{"success":true,"data":{"query":"Listing Context","limit":10,"count":9,"sources":["wiki_real_estate.hat","wiki_artificial_intelligence.hat","wiki_dallas.hat","web_1779060046.hat"],"synced":[],"results":[{"source":"wiki_real_estate.hat","text":"ARTICLE: Amortization (accounting)\nIn accounting, amortization is a method of obtaining the expenses incurred by an intangible asset arising from a decline in value as a result of use or the passage of time. Amortization is the acquisition cost minus the residual value of an asset, calculated in a systematic manner over an asset's useful economic life. Depreciation is a corresponding concept for tangible assets.  \nMethodologies for allocating amortization to each accounting period are generally the same as those for depreciation. However, many intangible assets such as goodwill or certain brands may be deemed to have an indefinite useful life and are therefore not subject to amortization (although goodwill is subjected to an impairment test every year).\nWhile theoretically amortization is used to account for the decreasing value of an intangible asset over its useful life, in practice many companies will amortize what would otherwise be one-time expenses through listing them as a capital expense on the cash flow statement and paying off the cost through amortization, having the effect of improving the company's net income in the fiscal year or quarter of the expense.\nAmortization is recorded in the financial statements of an entity as a reduction in the carrying value of the intangible asset in the balance sheet and as an expense in the income statement.\nUnder International Financial Reporting Standards, guidance on accounting for the amortization of intangible assets is contained in IAS 38. Under United States generally accepted accounting principles (GAAP), the primary guidance is contained in FAS 142.","score":45.103059609765616,"links":[]},{"source":"wiki_real_estate.hat","text":"The activity-based office\nThe activity-based office concept is said to increase productivity through the stimulation of interaction and communication while retaining employee satisfaction and reducing the accommodation costs. Although some research has gone into understanding the added value, there is still a need for sound data on the relationship between office design, its intentions and the actual use after implementation.\nThe concept of activity-based workplace has been implemented in organisations as a solution to improve office space efficiency. However, the question of whether or not office workers' comfort or productivity are compromised in the pursuit of space efficiency has not been fully investigated. There are obstacles and issues of concern when practicing the activity-based office concept. A study carried out in activity-based workplace settings reports that employees without an assigned desk complain of desk shortages, difficulty finding colleagues which limits immediate collaboration, wasted time finding and setting up a workstation, and limited ability to adjust or personalise workstations to meet individual ergonomic needs. Another study suggest the impact of office design on occupants' satisfaction, perceived productivity and health, pointing towards reduced time workers spent seated in ABW offices\nThe most recent study released in 2020 by Veldhoen + Company, the founders of Activity Based Working, was the biggest global research project on Activity Based Working. The research set out to understand the measurable impact of Activity Based Working and the drivers of success in Activity Based Working transitions. The research project was started in July 2019, and was impacted by the COVID-19 pandemic in 2020. The report included 32,369 responses spanning 11 countries, and explored questions used in Leesman Index surveys providing valuable context to understanding office workers' behaviour - with the opportunity to explore what factors would be most important as organisations transition to a post-pandemic return to the office. The data tells us not only what type of workplace to return to, but also how to do so.","score":36.327294707324214,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Algorithmic bias and fairness\nMachine learning applications can be biased if they learn from biased data. The developers may not be aware that the bias exists. Discriminatory behavior by some LLMs can be observed in their output. Bias can be introduced by the way training data is selected and by the way a model is deployed. If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination. The field of fairness studies how to prevent harms from algorithmic biases.\nOn 28 June 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as \"gorillas\" because they were black. The system was trained on a dataset that contained very few images of black people, a problem called \"sample size disparity\". Google \"fixed\" this problem by preventing the system from labelling anything as a \"gorilla\". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.\nCOMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend. In 2017, several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.\nA program can make biased decisions even if the data does not explicitly mention a problematic feature (such as \"race\" or \"gender\"). The feature will correlate with other features (like \"address\", \"shopping history\" or \"first name\"), and the program will make the same decisions based on these features as it would on \"race\" or \"gender\". Moritz Hardt said \"the most robust fact in this research area is that fairness through blindness doesn't work.\"\nCriticism of COMPAS highlighted that machine learning models are designed to make \"predictions\" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as recommendations, some of these \"recommendations\" will likely be racist. Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is descriptive rather than prescriptive.\nBias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.\nThere are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is distributive fairness, which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases, but it may conflict with anti-discrimination laws.\nAt the 2022 ACM Conference on Fairness, Accountability, and Transparency a paper reported that a CLIP‑based (Contrastive Language-Image Pre-training) robotic system reproduced harmful gender‑ and race‑linked stereotypes in a simulated manipulation task. The authors recommended robot‑learning methods which physically manifest such harms be \"paused, reworked, or even wound down when appropriate, until outcomes can be proven safe, effective, and just.\"","score":31.583209025129094,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"General purpose reasoning agents\nThe advent of LLMs created huge potential for further use of artificial agents, as agents based on them could have general purpose cognitive abilities. Agents run by LLMs (and occasionally non-LLM foundation models)  have similar vulnerability to adversarial attack as those run by decision tree models. The wider scope of actions for LLM agents has created new challenges for their verification, over and above those present for classical agents. For example, the LLM's neural network endows it with infinite domains, an especial challenge for traditional formal verification techniques. Academics began to study the problems involved in verifying LLM agents from 2018. Deployment of such agents began to accelerate in late 2023 after OpenAI's \"function-calling\" API was made available, and especially after Anthropic's late 2024 introduction of Model Context Protocol (MCP), a standardised way for LLM agents to gain contextual awareness, and to act on the world by calling various external tools. The rapid rollout of LLM agents following MCP's release has seen the task of agent verification receive increased attention within academia, and also from the private sector. In 2024 and 2025 several startups focusing on LLM agent verification have been founded in both Europe and the US to meet growing demand.","score":31.583209025129094,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Overview\nAI agents possess several key attributes, including goal-directed behavior, natural language interfaces, the capacity to use external tools, and the ability to perform multi-step tasks. Their control flow is frequently driven by large language models (LLMs). Agent systems may also include memory components, planning logic, tool interfaces, and orchestration software for coordinating agent components.\nAI agents do not have a standard definition. NIST has described agentic AI as an emerging area requiring standards for secure operation, interoperability, and reliable interaction with external systems.\nA common application of AI agents is the automation of tasks, for example booking travel plans based on a user's prompted request. \nCompanies such as Google, Microsoft and Amazon Web Services have offered platforms for deploying pre-built AI agents. Several protocols have been proposed for standardizing inter-agent communication, with examples including the Model Context Protocol, Gibberlink, and many others. Some of these protocols are also used for connecting agents with external applications.\nIn December 2025, Linux Foundation announced the formation of the Agentic AI Foundation (AAIF), with the goal of ensuring agentic AI evolves transparently and collaboratively.","score":31.583209025129094,"links":[]},{"source":"wiki_dallas.hat","text":"Notable people\nInternational relations\nThe city of Dallas has worked to build Sister & Friendship City relationships around the globe. These relationships help create and strengthen partnerships between Dallas and the international community. The program aims to build global cooperation at the municipal level by promoting cultural understanding and stimulating economic development between Dallas and its foreign counterparts.\n\nSister cities\nDallas's sister cities are:\n\nFriendship cities\nDallas has friendly relations with:\n\nSee also\nList of museums in North Texas\nNational Register of Historic Places listings in Dallas County, Texas\nTexas Triangle\nUSS Dallas, 3 ships\n2015 attack on Dallas police\n\nNotes\nReferences\nFurther reading\nExternal links\n\nOfficial website\nDallas from the Handbook of Texas Online\nDallas Public Library Search Results for Dallas County\n\n--- NEXT ARTICLE ---","score":29.280917192565077,"links":[]},{"source":"wiki_real_estate.hat","text":"Chawls\nHavelis\nIgloos\nHuts\nThe size of havelis and chawls is measured in Gaz (square yards), Quila, Marla, Beegha, and acre.\nSee List of house types for a complete listing of housing types and layouts, real estate trends for shifts in the market, and house or home for more general information.\n\nDevelopment\nReal estate development involves planning and coordinating of housebuilding, real estate construction or renovation projects. Real estate development can be less cyclical than real estate investing.\nThe price of real estate increases with demand and decreases with supply according to demand and supply.","score":27.551529804882808,"links":[]},{"source":"web_1779060046.hat","text":"API Reference Copy page Copy Built-in React Hooks Hooks let you use different React features from your components. You can either use the built-in Hooks or combine them to build your own. This page lists all built-in Hooks in React. State Hooks State lets a component “remember” information like user input. For example, a form component can use state to store the input value, while an image gallery component can use state to store the selected image index. To add state to a component, use one of these Hooks: useState declares a state variable that you can update directly. useReducer declares a state variable with the update logic inside a reducer function. function ImageGallery ( ) { const [ index , setIndex ] = useState ( 0 ) ; // ... Context Hooks Context lets a component receive information from distant parents without passing it as props. For example, your app’s top-level component can pass the current UI theme to all components below, no matter how deep. useContext reads and subscribes to a context. function Button ( ) { const theme = useContext ( ThemeContext ) ; // ... Ref Hooks Refs let a component hold some information that isn’t used for rendering, like a DOM node or a timeout ID. Unlike with state, updating a ref does not re-render your component. Refs are an “escape hatch” from the React paradigm. They are useful when you need to work with non-React systems, such as the built-in browser APIs. useRef declares a ref. You can hold any value in it, but most often it’s used to hold a DOM node. useImperativeHandle lets you customize the ref exposed by your component. This is rarely used. function Form ( ) { const inputRef = useRef ( null ) ; // ... Effect Hooks Effects let a component connect to and synchronize with external systems. This includes dealing with network, browser DOM, animations, widgets written using a different UI library, and other non-React code. useEffect connects a component to an external system. function ChatRoom ( { roomId } ) { useEffect ( ( ) => { const connection = createConnection ( roomId ) ; connection . connect ( ) ; return ( ) => connection . disconnect ( ) ; } , [ roomId ] ) ; // ... Effects are an “escape hatch” from the React paradigm. Don’t use Effects to orchestrate the data flow of your application. If you’re not interacting with an external system, you might not need an Effect. There are two rarely used variations of useEffect with differences in timing: useLayoutEffect fires before the browser repaints the screen. You can measure layout here. useInsertionEffect fires before React makes changes to the DOM. Libraries can insert dynamic CSS here. You can also separate events from Effects: useEffectEvent creates a non-reactive event to fire from any Effect hook. Performance Hooks A common way to optimize re-rendering performance is to skip unnecessary work. For example, you can tell React to reuse a cached calculation or to skip a re-render if the data has not changed since the previous render. To skip calculations and unnecessary re-rendering, use one of these Hooks: useMemo lets you cache the result of an expensive calculation. useCallback lets you cache a function definition before passing it down to an optimized component. function TodoList ( { todos , tab , theme } ) { const visibleTodos = useMemo ( ( ) => filterTodos ( todos , tab ) , [ todos , tab ] ) ; // ... } Sometimes, you can’t skip re-rendering because the screen actually needs to update. In that case, you can improve performance by separating blocking updates that must be synchronous (like typing into an input) from non-blocking updates which don’t need to block the user interface (like updating a chart). To prioritize rendering, use one of these Hooks: useTransition lets you mark a state transition as non-blocking and allow other updates to interrupt it. useDeferredValue lets you defer updating a non-critical part of the UI and let other parts update first. Other Hooks These Hooks are mostly useful to library authors and aren’t commonly used in the application code. useDebugValue lets you customize the label React DevTools displays for your custom Hook. useId lets a component associate a unique ID with itself. Typically used with accessibility APIs. useSyncExternalStore lets a component subscribe to an external store. useActionState allows you to manage state of actions. Your own Hooks You can also define your own custom Hooks as JavaScript functions. Previous Overview Next useActionState","score":27.12400931168676,"links":[]},{"source":"web_1779060046.hat","text":"API Reference Copy page Copy Built-in React APIs In addition to Hooks and Components , the react package exports a few other APIs that are useful for defining components. This page lists all the remaining modern React APIs. createContext lets you define and provide context to the child components. Used with useContext . lazy lets you defer loading a component’s code until it’s rendered for the first time. memo lets your component skip re-renders with same props. Used with useMemo and useCallback . startTransition lets you mark a state update as non-urgent. Similar to useTransition . act lets you wrap renders and interactions in tests to ensure updates have processed before making assertions. Resource APIs Resources can be accessed by a component without having them as part of their state. For example, a component can read a message from a Promise or read styling information from a context. To read a value from a resource, use this API: use lets you read the value of a resource like a Promise or context . function MessageComponent ( { messagePromise } ) { const message = use ( messagePromise ) ; const theme = use ( ThemeContext ) ; // ... } Previous <ViewTransition> Next act","score":18.56200465584338,"links":[]}]},"metadata":{},"timestamp":"2026-07-08T22:49:53.944Z"}