kakao·Jun 8, 2026·3 min readCurated summary

Key Players in the Agentic AI Ecosystem: MCP Player 10 Wraps Up, and What’s Next!

Kakao’s first MCP Player 10 competition showcased how developers are using Model Context Protocol (MCP) to build practical agentic AI services. More than 150 teams participated, and ten finalists were selected for solutions addressing childcare, startup support, culture, gaming, legal research, and safety. Kakao plans to expand this ecosystem through the upcoming Agentic Player 10 competition and deeper integration with Kakao Tools. ## The MCP Player 10 Competition - The competition ran from December 19, 2025, to January 18, 2026, on Kakao’s PlayMCP open platform. - It emphasized: - Creativity - Everyday usefulness - Technical stability - The goal was to encourage developers to create MCP servers that solve real-world problems with AI. - Ten teams were selected after internal evaluation and received a share of 21 million won in support funding, along with opportunities to collaborate with Kakao. ## Award-Winning MCP Services ### 어린이ZIP: AI Assistant for Childcare Teachers - Automates administrative work for daycare and kindergarten teachers. - Analyzes uploaded activity photos to generate drafts of parent notices and childcare journals. - Remembers child-specific details such as allergies and pickup arrangements. - Produces personalized responses in a warm, professional tone. ### SeedUp: Startup Support-Program Research - Collects and analyzes fragmented government startup-support announcements. - Summarizes eligibility requirements and relevant opportunities. - Helps founders develop application strategies. - Supports natural-language requests such as finding weekly deadlines or analyzing an uploaded announcement. ### Other Selected Services - **공유 비밀의 방:** An anonymous platform for sharing and empathizing with personal stories and AI conversations. - **바우만 16 안티에이징솔루션:** Recommends skincare routines using the Baumann 16 skin-type classification, cosmetic ingredient data, and skin pH analysis. - **아라드도우미:** A Dungeon & Fighter assistant using RAG and Vision AI to analyze patch notes, item trends, and optimized character builds. - **키즈허브:** Aggregates public data such as emergency-room availability, childcare waiting lists, and child-development information. - **택배추적기:** Combines package tracking with AI-based detection of smishing URLs in delivery-related messages. - **ArtBridge:** Recommends performances and exhibitions from approximately 200,000 records across nine cultural categories, using location, budget, and preferences. - **KidSafe:** Detects harmful language and emotional-crisis signals in children’s chatbot conversations, escalating serious cases to guardians or professional resources. - **LexiLink_ko:** Searches and organizes statutes, court precedents, and administrative interpretations through natural-language queries. All ten MCP servers are now officially available through the PlayMCP platform. ## PlayMCP’s Future Direction - PlayMCP will remain a developer-focused environment for building and distributing MCP servers. - Kakao Tools, available through ChatGPT for Kakao, will focus on helping general users experience MCP-based services. - Kakao plans to connect the two platforms more closely. - Kakao is considering managed infrastructure, including: - Kakao Cloud-based server support - Automated deployment - Greater operational responsibility for MCP service stability - PlayMCP may also support richer in-app interfaces through JSON-based widgets, similar to those already available in ChatGPT for Kakao. ## The Next Competition: Agentic Player 10 Kakao announced a second competition, Agentic Player 10, designed to connect developer-created agents with Kakao Tools and expose them to a broader audience. The program is positioned as an opportunity for startups and aspiring founders to test their services with real users and potentially bring their agents into KakaoTalk. Developers interested in building practical AI agents are encouraged to use PlayMCP and participate in Agentic Player 10 as the next step in Kakao’s expanding agentic AI ecosystem.

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ChatgptAI AgentsModel Context ProtocolRAG+3

toss·Jun 5, 2026·4 min readCurated summary

How the Toss Team Faces the AI Wave: AI Surf Day

Toss created **AI Surf Day**, a dedicated weekly time for employees to experiment with AI, share lessons, and redesign their workflows. Running on Fridays from April through June, the initiative aims to reduce the AI gap across technical and nontechnical roles by making experimentation collaborative and accessible. Its broader conclusion is that successful AI transformation depends less on formal programs than on culture, time, and people who actively share what they learn. ## AI Surf Day’s Purpose - Employees focus on their core work Monday through Thursday and reserve Friday for AI experimentation and practical application. - The program addresses anxiety and knowledge gaps, especially among nondevelopers who may struggle to identify useful AI information or find time to learn it. - Its concept comes from Jon Kabat-Zinn’s phrase: “You can’t stop the waves, but you can learn to surf.” - The goal is to help Toss become a company that works with AI as a foundation, not merely a workplace where individuals use AI tools. ## AI Surf Club - Employees can create or join informal groups focused on AI topics; roughly 200 clubs were formed at launch. - An **AI Antipattern Study** focused on failures and mistakes, turning participants’ experiences into a practical guide for avoiding common problems. - An **LLM Wiki** group explored how to organize scattered organizational knowledge across data engineering, machine learning, and business teams. - A beginner-focused “Step 0” group helped employees overcome basic technical barriers, such as installing agent tools and asking questions they felt were too fundamental. - A customer-protection team built an external-complaint monitoring portal in one month, along with automation for complaint-response drafts and classification. - A marketing team divided AI work into roles such as: - **Builder:** creates AI-powered tools and workflows - **Curator:** collects useful examples and resources - **Operator:** applies AI to repetitive work - **Scouter:** identifies new opportunities - The clubs emphasized reusable outputs and shared confidence, rather than isolated individual experimentation. ## AI Surf Weekly - Weekly sessions share successful internal AI applications, lessons learned, and current industry insights. - Toss connected employees with similar needs across different departments, enabling them to solve problems quickly by learning from existing internal examples. - Rather than prescribing specific tools, the program presents ideas and use cases that encourage employees to adapt solutions to their own work. - Examples included connecting a sales employee with an HR colleague who had built a similar tool, and pairing a marketer with a designer experienced in AI-powered automation. ## AI Surf Evangelists - Toss selected 142 employees across its affiliated companies and teams to promote AI adoption in their own organizations. - Evangelists were chosen through peer nominations, recognizing people who already shared useful discoveries and helped colleagues overcome AI-related obstacles. - Their responsibilities over three months include: - Reporting effective AI use cases - Sharing useful insights with colleagues - Hosting at least one meetup or workshop - Toss’s Culture team provides workshop templates and facilitation support. - Many teams have conducted workshops around redesigning their existing workflows with AI. - The program treats AI adoption as a team-level workflow redesign challenge, rather than simply measuring individual proficiency with AI tools. ## OpenAI Collaboration and Mini-Hackathon - Toss held a special AI Surf Day with OpenAI on May 15. - Hands-on sessions covered: - Codex-based development workflows for developers - ChatGPT Agent-based automation for nondevelopers - A 2.5-hour hackathon produced two notable projects: - An iOS workflow where Codex implements features, operates the simulator, tests the result, iterates on problems, and produces verification footage. - An agent that classifies thousands of daily Toss Place product records, sends reviewers links, and supports approval or rejection through an admin interface. - These projects demonstrated how AI can become a reusable agentic workflow rather than a one-time assistant. ## Culture Over Programs - Toss does not claim to have a fixed answer for managing AI’s rapid evolution. - The lasting value of AI Surf Day is the protected time for learning and experimentation, along with a culture where employees openly share results and failures. - Successful examples spread naturally across teams, while evangelist-led workshops translate experimentation into concrete changes in how work is performed. Organizations pursuing AI transformation can take a similar approach: create dedicated experimentation time, encourage peer-led learning, recognize existing champions, and focus on reusable workflow improvements rather than tool adoption alone.

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ChatgptAILarge Language ModelsAI Agents+4

stripe·Apr 2, 2026·3 min readCurated summary

Insights from Shoptalk 2026: How agents are changing retail

Agentic commerce is already reshaping retail, especially product discovery, embedded checkout, and customer engagement across AI-powered surfaces. However, retailers still lack a common strategy for managing product data, choosing channels, and deciding between first-party and third-party agent experiences. The article argues that success will depend not only on agent-compatible infrastructure, but also on strong brands, unified customer data, and frictionless checkout. ## Agentic Commerce Needs a Standard Framework - Retailers are experimenting with where to begin, which partners to use, and how to syndicate accurate product data across AI platforms. - Search and discovery are changing quickly: - Sephora is using loyalty data in its ChatGPT app to personalize recommendations and highlight benefits such as samples and free shipping. - OpenAI reported that more than half of its searches are discovery-oriented, with 70% containing detailed constraints or context. - AI agents increasingly function as storefronts. Brands that are not discoverable through these systems risk losing visibility. - Direct product feeds are becoming important because they provide agents with more structured and current information than web crawling. - Stripe’s Agentic Commerce Suite allows retailers to connect catalogs and syndicate them across supported agents without building separate integrations. - Many companies are using test-and-learn programs to measure how products are discovered, recommended, and purchased through AI surfaces. ## Commerce Is Expanding Beyond Chat Interfaces - Agentic commerce is appearing across: - Embedded checkout - Product discovery - Customer service - Catalog enrichment - Post-purchase systems - Meta demonstrated a Facebook checkout flow using the Agentic Commerce Protocol, allowing shoppers to move from an ad to product information, AI-generated review summaries, and in-app purchase. - New consumer brands may increasingly be built on agentic infrastructure, reducing customer acquisition costs and dependence on standalone websites. - Retailers must decide how much to invest in first-party experiences versus third-party agents across categories such as fashion, beauty, and home goods. - The market is unlikely to be controlled by one large language model or channel; instead, commerce will spread across many specialized applications and surfaces. ## Brand Trust Becomes More Important - As AI simplifies comparison shopping, trust, consistency, and emotional connection will play a larger role in brand selection. - New Balance is emphasizing consistent quality, store improvements, and better-trained associates rather than relying primarily on discounts. - Tapestry is studying Gen Z to maintain Coach’s relevance, while Victoria’s Secret is focusing on comforting, confidence-building store experiences. - Stitch Fix is using first-party customer data to power Stitch Fix Vision, an AI tool for personalized outfit visualization. - Retailers will need unified customer data and systems that preserve identity and context across websites, stores, apps, and AI agents. ## Checkout and Commerce Infrastructure Remain Fundamental - Customers arriving through agent-driven journeys may be ready to buy and less tolerant of checkout friction. - Stripe says its Optimized Checkout Suite selects payment methods using more than 100 signals and typically increases conversion by 2%–3%. - Core requirements remain unchanged: - Fast, branded checkout - Relevant payment methods - Effective fraud prevention - Connected online, in-store, and in-app commerce data - Stripe’s Agentic Commerce Suite is designed to let businesses connect their catalog and commerce systems once, then expand into compatible agents and channels. Retailers should begin with structured product data, measurable experiments, unified customer systems, and a frictionless checkout experience. Agentic channels are developing rapidly, but durable brand value and strong commerce fundamentals will remain essential as those channels multiply.

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ChatgptDatabase DesignStripeAgentic Commerce+4

line·Feb 23, 2026·4 min readCurated summary

Sharing the journey of LINE DEV

AI adoption at LY Corporation has moved beyond experimentation toward learning how to use these tools effectively in real work. The LINE DEV AI Reporters program connects scattered individual and team experiences through internal sharing sessions, helping practical lessons spread across the organization. Its central conclusion is that AI productivity depends not only on tools, but also on clear specifications, sound engineering practices, and a culture of continuous sharing. ## Turning Individual Experiments into Organizational Knowledge - AI enthusiasts across LY Corporation were independently experimenting with tools such as ChatGPT and Claude Code. - These experiences often remained limited to individuals or small teams. - AI Reporters brought together members of different roles and seniority who had experience sharing AI-related work. - Their goal was to turn personal trial and error into reusable organizational knowledge. ## Starting with Informal Personal Experiments - Early AI sharing sessions emphasized accessibility rather than polished success stories. - Multimedia Platform Dev’s Choi Jeong-min shared a “one service a day” vibe-coding experiment using Claude Code and Antigravity. - The experiment demonstrated that rapid implementation increases the importance of clearly defining what to build. - Sharing failures and unfinished experiments reduced the pressure to perform and encouraged more employees to try AI themselves. ## Applying AI to Real Development Work - As interest grew, discussions shifted from fun experiments to practical workplace applications. - Data Dev4’s Lee Yun-seong shared more than a month of project experience using Claude Code, project templates, and Vibe Kanban. - Developers spent more time on planning, design, review, and coordination while agents handled implementation. - Because the current codebase becomes the context for future agent work, poor architecture and coding styles can quickly be reproduced and amplified. - Continuous testing, refactoring, documentation, interface management, and architectural cleanup are therefore essential. - Skipping automated tests before commits led to increasing numbers of broken changes during later merges. - Humans remain responsible for ensuring that AI-generated code actually contributes to the project. - The most valuable skills increasingly involve task design, project management, system context, and meta-programming rather than implementation alone. - Developers can work in parallel with agents by planning the next task, researching requirements, and reviewing completed code while agents execute current work. ## Expanding from Teams to Organization-Wide Programs - Fintech Engineering organized a hands-on workshop covering the full path from idea to deployment. - Participants connected ChatGPT, Claude Code, and Stitch AI to plan, design, build, and complete a working service. - The integrated workflow helped participants understand how AI tools can support an entire product-development process, not just prototyping. - The GAI Study Group in the advertising organization broadened discussions to AI strategy, trends, agent behavior, developer workflows, and business applications. - Topics included: - AI agent reliability - Implementing interactions between PyTorch-based LLMs and MCP servers - Senior and junior developers’ vibe-coding workflows - NotebookLM-based RAG using wiki pages and Slack conversations - One session examined MCP internals by implementing JSON-RPC messaging and session-state management directly, revealing complexities hidden by libraries such as FastMCP. - Sessions were opened to participants and presenters from other teams, with some content published online for wider access. ## Building a Culture of Continuous Sharing - The most useful AI knowledge came from real workplace attempts, failures, and revisions—not only from polished documentation or external trends. - AI Reporters made existing but scattered experiences visible and connected them through presentations, Slack discussions, and monthly meetings. - Informal conversations such as “I tried this—how did it work for you?” helped normalize experimentation and learning from mistakes. - AI adoption is treated as an ongoing practice because tools and workflows continue to change. LY Corporation’s experience suggests that organizations should create lightweight, recurring forums where employees can share practical AI experiments. The combination of rapid experimentation, disciplined engineering, and open knowledge exchange allows individual discoveries to become lasting organizational capability.

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ChatgptAIPostgreSQLClaude Code+4

line·Dec 30, 2025Original article

A Business Trip to Japan After Only One (opens in new tab)

Joining the Developer Relations (DevRel) team at LINE Plus, a new employee was immediately thrust into a high-stakes business trip to Japan just one week after onboarding to support major global tech events. This immersive experience allowed the recruit to rapidly grasp the company’s engineering culture by facilitating cross-border collaboration and managing large-scale technical conferences. Ultimately, the journey highlights how a proactive onboarding strategy and a culture of creative freedom enable DevRel professionals to bridge the gap between complex engineering feats and community engagement. ### Global Collaboration at Tech Week * The trip centered on participating in **Tech-Verse**, a global conference featuring simultaneous interpretation in Korean, English, and Japanese, where the focus was on maintaining operational detail across diverse technical sessions. * Operational support was provided for **Hack Day**, an in-house hackathon that brought together engineers from various countries to collaborate on rapid prototyping and technical problem-solving. * The experience facilitated direct coordination with DevRel teams from Japan, Thailand, Taiwan, and Vietnam, establishing a unified approach to technical branding and regional community support. * Post-event responsibilities included translating live experiences into digital assets, such as "Shorts" video content and technical blog recaps, to maintain engagement after the physical event concluded. ### Modernizing Internal Technical Sharing * The **Tech Talk** series, a long-standing tradition with over 78 sessions, was used as a platform to experiment with "B-grade" humorous marketing—including quirky posters and cup holders—to drive offline participation in a remote-friendly work environment. * To address engineer feedback, the format shifted from passive lectures to **hands-on practical sessions** focusing on AI implementation. * Specific technical workshops demonstrated how to use tools like **Claude Code** and **ChatGPT** to automate workflows, such as generating weekly reports by integrating **Jira tickets with internal Wikis**. * Preparation for these sessions involved creating detailed environment setup guides and troubleshooting protocols to ensure a seamless experience for participating developers. ### Scaling AI Literacy via AI Campus Day * The **AI Campus Day** was a large-scale event designed for over 3,000 participants, aimed at lowering the barrier to entry for AI adoption across all departments. * The "Event & Operation" role involved creating interactive AI photo zones using **Gemini** to familiarize employees with new internal AI tools in a low-pressure setting. * Event production utilized AI-driven assets, including AI-generated voices and icons, to demonstrate the practical utility of these tools within standard business communication and video guides. * The success of the event relied on "participation design," ensuring that even non-technical staff could engage with AI concepts through hands-on play and peer mentoring. For organizations looking to strengthen their technical culture, this experience suggests that integrating new hires into high-impact global projects immediately can be a powerful onboarding tool. Providing DevRel teams the psychological safety to experiment with unconventional marketing and hands-on technical workshops is essential for maintaining developer engagement in a hybrid work era.

ChatgptAIGenerative AIGemini+5

line·Dec 19, 2025Original article

We held AI Campus Day to improve (opens in new tab)

LY Corporation recently hosted "AI Campus Day," a large-scale internal event designed to bridge the gap between AI theory and practical workplace application for over 3,000 employees. By transforming their office into a learning campus, the company successfully fostered a culture of "AI Transformation" through peer-led mentorship and task-specific experimentation. The event demonstrated that internal context and hands-on participation are far more effective than traditional external lectures for driving meaningful AI literacy and productivity gains. ## Hands-on Experience and Technical Support * The curriculum featured 10 specialized sessions across three tracks—Common, Creative, and Engineering—to ensure relevance for every job function. * Sessions ranged from foundational prompt engineering for non-developers to advanced technical topics like building Model Context Protocol (MCP) servers for engineers. * To ensure smooth execution, the organizers provided comprehensive "Session Guides" containing pre-configured account settings and specific prompt templates. * The event utilized a high support ratio, with 26 teaching assistants (TAs) available to troubleshoot technical hurdles in real-time and dedicated Slack channels for sharing live AI outputs. ## Peer-Led Mentorship and Internal Context * Instead of hiring external consultants, the program featured 10 internal "AI Mentors" who shared how they integrated AI into their actual daily workflows at LY Corporation. * Training focused exclusively on company-approved tools, including ChatGPT Enterprise, Gemini, and Claude Code, ensuring all demonstrations complied with internal security protocols. * Internal mentors were able to provide specific "company context" that external lecturers lack, such as integrating AI with existing proprietary systems and data. * A rigorous three-stage quality control process—initial flow review, final end-to-end dry run, and technical rehearsal—was implemented to ensure the educational quality of mentor-led sessions. ## Gamification and Cultural Engagement * The event was framed as a "festival" rather than a mandatory training, using campus-themed motifs like "enrollment" and "school attendance" to reduce psychological barriers. * A "Stamp Rally" system encouraged participation by offering tiered rewards, including welcome kits, refreshments, and subscriptions to premium AI tools. * Interactive exhibition booths allowed employees to experience AI utility firsthand, such as an AI photo zone using Gemini to generate "campus-style" portraits and an AI Agent Contest booth. * Strong executive support played a crucial role, with leadership encouraging staff to pause routine tasks for the day to focus entirely on AI experimentation and "playing" with new technologies. To effectively scale AI literacy within a large organization, it is recommended to move away from passive, one-size-fits-all lectures. Success lies in leveraging internal experts who understand the specific security and operational constraints of the business, and creating a low-pressure environment where employees can experiment with hands-on tasks relevant to their specific roles.

ChatgptAIGenerative AIModel Context Protocol+5

figma·Dec 10, 2025·2 min readCurated summary

Turn Your Prompts Into Visual Assets and Slide Decks With the Figma App in ChatGPT | Figma Blog

Figma has expanded its ChatGPT app beyond FigJam diagrams, allowing users to generate editable visual assets and presentation decks. ChatGPT provides initial creative directions, while Figma Buzz and Figma Slides support detailed editing, branding, resizing, and collaboration. The workflow is designed to move quickly from an idea or outline to a polished, team-ready result. ## Creating Visual Assets with Figma Buzz - Users can prompt ChatGPT to generate starting concepts for posters, invitations, digital ads, and video collages. - The app can produce multiple variations of a visual identity, layout, and copy. - Assets can then be opened in Figma Buzz for refinement, including: - Editing text and typography - Adjusting color palettes with blend modes - Applying brand-specific changes - Resizing designs for LinkedIn, X, Instagram, and other platforms - Figma Buzz is intended to make polished marketing assets accessible even to people without specialized design skills. ## Building Presentations in Figma Slides - ChatGPT can generate a Figma Slides deck from a user-provided outline or suggest a presentation flow. - Example decks include “year in review” templates organized around goals, priorities, highlights, and completed projects. - Users can select a generated option and continue editing collaboratively in Figma Slides. - Teams can replace generated images, adjust templates, and refine colors and typography to match brand guidelines. - Interactive features such as live polls, stamps, and alignment scales support feedback and discussion during presentations. ## Availability - The Figma app is available to ChatGPT users outside the European Union. - Figma Buzz and Figma Slides features are currently in beta within the app. The recommended workflow is to use ChatGPT for rapid ideation and first drafts, then move into Figma Buzz or Figma Slides for brand alignment, detailed editing, and collaborative refinement.

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ChatgptFigmaFigma SlidesOpenai+2

figma·Nov 25, 2025·3 min readCurated summary

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

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

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ChatgptAIFigma MakeUser Experience Design+2

figma·Oct 5, 2025·2 min readCurated summary

Turn Your ChatGPT Brainstorms Into FigJam Diagrams | Figma Blog

Figma’s new ChatGPT app turns brainstorms, sketches, uploaded files, and technical documents into editable FigJam diagrams. It supports flowcharts, sequence and state diagrams, and Gantt charts, helping users move quickly from exploration to collaborative artifacts. The feature is powered by Figma’s remote MCP server and is available to logged-in ChatGPT users outside the EU. ## Turning Conversations into Diagrams - Users can mention Figma in a prompt, such as “Figma, make a diagram from this sketch.” - ChatGPT can recommend the Figma app when diagramming is relevant. - Photos, drawings, PDFs, and other files can provide context. - Generated diagrams can be revised, expanded, or represented in alternative formats. - Figma plans to add more diagram types over time. ## Accelerating Design Iteration - Hand-drawn sketches can become shareable FigJam files. - Designers can ask ChatGPT to update diagrams or explore different visualizations. - Dense documents can be uploaded so ChatGPT can produce an initial draft. - This helps teams move ideas from informal notes or whiteboards into a collaborative workspace. ## Clarifying Technical Systems - Developers can use uploaded documentation and screenshots to create or update software architecture diagrams. - ChatGPT can research technical approaches using blogs and case studies, then visualize them. - Screenshots, such as a pricing page, can be used to map likely React component structures. - The resulting diagrams support system design discussions, technical communication, and interview preparation. ## Planning Products and User Experiences - Product managers can visualize tradeoffs, such as simplicity versus power in a permissions flow. - PRDs can be converted into user journey or process flowcharts. - Product, engineering, and design requirements can be combined into Gantt charts for launch planning. - ChatGPT supports individual exploration, while FigJam enables teams to review and iterate together. The feature is currently live for logged-in ChatGPT users outside the EU. It offers a practical workflow for using ChatGPT to generate a first visual draft and FigJam to refine, discuss, and collaborate on it.

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ChatgptFigmaFigjamReact+4

figma·Mar 13, 2025·3 min readCurated summary

Dylan Field and Garry Tan on design, AI, and the power of “locking in” | Figma Blog

AI is expanding what designers and product builders can explore, but it has not eliminated the need for human judgment, context, or craft. Dylan Field argues that design will become even more important as teams use AI to generate ideas and prototypes faster, while still needing expertise to turn them into thoughtful, polished products. The central challenge is closing the gap between making something work and making it work well. ## AI Expands the Design “Idea Maze” - Current AI systems primarily function as tools that augment people in specific tasks, rather than as general intelligence. - They lower the barrier to participating in design while also raising the ceiling on what experienced creators can accomplish. - AI allows teams to explore more branches of an “idea maze,” generating greater breadth during ideation. - However, meaningful progress still requires depth: teams must investigate, refine, and evaluate promising directions. ## The Value of Rapid Feedback and “Vibe Coding” - Terms such as “getting locked in,” “I’m cooking,” and “vibe coding” describe the flow state created by rapid experimentation. - Faster feedback loops help people move ideas from their heads onto the screen more fluidly. - Figma’s emphasis on play reflects the goal of making creative expression accessible and enjoyable, even for non-experts. - AI tools are increasingly effective at helping users start and prototype quickly. - The unresolved problem is helping users move from an exciting prototype to a finished, reliable product—an issue shared by both design and code-generation tools. ## Design Is More Than Functionality - Founders and teams increasingly recognize design as a source of product value. - The important question is no longer only whether software works, but how it works. - User experience, clarity, quality, and the overall interaction determine whether a product feels successful. ## Why Human Designers Still Matter - AI has developed along partly separate tracks: diffusion models address visual creation, while language models focus on reasoning and code generation. - It remains unclear how effectively these approaches can be combined into systems capable of true design. - Field describes design as “art as it applies to problem solving,” requiring more than producing an image or implementing a short specification. - Designers contribute context involving culture, brand, product experience, and the broader problem being addressed. - As software creation becomes more automated, the ability to supply judgment and context may make design an even more critical role. AI is best understood as a force multiplier for exploration and iteration, not a replacement for design expertise. Teams should use it to accelerate experimentation while preserving the human work required to select, shape, and finish products well.

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ChatgptFigmaAILarge Language Models+4

figma·Mar 19, 2024·3 min readCurated summary

AI + Design: Figma Users Tell Us What’s Coming Next | Figma Blog

Generative AI’s impact will depend not only on technical capability but also on how effectively it is designed into products and everyday workflows. A Figma survey of more than 1,800 designers, developers, and executives shows high expectations for AI, but limited evidence that current implementations are delivering meaningful value. The findings point to a risk of “AI feature fatigue” and suggest that thoughtful, user-centered design will determine whether AI becomes genuinely useful. ## Survey Scope and Methodology - Figma surveyed more than 1,800 users between February 26 and March 3, 2024. - Participants included designers, developers, and executives across the US, Canada, Australia, the UK, Japan, France, and Germany. - The research examines how organizations view AI’s near-term impact and how teams are incorporating it into products. ## High Expectations, Limited Results - 89% of respondents expect AI to affect their company’s products or services within 12 months. - 37% anticipate a “significant or transformative” impact. - Executives are especially likely to view AI as important to company goals. - Despite this optimism, 72% of people whose products include AI say it has only a minor or non-essential role. - Only about one-third report improvements in business metrics such as revenue, costs, or market share. - Fewer than one-third say they are proud of what they have shipped. ## The Risk of AI Feature Fatigue - Figma researchers observed growing indifference toward adding “yet another AI feature.” - More than 20% of teams building AI products identify failure to solve a real user need as a major challenge. - This concern is particularly strong among designers. - Fewer than half of respondents working on AI products or features have shipped anything, indicating that a larger wave of AI products may still be coming. - Organizations risk flooding the market with novelty features that users do not find useful. ## Designing AI Into Existing Products - One-third of respondents rank integrating AI coherently into existing products as a top challenge. - Simply adding AI without improving the overall user experience is unlikely to drive adoption. - Teams need to help users understand: - What AI tools are available - When those tools are useful - How AI improves existing workflows - The article uses ChatGPT as an example: its rapid adoption was driven partly by a simple, accessible conversational interface, even though the underlying model capabilities already existed. - Good design can make powerful technology more approachable, aligned with user expectations, and easier to use. ## Practical Implication AI products are more likely to succeed when they address concrete user problems rather than adding AI for its own sake. Organizations should prioritize coherent product integration, clear user experiences, and measurable improvements over ambitious but disconnected features.

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ChatgptFigmaAIUser Experience+2

figma·Sep 20, 2023·3 min readCurated summary

The Long and Short of It: Issue no.1 | Figma Blog

Figma’s first editorial issue argues that personal and professional growth often comes from stepping outside conventional roles, metrics, and workflows. It highlights human creativity in an AI era, alternative career paths, unconventional organizational structures, and the danger of reducing work to easily measured outcomes. The overall conclusion is that progress requires embracing ambiguity, risk, and broader definitions of success. ## Human Creativity in the Age of AI - John Maeda argues that people remain valuable because they think in circuitous, non-linear ways. - Unlike machines optimized for efficiency and shortcuts, humans can make unexpected creative leaps. - This “uphill thinking” involves greater uncertainty and risk, but can produce greater rewards. - The accompanying discussion explores how designers can work with AI without abandoning distinctly human innovation. ## UX Design as a Second Chance - CROP, a California nonprofit, trains formerly incarcerated people for careers in UX design. - Its program uses Figma to provide practical skills and an alternative path into the technology industry. - Fellow Ron Scott, released after 27 years in prison, sees product design as a way to influence how people interact with technology. - The program also reflects a broader belief that more people should have a voice in shaping products and services. ## Challenging Conventional Product Roles - Airbnb CEO Brian Chesky’s Config talk described how he helped rescue Airbnb by emphasizing design and eliminating the traditional product management function. - The decision sparked debate about the relationship between designers and product managers. - Figma presents the issue less as a true rivalry than as an opportunity to question inherited workplace structures. - Industry leaders suggest that teams may benefit from abandoning rigid titles and recognizing that everyone is working toward the same goal. ## Rethinking Productivity and Perspective - The issue points to criticism from Gergely Orosz and Kent Beck of attempts to measure developer productivity through narrow, quantifiable outcomes. - It asks whether an apparently unproductive colleague might instead be judged by an inadequate definition of success. - A reference to filmmaker Wong Kar Wai reinforces the importance of framing: what is excluded from view can matter as much as what is visible. ## Making Space for Better Ideas - The closing reflection argues that conversational interfaces such as ChatGPT can confine thinking to “chat boxes.” - Product designer Aosheng Ran suggests that giving ideas more space could reveal possibilities missed by conventional text-based interaction. Figma’s issue recommends looking beyond labels, efficiency metrics, and established roles. In practice, that means protecting room for creative detours, evaluating work more thoughtfully, and using technology to expand—not narrow—human possibilities.

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ChatgptFigmaAIUser Experience+3

figma·Aug 22, 2023·2 min readCurated summary

Give ideas more space with Jambot | Figma Blog

Jambot is a FigJam widget that brings ChatGPT’s generative capabilities into a collaborative, visual canvas. Figma created it to move beyond the limitations of linear chat, allowing people to ideate, branch into related topics, summarize discussions, and explore ideas together. The project reflects Figma’s broader view that AI interfaces should become more spatial, tangible, and multiplayer. ## From Chatbots to Creative Collaboration - Large language models can simulate spontaneous brainstorming and provide a broad base of knowledge. - ChatGPT’s conversational format is useful but can feel one-sided and restrictive during creative work. - Jambot was designed to make AI interaction more collaborative and adaptable to group ideation. ## Limitations of Linear Chat - Chat conversations present ideas in a one-dimensional sequence. - When ChatGPT offers multiple possibilities, exploring one path makes it difficult to return to another without scrolling and repeating questions. - Linear chat makes it unnatural to branch into related topics, compare alternatives, or see how ideas connect. ## A Visual Alternative - Jambot began as an internal Figma AI hackathon project described as “a visual version of ChatGPT.” - Its concept draws on networked-thinking tools such as Roam Research and Logseq, which link and organize ideas across pages. - The team was also inspired by Albus, which gives AI interaction a more visual structure. - LangChain influenced the idea of making sophisticated AI workflows visually tangible rather than requiring users to write code. ## Rethinking AI Interfaces - The team argues that users are currently “stuck in chat boxes,” much as they became dependent on video-call interfaces like Zoom. - Existing AI interfaces can feel primitive and command-line-like, despite decades of progress in graphical user interfaces. - Designers have an opportunity to develop new interaction patterns that provide more context, identity, and flexibility than simple conversational prompts. - A visual canvas can make AI more approachable while supporting branching ideas and shared participation. ## What Jambot Enables - Ideation and brainstorming directly inside FigJam. - Summarizing conversations or collections of ideas. - Riffing on concepts and extending them in multiple directions. - Collaborative exploration of AI-generated output within a multiplayer workspace. Jambot’s central recommendation is to treat AI as something that can inhabit richer environments than a chat window. By placing generative AI on a shared visual canvas, Figma aims to give teams more space to explore, connect, and develop ideas together.

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ChatgptLarge Language ModelsGenerative AIFigjam+2

figma·Jun 20, 2023·3 min readCurated summary

AI: The Next Chapter in Design | Figma Blog

Figma argues that AI will become a core platform capability reshaping the entire product-development process, not merely another feature. It can accelerate ideation, design, and coding while allowing teams to focus more on problem-solving and creative judgment. Figma announced its acquisition of Diagram as part of this strategy, positioning AI as a force that will change how products are designed, what experiences are created, and who participates in the process. ## Figma’s AI strategy and Diagram acquisition - Figma acquired Diagram, founded by Jordan Singer, whose GPT-3-powered “Designer” plugin generated design concepts from simple prompts. - The acquisition brings Diagram’s team into Figma and builds on Figma’s existing investment in machine learning. - Figma’s open API has already enabled nearly 100 community-built AI plugins. - The company views AI as a platform underlying the entire product-development workflow. ## AI across the product-development process - During discovery, AI could: - Generate and synthesize early ideas from prompts. - Summarize discussions and concepts. - During design, AI could: - Use existing designs and design systems to provide recommendations. - Surface relevant components and patterns. - Help teams produce first drafts faster. - During development, AI could: - Infer design context more effectively. - Generate higher-quality, production-ready code. - The broader goal is to help teams do more work faster while moving their attention toward higher-level problem-solving. ## How design may evolve from pixels to patterns - Design systems already shifted designers away from repetitive details such as border radii and toward composition, direction, and judgment. - Atomic elements such as pixels became reusable components, enabling faster and more consistent workflows. - AI could extend this progression by generating higher-level structures and patterns. - Designers may focus less on assembling basic login components and more on inventing entirely new ways to authenticate. - AI might also recommend color palettes based on a project’s emotional tone or theme. - This could move design beyond familiar interfaces toward smoother, more intuitive, and more human experiences. ## What product teams may design - AI systems such as ChatGPT are shifting interaction away from navigating websites and apps toward asking questions and receiving answers. - AI can reduce the gap between a user’s intention and the actions required to achieve it. - For example, instead of opening a ride-hailing app, entering a destination, comparing options, and requesting a ride, a user could simply say, “Get me to JFK.” - Product builders will need to reconsider whether existing interfaces can deliver the same outcome with fewer steps and decisions. ## The changing role of designers - Technological change has historically transformed design without eliminating the need for thoughtful designers. - Designers have adapted to new platforms, collaborative workflows, and hybrid work. - Figma expects AI to change design roles and collaboration, but frames that shift as an opportunity to spend more time on creative direction, curation, and meaningful problem-solving. The practical recommendation is to treat AI as a foundational design and development capability rather than a standalone feature. Teams should explore how it can remove repetitive work while preserving human judgment, taste, and responsibility for the experiences they create.

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ChatgptAIDesign SystemsMachine Learning+4