Prototyping

139 posts

figmaJul 31, 20262 min readCurated summary

AI Fluency Isn’t the Finish Line | Figma Blog

AI skills are increasingly viewed as essential, but Figma argues that tool fluency is only the starting point. As AI makes it easier to generate work, the more valuable capabilities are building shared systems, guiding teams toward decisions, and creating an environment where people can experiment together. The goal is not for one person to work dramatically faster alone, but for entire teams to move faster collectively. ## Become an Internal Product Builder - Individual AI expertise has greater impact when turned into shared tools that benefit the whole team. - Useful examples include: - Prototyping agents - Brand plugins - Shared prompt libraries - Internal prototyping playgrounds - Figma researcher Shane Johnston used AI to build an interactive website for exploring the company’s AI report data, making the information accessible to cross-functional stakeholders. - Figma’s Brand Studio created an image-effect generator in Figma Make so teammates could apply custom, on-brand textures to designs with one click. - AI enables more employees—not just engineers—to identify workflow friction and build tools that solve it. - The broader opportunity is shifting from one person working “10x faster” to the entire team becoming more productive. ## Guide People to a Decision - When AI can produce dozens of possible directions quickly, evaluating and selecting among them becomes a core product skill. - Effective facilitation requires involving the right stakeholders, including: - People with dissenting or contrarian perspectives - Colleagues with historical context - Experts who can identify operational, security, or governance risks - One team discovered that an internally vibe-coded app exposed sensitive company project information, illustrating why data governance experts should be involved early. - Teams should provide context before review meetings through: - Prototype demonstrations - Loom videos - Annotated FigJam files - At Figma, these materials help shift meetings away from explaining options and toward discussing trade-offs and making decisions. - Facilitators should ensure discussions reach a clear outcome by inviting quieter participants, clarifying vague recommendations, asking forward-moving questions, and confirming next steps. ## Share Bad Ideas - AI adoption is occurring at different speeds across teams and organizations. - The report found that: - 20% of respondents said individual contributors were advancing faster than their organizations could support. - 27% said leadership was pushing AI adoption while teams struggled to keep up. - Without deliberate knowledge-sharing and collaboration, the gap between early adopters and less experienced users can continue to widen.

Prototyping AI Large Language Models Figma Make+2

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.

Prototyping AI Design Systems Product Development+2

figmaJul 9, 20262 min readCurated summary

GPT-5.6 is Now Available in Figma Make | Figma Blog

GPT-5.6 is now available in Figma Make, where Figma says it improves both the speed and quality of AI-generated prototypes. The model is designed to produce stronger first passes, preserve existing designs more faithfully, and recover from errors without stopping. Figma’s examples suggest it can move teams from prompts or static designs to functional, responsive prototypes with less iteration. ## Faster exploration and error recovery - GPT-5.6 can turn complex prompts into working prototypes quickly, helping teams explore multiple ideas in one session. - In Figma’s stock-tracking app evaluation, it created: - An interactive dashboard - Sample prices and performance data - Keyboard shortcuts and search - A dark, gothic visual style - The model is described as more token-efficient, helping users make better use of Figma Make credits. - When builds encounter errors, GPT-5.6 can investigate and self-heal instead of stopping. Figma reports that it independently diagnosed and fixed a blank build. ## Faithful design-to-code conversion - GPT-5.6 can build prototypes from existing design specifications or Figma Design files. - In a nature sound player test, it preserved: - Layout and visual hierarchy - Spacing, proportions, and styling - A multi-track timeline and sound library - It also implemented functional interactions, including: - Play, pause, and skip controls - Working audio playback - Multiple playable tracks - Audio-responsive visual effects ## Higher-quality first passes - Figma says GPT-5.6 produces polished initial prototypes with functional interactions and responsive layouts. - A bookshelf e-commerce example included: - Product descriptions, measurements, and care information - Populated information dropdowns - An interactive product photo library - A clickable navigation menu - The prototype adapted reliably across different screen sizes without additional prompting. - Stronger first passes allow teams to spend more time refining ideas collaboratively rather than repairing basic implementation problems. GPT-5.6 is available through Figma Make’s model selector. Users can select it directly in Make and consult Figma’s help center for guidance on choosing and using AI models.

Prototyping Figma Make Design To Code Responsive Design+3

figmaJul 3, 20262 min readCurated summary

7 Questions We Had Going Into Config Leadership Collective | Figma Blog

The post captures lessons from Figma’s Config Leadership Collective, where more than 1,300 design, product, and engineering leaders discussed leading through AI-driven change. Its central argument is that successful leadership depends less on rigid processes or tool expertise and more on adaptability, human-centered design, judgment, collaborative teams, and supported experimentation. ## Leading Change Through Experimentation - Leaders are learning alongside their teams as AI rapidly changes established workflows. - Rather than adopting fixed processes, they emphasize adaptability and continuous adjustment. - Executives are experimenting directly with new tools, prototyping ideas, and sharing failures. - Teams need permission to explore, take risks, and remain enthusiastic even when experiments fail. ## Preserving Human-Centered Design Fundamentals - AI has changed methods, but core principles remain important: - Understand users and their workflows. - Continue prioritizing craft and quality. - Design for people rather than simply following new tools. - Leaders warn against “chasing the tool” at the expense of human needs and thoughtful design. ## Expertise Is Moving Toward Judgment - AI can increasingly handle execution and task completion. - Human expertise is becoming more valuable in higher-order activities such as: - Taste - Discernment - Contextual decision-making - Evaluating and editing AI-generated work - Expertise now means selecting the best answer for a particular situation, not simply knowing a single correct answer. ## Restructuring Teams for the AI Era - AI is blurring traditional boundaries between design, product, engineering, and other disciplines. - Airbnb is organizing work into small, self-contained pods that resemble startups. - These pods combine core product roles with perspectives such as data science or business expertise. - Strong editing judgment, diverse viewpoints, and constructive disagreement are treated as essential. - Effective teams should be scrappy, vocal, ambitious, and willing to challenge one another. ## Helping Teams Adopt New Tools - Adoption requires education, infrastructure, and psychological safety—not just instructions to use AI. - Expedia is building dedicated support and training to help employees become fluent with AI tools. - OpenAI recommends starting with small, low-risk tasks instead of imposing large automation programs from the top down. - A simple use case, such as summarizing a long Slack thread, can demonstrate value and encourage broader adoption. The practical recommendation is to lead AI adoption as an ongoing learning process: experiment personally, preserve user-centered standards, hire for judgment and curiosity, build cross-functional teams, and introduce tools through manageable, well-supported steps.

Prototyping AI User Experience Product Design+2

figmaJun 24, 20263 min readCurated summary

Figma’s 2026 AI Report: Can AI Help Us Collaborate Better? | Figma Blog

AI is shifting from a tool for individual productivity into a driver of team collaboration. Figma’s research shows that 41% of respondents believe AI is already changing how teams work together, up from 7% two years ago. The report concludes that shared workspaces, stronger design judgment, and coordinated adoption matter more than simply making individuals faster. ## AI Is Moving Work from Solo to Collaborative - Figma’s report draws on 8,403 survey responses and 639 interviews across ten markets. - Designers and developers are increasingly crossing into each other’s work: - Designers participating in development rose from 21% to 41%. - Developers doing design work increased from 44% to 60%. - Seventy-six percent of product builders say at least half their work happens on the canvas, while six in ten spend most of their time there. - Unlike terminals or prompts, a shared canvas lets teams explore ideas, compare designs, give feedback, and solve problems together. ## Design and Judgment Matter More in the AI Era - AI can generate products, copy, and assets quickly, but it cannot decide what is worth building. - As creation becomes cheaper and faster, teams must focus more on product choices, differentiation, user experience, and trade-offs. - Ninety percent of respondents say design is at least as important as before AI; nearly 60% consider it more important. - Developers increasingly share this view, with 65% saying design has become more important. - Collaborative decision-making helps teams develop sharper judgment instead of optimizing only for individual output. ## Four Patterns of AI Adoption - The report identifies four organizational approaches: - **Unified:** Individuals and leadership advance AI adoption together (36%). - **Directive:** Adoption is driven from the top down (27%). - **Grassroots:** Practitioners lead adoption from the bottom up (20%). - **Nascent:** AI adoption remains at an early stage (18%). - Directive and grassroots organizations both experience friction when teams lack shared practices and communication. - The main challenge is organizational alignment, not simply access to AI tools. ## Building Shared AI Practices - Grassroots adopters should make successful workflows visible, create structure, and promote shared spaces. - Leaders introducing AI should close the gap between strategy and everyday practice. - The goal is not for one person to move faster, but for the whole organization to make better decisions and move quickly together. Teams should treat AI adoption as a collaborative design and organizational challenge: establish shared workflows, keep work visible, and use AI to improve collective judgment rather than only individual productivity.

Prototyping Figma AI Collaborative Design+2

tossJun 19, 20263 min readCurated summary

If You Asked a Designer to Make Anything with AI

Toss Design Chapter’s AI Contest invited designers to build anything with AI, resulting in 122 projects over one month. The examples show that designers primarily used AI to improve existing work—making it faster, more persuasive, and higher quality—rather than creating entirely new kinds of work. The article recommends starting with a frustrating, repetitive task or a frequently repeated communication problem. ## Automating Repetitive Work - A color-extraction tool automatically identifies and adjusts colors from images for use in UI. - Color extraction had been an unresolved challenge at Toss because results varied widely by image. - Designers used AI to draft the logic, test it against many sample images, and rapidly refine it. - The resulting system is now used for product-card colors in Toss Shopping. ## Reducing Collaboration Costs with a Personal Bot - A Slack bot was trained on a designer’s knowledge, past discussions, and reference materials. - It creates draft answers to the many design and requirements questions the designer receives each day. - Team members can send the draft as-is or revise it before responding. - The bot learns from those revisions, improving its answers to similar questions over time. - The designer described the result as feeling like becoming “1.5 people,” and other Toss designers began creating their own bots. ## Persuading Through Interactive Prototypes - A designer built a functioning prototype of a stock-trading desktop interface instead of presenting only static screens. - Users could drag panels, rearrange them, and resize windows, with the interface responding accordingly. - Showing the intended interactions directly reduced the risk that design ideas would be misunderstood during development. - The working prototype helped align designers and developers and persuade the product owner. ## Pushing Quality Within Tight Deadlines - AI-generated motion graphics were created for the key visual of Toss Bank’s recruitment website. - Each job category needed its own animation despite a very short schedule. - The designer created the foundational images manually and repeatedly refined Kling prompts to achieve the desired results. - Human-designed starting and ending frames combined with AI-generated motion allowed all category animations to be completed in a single day. ## Four Ways to Start Using AI - **Efficiency:** Hand off one especially annoying repetitive task to AI. - **Replication:** Build a bot to answer questions you repeatedly handle yourself. - **Persuasion:** Turn designs that require verbal explanation into working prototypes. - **Quality:** Use AI to reach a higher level of polish within a limited timeframe. The practical recommendation is to begin with an existing task rather than searching for an entirely new AI application. Choose one area where AI can save time, communicate intent more clearly, or help raise the final quality.

Prototyping AI Slack Prompt Engineering+3

figmaMay 28, 20263 min readCurated summary

4 New Ways to Go From Idea to Product With AI Tools | Figma Blog

AI tools are reshaping product development by enabling teams to prototype, test, and refine ideas earlier and across both code and design. The article argues that working prototypes can expose problems that static mockups miss, while preserving design context throughout the path to production. It illustrates this shift through examples from FloQast, Merkle, Affirm, and Accor. ## AI-enabled product workflows - Product teams are: - Prototyping earlier instead of relying solely on traditional requirements documents. - Testing ideas in code before finalizing designs. - Exploring more possibilities at greater scale. - Carrying design-system context into implementation. - Figma presents these practices as ways to balance faster iteration with deliberate product decisions. ## Testing constraints in code AI coding tools make it easier for non-developers and product teams to build functional prototypes involving: - Multi-step workflows. - Conditional behavior based on user permissions or data. - Actions that trigger subsequent actions. - Realistic backend logic and data relationships. A prototype can then be moved into Figma with Codex to Figma for collaborative exploration and refinement. If implementation work continues in code, teams can move the design back through MCP while retaining the relevant design context. ## FloQast’s complex workflow prototype ### The challenge - FloQast needed to redesign an accounting workflow for investigating discrepancies. - Users previously had to move between multiple pages to: - Find an issue. - Investigate it. - Resolve it. - The team wanted one page where users could see tasks, identify blocked work, and take action. - Because the workflow depended on interconnected steps, real data, and business logic, a static mockup could not fully validate the concept. ### The unlock - UX manager Benjamin Ellis built a working prototype with an AI coding tool. - The prototype included: - A simulated backend. - Realistic data based on an actual customer’s workflows. - Clickable scenarios where completing one task affected the next. - Testing the workflow revealed interactions that appeared sound in a design mockup but failed when subjected to realistic conditions. ### The impact - The team and designer committed to a direction only after testing it against real scenarios. - They identified interaction problems earlier. - The approach reduced later surprises and increased confidence in the final design. ### When this approach is useful - When behavior depends on permissions, data, or sequential actions. - When a small fix is faster to make directly in code. - When designers and developers need a working example to scope a complex experience together. ## Exploring with AI on the canvas The next section introduces using AI directly in the Figma canvas to explore product possibilities. The provided excerpt ends before describing the specific workflow or company example. Teams should use code-backed prototypes when logic and real data are central to the experience, then bring those prototypes into collaborative design tools to refine decisions with greater confidence.

Prototyping Figma Design Systems Model Context Protocol+4

figmaApr 21, 20262 min readCurated summary

How AI Leaders Are Borrowing From the Design Playbook | Figma Blog

AI transformation requires more than deploying new tools; it requires redesigning how organizations work. Figma argues that the most effective AI leaders adopt design practices—hands-on experimentation, close observation of workflows, and rapid prototyping—to turn adoption and innovation into meaningful business change. ## AI Leadership as Organizational Design - New AI innovation and acceleration roles are emerging to improve workflows, speed product launches, and expand tool adoption. - These leaders often coordinate AI strategy across product, support, internal operations, and technology investments. - A major risk is “performative progress”: adopting tools for appearances without changing the underlying systems and processes. - Effective leaders connect technology, teams, workflows, and business outcomes. ## Learn the Material by Using It Yourself - Leaders need firsthand experience with AI tools rather than relying only on strategic or executive-level perspectives. - Prompting, building agents, and experimenting across different tools reveals practical limitations, trade-offs, and adoption barriers. - Personal projects—such as planning travel, organizing events, or managing volunteer work—can provide low-risk opportunities to develop AI fluency. - Leaders cannot effectively guide organizations through probabilistic technologies without understanding how those technologies behave in real situations. ## Observe How Teams Actually Work - Understanding AI use across the business requires studying workflows, not just tools and their outputs. - Useful signals include Slack discussions, survey responses, usage patterns, frustrations, and points where employees get stuck. - An automation may appear successful technically but fail because it adds friction to an already complicated process. - When adoption stalls, teams may be routing around the official solution and creating unofficial alternatives; observing this behavior helps identify the real problem. ## Turn Ideas Into Prototypes - Ideas often fail because teams cannot visualize or evaluate them, not because the ideas themselves are flawed. - Prototyping converts abstract AI concepts into tangible experiences that teams can discuss and test. - Tools such as Figma Make can help leaders and teams explore concepts quickly and make early possibilities easier to understand. - Design combines observation with action: leaders should learn from real behavior, then use prototypes to test potential solutions. AI leaders should therefore combine technical curiosity with design discipline: use the tools personally, study how people work, and prototype proposed changes before attempting broad implementation.

Prototyping AI AI Agents Automation+2

tossApr 9, 20263 min readCurated summary

It Almost Ended Up Ugly - The Making of Toss Front 2

Toss redesigned its Front 2 payment terminal by addressing real-world usability problems rather than settling for technically workable solutions. The redesign moved NFC to the front, made the card reader field-replaceable, and reworked the internal structure to simplify removal. The result was a smaller, cleaner device that improved both customer experience and repairability. ## Moving NFC to the Front - The first-generation terminal placed NFC on the right side because other components interfered with the signal. - This was inconvenient in narrow retail spaces, where users had little room to tap cards or phones. - Several alternatives were tested: - Moving the card reader upward made card insertion awkward and strained users’ wrists. - Enlarging the top made the vertically oriented terminal look excessively long and displaced the camera, complicating barcode and face-payment use. - Placing NFC around the camera caused interference, producing camera shake and reducing recognition accuracy. - The team reframed the problem by asking whether the display’s metal backing could be replaced. - A customized plastic backing allowed NFC signals to pass through while reinforced glass preserved the display’s rigidity. - Despite higher manufacturing complexity and cost, the design was successfully mass-produced, enabling reliable front-facing NFC without sacrificing appearance. ## Designing a Replaceable Card Reader - The first-generation card reader was integrated into the main body, so failures required repairing or replacing the entire terminal. - Repairs took more than a week on average, forcing stores to use backup devices and distributors to maintain extra inventory. - Front 2 introduced a docked, replaceable card reader designed for easy on-site replacement. - A USB-C connector was selected because users already understand how to connect and disconnect it. - The connector provided stable attachment without exposing additional brackets or mechanisms, preserving the product’s clean appearance. ## Making Removal Simple - Once the reader used USB-C, the team needed a way to remove it without adding buttons, levers, or protruding parts. - The simplest approach was to insert the reader from the front and push it out from behind, but existing power and network connections blocked the necessary space. - Instead of adding another mechanism, the team redesigned the internal layout from scratch. - The circuit board and connectors were tilted toward the top of the device, requiring redesigned and inverted cable components. - This created enough room to remove the reader without tools. - The revised layout also made the cables easier to see and connect. ## Results of Front 2 - Front-facing NFC made payments more natural on crowded counters. - The USB-C dock reduced the difficulty of replacing a failed card reader. - The terminal became smaller while retaining its visual simplicity and design quality. - Front 2 surpassed the first generation’s sales immediately after launch, while previous customer complaints shifted toward positive feedback. ## Building Quality Through Persistent Questions - The article argues that product quality comes from repeatedly challenging an acceptable technical solution. - The team focused on finding answers that were right for users and the overall product experience, not merely answers that functioned. - For similar design problems, the recommended questions are: - Can everyone use the design easily, including in edge cases? - What is the fundamental problem? - Is the current solution truly the best one? - If not, can the problem and solution direction be redefined? The practical lesson is to keep revisiting the problem until the solution is not only feasible, but genuinely appropriate for users.

Prototyping User Experience Product Design Nfc+4

figmaApr 8, 20263 min readCurated summary

How Figmates Used Figma AI to Take Delight to the Next Level | Figma Blog

Figma’s 2026 April Fun Day project, “FigCade,” used Figma Make, Figma Weave, and the Figma MCP server to create six playable mini-games in only a few days. The tools helped the team rapidly prototype ideas, explore visual styles, produce media, and translate designs into code. The project demonstrated how AI can make design and development more collaborative and iterative. ## Building a playful canvas experience - April Fun Day is Figma’s annual tradition of adding playful surprises and Easter eggs for its community. - This year, the team brought six mini-games directly into the Figma canvas for one week. - The project also gave employees an opportunity to experiment beyond their usual roles and push Figma’s tools in new ways. - The resulting FigCade included games such as: - **2Fast2Figma**, a timed quiz about Figma facts. - **FigPalette** and **Diabolical Magic Square**, featured in the game menu. ## Rapid prototyping with Figma Make - Figma Make helped the team turn ideas into working prototypes quickly. - An early concept for 2Fast2Figma was created on a Sunday morning and became functional that afternoon. - The team generated multiple prototypes, tested them with others, and iterated based on feedback. - This established a fast workflow: build something quickly, review it, align with the team, and refine it. ## Exploring visuals with Figma Weave - Figma Weave helped designers generate and explore visual assets more efficiently. - Designer Lesley Moon used it to create felt-style textures and assets, including the project’s textured cursor. - Generating many variations quickly expanded the range of visual themes the team could consider. - Weave was also used to develop the April Fun Day trailer: - Product Manager Tara Nadella explored the initial concept. - Motion Designer Fifi Law used those explorations and Lesley’s visuals to produce the final trailer in one day. ## Connecting design and code with MCP - The Figma MCP server helped developers turn design explorations into implementation. - Engineer Steven Noto used Claude and GitHub Copilot with MCP authentication. - By sharing links to specific Figma components, the coding agents could access design context and generate code matching the intended specifications. - The team moved back and forth between design and development, using AI to reduce the distance between visual concepts and working software. ## Practical takeaway FigCade illustrates how combining rapid prototyping, generative visual tools, and design-aware coding assistance can help small teams create polished interactive experiences quickly. The strongest results came from treating AI as part of an iterative design-and-development process rather than as a replacement for human direction.

Prototyping Generative AI Github Copilot Figma Make+2

tossApr 7, 20263 min readCurated summary

Layers of your time : Celebrating the time spent with Toss

The article argues that effective internal branding is not about making attractive company merchandise, but about designing meaningful experiences around employees’ time and contributions. Through an eight-month redesign of Toss’s work-anniversary gift, the designer created a layered light that visually represents accumulated years, protected quality despite production delays, and carefully designed the delivery experience. The project concludes that strong internal branding requires a clear reason, end-to-end experience design, and unwavering standards. ## From Merchandise to a Celebration of Time - Toss celebrates employees’ work anniversaries with annual gifts such as medals, wine, and cubes. - Over time, some employees began giving the gifts away, suggesting they had become clutter rather than meaningful keepsakes. - The redesign aimed to: - Sincerely celebrate each employee’s time at the company. - Show appreciation for the six-month gap while the gift was being redesigned. ## Three Criteria for the New Gift The new product had to: - Avoid being immediately stored away in a drawer. - Physically show the accumulation of time. - Remain beautiful whether celebrating one year or ten years. A layered lamp was chosen because employees could add one disk for each anniversary. As the disks accumulated, the layers of light became deeper, making the passage of time visible through the object’s structure. ## Hardware Development and Quality Control - The designer had no previous hardware or lighting-production experience. - The team repeatedly tested: - Disk thickness, including differences as small as 0.5 mm. - The spacing between the lamp body and disks. - Light intensity as more disks were added. - The product was divided into two versions: - White for years 1–10. - Black from year 11 onward, symbolizing the beginning of a new period. - Dozens of defects appeared during final factory inspection. - Rather than compromise quality to meet the schedule, distribution was delayed. - Approximately 5,000 lamps were individually inspected and improved. ## Giving the Product a Warm Voice - The lamp was named **Layered Lighting**. - The phrase **“Layers of your time at toss”** was engraved on the lamp and packaging. - The communication emphasized remembrance and celebration rather than corporate motivation. - Serif typography, carefully matched packaging, and handwritten name cards created a warmer, more personal experience. ## Designing the Moment of Delivery - Instead of asking employees to pick up their gifts, the team placed them directly at employees’ desks. - The redesigned process gave employees the correct number of disks for their accumulated tenure. - The intended experience was: - Discovering the gift on a Monday morning. - Opening the box and reacting with surprise. - Taking photos and sharing the moment with colleagues. - Over one weekend, the team placed gifts at 2,500 desks among approximately 3,900 employees. - The operation took 26 hours. - Employee photos and reactions spread across Slack and Instagram, including one memorable comment: “I now have a reason to stay another year.” ## Principles of Good Internal Branding - **Start with the reason:** Every object or graphic should clearly communicate why it exists. - **Design the experience, not just the object:** The interaction begins before the product is opened and includes how it is received and shared. - **Protect the standard until the end:** Internal projects are easy to compromise because their results may not be immediately visible, making a clear standard essential. Good internal branding helps employees feel that they belong to a good team. That sense of belonging can strengthen engagement and ultimately improve the quality of the work they create.

Prototyping User Experience Product Development Design+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.

Prototyping Figma AI Model Context Protocol+3

figmaMar 10, 20262 min readCurated summary

5 Design Skills To Sharpen in the AI Era | Figma Blog

AI is changing product creation by accelerating experimentation and expanding who can participate in design. Figma argues that designers should strengthen adaptable, technology-oriented skills rather than rely only on traditional craft. The first priority is becoming fluent with AI tools and learning to prompt them effectively, while maintaining human judgment and design fundamentals. ## AI Fluency and Prompting - AI skills are becoming essential for designers and increasingly important in non-design roles such as product management, development, and marketing. - More than half of designers and hiring managers consider AI design capabilities—such as rapid prototyping and “vibe coding”—important hiring skills. - Among designers who adopted AI during the past year: - 91% say it helps them create better designs. - 89% say it helps them work faster. - AI can support many activities, including: - Editing images directly within a workflow. - Building prototypes instead of writing traditional product requirements documents. - Testing assumptions and creating tangible artifacts for team alignment. ## Writing Better Prompts - Clear, structured prompts produce more reliable AI-generated results. - Figma recommends organizing prompts around: - The task - Context - Required elements - Behavior - Constraints - Prompting is presented as a repeatable design practice, not merely a way to get a one-off output. - Strong prompts help turn AI into a consistent design partner rather than an unpredictable experimentation tool. ## Broader Changes to Design Work - AI is lowering barriers to participation and blurring boundaries between product roles. - Designers are increasingly expected to work across disciplines and use AI to extend their capabilities. - Prototyping is becoming a faster way to communicate ideas, validate assumptions, and build momentum than relying solely on written documentation. Designers should build practical fluency with AI tools, practice structured prompting, and use prototypes to make ideas concrete—while applying their own judgment to guide and evaluate the results.

Prototyping Figma AI Figma Make+2

figmaMar 10, 20262 min readCurated summary

Vishal Kapoor’s 10 Rules for Building Honest Products with AI | Figma Blog

AI product development is ultimately a trust challenge, not merely a technical one. Vishal Kapoor argues that AI should accelerate exploration and execution without replacing human judgment, empathy, or accountability. His approach centers on building products that remain transparent, secure, emotionally aware, and honest—especially in sensitive areas such as personal finance. ## Start with First-Principles Thinking - Break complex problems into their fundamental components before reaching for an AI solution. - AI can accelerate ideation and iteration, but it cannot replace human intuition, taste, or a distinctive product perspective. - Question basic assumptions to uncover better alternatives. For example, Affirm challenges why customers receive three payment-plan options rather than one, five, or a customizable plan. - Thoughtful disagreement among people remains essential for generating meaningful insights; AI is best used to explore possibilities more quickly. ## Stay Close to Human Emotions - Product teams should regularly observe customers, conduct UX research, read app-store reviews, monitor social media, and speak directly with users. - Metrics and dashboards identify patterns, but they do not fully explain the emotions behind customer behavior. - Financial products especially require sensitivity to anxiety, frustration, trust, and relief—not just transactional outcomes. - Affirm uses an internal AI tool called Pluto to investigate recent customer disappointments, while still relying on human observation and empathy to interpret those experiences. ## Treat AI as a Teammate - AI is neither a guaranteed productivity multiplier nor an inevitable replacement for employees; it is another participant in a collaborative product-development process. - Tools such as Figma Make help teams convert customer insights into prototypes and test ideas faster. - AI can audit large numbers of screens and interaction patterns across web, mobile, and desktop experiences, identifying outdated or inconsistent designs. - Moving repetitive auditing and prototyping work from engineers to designers and product managers increases iteration speed and creates more room for creativity. ## Test the Edge Cases - Trustworthy products cannot be designed only around the happy path. - Teams should deliberately explore unusual inputs, failure modes, and unexpected customer situations rather than assuming normal usage. - The article begins this rule by emphasizing that authentic product quality depends on examining the difficult and overlooked scenarios where users are most likely to encounter confusion or harm. The overall recommendation is to use AI aggressively for exploration, prototyping, and repetitive analysis—but keep humans responsible for defining the problem, understanding customers, challenging assumptions, and ensuring the final product is honest.

Prototyping AI User Experience Product Design+2

figmaMar 4, 20262 min readCurated summary

3 Ways Teams Are Building Conviction Faster With Figma Make | Figma Blog

Product teams are using Figma Make to turn abstract product ideas into interactive prototypes earlier in the process. By making concepts tangible, PMs can align designers and engineers, test assumptions, collect feedback, and build conviction before significant development begins. The article highlights examples from ServiceNow, Ticketmaster, and Affirm. ## Prototyping Instead of Relying Only on PRDs - Product managers traditionally translate customer needs, design goals, and engineering constraints into shared decisions. - Figma Make lets them create prototypes that demonstrate both a product’s appearance and behavior. - Interactive prototypes provide more useful early feedback than static mockups or abstract explanations. - Teams can identify problems, test ideas with users, and adjust direction before implementation progresses too far. - Earlier visibility helps teams make better-informed decisions and build products that more closely meet user needs. ## Bringing Complex Product Thinking Into Shared Focus - At ServiceNow, Product Director Ram Devanathan works with a design team serving multiple product groups, making dedicated design support difficult to obtain. - He needed to redesign a configuration page containing 15–20 settings, including technical options affecting incident prioritization and system load. - The initial mockup was functional but did not fully communicate the desired hierarchy, guidance, or tone. - Ram used Figma Make to transform the mockup into a clearer prototype: - Settings were grouped logically. - Simpler options appeared first. - Tooltips explained individual settings. - A warning clarified that changes required restarting the service. - The prototype gave Ram and the designer a shared, concrete representation of the intended experience. - Figma Make templates can also embed design systems and UX patterns, allowing PMs to iterate consistently without requiring designers for every early exploration. - Ram found that showing the idea directly was much more effective than describing it abstractly, helping the team reach agreement faster. ## Validating Features Before Building - The article next introduces Ticketmaster’s use of Figma Make for validating new features before development. - Ticketmaster applies prototypes to situations involving high-demand concert ticket purchases and internal dashboards for monitoring sales and troubleshooting issues. - The provided excerpt ends before explaining the specific feature-validation process or the third approach involving Affirm. Figma Make is presented less as a replacement for design or engineering and more as an early collaboration and validation tool. Product teams can use it to communicate complex behavior, explore alternatives, and secure alignment before committing substantial resources.

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