - **Dev Mode updates** aim to move teams from designs being merely “design ready” to being fully “dev complete.” - Additional improvements target **Auto Layout, UI kits, and the prototype viewer**. ## Availability - Figma AI and UI3 were announced as limited betas with gradual rollout. - Users can join the waitlist through Figma’s help menu by selecting **“Join UI3 + AI waitlist.”** Figma’s overall direction is to support the full path from idea generation through design, presentation, and development, while using AI to automate routine tasks and expand creative exploration.

AI Design Systems Large Language Models Prototyping+3

figmaJun 25, 20243 min readCurated summary

Ovetta Sampson on Inputs and Outputs | Figma Blog

Minimum viable data is the idea that AI projects should begin with representative, high-quality data—not with the most powerful model or feature. Ovetta Sampson argues that model outputs are overwhelmingly determined by their inputs, which reflect human choices and historical biases. Product builders should therefore question whether AI is necessary, who it serves, and whether the data is equitable enough to avoid harming overlooked groups. ## Data Quality Shapes AI Outcomes - The quality of an AI system depends primarily on the data used to train and operate it. - Decisions about what data to collect, exclude, label, and measure determine who the system recognizes and how it behaves. - Data is never purely objective: it is generated, engineered, and transformed by people. - Treating data as disconnected from human lives can produce “traumatized data sets,” embedding social, cultural, and economic harms into models. ## The Consequences of Omission - Historical datasets often exclude entire groups: - U.S. credit and mortgage models were developed before women could independently obtain mortgages or credit cards. - The U.S. Census did not recognize LGBTQ individuals until 2021, despite those people existing in earlier populations. - When people are absent from the data, models may fail to serve them or may expose them to harmful decisions. - The central question is not simply whether data exists, but whether it represents the people affected by the system. ## Define the Problem Before Choosing AI - Teams should first identify the problem they are trying to solve and determine whether ML or AI is appropriate. - The fact that a problem can be addressed with AI does not mean it should be. - Builders should ask: - Who is the product for? - Is the data equitable and sufficiently high quality? - What is the minimum data and technology needed? - Could the proposed solution increase human risks or reduce people to data points? - Minimum viable data means collecting what is necessary for a useful, responsible solution rather than indiscriminately gathering more data. ## Putting People Back in Control - Product builders and the public need to participate in decisions about how AI systems are designed and governed. - Important questions include who defines “good” data, who decides what enters a training set, and how much data is truly required. - Sampson recommends learning from work such as *Weapons of Math Destruction*, *Ghost Work*, and research on the lack of attention given to data work in AI development. AI development should start with the people affected by a system and the data needed to represent them fairly. Choosing the smallest appropriate dataset and validating its quality can be more responsible—and more effective—than pursuing larger models or unnecessary AI features.

AI Machine Learning Generative AI Data Quality+2

figmaJun 25, 20242 min readCurated summary

Meet Figma AI: Empowering Designers with Intelligent Tools | Figma Blog

Figma AI is a suite of tools designed to help designers overcome creative blocks, work faster, and explore ideas more easily. Rather than treating AI as hype, Figma presents it as a practical way to solve common workflow problems, including finding existing designs and components. The features are initially available in a limited beta and are free during 2024, though usage limits and future pricing may change. ## Figma’s Practical Approach to AI - Figma AI builds on the company’s earlier AI features for FigJam. - The tools target several stages of design work: - Finding inspiration and existing assets - Exploring different design directions - Automating repetitive tasks - Generating interfaces from text prompts - Figma emphasizes helping designers remain efficient and creative rather than replacing their judgment. ## Visual Search - Visual Search allows users to find similar designs by: - Uploading an image - Selecting part of a canvas - Entering a text query - Results are drawn from team files the user can access. - Relevant frames can be inserted directly into the current working file. - Figma plans to expand search to Community files, with attribution, links to source files, and access to creators’ other work. ## AI-Enhanced Asset Search - Asset Search now uses semantic understanding rather than relying only on exact keyword matches. - A search such as “primary button” can find a component named `btn_large`. - The system considers the meaning and typical use of design elements, making components in large or complex design systems easier to discover. - The goal is to make finding assets feel more natural and reduce time spent searching through files and libraries. ## Beta Availability and Pricing - Figma AI and UI3 are being rolled out through a limited beta. - Users can join through Figma’s help menu by selecting **“Join UI3 + AI waitlist.”** - Features are free during the beta period, which runs through 2024. - Figma may introduce beta usage limits as it evaluates demand and infrastructure costs. - Pricing for general availability will be announced later. Figma AI is positioned as an assistive layer within the existing design workflow: it helps users locate useful starting points and reduce friction while leaving creative decisions with the designer.

AI Design Systems User Experience Generative AI+2

figmaJun 25, 20242 min readCurated summary

Making Figma Work Better for Freelancers and Agencies | Figma Blog

Figma is revising its billing model to better support freelancers and agencies that work across multiple clients. The company acknowledges that its organization-focused plans create licensing and administrative overhead, and proposes connected workspaces, easier project transfers, and stronger billing controls. These changes aim to make external collaboration easier while reducing unexpected seat charges. ## Connected Workspaces for External Collaboration - Figma is developing connected workspaces that link separate Figma accounts. - Freelancers and agencies will be able to collaborate with clients and co-edit files using their existing seats. - The feature is expected to roll out early the following year, reducing the need for multiple licenses. ## Easier Project Transfers Figma plans to improve transferring work between accounts and plans so freelancers can hand projects to clients with less friction. - Users will be able to transfer a copy while retaining the original. - Collaborators can be removed during transfer to prevent accidental paid-seat upgrades. - Work can be transferred across Professional, Organization, and Enterprise plans. - These improvements went live on August 20. ## More Visibility and Control Over Billing Figma’s browser-based collaboration model lets users begin editing immediately, but it can leave admins responsible for remembering to downgrade seats after a project ends. - Admins can enable daily, weekly, or monthly notifications showing who upgraded and what action caused the upgrade. - New accounts receive these notifications by default: - Weekly on Professional plans - Monthly on Organization and Enterprise plans - Admins can set new users’ default role to “viewer-restricted,” requiring explicit approval before they receive a paid seat. - Figma says broader billing-architecture improvements are still underway. Figma’s immediate tools help admins monitor and approve seat changes, while connected workspaces and improved transfers address the larger structural challenges freelancers and agencies face.

Figma Figjam Collaboration Figma Design+1