{"success":true,"data":{"query":"Security Architect","limit":10,"count":10,"sources":["wiki_artificial_intelligence.hat","wiki_real_estate.hat","wiki_dallas.hat","web_1779060034.hat"],"synced":[],"results":[{"source":"wiki_artificial_intelligence.hat","text":"Open source\nActive organizations in the AI open-source community include Hugging Face, Google, EleutherAI and Meta. Various AI models, such as Llama 2, Mistral or Stable Diffusion, have been made open-weight, meaning that their architecture and trained parameters (the \"weights\") are publicly available. Open-weight models can be freely fine-tuned, which allows companies to specialize them with their own data and for their own use-case. Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitate bioterrorism) and that once released on the Internet, they cannot be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.","score":63.16641805025819,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"ARTICLE: Artificial intelligence\nArtificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in engineering, mathematics and computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.\nHigh-profile applications of AI include advanced web search engines, chatbots, virtual assistants, autonomous vehicles, and play and analysis in strategy games (e.g., chess and Go). Since the 2020s, generative AI has become widely available to generate images, audio, and videos from text prompts.\nThe traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception, as well as support for robotics. To reach these goals, AI researchers have used techniques including state space search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields. Some companies, such as OpenAI, Google DeepMind and Meta, aim to create artificial general intelligence (AGI) – AI that can complete virtually any cognitive task at least as well as a human.\nArtificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest increased substantially after 2012, when graphics processing units began being used to accelerate neural networks, and deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture. In the 2020s, an AI boom has coincided with advances in generative AI, which allowed for the creation and modification of media. In addition to AI safety and unintended consequences and harms from the use of AI, ethical concerns, AI's long-term effects, and potential existential risks have prompted discussions of AI regulation.","score":53.16641805025819,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"History\nClassical agents\nThe theoretical underpinnings for artificial (inorganic) agents emerged in the mid 20th century, with establishment of cybernetics and artificial intelligence. Oliver Selfridge's 1958 Pandemonium - A Paradigm for Learning paper was an important early theoretical contribution in establishing agent oriented architecture. Practical implementations of agents for real world applications began to become widespread in the 1990s, after the introduction of the belief–desire–intention software model (BDI), and agent-oriented programming. Pure digital agents were deployed in computer infrastructure for purposes such as monitoring, while agents connected to real-world sensors and actuators were increasingly used in industrial control systems.  \nWhile the concept of artificial agents was interwoven with early artificial intelligence studies right from the start, early agents lacked general purpose reasoning capabilities, often only having simple if then logic. Even a device as simple as a thermostat, which has a sensor and a means of acting, can be considered a proto agent in this sense. Verifying the behaviours of a simple single agent system is not generally especially difficult, but it can be a different matter when several simple agents coexist in the same system. Craig Reynolds's work on boids showed that relatively complex, \"intelligent\" behaviour can emerge from a number of such simple agents working together in a Multi-agent system (MAS). By the 1990s, even the behaviour of a single agent system could sometimes be quite complex; in accordance with the Belief–desire–intention software model, agents could have believes that might evolve over time. Agents were increasingly introduced that were controlled by quite large decision tree models, which had new vulnerabilities to adversarial attack.  It was becoming increasingly apparent that traditional software verification methods had limitations for testing such agents, or even for the more primitive type of agents when they were deployed as part of a MAS.\nIt was the use of agents for industrial control systems, sometimes associated with robotics, that lent urgency to the practice of agent verification. Informal testing might be acceptable for digital agents used say to monitor whether each of an organisation's computer is properly licensed. But with an increasing potential for faulty agents to result in a failure that might cause a large fire to break out at a chemical manufacturing plant, a botched medical operation or even a crashed aircraft, the need to develop reliable means of verifying the behaviour of such agents was considered urgent. The Foundation for Intelligent Physical Agents was established in 1996. In the late 90s, a growing number of industry and university based scientists began working on the problem, with researchers publishing papers on the verification of both single and multi agent systems. Much of this work showed how formal verification techniques like model checking could be used to gain a high level of assurance that agent based systems would conform with their specification. A 2018 systematic review covering 231 studies found that model checking was the most common technique for agent verification, with theorem proving the second most commonly used formal verification method. In the first two decades of the 20th century, agents run by AI became more common, with Siri and Alexa being well known examples. But such agents still lacked general reasoning capabilities and did not pose new pressing problems for agent verification.","score":53.16641805025819,"links":[]},{"source":"wiki_real_estate.hat","text":"History\nThe first known reference to an activity-based analysis of office work modes was by American architect Robert Luchetti in the late 1970s. in 1983, Luchetti co-invented the now widely accepted concept of the office as a series of \"activity settings\". In an activity settings-based environment, multiple settings are provided which have different technical and physical attributes assembled to support the variety of performance \"modes\" that take place in a work environment.\nThe term \"Activity Based Working\" was first coined in the book the Art of Working by Erik Veldhoen, a Dutch consultant with Veldhoen + Company, and author of the book The Demise of the Office.  Activity Based Working was first implemented in the Netherlands by Interpolis in collaboration with Veldhoen + Company in the nineties. Interpolis is one of largest insurance companies in the Netherlands. The company gained wide recognition with its advertising campaign \"Interpolis. Crystal clear\", which was adopted from their vision and brought to life in their new way of working.","score":45.103059609765616,"links":[]},{"source":"wiki_real_estate.hat","text":"See also\n\n\n== References ==\n\n--- NEXT ARTICLE ---\n\nARTICLE: Architect of record\nArchitect of record is the architect or architecture firm whose name appears on a building permit issued for a specific project on which that architect or firm performed services.\n\nIssuance of building permits\nBuilding permits are issued by a government agency with the authority in a certain jurisdiction to regulate building construction and enforce building codes. Generally, the building contractor submits the application for the permit to the regulatory authority,  along with a building project's drawings and specifications (called collectively \"construction documents\"). But in some jurisdictions, the architect is required to submit the construction documents needed to obtain the building permit. With some construction projects, more than one building permit is issued. That occurs when several different architects perform services on discrete parts of a single building project. More than one architect of record, therefore, would exist in such a case.","score":45.103059609765616,"links":[]},{"source":"wiki_real_estate.hat","text":"Definition\nA building is 'a structure that has a roof and walls and stands more or less permanently in one place'; \"there was a three-storey building on the corner\"; \"it was an imposing edifice\". In the broadest interpretation a fence or wall is a building. However, the word structure is used more broadly than building, to include natural and human-made formations and ones that do not have walls; structure is more often used for a fence. Sturgis' Dictionary included that \"[building] differs from architecture in excluding all idea of artistic treatment; and it differs from construction in the idea of excluding scientific or highly skillful treatment.\"\nStructural height in technical usage is the height to the highest architectural detail on the building from street level. Spires and masts may or may not be included in this height, depending on how they are classified. Spires and masts used as antennas are not generally included. The distinction between a low-rise and high-rise building is a matter of debate, but generally three stories or less is considered low-rise.","score":45.103059609765616,"links":[]},{"source":"wiki_dallas.hat","text":"Architecture\nDallas's skyline has twenty buildings classified as skyscrapers, over 490 feet (150 m) in height. Despite its tallest building not reaching 980 feet (300 m), Dallas does have a signature building in Bank of America Plaza which is lit up in neon but falls outside the top two hundred tallest buildings in the world. Although some of Dallas's architecture dates from the late 19th and early 20th centuries, most of the notable architecture in the city is from the modernist and postmodernist eras. Iconic examples of modernist architecture include Reunion Tower, the John Fitzgerald Kennedy Memorial, I. M. Pei's Dallas City Hall and the Morton H. Meyerson Symphony Center. Good examples of postmodernist skyscrapers are Fountain Place, Bank of America Plaza, Renaissance Tower, JPMorgan Chase Tower, and Comerica Bank Tower. Downtown Dallas also has residential offerings in downtown, some of which are signature skyline buildings.\nSeveral smaller structures are fashioned in the Gothic Revival style, such as the Kirby Building, and the neoclassical style, as seen in the Davis and Wilson Buildings. One architectural \"hotbed\" in the city is a stretch of historic houses along Swiss Avenue, which has all shades and variants of architecture from Victorian to neoclassical. The Dallas Downtown Historic District protects a cross-section of Dallas commercial architecture from the 1880s to the 1940s.","score":29.280917192565077,"links":[]},{"source":"wiki_dallas.hat","text":"East Dallas\nEast Dallas is the location of Deep Ellum, an arts area close to Downtown, the Lakewood neighborhood (and adjacent areas, including Lakewood Heights, Wilshire Heights, Lower Greenville, Junius Heights, and Hollywood Heights/Santa Monica), Vickery Place and Bryan Place, and the architecturally significant neighborhoods of Swiss Avenue and Munger Place. Its historic district has one of the largest collections of Frank Lloyd Wright-inspired prairie-style homes in the United States. In the northeast quadrant of the city is Lake Highlands, one of Dallas's most unified middle-class neighborhoods.\n\nOak Cliff\nSouthwest of Downtown lies Oak Cliff. Once a separate city founded in the mid-1800s, Oak Cliff was annexed in 1903 by Dallas. As one of the oldest areas in Dallas, the hilly North Oak Cliff is home to 5 of the 13 conservation districts in Dallas including the architecturally significant Kessler Park neighborhood and trendy Bishop Arts District.","score":29.280917192565077,"links":[]},{"source":"wiki_dallas.hat","text":"Transit systems\nDallas Area Rapid Transit (DART) is the Dallas-area public transportation authority that provides rail, buses and HOV lanes to commuters. DART began operating the first light rail system in Texas in 1996, and it is now the largest operator of light rail in the US. Today, the system is the seventh-busiest light rail system in the country with approximately 55 stations on 72 miles (116 km) of light rail, and 10 stations on 35 miles (56 km) of commuter rail. It includes four light rail lines and a commuter line: the Red Line, the Blue Line, the Green Line, the Orange Line, and the Trinity Railway Express.\nThe Red Line travels through Oak Cliff, South Dallas, Downtown, Uptown, North Dallas, Richardson and Plano, while the Blue Line goes through Oak Cliff, Downtown, Uptown, East Dallas, Lake Highlands, and Garland. The Red and Blue lines are conjoined between 8th & Corinth Station in Oak Cliff through Mockingbird Station in North Dallas. The two lines service Cityplace Station. The Green Line serves Carrollton, Farmers Branch, Love Field Airport, Stemmons Corridor, Victory Park, Downtown, Deep Ellum, Fair Park, South Dallas, and Pleasant Grove.\nThe Orange Line initially operated as a peak-service line providing extra capacity on portions of the Green and Red Lines (Bachman Station on the Green Line, through the Downtown transit mall, to Parker Road Station on the Red Line making a \"U\"-shape). However, the first stage of the Orange Line opened on December 6, 2010, extending its west end from Bachman to Belt Line Station in Irving. The second and final phase opened in August 2014 and provided DFW Airport with rail service. DFW Airport Terminal A station is the terminus for the Orange Line and connects Skylink.\nThis provides passengers the convenience of disembarking the DART rail, proceeding to security check-in and immediately boarding Skylink to be quickly transported to their desired terminal. The Blue Line has also been extended by 4.5 miles (7.2 km) to serve Rowlett at the Rowlett Park & Ride facility.\nIn August 2009, the Regional Transportation Council agreed to seek $96 million in federal stimulus dollars for a trolley project in Dallas and Fort Worth. The Oak Cliff Transit Authority took the lead with leaders envisioning a streetcar line that would link Union Station and the Dallas Convention Center in Downtown to Oak Cliff, Methodist Medical Center, and the Bishop Arts District via the Houston Street Viaduct.\nDallas was awarded a $23 million TIGER grant towards the $58 million Dallas Streetcar Project in February 2010.\nIn addition to light rail, Amtrak's Texas Eagle also serves Union Station, providing daily service east to Chicago and west to San Antonio, and thrice-weekly service west to Los Angeles. The Trinity Rail Express terminates at Union Station and T&P Station.","score":29.280917192565077,"links":[]},{"source":"web_1779060034.hat","text":"Menu Using App Router Features available in /app Latest Version 16.2.6 This page is also available as Markdown at /docs/app/guides.md . For an index of Next.js documentation , see /docs/llms.txt . Copy page Guides Last updated May 13, 2026 AI Coding Agents Learn how to configure your Next.js project so AI coding agents use up-to-date documentation instead of outdated training data. Analytics Measure and track page performance using Next.js Speed Insights Authentication Learn how to implement authentication in your Next.js application. Backend for Frontend Learn how to use Next.js as a backend framework Caching (Previous Model) Learn how to cache and revalidate data using fetch options, unstable_cache, and route segment configs for projects not using Cache Components. CDN Caching Learn how CDN caching works with Next.js, including what works today, cache variability, and the direction toward pathname-based cache keying. CI Build Caching Learn how to configure CI to cache Next.js builds Content Security Policy Learn how to set a Content Security Policy (CSP) for your Next.js application. CSS-in-JS Use CSS-in-JS libraries with Next.js Custom Server Start a Next.js app programmatically using a custom server. Data Security Learn the built-in data security features in Next.js and learn best practices for protecting your application's data. Debugging Learn how to debug your Next.js application with VS Code, Chrome DevTools, or Firefox DevTools. Deploying to Platforms Understand which Next.js features require specific platform capabilities and how to choose the right deployment target. Draft Mode Next.js has draft mode to toggle between static and dynamic pages. You can learn how it works with App Router here. Environment Variables Learn to add and access environment variables in your Next.js application. Forms Learn how to create forms in Next.js with React Server Actions. How Revalidation Works A deep dive into how Next.js revalidates cached content, including the tag system, cache consistency, and multi-instance coordination. ISR Learn how to create or update static pages at runtime with Incremental Static Regeneration. Instrumentation Learn how to use instrumentation to run code at server startup in your Next.js app Internationalization Add support for multiple languages with internationalized routing and localized content. JSON-LD Learn how to add JSON-LD to your Next.js application to describe your content to search engines and AI. Lazy Loading Lazy load imported libraries and React Components to improve your application's loading performance. Development Environment Learn how to optimize your local development environment with Next.js. Next.js MCP Server Learn how to use Next.js MCP support to allow coding agents access to your application state MDX Learn how to configure MDX and use it in your Next.js apps. Memory Usage Optimize memory used by your application in development and production. Migrating Learn how to migrate from popular frameworks to Next.js Migrating to Cache Components Learn how to migrate from route segment configs to Cache Components in Next.js. Multi-tenant Learn how to build multi-tenant apps with the App Router. Multi-zones Learn how to build micro-frontends using Next.js Multi-Zones to deploy multiple Next.js apps under a single domain. OpenTelemetry Learn how to instrument your Next.js app with OpenTelemetry. Package Bundling Learn how to analyze and optimize your application's server and client bundles with the Next.js Bundle Analyzer for Turbopack, and the `@next/bundle-analyzer` plugin for Webpack. PPR Platform Guide A guide for platform engineers on implementing PPR support, from basic origin rendering to optimized CDN integration. Prefetching Learn how to configure prefetching in Next.js Preserving UI state Learn how React's Activity component preserves UI state across navigations in Next.js and how to control what resets. Preventing Flash Learn how to correct server-rendered content before the browser paints, avoiding visible flash when the page hydrates. Production Recommendations to ensure the best performance and user experience before taking your Next.js application to production. PWAs Learn how to build a Progressive Web Application (PWA) with Next.js. Public pages Learn how to build public, \"static\" pages that share data across users, such as landing pages, list pages (products, blogs, etc.), marketing and news sites. Redirecting Learn the different ways to handle redirects in Next.js. Rendering Philosophy Learn how Next.js treats static and dynamic rendering as a spectrum at the component level, and what this means for deployment. Sass Style your Next.js application using Sass. Scripts Optimize 3rd party scripts with the built-in Script component. Self-Hosting Learn how to self-host your Next.js application on a Node.js server, Docker image, or static HTML files (static exports). SPAs Next.js fully supports building Single-Page Applications (SPAs). Static Exports Next.js enables starting as a static site or Single-Page Application (SPA), then later optionally upgrading to use features that require a server. Streaming Learn how streaming works in Next.js and how to use it to progressively render UI as data becomes available. Tailwind CSS v3 Style your Next.js Application using Tailwind CSS v3 for broader browser support. Testing Learn how to set up Next.js with four commonly used testing tools — Cypress, Playwright, Vitest, and Jest. Third Party Libraries Optimize the performance of third-party libraries in your application with the `@next/third-parties` package. Upgrading Learn how to upgrade to the latest versions of Next.js. Videos Recommendations and best practices for optimizing videos in your Next.js application. View transitions Learn how to use view transitions to communicate meaning during navigation, loading, and content changes in a Next.js app. Was this helpful? supported. Send","score":27.12400931168676,"links":[]}]},"metadata":{},"timestamp":"2026-07-08T22:49:09.675Z"}