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gitlabJan 14, 2026Original article

Getting started with GitLab Duo Agentic Chat (opens in new tab)

GitLab Duo Agentic Chat marks a shift from traditional Q&A chatbots to autonomous AI collaboration partners integrated directly into the software development lifecycle. By leveraging specialized agents and context-aware large language models, the platform enables developers to automate complex tasks like code refactoring, security remediation, and issue triaging. This system serves as a centralized interface across both the GitLab Web UI and IDEs to streamline workflows from initial planning to production deployment. ## Capabilities of Agentic AI * **Autonomous Actions:** The system can move beyond simple chat by creating files, modifying existing code, and opening merge requests on behalf of the user. * **Deep Context Integration:** Agents have access to the full GitLab ecosystem, including issues, epics, Git commits, CI/CD pipelines, and security scans. * **Extensibility:** Through the Model Context Protocol (MCP), the chat can integrate with external services to expand its functional scope. * **Information Retrieval:** Users can query project architecture or use GitLab Query Language (GLQL) to pull specific project analytics and insights. ## Model and Agent Customization * **Flexible Model Selection:** Users and administrators can choose from different LLMs based on task requirements, with configuration available at both the group and individual user levels. * **Specialized Agents:** The platform features dedicated agents for specific roles, such as the **Planner Agent** for product management and the **Security Analyst Agent** for vulnerability management. * **Contextual Switching:** In IDEs, users can switch between agents via a dropdown menu, while the Web UI allows for agent selection when starting new chat sessions. ## Specialized Workflow Use Cases * **Project Planning:** The Planner Agent can break down epics into smaller tasks, list high-priority bugs, and generate technical requirements for new features. * **Security Remediation:** Security-focused agents can explain vulnerabilities in simple terms, identify false positives in scans, and suggest specific code fixes for SQL injection or XSS risks. * **Troubleshooting and Debugging:** The system can analyze CI/CD pipeline logs to identify why a build failed and suggest optimizations for job performance. * **Legacy Modernization:** Specific prompts can guide the AI to refactor code to follow SOLID principles or create migration plans for modernizing legacy languages like COBOL to Java or Python. ## Access and Integration * **Interface Options:** The chat is accessible via a collapsible sidebar in the Web UI and through dedicated plugins in popular IDEs. * **Future Development:** While currently limited to UI and IDE interfaces, a GitLab Duo CLI is in development to bring agentic capabilities to the terminal. To get the most out of GitLab Duo Agentic Chat, it is recommended to transition between specialized agents as you move through different project phases. Using the Security Analyst for code reviews and the Planner for backlog grooming ensures that the underlying models are optimized for the specific metadata and constraints of those tasks.

Database Design AI Agents CI/CD Gitlab+4

gitlabJan 14, 2026Original article

How to customize GitLab Duo Agent Platform (opens in new tab)

The GitLab Duo Agent Platform provides a multi-layered framework for customizing AI behavior to align with specific team workflows and coding standards. By leveraging configuration files at the user, workspace, and project levels, teams can ensure that AI-driven assistance remains context-aware and adheres to internal development policies. This extensibility allows organizations to move from generic AI interactions to highly specialized automation that respects unique architectural patterns and security requirements. ### Levels of Customization GitLab offers a hierarchical approach to tailoring agent behavior, ensuring the right balance between global consistency and project-specific flexibility: * **User-level:** Personal preferences and rules applied across all projects, typically stored in the user’s home directory (e.g., `~/.gitlab/duo/`). * **Workspace-level:** Project-specific configurations located in the repository root that override user-level settings for that specific codebase. * **Project-level:** The creation of entirely custom agents and workflows managed within a specific project to handle complex, specialized tasks. ### Custom Rule Configuration Custom rules provide a mechanism to enforce specific coding styles and instructional sets without repeating prompts in every interaction. * **File implementation:** Rules are defined in `chat-rules.md` files located either in the user's home directory for global application or within the `.gitlab/duo/` directory for project-specific application. * **Functional scope:** They are best used for granular instructions such as forcing the use of the Vue 3 Composition API, requiring JSDoc comments for public functions, or mandating single quotes for strings. * **Governance:** Teams are encouraged to use GitLab Code Owners to manage who can approve changes to these rules, ensuring that AI behavior remains aligned with official team standards. ### Architectural Control with AGENTS.md The platform supports `AGENTS.md`, an industry-standard configuration file used to define broader agent personality, tone, and deep repository context. * **Versatility:** Unlike basic rules, `AGENTS.md` is consumed by both foundational and custom flows and can be understood by external agents like Claude Code. * **Contextual Depth:** These files can be placed in subdirectories to provide specific instructions for different parts of a monorepo, helping the agent understand complex folder structures and internal dependencies. * **Key Parameters:** It typically controls high-level preferences such as security protocols (e.g., "never suggest hardcoding secrets"), documentation requirements, and preferred tool usage. ### Technical Requirements and Deployment Implementing these customizations requires specific environment versions to ensure compatibility across the GitLab ecosystem. * **GitLab Version:** Requires GitLab 18.8 or later. * **IDE Support:** For VS Code users, the GitLab Workflow extension must be version 6.60 or later. * **Update Cycle:** Changes to `AGENTS.md` or custom rules generally require starting a new chat session or triggering a new flow to take effect. To achieve the best results, teams should adopt a "standardize-then-specialize" approach: establish global security and documentation rules at the user level, while using workspace-level `AGENTS.md` files to define the unique architectural patterns and tech stacks of individual projects.

Database Design Machine Learning TypeScript Gitlab Duo+4

gitlabJan 14, 2026Original article

Get started with GitLab Duo Agent Platform: The complete guide (opens in new tab)

The GitLab Duo Agent Platform represents a shift in AI-assisted development by moving from individual chat-based interactions to a collaborative multi-agent orchestration layer. By integrating specialized AI agents throughout the software development lifecycle, the platform transforms linear DevSecOps workflows into parallel processes that leverage full project context for tasks like security scanning and code refactoring. This architecture allows development teams to delegate routine technical burdens to autonomous agents, focusing human efforts on high-level innovation and complex problem-solving. ### Orchestrating the DevSecOps Lifecycle The platform functions as a central intelligence layer that connects AI agents to the broader GitLab ecosystem. * Agents access comprehensive project context, including source code management, CI/CD pipelines, issue tracking, and security scan results. * Specialized agents can be assigned to specific technical domains such as research, refactoring, and automated testing. * The system enables asynchronous collaboration, allowing multiple agents to work on different stages of a project simultaneously. ### Evolution from Duo Enterprise to Agentic AI The Duo Agent Platform is a superset of previous GitLab AI offerings, moving beyond simple 1:1 user-to-AI interactions. * GitLab Duo Pro focused on individual IDE productivity through code suggestions and basic chat. * GitLab Duo Enterprise expanded AI to the wider software lifecycle but remained primarily a 1:1 Q&A experience. * The Agent Platform introduces a many-to-many collaboration model where teams and multiple specialized agents interact autonomously to handle production-ready workflows. ### Advanced Integration and Customization To support enterprise-grade automation, the platform provides a roadmap for scaling AI from basic interactions to production environments. * Integration with the Model Context Protocol (MCP) allows for expanded data access and agent capabilities. * The platform supports a progression from initial agent interactions to full workflow customization and production-ready automation. * Developers can leverage the eight-part guide series to move from foundational concepts to advanced technical implementations. To maximize the benefits of agentic AI, organizations should transition from viewing AI as a simple Q&A tool to treating it as an orchestration layer. Teams are encouraged to explore the complete introductory series to begin delegating routine maintenance and security tasks to specialized agents, thereby accelerating overall delivery speed.

Machine Learning CI/CD Gitlab DevSecOps+3

gitlabJan 14, 2026Original article

Monitor, manage, and automate AI workflows (opens in new tab)

The GitLab Duo Agent Platform’s Automate capabilities provide a centralized framework for managing, executing, and monitoring AI-driven development workflows within the software development lifecycle. By integrating event-driven triggers and detailed session logging, the platform allows developers to transition from manual AI interactions to fully autonomous, production-ready processes. This orchestration layer ensures that AI agents are not only performant but also transparent and easy to audit across projects. ## Resource Management for Agents and Flows The Automate hub serves as the control center for organizing AI resources, distinguishing between agents (entities that perform tasks) and flows (structured sequences of actions). * Resources are categorized into "Enabled" (those available for project use) and "Managed" (those created and owned specifically by the project). * Custom agents and flows must be enabled at the top-level group before they can be activated for specific projects. * Users can expand their automation library by browsing and enabling pre-configured resources from the GitLab AI Catalog. ## Event-Driven Automation with Triggers Triggers allow AI agents to respond automatically to specific actions within the GitLab interface, eliminating the need for manual invocation. * Automation can be initiated through three primary event types: user mentions (e.g., `@agent-name`), issue/MR assignments, or reviewer assignments. * When a trigger is activated, the system identifies the associated flow, executes the agent, and posts the final results directly back to the relevant issue or merge request. * Common use cases include using the `/assign` quick action to trigger a CI/CD optimizer or a code explanation agent. ## Workflow Monitoring and Session Transparency The Sessions interface provides a detailed audit trail for every execution, offering visibility into the "black box" of AI decision-making. * The Activity tab tracks step-by-step reasoning, showing exactly which tools the agent used and the results of individual actions. * Execution statuses are monitored in real-time, with labels such as Running, Finished, Failed, or Input Required. * The Details tab provides deep technical context by linking directly to Runner job logs, including system messages and full tool invocation outputs. ## Practical Conclusion To maximize the utility of the GitLab Duo Agent Platform, teams should move beyond experimental chat prompts and begin configuring triggers for repetitive tasks like code review assignments or issue triaging. Utilizing the Sessions tool is recommended during the initial rollout phase to verify agent reasoning and ensure that custom flows are interacting correctly with project data before full-scale deployment.

Machine Learning Gitlab DevSecOps Event Driven Automation+4

gitlabJan 14, 2026Original article

AI Catalog: Discover, create, and share agents and flows (opens in new tab)

The GitLab AI Catalog serves as a centralized repository designed to streamline the discovery, creation, and distribution of AI agents and automated flows across an organization. By providing a structured environment for managing foundational and custom AI assets, it fosters team collaboration and ensures consistency throughout the development lifecycle. Ultimately, the catalog enables developers to scale AI-driven automation from experimental private prototypes to production-ready, instance-wide solutions. ## Discovering and Enabling AI Assets * The catalog acts as a central hub for two distinct asset types: Agents, which handle on-demand or context-specific tasks, and Flows, which are multi-step automations that orchestrate multiple agents. * Users can browse assets via the Explore menu, inspecting titles, descriptions, and visibility statuses before implementation. * To utilize an asset, it must first be added to a top-level group via the "Enable in group" button and then activated within specific projects. * The duplication feature allows teams to copy existing agents or flows to serve as templates for further customization. ## Development and Configuration * Custom agents are built by defining specialized system prompts and configuring specific tool access, such as granting read-only permissions for code and merge requests. * Custom flows utilize a YAML-based structure to define complex behaviors, incorporating components like prompts, routers, and agent hierarchies. * New assets are typically assigned a unique display name (e.g., `ci-cd-optimizer`) and initially set to private visibility to allow for safe experimentation. * Effective creation requires thorough documentation of prerequisites, dependencies, and specific use cases to ensure the asset is maintainable by other team members. ## Managing Visibility and Sharing * Private visibility restricts access to project members with at least a Developer role or top-level group Owners, making it ideal for sensitive or team-specific workflows. * Public visibility allows anyone on the GitLab instance to view and enable the asset in their own projects. * Best practices for sharing include using descriptive, purpose-driven names like `security-code-review` rather than generic identifiers. * Organizations are encouraged to validate and test assets privately before moving them to public status to ensure they solve real problems and handle edge cases. ## Versioning and Lifecycle Management * GitLab employs automated semantic versioning (e.g., 1.1.0) where any change to a prompt or configuration triggers an immutable version update. * The platform uses "version pinning" to ensure stability; when an asset is enabled, projects remain on a fixed version rather than updating automatically. * Updates are strictly opt-in, requiring users to manually review changes and click an "Update" button to adopt the latest version. * Version history and current status can be monitored through the "About" section in the Automate menu for both agents and flows. To maximize the benefits of the AI Catalog, organizations should establish a clear transition path from private experimentation to public sharing. By leveraging version pinning and granular tool access, teams can safely integrate powerful AI automations into their development workflows while maintaining full control over environment stability and security.

Database Design Gitlab Agents Flows+4

lineJan 14, 2026Original article

Building an Enterprise LLM (opens in new tab)

LY Corporation’s engineering team developed an AI assistant for their private cloud platform, Flava, by prioritizing "context engineering" over traditional prompt engineering. To manage a complex environment of 260 APIs and hundreds of technical documents, they implemented a strategy of progressive disclosure to ensure the LLM receives only the most relevant information for any given query. This approach allows the assistant to move beyond simple RAG-based document summarization to perform active diagnostics and resource management based on real-time API data. ### Performance Limitations of Long Contexts * Research indicates that LLM performance can drop by 13.9% to 85% as context length increases, even if the model technically supports a large token window. * The phenomenon of "context rot" occurs when low-quality or irrelevant information is mixed into the input, causing the model to generate confident but incorrect answers. * Because LLMs are stateless, maintaining conversation history and processing dense JSON responses from multiple APIs quickly exhausts context windows and degrades reasoning quality. ### Progressive Disclosure and Tool Selection * The system avoids loading all 260+ API definitions at once; instead, it analyzes the user's intent to select only the necessary tools, such as loading only Redis-related APIs when a user asks about a cluster. * Specific product usage hints, such as the distinction between private and CDN settings for Object Storage, are injected only when those specific services are invoked. * This phased approach significantly reduces token consumption and prevents the model from being overwhelmed by irrelevant technical specifications. ### Response Guidelines and the "Mock Tool Message" Strategy * The team distinguished between "System Prompts" (global rules) and "Response Guidelines" (situational instructions), such as directing users to a console UI before suggesting CLI commands. * Injecting specific guidelines into the system prompt often caused "instruction conflict," where the LLM might hallucinate information to satisfy a guideline while ignoring core requirements like using search tools. * To resolve these conflicts, the team utilized "ToolMessages" to inject guidelines; by formatting instructions as if they were results from a tool execution, the LLM treats the information as factual context rather than a command that might override the system prompt. To build a robust enterprise LLM service, developers should focus on dynamic context management rather than static prompt optimization. Treating operational guidelines as external data via mock tool messages, rather than system instructions, provides a scalable way to reduce hallucinations and maintain high performance across hundreds of integrated services.

AI Large Language Models Prompt Engineering RAG+4

gitlabJan 14, 2026Original article

Understanding flows: Multi-agent workflows (opens in new tab)

The GitLab Duo Agent Platform introduces flows as a sophisticated orchestration layer that allows multiple specialized AI agents to collaborate on complex, multi-step developer workflows. Unlike standard interactive agents, flows are designed to work autonomously and asynchronously on GitLab’s platform compute, executing tasks ranging from initial requirement analysis to final merge request creation. This architecture enables teams to offload repetitive or high-compliance tasks to a background process that integrates directly with the existing GitLab ecosystem. ## Core Mechanics of Multi-Agent Flows * Flows function as event-driven systems triggered by specific actions such as @mentions, issue assignments, or being designated as a reviewer on a merge request. * Execution occurs on GitLab's platform compute, removing the need for users to maintain separate infrastructure for their automation logic. * While standard agents are interactive and synchronous, flows are designed to be autonomous, gathering context and making decisions across various project files and APIs without constant human intervention. * The system supports background processing, allowing developers to continue working on other tasks while the flow handles complex implementations or security audits. ## Foundational and Custom Flow Categories * Foundational flows are production-ready, general-purpose workflows maintained by GitLab and accessible through standard UI controls and IDE interfaces. * Custom flows are specialized workflows defined via YAML that allow teams to tailor AI behavior to unique organizational requirements, such as specific coding standards or regulatory compliance like PCI-DSS. * Custom flows utilize a YAML schema to define specific components, including "Routers" for logic steering and "Toolsets" that grant agents access to GitLab API functions. * Real-world applications for custom flows include automated security scanning, documentation generation, and complex dependency management across a project. ## Technical Configuration and Triggers * Flows are triggered through simple Git commands and UI actions, such as `/assign @flow-name` or `/assign_reviewer @flow-name`. * The configuration for a custom flow includes an "ambient" environment setting and defines specific `AgentComponents` that map to unique prompts and toolsets. * Toolsets provide agents with capabilities such as `get_repository_file`, `create_commit`, `create_merge_request`, and `blob_search`, enabling them to interact with the codebase programmatically. * YAML definitions also manage UI log events, allowing users to track agent progress through specific hooks like `on_tool_execution_success` or `on_agent_final_answer`. To maximize the value of the GitLab Duo Agent Platform, teams should identify repetitive compliance or boilerplate implementation tasks and codify them into custom flows. By defining precise prompts and toolsets within the YAML schema, organizations can ensure that AI-driven automation adheres to internal domain expertise and coding standards while maintaining a high level of transparency through integrated UI logging.

AI Agents Gitlab React TypeScript+4

cloudflareJan 14, 20262 min readCurated summary

What came first- the CNAME or the A record

A memory-optimization change in Cloudflare’s 1.1.1.1 resolver accidentally reordered DNS records, placing CNAMEs after A/AAAA records. Although DNS record order is generally considered irrelevant, some clients—including glibc’s `getaddrinfo`—process answers sequentially and require CNAMEs to appear first. The resulting failures affected users globally until Cloudflare reverted the release. ## Incident Timeline - **December 2, 2025:** The record-reordering change was added. - **December 10:** It reached the testing environment. - **January 7, 2026:** Global deployment began. - **January 8, 17:40 UTC:** The change reached 90% of servers. - **18:19:** The incident was declared. - **18:27:** The release was reverted. - **19:55:** The revert completed and the impact ended. ## How CNAME Chains Are Resolved - A hostname may point through multiple aliases before reaching an A or AAAA record: - `www.example.com → cdn.example.com → server.cdn-provider.com → 198.51.100.1` - Each record has its own TTL and may expire independently. - If only part of a chain expires, 1.1.1.1 can reuse the cached portion and resolve only the missing records. - The resolver then combines the cached CNAME records with newly resolved address records. ## The Memory Optimization That Changed Ordering - Previously, the resolver created a new list: - Insert the existing CNAME chain first. - Append the newly resolved A/AAAA records afterward. - The optimization avoided allocations and copies by appending new CNAME records directly to the existing answer list. - This caused some responses to place address records before CNAME records. ## Why Some DNS Clients Failed - Many clients treat answer-section ordering as irrelevant, but some parse records sequentially. - These clients: - Start by looking for records matching the original queried name. - Update the expected name when they encounter a CNAME. - Accept the corresponding A or AAAA record only after that update. - With the expected order, the client sees the CNAME first and then accepts the address record. - With the address record first, it ignores the address because it does not yet match the expected name. After encountering the CNAME, there are no records left to process, so it reports an empty response. - The affected implementation included glibc’s `getaddrinfo`, widely used for DNS resolution on Linux. The incident demonstrates that even seemingly insignificant DNS response ordering can matter in practice. Resolver implementations should preserve CNAME-before-address ordering, and DNS clients should avoid assuming that record order is meaningful unless the protocol explicitly requires it.

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Rust Dns Networking Glibc+3

cloudflareJan 13, 20263 min readCurated summary

What we know about Iran’s Internet shutdown

Iran’s government effectively disconnected the country from the global Internet on January 8, 2026, amid escalating nationwide protests. Cloudflare observed a near-total loss of traffic after major Iranian networks withdrew most announced IPv6 address space and then lost connectivity almost entirely. Brief access windows on January 9 quickly ended, and the shutdown remained in place through January 10. ## Background and Earlier Shutdowns - Iran has previously restricted Internet access during protests: - More than five days of disruption followed fuel-price protests in November 2019. - Connectivity was disrupted across multiple providers during protests after Mahsa/Zhina Amini’s death in September 2022. - Internet traffic had already been below normal at the beginning of 2026, suggesting connectivity problems preceded the complete shutdown. ## Connectivity Collapsed on January 8 - At 11:50 UTC, Iranian networks reduced announced IPv6 address space by 98.5%, from over 48 million `/48` blocks to roughly 737,000. - This caused IPv6’s share of human-generated traffic to fall from about 12% to 2%, before IPv6 traffic nearly disappeared later that afternoon. - Between 16:30 and 17:00 UTC, overall traffic dropped by nearly 90%. - Major providers affected included: - MCCI (AS197207) - IranCell (AS44244) - TCI (AS58224) - By approximately 18:45 UTC, traffic from Iran had fallen effectively to zero, indicating a nationwide disconnection from the global Internet. ## Brief Connectivity on January 9 - Internal traffic remained below 0.01% of pre-shutdown peaks. - Access to Cloudflare’s `1.1.1.1` DNS resolver briefly returned around 10:00 UTC, producing a short-lived spike in requests. - Several universities also regained connectivity temporarily, including the University of Tehran, Sharif University of Technology, Tehran University of Medical Science, and Tarbiat Modares University. - Traffic from these networks disappeared again by roughly 15:00 UTC. ## Filtering Changes Before the Shutdown - HTTP/3 and QUIC usage declined sharply before the full outage. - On IranCell, HTTP/3 usage fell from as high as 40% to 5% by December 31 and continued declining. - On TCI, HTTP/3 dropped below 5% around January 3. - These changes may indicate increasingly severe filtering and upgraded whitelisting, according to MahsaNet. ## Ongoing Disconnection - Since January 10, Iran’s Internet traffic has shown no significant recovery. - Traffic remains at only a fraction of one percent of previous levels. - Cloudflare continues monitoring the situation through Radar’s traffic and routing data. The available measurements strongly indicate a deliberate, nationwide Internet shutdown rather than an ordinary network failure. Cloudflare Radar’s traffic and routing pages provide the most practical way to follow any restoration or further changes.

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Dns Bgp Ipv6 Quic+4

pinterestJan 13, 20263 min readCurated summary

PinLanding: Turn Billions of Products into Instant Shopping Collections with Multimodal AI

PinLanding is a production pipeline for turning billions of products into searchable shopping collections using multimodal AI. Rather than relying mainly on historical queries or manual curation, it derives structured product attributes from images and metadata, then aligns those attributes with real user search behavior. The system combines multimodal LLMs, embedding-based consolidation, a CLIP-style classifier, and distributed infrastructure to produce scalable, precise shopping feeds. ## Understanding Shopping Intent - Pinterest analyzes search history, autocomplete use, filters, and browsing paths to estimate shopping demand. - Existing systems handle high-volume queries such as “black cocktail dress” well, but provide weaker coverage for: - Long-tail queries - Conversational requests - Contextual intents such as “what to wear for an Italian summer vacation” - The analysis identifies: - Product areas with strong demand but poor collection coverage - Important attribute dimensions, including color, occasion, style, fit, price, and brand - The goal is to expand and improve collection coverage, not replace query understanding. ## Generating and Curating Shopping Topics - Each product is represented by an image plus metadata such as title, description, merchant tags, and price. - A vision-language model generates normalized key-value attributes rather than free-form descriptions. - Raw model output has high recall but produces: - Excessively specific attributes - Near-duplicates such as “boho,” “bohemian,” and “boho-chic” - Sparse attributes that apply to very few products - PinLanding builds a compact vocabulary through: - Frequency filtering to remove rarely useful attributes - Embedding-based clustering to merge semantically similar terms - Manual and LLM-assisted review - An LLM judge evaluates generated topics for semantic coherence, realistic shopping intent, and alignment with natural search phrasing. ## Scalable Attribute Assignment - Running the vision-language model over every product is too expensive and operationally fragile. - PinLanding trains a CLIP-inspired dual encoder: - One encoder embeds product images and text - Another embeds attribute phrases - Matching product-attribute pairs are trained as positives, while mismatches are negatives - A bidirectional contrastive loss aligns related products and attributes. - At inference, products and attributes are embedded once, and attributes are assigned when similarity exceeds a calibrated threshold. - This produces fewer distinct attributes while increasing the average number assigned to each product, creating a denser and more consistent attribute graph. ## Distributed Feed Construction - Ray handles large-scale batch inference across millions of products and topics. - The pipeline separates: - CPU-based image and metadata loading, tokenization, and serialization - GPU-based classifier inference - Streaming allows preprocessing and inference to overlap, while heterogeneous CPU and GPU clusters can scale independently. - The classifier pipeline reportedly completes in about 12 hours using eight NVIDIA A100 GPUs, at an estimated cost of roughly $500 per training run. - Feed construction uses approximate-nearest-neighbor techniques and strict attribute matching. - Topics are represented as attribute tuples, such as: - Category: dress - Color: yellow - Season: summer - Occasion: party - Apache Spark computes topic-product relevance using shared attributes and confidence weights, with partitioning and overlap filters reducing unnecessary candidate comparisons. The core recommendation is to combine user-behavior signals with content-first multimodal modeling. This approach can expand shopping coverage into conversational and long-tail intents while remaining practical through attribute consolidation, contrastive retrieval, and distributed inference.

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Multimodal AI Apache Spark Contrastive Learning Clip+4

metaJan 12, 2026Original article

CSS at Scale With StyleX (opens in new tab)

Scaling CSS within massive codebases presents unique challenges that traditional styling methods often struggle to solve effectively. Meta’s StyleX addresses these issues by offering a system that combines the intuitive ergonomics of CSS-in-JS with the runtime performance of static CSS. By prioritizing atomic styling and definition deduplication, StyleX minimizes bundle sizes and has become the primary styling standard across Meta's entire suite of applications. ### Performance-Driven Styling Architecture * Combines a CSS-in-JS developer experience with a compiler that outputs static CSS to ensure high performance and zero runtime overhead. * Utilizes atomic styling to break down CSS into small, reusable classes, which prevents style sheets from growing linearly with the size of the codebase. * Automatically deduplicates style definitions during the build process, significantly reducing the final bundle size delivered to the client. * Exposes a simple, consistent API that allows developers to manage complex styles and themes while maintaining type safety. ### Standardization and Industry Adoption * Serves as the foundational styling system for Meta’s most prominent platforms, including Facebook, Instagram, WhatsApp, Messenger, and Threads. * Gained significant industry traction beyond Meta, with large-scale organizations such as Figma and Snowflake adopting it for their own web applications. * Acts as an open-source force multiplier, allowing Meta engineers and the broader community to collaborate on solving CSS-at-scale problems. * Provides a mature ecosystem that bridges the gap between the flexibility of JavaScript-based styling and the efficiency of traditional CSS. For engineering teams managing large-scale web applications where bundle size and styling maintainability are critical, StyleX offers a battle-tested framework. Developers can leverage this tool to achieve the performance of static CSS without losing the expressive power of modern JavaScript tooling.

CSS Atomic Css Stylex Css In Js+2

awsJan 12, 2026Original article

AWS Weekly Roundup: AWS Lambda for .NET 10, AWS Client VPN quickstart, Best of AWS re:Invent, and more (January 12, 2026) (opens in new tab)

The AWS Weekly Roundup for January 2026 highlights a significant push toward modernization, headlined by the introduction of .NET 10 support for AWS Lambda and Apache Airflow 2.11 for Amazon MWAA. To encourage exploration of these and other emerging technologies, AWS has revamped its Free Tier to offer new users up to $200 in credits and six months of risk-free experimentation. These updates collectively aim to streamline serverless development, enhance container storage efficiency, and provide more robust authentication options for messaging services. ### Modernized Runtimes and Orchestration * AWS Lambda now supports .NET 10 as both a managed runtime and a container base image, with AWS providing automatic updates to these environments as they become available. * Amazon Managed Workflows for Apache Airflow (MWAA) has added support for version 2.11, which serves as a critical stepping stone for users preparing to migrate to Apache Airflow 3. ### Infrastructure and Resource Management * Amazon ECS has extended support for `tmpfs` mounts to Linux tasks running on AWS Fargate and Managed Instances; this allows developers to utilize memory-backed file systems for containerized workloads to avoid writing sensitive or temporary data to task storage. * AWS Config has expanded its monitoring capabilities to discover, assess, and audit new resource types across Amazon EC2, Amazon SageMaker, and Amazon S3 Tables. * A new AWS Client VPN quickstart was released, providing a CloudFormation template and a step-by-step guide to automate the deployment of secure client-to-site VPN connections. ### Security and Messaging Enhancements * Amazon MQ for RabbitMQ brokers now supports HTTP-based authentication, which can be enabled and managed through the broker’s configuration file. * RabbitMQ brokers on Amazon MQ also now support certificate-based authentication using mutual TLS (mTLS) to improve the security posture of messaging applications. ### Educational Initiatives and Community Events * New AWS Free Tier accounts now include a 6-month trial period featuring $200 in credits and access to over 30 always-free services, specifically targeting developers interested in AI/ML and compute experimentation. * AWS published a curated "Best of re:Invent 2025" playlist, featuring high-impact sessions and keynotes for those who missed the live event. * The 2026 AWS Summit season begins shortly, with upcoming events scheduled for Dubai on February 10 and Paris on March 10. Developers should take immediate advantage of the new .NET 10 Lambda runtime for serverless applications and review the updated ECS `tmpfs` documentation to optimize container performance. For those new to the platform, the expanded Free Tier credits provide an excellent opportunity to prototype AI/ML workloads with minimal financial risk.

Cloud Computing Serverless Aws Lambda Dotnet+4

figmaJan 12, 20262 min readCurated summary

The New Business Case For Design Systems | Figma Blog

Design systems are no longer merely efficiency tools or static component libraries; they are strategic investments that can influence revenue, customer loyalty, global expansion, and product quality. Research from the Design Executive Council shows that organizations are increasingly measuring design-system value through customer and business outcomes, not just reduced rework or faster handoffs. The strongest business case connects design-system work to metrics executives already care about. ## Linking Design Systems to Customer Outcomes - Teams can measure design-system impact through adoption, retention, engagement, satisfaction, and support metrics. - Freshworks attributed its design system to: - A 28% reduction in customer service costs - Faster support-ticket resolution - SAP collects more than one million in-app user feedback data points to improve its design system. - Freshworks uses CSAT scores, A/B tests, and funnel diagnostics to identify onboarding friction and guide new components, patterns, and features. - These metrics help design teams demonstrate business value while creating a roadmap for improving customer experience and product “stickiness.” ## Scaling Company Values and Product Quality - Design systems can scale not only brand identity but also company principles and product standards. - Linear uses its design system to support a culture of craft and quality, which contributes to customer loyalty and net revenue retention. - The system is intentionally flexible and continuously updated rather than governed by rigid rules. - Its goal is to ensure that products feel thoughtfully crafted while allowing teams to adapt components as needed. ## Supporting Global Growth and Localization - Design systems help companies expand internationally while maintaining consistency, brand identity, and cultural relevance. - Hyundai Motor Group uses one system across more than 30 vehicle models and three brands—Hyundai, Kia, and Genesis—while preserving each brand’s distinct identity. - Grammarly built localization into its design-system strategy from the beginning by: - Employing in-house linguists - Accounting for cultural nuances - Treating right-to-left readability as a core design input - Distributed teams across North America, South Korea, and Poland use a shared foundation to handle different languages, hardware constraints, screen sizes, and cultural expectations. - Hyundai’s 42dot uses custom Figma plugins to test multilingual user experiences. The practical recommendation is to frame a design system around outcomes that business leaders already value—customer satisfaction, retention, revenue, global growth, and product quality. Productivity improvements remain useful, but the most persuasive evidence comes from showing how design-system decisions change customer and business performance.

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Figma Design Systems User Experience Design Tokens+2

googleJan 12, 2026Original article

Dynamic surface codes open new avenues for quantum error correction (opens in new tab)

Google Research has demonstrated the operation of dynamic surface codes for quantum error correction, marking a significant shift from traditional static circuit architectures. By alternating between different circuit constructions and re-tiling "detecting regions" in each cycle, these dynamic circuits offer greater flexibility to avoid hardware defects and suppress correlated errors. Experimental results on the Willow processor show that these methods can match the performance of static codes while significantly simplifying the physical design and fabrication of quantum chips. ## Error Triangulation via Dynamic Detecting Regions Quantum error correction (QEC) functions by localizing physical errors within specific "detecting regions" over multiple cycles to prevent them from affecting logical information. While standard surface codes use a static, square tiling for these regions, dynamic codes periodically change the tiling pattern. * Dynamic circuits allow the system to "deform" the detecting regions in spacetime, providing multiple perspectives to triangulate errors. * This approach enables the use of different gate types and connectivity layouts that are not possible with fixed, repetitive cycles. * The flexibility of dynamic re-tiling allows the system to sidestep common superconducting qubit issues such as "dropouts" (failed qubits or couplers) and leakage out of the computational subspace. ## Quantum Error Correction on Hexagonal Lattices Traditional square lattices require each physical qubit to connect to four neighbors, which creates significant overhead in wiring and coupler density. Dynamic circuits enable the use of a hexagonal lattice, where each qubit only requires three couplers. * The hexagonal code alternates between two distinct cycle types, utilizing one of the three couplers twice per cycle to maintain error detection capabilities. * Testing on the Willow processor showed that scaling the hexagonal code from distance 3 to 5 improved the logical error rate by a factor of 2.15, matching the performance of standard static circuits. * Reducing coupler density simplifies the optimization of qubit and gate frequencies, leading to a 15% improvement in simulated error suppression compared to four-coupler designs. ## Walking Circuits to Mitigate Leakage Superconducting qubits are prone to "leakage," where a qubit exits its intended computational states (0 and 1) into a higher energy state (2). In static circuits, repeated measurements on the same physical qubits can cause these leakage errors to accumulate and spread. * "Walking" circuits solve this by shifting the roles of data and measurement qubits across the lattice in each cycle. * By constantly moving the location where errors are measured, the circuit effectively "flushes out" leakage and other correlated errors before they can damage logical information. * Experiments confirmed that walking circuits achieve error suppression equivalent to static circuits while offering a more robust defense against long-term error correlations. ## Flexibility with iSWAP Entangling Gates Most superconducting quantum processors are optimized for Controlled-Z (CZ) gates, but dynamic circuits prove that QEC can be effectively implemented using alternative gates like iSWAP. * The research team demonstrated a dynamic surface code that utilizes iSWAP gates, which are native to many quantum hardware architectures. * This flexibility ensures that QEC is not tethered to a specific gate set, allowing hardware designers to choose entangling operations that offer the highest physical fidelity for their specific device. The move toward dynamic surface codes suggests a future where quantum processors are more resilient to manufacturing imperfections. By adopting hexagonal layouts and walking circuits, developers can reduce hardware complexity and mitigate physical noise, providing a more scalable path toward fault-tolerant quantum computing.

Quantum Computing Superconducting Qubits Quantum Error Correction Logical Qubits+3

googleJan 12, 2026Original article

Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR (opens in new tab)

Google Research has introduced MedGemma 1.5 4B and MedASR, expanding its suite of open medical AI models to support more complex clinical workflows. These updates significantly enhance the interpretation of high-dimensional imaging and medical speech-to-text, providing a compute-efficient foundation for healthcare developers to build upon. By maintaining an open-access model available on Hugging Face and Vertex AI, Google aims to accelerate the integration of multimodal AI into real-world medical applications. ### Multimodal Advancements in MedGemma 1.5 The latest update to the MedGemma 4B model focuses on high-dimensional and longitudinal data, moving beyond simple 2D image interpretation. * **3D Medical Imaging:** The model now supports volumetric representations from CT scans and MRIs, as well as whole-slide histopathology imaging. * **Longitudinal Review:** New capabilities allow for the review of chest X-ray time series, helping clinicians track disease progression over time. * **Anatomical Localization:** Developers can use the model to identify and localize specific anatomical features within chest X-rays. * **Document Understanding:** Enhanced support for extracting structured data from complex medical lab reports and documents. * **Edge Capability:** The 4B parameter size is specifically designed to be small enough to run offline while remaining accurate enough for core medical reasoning tasks. ### Medical Speech-to-Text with MedASR MedASR is a specialized automated speech recognition (ASR) model designed to bridge the gap between clinical dialogue and digital documentation. * **Clinical Dictation:** The model is specifically fine-tuned for medical terminology and the unique nuances of clinical dictation. * **Integrated Reasoning:** MedASR is designed to pair seamlessly with MedGemma, allowing transcribed text to be immediately processed for advanced medical reasoning or summarization. * **Accessibility:** Like other HAI-DEF models, it is free for research and commercial use and hosted on both Hugging Face and Google Cloud’s Vertex AI. ### Performance Benchmarks and Community Impact Google is incentivizing innovation through improved performance metrics and community-driven challenges. * **Accuracy Gains:** Internal benchmarks show MedGemma 1.5 improved disease-related CT classification by 3% and MRI classification by 14% compared to the previous version. * **MedGemma Impact Challenge:** A Kaggle-hosted hackathon with $100,000 in prizes has been launched to encourage developers to find creative applications for these multimodal tools. * **Model Collection:** The update complements existing tools like the MedSigLIP image encoder and the larger MedGemma 27B model, which remains the preferred choice for complex, text-heavy medical applications. Developers and researchers are encouraged to utilize MedGemma 1.5 for tasks requiring efficient, offline multimodal processing, while leveraging MedASR to automate clinical documentation. By participating in the MedGemma Impact Challenge, the community can help define the next generation of AI-assisted medical diagnostics and workflows.

AI Generative AI Multimodal AI Speech To Text+5