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aws3 min readCurated summary

Amazon EC2 C9g and C9gd instances powered by AWS Graviton5 processors are now available | Amazon Web Services

Amazon EC2 C9g and C9gd instances, powered by AWS Graviton5, are now generally available for compute-intensive workloads. They provide up to 25% better performance per vCPU than C8g, faster DDR5 memory, larger caches, and improved networking and EBS bandwidth. C9gd adds local NVMe SSD storage, making it suitable for workloads requiring both high CPU performance and low-latency temporary storage. ## Graviton5 Performance Improvements - Up to 25% higher performance per vCPU than previous-generation C8g instances. - DDR5 memory running at 8800 MT/s, described as the fastest memory available in a cloud processor instance. - Five times more L3 cache than Graviton4-based instances. - Up to three times higher packet-processing performance than Graviton4. - Benefits include faster in-memory analytics, higher throughput, and more responsive real-time applications. ## C9g and C9gd Workloads - C9g is designed for compute-heavy applications using Amazon EBS, including: - Batch processing - Video encoding - Distributed analytics - CPU-based machine learning inference - Agentic AI workloads - C9gd adds local NVMe SSD storage for: - HPC simulation scratch space - Machine learning inference caches - Ad-serving buffers - Other low-latency temporary-storage use cases - C9gd delivers up to 30% higher local storage performance than the previous generation. ## Networking, Storage, and Configuration - Available in 11 sizes from medium through 48xlarge, plus bare metal. - Up to 15% higher network bandwidth and 20% higher EBS bandwidth on average compared with the prior generation. - The largest instances provide up to 100 Gbps networking and 72 Gbps EBS bandwidth. - Instance Bandwidth Configuration allows up to 25% of bandwidth to be shifted between EBS and VPC networking. - Support includes ENA Express, up to 128 EBS volumes, and On-Demand, Spot, Savings Plans, Dedicated Instances, and Dedicated Hosts. - NVMe-equipped instances expose detailed I/O statistics, including latency histograms by I/O size at one-second granularity through CloudWatch or `nvme-cli`. ## Nitro Isolation Engine - C9g and C9gd are the first compute-optimized EC2 instances to use the AWS Nitro Isolation Engine. - The Rust-based Nitro System component isolates virtual machines by mediating access to memory, CPU register state, and I/O devices through a minimal API set. - AWS provides additional technical documentation covering the engine and its formal verification results. ## Availability - The instances are available in US East (Ohio and Northern Virginia), US West (Oregon), and Europe (Frankfurt). - They can be launched through the AWS Management Console, CLI, or SDKs, with more regions planned. For compute-intensive workloads, C9g is the general-purpose choice, while C9gd is preferable when fast local NVMe storage is also required.

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RustAmazon Ec2Aws Graviton5Aws Nitro System+4
aws3 min readCurated summary

Automate public TLS certificate issuance with ACME support in AWS Certificate Manager | Amazon Web Services

AWS Certificate Manager (ACM) now supports public TLS certificate issuance through ACMEv2, enabling automation with clients such as Certbot, cert-manager, and acme.sh. This addresses increasingly short certificate lifetimes—100 days from 2027 and 47 days by 2029—while centralizing certificate governance and visibility in AWS. The service combines Amazon Trust Services certificates with IAM, domain scopes, CloudTrail, CloudWatch, and ACM expiry notifications. ## Why ACME Automation Matters - Manual certificate renewal risks outages and browser errors. - ACME is an open protocol used by Let’s Encrypt and many certificate clients. - ACM’s managed ACME endpoint allows organizations to continue using standard ACME tooling without relying on separate certificate authorities. - Certificates issued through ACME appear alongside other ACM certificates in centralized searches and dashboards. ## Centralized Governance and Monitoring - PKI administrators can create one or more managed ACME endpoints. - IAM roles can be associated with ACME accounts to control which domains clients may request. - Endpoint-level domain scopes restrict requests to: - Exact domains - Subdomains - Wildcard certificates - AWS CloudTrail records certificate requests for auditing. - Amazon CloudWatch provides operational metrics. - ACM sends notifications as certificates approach expiration. - Centralized controls reduce the need for separate lifecycle-management products or custom policy systems. ## Endpoint and Domain Configuration - Administrators create a public ACME endpoint in ACM. - Certificates are issued by Amazon Trust Services and are trusted by standard browsers and operating systems. - Supported key types include: - ECDSA P-256 by default - RSA 2048 - ECDSA P-384 - Domains are validated once at the endpoint level using DNS validation. - Route 53 hosted zones can be updated automatically; other DNS providers require manual creation of the supplied CNAME record. - Application owners do not need access to DNS credentials. ## External Account Binding - Clients register with the ACME server using External Account Binding (EAB). - EAB consists of: - A key identifier - An HMAC key - After registration, the ACME client creates its own asymmetric key pair for subsequent authentication. - EAB credentials can be named and given expiration dates, limiting their lifetime and exposure. ## Using Existing ACME Clients - ACM provides command examples for Certbot and acme.sh. - A Certbot request uses: - The ACM ACME directory URL - EAB key ID and HMAC key - An issuance timeout - The requested domain - Clients can run in containers such as `certbot/certbot`. - The same endpoint can support existing ACME-compatible automation workflows. ACM’s ACME support is best suited to organizations that want automated public certificate issuance without sacrificing centralized policy, auditing, and monitoring. Administrators should validate domains centrally, limit wildcard access where possible, use short-lived EAB credentials, and integrate the endpoint with their existing ACME client automation.

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Tls CertificatesPkiAcmeAws Certificate Manager+4
meta2 min readCurated summary

10 Years of Meta’s Commitment to Python

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

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AIMachine LearningOpen SourcePython+3
naver1 min readCurated summary

Starting an AI Agent Company: From Incorporation to Syncing Anywhere

The content appears to be a minimal NAVER D2 landing page rather than a substantive tech blog post. It lists navigation links to D2 News, About D2, NAVER Developers, DEVIEW, OpenSource, and D2 STARTUP FACTORY, followed by a NAVER copyright notice. ## Page Navigation - Naver D2 - Hello world - D2 News - About D2 - NAVER Developers - DEVIEW - OpenSource - D2 STARTUP FACTORY ## Footer - Copyright © NAVER Corp. All Rights Reserved. No technical argument, detailed sections, or practical recommendations are included in the provided content.

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Open SourceSoftware DevelopmentDeveloper Community
google3 min readCurated summary

Introducing TabFM: A zero-shot foundation model for tabular data

TabFM is a foundation model for tabular classification and regression that uses in-context learning instead of dataset-specific training. It processes an entire table in one forward pass, eliminating much of the usual hyperparameter tuning and feature engineering. The authors report that it performs competitively with or better than tuned tree-based models, with planned integration into BigQuery. ## The Challenge of Traditional Tabular ML - Tabular prediction supports applications such as churn prediction and fraud detection. - Algorithms including XGBoost, AdaBoost, and random forests remain widely used. - Building reliable models typically requires: - Hyperparameter optimization - Cross-validation - Domain-specific feature engineering - Repeated model training for each dataset ## Tabular Prediction Through In-Context Learning - TabFM treats tabular prediction as an in-context learning problem. - Users provide historical training rows and target rows together as a single input. - The model learns relationships between rows and columns during inference without updating model weights. - This enables zero-shot predictions on previously unseen datasets. ## Hybrid Architecture TabFM combines ideas from TabPFN and TabICL to address the two-dimensional, order-independent nature of tables. - **Alternating row and column attention** - Attends across both features and examples. - Learns feature interactions and dependencies directly from the table. - Reduces the need for manually engineered features. - **Row compression** - Converts the contextualized information for each row into a dense vector. - **In-context Transformer** - Operates on compressed row representations rather than the full table grid. - Reduces computation and supports larger datasets efficiently. ## Training with Synthetic Data - Large, diverse real-world tables are difficult to obtain because they often contain proprietary or sensitive information. - TabFM is trained entirely on hundreds of millions of synthetic datasets. - Structural causal models generate tables using varied random functions and relationships. - The goal is to reproduce the broad range of distributions and feature interactions found in real-world data. ## Benchmark Results and Model Variants - Evaluation used TabArena, an Elo-based benchmark covering: - 38 classification datasets - 13 regression datasets - Dataset sizes from 700 to 150,000 samples - **TabFM** - Works out of the box. - Produces predictions in one forward pass without tuning or cross-validation. - **TabFM-Ensemble** - Adds cross features and SVD-derived features. - Uses a 32-model ensemble with non-negative least squares to optimize weights. - Applies Platt scaling for classification calibration. - The authors state that TabFM consistently outperforms heavily tuned conventional supervised algorithms. ## Availability and BigQuery Integration - TabFM is available through Google’s Hugging Face and GitHub repositories. - Google plans to integrate it into BigQuery. - Users will be able to run regression and classification through an `AI.PREDICT` SQL command without specialized machine-learning expertise. TabFM’s main promise is to make high-quality tabular prediction as simple as supplying a dataset and requesting predictions. Its zero-shot approach could reduce the engineering burden of traditional workflows, though practical adoption will depend on benchmark reproducibility, inference costs, and performance across specific production datasets.

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TransformerSynthetic DataHugging FaceXgboost+4
discord2 min readCurated summary

Discord is Now on Meta Quest: Reach Out to Your Servers While in VR

Discord is now available directly through Meta’s Horizon App Store, eliminating the need for sideloading, browser workarounds, or a second device. The Meta Quest app provides the full Discord experience in VR, including servers, direct messages, group messages, voice calls, video chats, and gameplay streaming. Eligible users who try the app may also receive a free month of Nitro. ## Full Discord Access in VR - Users can download Discord directly from the Meta Horizon App Store. - The app includes: - All Discord servers - Direct messages and group DMs - Voice calls using the Quest’s built-in microphone - Discord can remain pinned in the VR playspace while users play games such as *Beat Saber* or spend time in VRChat. - Conversations can appear as a floating virtual window alongside other VR activities. ## Meta Avatar Video Chats and Gameplay Streaming - Users can join Discord video calls as their Meta Avatar. - Quest gameplay can be streamed directly to Discord. - This removes previous workarounds involving browser-based streaming and sharing browser tabs. - The feature supports social interaction between users in VR and friends on PCs, phones, or consoles. ## Free Nitro Promotion - New or returning Nitro users may qualify for one free month. - Eligibility is offered after logging into Discord through a Meta Quest headset. - Nitro benefits include: - Higher-quality Go Live streaming - Additional profile customization options - Access to more than 50 games through Xbox Game Pass - Users can check eligibility through Discord’s support documentation. The article recommends downloading Discord from the Quest app store to keep servers, calls, and gaming conversations accessible while in VR. Qualifying users should also claim the promotional Nitro month.

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DiscordVirtual RealityGame StreamingXbox Game Pass+3
gitlab2 min readCurated summary

Claude Sonnet 5 on GitLab: More reliable, more efficient

Claude Sonnet 5 is now available on GitLab Duo Agent Platform through GitLab’s AI Gateway across all tiers and deployment models. GitLab reports that it is the first model to complete every task in its evaluation suite, compared with 93.8% for Sonnet 4.6, while resolving 8.8% more issues. The result is intended to make software-engineering agents more reliable, efficient, and suitable for production-scale workflows. ## More Reliable Agent Runs - Sonnet 5 is designed for multi-step development tasks, code generation, and automated workflows. - Completing every benchmark task reduces failures that require diagnosis, reprompting, and verification. - GitLab highlights applications including: - Multi-file refactoring - Test generation - Security investigations across repository history - Pipeline-failure analysis - Higher completion rates allow developers to review agent output instead of repeatedly restarting incomplete runs. ## Lower Cost Through Efficiency - Reliability and resource efficiency reduce the effective cost of completed agent tasks. - Models on GitLab Duo consume GitLab Credits at different rates. - Teams can control spending by using a model whose cost and performance fit routine development work. - GitLab directs users to its Credits documentation for model-specific consumption rates. ## Selecting the Right Model - Sonnet 5 is positioned as a dependable default for everyday software-development tasks. - Sonnet-class models aim to balance quality, speed, and cost. - Claude Opus 4.8 remains available for complex, long-running tasks requiring greater reasoning depth. - Teams can select models per task through model selection in their GitLab instance. ## Availability - Claude Sonnet 5 is available immediately on GitLab Duo Agent Platform through the AI Gateway. - It runs on GitLab Credits and is offered across all tiers and deployment models. - New users can start through a Duo Agent Platform trial or GitLab Free, while Premium and Ultimate subscribers can use included credits. Overall, GitLab presents Claude Sonnet 5 as a more reliable and economical choice for routine agent-assisted development, with more powerful models available when tasks require deeper reasoning.

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AI AgentsDevSecOpsGitlab Duo Agent PlatformAi Gateway+2
google3 min readCurated summary

Expanding our Heat Resilience data to 50+ global cities

Google Research has expanded its building-level rooftop reflectivity dataset from 14 pilot cities to more than 50 cities across nine countries. By combining Sentinel-2 satellite data with 30-centimeter commercial imagery and machine learning, the project identifies roofs where reflective “cool roof” interventions could reduce urban heat. The data is publicly available through a new Google Earth Engine app for planners and researchers. ## Why Rooftop Reflectivity Matters - Extreme heat contributes to approximately 500,000 deaths annually. - Urban heat islands make cities warm faster than the global average. - Dark roofs, pavements, and limited vegetation increase heat absorption. - Reflective roofs reduce the solar energy absorbed by buildings and can lower local surface temperatures. - Earlier pilot data from 14 cities supported cool-roof ordinances and broader climate adaptation plans. ## Building-Level Albedo Mapping - Sentinel-2 provides global albedo data but at 10-meter resolution, which cannot reliably distinguish individual rooftops. - Google Research fused Sentinel-2’s spectral and global coverage with 30-centimeter Airbus Pléiades Neo imagery. - Machine learning and radiometric calibration reconstruct detailed reflectance profiles at the urban-pixel level. - Validation against airborne hyperspectral measurements in Boulder, Colorado, produced an RMSE of 0.04. - The resulting maps allow planners to prioritize large, low-reflectivity buildings for cool-roof retrofits. - The modeling suggests targeted interventions could reduce extreme urban heat by up to 0.5°C globally. ## Heat Resilience Earth Engine App - Displays building-level albedo using rooftop centroids to highlight low-reflectivity surfaces. - Provides baseline analyses and supports monitoring changes over time. - Allows users to download high-resolution data for local studies and policy development. - Offers a nested view that moves from census-tract summaries to individual buildings. ## Expanded Global Coverage - The dataset now covers more than 50 cities in nine countries. - Newly included urban areas span Europe, Brazil, and the United States. - Examples include London, Athens, Barcelona, Rio de Janeiro, São Paulo, Los Angeles, Austin, and New York City. - The open dataset is intended to help municipalities accelerate reflective-surface programs. ## Access and Collaboration - The interactive app and datasets are publicly available through Google’s Heat Resilience site. - The methodology is described in the Nature Communications paper “Estimating high-resolution albedo for urban applications.” - The work was developed by Google Research in collaboration with the World Resources Institute. Cities can use the app to identify the buildings and neighborhoods where cool-roof investments are likely to have the greatest heat-reduction benefits.

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Machine LearningGeospatial DataSatellite ImageryEarth Engine+3
discord2 min readCurated summary

Cost Attribution in Discord’s API

Discord’s API runs from a shared Python codebase with more than 1,700 endpoints and 700 background tasks across hundreds of Kubernetes deployments. While existing observability tracks performance and reliability, Discord lacked a way to understand hosting costs by product feature or endpoint. Because deployments share code and workers handle multiple features concurrently, the solution was to extend application profiling to allocate deployment costs according to the time spent serving each feature. ## A Large, Continuously Deployed API - Discord operates a unified Python codebase containing: - Over 1,700 API endpoints - Around 700 background tasks - Engineers deploy changes daily to several hundred Kubernetes deployments. - Phased rollouts and instrumentation help monitor: - Latency - Throughput - Error rates - These metrics make it possible to detect regressions affecting users or infrastructure. ## The Missing Cost Dimension - Discord wanted to determine how hosting costs were distributed across product features. - Example questions included: - How much does it cost to send and receive messages? - What does it cost to start a stream or send a Nitro gift? - How do feature costs change over time? - Did a recent code change materially affect a team’s hosting spend? - The goal was to measure costs at both: - Individual endpoint level - Broader feature level, such as chat ## Why Kubernetes Deployment Costs Were Insufficient - Cloud providers can generally report costs by Kubernetes deployment. - However, Discord’s deployments do not map cleanly to product features: - The same codebase runs across all deployments. - Each deployment handles a particular subset of HTTP traffic or background tasks. - Splitting deployments further would make the system impractical to operate. - Discord therefore needed cost attribution without changing its deployment topology. ## Allocating Costs Through Profiling - API worker processes handle multiple tasks concurrently. - A single worker may simultaneously perform work for many different features. - Existing traffic isolation was not detailed enough for feature-level cost analysis. - Discord’s approach was to allocate a deployment’s cost based on the amount of time spent executing code associated with each feature. - By extending its application profiling tools, Discord could track this execution time and use it to estimate feature and endpoint hosting costs. In practice, the profiling-based approach provides a way to analyze infrastructure spending within shared deployments, without requiring separate services or Kubernetes environments for every product feature.

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KubernetesObservabilityPythonAPI Design+2
aws3 min readCurated summary

AWS Weekly Roundup: Agentic CX designer for Amazon Connect Customer, EC2 AMI Watermarks, Open Governance for MySQL, and more (June 29, 2026) | Amazon Web Services

The AWS Weekly Roundup highlights tools aimed at making AI, infrastructure management, and cloud operations faster and more accessible. The main announcement is Amazon Connect Customer’s no-code Agentic CX designer, which lets business teams create governed AI customer experiences without relying on lengthy engineering backlogs. Other updates cover isolated serverless compute, AMI governance, guided migrations, AI-assisted security investigations, and broader community initiatives. ## Agentic Customer Experience Design - Amazon Connect Customer launched the Agentic CX designer (NLX) in preview. - The no-code canvas enables business teams to design, test, simulate, and deploy voice and digital self-service experiences. - It combines agentic and deterministic AI within a governed workflow. - AWS also introduced Live Sync in preview, allowing web or mobile interfaces to update in real time as customers speak or type. - Customers could, for example, complete forms or open product pages while continuing a voice conversation. ## New AWS Infrastructure and Operations Features - **AWS Lambda MicroVMs** - Provides VM-level isolation with near-instant startup and resume times. - Supports suspending and resuming execution for up to eight hours. - Targets multi-tenant applications running user-generated or AI-generated code. - **Amazon EC2 AMI Watermarks** - Embeds custom identifiers in private AMIs. - Watermarks persist across copies, Regions, and account shares. - Works with Allowed AMIs and Declarative Policies to enforce approved-image usage. - **AWS Outposts lifecycle management** - Adds self-service configuration, quoting, ordering, subscription management, renewal, and decommissioning. - A new quoting tool provides rapid cost estimates and identifies account or regional constraints. ## AI-Assisted Developer and Migration Tools - **Amazon MSK AI Agent Skills** gives coding assistants such as Kiro, Claude Code, and Cursor operational guidance for Amazon MSK. - It supports Kafka sizing, configuration, troubleshooting, monitoring, and migrations to MSK Express. - **Amazon OpenSearch Service Migration Assistant** now offers agent-guided migrations from Solr, Elasticsearch, and OpenSearch to managed clusters or OpenSearch Serverless. - The migration tooling adds live traffic capture and replay for Solr workloads. ## AI-Powered Security Investigations - Amazon GuardDuty’s AI-powered investigations entered preview. - It analyzes findings, account context, related activity from the previous 90 days, knowledge graphs, and threat intelligence. - Investigations produce confidence-scored assessments, MITRE ATT&CK classifications, and recommended actions to help distinguish real threats from benign activity. ## Open Governance and AWS Community Updates - Oracle announced a community governance model for MySQL, including four non-Oracle seats on a new Steering Committee and a public GitHub presence. - AWS supports the initiative and contributes fixes upstream. - AWS Certification holders can renew eligible Associate and Professional certifications for an additional year through selected Skill Builder training and hands-on labs instead of retaking an exam. - The 2026 All Builders Welcome Grant offers selected early-career builders conference admission, airfare, and lodging for AWS re:Invent. AWS’s latest releases broadly point toward more self-service cloud management: business users can design AI experiences, developers can receive operational guidance from coding assistants, and teams can apply stronger controls to infrastructure and security workflows.

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AWSApache KafkaServerlessAws Lambda+4
github1 min readCurated summary

Inside the Advisory Database and what happens when vulnerability volume breaks records

Madison Ficorilli is a vulnerability transparency advocate and senior security manager at GitHub. She leads the advisory database curation team and contributes to vulnerability reporting, response, and disclosure through several industry organizations. Her perspective combines current leadership experience with prior roles in incident response and vulnerability coordination. ## Leadership at GitHub - Leads GitHub’s advisory database curation team. - Focuses on improving vulnerability transparency and the quality of security advisory information. ## Industry and Open Source Security Work - Co-chairs a relevant Open Source Security Foundation (OpenSSF) working group. - Serves on the CVE Program Board. - Advocates for effective vulnerability reporting, response, and disclosure practices. ## Professional Background - Previously worked as a product incident response analyst at GitHub. - Served as a vulnerability coordinator at the CERT Coordination Center at Carnegie Mellon University’s Software Engineering Institute. Her career reflects deep expertise across vulnerability coordination, incident response, database curation, and security disclosure policy.

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GithubVulnerability ManagementIncident ResponseCve+2
netflix3 min readCurated summary

GenPage: Towards End-to-End Generative Homepage Construction at Netflix

GenPage is Netflix’s end-to-end generative approach to building personalized homepages. Instead of separately ranking rows and items, one transformer autoregressively generates the entire page—including rows, entities, and layout—from user and request context. In production, it outperformed Netflix’s mature multi-stage recommender on a core engagement metric while reducing serving latency by 20%. ## Reframing Homepage Recommendation - Netflix’s homepage is a personalized two-dimensional structure, not a single ranked list. - Traditional systems use separate candidate-generation and ranking stages for rows and entities. - GenPage treats homepage construction as a prompt-response task: - The prompt contains user history, profile information, and request context. - The response is the complete homepage generated autoregressively. - The approach aims to: - Replace complex multi-stage pipelines with one end-to-end model. - Optimize the whole page using reinforcement learning. - Capture interactions such as diversity and the trade-off between high-value rows and continued browsing. - Scale more predictably with additional data, compute, and model capacity. - Support new content types, layouts, UI components, and personalized artwork with fewer architectural changes. ## Production Challenges and Results - Real-time generation makes serving latency a major constraint. - The system must address: - Cold-start entities in a constantly changing catalog. - Shifting user interests and cultural trends. - Product and business rules that constrain generated pages. - An online A/B test against Netflix’s optimized production recommender produced: - Statistically significant improvement on Netflix’s primary launch engagement metric. - A 20% reduction in end-to-end serving latency. - Offline experiments found that: - Improving the prompt helped more than increasing model capacity in the tested regime. - Reinforcement-learning post-training improved homepage diversity, even though diversity was not an explicit objective. ## Tokenizing Context and Pages - Each training example contains: - **Context:** user history, profile attributes, and request information. - **Page:** displayed rows and entities in layout order. - **Feedback:** interactions such as plays, thumbs-up, and abandonment. - Context and page are tokenized as model inputs and outputs. - Feedback is used to derive reward and supervision signals rather than being directly generated. ## Domain-Specific Tokenization - GenPage uses a custom recommender-system tokenizer instead of a general-purpose text tokenizer. - This reduces sequence length and improves inference cost and latency. - For example, an action such as watching *Orange Is the New Black* can be represented with four tokens: - Entity ID - Action type - Time bucket - Duration bucket - Direct token mappings to product concepts, such as rows and entities, also make it easier to enforce generation rules and business constraints. ## Context Representation - User-history tokens encode: - Action type - Entity ID - Timestamp - Duration - The history includes explicit signals, such as playback, adding titles to My List, and thumbs-up, as well as implicit signals such as trailer views and detail-page visits. - Profile tokens represent attributes including language and profile type. - Request-context tokens include time of day, day of week, and device. - Long data sources, such as complete impression histories, are summarized to control sequence length and cost. - These summaries improve practicality but introduce handcrafted prompt engineering; learning to compress such information end to end remains a future direction. - Special segment markers help the model distinguish between different context sources.

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Generative AITransformerReinforcement LearningPersonalization+2
naver1 min readCurated summary

Solve End-to-End User Monitoring in One Go with RUM!!

The content is a minimal NAVER D2 webpage outline rather than a substantive tech blog post. It contains navigation links to D2 News, About D2, NAVER Developers, DEVIEW, OpenSource, and D2 STARTUP FACTORY, along with a copyright notice. ## Site Navigation - “Hello world” appears as the primary page content or placeholder. - Links are provided to: - D2 News - About D2 - NAVER Developers - DEVIEW - OpenSource - D2 STARTUP FACTORY ## Footer - Copyright © NAVER Corp. All Rights Reserved. No technical argument, explanation, or conclusion is included in the provided text.

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Open SourceSoftware DevelopmentDeveloper CommunityStartups
figma3 min readCurated summary

Start Anywhere, a Magazine by Figma | Figma Blog

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

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FigmaAIReactDesign To Code+1
figma2 min readCurated summary

Meet the Artists Behind Figma's Season 5 Collection | Figma Blog

Figma’s Season 5 merchandise collection was created with eight artists from around the world, each bringing a distinct creative voice. Organized around play, iteration, and hands-on tools, the collection embraces experimentation, imperfection, and unexpected character. The result is an eclectic range of apparel and accessories featuring playful illustrations and tactile, artistic styles. ## Collection Themes - **Play:** Making freely without overthinking. - **Iteration:** Allowing ideas to develop through repetition and refinement. - **Tools:** Celebrating the physical process of creating by hand. - Figma’s Brand Studio selected artists whose existing work reflected these ideas. - The collection debuted at Config, Figma’s annual conference for product makers. ## Quentin Chambry: Repetition and Spontaneity - **Based in:** Tokyo, Japan - **Influences:** Skateboarding, street culture, graffiti, Japanese ceramics, and Tokyo pop culture - **Tools:** Black marker and white paper - Quentin fills hundreds of pages in a session, repeating movements until unexpected shapes emerge. - Using marker rather than pencil prevents erasing, making the process more immediate and exciting. - His designs include smiling figures and cubes featured on shirts, hats, and sweatshirts. - The work reflects play through exploration, editing, and accepting imperfections. ## Meazo: Childlike Creativity - **Based in:** Leicestershire, England - **Influences:** His children’s drawings, DIY illustrators, and tattoo artists - **Tool:** An old, cumbersome drawing app on his phone - Meazo intentionally uses a tool he cannot fully control to avoid becoming overly self-critical. - He combines familiar objects—such as butterflies, clocks, and tomatoes—with surreal transformations. - His designs feature colorful characters, including a cheerful tomato with the phrase “time to make!” - The artwork appears on hats, socks, and long-sleeve shirts, balancing comfort and strangeness. Figma’s Season 5 collection demonstrates how playful constraints, repetition, and imperfect tools can produce distinctive visual ideas. Its strongest recommendation is implicit: approach creative work with curiosity, experimentation, and less concern for making something “good” immediately.

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FigmaDesign ToolsGraphic DesignConfig+1