14 posts
toss This is the English version of a previously published article. What Is the Software 3.0 Era? In June 2025, Andrej Karpathy gave a talk at Y Combinator AI Startup School. He broke software's evolution into three stages. Software 1.0: What we've done for decades. Writing explicit…
line 들어가며 지난 엔터프라이즈 LLM 서비스 구축기 1: 컨텍스트 엔지니어링에서는 260개의 도구와 수백 페이지의 문서를 다루는 환경에서 LLM에게 필요한 정보만 골라서 제공하는 '점진적 공개' 전략을 공유해 드렸습니다. 1편이 AI에게 '무엇을 전달할 것인가?'에 대한 답이었다면, 이번 2편은 그 다음 질문으로 넘어갑니다. 정제된 맥락을 전달받는 에이전트를 '어떻게 만들 것인가?'입니다. 본격적인 이야기에 앞서, 먼저 현재 Flava AI 어시스턴트(이하 FAA)의 실전 성적표를 공개합니다. 저희…
cloudflare How we rebuilt Next.js with AI in one week 2026-02-24 Steve Faulkner *This post was updated at 12:35 pm PT to fix a typo in the build time benchmarks. Last week, one engineer and an AI model rebuilt the most popular front-end framework from scratch. The result, vinext (pronounce…
spotify How We Release the Spotify App: A Look Under the Hood (Part 2) Introduction In Part 1 of “How We Release the Spotify App”, we gave an overview of how the Spotify release process works. In Part 2, we will peek under the hood at the tooling that makes it possible. To understand wh…
cloudflare Building vertical microfrontends on Cloudflare’s platform 2026-01-30 Brayden Wilmoth Updated at 6:55 a.m. PT Today, we’re introducing a new Worker template for Vertical Microfrontends (VMFE). This template allows you to map multiple independent Cloudflare Workers to a single dom…
gitlab 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.
toss Toss Design System (TDS) argues that as organizations scale, design systems often become a source of friction rather than efficiency, leading teams to bypass them through "forking" or "detaching" components. To prevent this, TDS treats the design system as a product that must adapt to user demand rather than a set of rigid constraints to be enforced. By shifting from a philosophy of control to one of flexible expansion, they ensure that the system remains a helpful tool rather than an obstacle. ### The Limits of Control and System Fragmentation * When a design system is too rigid, product teams often fork packages to make minor adjustments, which breaks the link to central updates and creates UI inconsistencies. * Treating "system bypasses" as user errors is ineffective; instead, they should be viewed as unmet needs in the system's "supply." * The goal of a modern design system should be to reduce the reason to bypass the system by providing natural extension points. ### Comparing Flat and Compound API Patterns * **Flat Pattern:** These components hide internal structures and use props to manage variations (e.g., `title`, `description`). While easy to use, they suffer from "prop bloat" as more edge cases are added, making long-term maintenance difficult. * **Compound Pattern:** This approach provides sub-components (e.g., `Card.Header`, `Card.Body`) for the user to assemble manually. This offers high flexibility for unexpected layouts but increases the learning curve and the amount of boilerplate code required. ### The Hybrid API Strategy * TDS employs a hybrid approach, offering both Flat APIs for common, simple use cases and Compound APIs for complex, customized needs. * Developers can choose a `FlatCard` for speed or a `Compound Card` when they need to inject custom elements like badges or unique button placements. * To avoid the burden of maintaining two separate codebases, TDS uses a "primitive" layer where the Flat API is simply a pre-assembled version of the Compound components. Design systems should function as guardrails that guide developers toward consistency, rather than fences that stop them from solving product-specific problems. By providing flexible architecture that supports exceptions, a system can maintain its relevance and ensure that teams stay within the ecosystem even as their requirements evolve.
toss At Toss Income, QA Manager Suho Jung successfully automated complex E2E testing for diverse tax refund services by leveraging AI as specialized virtual team members. By shifting from manual coding to a "human-as-orchestrator" model, a single person achieved the productivity of a four-to-five-person automation team within just five months. This approach overcame the inherent brittleness of testing long, React-based flows that are subject to frequent policy changes and external system dependencies. ### Challenges in Tax Service Automation The complexity of tax refund services presented unique hurdles that made traditional manual automation unsustainable: * **Multi-Step Dependencies:** Each refund flow averages 15–20 steps involving internal systems, authentication providers, and HomeTax scraping servers, where a single timing glitch can fail the entire test. * **Frequent UI and Policy Shifts:** Minor UI updates or new tax laws required total scenario reconfigurations, making hard-coded tests obsolete almost immediately. * **Environmental Instability:** Issues such as "Target closed" errors during scraping, differing domain environments, and React-specific hydration delays caused constant test flakiness. ### Building an AI-Driven QA Team Rather than using AI as a simple autocomplete tool, the project assigned specific "personas" to different AI models to handle distinct parts of the lifecycle: * **SDET Agent (Claude Sonnet 4.5):** Acted as the lead developer, responsible for designing the Page Object Model (POM) architecture, writing test logic, and creating utility functions. * **Documentation Specialist:** Automatically generated daily retrospectives and updated technical guides by analyzing daily git commits. * **Git Master:** Managed commit history and PR descriptions to ensure high-quality documentation of the project’s evolution. * **Pair Programmers (Cursor & Codex):** Handled real-time troubleshooting, type errors, and comparative analysis of different test scripts. ### Technical Solutions for React and Policy Logic The team implemented several sophisticated technical strategies to ensure test stability: * **React Interaction Readiness:** To solve "Element is not clickable" errors, they developed a strategy that waits not just for visibility, but for event handlers to bind to the DOM (Hydration). * **Safe Interaction Fallbacks:** A standard `click` utility was created that attempts a Playwright click, then a native keyboard 'Enter' press, and finally a JS dispatch to ensure interactions succeed even during UI transitions. * **Dynamic Consent Flow Utility:** A specialized system was built to automatically detect and handle varying "Terms of Service" agreements across different sub-services (Tax Secretary, Hidden Refund, etc.) through a single unified function. * **Test Isolation:** Automated scripts were used to prevent `userNo` (test ID) collisions, ensuring 35+ complex scenarios could run in parallel without data interference. ### Integrated Feedback and Reporting The automation was integrated directly into internal communication channels to create a tight feedback loop: * **Messenger Notifications:** Every test run sends a report including execution time, test IDs, and environment data to the team's messenger. * **Automated Failure Analysis:** When a test fails, the AI automatically posts the error log, the specific failed step, a tracking EventID, and a screenshot as a thread reply for immediate debugging. * **Human-AI Collaboration:** This structure shifted the QA's role from writing code to discussing failures and policy changes within the messenger threads. The success of this 5-month experiment suggests that for high-complexity environments, the future of QA lies in "AI Orchestration." Instead of focusing on writing selectors, QA engineers should focus on defining problems and managing the AI agents that build the architecture.
figma Make your site interactive with code layers Inside Figma Product updates Prototyping Design Motion Tips & inspiration AI News Figma Sites Today we’re launching code layers—a new way to build custom interactions in Figma Sites. There’s no limit to what you can build on the web, b…
discord Discord leverages React and React Native to maintain high development velocity across desktop and mobile platforms while navigating the performance constraints of cross-platform frameworks. After initially avoiding React Native for Android due to hardware limitations, the company transitioned its client in 2022 by utilizing the Hermes JavaScript engine and targeting improved device capabilities. This strategic shift has allowed for significant efficiency gains, including a 50% reduction in median startup times as the team shifts focus toward optimizing experiences for power users. ### Cross-Platform Development and Android Transition * Discord uses React for desktop and React Native for mobile to allow unified feature shipping across platforms. * The company originally delayed adopting React Native on Android to avoid performance degradation on a wide range of hardware. * The formal transition to React Native for Android occurred in 2022, spurred by the introduction of Hermes, a JavaScript engine optimized for React Native. ### Performance Optimization and Efficiency * React Native introduced specific trade-offs, particularly regarding startup times on lower-end Android devices. * Discord’s engineering team successfully halved median startup times throughout 2023 through focused architectural improvements. * Current development efforts are prioritizing performance for "power users" who push the app's limits, ensuring stability and speed for high-intensity use cases. Discord’s journey highlights that while cross-platform frameworks like React Native require rigorous optimization—especially on Android—the trade-off for development speed and feature parity is increasingly viable through modern tools like the Hermes engine.
figma React at 60fps: improving scrolling comments in Figma Inside Figma Product updates Engineering Quality & performance
figma Introducing: Figma to React Inside Figma React Engineering Product updates Productivity
figma 5 books that shaped the design approach of Airbnb’s Jem Gold If you surfed design Twitter this week, you probably saw chatter about Airbnb’s new open source library for rendering React code as Sketch designs. It’s a pioneering approach that brings design and engineering closer t…
figma 5 books that shaped the design approach of Airbnb’s Jem Gold If you surfed design Twitter this week, you probably saw chatter about Airbnb’s new open source library for rendering React code as Sketch designs. It’s a pioneering approach that brings design and engineering closer t…