AI

331 posts

toss3 min readCurated summary

Why Does Our Team’s Documentation Fail? (2)

Documentation succeeds not through individual resolve, but through centralized knowledge, clear purpose, and systems that reduce the fear of being wrong. Interviews across a commerce domain and a server-development chapter show that documentation strategies must match an organization’s existing maturity, audience, and work patterns. AI lowers the cost of writing and sharing knowledge, while also requiring more deliberate document organization. ## Lessons from Early Attempts - The commerce wiki was initially designed as a self-service platform supported by workshops and guilds. - Workshops could encourage a first contribution, but sustaining second, third, and later contributions was difficult. - In the Ads domain, documentation was already strong, so the better approach was to respect existing conventions and help people locate knowledge rather than create another system. - Organizations with little documentation need to build foundational knowledge; organizations with mature documentation need better discovery and maintenance. ## Reducing the Fear of Asking and Writing - Developers often avoid asking questions because doing so publicly reveals what they do not know. - They may also hesitate to publish documentation because they fear their knowledge could be inaccurate. - The team addresses this through: - **“Lee’s Development Consultation Week,”** which normalizes questions and encourages teammates to answer questions Lee cannot address. - **A daily knowledge bot, “Ha,”** which shares short server-development tips automatically. - Correcting or adding to an existing shared post feels easier than writing a complete document from scratch. ## How AI Has Changed Documentation - AI makes it faster to create initial drafts and distribute knowledge through chatbots. - It also enables measurement of knowledge flow, including: - The number of questions asked. - Whether teammates provide answers. - The quality and content of those answers. - The number of new documents and week-over-week growth. - These metrics reveal knowledge gaps and recurring questions without manually reviewing every channel. - AI also creates a need for more detailed internal context than human readers typically require. - Commerce therefore separates: - Central, human-friendly documentation managed by technical writers. - Team-repository documentation containing detailed, team-specific context useful to AI but unnecessary for everyone else. ## Shared Principles and Organizational Differences - Both domains and chapters should centralize knowledge and avoid spreading it across too many channels. - Domains typically: - Connect documentation to products and code. - Change rapidly. - Serve a broad and varied audience. - Chapters typically: - Document conventions, working methods, and professional knowledge. - Change more slowly. - Focus on productivity and capability development. - Have a clearer audience, such as a specific role or discipline. - Domain documentation should be understandable even to non-developers, since developers may also lack context outside their specialties. - Separating guides, capability-based policies, glossaries, and metrics helps different readers find documents suited to their needs. ## Where to Begin - First diagnose the organization’s current documentation maturity by asking what people do when they get stuck: - **Ask coworkers or search chat:** foundational documentation is largely missing. - **Search documents:** assess whether information is easy to find; fill gaps if searches fail. - **Ask an AI or bot:** evaluate whether answers are accurate and whether the underlying documents are complete, current, centralized, and sufficiently contextualized. - Define the specific problem driving documentation, rather than starting with a vague goal. - Begin with a focused need, such as creating a glossary for inconsistent terminology or building references for sharing knowledge with other teams. The practical recommendation is to centralize knowledge, identify the organization’s biggest documentation gap, and build a low-friction system where people can contribute, correct, and consume information without relying solely on personal effort.

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toss4 min readCurated summary

Going Beyond Expertise

Technical Writers (TWs) can contribute far beyond writing documentation: they can lead product teams and build systems that turn knowledge into an organizational asset. Toss’s Knowledge System Team created “todoc,” an internal platform that makes documentation easier to write, centralizes scattered knowledge, and enables AI access. Its broader goal is to make documentation emerge naturally from daily work and remain accurate without constant manual maintenance. ## TWs as Product Owners and Makers - The author leads a product team of developers, designers, and TWs. - Their responsibilities include: - Setting product direction, roadmap, and priorities - Interviewing users and bringing insights to the team - Planning features - Building features directly with AI tools - TW expertise is especially valuable because TWs have deeply considered: - Why documents are difficult to read - What makes documentation effective - How information should be structured for AI consumption ## Why Toss Built Todoc Todoc was launched to address weaknesses in Toss’s existing documentation environment. - Static-site-generated documentation required users to: - Clone a repository - Write Markdown - Submit pull requests - Wait for review - This workflow was familiar to developers but created major barriers for designers, PMs, and other non-developers. - Existing documentation tools accumulated outdated policies, unfinished notes, and unexplained content, creating “documentation debt.” - Knowledge was fragmented across: - Static sites - Documentation tools - Code - Collaboration messengers - Individual employees’ knowledge After its beta launch, Todoc grew to more than 500 documents and 40,000 valid pages, with over 1,000 monthly users. ## Todoc’s Four Core Values ### Easy Documentation for Everyone - Anyone can create or edit documents immediately. - Content can be connected from GitHub, documentation tools, internal messengers, and other sources. - The platform removes the technical and procedural barriers to documentation. ### AI-Ready Knowledge - Well-organized documentation can be used by team bots and other AI tools. - Todoc supports API, CLI, and MCP access. - Teams use it for request bots, product specifications, and other workflows. ### A Single Source of Truth - Todoc consolidates scattered sources into complete, centralized documents. - Users can determine which information is current without searching across multiple systems. - The platform serves as the organization’s SSoT (Single Source of Truth). ### Scalable Infrastructure - Teams no longer need to select, build, or maintain their own documentation infrastructure. - Each team can have its own space on a shared platform. - The model is being expanded to Toss affiliates. ## Automating Documentation Quality and Maintenance Lowering the barrier to writing creates a new challenge: maintaining quality. - TW judgment is being converted into: - AI proofreading - Automated document reviews - Bots that generate initial drafts - Todoc is also designed to create documentation automatically from: - Decisions and discussions in internal messengers - Code changes - Ongoing project conversations - The system aims to update documents without relying on someone remembering to maintain them. - It evaluates whether knowledge is still valid by checking: - Whether policies match implemented code - Whether information is actively used - How recently it was updated ## The Evolution of TW Expertise The role is shifting from writing excellent documents manually to designing systems that consistently produce and maintain excellent documentation. - Experience understanding why documents are hard to read becomes standards for human- and AI-readable content. - Judgments about what makes a good document become criteria for AI review and automated editing. - Expertise in identifying outdated information becomes a system for validating knowledge. - TWs increasingly focus on: - Creating places where knowledge can gather - Defining quality standards - Encoding human judgment into systems - Generating documentation through normal work - Keeping knowledge continuously updated The practical vision is an organization where outdated documents trigger their own notifications, project work leaves behind organized records, and recurring explanations are preserved for future employees. Toss’s Technical Writing Chapter is therefore working to systematize TW expertise and establish documentation governance so teams can document effectively without constant manual intervention.

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toss3 min readCurated summary

If You Asked a Designer to Make Anything with AI

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

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cloudflare3 min readCurated summary

Celebrating 12 years of Project Galileo

Project Galileo, launched by Cloudflare 12 years ago, provides free cybersecurity services to more than 3,400 civil society websites across 120 countries. Its anniversary report shows that journalists, human rights groups, and nonprofits face more frequent and intense attacks than other Internet users, especially during politically sensitive work. Cloudflare is responding with expanded research, case studies, partnerships, and a call for accessible security protections. ## Project Galileo’s Mission and Reach - The program protects journalists, human rights defenders, and nonprofit organizations from being forced offline. - It now supports more than 3,400 websites in 120 countries. - Cloudflare’s global network spans more than 335 cities in 125 countries, with over 20% of the web behind its infrastructure. ## Cyberattacks Targeting Civil Society Cloudflare’s first comprehensive annual report compares threats against civil society with attacks against Internet users more broadly. - DDoS attacks were the most common threat, often lasting for days or weeks. - Civil society organizations faced website vulnerability exploitation attempts at more than seven times the rate of other Cloudflare customers. - Media organizations were especially affected. - Journalists working in exile received nearly four times more malicious traffic than journalism organizations overall. - Almost 10% of emails processed for civil society organizations contained potential phishing material. - Attacks often coincided with investigative reporting, public advocacy, or other critical organizational activities. Cloudflare calls for affordable cybersecurity, greater transparency around cyberattacks and Internet shutdowns, and default integration of AI-aware and post-quantum protections. The company plans to publish the report annually to track changing threat patterns. ## Case Studies of Project Galileo Participants Sixteen case studies illustrate the varied security needs of participating organizations, including: - Digital rights groups such as SHARE Foundation. - Investigative and independent media organizations, including OCCRP, elTOQUE, and China Digital Times. - Organizations documenting conflict and human rights abuses, such as Ukraine War Archive. - Research and public-interest institutions including Our World in Data and the Bulletin of Atomic Scientists. - Environmental, legal, scientific, and humanitarian groups such as Sea Shepherd Brazil, Activist Rights, and the Royal Meteorological Society. ## Expanding the Partner Network Project Galileo depends on 59 civil society partners that review and approve applications. - Partners contribute local expertise and help identify organizations that need protection. - Previous collaborations produced initiatives such as email security with Protect.ngo and Internet measurement work through UNICEF’s Giga project. - Cloudflare has focused on expanding access beyond North America and Europe through regional events and partnerships. - Recent Asia-Pacific partners include EngageMedia and the OpenCulture Foundation. - The anniversary announcement introduces three additional partners serving journalists, including the International Center for Journalists and Media Cluster Norway. Project Galileo’s next phase combines threat intelligence, direct protection, regional partnerships, and specialized services for journalism organizations. Its broader recommendation is that reliable cybersecurity should be treated as essential infrastructure for civil society and public discourse.

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figma2 min readCurated summary

Direct Every Frame with Runway Aleph 2.0, Now in Figma Weave | Figma Blog

Runway Aleph 2.0 is now integrated into Figma Weave, giving creators precise, frame-level control over video edits. The model supports longer clips, reference images, and sequential creative decisions while preserving footage that users do not ask to change. It also enables substantial transformations—such as new camera angles, characters, or environments—without requiring a reshoot. ## More Time, More Control - Aleph 2.0 supports video clips up to 30 seconds, allowing users to direct complete scenes. - Reference images can guide the visual style and appearance of edits. - Changes are applied across relevant frames while preserving unaffected elements. - Subject-specific edits follow that subject throughout the footage. ## Sequenced Creative Workflows - The Aleph 2.0 node in Figma Weave supports connected, step-by-step workflows. - Creators can preview edits before committing them. - Multiple decisions can be refined progressively rather than being forced into a single prompt. - The workflow mirrors traditional creative development on a visual canvas. ## Extending Existing Footage - Users can alter a scene beyond the limits of the original recording. - Possible changes include: - Adjusting the camera angle - Adding new characters - Transforming the environment - Multiple creative directions can be explored side by side without restarting from scratch. - The creator defines the desired conditions, while Aleph 2.0 generates the revised video. ## Pricing and Resources - Figma says pricing will soon scale according to input length, potentially lowering costs for some use cases. - Users can learn more through Figma’s help center, community templates library, and Weavy’s knowledge center. Figma Weave users can use Aleph 2.0 to move from broad AI generation toward more controlled, iterative video direction—making it useful for experimentation, editing, and visual development without reshooting footage.

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gitlab3 min readCurated summary

GitLab named a Leader in the 2026 Gartner® Magic Quadrant™ for DevSecOps Platforms

GitLab says Gartner named it a Leader in the 2026 Magic Quadrant for DevSecOps Platforms for the fourth consecutive year. The company argues that AI agents have accelerated coding but shifted bottlenecks to pipelines, security, deployments, governance, and costs. GitLab positions its unified platform as the control layer that turns agent-generated code into secure, compliant, production-ready software. ## AI Requires a Control Layer - Enterprises increasingly use multiple coding agents, but often lack centralized governance over: - Which agents can run - What data they can access - Which actions they can take - How their activity is audited - GitLab combines source control, CI/CD, security, deployment, policies, and planning in one platform. - Changes made by developers or agents can be evaluated against existing code, pipelines, and organizational policies before reaching production. ## Enterprise-Scale DevSecOps - GitLab highlights customer examples: - Ericsson reportedly cut deployment time in half. - Southwest uses GitLab for mission-critical airline operations. - Barclays and other regulated organizations use it while maintaining security and compliance requirements. - The platform supports multi-tenant SaaS, single-tenant SaaS, self-managed, and air-gapped environments. - Customers can use self-hosted AI models and integrate existing tools and AI services while maintaining a unified governance boundary. ## Reliability and Availability - Gartner recognized GitLab’s strengthened service-level agreements. - GitLab offers Ultimate customers on GitLab.com and GitLab Dedicated a 99.9% monthly availability commitment. - Eligible customers can receive service credits when availability falls below that threshold. ## New Capabilities for Speed and Governance GitLab announced five innovations intended to coordinate developers, agents, and software delivery: - **Next-generation source code management:** Claimed testing showed up to 50× faster performance and up to 1,000× less network data transfer. - **GitLab Orbit:** A context graph connecting code, work items, pipelines, deployments, and production signals. With Claude Code, GitLab reports tasks running up to 11× faster, using up to 4.5× fewer tokens and producing up to 45× fewer hallucinations. - **Security and governance agents:** Designed to address security and compliance gaps as agent usage expands. - **Agentic triggers:** Automate handoffs between developers and agents without requiring manual coordination. - **GitLab Flex agreements:** Allow customers to adjust spending across GitLab products and capabilities without changing contracts. GitLab’s central recommendation is to standardize development and AI-assisted delivery on one platform, context graph, and governance boundary. The Gartner recognition supports that positioning, although Gartner notes that its Magic Quadrant reflects analyst opinions and should not be interpreted as an endorsement or a recommendation to select the highest-rated vendor.

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gitlab2 min readCurated summary

Introducing the 2026 EMEA GitLab Partner Award winners

GitLab announced its 2026 EMEA Partner Award winners, recognizing organizations that drove customer success, technical innovation, certification, business growth, and joint marketing. The awards highlight partners helping enterprises adopt DevSecOps, cloud-native platforms, managed services, and AI-enabled software development across the region. ## Regional Partners of the Year - **Central Europe: cc cloud GmbH** — Combines infrastructure and DevOps expertise to manage cloud applications, platforms, and IT operations. - **Northern Europe: Eficode** — Supports more than 1,600 customers through consulting, managed services, toolchain implementation, and AI-augmented development. - **Southern Europe: Kiratech** — Helps enterprises modernize infrastructure using cloud-native, DevOps, and PlatformOps practices. - **Eastern Europe and Israel: Bynet** — An established systems integrator supporting enterprise IT, cloud, cybersecurity, modernization, DevSecOps, and AI adoption. ## Technical and Enablement Awards - **Best Technical Solution/Project: Capgemini | Sogeti** — Recognized for impactful, complex technical solutions using AI-driven quality engineering, data, and cloud capabilities. - **Most Certified and Enabled Partner: Devoteam** — Awarded for having the largest number of GitLab-certified professionals. - **Rookie of the Year: ITDOTCOM** — A Uzbekistan-based technology distributor that achieved rapid success supporting software, infrastructure, cybersecurity, and business automation across Central Asia. ## Growth and Collaboration Awards - **First Order Master: Linux Polska** — Recognized for winning new customers and business through open-source consulting, DevOps, automation, containerization, and data analytics. - **Co-marketing Partner of the Year: Conoa, a PROACT Company** — Honored for joint marketing efforts and expertise in Kubernetes, cloud-native technologies, container platforms, and managed operations. The awards demonstrate the breadth of GitLab’s EMEA partner ecosystem, from regional systems integrators and cloud specialists to technical consultants and Kubernetes providers. Together, these partners are helping customers modernize delivery practices and adopt DevSecOps and AI capabilities.

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toss4 min readCurated summary

Designing the Work You Do Every Day

A product designer at Toss Bank transformed her personal task-management routine instead of accepting repetitive manual work as unavoidable. She built an AI-powered desktop widget that collects Slack messages, summarizes them into actionable tasks, preserves their context, and helps her focus on prioritization. What began as a personal solution revealed a broader problem shared across roles and spread throughout her team. ## From Manual Task Tracking to Workflow Design - For two and a half years, she manually copied tasks, feedback, discussion links, and requests from Slack into Notion or Slack lists. - As her responsibilities expanded to three teams, daily tasks grew from roughly 10 to more than 20. - She reframed the issue as a product-design problem: - **User:** herself - **Real goal:** completing the most important work without missing anything - **Main friction:** copying, organizing, and locating context - **Ideal state:** tasks collected automatically, leaving only prioritization to manage - This led to three core requirements: - AI should register tasks directly from Slack. - Each task should retain its source thread and document links. - Priorities should remain visible in an always-present widget. ## Teaching AI to Understand Work Context - Adding a specific emoji to a Slack message sends it to a designated channel. - Claude Code reads the message and converts it into a task with: - A concise summary - The relevant team tag - A link to the original Slack thread - The hardest part was turning long, contextual Slack conversations into one clear action. - For example, a request about an error during a loan-extension application becomes “Check loan-extension error case.” - She created writing guidelines and examples defining: - What qualifies as a good task - How teams should be categorized - Which expressions and sentence structures to use - The goal was for AI-generated tasks to sound like something she would have written herself. - Refining the AI’s output was less about coding than encoding her judgment about what constitutes a real task. ## Designing the Widget Experience - Making the widget feel natural required detailed interaction design and repeated implementation. - She rebuilt the code to refine the expand-and-collapse behavior. - The drag interaction took nearly a week to complete. - Explaining seemingly obvious behaviors to AI forced her to define her own requirements more precisely. - In this sense, working with AI became a process of clarifying thoughts and translating them into explicit language. ## Replacing Anxiety with Prioritization - She no longer needs to open Slack or Notion repeatedly to remember her tasks. - The always-visible widget removed a previously unnoticed source of friction. - AI now handles collecting and organizing work, reducing the mental energy spent on administration. - She can concentrate on deciding what matters most instead of worrying that something has been forgotten. ## A Personal Problem Shared by the Team - Although the widget was initially built for personal use, many colleagues adopted it. - Developers unexpectedly became active users, reporting bugs and suggesting features. - The usual designer–developer relationship reversed: developers raised issues while she fixed and redeployed the tool. - This showed that task collection, prioritization, and context management are common problems across job functions. - The tool spread not because its concept was revolutionary, but because it addressed an existing, widely felt inconvenience. ## Applying the Method - Identify the most frequent “not really work” task from the past week: - Copying information - Searching for context - Organizing lists - Define the problem as a product: - Who is the user? - What are they truly trying to accomplish? - Where is the greatest friction? - What does success look like? - Examine why existing tools do not solve the problem. - Start with the smallest version that can be useful immediately. The practical lesson is to treat repetitive coordination work as something that can be designed away. Instead of searching for a perfect general-purpose tool, build a small solution around the specific context, habits, and judgments that existing products cannot know.

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grammarly2 min readCurated summary

Say It, Then Send It with Speech to Text

Writing on a phone remains difficult because typing is slow and native dictation produces messy transcripts. Grammarly Keyboard’s speech-to-text feature aims to solve this by converting natural speech into polished, send-ready text directly within any iOS app. It removes filler words, verbal corrections, and grammar errors while preserving the speaker’s tone and intent. ## Polished Dictation Anywhere - Speech-to-text is built into the Grammarly Keyboard. - Users tap the microphone, speak naturally, and receive cleaned-up text in the active app. - The feature supports multiple languages, accents, and speaking styles. - Users can switch between dictation and typing or further edit text with Grammarly’s keyboard. - Grammarly’s AI assistant is also available within the same keyboard. ## Designed for Mobile Writing - The feature targets frequent mobile writers, including professionals, students, and people capturing ideas while away from a desk. - Noise reduction helps improve recording quality. - Recording starts only when the user taps the microphone. - An on-keyboard indicator and iOS’s orange status light show when the microphone is active. - Audio is deleted after transcription and is not stored, linked to the user’s account, or used for model training. ## How to Get Started - Download Grammarly for iOS from the App Store. - Add Grammarly under **Settings → General → Keyboard → Keyboards → Add New Keyboard**. - Enable full access so the keyboard can operate across apps. - Open any text field, tap the Grammarly microphone, and begin speaking. Grammarly’s speech-to-text is presented as a practical alternative to raw phone dictation, especially for users who want fast, polished mobile messages without manual cleanup.

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google3 min readCurated summary

Research into how AI can help users understand skin conditions

Google Research examined how AI tools can help non-experts understand skin conditions and decide what to do next. In a large study, AI substantially improved people’s ability to identify possible conditions, but it did not reliably improve the accuracy of recommended next steps. The research therefore argues that dermatology AI should be designed around human decision-making, safety, and clear guidance—not diagnosis alone. ## Why Dermatology AI Needs Human-Centered Research - More than half of adults use the Internet for health information, and about one-third use AI. - People often lack the medical vocabulary needed to search effectively—for example, searching for “red dots on legs” instead of “palpable purpura.” - Google Research has developed dermatology AI models, validated their generalization, and released datasets such as SCIN. - Earlier research found that online tools can improve condition recognition without necessarily helping people choose appropriate next steps. - The researchers emphasize studying how people interpret and act on AI-generated information. ## Large-Scale Evaluation of an AI Information Tool - A JAMA Dermatology study involved 2,345 participants reviewing de-identified skin-condition cases with images and structured medical histories. - Participants were assigned to one of three groups: - **Standard-search control:** Used familiar text-based search tools. - **AI group:** Used a prototype showing 3–7 AI-predicted conditions, textbook images, and information about symptoms and treatments. - **“Wizard of Oz” control:** Used the same interface, but with dermatologist-provided differential diagnoses presented as if generated by AI. - The AI interface increased participants’ willingness to name a condition: - More than 62% attempted a diagnosis with AI. - Only 41% did so using standard search. - Accuracy also improved: - AI users correctly identified a matching condition about 23% of the time. - Standard-search users achieved 8%. - The “perfect-prediction” interface reached 36%, showing that even accurate candidate lists did not make users nearly perfect. - AI users reported greater confidence, satisfaction, and satisfaction with the time spent searching. ## Identifying a Condition Does Not Guarantee Safe Action - The prototype intentionally avoided prescribing actions or making individualized diagnoses. - Treatment information was dermatologist-written and based on the condition name, rather than the severity or details of the specific case. - Choosing the right next step—such as home care, routine care, or urgent evaluation—remained difficult. - Next-step accuracy improved only slightly in the “Wizard of Oz” group, from 60% in the standard-search control to 63.5%. - The standard AI group showed no statistically significant improvement. - AI users were slightly more likely than control participants to recommend a less urgent action than dermatologists would: 30% versus 27%. - These findings show that identifying possible conditions is insufficient without stronger safety-oriented guidance. ## Studying Real Users and Diverse Communities - The researchers also conducted a qualitative study, published at ACM CHI, to examine how people use AI for their own active skin concerns. - The project partnered with Stanford’s Healthcare AI Applied Research Team and the Santa Clara Family Health Plan. - The community included many Medi-Cal users who rely on a healthcare safety net. - Researchers aimed to gather richer feedback than survey-based studies provide by observing real-world use. - Because participants spoke four primary languages, the application was translated into those languages, with multilingual volunteers or staff available to support communication. AI can make dermatology information easier to find and improve recognition of possible conditions, but it should not be treated as a substitute for professional judgment. Future tools should focus equally on urgency assessment, personalized context, uncertainty, and clear recommendations for when to seek medical care.

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gitlab3 min readCurated summary

GitLab Flex: Commit once, reshape your seats and AI spend

GitLab Flex addresses the unpredictability introduced by agentic software development, where seat counts, AI consumption, and desired capabilities can change throughout the year. Instead of fixing these needs in a traditional annual contract, Flex provides one annual spending commitment that can be reallocated monthly. GitLab’s conclusion is that organizations can adopt new capabilities and adjust usage without renegotiation or re-procurement. ## Fixed Contracts, Moving Needs - Agentic development creates uncertainty around: - The number of platform seats required as teams and contractor mixes change. - The amount of AI usage driven by evolving use cases and technology. - Which new capabilities organizations will adopt during the contract term. - Traditional contracts require customers to estimate all three in advance. - Overestimating leads to unused seats and capacity, while underestimating can delay adoption through additional procurement cycles. ## One Annual Commitment, Adjusted Monthly - GitLab Flex uses a single annual dollar commitment based on a published rate card. - Customers can allocate that commitment across: - Premium and Ultimate platform seats. - GitLab Credits for services such as Duo Agent Platform, hosted runners, and artifact management. - Eligible usage-based capabilities introduced after signing. - It applies across GitLab.com, Self-Managed, air-gapped, and Dedicated deployments. - Customers can shift unused seat reservations toward other seats or AI usage without amending the agreement. - Usage above the annual commitment is billed on demand at $1 per credit or the negotiated per-seat rate. ## Combined Seats, Credits, and Deployment Types - A single agreement can combine platform seats and credit-metered services. - Larger commitments provide volume discounts across the rate card. - Organizations can change their mix of seats, credits, and deployment models during the term. - Unlike models that separate licenses and usage credits, Flex allows budget to move between them. ## Pricing and Spending Controls - Reserved capacity is priced below unplanned usage. - Subscription-level and per-user caps help control spending. - Project- and group-level administrative controls provide additional oversight. - Unreserved seats use the same effective negotiated rate as reserved seats. - Cloud-connected customers are billed automatically; air-gapped customers are invoiced twice yearly. ## Existing Contracts and Availability - GitLab Premium and Ultimate remain available through direct seat pricing. - Existing customers may keep their current plans through renewal. - Flex does not change the capabilities included in those tiers. - Customers approaching renewal can compare Flex with their current contract using projected seat and AI usage. - Flex orders are available now, with fulfillment rolling out throughout the quarter. GitLab Flex is best suited to organizations that expect their workforce, AI consumption, or deployment requirements to change frequently. Its main benefit is financial and operational flexibility: one agreement lets customers rebalance spending monthly instead of waiting for renewal or reopening procurement.

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discord2 min readCurated summary

Game On: Discord Is Backing the Next Generation of Dutch Gaming Founders

Discord and Techleap are launching the Gaming Founders Circle to support five ambitious Dutch gaming companies through the end of 2026. The program aims to help founders scale by connecting them with peers, investors, industry networks, and Discord leadership. It reflects Discord’s broader view that gaming grows through strong relationships among developers, players, and business communities. ## A New Support Program for Dutch Gaming - Discord is partnering with: - **Techleap**, which supports Dutch scaleups. - **The Dutch Games Association**, which provides connections to the local games industry. - The Netherlands is especially significant to Discord because it hosts the company’s European headquarters. - Discord says its platform offers developers direct access to players and real-time feedback: - More than 90% of Discord users play games. - Over 80 million users participate in more than 10,000 gaming communities. ## The Five Founders in the Cohort The first Gaming Founders Circle cohort represents a broad range of gaming technologies and business models: - **VaultN**: Digital distribution infrastructure used by publishers such as Bethesda, 2K, and Take-Two. - **Poki**: A self-funded web gaming platform with 90 million monthly players. - **MAXYMUM**: AI-powered tools for game design, recently validated at GDC. - **YOM**: Decentralized cloud gaming infrastructure combining blockchain and streaming. - **Immens**: A European game engine designed around AI and led by an experienced industry veteran. Discord, Techleap, and the Dutch Games Association selected the companies jointly. ## What Participants Receive The program focuses on practical challenges faced by growing gaming companies, including: - **Peer learning** on hiring, fundraising, distribution, community building, and international expansion. - **Investor access** through Techleap’s Dutch and international network, particularly important as later-stage studio funding becomes more difficult. - **Industry connections** through Discord’s gaming ecosystem. ## Program Milestones - The program runs through the end of **2026**. - In **October**, participants will travel to San Francisco for: - A meeting with Discord leadership. - An investor dinner hosted by Techleap and Prince Constantijn. - The initiative concludes at the **Dutch Games Awards in November**. The Gaming Founders Circle is intended to strengthen Dutch gaming by giving founders the relationships, knowledge, and capital connections needed to grow. Discord’s recommendation is clear: supporting individual companies while building stronger connections across the entire national games sector.

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figma3 min readCurated summary

You Never Stop Cultivating Taste | Figma Blog

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

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toss4 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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spotify3 min readCurated summary

Encoding Your Domain Expert: The Context Layer Behind Spotify's Data Assistant | Spotify Engineering

Spotify’s data assistant, Vedder, relies less on model size than on carefully curated domain context. With more than 70,000 datasets, schemas alone cannot capture business definitions, data quality issues, or preferred query patterns. Spotify’s solution is a cluster-based context layer owned by domain experts, making AI-generated SQL more reliable, transparent, and maintainable. ## Why Schemas Alone Are Not Enough - Spotify has petabytes of data across more than 70,000 datasets, making it impossible to provide an LLM with the entire warehouse. - Even large context windows cannot represent all available schemas effectively. - Schema types and column names omit critical meaning, such as: - Which values represent test or legacy data - What “active user” means in a particular domain - Which tables or columns are authoritative - Without this context, an AI assistant may confidently choose the wrong dataset. ## Spotify’s Data Agent - Users ask questions in natural language, and the agent: - Selects the relevant context - Generates SQL - Executes it against the warehouse - Returns the answer, query, and sources - It uses a ReAct loop to reason, call tools, inspect results, and revise its approach. - Users can see how an answer was produced rather than receiving an opaque result. - The assistant is available through: - Slack - An MCP server for IDEs and AI tools - A dedicated web interface - Since August 2025, it has supported more than 2,100 users, 13,000 conversations, and 60,000 messages across 177 domain clusters. ## The Cluster Model Spotify organizes data domains into “clusters,” each owned by a named team of experts. A cluster contains: - **Datasets** - Relevant warehouse tables with schemas and profiling - Column cardinality, common values, and partition information - Details that help the model construct accurate filters and queries - **Pairs** - Expert-approved natural-language questions paired with SQL - Examples of both query patterns and domain semantics - **Docs** - Business terminology and definitions - Known data pitfalls - Guidance about which columns to use or avoid Clusters can represent organizations, initiatives, or specialized areas of interest. Domain experts decide what belongs in each cluster and which examples best represent correct practice. ## Why Human Curation Matters - Spotify considered automatically generating training pairs from historical query logs. - That approach produced unreliable results because query history contains: - Exploratory analysis - Debugging queries - One-off investigations - Incorrect table choices - Technically valid but misleading patterns - Cluster curators accepted only 12.5% of the proposed question-SQL pairs. - Experts therefore determine what is canonical and trustworthy, while the model uses that curated knowledge to answer more users. - The goal is not to replace data specialists, but to scale their judgment and expertise. ## Keeping Context Current - Data models and business logic change continuously. - Cluster health scores monitor signals such as: - Underlying data quality - Whether curated SQL still works after schema changes - Coverage of users’ real questions - Reproducibility of generated SQL - Renamed columns or deprecated tables can immediately reduce the validity of existing examples. - Cluster owners use health dashboards and recommended actions to prioritize maintenance. ## Learning from Every Conversation - Vedder records conversations, queries, answers, generated SQL, and user feedback. - Cluster owners use this information to identify missing documentation, weak examples, and emerging needs. - Each approved example or clarified definition improves future answers. - The system treats context as an ongoing product that requires ownership and maintenance, not a one-time upload of metadata. Spotify’s approach suggests that trustworthy enterprise AI depends on a maintained context layer: curated datasets, expert-approved examples, clear documentation, and continuous feedback. The model supplies reasoning and automation, but domain experts remain responsible for defining what the data means.

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