User Experience Design

figmaJul 16, 20262 min readCurated summary

Sightlines Issue no.1: Insights from Config | Figma Blog

Figma’s first *Sightlines* issue distills lessons from Config’s Leadership Collective about leading design, product, and engineering teams in the AI era. Although organizations are still experimenting with AI, core leadership principles remain unchanged: build curious teams, preserve quality, collaborate openly, and rely on human judgment. As AI accelerates production, taste and thoughtful editing become increasingly important differentiators. ## Navigating Leadership in the AI Era - Leaders are still determining how to integrate AI into products, teams, and workflows. - The central challenge is gaining AI’s speed without sacrificing quality. - Many leaders are learning alongside their teams rather than presenting themselves as having all the answers. ## Fundamentals That Still Matter - Strong leadership continues to depend on: - Curious, critical-thinking teams - High standards for craft - Clear judgment about what makes work effective - Leaders are encouraging a beginner’s mindset by: - Starting from first principles - Experimenting with new tools and processes - Prototyping directly with their teams ## Collaboration and Human Judgment - AI tools can encourage isolated, individual workflows, so leaders are emphasizing collaboration more strongly. - Effective practices include: - Open critiques and feedback - Showing work early - Debating outcomes collectively - As AI automates more creation, human “taste”—the ability to judge, refine, and select high-quality work—becomes the key differentiator. ## Lessons from Industry Leaders - Teo Connor of Airbnb argues that an increase in mediocre AI-generated work will make strong editing and creative judgment more valuable. - Jen Dunnam emphasizes designing for people rather than chasing tools, noting that human needs remain constant. - Ian Silber of OpenAI recommends trusting capable teams and accepting that leaders cannot oversee every detail. - Jeetu Patel of Cisco describes meticulous design as a way to demonstrate care and create an emotional connection with customers. The practical recommendation is to adopt AI with experimentation and openness while preserving the human practices that sustain quality: collaboration, critical thinking, empathy, strong standards, and continual cultivation of taste.

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User Experience Design AI Design Systems Prototyping+2

figmaJun 8, 20263 min readCurated summary

You Never Stop Cultivating Taste | Figma Blog

Mastery is not just learning tools or techniques; it is developing a distinctive point of view through repeated practice and intentional choices. Figma’s Loredana Crisan argues that “taste” is cultivated continuously through care for one’s craft, empathy for users, and disciplined attention to detail. AI can expand creative exploration, but it cannot replace the judgment that makes work personal and meaningful. ## Taste Is Built Through Practice - Expertise requires understanding both the material and the tools of a craft. - Crisan compares design to piano and music composition: technical correctness matters less than knowing why choices create emotion and impact. - Taste develops through: - Consistent practice - Mentorship and critique - Feedback and collaboration - Sustained creative attention - Developing a point of view is the most time-consuming part of mastery—and it never truly ends. ## Taste Is a Form of Care - Taste is visible when work feels intentional, refined, and thoughtfully executed. - Dieter Rams’ Braun products demonstrate this principle by considering not only an object’s function but also its surroundings, physical interactions, and overall experience. - Taste is not universal popularity; different designers can have different sensibilities while showing equal intentionality. - In product design, taste appears in trade-offs such as: - Form versus function - Expressiveness versus legibility - What to include versus what to omit - Which compromises to accept or reject - Taste comes from both love of the craft and care for the people using the result. - Designers should test details across varied contexts, including screen sizes, color profiles, languages, devices, transitions, and uncommon user states. ## What Designers With Taste Demonstrate When hiring for taste, Crisan looks for three qualities: - **Discernment:** The ability to identify what is not working and explain why with nuance. - **Empathy:** Attention to the person experiencing the interface, including needs that may not be obvious. - **Creative energy:** A persistent drive to make, experiment, and pursue side projects or unresolved problems. ## AI Expands Exploration but Cannot Replace Judgment - AI may reduce the labor involved in producing work, but accepting its first output would undermine the iterative process required for quality. - Examples such as James Dyson’s 5,127 prototypes illustrate how refinement and rejection are central to creative mastery. - Better tools increase the distance a creator can travel between an idea and its execution, but the vision still comes from the creator. - AI can help generate more possibilities, while taste determines which possibilities are worth developing. - A creator’s distinctive voice emerges from accumulated, intentional decisions repeated over time. The practical recommendation is to use AI and other tools to explore broadly, while continuing to practice, critique, refine, and care deeply about both the craft and the people who experience the final result.

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User Experience Design AI Design Systems Product Design+1

grammarlyMar 27, 20263 min readCurated summary

How to Create a Chatbot Step by Step: A Beginner’s Guide

Chatbot development begins with a focused purpose, not technology. By choosing the right interaction method, chatbot type, and platform, organizations can automate routine tasks and improve user experiences without necessarily needing developers. Successful chatbots also require deliberate conversation design, testing, monitoring, and continuous improvement. ## What Chatbots Can Do - Simulate conversations through text or voice. - Answer frequently asked questions and provide information. - Handle structured tasks such as: - Checking order status - Booking appointments - Explaining policies - Guiding users through onboarding - AI-powered and hybrid chatbots can manage follow-up questions and more complex, multistep interactions. - They can reduce repetitive work, improve response consistency, and help users reach solutions faster. ## Define the Chatbot’s Goal - Identify two or three specific tasks the chatbot should handle. - Define the target audience, such as customers, employees, or students. - Establish success metrics, including: - Fewer support tickets - Faster response times - Higher task-completion rates - A narrow, well-defined purpose makes the chatbot easier to design, test, and refine. ## Choose the Interaction Method - Decide whether the chatbot will be text-based or voice-based. - Text is generally simpler to build. - Voice requires additional technical setup. - Choose where it will operate, such as: - A website - Mobile application - Messaging platform - Internal company tool - Determine how conversations begin, whether through typed messages, preset options, or proactive prompts. - The access point and interaction style directly affect development and maintenance requirements. ## Select the Chatbot Type - **Rule-based chatbots** use predefined flows, menus, and decision trees for predictable requests. - **Keyword-based chatbots** respond to specific words or short phrases, such as “pricing” or “hours.” - **AI chatbots** use artificial intelligence and natural language processing to handle varied questions and contextual follow-ups, but require more testing and oversight. - **Hybrid chatbots** combine structured rules for common tasks with AI for open-ended questions. - The choice determines the chatbot’s flexibility, behavior, complexity, and ongoing management effort. ## Choose a Building Platform - **No-code platforms** such as Chatling, Voiceflow, Zapier, and Landbot use visual interfaces and are suitable for beginners and simple chatbot tasks. - **Low-code or full-code approaches** using technologies such as Python, Node.js, or AI frameworks provide greater customization and integration capabilities. - Platform selection should account for: - Cost - Integrations - Analytics - Scalability - Data protection - Required technical expertise ## Design the Conversation Flow - Map typical conversations before implementing the chatbot. - Planning helps identify missing responses, avoid dead ends, and create a smoother user experience. - Traditional chatbots generally use structured decision paths, while AI chatbots support more flexible conversations. - The flow should reflect the chatbot’s purpose and provide a clear route for completing tasks or escalating complex issues. ## Ongoing Improvement - Building and launching the chatbot is only the beginning. - Chatbots should be tested before release and monitored afterward. - Regular refinement, accurate training data, configuration updates, and performance reviews help maintain quality over time. A practical approach is to start with a narrow use case and a simple platform, then expand as user needs and performance data become clearer. Choose AI or custom development only when the chatbot requires more flexibility, deeper integrations, or complex conversational capabilities.

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User Experience Design Database Design Natural Language Processing No Code Tools+3

figmaMar 17, 20262 min readCurated summary

Design’s Influence Is Expanding, and Here’s Why That Feels Hard | Figma Blog

Design is expanding into more products, interactions, and strategic decisions, especially as AI introduces new software categories and interfaces. Although AI makes design work faster, it also increases output, expectations, and workload rather than reducing effort. This leaves designers divided: the field is growing, but many are unsure whether it is improving. ## Design’s Expanding Influence - Each technological shift—from graphical interfaces to the web and mobile apps—has increased design’s scope. - AI is creating new categories such as agent orchestration systems and answer engines. - Existing products are gaining generative, conversational, and predictive features. - Users now interact through prompts, speech, and image uploads, creating new design challenges: - Translating ambiguous input into clear intent - Making automated experiences understandable and human - Designing beyond traditional screen-by-screen navigation - Survey results show mixed sentiment: - 36% of designers think the profession has improved - 35% think it has worsened - 29% see no change - Meanwhile, 82% of hiring managers say demand for designers has increased or remained steady, though only 20% believe the industry itself is improving. ## AI Expands the Work - AI helps teams address new design problems more quickly, but it does not necessarily reduce the amount of work. - Product builders reported a 17.5% year-over-year increase in the number of tasks they perform. - Research from UC Berkeley found that AI users work faster while also taking on more tasks and working longer hours. - Workers often feel more productive without feeling less busy. ## The Jevons Paradox in Design - As AI makes creation cheaper and easier, teams produce more designs, explore more options, and iterate more deeply. - This follows the Jevons Paradox: efficiency increases can lead to greater overall consumption rather than reduced consumption. - Software development experienced a similar pattern when cloud infrastructure made releases easier, resulting in more frequent releases and redesigns. - AI has changed the rhythm and volume of design work rather than eliminating it. Designers should view AI as a force multiplier, not a shortcut to less work. Its benefits will depend on managing rising expectations and workload while developing clearer approaches to complex, automated interactions.

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User Experience Design AI Generative AI Cloud Infrastructure+3

figmaMar 16, 20262 min readCurated summary

Issue no.15: The State of Design | Figma Blog

AI is reshaping design by blurring the boundary between code and canvas, while expanding—not eliminating—the need for designers. Figma’s research suggests designers are adapting to new expectations by strengthening both AI-related capabilities and enduring creative fundamentals. The future favors people who can move fluidly across tools, teams, and stages of product development. ## AI’s impact on design work - 91% of surveyed designers say AI tools are helping them improve their work. - “Better design” means different things to different designers, including: - Visual polish - More thoughtful problem-solving - More intuitive user experiences - These differing priorities influence how designers understand and experience their jobs. - Design is increasingly defined by outcomes and problem-solving rather than by a single medium. ## Design hiring remains strong - AI is not reducing demand for designers according to Figma’s research. - 82% of surveyed hiring managers say their need for designers has either remained stable or increased. - Demand is growing beyond technology companies. - Organizations are seeking designers who can help translate new AI capabilities into useful products and experiences. ## Skills for the AI era - Designers are exploring emerging practices such as: - Prompting - MCP-related workflows - Connecting AI tools and processes - Translating between design, engineering, product, and other teams - AI-specific skills complement rather than replace foundational design abilities. - Communication, judgment, craft, and the ability to understand user and business needs remain essential. - The strongest designers are likely to combine technical fluency with human-centered thinking. ## Product teams are prototyping earlier - Product managers are using Figma Make to explore ideas and build conviction more quickly. - Teams at ServiceNow, Ticketmaster, and Affirm use prototypes to: - Communicate complex product behaviors - Test and develop ideas - Make better roadmap decisions - Prototyping is becoming accessible beyond traditional design roles. ## Code and canvas converge - Ideas can begin in code, visual design, or anywhere in between. - Figma presents the future of design as a continuous movement between code and canvas. - This shift makes designers less defined by their tools and more by their ability to shape ideas across mediums. Designers should treat AI as an extension of their creative and problem-solving toolkit, while continuing to develop core design judgment, communication, and craft. The most valuable practitioners will be those who can connect AI-enabled workflows with strong product thinking and cross-functional collaboration.

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User Experience Design Figma AI Prototyping+3

figmaNov 25, 20253 min readCurated summary

Version Control: One Founder’s Mission to Save Local Farms with Figma Make | Figma Blog

Aaron Veale used Figma Make to build Planet Food, a marketplace connecting British Columbia farmers with Vancouver restaurants, in under three weeks. Motivated by the financial crisis facing local farms, he used rapid AI-assisted prototyping to validate the idea directly with farmers and chefs. The project shows how prompt-to-app tools can help founders move quickly while tailoring products to real user workflows. ## The problem facing local farms - British Columbia farmers lost a record CAD $457 million last year, with the sector operating at a net loss since 2017. - Rising costs, disrupted supply chains, regulatory changes, and factory-farm competition are pushing small growers out of business. - Farmers are skilled at producing food but often lack time and resources for marketing and sales. - Large distributors can pressure farmers into selling produce at a loss. - Veale envisioned a marketplace linking farms directly with restaurants seeking high-quality local ingredients. ## Building the first marketplace prototype - Veale spent six weeks interviewing farmers before developing the product. - Planet Food required two connected systems: - **Farm OS:** Farmers record and categorize available produce. - **Restaurant OS:** Chefs search for and order ingredients. - He built both systems in parallel using separate Figma Make projects. - Roughly 20 prompts produced the first prototype in a single day. - Early versions became conversation starters that Veale could show farmers and restaurants for immediate feedback. ## Designing for farmers’ daily reality - The app uses dark mode to reduce glare for farmers working outdoors. - Because farmers may work 12–16-hour days, tasks were designed to take fewer than three clicks. - Veale prioritized simple workflows over feature-heavy interfaces. - He used screenshots of familiar interactions, such as swipes and slide-ups, as prompt references. - Figma Make allowed him to refine the product’s mobile-first interface without relying on a large engineering team. ## Humanizing the product through personas - Veale used his design and filmmaking background to treat prompting as a form of storytelling. - He created detailed personas describing users’ traits, motivations, responsibilities, and pain points. - The farmer persona emphasized: - Small or midsize British Columbia operations - Limited administrative capacity - Seasonal workloads and slim teams - The need for fair prices and predictable income - Common frustrations included manually updating spreadsheets, guessing restaurant demand, and overselling or underselling due to poor synchronization. - Veale used ChatGPT to turn these personas and the onboarding flow into more detailed Figma Make prompts. - Custom icons and branded interactions helped make the interface more approachable and engaging. ## Speed as a startup advantage - Figma Make enabled Veale to move from an idea to a functioning MVP in weeks rather than following the traditional fundraising-and-development sequence. - Demonstrating an actively used product gave him stronger evidence of market demand and a potential signal for investors. - The process also let him remain closely involved in product design instead of compromising his vision through multiple layers of implementation. Planet Food illustrates how AI-assisted development can accelerate product validation while keeping design grounded in user research. For founders addressing urgent problems, rapid prototyping combined with direct customer feedback can be more valuable than waiting to assemble a conventional product team.

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User Experience Design AI Figma Make Chatgpt+2

googleNov 17, 2025Original article

Generative UI: A rich, custom, visual interactive user experience for any prompt (opens in new tab)

Google Research has introduced a novel Generative UI framework that enables AI models to dynamically construct bespoke, interactive user experiences—including web pages, games, and functional tools—in response to any natural language prompt. This shift from static, predefined interfaces to AI-generated environments allows for highly customized digital spaces that adapt to a user's specific intent and context. Evaluated through human testing, these custom-generated interfaces are strongly preferred over traditional, text-heavy LLM outputs, signaling a fundamental evolution in human-computer interaction. ### Product Integration in Gemini and Google Search The technology is currently being deployed as an experimental feature across Google’s main AI consumer platforms to enhance how users visualize and interact with data. * **Dynamic View and Visual Layout:** These experiments in the Gemini app use agentic coding capabilities to design and code a complete interactive response for every prompt. * **AI Mode in Google Search:** Available for Google AI Pro and Ultra subscribers, this feature uses Gemini 3’s multimodal understanding to build instant, bespoke interfaces for complex queries. * **Contextual Customization:** The system differentiates between user needs, such as providing a simplified interface for a child learning about the microbiome versus a data-rich layout for an adult. * **Task-Specific Tools:** Beyond text, the system generates functional applications like fashion advisors, event planners, and science simulations for topics like RNA transcription. ### Technical Architecture and Implementation The Generative UI implementation relies on a multi-layered approach centered around the Gemini 3 Pro model to ensure the generated code is both functional and accurate. * **Tool Access:** The model is connected to server-side tools, including image generation and real-time web search, to enrich the UI with external data. * **System Instructions:** Detailed guidance provides the model with specific goals, formatting requirements, and technical specifications to avoid common coding errors. * **Agentic Coding:** The model acts as both a designer and a developer, writing the necessary code to render the UI on the fly based on its interpretation of the user’s prompt. * **Post-Processing:** Outputs undergo a series of automated checks to address common issues and refine the final visual experience before it reaches the browser. ### The Shift from Static to Generative Interfaces This research represents a move away from the traditional software paradigm where users must navigate a fixed catalog of applications to find the tool they need. * **Prompt-Driven UX:** Interfaces are generated from prompts as simple as a single word or as complex as multi-paragraph instructions. * **Interactive Comprehension:** By building simulations on the fly, the system creates a dynamic environment optimized for deep learning and task completion. * **Preference Benchmarking:** Research indicates that when generation speed is excluded as a factor, users significantly prefer these custom-built visual tools over standard, static AI responses. To experience this new paradigm, users can select the "Thinking" option from the model menu in Google Search’s AI Mode or engage with the Dynamic View experiment in the Gemini app to generate tailored tools for specific learning or productivity tasks.

User Experience Design AI Large Language Models Gemini+4

figmaSep 11, 20253 min readCurated summary

Is the App Layer Where AI Proves Its Value? | Figma Blog

AI’s next breakthrough may come less from larger models than from the application layer that makes them useful and accessible. Like graphical interfaces made personal computers mainstream, well-designed AI products can translate complex capabilities into intuitive, context-specific experiences. The products that succeed will combine reliable infrastructure with thoughtful interaction design and emotional resonance. ## From MS-DOS to the App Layer - Today’s prompt-driven AI resembles the MS-DOS era: powerful, but requiring users to know how to issue precise commands. - Existing models have a “capabilities overhang,” meaning much of their potential remains difficult to access. - Personal computers became mainstream through graphical user interfaces, not MS-DOS itself. - Similarly, browsers, search engines, smartphone apps, and services such as Uber and Instagram transformed underlying technology into everyday tools. ## Design Makes Technology Adoptable - Building an app layer is not enough; adoption depends on the quality of the interactions surrounding the technology. - Successful products combine functionality with intuitive design: - Pinch-to-zoom and inertial scrolling on smartphones - Live maps in Uber - Simple navigation in browsers and search engines - AI products will need new interaction patterns that make model capabilities feel natural rather than like conversations with a raw chatbot. ## AI Products Must Be Context-Specific - Most people will use AI through specialized products rather than directly interacting with language models. - Effective AI applications will adapt their content, tone, interface, and responses to particular audiences and situations. - The Good Inside parenting app illustrates this approach: - It uses a chatbot trained on Dr. Becky’s parenting guidance. - Vague prompts receive empathetic, actionable advice. - Simple cards, a calm color palette, readable typography, and subtle animations create a reassuring experience. - The same principle applies to products for lawyers, doctors, designers, artists, and other professional or consumer groups. ## The Interface Can Matter More Than the Model - User reactions to GPT-5’s simplified model picker showed that interface changes can provoke stronger responses than improvements to model capability. - This does not make the underlying models unimportant, but users primarily experience AI through how its capabilities are packaged and presented. - Atlassian’s acquisition of The Browser Company suggests that even browsers may evolve into active AI interfaces that help applications work together, rather than merely displaying tabs. ## Design as a Competitive Advantage - AI products will compete on the feelings and confidence they create: - Support for parents - Inspiration for artists - Confidence for lawyers - Product teams must choose interactions that present AI outputs seamlessly while maintaining reliable, scalable systems. - Many new AI applications will emerge, but the strongest may distinguish themselves through design and become as transformative as graphical user interfaces were for computing. The practical opportunity for AI builders is to focus not only on model performance, but on designing specialized, emotionally resonant products that turn raw capability into useful everyday experiences.

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User Experience Design Large Language Models Generative AI Product Design+3

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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User Experience Design Figma Product Design Web Development+2

figmaFeb 6, 20242 min readCurated summary

Power up your designer-developer handoff with Figma and Jira | Figma Blog

Product teams achieve higher velocity not through speed alone, but through tools, processes, and rituals that keep design and development aligned. Figma argues that reducing communication gaps—especially in hybrid and growing organizations—helps teams preserve “flow” and ship better products faster. High-fidelity prototypes, work-in-progress sharing, and connected workflows such as Figma for Jira are presented as practical ways to support that alignment. ## Building a Shared Understanding with Prototypes - As companies grow, teams tend to become siloed and processes slow down. - Designers and developers may interpret the same requirements differently, making continuous communication essential. - High-fidelity, interactive prototypes provide a shared visual and functional reference: - Developers can understand user flows, pop-ups, dropdowns, and interactive states. - Teams avoid the ambiguity of static mockups or fragmented prototypes spread across many linked pages. - At One.com, designers in Denmark meet regularly with developers in India to review requirements, blockers, and potential problems around a working Figma prototype. - The prototype also preserves meeting context for remote and asynchronous collaborators, allowing people to continue working effectively after the meeting ends. ## Protecting Maker Time and Team Flow - Paul Graham’s “Maker’s Schedule, Manager’s Schedule” is used to illustrate why uninterrupted time matters to designers and developers. - Product teams rarely work in ideal conditions: calendars are crowded, requirements change, and hybrid work introduces additional coordination challenges. - Maintaining velocity therefore requires deliberate foundations: - Clear communication - Shared artifacts - Regular alignment rituals - Processes that reduce interruptions and uncertainty - Daily standups, such as those used by Condé Nast’s product development team, help teams discuss progress, priorities, and possible roadblocks. ## Connecting Design and Development Workflows - Figma positions its Jira integration as a way to keep design context connected to implementation work. - Embedding design information in development workflows helps developers understand not only what to build, but how the intended experience should behave. - The broader goal is to reduce handoff friction and keep different teams working from compatible sources of information. Teams can improve designer-developer handoff by treating prototypes and collaboration processes as part of the product infrastructure—not as final-stage documentation. Use interactive prototypes, share work early, maintain regular alignment, and connect design artifacts directly to development tools such as Jira.

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User Experience Design Figma Prototyping Product Development+2

figmaNov 8, 20232 min readCurated summary

Little Big Updates: When going big means thinking small | Figma Blog

Figma argues that meaningful product progress often comes from small, frequent improvements rather than headline features. Because designers may spend 40 or more hours a week in Figma, reducing friction in everyday workflows can have a greater cumulative impact than adding rarely used capabilities. Its annual “Little Big Updates” initiative formalizes this focus on quality, usability, and user joy. ## Why Small Improvements Matter - Subtle changes can save users clicks, eliminate recurring frustrations, and fix long-standing bugs. - Figma users invest heavily in learning the tool, creating a responsibility for Figma to continually improve it. - Quality-of-life updates are difficult to market, but often affect the actions users perform most frequently. - A small improvement repeated hundreds of times a day can matter more than a flashy feature used occasionally. ## The Origin of Little Big Updates - Figma originally released product updates weekly. - After accumulating four updates, the team released one per day from Monday through Thursday. - Users compared the experience to opening a new present each day. - This response inspired the “Little Big Updates” format, which gives individual improvements their own attention. ## Examples of High-Impact Details - Figma fixed paste behavior that previously placed content in seemingly random locations. - Although the change was not headline-worthy, it removed a frustration users encountered hundreds of times daily. - Text selection when switching between frames was also improved, eliminating unnecessary double-clicking. - These examples show how workflow friction can be more important than feature novelty. ## Prioritizing Big and Small Features - Major initiatives, such as AI features for FigJam, require deliberate planning and coordination. - Large features may need to be sequenced carefully so related capabilities work together. - Small improvements should be prioritized differently: teams should avoid over-planning and allow decisions to remain decentralized. - Individual teams are often best positioned to identify which usability improvements will have the greatest effect. - Every team should treat product quality as an ongoing responsibility, not as a secondary concern. Figma’s recommendation is to balance ambitious new capabilities with sustained attention to everyday details. Product teams can create substantial user value by identifying frequent sources of friction and steadily removing them.

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User Experience Design Figma AI Figjam+2

figmaAug 24, 20212 min readCurated summary

FIT’s principles for fostering a collaborative classroom | Figma Blog

Professor Christie Shin of the Fashion Institute of Technology uses Figma to create a virtual classroom centered on systems thinking, frequent feedback, and open collaboration. Her approach treats design as an evolving process rather than a finished object, helping students build habits that mirror professional design teams. She organizes coursework into Figma teams, projects, files, and templates while preserving space for experimentation and individual expression. ## Celebrate Early Work - Students share unfinished ideas regularly instead of waiting for polished presentations. - They visit one another’s files and provide feedback both verbally and directly in Figma. - This reduces the fear of exposing imperfect work and reinforces design as a way of thinking. ## Guide the Process While Leaving Room to Grow - Coursework—from exercises to final presentations—is kept in Figma. - Students receive structured files with named pages and templates for each activity. - The infinite canvas leaves room for sketches, iterations, and exploratory work. - Each file becomes a “creative journey,” documenting research, summaries, wireframes, and development over time. ## Work Individually, Together - Students complete individual projects within study groups. - For a project involving an MTA app design system and video case study, students presented progress incrementally rather than waiting for final critiques. - Continuous peer feedback reflects real-world cross-functional collaboration and helps students become less attached to initial solutions. ## Embrace Influences - Students are encouraged to share work openly instead of hiding ideas to prevent others from copying them. - Shin frames influence as a normal and valuable part of design practice. - Each project requires students to collect and credit visual references from professional designers and classmates. - Discussing these influences during critiques makes the design process more transparent. The article recommends combining clear structure with open collaboration: provide students with organized tools and milestones, but give them room to experiment, share early work, learn from peers, and acknowledge the influences shaping their designs.

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User Experience Design Figma Design Systems Wireframing+2

figmaSep 29, 20202 min readCurated summary

Building an open and inclusive design process | Figma Blog

The post argues that inclusive products emerge from inclusive design processes built on trust, empathy, transparency, and collaboration. Teams should look beyond ideal users, involve localization and accessibility perspectives early, and create open working practices—especially in remote environments. These approaches improve both the product and the way teams understand one another. ## Designing for More Than the “Perfect” Use Case - User research should include diverse users, situations, and challenges—not only the primary persona. - Designers are encouraged to experience products “in the field,” such as considering delivery workers dealing with broken elevators or unreliable GPS. - Inclusive thinking can reveal accessibility needs that are: - Permanent, such as visual or motor limitations - Temporary or situational, such as hearing difficulties in crowded spaces or using a phone with a shattered screen - Small improvements to copy, color contrast, font size, and layout are useful, but accessibility requires broader organizational prioritization. - Teams should establish clear ownership for accessibility. - Designing for essential needs often produces features that benefit everyone. ## Making Localization Part of Product Design - Localization involves more than translating text; it requires attention to syntax, cultural nuance, and how designs adapt across languages. - Deliveroo replaced a siloed handoff process with the Phrase Figma plugin, allowing designers and localization teams to review localized prototypes earlier. - Localization experts can identify inaccurate translations or insufficient space before engineers build the final pages. - Seeing the complete user journey gives localization teams more context and improves collaboration. - Localization becomes an integrated part of product development rather than a transactional final step. ## Creating Transparent Team Processes - Remote work increases the need for visibility into teammates’ work, availability, and priorities. - Kate Pincott’s Team Capacity Template helps teams map weekly schedules and identify meeting or workload gaps. - Regular meetings and short stand-ups can replace informal office interactions and maintain personal connection. - Teams should make room for what matters during difficult periods rather than optimizing only for efficiency. - Shared working sessions, such as open Zoom rooms, can recreate some of the spontaneous collaboration of an office. Inclusive design is ultimately a process practice: broaden the perspectives involved, invite feedback early, and make work visible. Teams that build these habits are better equipped to create products that work across users, cultures, and circumstances.

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User Experience Design Figma Accessibility Remote Collaboration+2

figmaMar 5, 20193 min readCurated summary

How Notion pulled itself back from the brink of failure | Figma Blog

Notion nearly failed in 2015 because its original product and technology stack did not match what users wanted. Founders Ivan Zhao and Simon Last moved to Kyoto, rebuilt the product from scratch, and used an intensive, collaborative design process to create Notion 1.0. The relaunch succeeded because it combined powerful customization with a simple, approachable user experience. ## Rebuilding Notion from the Brink - Notion’s first version was a programming-oriented tool intended to help nontechnical users build software. - The founders realized they had focused on their own vision rather than customer needs. - With funding running low, they dismissed their team, sublet their office, and relocated to Kyoto to reduce expenses and concentrate on rebuilding. - Zhao spent as many as 18 hours a day designing and iterating on the new product. - The mission remained the same: enable people to create tools tailored to their own problems without writing code. ## Collaborative Design Under Pressure - Zhao and Last worked closely across design and engineering, switching roles as needed. - Figma’s multiplayer capabilities allowed them to work in the same files simultaneously, brainstorm quickly, and explore product problems together. - This collaboration helped them move faster during their year-long rebuild. - Notion 1.0 launched in March 2018 and quickly reached the top of Product Hunt. - The product later reached one million users with only seed funding and earned praise for its user experience. ## Simplicity Despite Powerful Features - Notion’s new-user home screen was intentionally minimal, using a small set of simple icons. - Its visual style drew inspiration from classic interfaces, including Susan Kare’s iconography and the look of Windows 95. - Zhao argues that design is central because users value how a daily tool feels, just as they care about the physical qualities of a hammer or knife. - The product’s challenge was to make extensive functionality feel approachable rather than overwhelming. ## Create Many Variations, Then Choose One - Zhao repeatedly duplicated user flows and changed small details such as icons, wording, and layout. - Notion’s broader philosophy is to explore many permutations before selecting the strongest solution. - Team members are encouraged to produce rough drafts, including unconventional or bad ideas, instead of committing too early. - Designers, copywriters, engineers, and illustrators all use this iterative approach. - Teammates then critique and stress-test the alternatives, narrowing them down to the best option. - Zhao credits this process with helping create Notion’s distinctive brand. ## Design as a Thinking Tool - Notion treats design as part of the thinking process, not merely as a final production stage. - The team develops ideas visually in Figma from the beginning, using it as a flexible scratchpad. - This design-centered approach extends across the small company, involving more than just dedicated designers. Notion’s recovery demonstrates how a failing product can be transformed by listening more closely to users, rebuilding around their needs, and creating space for rapid, collaborative experimentation. Teams facing similar pressure can benefit from exploring many options early, testing them together, and protecting simplicity even when the underlying product is powerful.

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User Experience Design Figma Product Design Real Time Collaboration+3

figmaOct 29, 20183 min readCurated summary

Designers use Figma to bring Uber to the unbanked | Figma Blog

Uber’s expansion into emerging markets required designing for differences in language, literacy, devices, culture, location, and user roles. For its cash-payment initiatives, product designer Femke van Schoonhoven adopted Figma after fragmented tools made collaboration and research iteration difficult. Figma became a shared, web-based source of truth that reduced communication overhead, improved engineer handoff, and enabled distributed teams to work together more effectively. ## Designing for Diverse Markets - Uber serves millions of riders across dozens of countries, requiring experiences that work across very different local contexts. - Designers conduct extensive research, including riding with drivers and testing designs in real-world environments. - Emerging-market design must account for: - Language and literacy - Device limitations - Culture and location - Whether the user is a rider, driver, or another participant - Cash payments were particularly important for reaching users without access to traditional banking. ## Problems with the Previous Workflow - The Cash team used separate tools for design, prototyping, sharing, feedback, and engineering handoff. - Designers had to repeatedly import and export files and communicate project status outside the design tool. - During field research, only one person could edit a design at a time, preventing real-time iteration with teammates. - This made it difficult to respond immediately to feedback from Uber drivers. ## Figma Enables Real-Time Collaboration - Van Schoonhoven moved existing files into Figma through a simple drag-and-drop process. - She could create prototypes using local languages and realistic, in-context materials. - Multiple team members could edit the same design simultaneously, whether working nearby or across countries. - Figma’s file organization made designs easier to locate and maintain. ## A Single Source of Truth - Stakeholders could access the latest designs directly instead of requesting updates by email. - Figma reduced communication overhead by an estimated 75 percent. - Engineers could be invited into design files, view current changes, and comment in context. - Early engineering involvement made handoff faster and less error-prone. - The shared workspace helped replace siloed work with a central design repository. ## Supporting Distributed Projects - Figma provided the collaborative foundation for projects involving designers in Amsterdam, San Francisco, and other locations. - Teams could access shared Uber platform components and work from the same current files. - Because the tool was web-based, stakeholders could share designs and obtain approvals more quickly. Figma helped Uber’s designers adapt payment experiences for unbanked users by combining rapid iteration, shared context, and cross-location collaboration. For distributed product teams working across complex markets, a centralized collaborative design environment can substantially reduce friction from research through engineering handoff.

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