AI

331 posts

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.

AI Figma Product Design 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.

AI User Experience Prototyping Product Design+2

githubJul 1, 20261 min readCurated summary

6 security settings every GitHub maintainer should enable this week

Joseph is a cybersecurity and AI expert who creates software and educational content to help developers build more securely. His open-source game, videos, and international speaking engagements have reached a broad audience, combining practical security guidance with accessible explanations. ## Cybersecurity and AI Leadership - Develops software and content focused on secure development. - Helps shape how developers approach cybersecurity and AI. ## Open-Source Security Education - Created the open-source game [gh.io/scg]. - More than 10,000 developers have used it to build future-proof security skills. ## Educational Videos - His videos have received over 2.8 million views. - Simplifies complex security topics into actionable advice for a global audience. ## International Speaking - Delivered 79 talks across 25 countries in the past four years. - Known for combining technical insight with energetic stage presence. Overall, Joseph’s work spans hands-on tools, accessible education, and public speaking, making cybersecurity knowledge more practical and widely available to developers.

AI Open Source Cybersecurity Secure Software Development

cloudflareJul 1, 20264 min readCurated summary

Content Independence Day, one year on- building the business model for the agentic Internet

Cloudflare argues that generative AI has rapidly replaced the traditional web model in which publishers traded content access for search referrals. With AI now driving much of online discovery and crawler activity, content is increasingly consumed without users visiting its source. The company says a new market is emerging in which transparency, access controls, scarcity, and licensing can help publishers regain economic value. ## AI’s rapid transformation of the Internet - Generative AI adoption has reached more than 2.5 billion regular users—over 30% of humanity—in roughly 3.5 years, reportedly more than twice the adoption speed of smartphones. - Users now spend only about 15 minutes on the open web for every hour spent searching for information. - Instead of visiting and comparing multiple websites, users increasingly receive consolidated answers directly from AI systems. - More than 50% of Internet traffic is now non-human, marking the arrival of what Cloudflare calls the “agentic Internet.” ## Crawlers are increasingly focused on AI - AI training accounted for 52% of crawler requests in June 2026, up from 22% in spring 2025. - Mixed-use crawlers, combining search, agent activity, and training, represented more than 36% of crawler traffic. - Traditional search crawlers make up a smaller share of activity, even though they remain important for sending visitors to publishers. - Mixed-purpose crawling makes it difficult for site owners to remain visible to AI-driven discovery without also giving away content for training without compensation. ## The traditional web business model is breaking down - Historically, publishers allowed search engines to crawl their content in exchange for visibility and referral traffic. - AI systems now answer questions, conduct research, compare products, and complete tasks without necessarily sending users to original sources. - Content can therefore be crawled, indexed, and monetized by AI companies while the original publisher receives little or no traffic. - News and media organizations experienced the disruption first, but retail, software, IT, finance, and other sectors are also affected. - Some heavily crawled categories have seen human traffic fall by as much as 40% in under a year. - Publishers are preparing for “Google Zero,” in which search referrals provide little meaningful traffic. ## The impact extends across industries - Any organization publishing proprietary information online may need a strategy for AI access and monetization. - The issue affects not only traditional publishers but also businesses whose websites contain valuable product, technical, financial, or industry knowledge. - Cloudflare frames the sustainability of online content as an economic and public-interest concern because the Internet remains a major global information resource. ## Building a market for content Cloudflare says Content Independence Day focused on three goals: - Give site owners transparency and control over how their content is accessed and monetized. - Create scarcity by allowing publishers to restrict or selectively permit AI access. - Establish a marketplace where publishers and AI companies can discover, license, and price content. According to the post, these efforts have helped create the early conditions for a monetized content market. ## Control and data create negotiating power - Cloudflare’s attribution, business intelligence, and enforcement tools let publishers observe AI access at the network level. - These tools provide stronger practical enforcement than voluntary mechanisms such as `robots.txt`. - Publishers can identify: - How often LLMs attempt to access their content - Which competing AI systems are crawling their sites - Which URLs are most in demand - The relationship between crawling and referrals - Restricting or controlling access creates scarcity, which gives publishers leverage in licensing negotiations. - Better operational data reduces information asymmetry and allows content owners to negotiate with evidence rather than guesswork. Ultimately, the post recommends treating online content as an economic asset rather than an unlimited free input. Publishers should measure AI consumption, control access, and pursue licensing arrangements so that the agentic Internet can support content creation instead of undermining it.

AI Large Language Models Generative AI Cloudflare+3

figmaJul 1, 20262 min readCurated summary

Trust You Can Verify: Figma Is Now ISO 42001 Certified | Figma Blog

Figma has achieved ISO/IEC 42001:2023 certification, making its AI governance independently verifiable rather than based solely on company assurances. An ANAB-accredited certification body, Schellman, audited Figma’s policies, risk management, data practices, and AI development processes. The certification is intended to give customers—especially regulated organizations—stronger evidence for vendor assessments, regulatory reviews, and board reporting. ## Why Independent Verification Matters - Vendors can describe their AI controls through questionnaires, whitepapers, and documentation, but those materials remain self-reported. - ISO 42001 requires an accredited third party to evaluate whether an organization’s AI management system meets an international standard. - Figma says this provides more reliable evidence than simply claiming to practice responsible AI governance. ## Scope of Figma’s Certification - The certification covers the AI Management System governing how Figma designs, develops, and operates AI features. - It applies across: - Figma Design - Figma Make - FigJam - Dev Mode - Figma Sites - Figma Slides - Figma Draw - Figma Buzz - Figma Weave ## What the Audit Evaluated - The audit took place in two stages: - **Stage 1:** Reviewed the design of Figma’s AI Management System, including documentation, policies, and risk methodology. - **Stage 2:** Tested operational effectiveness through staff interviews, process observation, and control evaluations. - Auditors assessed 38 controls across nine areas: - AI impact assessment - Governance and accountability - AI-specific risk management - AI system lifecycle management - Data governance - Third-party AI risk - Monitoring and performance evaluation - Human oversight - Responsible use of AI systems - Figma emphasizes that the certification validates implementation, not merely the existence of written policies. ## Relevance for Customers - The certification gives customers evidence they can reference in: - Vendor risk assessments - Board reporting - Regulatory submissions - AI procurement processes - It is particularly relevant to financial services, healthcare, insurance, and public-sector organizations with strict security, privacy, and regulatory requirements. - Figma connects the certification to the EU AI Act and emerging procurement standards, which increasingly require demonstrable governance rather than vendor promises. ## Ongoing Commitment - Figma plans to continue submitting its AI governance practices to independent verification as its AI capabilities evolve. - Its certificate and broader compliance documentation are available through `compliance.figma.com`. - The certificate can also be verified through Schellman’s directory, and Figma says it will update its documentation when governance changes affect customer risk assessments. ISO 42001 certification represents a baseline for Figma’s ongoing AI governance efforts, giving customers independently audited evidence they can use when evaluating the company’s AI products.

AI Data Governance Ai Governance Security Compliance+3

metaJun 30, 20262 min readCurated summary

10 Years of Meta’s Commitment to Python

Meta marks its 10th consecutive year sponsoring the Python Software Foundation (PSF), emphasizing that Python is central to its infrastructure, products, and AI work. The company views sponsorship as both a responsibility to the open-source community and a strategic investment in the long-term health, security, and innovation of the technology it relies on. ## Python’s Role at Meta - Python is Meta’s most widely used programming language. - It supports infrastructure for products including Instagram and Threads, as well as AI research and data-driven initiatives. - Meta engineers contribute directly to Python’s development, including core maintenance and Python Enhancement Proposals. - Meta’s open-source contributions include: - PyTorch, originally developed at Meta before becoming an independent foundation. - Pyrefly, a fast Python type checker and language server. - Meta expects Python to remain important as it expands AI capabilities and scales its infrastructure. ## Why Meta Supports the PSF - Open-source adoption creates a shared responsibility to maintain a healthy, secure, and sustainable ecosystem. - PSF funding supports the Developer-in-Residence program, enabling full-time developers to work on Python improvements that might otherwise be neglected or left to volunteers. - Sponsorship helps strengthen PyPI, including critical security improvements that protect package distribution and consumption. - Funding also supports education and community development through: - PyCon US workshops, summits, and discounted or free passes. - Fundraising and support for groups such as PyLadies. - Meta considers these efforts an investment in the tools, infrastructure, and people behind its own technology stack. ## Ways to Support the Python Software Foundation - Individuals can make one-time donations or become PSF members. - Membership may include voting rights and can be supported through financial contributions or volunteer time. - Organizations can become annual sponsors at different contribution levels. - Sponsorship offers public recognition, community engagement opportunities, event participation, and—in higher tiers—greater visibility and invitations to special initiatives. Meta concludes by thanking Python’s maintainers, contributors, educators, and advocates, while encouraging other individuals and organizations to help sustain the language through PSF donations, membership, or sponsorship.

AI Machine Learning Open Source Python+3

figmaJun 29, 20263 min readCurated summary

Start Anywhere, a Magazine by Figma | Figma Blog

Figma’s 2026 *Start Anywhere* magazine explores how new tools are expanding the ways people begin and develop creative work. Its central argument is that as motion, code, and AI become more integrated into the design canvas, creative workflows are converging and becoming more expressive. Rather than prescribing one starting point, Figma encourages designers of all experience levels to choose an entry point and keep exploring. ## A Magazine About Design’s Changing Landscape - The magazine was created for Config 2026 by No Ideas, featuring work by several artists and designers. - Figma’s annual publication aims to capture what matters in design beyond rapidly changing product releases. - This year’s theme reflects the difficulty—and freedom—of writing about tools that evolve quickly. - The enduring principles are curiosity, patience, and understanding what something is before focusing on what it does. ## More Materials on the Canvas - Figma Motion introduces a timeline directly into the canvas, allowing designers to work with movement alongside components, variables, and collaborators. - Motion design principles remain important even as tools become easier to use: - Timing and mechanics give movement meaning. - Foundational craft helps designers make better creative decisions. - Code is also moving into Figma’s shared multiplayer environment. - Code layers allow teams to explore design and implementation side by side, making code a more direct part of the design process. ## The Design-to-Code Loop - As work moves fluidly between code and canvas, design and development workflows increasingly converge. - The magazine examines how this connected process can: - Enable faster experimentation. - Support multiple directions in parallel. - Improve collaboration between designers, engineers, and AI-focused teams. - Carry ideas more smoothly from early exploration into production. ## AI as a New Path from Idea to Product - AI tools are changing where product work begins and how ideas move through the development process. - The magazine presents examples from four organizations using AI in different ways. - These approaches suggest that AI can influence: - Ideation and initial exploration. - Product design and iteration. - The transition from design concepts to working software. - The continuity of ideas through production. ## Imagining Future Human–Computer Interaction - The “Future states” section asks what it might mean to reduce the gap between human and machine intelligence. - Contributors imagine software that interacts in more human-centered ways, including: - Interfaces that respond to users’ emotions. - Systems that help people anticipate the consequences of decisions. - New forms of interaction beyond traditional interfaces. ## Who the Magazine Is For - The publication addresses: - Beginners with ideas but no clear starting point. - Experienced practitioners looking for new creative possibilities. - Anyone who wants to produce effective design efficiently. - Its three covers represent different prompts and entry points into the same broader questions. The practical message is to start wherever the most interesting possibility appears—whether in design, motion, code, or AI—and continue iterating. The tools may change quickly, but curiosity and strong creative fundamentals remain useful across every workflow.

AI Figma React Design To Code+1

tossJun 26, 20264 min readCurated summary

While Others Test Toss, We Build the Way to Test.

Every week, Toss releases a new version containing roughly 300–400 code changes, making quality assurance essential to protecting users from broken critical features. The QA Platform team combines smoke tests, regression testing, release monitoring, automation, and AI-assisted tools to make releases reliable and efficient. Its central lesson is that teams often want QA work handled responsibly—not merely more tools to operate themselves—so the team is shifting toward directly owning more of the testing process. ## Weekly Release Quality - QA begins when a Release Candidate is available. - **Toss Doctor** runs smoke tests covering core flows such as login through account deletion. - A pull-request analyzer identifies changed areas, potential impact, bug risk, and testing priorities. - **Toss Checker** performs regression testing to ensure new changes have not damaged stable functionality. - After release, the team monitors crash metrics and decides whether an immediate hotfix is necessary or whether a safer fix can wait for the next release. - Custom dashboards track crashes and hotfixes, including causes and prevention measures. - The team also supports product groups starting QA, improves internal tools, and helps establish organization-wide QA processes. ## Defining Toss’s Quality Standard The team’s goal is to go beyond basic testing and establish consistent quality standards across Toss. - **Reliable releases every time:** Quality must remain dependable week after week, not just during individual successful launches. - **High-quality testing:** The focus is on finding defects that could become real incidents, rather than simply increasing test volume. - **Efficient quality assurance:** Manual repetition alone cannot keep pace with the company’s release speed, so automation and sustainable workflows are necessary. - AI is intended to handle suitable decisions and repetitive work, allowing people to focus on areas requiring human judgment. ## Building the Tossion Platform Commercial tools did not provide enough flexibility for Toss’s release pace and evolving AI experiments, so the team built its own platform, **Tossion**. - Tossion replaced TestRail and brought test-case creation, execution, and result tracking into one system. - Multiple bots were consolidated into **Toss Butler**, optimized for the team’s workflow. - **PRCheck** analyzes pull requests and highlights where testers should focus. - **tcgen** uses PRDs, design documents, and surrounding context to generate initial test cases for review. - An automation testing platform displays manual and automated test results together. - **Crash Trend** tracks crash patterns using metrics tailored to Toss. - A hotfix dashboard categorizes causes and records measures intended to prevent recurrence. - These tools are connected by one objective: handling the growing volume of weekly changes more effectively. ## Learning What Teams Really Need The team initially assumed that making test-case creation easier would encourage more people to test. However, tcgen received less adoption than expected. - Users did not necessarily want better tools for doing QA themselves. - What they really wanted was for someone to perform testing quickly and accurately while taking responsibility for its quality. - Providing a tool could feel less like removing work and more like assigning a new task. - As a result, the team shifted toward directly handling more testing and pursuing tenfold efficiency without transferring responsibility to product teams. ## Staying Flexible as AI Evolves AI has solved many problems but has also made long-term planning difficult. - Initial hypotheses may be only partly correct, as the tcgen experience demonstrated. - Tools and methods can become outdated rapidly as AI capabilities change. - The team discarded an API testing tool, **API Labs**, after only eight hours when it proved misaligned with its goals. - Tossion, Toss Doctor, Toss Checker, and internal skills are designed with replacement in mind rather than as permanent, finished systems. - AI can accelerate tool creation, but people must still define what quality means, establish priorities, and decide what standards must be preserved. The QA Platform team’s ongoing approach is to build adaptable systems, learn from actual usage, and remain willing to replace anything that no longer serves its purpose. Its upcoming work will explore Tossion, the release-gate tools, regression automation, and intelligent AI bots in greater detail.

AI Test Automation Regression Testing Release Management+4

figmaJun 24, 20262 min readCurated summary

Principles in Motion | Figma Blog

Motion design extends graphic design into time, using rhythm, pacing, sound, and sequencing to communicate ideas. The Figma designers argue that effective motion combines clear intent with an understanding of physics and human perception. Rather than copying trends, designers should draw inspiration from nature, film, art, and real-world movement. ## Motion Turns Design into a Sequence - Graphic design communicates through static images; motion adds: - Rhythm and pacing - Transformation and character - Easing and timing - Sound and synchronization - Motion allows designers to divide a story across multiple frames instead of forcing every idea into one image. - Timing can create emotion and direct attention, much like beats structure music. - Unexpected connections between sound and image can produce “happy accidents” and richer results. ## Physics as a Foundation - Real-world physics provides a reference for making animated movement feel believable. - A bouncing ball, for example: - Moves quickly after impact - Rises and slows near its highest point - Falls again - Loses height with each bounce - Viewers often recognize when motion feels “good” because it reflects familiar physical behavior, even if they cannot explain why. ## Finding More Original Motion References - Relying only on existing motion-design examples can lead to predictable trends. - Designers can develop more distinctive work by studying: - Movement in nature - Film storytelling and editing - Gestures and forms in art and design - These broader references help motion communicate character and meaning beyond standard bouncing shapes and rectangles. ## Core Motion Principles - **Ease in/ease out:** Controls acceleration and deceleration. - **Anticipation:** Prepares the viewer for an upcoming action. - **Overshoot:** Moves slightly beyond the destination before returning. - **Follow-through:** Keeps secondary elements moving after the main action ends. - **Hold:** Pauses so viewers can process an event. - **Settle:** Adds subtle final movement as an object comes to rest. ## Transitions and Continuity - Easing determines how movement begins, changes speed, and settles. - Match cuts connect separate shots through a shared movement. - Cutting at the fastest point of an action can make transitions feel seamless. - Transitions link individual story beats and help the overall piece feel cohesive. Motion works best when designers treat time as a storytelling material. Grounding movement in physics, using sound thoughtfully, and drawing from varied real-world references can make animation clearer, more expressive, and less predictable.

AI Figma Motion Design Animation+2

figmaJun 24, 20263 min readCurated summary

What Does the Future of Software Look Like? | Figma Blog

AI may reshape software around more human, contextual interactions rather than fixed menus and mechanical commands. The post argues that future interfaces could understand intent through voice, gesture, emotion, and situation, adapting their behavior to each person. Instead of forcing users to adapt to increasingly powerful systems, software could meet users where they are while encouraging focus, presence, and healthier technology habits. ## Ephemeral Tools - Controls appear only when users select an object and indicate what they want to do. - Contextual options replace persistent menus, panels, and modes. - A video editor, for example, might show timing, pacing, alternate cuts, and sound options around a selected clip. - This lets creators focus on decisions and intent rather than remembering how software is organized. ## Magic Marker - Users interact through a combination of voice, cursor movement, gestures, sound effects, and body language. - Someone could circle an object, drag it into position, and verbally request a change. - AI would interpret these signals together, making it feel more like collaborating with a teammate. - This reduces the need for precise prompt engineering or complex document references. ## Adaptive Presence - Intelligent systems adjust their communication style and level of assistance based on user behavior. - They might offer structured guidance when someone is confused, step back when help is unnecessary, or switch between text, voice, and visuals. - Software could change pacing, simplify language, and divide information into smaller steps. - This approach is especially valuable in healthcare and education, where differences in user readiness can have serious consequences. ## Empathetic Flows - Interfaces could infer emotional states from typing speed, stylus pressure, speech patterns, facial expressions, and repeated revisions. - A food app might reduce choices when someone appears overwhelmed. - A creative tool could become quiet when the user is concentrating, while a hotel app might stop promoting upgrades when the guest seems tired. - Rather than requiring users to explicitly state what they need, systems would respond to behavioral signals. ## Situational Cues - Sound, motion, pacing, progress indicators, and visual transitions can help users understand where they are in an experience. - Earlier digital products used cues such as dial-up sounds, progress bars, and “You’ve got mail” announcements to provide orientation. - Future interfaces should counteract the overstimulation caused by attention-driven notifications. - Persistent progress indicators, transition sounds, and consistent visual language could help users regulate their attention and nervous systems. ## Spatial Tuning - Users could control software through bodily movement instead of conventional tapping and clicking. - Examples include shaping music with hand movements, navigating augmented reality by changing body orientation, or adjusting design elements through gestures. - These interactions demand attention and presence, making them harder to rush or automate. - Technology becomes an experience that intentionally slows users down rather than continually rewarding speed. ## Mash-Ups - Future systems could combine any two inputs—files, objects, sounds, locations, or physical gestures—to create something new. - The system would synthesize the combined inputs while blending their structure, tone, and meaning. - Possible examples include merging a playlist with a city map or combining digital objects through touch or gestures. The overall recommendation is to design AI-powered software around human intent, context, emotion, and physical presence. The most successful future interfaces may be those that make technology feel less like a collection of controls and more like an adaptable, considerate collaborator.

AI User Experience Human Computer Interaction Augmented Reality+3

figmaJun 24, 20263 min readCurated summary

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

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

AI Figma Prototyping Collaborative Design+2

kakaoJun 23, 20263 min readCurated summary

Solving Social Problems with AI Beyond Development

The first “SSAFY X Kakao Tech Bootcamp AI Hackathon” brought together 90 trainees from 12 teams to use AI for solving real social problems. Rather than focusing only on coding competition, the event emphasized public value, practical service prototypes, expert feedback, and collaboration across different training programs. It demonstrated that future developers need both technical ability and the capacity to work with others on meaningful problems. ## Connecting Kakao and Samsung’s Developer Programs - Held June 13–14 at Kakao’s AI Campus in Yongin. - Organized jointly by Kakao Tech Bootcamp and Samsung’s SSAFY program. - Participants came from two major digital-training initiatives supported by Korea’s K-Digital Training program. - The event aimed to create opportunities for collaboration and growth among future AI developers. ## Applying AI to Everyday Social Problems - Teams selected challenges from the government’s “Top 10 AI Projects for People’s Livelihoods.” - Topics included: - Small-business support - Voice-phishing prevention - Child and youth protection - Maritime safety - Over two intensive, sleepless days, teams: - Defined a specific social problem - Designed solutions from the user’s perspective - Built AI-powered service prototypes - The hackathon stressed that AI’s value depends not only on technical advancement, but also on how effectively it improves society. ## Practical Mentoring from Government and Industry - Officials from agencies including the National Police Agency, Ministry of Justice, and Ministry of Gender Equality and Family provided policy and field expertise. - Kakao developers delivered lectures and technical mentoring based on real-world service development. - Teams refined their ideas through questions, feedback, and discussions with experts. - This allowed trainees to connect classroom learning with actual policy and operational challenges. ## Collaboration Across Different Backgrounds - Kakao Tech Bootcamp and SSAFY use different educational approaches, giving participants varied experiences and strengths. - Teams worked with people they had not previously met and actively discussed how to incorporate AI into their products. - Participants discovered new perspectives and solutions by sharing their knowledge. - Many came to recognize communication and teamwork as essential skills alongside technical competence. ## Projects and the Future Developer Ecosystem - Five teams received awards after the final presentations. - The Ministry of Employment and Labor award went to “Golden Time” for **DRIFT**, an AI service supporting maritime rescue when communications are unavailable. - Kakao’s CEO award went to “SSAIKA” for **Mindam**, an AI-based civil complaint intake and processing service. - Other awards were presented by Samsung Electronics, the Korea Chamber of Commerce and Industry, and the Korea Radio Promotion Association. - Although the total prize money was 15 million won, the article identifies hands-on experience solving social problems as the participants’ more important achievement. - Kakao has trained more than 660 digital professionals since joining the K-Digital Training initiative in 2022. The hackathon suggests that AI education should combine technical training with real-world projects, expert guidance, and cross-organizational collaboration. Kakao plans to expand these practical opportunities to support developers who can turn technology into social value.

AI Software Development Collaboration Ai Hackathon+2

stripeJun 23, 20263 min readCurated summary

Four travel and hospitality trends from HITEC 2026

Hospitality’s AI opportunity is growing, but most operators lack the data, infrastructure, and operational systems needed to turn investment into measurable returns. AI is reshaping how travelers discover and book hotels, while fragmented data and outdated payment systems create lost revenue and guest frustration. The strongest strategy is to connect accurate data, intelligent workflows, and seamless payments so technology improves the experience without becoming visible to guests. ## AI Is Changing the Direct-Booking Battle - Hotels historically relied on SEO to compete with OTAs such as Expedia and Booking.com. - AI-generated search answers are reducing traditional website traffic: - 65% of Google searches with AI Overviews end without a click. - The figure rises to 78% on mobile. - Traditional search traffic is declining by about 25%. - AI systems prioritize accurate, structured, machine-readable information rather than keyword density and backlinks. - More than 90% of accommodation websites are reportedly undetected by AI models. - Hotels should audit whether AI tools can correctly describe: - Room categories - Amenities - Policies and cancellation terms - Local context - Real-time availability - Winning direct bookings will require both AI discoverability and a modern checkout experience supporting local currencies, payment methods, and fraud protection. ## Hospitality AI Is Held Back by Fragmented Data - Only about 25% of hospitality businesses are actively scaling AI, and fewer than 10% are considered “AI future-built.” - Property management, CRM, loyalty, food and beverage, and payment systems often operate in silos. - Incomplete data weakens: - Personalization - Guest profiles - Financial reconciliation - Operational decision-making - The main challenge is not building AI features but operationalizing them reliably in real workflows. - Successful examples connect live data to timely actions: - Delta’s AI concierge uses customer and operational data to provide context-aware support. - Wynn’s revenue managers receive predictive alerts and recommended actions. - For most operators, better data connectivity matters more than using a more advanced AI model. ## Payment Friction Directly Affects Revenue - Payments are increasingly viewed as a competitive capability rather than a back-office commodity. - Survey findings cited in the article include: - 90% of executives consider payments important to growth. - 37% say limited payment options most harm the guest experience. - 58% report that fraud tools block legitimate transactions. - 74% say fragmented systems create excessive reconciliation work. - Guests may abandon a hotel when their preferred payment method is unavailable, shifting the booking to an OTA that supports it. - Modern payment infrastructure allows smaller operators to offer international payment methods and currencies without building large in-house teams. ## Invisible Technology Creates the Best Guest Experience - Guests have little tolerance for technology failures and may simply avoid returning rather than complain. - Effective hospitality technology should anticipate needs without drawing attention to itself. - The desired experience includes details such as: - A room set to the guest’s preferred temperature - Familiar television channels - Preferred pillow firmness - Hospitality is moving from remembering information guests explicitly provided to predicting preferences based on connected guest data. Operators should prioritize clean, connected data, AI systems tied to real operational actions, and flexible payment infrastructure. The goal is not to add AI for its own sake, but to make booking and stays more seamless while quietly improving revenue, efficiency, and guest loyalty.

AI Large Language Models AI Agents Seo+4

tossJun 23, 20264 min readCurated summary

5. Technical Writer, A Decision to Disappear

Toss’s technical writing team argues that documentation is essential context for AI, but manually maintaining thousands of documents is impossible with only three technical writers serving roughly 4,000 people. Their solution is to automate the technical writer’s work by teaching AI the team’s implicit standards and embedding those standards into reusable Skills. The initial system supported document creation and review, but adoption remained low because users still had to install, invoke, and supply information to the AI manually. ## Why Toss Wanted to Automate Technical Writing - Documentation gives AI the organizational context it needs to work effectively. - Toss has approximately 4,000 employees but only three technical writers. - Reviewing documents individually does not scale, especially in a fast-moving organization where features change or disappear before documentation is complete. - The team’s goal to “eliminate technical writers” means transferring routine writing and editing work to AI, not abandoning documentation quality. ## Teaching AI Technical Writing Principles - The team analyzed existing technical writing review comments to identify how writers evaluate documents. - Existing writing guidelines were converted into explicit principles, such as: - Focus each page on one subject. - Present value before implementation details. - Each principle was supplemented with incorrect and correct examples so AI would understand the intent rather than apply rules mechanically. - Common document types were converted into templates. - Templates include: - Instructions explaining what each section should contain. - `(required)` markers for information that must not be omitted. - For example, an ADR template requires an overview, context, considered alternatives, decision, and rationale, while also allowing optional sections such as expected outcomes and related references. ## Skill for Writing New Documents The document-writing Skill reproduces the four stages a technical writer typically follows: - **Clarify the purpose:** Ask about the project, document goal, audience, level of detail, source materials, and expected structure. - **Design the structure:** Use a standard structure or select a relevant template, such as onboarding guides, meeting notes, or PRDs. - **Write the content:** Apply technical writing and MDX rules while using templates as structural guidance. - **Review the draft:** Check for awkward wording, missing information, and other quality issues. The Skill also distinguishes between required and optional template sections: - Required sections remain in the draft even when source information is incomplete. - Missing information is represented with questions or comments rather than guesses. - Optional sections are omitted when there is not enough source material to complete them. ## Skill for Reviewing and Improving Documents - The team initially converted past review comments into a checklist. - This produced poor results: AI overlooked important issues while generating unnecessary comments. - The problem was that good writing follows relatively stable principles, whereas bad writing can fail in many different ways. - The revised workflow lets AI independently: - Read the technical writing principles. - Analyze the document. - Identify violations. - Explain the issue and suggest revised wording. - Perform a final checklist-based review. - Previous review comments are now used as examples of how principles apply, rather than as a rigid list of required findings. - One example principle requires descriptions of parameters or properties to include their meaning, accepted format, and usage example—not merely a type such as `date: string`. ## Low Adoption Revealed a Usability Problem - Despite creating both Skills, the team found that few employees used them. - Users still had to: - Download and install the Skill manually. - Understand CLI-based setup, which was unfamiliar to non-developers. - Remember to invoke the Skill whenever they began writing documentation. - Find and provide all relevant source materials themselves. - The team concluded that improving the AI’s capabilities was not enough; the workflow also had to reduce the effort required from users. The main lesson is that AI-based documentation succeeds only when organizational knowledge, writing principles, and templates are encoded clearly—and when the system is integrated into everyday work so employees do not have to remember to use it or prepare everything manually.

AI Prompt Engineering Technical Writing Cli+3

tossJun 23, 20264 min readCurated summary

6. Beyond Tools: Standards and Responsibility

Toss’s commerce domain found that reliable organizational knowledge cannot be created by writing more documents or adding automation alone. Sustainable knowledge management requires clear standards for what should be documented, who owns it, how it is maintained, and which sources can be trusted. The proposed solution combines AI-assisted documentation with domain-level responsibility and company-wide governance. ## The Limits of Writing Alone - A commerce wiki consolidated terminology, onboarding material, code references, and policy documents. - This reduced confusion over terms such as “seller” and “store” and gave teams a shared starting point. - However, product and policy changes happened faster than one Technical Writer could document them. - Important knowledge also appeared in policy changes, temporary experiments, and chat discussions that were difficult to track manually. ## Why Culture and Participation Were Not Enough - The team promoted documentation through: - A weekly “Commerce Wiki News” newsletter - A policy-question channel and bot - AI documentation workshops - A documentation guild - These efforts increased requests, wiki usage, and adoption of official terminology. - Participation rarely continued beyond an individual’s first document because documentation was not part of normal work priorities. - Writers lacked guidance on: - What information to preserve - How much detail to include - Which audience to target - How to verify whether a document was correct - Documentation became sustainable only when it was treated as a team responsibility embedded in existing workflows. ## AI Automation Reveals the Governance Problem - AI now creates draft documents nightly from two signals: - Product deployment and policy-change announcements - Questions that the commerce Q&A bot cannot answer - AI gathers supporting context and produces drafts, while humans verify the evidence and approve them. - This removes the burden of starting documents from a blank page. - Automation also exposed new problems: - Duplicate or overlapping documents - Unclear authoritative sources - Outdated policies being used in bot answers - Difficulty distinguishing current policies from completed experiments - Automation can collect and draft information, but it cannot decide who owns a policy or whether a document should still be trusted. ## Knowledge Standards and Governance - The focus shifted from “How do we create more documents?” to “How do we create knowledge people can trust?” - Toss’s knowledge-management standards state that teams should: - Preserve recurring questions, important decisions, and information needed by newcomers. - Organize knowledge so both people and AI can find it. - Connect documents to work tools such as Q&A bots and GitHub. - Assign owners and review cycles to keep information accurate and current. - Possible classification systems include: - **Technical layers** for teams with clear data or system flows - **Service domains** for teams responsible for multiple service areas - **Functional units** for systems with distinct feature boundaries - Information becomes organizational knowledge only when it helps people understand situations and make better decisions, with sufficient context and verification. ## The Role of the Knowledge Committee - The Knowledge Committee defines and maintains company-wide documentation standards and resolves conflicts between organizational rules. - Unlike a voluntary guild, it has designated members with decision-making authority. - Governance operates at two levels: - The Technical Writing Chapter manages shared standards for sources, ownership, document status, and lifecycle. - Individual domains decide how those standards apply locally, including ownership, update schedules, and retirement rules. - This balance prevents both inconsistent practices across teams and overly centralized rules that ignore local realities. - For example, commerce teams may need separate handling for permanent deployments and temporary experiments so expired policies do not remain authoritative. The practical recommendation is to treat knowledge management as an operating system for the organization, not a documentation project. AI can reduce the effort of capturing knowledge, but clear ownership, review processes, lifecycle rules, and governance are necessary to keep that knowledge reliable and useful.

AI Automation Knowledge Management Technical Writing+3