Techlist.io - Korean Tech Blog Curator

figmaJul 2, 20262 min readCurated summary

Issue no.17: Build with joy | Figma Blog

Figma’s Issue No. 17, “Build with joy,” celebrates the creative motivation behind making things: turning a vision into reality. Inspired by Config 2026, it presents tools, ideas, and stories that encourage designers to explore their own paths while developing stronger creative judgment. The central message is that tools help, but care, taste, and a clear point of view ultimately shape meaningful work. ## Config 2026 and Choosing Your Own Path - Figma introduces new tools and materials for its canvas, enabling teams to extend existing workflows and move faster. - The accompanying Config magazine uses a choose-your-own-adventure format, allowing readers to explore topics in any order. - Its broader focus is how teams are adapting established processes to reach new creative and professional goals. ## Cultivating Taste - Chief Design Officer Loredana Crisan defines taste as more than recognizing quality; it also involves sustaining the energy and care required to achieve it. - Her experience playing music helped shape her understanding of creative judgment. - The article emphasizes that taste develops continuously through practice, attention, and a strong point of view. - Crisan identifies care for the user, the craft, and the final result as essential qualities. ## Motion as Design in Time - Figma’s Brand Studio offers an introduction to motion design. - Motion is framed as the combination of design and time, with timing and movement affecting how an idea is perceived. - Small distinctions—such as whether an animation feels like a “whoosh” or a “whoop”—can significantly change its character and meaning. ## Color as Cultural Language - Pantone’s color experts explain how colors acquire cultural associations and influence behavior. - Familiar examples include Coca-Cola red, Barbie pink, and Brat green. - Brands use color to create meaning, communicate identity, and help drive cultural trends. - Color is presented not merely as decoration, but as a system of shared social and emotional signals. ## Further Exploration - Dylan Field recaps Config 2026 and the new dimensions Figma is bringing to its canvas. - Figma’s community imagines future software, including systems that respond to moods and tools that predict what comes next. - The latest Figma merchandise collection follows eight illustrators from initial sketches through production. Figma’s recommendation is ultimately to build with curiosity and joy: use new tools as a starting point, but rely on care, experimentation, and cultivated taste to determine what is worth making.

Figma Design Systems Motion Design Color Theory+1

awsJul 1, 20262 min readCurated summary

Upgrade Amazon EKS clusters with confidence using Kubernetes version rollbacks | Amazon Web Services

Amazon EKS now supports rolling back Kubernetes minor-version upgrades within seven days, providing a safety net that open-source Kubernetes traditionally lacks. The rollback returns a cluster to its previously validated production version rather than placing it in an emulated transitional state. This can help organizations upgrade more confidently, reduce lengthy upgrade cycles, and avoid remaining on outdated versions. ## Kubernetes Version Rollbacks - Rollbacks support one minor version at a time, matching EKS upgrade behavior. - Administrators can revert an upgrade such as Kubernetes 1.35 to 1.34 within the seven-day rollback window. - EKS uses cluster insights to check rollback readiness, including: - Node version compatibility - Add-on dependencies - Other potential upgrade issues - The `--force` option bypasses these checks when administrators need to proceed quickly. - The capability applies to both self-managed-node clusters and clusters using AWS-managed infrastructure. ## EKS Auto Mode Rollbacks - EKS Auto Mode must roll back both the control plane and managed nodes. - Node rollback operations honor pod disruption budgets, so completion time depends on workload configuration. - A cancel API allows administrators to stop an in-progress node rollback. - Disruption budgets are never bypassed automatically; users can modify or remove them if they need to accelerate the process. ## Rollback Experience and Availability - Rollbacks can be initiated from the EKS console after selecting an eligible cluster. - The console displays the remaining rollback window and relevant readiness insights. - In the example, the control plane rollback took about 20 minutes, while Auto Mode nodes rolled back according to their disruption budgets. - Rollbacks are available at no additional cost in all commercial AWS Regions where EKS operates. - Control plane rollbacks support all EKS clusters; node rollbacks are available for EKS Auto Mode. - Supported Kubernetes versions include those under standard or extended EKS support. Administrators can use the EKS console or documentation to begin using rollbacks, making incremental Kubernetes upgrades safer and easier to recover from.

Kubernetes AWS Amazon Eks Eks Auto Mode+3

metaJul 1, 20263 min readCurated summary

Meta’s AI Storage Blueprint at Scale

Meta argues that AI progress increasingly depends on storage that can deliver massive datasets with predictable, low latency. Traditional BLOB-storage designs optimized for durable, cost-efficient HDD storage create metadata and proxying bottlenecks that stall GPUs and slow research. Meta is therefore rebuilding its storage foundation around unified metadata, direct client-to-storage access, and regional deployments colocated with GPUs. ## Storage Architecture and AI’s Growing Demands - Meta operates hundreds of exabyte-scale storage clusters supporting products such as Facebook, Instagram, Meta AI, Ads, and internal databases. - Its storage APIs are built on Tectonic, a horizontally scalable block layer providing: - High durability and availability through erasure coding - HDD and flash tiering - Placement of hot, warm, and cold data - Multi-tenant regional storage - BLOB-storage layers built on Tectonic provide globally scalable object storage and configurable durability/availability policies. - Meta’s training systems historically used an NFS-like filesystem interface over Tectonic, but are increasingly moving to BLOB storage for unified access to massive data lakes and higher performance. ## Why Storage Latency Limits GPU Utilization - AI workloads require bursty and sustained high throughput with predictable worst-case latency. - Training runs use hundreds of thousands of GPUs processing data in batches and periodically synchronizing state. - A single slow GPU can delay synchronization and extend the completion time for every GPU. - Data loaders prefetch future batches while GPUs process current ones, but high-latency storage reads can still create GPU stalls. - These stalls directly increase training costs and extend time to market. ## Problems with the Legacy BLOB Architecture - The older service-oriented design accumulated multiple stateful layers, each with its own metadata store. - A single `getObject("/bucket/path")` request could require lookups across the namelayer, volumeslayer, and containerlayer. - Cross-region metadata requests could add hundreds of milliseconds, and one slow lookup could delay the entire operation. - The architecture’s original assumptions no longer matched AI requirements: - **Latency:** AI needs bounded pMax latency, not merely acceptable average performance. - **Reliability:** AI requires high availability, but does not always need global replication by default. - **Cost:** Flash is necessary for AI-level IOPS, making storage cost-per-byte less important. - **Power:** Power used by storage competes directly with power available for GPUs. ## Rebuilding the Storage Foundation Meta redesigned the system around three major changes: - **Unified metadata schema** - Metadata from separate layers was consolidated into a flat schema backed by ZippyDB. - Path resolution can now use O(1) lookups to map objects to `(blockId, offset, size)` locations. - **Direct data access** - The dataplane proxy was removed. - A “fat client” SDK streams data directly from Tectonic storage servers. - This reduces latency, increases throughput, and lowers storage power consumption. - **Regional deployment** - The BLOB stack can operate regionally or globally. - Regional instances are colocated with GPUs in AI regions, reducing cross-region access. With the new flow, the SDK requests a read plan from the API server, which performs the metadata lookup and returns storage locations. The SDK’s embedded Tectonic BlockClient then reads directly from the underlying blocks, adding essentially no extra dataplane overhead. The redesigned architecture is intended to improve GPU utilization, reduce latency, and preserve power for computation. The provided excerpt ends as Meta begins discussing how it handles workload spikes and hot spots during data and checkpoint loading.

Ai Training Object Storage Distributed Storage Erasure Coding+3

githubJul 1, 20261 min readCurated summary

6 security settings every GitHub maintainer should enable this week

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

AI Open Source Cybersecurity Secure Software Development

cloudflareJul 1, 20263 min readCurated summary

Making AI search smarter

Cloudflare argues that AI-powered search has broken the traditional bargain in which publishers exchanged crawl access for traffic and revenue. AI summaries increasingly answer users’ questions without sending them to source sites, leaving publishers to choose between reduced visibility and uncompensated content use. The company proposes rebuilding this relationship through smarter crawling and payments tied to actual content usage. ## Rebuilding the Bargain - Cloudflare’s responsible AI bot principles emphasize: - Transparency about a bot’s identity and purpose - Respect for site owners’ choices - Good-faith behavior - Blocking unwanted crawlers protects publishers but does not create a sustainable business model. - Cloudflare’s broader goal is to make AI search beneficial to users, AI companies, and content creators. ## Making AI Search Smarter - Cloudflare is launching a research program using signals from its global network, which covers more than 20% of the web. - These signals may identify: - Which pages are fresh or have genuinely changed - Which content attracts human and automated traffic - Which sources are high quality and relevant - Answer engines could use this information to surface better content and avoid repeatedly crawling unchanged pages. - More than 50% of traffic from legitimate crawlers reportedly goes toward re-fetching unchanged pages. - Reducing unnecessary crawls would lower: - AI companies’ compute costs - Publishers’ server load and bandwidth expenses - The program is intended to be neutral, limited to search, and will not share content or train foundation models. - Cloudflare plans to publish results and make the capability broadly available later in the year. ## From Pay Per Crawl to Pay Per Use - Cloudflare’s existing Pay Per Crawl model lets publishers charge AI companies for accessing their content. - Cloudflare says crawling is an imperfect measure of value: - A page may be crawled once but cited in thousands of answers. - It may also be crawled repeatedly without ever being used. - The company is therefore experimenting with Pay Per Use, where compensation reflects how often content contributes to search results or answers. - Early partners include Ceramic.ai and You.com. - Ceramic’s pay-per-query model pays publishers when their content appears in Ceramic search results. - Cloudflare’s network is intended to help AI companies scale these payment systems across millions of participating content owners. Cloudflare’s proposed model combines efficient, change-aware crawling with compensation based on actual content use. If adopted broadly, it could give publishers better control, reduce needless infrastructure costs, and create a more sustainable economic relationship between AI search services and the web.

Cloudflare Bot Management Ai Search Web Crawling+2

cloudflareJul 1, 20263 min readCurated summary

Announcing the Monetization Gateway: charge for any resource behind Cloudflare via x402

Cloudflare is introducing the Monetization Gateway, which will let customers charge for web pages, datasets, APIs, and MCP tools protected by Cloudflare. It combines payment rules, access control, metering, and payment verification at the edge, initially using stablecoins through the x402 protocol. The goal is to make low-cost, usage-based payments practical for AI agents without requiring sellers to build billing infrastructure or onboard every buyer. ## The Web’s Shift Toward Usage-Based Payments - The traditional web monetized human attention through advertising, subscriptions, and e-commerce. - AI agents consume resources without viewing ads or maintaining subscriptions, creating a need for pricing based on actual usage. - Potential models include: - Charging a few cents per search. - Combining a base fee with usage charges, such as per megabyte uploaded. - Charging only when an outcome succeeds, such as a resolved support escalation. - Agents may generate thousands of requests and micropayments, making per-request, per-token, or outcome-based pricing more appropriate than monthly or per-seat plans. - Existing usage billing has generally required API keys, known customers, extensive accounting systems, and costly payment infrastructure. - Stablecoins such as USDC and Open USD can support sub-cent payments with low fees and settlement times under a second. ## Cloudflare’s Role in Usage-Based Billing - Cloudflare can use its position as a proxy between buyers and sellers to combine payment verification with the request path. - Metering, payment exchange, and settlement can occur away from the customer’s origin. - Customers retain control over: - Pricing. - Access rules. - Revenue. - Sellers will not need to onboard each buyer or build a complete billing system; they can define rules that determine when agents must pay. ## How x402 Payments Work - x402 uses HTTP’s `402 Payment Required` status code to add payments directly to ordinary web requests. - The flow is: - A client requests a protected resource. - The server returns a 402 response containing the price, accepted asset, and payment destination. - The client pays and retries the request with proof of payment. - A facilitator verifies the payment. - The server returns the requested resource. - There are no checkout redirects or separate payment APIs. - Payments settle peer-to-peer directly into the seller’s wallet. - x402 is well suited to machine payments because: - It supports very small transactions. - Buyers do not need an account with the seller. - The payment itself acts as the access credential. - Stablecoins offer fast settlement, low fees, and no chargebacks. ## Monetization Gateway Capabilities - Customers will define payment policies through a dedicated rules API using expressions similar to other Cloudflare rules. - The system will apply to traffic such as tokens, APIs, MCP tool calls, and datasets. - Enforcement will run across Cloudflare’s network in more than 330 cities, allowing payment handshakes to occur near buyers while reducing latency and protecting origins. - Planned functionality includes charging for specific REST methods and routes, such as requiring $0.01 for each `GET` or `POST` request to `/api/premium/*`. - The gateway is also intended to support variable pricing for tasks with different costs. Cloudflare’s approach is to make micropayments a native part of HTTP access, enabling businesses to charge agents directly for the resources they consume without constructing their own payment and accounting systems.

AI Agents Model Context Protocol Cloudflare API+4

cloudflareJul 1, 20264 min readCurated summary

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

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

AI Large Language Models Generative AI Cloudflare+3

cloudflareJul 1, 20263 min readCurated summary

Your site, your rules: new AI traffic options for all customers

Cloudflare is replacing its broad “Block AI Bots” approach with finer controls based on what automated systems do: Search, Agent, or Training. The goal is to let website owners preserve discoverability and useful automation while blocking uncompensated model training and other unwanted access. These controls will be available to all Cloudflare customers, including Free-tier users. ## Why AI traffic needs more nuance - The traditional crawler exchange—content in return for referrals—has weakened as AI systems increasingly consume content without sending traffic back. - Website owners previously faced a binary choice: - Allow AI access to remain discoverable. - Block automation and protect content at the risk of losing visibility. - This tradeoff particularly harms small sites and can favor established search providers that use the same crawlers for search and training. ## A behavior-based AI taxonomy Cloudflare will classify automated traffic by its purpose rather than simply labeling bots as “AI”: - **Search** - Collects or indexes content to answer future queries. - Builds a database proactively. - Should generally provide referrals or other fair compensation. - **Agent** - Acts in real time on behalf of a person. - Includes chat-fetch bots such as ChatGPT-User and browser-use agents driven by Gemini or Claude. - Visits a site to complete a specific task for a human. - **Training** - Collects content to train or fine-tune a model. - Permanently incorporates data into the model’s underlying architecture. Bots may have multiple classifications. Cloudflare encourages operators to separate Search, Agent, and Training crawlers so site owners can understand and control their access more effectively. ## New controls for AI traffic - Cloudflare is adding separate controls for Search, Agent, and Training traffic. - These options replace the need for a single all-or-nothing AI blocking decision. - The controls will be available to all customers, including those on the Free plan. - Cloudflare will continue tracking other automated behaviors, such as ad verification, feed fetching, and agentic transactions. ## New default rules Starting September 15, 2026: - For new domains, **Training** and **Agent** crawlers will be blocked by default on pages displaying ads. - **Search** crawlers will remain allowed by default because they are more likely to send visitors back. - The policy treats ads as an indication that human attention—and therefore monetizable traffic—is the intended outcome. - Multi-purpose crawlers will be governed by all of their classifications, using the most restrictive applicable rule. - As a result, crawlers such as Googlebot, Applebot, and BingBot may be blocked when customers choose to block Training traffic. - Website owners can opt out of the new defaults through Cloudflare Security settings before September 15. Cloudflare’s recommendation is to manage AI access by behavior: allow Search when referrals matter, permit Agents when real-time user tasks are valuable, and block Training where content reuse is not adequately compensated.

AI Agents Cloudflare Bot Management Web Crawlers+3

cloudflareJul 1, 20262 min readCurated summary

Unmasking the crawls with Attribution Business Insights

Cloudflare argues that the traditional exchange between crawlers and publishers has broken down as AI bots extract content without sending meaningful referral traffic. This creates lost revenue for publishers while increasing hosting costs, making granular traffic attribution essential. Its new Attribution Business Insights dashboard aims to help site owners identify which bots provide value and make informed decisions about access, blocking, and commercial relationships. ## The Internet’s Changing Economics - Traditional search engines generally crawled content a few times for each visitor they referred. - That crawl-to-referral balance supported advertising, affiliate revenue, subscriptions, and direct audience relationships. - AI crawlers increasingly create a “zero-click” ecosystem by summarizing content without directing users to the original publisher. - Cloudflare observed AI crawl-to-referral ratios ranging from 118:1 to nearly 50,000:1. - Publishers face both reduced traffic-based revenue and higher infrastructure costs from unproductive automated access. ## Attribution Business Insights Dashboard - The dashboard is available to Cloudflare Bot Management customers. - It provides an immediate view of bot activity without requiring extensive manual analytics filtering. - It measures: - Human versus bot traffic to content pages. - Overall and operator-specific crawl-to-referral ratios. - Crawl-to-referral trends over 24 hours, seven days, or 30 days. - Top bots by traffic volume, country, bandwidth usage, and current allow/block status. - AI crawlers are classified by behavior: - **Training:** collecting data for future large language models. - **Search:** refreshing indexes used by retrieval-augmented generation. - **Agent:** supporting automated interactions that return answers to users. ## Turning Traffic Data into Business Strategy - Site owners can use high-level metrics to evaluate whether their content security policies are effective. - More detailed operator-level data helps publishers understand how individual AI companies use their content. - Comparing operators can support negotiations about: - Blocking or allowing specific crawlers. - Licensing content. - Reconsidering existing commercial agreements. - Prioritizing relationships with companies that provide meaningful compensation or referrals. - The dashboard is intended to give publishers concrete evidence—such as comparative crawl volumes and referral performance—when discussing content access with AI companies. Cloudflare’s recommendation is effectively to stop treating all crawlers alike. Publishers should use crawl-to-referral ratios, resource consumption, crawler purpose, and commercial value to decide which bots deserve access and under what conditions.

Large Language Models RAG Ai Crawlers Web Crawling+4

gitlabJul 1, 20261 min readCurated summary

GitLab Patch Release: 18.8.11 | GitLab Docs

GitLab 18.8.11 is an out-of-band patch release for Community and Enterprise Editions, released July 1, 2026. It fixes a regression introduced by the Rails 7.2 upgrade that could cause database connections to leak when database load balancing is enabled. The release contains no security fixes or new migrations. ## Database Connection Leak Fix - Resolves database connection leaks affecting installations that use database load balancing. - Intended to improve stability for customers upgrading to the required GitLab 18.8 stop. ## Upgrade Considerations - No new database migrations are included. - Multi-node deployments should not require downtime. - Omnibus packages normally stop GitLab, run migrations, and restart during upgrades, regardless of upgrade size. - Administrators can disable this automatic reconfiguration for upgrades by creating: ```text /etc/gitlab/skip-auto-reconfigure ``` ## Updating and Subscriptions - Administrators should follow GitLab’s update instructions to install 18.8.11. - Premium and Ultimate features require a paid subscription, while GitLab.com provides access to GitLab-hosted infrastructure. Upgrade to 18.8.11 particularly if database load balancing is enabled, as it addresses a stability-related connection leak without introducing migration or downtime requirements.

Gitlab Database Migrations Ruby On Rails Database Load Balancing+2

figmaJul 1, 20263 min readCurated summary

Building Accessibility Into a Canvas-Based Product | Figma Blog

Figma’s canvas-based rendering enables performance features such as infinite zoom and real-time collaboration, but it removes the browser’s built-in accessibility support. To restore that support, Figma built a synchronized “Mirror DOM” that represents the canvas in ordinary DOM elements for screen readers and keyboard users. The system combines an internal accessibility tree, React-rendered mirror elements, bidirectional selection syncing, and announcements for non-navigational changes. ## Why Canvas Requires a Different Accessibility Strategy - Figma renders designs on a canvas rather than with traditional HTML and DOM. - This improves performance but leaves the browser’s accessibility tree nearly empty. - Unlike a conventional web app with semantic elements such as `<button>`, `<p>`, and `<img>`, Figma’s canvas effectively has only one focus-holding `<input>`. - Without additional work, screen readers cannot navigate or meaningfully interpret the layers in a Figma file. ## Synthesizing an Accessibility Tree - Browsers normally derive an accessibility tree from the DOM, semantic HTML, ARIA attributes, and computed state. - Figma created its own internal accessibility tree to provide equivalent non-visual information for each design layer. - Each layer receives an accessible summary describing the role and content a screen reader should announce. - Summaries vary according to context: - In prototypes, editing-related layers can be omitted, while text and interactive roles are preserved for viewers. - In editing mode, structures such as autolayout frames need to remain available. - The tree is flattened by removing omitted nodes and connecting their relevant descendants. - Figma builds the tree initially, then applies surgical updates as documents change instead of rebuilding everything. ## Rendering the Mirror DOM - A recursive React component converts the internal accessibility tree into DOM elements. - Each component subscribes to accessibility data for one design layer and renders its role, label, and children. - React’s incremental updates help keep DOM changes minimal as the design changes. - The resulting elements are invisible to sighted users but available to assistive technologies. ## Synchronizing Canvas and Screen Reader Interaction - Figma maintains bidirectional synchronization between the visual canvas and the Mirror DOM. - Selecting a layer on the canvas moves focus to the corresponding DOM element. - When a screen reader user navigates the Mirror DOM, Figma updates the canvas selection accordingly. - This connects non-visual navigation with the editor’s visual state. ## Announcing Changes - A separate announcement system communicates changes that are not primarily navigational. - It reports actions such as nudging objects, switching tools, and other updates that would normally be apparent visually. - Together with the Mirror DOM, these announcements help screen reader users understand both the document structure and ongoing editor activity. Figma’s approach shows that accessibility can be rebuilt for canvas applications by maintaining a semantic representation alongside the rendering layer. Applications that prioritize canvas performance should provide a synchronized accessibility model rather than relying on the canvas itself to expose meaning to assistive technologies.

React Accessibility Screen Readers Dom+3

datadogJul 1, 20263 min readCurated summary

How we measure data completeness at scale

Datadog built a real-time data-completeness system to ensure that every customer’s telemetry is available for dashboards, alerts, queries, and AI-driven decisions. Because ingestion spans hundreds of distributed paths and customers may send delayed or retried data, global or watermark-based tracking is unreliable. The system instead tracks payloads segment by segment, using idempotent create and acknowledgment events to identify losses and calculate end-to-end completeness. ## Defining Completeness at Datadog’s Scale - Completeness means every ingested payload—metrics, logs, spans, or other telemetry—is ultimately available to customers. - The system must measure completeness: - Across hundreds of services and ingestion paths - For each individual customer - In real time - With enough detail to identify where degradation occurred - Customer traffic may take different routes because of partitioning, isolation, and traffic patterns. - Metrics and APM pipelines can each involve hundreds or tens of distinct paths, creating a large number of possible failure points. - The completeness system must remain independent of the services it monitors so it can provide trustworthy diagnostics during incidents. ## Tracking Completeness by Pipeline Segment - Datadog considered watermark-based tracking, but delayed customer data, replayed traffic, and pipeline loops made predictable watermarks impractical. - Pipelines are divided into segments representing steps within or between services. - For example, intake-in to intake-out is one segment. - Intake-out to processing-in is another. - Each segment is measured independently, allowing engineers to locate degradation within a service or between services. - Segment-level tracking also adapts to pipelines whose branches appear or disappear over time. ## Counting Creates and Acknowledgments - When a payload enters a segment, the system records a create event. - When it exits, the system records an acknowledgment using the payload’s unique identifier. - Comparing creates with acknowledgments reveals whether payloads were lost in that segment. - Events are organized into time buckets based on when the payload first entered Datadog, using a Datadog-controlled timestamp rather than the customer’s clock. - Each identifier has a state per segment: - Created - Acknowledged - Acknowledged before the create event arrived - Duplicate create or acknowledgment events are ignored, making the system idempotent despite retries and event reordering. ## Calculating End-to-End Completeness - Segment completeness is the ratio of payloads exiting a segment to those entering it. - For sequential services, overall completeness is calculated by multiplying segment ratios. - Parallel branches require a different approach: - Treating branches as one pipeline would make completeness wait for the slowest branch. - Instead, Datadog uses a weighted average, giving each branch influence proportional to the volume it processes. - In the example, one branch reaches 94% completeness by multiplying 98% and 96% across two sequential services, while another branch reaches 100%. - Combining these branch measurements produces a more accurate view of currently available data without incorrectly marking all data incomplete because one branch is slower. ## Practical Conclusion Segment-level, identifier-based tracking gives Datadog a real-time and customer-specific view of data completeness. It both supports reliable end-to-end calculations and helps humans or automated systems quickly determine where ingestion problems are occurring.

Observability Application Performance Monitoring Apache Kafka Distributed Systems+3

figmaJul 1, 20263 min readCurated summary

Got Skills? Make the Figma Agent a Better Collaborator | Figma Blog

Figma’s custom skills turn team knowledge and workflows into reusable instructions for the Figma agent. They complement design systems by adding guidance such as brand voice, critique methods, writing standards, and review processes. Figma’s experience suggests that any repeated task or team-specific judgment can become a shared skill that improves consistency and collaboration. ## Custom Skills Capture Team Expertise - A skill is a reusable set of plain-English instructions for the Figma agent. - Skills can be triggered in chat with a forward slash (`/`). - Teams and organizations can publish skills so members do not have to recreate prompts or explain workflows repeatedly. - They are particularly useful for practices that are easy to use but difficult to document and often exist only in someone’s head. ## A Second Opinion on Demand Skills can provide focused critique and help teams apply shared standards. - **Simulate stakeholder feedback:** Figma created a skill based on CEO Dylan’s comments, allowing designers to pressure-test work before a review. - **Apply UX writing standards:** A skill based on Figma’s style guide checks capitalization, punctuation, and other consistency issues. - **Review work as a new user:** The agent can assess an experience from a first-time user’s perspective, exposing friction and missing context that experts may overlook. - Design systems supply components, patterns, and UI elements; skills add broader team expertise such as compliance rules, product principles, and critique frameworks. ## Build Once, Use Everywhere Repeated team rituals are strong candidates for automation through skills. - **Catch-me-up:** Summarizes recent file or project activity so returning teammates can quickly understand what happened without searching comment threads. - **Crit preparation:** Interviews the designer about the project, persona, scope, and audience, then creates a critique page with guided discussion prompts. - Figma’s crit-prep skill draws on Nielsen Norman Group best practices to encourage more effective research questions. - **Crit recap:** Organizes feedback into themes, decisions, action items, and deferred items. - Recaps can be placed on the canvas or copied into Slack, helping preserve decisions and keep follow-up work visible. ## Connecting Existing Tools The article begins describing how skills become more powerful when they can draw on the tools a team already uses, suggesting that skills can connect workflows and information across the organization. The provided excerpt ends before giving the specific examples or implementation details. Teams should start by identifying repeated tasks, recurring meetings, or expert review processes and turn those into shared slash-command skills.

Figma Design Systems AI Agents Accessibility+2

figmaJul 1, 20262 min readCurated summary

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

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

AI Data Governance Ai Governance Security Compliance+3

awsJun 30, 20262 min readCurated summary

Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode | Amazon Web Services

AWS CloudFormation Express mode speeds deployments by marking them complete once resource configuration is applied, rather than waiting for full stabilization checks. AWS says this can reduce deployment times by up to four times, while resources continue becoming operational in the background. It is intended for rapid infrastructure iteration and scenarios where eventual stabilization is acceptable, not workflows requiring resources to be fully ready before proceeding. ## How Express Mode Works - Standard CloudFormation deployments wait for post-configuration stabilization checks. - Express mode completes earlier, immediately after configuration is applied. - Resources continue stabilizing asynchronously. - CloudFormation retries dependent resources that encounter transient provisioning failures. - The provisioning process itself is unchanged; only the point at which deployment completion is reported changes. ## Performance Improvements - Creating an SQS queue with a dead-letter queue took: - Standard mode: 64 seconds - Express mode: up to 10 seconds - Deleting a Lambda function with attached network interfaces took: - Standard mode: 20–30 minutes - Express mode: up to 10 seconds in AWS’s benchmark ## Best Use Cases - Iteratively building infrastructure one component at a time. - Testing individual application components. - AI-assisted infrastructure development requiring sub-minute feedback. - Production workflows that can tolerate resources stabilizing after deployment completion. ## Enabling Express Mode - In the AWS Console, select **Enable** under stack deployment options. - With the CLI or SDKs, set the deployment configuration mode to `EXPRESS`: ```bash aws cloudformation create-stack \ --stack-name my-app \ --template-body file://template.yaml \ --deployment-config '{"mode": "EXPRESS", "disableRollback": true}' ``` - AWS CDK supports: ```bash cdk deploy --express ``` - No CloudFormation template changes are required. - Express mode supports existing templates, change sets, nested stacks, and IaC or AI tools such as Kiro. - Enabling it on a parent stack also applies it to nested stacks. ## Rollback and Operational Considerations - Rollback is disabled by default in Express mode to maximize iteration speed. - For production use, rollback can be restored with `"disableRollback": false`. - Teams should otherwise provide monitoring and cleanup procedures for failed deployments. - IAM templates should continue following least-privilege principles. ## Availability - Express mode is available at no additional cost in all AWS commercial Regions. - AWS recommends standard deployment behavior when resources must be fully operational before traffic shifting or testing. For fast development and AI-driven infrastructure iteration, Express mode is a useful optimization. Use it selectively, while retaining standard mode—or explicitly enabling rollback—when deployment readiness and failure recovery are critical.

Aws Lambda Infrastructure As Code Aws Cli Aws Cloudformation+4