Product Design | Techlist.io

figmaJul 9, 20262 min readCurated summary

How to Lead Design Teams Through the AI Era | Figma Blog

Jen Dunnam argues that design leaders should respond to AI-driven change with calm, deliberate experimentation rather than panic. The fundamentals of design remain human-centered, so teams should prioritize clear thinking, strong principles, and the ability to turn insights into products. Her approach emphasizes investing in emerging talent while hiring and developing designers who can challenge assumptions. ## Lead with Calm - Leaders should steady their teams instead of adding to the urgency already felt by ambitious designers. - Break AI-related change into manageable steps: - Choose an approach. - Experiment with appropriate tools. - Refine design principles. - Learn from the results. - Designers should avoid chasing every new capability simply because it is novel or impressive. - AI may transform workflows, but designing for human needs remains the central responsibility. ## Hire for Critical Thinking - Dunnam would invest more heavily in designers fresh out of school, many of whom are disadvantaged by today’s pressure to ship quickly. - Pair early-career designers with experienced practitioners who can help turn ideas into shippable products. - Look for researchers who can move beyond gathering insights and contribute decisively to product direction. - Critical thinking has become especially valuable as AI tools make polished but potentially shallow solutions easier to produce. - Interviewers should ask candidates: - Where did they disagree with a stakeholder? - How did they push back? - What product decision still bothers them? - These questions reveal whether candidates can challenge attractive but poorly reasoned solutions. Dunnam’s practical recommendation is to keep teams grounded in human-centered design, combine emerging and experienced talent, and hire people with the judgment to question what appears easy, polished, or technologically exciting.

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Product Design Figma AI Human Centered Design+2

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.

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Product Design AI User Experience 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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Product Design AI Design Systems User Experience Design+1

figmaMay 22, 20262 min readCurated summary

What Matters When Anyone Can Build | Figma Blog

AI has made building products faster and more accessible, but speed alone no longer creates an advantage. When anyone can ship, the differentiators become choosing the right direction and shaping the result with care. Figma’s Yuhki Yamashita argues that strong teams combine rapid exploration, deliberate decision-making, and relentless craft. ## Choosing What’s Worth Building - The abundance of possible ideas makes it easy to commit prematurely to the first promising concept. - Iterating deeply on one idea can become “local hill-climbing,” refining a path without questioning whether it is the right one. - AI tools can worsen tunnel vision by accelerating a chosen direction without challenging its assumptions. - Traditional strategic methods, such as MECE option mapping, encourage breadth but can remain too abstract to create conviction. - A stronger approach combines breadth and depth: - Explore several distinct directions in parallel. - Turn each direction into a realistic, end-to-end interactive prototype. - Compare actual user experiences rather than abstract diagrams or wireframes. - Invite teammates and AI agents to react and build on ideas collectively. - This creates a more collaborative, parallel way of working instead of a siloed, sequential process. ## Making the Product Yours - AI-generated products tend to converge on familiar patterns and statistically likely solutions. - “Good enough” becomes easy to produce and easy to accept, creating interchangeable products. - The main danger is passivity: accepting the first convincing result because it looks polished. - Craft requires active judgment: - Question every decision. - Revisit and refine multiple times. - Remove unnecessary elements. - Push beyond the first few acceptable versions. - Develop a distinct point of view. - As AI raises the baseline quality of products, differentiation will come less from tools or execution speed and more from the care and intention behind the final result. ## What Matters Now - The essential capabilities are **speed, direction, and craft**. - The best teams do not treat these as competing priorities: - They move quickly. - They choose deliberately. - They refine relentlessly. - In a world where nearly anything can be built, the lasting advantage lies in deciding what deserves to exist and shaping it into something distinctive.

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Product Design AI User Experience Product Development+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.

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Product Design User Experience Prototyping Nfc+4

tossApr 3, 20263 min readCurated summary

Why Toss reduced its design roles to two

On April 1, Toss’s Design Chapter consolidated six design roles into two: Product Designer and Visual Designer. The change reflects how role boundaries had already blurred as designers crossed disciplines and technology reduced the importance of tool-specific expertise. Toss’s central argument is that designers should be organized around judgment and user problems—not the tools, media, or screens they work with. ## Why Role Boundaries Became a Problem - The previous structure separated designers by tools and outputs rather than by the decisions they made. - Ambiguity emerged in areas such as: - Whether interaction in a design system belonged to Platform or Interaction Designers - Whether interactive graphics should be handled through Lottie, code, or UI design - Whether expanding a PC product to mobile belonged to a Tools Product Designer or Product Designer - These divisions sometimes determined ownership based on medium instead of capability or context. ## Designers Were Already Crossing Disciplines - Tools Product Designers began designing mobile products. - Interaction Designers worked on parts of internal design tools. - Graphic Designers created semantic icon systems. - Platform Designers built interactive web pages. - Brand Designers with visual-design backgrounds worked on lighting products. - AI and other tools have shortened the time needed to learn formerly specialized skills, including: - Video and Lottie production - Figma prototyping - Coding interactive experiences - As tool proficiency becomes less differentiating, the ability to judge what creates a good experience becomes more important. ## Product Designer - Product Designer and Tools Product Designer were merged into one role. - The distinction between mobile and PC disappeared. - The role now focuses on: - Understanding the user’s context and problems - Deciding how those problems should be solved - Designing across screen sizes and product environments ## Visual Designer - Platform, Interaction, Graphic, and Brand Designers were combined into Visual Designer. - Visual Designers are expected to work across media and produce what the experience requires, such as: - Building interactions within systems - Creating icons for prototypes - Designing interactive web experiences - The defining capability is visual judgment: deciding what is beautiful, appropriate, and correct. - The title was chosen to emphasize visual decision-making rather than a specific medium or technique. ## Lessons from Other Industries - Disney animation reduced many physical and intermediate production steps through software while preserving stages requiring important creative judgment. - Digital audio workstations allow artists such as Billie Eilish and Finneas to compose, perform, record, and mix with a laptop, but human judgment about what sounds good remains essential. - Digital cinema and streaming weakened the historical distinction between film and television production. - Across these industries, tools converged while the value of creative judgment increased. ## What Comes Next - The new job structure will not immediately change how people work. - Toss still needs to redesign hiring standards, onboarding, and career-development paths. - The consolidation is intended to give designers broader ownership and more room to make decisions across disciplines, ultimately improving the experiences delivered to users.

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Product Design Figma Design Systems Interaction Design+4

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.

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Product Design AI User Experience Prototyping+2

tossFeb 27, 20263 min readCurated summary

From Intern to Solo Designer: Growth

As a Toss Bank product design intern, Jeon Nuri designed experiments to improve non-member sign-up conversion. She prioritized the funnel using speed and impact, studied previous experiments, and learned that clear, narrowly defined hypotheses were more valuable than constantly generating new ideas. The experience showed that failed experiments can still guide better decisions when they produce actionable learning. ## Prioritizing the Right Funnel Stage - The largest drop-offs occurred in the intro, consent, and identity-verification screens. - Consent and identity verification were shared modules requiring legal and compliance review, making rapid iteration difficult. - The intro screen could be changed more quickly and had the potential to affect the greatest number of users. - Based on this speed-versus-impact assessment, she chose the intro screen as the starting point. ## Learning from Previous Experiments - Instead of immediately designing new concepts, she reviewed existing experiments, including both winners and unsuccessful variations. - She examined: - The problem each experiment addressed - The reasoning behind its hypothesis - How the test variation was designed - Experiments from unrelated screens were also useful because their problem definitions and hypothesis structures could be adapted. - The main lesson was that inexperienced experimenters benefit more from systematically analyzing existing learning than from rushing to create new ideas. ## First Experiment: A Counselor Concept - The first variation presented benefits as if they were being recommended by a counselor and offered a small number of choices. - The hypothesis was vague: fewer choices would increase conversion. - The result was negative: - Click-through rate fell by more than 10%. - Conversion rate fell by more than 3%. - The design actually introduced more choices than the original, which had only one CTA button. - The experiment also failed to consider why users had entered the screen and whether they needed recommendations. - This led her to analyze the existing screen and user context before creating a hypothesis. ## Identifying and Solving Concrete Problems - Rather than inventing an entirely new design, she identified two specific weaknesses in the existing version: - The copy did not clearly communicate benefits users cared about. - Images loaded slowly, taking two to three seconds on low-end devices. - Previous experiments showed that users responded well to messages about high interest rates and receiving interest daily. - She incorporated those themes into the copy and optimized the visuals with newer graphics and lower-weight image formats. - Both click-through rate and conversion rate increased, demonstrating that a hypothesis grounded in clear problems can provide a stable direction for design. ## Making Benefits Easier to Imagine - Building on the earlier results, she changed functional wording into language that helped users imagine a concrete situation and immediate benefit. - Instead of simply explaining that interest could be earned after depositing money for one day, the revised copy foregrounded the moment when users would experience the benefit. - Copy alone increased CTR by 5% and also produced a meaningful improvement in CVR. - The result reinforced that different expressions of the same information can create significantly different first impressions. ## Principles for Designing Experiments - Break the funnel into stages and prioritize opportunities by speed and potential impact. - Understand the existing context before defining the core problem. - Study previous experiments through their hypotheses and problem definitions, not just their numerical outcomes. - Establish a clear hypothesis and success metric before designing the variation. - Make sure the experiment visibly tests the stated hypothesis. - Treat failure as input for the next decision rather than as wasted effort. A practical starting point for new designers is to begin with a small, focused experiment—but make the hypothesis precise enough to guide both the design and the next iteration.

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Product Design A B Testing Data Driven Design Ux Writing+3

figmaFeb 11, 20262 min readCurated summary

State of the Designer 2026: Designers Are Leaning Into the Messy Middle | Figma Blog

Designers are navigating rapid change by using AI as a complement to—not a replacement for—human craft. Figma’s 2026 survey of 906 designers finds that AI is helping many work faster, collaborate better, and improve quality, while strong craft remains central to satisfaction and business performance. The report’s overall conclusion is optimistic: designers are embracing uncertainty and turning new pressures into creative momentum. ## AI Improves Speed, Collaboration, and Quality - The survey was conducted by NewtonX across North America, APAC, Europe, LATAM, and the Middle East. - Respondents answered in English, Spanish, French, Italian, Portuguese, Japanese, and Korean. - 89% of designers say AI helps them work faster. - 80% say it improves collaboration. - 91% believe AI tools improve their designs, countering concerns that AI-generated work will reduce quality. - Designers who actively use AI are 25% more likely to report job satisfaction. - AI users are also more likely to say they drive business impact and contribute to company growth. - By automating or accelerating workflow tasks, AI gives designers more time for high-impact ideas. ## Craft Remains a Human Differentiator - As AI makes prototyping more accessible, craft becomes a key way for products to stand out. - Designers define craft in several ways: - Visual polish: 58% - Thoughtful problem-solving: 47% - Clear, intuitive UX: 36% - Emotion and delight: 35% - Consistency across products: 15% - Craft can mean technical skill, careful execution, intentional decisions, artistry, or solving difficult product problems. - Designers who associate craft with visible emotional and creative outcomes often receive more recognition than those whose craft involves less visible tactical work. ## Design Excellence Supports Morale and Growth - Designers are twice as likely to feel positive about their work when leaders prioritize design excellence. - Teams that value craft report stronger morale, faster business growth, and a clearer sense of momentum. - Leadership support, recognition, and opportunities for development help designers maintain quality while adapting to new tools. - The report links investment in craft with better outcomes for both designers and their organizations. Organizations should treat AI as a way to extend designers’ capabilities while continuing to invest in human judgment, creativity, quality, and recognition.

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Product Design Figma AI User Experience+2

figmaFeb 9, 20263 min readCurated summary

Why Demand for Designers Is on the Rise | Figma Blog

Companies are investing more in design, with 82% of leaders reporting that demand has either increased or remained steady. The post argues that AI is not reducing the need for designers; instead, it is increasing demand for people who can use AI tools, design AI products, and connect design with strategy and business growth. Fast-growing organizations are leading this hiring momentum, while employers increasingly favor experienced, AI-fluent candidates. ## Design hiring is increasing across industries - Nearly half of hiring managers say demand for designers has increased, and most of them report growth of at least 10%; more than a quarter report increases of 25% or more. - Technology companies lead hiring, but demand is also growing in sectors such as retail, publishing, aviation, and other non-tech industries. - Companies are hiring designers to improve digital experiences, strengthen online presence, and create new customer value. - Planned hiring varies by company growth: - 46% of fast-growing companies expect to increase hiring. - 40% of average-growth companies plan to do so. - 33% of slower-growth companies expect increased hiring. - High-growth companies view design as a way to test ideas earlier, move faster, differentiate products, and drive revenue. - Although only 20% of managers believe the overall hiring market is improving, 40% plan to add design headcount within six months. - Design job postings among Designer Fund portfolio companies reportedly rose about 60% in 2025 compared with 2024. ## AI is fueling demand for designers - Rapid advances in AI models and tools are creating demand for designers who can immediately work with evolving AI processes. - Employers want both: - Proficiency with AI tools in everyday design workflows. - Experience designing AI-powered products. - 73% of hiring managers report an increasing need for AI-tool proficiency. - 79% report an increasing need for knowledge of designing AI products. - AI fluency is increasingly treated as a hiring requirement rather than an optional advantage. - Companies are prioritizing candidates who combine technical ability, strategic thinking, experimentation, and approaches such as human-in-the-loop and human-augmented AI. ## Seniority and broader judgment matter - The article begins a discussion of companies prioritizing senior talent, particularly as teams face pressure to deliver quickly. - Hiring managers are looking for designers with strong skills, judgment, and experience—not only executional ability. - The combination of design expertise, strategic thinking, and AI capability is becoming increasingly valuable. Designers can improve their prospects by developing practical AI fluency alongside core design skills, learning how to design AI products, and demonstrating strategic judgment and business impact.

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Product Design Figma AI User Experience+1

figmaDec 2, 20253 min readCurated summary

Double Click: What Does It Mean To Be A Designer In The Age Of AI? | Figma Blog

AI is blurring the boundaries between design, development, and product management, making traditional job titles less stable. Figma argues that titles still matter, however: they communicate expertise, shape expectations, support career development, and contribute to professional identity. As AI changes the tasks within jobs, people may increasingly identify as generalists or combinations of roles rather than occupying a single fixed profession. ## The rise of hybrid roles - Figma reports that **64% of product builders identify with two or more roles**. - AI is taking on increasingly specialized tasks, increasing the value of people who can connect ideas across disciplines. - Designers, developers, and product managers are increasingly working across traditional boundaries. - Jobs can be understood as “bundles of tasks” whose importance changes as technology and industry needs evolve. ## Why titles still matter - Titles provide shorthand for understanding someone’s expertise, responsibilities, and status. - They help establish expectations when people meet or collaborate for the first time. - Professional titles can support career ladders and communicate alignment with the values of an industry. - Research cited from Adam Grant found that allowing employees to choose their own titles improved psychological safety and reduced emotional exhaustion by up to 10% over five weeks. - Professional organizations and certifications reinforce the importance of titles in fields such as architecture, engineering, and medicine. ## Titles evolve with technology - The meaning of “designer” has changed from a focus on physical objects and print to digital products and software. - “Software engineer” emerged in the 1960s as the industry confronted the complexity of building software for increasingly powerful computers. - New technology continually creates, reshapes, and sometimes eliminates roles; “prompt engineer” is presented as a recent example. - During the dot-com era, some professionals combined responsibilities spanning product management, program management, development, and art. ## Roles shape expectations and identity - Titles influence how others perceive a person’s expertise and what work they are expected to perform. - Nikolas Klein describes himself as “a product designer in a PM trenchcoat,” showing how people may retain one identity while operating in another role. - Moving into product management made Klein’s strategic and service-design skills more visible and reduced assumptions that his work centered mainly on visual design. - Developer advocate Jake Albaugh views roles as potentially limiting because their definitions change as expertise grows. - At the same time, adopting the title “software engineer” after working as a web designer gave Albaugh a sense of confidence and recognition. Organizations and individuals will likely need to treat titles as flexible signals rather than rigid boundaries. The most durable professional identity may come from the value someone creates and the connections they make—not from a single fixed job label.

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Product Design Figma AI Prompt Engineering+3

kakaoNov 19, 2025Original article

How the POPM Course Became a (opens in new tab)

Kakao developed its internal POPM (Product Owner/Product Manager) training program by treating the curriculum itself as an evolving product rather than a static lecture series. By applying agile methodologies such as data-driven prioritization and iterative versioning, the program successfully moved from a generic pilot to a structured framework that aligns teams through a shared language of problem-solving. This approach demonstrates that internal capability building is most effective when managed with the same rigor and experimentation used in software development. ## Strategic Motivation for POPM Training * Addressed the inherent ambiguity of the PO/PM role, where non-visible tasks often make it difficult for practitioners to define their own growth or impact. * Sought to resolve the disconnect between strategic problem definition (PO) and tactical execution (PM) within Kakao’s teams. * Prioritized the creation of a "common language" to allow cross-functional team members to define problems, analyze metrics, and design experiments under a unified structure. ## Iterative Design and Versioning * The program transitioned through multiple "versions," starting with an 8-session pilot that covered the entire lifecycle from bottleneck exploration to execution review. * Based on participant feedback regarding high fatigue and low efficiency in long presentations, the curriculum was condensed into 5 core modules: Strategy, Metrics, Experiment, Design, and Execution. * The instructional design shifted from "delivering information" to "designing a rhythm," utilizing a "one slide, one question, one example" rule to maintain engagement. ## Data-Driven Program Refinement * Applied a "Product Metaphor" to education by calculating "Opportunity Scores" using a matrix of Importance vs. Satisfaction for each session. * Identified "Data/Metrics" as the highest priority for redesign because it scored high in importance but low in satisfaction, indicating a structural gap in the teaching method. * Refined the "features" of the training by redesigning worksheets to focus on execution routines and converting mandatory practice tasks into selective, flexible modules. ## Structural Insights for Organizational Growth * Focused on accumulating "structure" rather than just training individuals, ensuring that even as participants change, the framework for defining problems remains consistent within the organization. * Designed practice sessions to function as "thinking structures" rather than "answer-seeking" exercises, encouraging teams to bring their training insights directly into actual team meetings. * Prioritized scalability and simplicity in the curriculum to ensure the structure can be adopted across different departments with varying product needs. To build effective internal capabilities, organizations should treat training as a product that requires constant maintenance and versioning. Instead of focusing on one-off lectures, leaders should design structural "rhythms" and feedback loops that allow the curriculum to evolve based on the actual pain points of the practitioners.

Product Design Product Management Data Analysis Metrics+3

tossNov 6, 2025Original article

Creating the worst experience at Toss (opens in new tab)

Toss designer Lee Hyeon-jeong argues that business goals and user experience are not mutually exclusive, even when integrating controversial elements like advertising. By identifying the intersection between monetization and usability, her team transformed intrusive ads into value-driven features that maintain user trust while driving significant revenue. The ultimate conclusion is that transparency and appropriate rewards can mitigate negative feedback and even increase user engagement. ### Reducing Friction through Predictability and Placement * Addressed "surprise" ads by introducing clear labeling, such as "Watch Ad" buttons or specifying ad durations (e.g., "30-second ad"), which reduced negative sentiment without decreasing revenue. * Discovered that when users are given a choice and clear expectations, their anxiety decreases and their willingness to engage with the content increases. * Eliminated "flow-breaking" ads that mimicked functional UI elements, such as banners placed inside transaction histories that users frequently mistook for personal bank records. * Established a design principle to place advertisements only in areas that do not interfere with information discovery or core user navigation tasks. ### Transforming Advertisements into User Benefits * Developed a dedicated B2B ad platform to scale the variety of available advertisements, ensuring that users receive ads relevant to their specific life stages, such as car insurance or new credit cards. * Shifted the internal perception of ads from "noise" to "benefits" by focusing on the right timing and high-quality matching between the advertiser and the user's needs. * Institutionalized regular "creative ideation sessions" to explore interactive formats, including advertisements that respond to phone movement (gyroscope), quizzes, and mini-games. * Leveraged long-term internal experiments to ensure that even if an idea cannot be implemented immediately, it remains in the team's "creative bank" for future product opportunities. ### Optimizing Value Exchange through Rewards * Conducted over a year of A/B testing on reward thresholds, comparing small cash amounts (1 KRW to 200 KRW), non-monetary items (gifticons), and high-stakes lottery-style prizes. * Analyzed the "labor intensity" of ads by adjusting lengths (10 to 30 seconds) to find the psychological tipping point where users felt the reward was worth their time. * Implemented a high-value lottery system within the Toss Pedometer service, which successfully transitioned a loss-making feature into a profitable revenue stream. * Maintained user activity and satisfaction levels despite the increased presence of ads by ensuring the "worst-case experience"—viewing ads for no gain—was entirely avoided. Product teams should stop viewing business requirements and UX as a zero-sum game. By focusing on user psychology—specifically transparency, non-disruption, and fair value exchange—it is possible to achieve aggressive business targets while maintaining a sustainable and trusted user environment.

Product Design User Experience A B Testing Gamification+3

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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Product Design Large Language Models Generative AI Interaction Design+3

figmaSep 9, 20252 min readCurated summary

Issue No.12: New Roles, New Rules | Figma Blog

Figma’s “New roles, new rules” highlights how AI and faster iteration are blurring traditional boundaries between product roles. Product managers, designers, and developers are increasingly working across disciplines, using prototypes and shared principles to collaborate directly. The issue argues that effective teams are replacing rigid handoffs with experimentation, co-creation, and better systems for guiding AI. ## Shifting Roles - Research found that: - 64% of product builders identify with two or more roles. - 56% of non-designers perform design-related work. - Faster development cycles and AI tools are enabling people to contribute further outside their formal specialties. - As responsibilities expand, teams are also reconsidering how they manage time, ownership, and collaboration. ## Prototyping to Create Shared Understanding - Figma product designer Natasha Tenggoro struggled to explain how video playback should work in Figma Buzz. - Instead of relying on verbal descriptions, she used Figma Make to build prototypes herself. - The prototypes helped the team reach three “aha” moments and provided a clearer basis for discussion. - The example demonstrates how building interactive artifacts can replace lengthy explanations and accelerate alignment. ## Music-Inspired Design in Figma Draw - Figma Draw’s new scatter brushes were shaped by musical concepts such as tempo, texture, and volume. - Designers translated the character of genres including Honky-tonk, Screamo, Doo-wop, and Vaporwave into brush behavior. - The release adds 10 scatter brushes, giving designers more control over attributes such as gap, wiggle, and jitter. ## Duolingo’s Collaborative Method - Duolingo’s Math team is changing the traditional design-to-engineering handoff for its math games. - Designers and engineers work through co-creation, scrappy prototypes, and continuous experimentation. - Shared principles—including “show, don’t tell” and distinguishing between a v1 and an MVP—help the team move quickly while refining ideas. - Collaboration is treated as an ongoing product practice rather than a stage that ends when design is handed off. ## Broader Changes in Creative Work - Agencies and freelancers are also abandoning rigid client boundaries and involving clients throughout the creative process. - Design systems can improve AI-generated code by giving agents structured, relevant, and brand-consistent input. - MCP servers are presented as an important connection between design systems and AI-powered workflows, helping agents produce more useful output. Overall, the issue recommends embracing broader roles and replacing formal handoffs with shared prototypes, collaborative iteration, and well-structured design systems—especially as AI becomes more involved in product development.

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Product Design Figma AI Design Systems+2