AI

331 posts

cloudflare3 min readCurated summary

Defeating the deepfake: stopping laptop farms and insider threats

Trust is becoming a critical security weakness as attackers use stolen identities, AI-generated deepfakes, and laptop farms to impersonate remote workers. Traditional zero trust controls verify devices and credentials but often fail to verify the actual person behind them. Cloudflare’s partnership with Nametag adds identity verification during onboarding and risk-based controls afterward, aiming to prevent fraudulent workers from accessing corporate systems. ## The Rise of Remote Worker Fraud - Organized groups, including North Korean operations, use “laptop farms” to infiltrate companies. - Devices are shipped to domestic addresses, physically connected to KVM switches, and remotely operated by fraudulent workers. - Attackers use stolen identities, generative AI for interviews, and deepfake tools to create convincing government IDs and selfies. - Valid credentials and corporate-issued devices can make these users appear legitimate to standard zero trust systems. ## Why Traditional Insider Threat Defenses Fall Short - DLP and UEBA tools typically detect suspicious behavior only after an attacker has gained access. - Conventional onboarding often trusts: - The identity provided by a new hire - The shipping address receiving the laptop - Credentials sent to a personal email address - Zero trust policies commonly verify device posture, location, and account permissions—but not whether the person is genuinely the employee. ## Identity-Verified Zero Trust - Cloudflare Access is adding Nametag’s workforce identity verification to its existing policy checks. - Nametag verifies that the person receiving, configuring, and using a device is: - A real person - The legitimate person named in the identity documents - The authorized employee - Verification occurs before access to email, code repositories, or other internal resources is granted. ## How the Nametag Integration Works - Nametag integrates with Cloudflare Access through OpenID Connect (OIDC). - It can operate as the primary identity provider or as an additional evaluation factor alongside Okta or Microsoft Entra ID. - A typical onboarding flow includes: - The user attempts to access an onboarding portal. - Cloudflare redirects them to Nametag. - The user provides a work email, takes a selfie, and scans a government-issued ID. - Nametag’s Deepfake Defense technology uses cryptography, biometrics, and AI to detect fake identities, injection attacks, and presentation attacks such as printed photos. - A successful verification returns an ID token to Cloudflare, which applies its Access policies. - The process reportedly takes less than 30 seconds, and biometrics are not retained afterward. ## Layered Insider Threat Protection - Identity verification complements Cloudflare’s existing controls: - API-driven DLP for detecting data exfiltration - Remote Browser Isolation for reducing browsing risks - Shadow IT reporting and CASB capabilities for identifying unmanaged services and misconfigurations - Together, these controls distinguish between knowing which account is connecting and knowing who is actually behind the keyboard. ## Continuous Verification - Initial identity checks are not sufficient because legitimate credentials can later be sold or compromised. - Cloudflare Access uses user risk scores to support context-aware policies. - A sudden increase in risk can trigger access revocation for one or multiple applications. Organizations facing remote hiring and insider-threat risks should supplement device and credential verification with strong identity assurance at onboarding, followed by continuous, risk-based monitoring.

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figma2 min readCurated summary

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

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

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cloudflare3 min readCurated summary

Introducing the 2026 Cloudflare Threat Report

Cloudflare’s 2026 Threat Report argues that cyberattacks are shifting from brute-force intrusion toward high-trust exploitation. Attackers increasingly prioritize “Measure of Effectiveness” (MOE)—the greatest operational result for the least effort—using stolen tokens, AI, trusted cloud services, and social engineering rather than costly custom exploits. The report concludes that defenders must focus on identity, integrations, infrastructure resilience, and continuous monitoring of legitimate tools. ## Measure of Effectiveness (MOE) - MOE measures the ratio between an attacker’s effort and the operational outcome. - Threat actors favor: - Stolen session tokens over expensive zero-day exploits. - Reputation-based infrastructure such as LotX over custom servers. - AI-assisted automation over manually written tooling. - The most dangerous actors are those able to combine intelligence and technology into continuous, high-speed operations. ## Eight trends shaping the 2026 threat landscape - **AI-driven attacker operations** - Generative AI supports real-time network mapping, exploit development, and deepfake creation. - Lower-skilled attackers can now conduct more sophisticated, high-impact campaigns. - **State-sponsored infrastructure pre-positioning** - Groups such as Salt Typhoon and Linen Typhoon are targeting North American telecommunications, government, commercial, and IT services. - Their goal is to maintain access that can provide long-term geopolitical leverage. - **Over-privileged SaaS integrations** - Third-party APIs can expand a single compromise across hundreds of organizations. - The GRUB1 breach of Salesloft demonstrates the risks created by excessive integration privileges. - **Weaponized trusted cloud tools** - Attackers use services such as Google Calendar, Dropbox, GitHub, Google Drive, Microsoft Teams, and Amazon S3 to conceal malicious activity. - Legitimate enterprise traffic makes command-and-control communications harder to distinguish from normal use. - **Deepfake-based insider placement** - North Korean operators are using fraudulent identities and deepfakes to place remote IT workers inside Western companies. - These operatives support espionage and illicit revenue generation. - **Session-token theft** - Infostealers such as LummaC2 harvest active authentication tokens. - Attackers can then bypass multi-factor authentication and begin post-authentication activity. - **Internal brand spoofing** - Phishing-as-a-service tools exploit mail-relay blind spots where sender identity is not re-verified. - This enables convincing impersonation messages to arrive directly in trusted user inboxes. - **Hyper-volumetric DDoS attacks** - Botnets such as Aisuru are generating increasingly large distributed denial-of-service attacks. - The speed and scale of these attacks can overwhelm infrastructure before human responders can react. ## Living off legitimate cloud infrastructure - Attackers increasingly avoid known malicious servers and instead use legitimate SaaS, IaaS, and PaaS platforms. - Cloud services can be used to host payloads, redirect victims, deliver malware, or scale campaigns. - Amazon SES and SendGrid, for example, can be abused for phishing and malware distribution. - This “living off the land” approach—or “living off anything-as-a-service”—allows attackers to hide behind the reputation and normal traffic patterns of trusted providers. - Cloud-resource abuse is evolving from opportunistic infrastructure misuse into a deliberate nation-state strategy. Defenders should treat identity tokens, SaaS permissions, cloud activity, and trusted integrations as critical security boundaries. Organizations need least-privilege access, stronger token protection, continuous monitoring, automated DDoS mitigation, and detection that evaluates behavior—not just whether a service is legitimate.

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figma3 min readCurated summary

Our Config 2026 Speakers on the Biggest Opportunities With AI | Figma Blog

Figma’s Config 2026 speakers see AI as more than a productivity tool: it is expanding the scope of creative work, from software and music to fashion and manufacturing. Their perspectives emphasize human direction, participation, taste, and intention as AI accelerates experimentation. The central opportunity is to use AI to extend creative capacity without losing the distinctly human role of shaping meaning and purpose. ## AI as a New Creative Medium - Holly Herndon describes software as one of the defining artistic mediums of the current era. - AI enables studios to take on more complex projects, shifting creative roles toward orchestration. - Herndon and Mat Dryhurst’s *Starmirror* treats AI models as collective, public endeavors: - Visitors and local choirs contribute vocal data. - The data will train a new AI choir. - Participants engage with both the model’s inputs and outputs. - The project demonstrates how creative work can keep humans actively involved rather than treating AI as an isolated generator. ## Connecting Digital Creativity to the Physical World - Danit Peleg argues that AI will increasingly create tangible objects, not just digital designs. - AI is likely to influence: - Manufacturing - Architecture - Fashion - Wearable textiles - Peleg uses AI agents throughout her production pipeline, from initial concepts through fabrication. - Figma Weave, created after Figma’s acquisition of Weavy, is intended to expand AI-native capabilities for: - Image and video generation - Animation and motion design - VFX creation and editing - These tools point toward workflows where digital concepts can move more directly into physical production. ## Creativity as Attention and Care - Vicki Tan connects creativity with decision-making: both involve following questions and intuition despite uncertainty. - She argues that creativity is not primarily originality or talent, but care, attention, and sustained engagement with an idea. - Her interpretation of the French word *attendre*—to wait for or tend to—frames creativity as allowing meaning to emerge over time. - Rather than constantly seeking something completely new, creators can begin by noticing what already feels personal, meaningful, or instinctively theirs. ## Rethinking Creative Work in 2026 - The featured speakers come from varied fields, including art, fashion, behavioral design, software strategy, and emerging technology. - Their work challenges older assumptions about creativity and encourages experimentation with new processes. - AI’s greatest value may lie in amplifying human judgment, participation, and creative intent rather than replacing them. Creators should treat AI as an expandable medium and collaborator while preserving the human practices—attention, taste, participation, and purpose—that give creative work meaning.

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aws2 min readCurated summary

Transform live video for mobile audiences with AWS Elemental Inference | Amazon Web Services

AWS Elemental Inference is a fully managed AI service that transforms landscape live and on-demand video into mobile-ready vertical formats and automatically generates clips. It works in real time alongside AWS Elemental MediaLive, reducing 6–10 seconds of latency compared with minutes of traditional postproduction. AWS aims to help broadcasters publish content to TikTok, Instagram Reels, YouTube Shorts, and similar platforms without manual editing or specialized AI expertise. ## Mobile-Optimized Video Transformation - Smart Crop reformats landscape broadcasts into a 9:16 vertical format. - AI tracks subjects and keeps important action visible while preserving broadcast quality. - The service can process live content as it is being broadcast, helping publishers capture viral moments quickly. - Clip generation identifies notable events—such as game-winning plays in soccer or basketball—and produces clips for rapid distribution. ## Deployment and Workflow Integration - Users can create and manage feeds through the standalone AWS Elemental Inference console. - A feed contains feature configurations and moves from `CREATING` to `AVAILABLE`. - Outputs can be configured for vertical cropping or clipping; clip outputs require a name, the `Clipping` type, and an `ENABLED` status. - AWS Elemental Inference can also be enabled directly in existing AWS Elemental MediaLive channels without changing the surrounding video architecture. - MediaLive includes an AWS Elemental Inference tab showing the service ARN, data endpoints, feed outputs, enabled features, and operational status. ## Real-Time Agentic AI Processing - The service analyzes video continuously and independently performs cropping and clip-generation workflows. - Its agentic AI operates without human prompting or manual intervention. - Multiple AI features run in parallel against the same stream through a “process once, optimize everywhere” model. - Fully managed foundation models are automatically updated and optimized, removing the need for dedicated AI infrastructure or specialist teams. - Processing latency is approximately 6–10 seconds. ## Availability and Pricing - AWS Elemental Inference is initially available in: - US East (N. Virginia) - US West (Oregon) - Europe (Ireland) - Asia Pacific (Mumbai) - It can be accessed through the MediaLive console or MediaLive APIs. - Consumption-based pricing charges for the features used and video processed, with no upfront commitment. - AWS plans additional capabilities and tighter integration with other Elemental services, including features aimed at video monetization. AWS Elemental Inference is best suited to broadcasters and streamers that need to repurpose live content for mobile platforms quickly. Organizations already using MediaLive can add automated cropping and clip generation with minimal architectural change.

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grammarly2 min readCurated summary

Superhuman Go Scales Agent Ecosystem With New Partner Agents From Box, Gamma, and Wayground

Superhuman Go expands Grammarly’s workflow assistant with agents that connect enterprise knowledge, create visual content, support learning, and improve communication. These integrations let users work directly from their existing context instead of switching between tools, while keeping tasks such as document reuse, presentation creation, research, and feedback in one workflow. ## Enterprise Knowledge and Workflow Automation - **Box** connects document repositories to Go, allowing users to: - Create Box documents in the appropriate folders. - Search existing files for summaries, extracted information, and reusable knowledge. - Find the latest document versions while keeping Box as the source of truth. - **Common Room** brings buyer intelligence from multiple channels into users’ workflows. - **Fireflies** surfaces meeting summaries, action items, and key decisions to speed up follow-up. - **Parallel** checks facts, recommends citations, and adds real-time data for more credible work. - **Latimer** combines internal search with bias detection to support precise, fair writing. ## Visual Content Creation - **Gamma** turns notes, documents, and meeting recaps into polished, structured presentation decks. - **Napkin AI** converts written content into visual frameworks designed to improve clarity and drive action. ## Interactive Learning - **Wayground** creates quizzes and flashcards from content visible on screen, including emails, documents, slides, and web pages. - **Quizlet** transforms notes, essays, and other written materials into flashcards with a single prompt. - **Speechify** supports listening at speeds up to 4.5 times faster using AI voices designed to improve comprehension. ## Communication, Feedback, and Compliance - **Radical Candor®** helps users handle difficult feedback using Kim Scott’s framework. - **Saifr** assists financial organizations with clear, compliant public communications by detecting regulatory risks and suggesting safer language. ## Building Custom Agents - The Superhuman Agents SDK and MCP client allow organizations to build agents that operate across Go. - The SDK is currently in private beta, with applications available for organizations interested in developing their own agents. Superhuman Go is available to Grammarly users through its Chrome and Edge browser extensions, with Mac and Windows support planned. Together, the integrations position Go as a central workspace for turning existing information into documents, presentations, learning materials, research, and compliant communications without constant tool switching.

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line4 min readCurated summary

Sharing the journey of LINE DEV

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

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figma2 min readCurated summary

The Future of Design Is Code and Canvas | Figma Blog

The post argues that the future of product creation combines code with visual design canvases rather than treating them as separate, linear stages. Figma’s integration with Claude Code lets developers send rendered browser work into Figma as editable layers, enabling teams to explore alternatives visually and move changes back into code. The broader goal is to help builders avoid tunnel vision and choose better solutions before committing to implementation. ## Code and Canvas as Complementary Tools - Code is powerful for building and expressing ideas, while the canvas is better for comparing and navigating many possibilities. - Figma supports: - Divergent exploration of multiple approaches - Side-by-side comparison of designs - Direct manipulation of visual details - Big-picture evaluation before implementation ## Claude Code to Figma - Users can install the Figma MCP and type “Send this to Figma” in Claude Code. - The browser’s rendered state is translated into fully editable Figma layers. - After refining the design in Figma, Figma MCP can transfer design changes back into the codebase. - This creates a bidirectional workflow between production code and visual design. ## Moving Beyond Linear Workflows - Traditional product development often followed a sequence: brainstorm, design, then code. - AI and connected tools allow work to begin in a terminal, prompt box, visual interface, or sketch and move between formats. - Teams can now reconsider direction during development instead of simply advancing the first workable concept. ## Design as the Main Differentiator - As AI makes it easier to generate almost any articulated possibility, the difficult work becomes identifying the best solution. - Design judgment, craft, and point of view remain essential. - Figma positions the canvas as a space for stepping back, examining alternatives, and escaping the momentum of building the first version. The practical recommendation is to combine code-driven speed with canvas-based exploration: use code to create, Figma to compare and refine, and MCP integrations to keep both workflows connected.

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figma2 min readCurated summary

From Claude Code to Figma: Turning Production Code into Editable Figma Designs | Figma Blog

Claude Code to Figma lets users capture working interfaces from production, staging, or localhost and convert them into editable Figma frames. The workflow combines code’s speed for building functional prototypes with Figma’s strengths in collaboration, comparison, and exploration. Its central argument is that teams can move faster without stopping at the first working implementation. ## From Code to an Editable Canvas - Developers can capture real UI screens from Claude Code workflows. - Captured screens can be pasted into any Figma file as editable frames. - The workflow supports interfaces running in production, staging, or locally. - Multiple screens can be captured in one session, preserving flow sequence and context. ## Start Anywhere, Then Collaborate - Code-first exploration is fast but often isolated: one person manages the branch, server, and context. - Sharing screenshots, recordings, or local builds creates friction when feedback is needed. - Once imported into Figma, screens can be organized, duplicated, refined, annotated, and shared. - Teams can discuss and explore the interface without switching environments or modifying code for every idea. ## Build the Best Idea, Not Just the First One - AI makes it easier to produce an initial prototype quickly, shifting attention toward evaluating alternatives. - Figma Make supports a similar workflow by bringing generated prototypes onto the design canvas. - Claude Code to Figma extends this approach to code-created interfaces. - Both workflows aim to turn an initial tangible result into deeper design exploration. ## Explore Systems and Variations Visually - Side-by-side frames make patterns, inconsistencies, gaps, and trade-offs easier to identify. - Teams can duplicate frames, rearrange steps, and test structural changes without reimplementing code. - Keeping alternatives visible supports continued exploration, including previously rejected ideas. - Designers, engineers, and product managers can make decisions using the same high-fidelity artifact. - Shared context helps surface questions and resolve direction earlier. Claude Code to Figma is intended as a bridge between functional prototyping and collaborative design. Teams can use code to quickly discover what works, then move the result into Figma to compare options, gather feedback, and establish shared direction.

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dropbox2 min readCurated summary

Insights from our executive roundtable on AI and engineering productivity

Dropbox argues that AI improves engineering productivity only when tied to measurable business outcomes rather than adopted for its own sake. The company has expanded AI use across the software development lifecycle, while recognizing trade-offs involving quality, maintenance, and organizational change. Its executive roundtable concluded that leadership, formal AI competency, and stronger outcome measurement will be central to realizing AI’s potential. ## Dropbox’s AI Adoption Strategy - Dropbox made AI adoption a company-wide priority with leadership sponsorship, enabling teams to experiment more easily and reducing delays in approving new tools. - Engineers use AI across code review, documentation, debugging, testing, and other stages of development. - Because Dropbox operates a large, multilingual monorepo, it combines commercial tools such as Claude Code and Cursor with internally built systems. - One internal tool detects failed pull-request builds and uses Dropbox’s AI platform to suggest fixes. - Most developers now use at least one AI tool. - Dropbox tracks monthly pull-request throughput per engineer and has observed higher output among developers who use AI coding tools more actively. - The company also monitors engineer sentiment, reporting increased positive sentiment and reduced negative sentiment as adoption improves. ## Focus of the Executive Roundtable Leaders from multiple companies discussed engineering productivity and AI in rotating peer groups organized around three themes: - **Measuring impact** - Identifying ways to measure AI-driven productivity gains. - Connecting engineering improvements to broader business results. - **Leadership alignment** - Establishing how executives should communicate AI deployment progress. - Determining the appropriate pace and scope of adoption. - **The human element** - Recruiting, evaluating, and developing AI-capable employees. - Applying lessons from developer productivity to help non-engineering teams work more effectively. ## Lessons About AI and Productivity - **Balance is essential:** Faster development must not come at the expense of software quality or increased long-term maintenance costs. - **Leadership sets standards:** Technical managers play a key role in defining responsible and effective AI usage norms. - **AI skills should be formalized:** Including AI competency in career frameworks demonstrates that it is a lasting strategic capability rather than a temporary trend. - **Extra capacity needs direction:** Dropbox is currently using productivity gains to address technical debt, complete migrations, and improve reliability. ## Priorities for 2026 Dropbox’s main unresolved challenge is linking engineering productivity metrics to tangible business outcomes. Its next phase will focus on mapping AI-driven gains to specific results, extending operational discipline beyond engineering, and improving end-to-end product velocity.

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figma2 min readCurated summary

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

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

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kakao2 min readCurated summary

Introducing the new Kanana-o

Kanana-o is Kakao’s new Korean-focused omni-modal AI model, designed to understand and generate text, images, and audio naturally. Kakao is opening a closed beta for the Kanana-1.5-o-9.8b-2602 model to gather feedback from developers and partners before commercial release. The service emphasizes practical experimentation rather than large-scale traffic handling. ## Model Capabilities - Supports simultaneous processing of multiple modalities, including text, images, and audio. - Specializes in: - Deep understanding of Korean language, culture, and user intent. - Natural Korean speech with expressive intonation, pacing, and emotion. - Flexible applications such as podcast narration, multi-turn conversations, and multi-speaker text-to-speech. - Balances text-generation speed with audio-processing speed to produce more natural spoken responses. ## API Beta Service - **Service:** Kanana-o API Beta - **Model:** Kanana-1.5-o-9.8b-2602 - **Beta period:** February 27–May 27, 2026 - **Access:** Selected testers receive a fixed number of daily API uses during the beta. - The closed beta is intended for meaningful developer testing and feedback, not high-volume production workloads. ## Application and Selection - Applicants should visit [omni.kanana.ai](https://omni.kanana.ai/), sign in with a Kakao account, and submit information about: - Their organization or affiliation - Intended purpose - Expected technical scenarios - Selected applicants will receive invitations and API documentation through KakaoTalk notifications starting February 27. - Kakao is seeking developers, students, startups, and researchers with concrete implementation plans. - Specific proposals—such as building a visual shopping assistant for people with visual impairments—are favored over general interest in trying AI. Developers interested in exploring Korean-language, audio, and vision applications can apply for the beta with a clearly defined use case and prototype plan.

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figma3 min readCurated summary

Why Demand for Designers Is on the Rise | Figma Blog

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

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line4 min readCurated summary

Scaling to Infinity: LY Corporation’s

LY Corporation’s observability team evolved its time-series database to handle rapidly growing infrastructure and Kubernetes workloads. After outgrowing MySQL and OpenTSDB, the team built an engine optimized for high-cardinality metrics, low-latency queries, and seamless API compatibility. Its architecture now combines in-memory, Cassandra, and S3-compatible storage, enabling cost-efficient scaling while supporting trillions of daily metrics. ## Why Time-Series Storage Matters - Metrics record system state as timestamped numerical values. - They support dashboards, threshold-based alerts, and predictive analysis using tools such as ARIMA and Prophet. - Even a small metric record can consume about 280 bytes when timestamps, values, and tags are included. - One CPU metric collected every 15 seconds requires roughly 562 MiB per server annually; across 1,000 servers, this grows to about 548 GiB before adding memory, disk, and network metrics. - High-cardinality cloud environments make both storage cost and query latency critical operational concerns. ## Moving Beyond MySQL and OpenTSDB - MySQL initially became inadequate as the organization moved from SOA to MSA: - Write load increased sharply. - Storage costs and capacity requirements grew. - Query latency worsened for large datasets. - Rigid schemas could not easily represent changing cloud resources. - MySQL sharding provided temporary relief but could not support high-resolution metrics collected at intervals under one minute. - OpenTSDB, introduced in 2016 on Apache HBase, improved write performance but had important limitations: - Tag growth harmed UID-table lookup performance. - Metadata was restricted to a narrow character set. - Large queries required cache warm-up procedures. - These constraints led to the development of an internal database beginning in 2018. ## Building the Internal Time-Series Database - The 2019 engine was designed around: - Flexible protocol support independent of a particular agent. - Linear scalability without downtime. - Low-latency processing of high-resolution metrics. - Strong availability during failures. - Inspired by Meta’s Gorilla research, the team used access patterns in which most queries target recent data. - Frequently accessed metrics were kept in an in-memory database, while colder data was stored in Apache Cassandra. - The new engine enabled metric volumes to grow by more than 200 billion records annually while preserving existing APIs. - Users benefited from the new backend without migration work or code changes. ## Scaling for Kubernetes Workloads - Kubernetes introduced rapidly changing pods, dynamically allocated volumes, and much higher metric churn. - Both major storage layers encountered scaling problems: - IMDB initially required adding identical hardware, limiting expansion options. - Cassandra rebalancing could take tens of hours because of its data volume. - The team improved IMDB with weighted load balancing so nodes with different capacities could be used effectively. - Storage was divided into tiers: - Recent 14-day data remained in Cassandra for high-performance access. - Older data was moved to S3-compatible storage. - This reduced Cassandra dependency, lowered costs, simplified operations, and enabled more flexible hardware and Kubernetes-based deployment. ## Writing and Reading Through S3 - The write path separates data processing from long-term storage: - A Dumper reads metric slots from IMDB. - It converts them into internally defined sub-blocks. - A Block Dumper combines sub-blocks into blocks and writes them to S3. - A Storage Gateway reads the blocks for queries and caches them on local disks. - Disk caching initially caused excessive page-cache use and rapid memory exhaustion. - Direct I/O was considered but withdrawn after the cloud storage team warned that it consumed too much shared bandwidth. - Through cross-team collaboration, the team adopted a B+ tree-based cache that made better use of the kernel page cache without overloading infrastructure. ## Future Direction: From Storage to Intelligence - The team aims to move beyond recording metrics toward prediction and AI-assisted operations. - Achieving this requires consolidating time-series data currently scattered across internal systems. - A key requirement is to perform this integration without imposing migration work or breaking changes on users. - The broader goal is an observability platform that turns unified metrics into predictive and intelligent operational capabilities. The main recommendation is to design time-series platforms around real access patterns, tier storage according to data age, and preserve compatibility while evolving the backend. At extreme scale, careful storage architecture and collaboration across infrastructure teams are as important as raw database performance.

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kakao2 min readCurated summary

Recruiting new Kakao AI Ambassadors ‘

Kakao is recruiting 100 participants for its expanded KANANA 429 AI ambassador program. The five-month program introduces separate tracks for AI experts, creators, and university students, offering opportunities to test Kakao AI services, create content, and provide feedback. Applications close at noon on February 19, 2026. ## Program Purpose and Name - KANANA 429 promotes Kakao’s AI technologies and gathers user feedback. - “429” references the HTTP status code “Too Many Requests,” representing people with abundant enthusiasm and ideas about AI. - The program builds on Kakao’s first ambassador cohort, which included 20 participants. ## Results from the Previous Cohort - Participants communicated through KakaoTalk Open Chat. - They attended monthly offline meetups, informal group activities, and networking sessions with Kakao employees. - They previewed new Kakao AI services and exchanged feedback. - The cohort produced roughly 100 reviews and other pieces of content about Kakao’s AI services, models, and technologies. - Kakao selected and awarded five outstanding ambassadors. ## New Tracks and Benefits - **AI experts:** Test Kakao’s latest AI services and models and write in-depth reviews. - **Creators:** Produce content demonstrating practical ways to use Kakao AI. - **University students:** Promote the program on and off campus and collect user opinions. - The activity period has increased from three to five months. - Selected ambassadors receive AI service usage opportunities worth approximately 1 million won, along with additional benefits and special merchandise. ## Application and Schedule - Applicants must publish content related to Kakao AI and submit its URL through the recruitment page. - Applications are accepted until noon on February 19, 2026. - Every applicant receives a one-month free Kakao Emoticon Plus subscription. - Selected participants will be notified individually through Kakao’s official KakaoTalk channel on March 4. - The opening ceremony is scheduled for March 13 at Kakao AI Campus. Kakao is seeking applicants who are genuinely interested in AI and willing to communicate openly while helping shape and spread its AI services.

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