{"version":3,"file":"script_main.D8kDHZv8.mjs","names":["useRef","useState","_Fragment","isBrowser","useReducer","isBrowser","useState","isBrowser","useState","_Fragment","Suspense","isBrowser","useState","_Fragment","Overlay","Suspense","useState","useCallback","v0","v1","v2","__FramerMetadata__","getLocalizedValue","valuesByLocaleId","localizedValues","React.Fragment","getLocalizedValue","valuesByLocaleId","localizedValues","addPropertyOverrides","cycleOrder","serializationHash","variantClassNames","transition1","negate","Transition","React.useContext","React.useMemo","humanReadableVariantMap","Variants","React.Fragment","getProps","createLayoutDependency","React.forwardRef","useRef","React.useId","SVG","RichText","getLocalizedValue","_BrandingLogoTextFonts","_BrandingLogoText","_CoreTextFonts","_CoreText","UnderlineButton","serializationHash","variantClassNames","matchVariant","React.useContext","React.useMemo","humanReadableVariantMap","React.Fragment","getProps","React.forwardRef","useRef","React.useId","React.useRef","getLocalizedValue","_Fragment","RichText","getLocalizedValue","_BrandingLogoText","_CoreText","Footer","isBrowser","useRef","React.useId","getLocalizedValue","PropertyOverrides","RichText","React.Fragment","React.createElement","Main_New","Framer.PageRoot","Framer.lazy","Framer.useCurrentRoute","Framer.useLocaleInfo","Framer.useRouter","React.Fragment","React.memo","React.useCallback","React.useEffect","React.useRef","React.useState","React.useLayoutEffect","Framer.LazyValue","Framer.GracefullyDegradingErrorBoundary","Framer.PageEffectsProvider","Framer.patchRoutesForABTesting","Framer.inferInitialRouteFromPath","Framer.yieldToMain","React.startTransition","Framer.ErrorPlaceholder"],"sources":["https:/framerusercontent.com/modules/06YBUGrb4WVyU6mVyu9R/uAn16GNIiKLh48olFHop/ScrollbarPro.js","https:/framerusercontent.com/modules/3Y415ULVCZCEMX6zFQMg/ixblIOb2eENMczEskhtt/SEOPro.js","https:/framerusercontent.com/modules/lfM9wQ53RTva0nHSOMAz/wrt794kZfKO30nR15sTj/LeaderPro.js","https:/framerusercontent.com/modules/RFM6zI5MxOiqwwNiQep4/2CcJhEanvh9oX5JHRkd8/send.js","https:/framerusercontent.com/modules/tfq8EDfrazNLXzc9LJte/hLcdFz4t8UjInDMvzq41/utils.js","https:/framerusercontent.com/modules/zvf2WTzc98u6EX2y7YDj/dsQTNWNk09IMnZEHMfzP/consent.js","https:/framerusercontent.com/modules/HKzIAGtbudIGLRAteuFH/Nhaar5CYKIbxxEaVvTJs/inEU.js","https:/framerusercontent.com/modules/0oeZpJVursioGAbwgB9e/atthcI0SmGvO4HAxG9e5/region.js","https:/framerusercontent.com/modules/EkwkxVF9vkTs720qqBC8/L9fJK0tucMOkjZFRszrm/Banner.js","https:/framerusercontent.com/modules/GbX8S6ghmyszcS2GLR2F/7p40RDrpC9PBWkQoJaco/Cookies.js","https:/framerusercontent.com/modules/oWVHHLIj4Q7woeIlKzbt/dNcsiLsuC8uzH8lzqFBp/Input.js","https:/framer.com/m/framer/InputSites.js@1.16.3","https:/framerusercontent.com/modules/tEJqoun4MtBed1OjNwKy/IoUlFnqTeq8fqEqmFsht/Input_Mailchimp.js","https:/framerusercontent.com/modules/ZIclRrVgv8NvtYH3kije/rhB3A17qhO9mwBIAXqZV/dglb0htIf-0.js","https:/framerusercontent.com/modules/ZIclRrVgv8NvtYH3kije/rhB3A17qhO9mwBIAXqZV/dglb0htIf.js","https:/framerusercontent.com/modules/q7odCLKIFUPvUf73Ef4H/7PTxUGgExnFJJnI9AtsJ/xTJjsfbbw-0.js","https:/framerusercontent.com/modules/q7odCLKIFUPvUf73Ef4H/7PTxUGgExnFJJnI9AtsJ/xTJjsfbbw.js","https:/framerusercontent.com/modules/vSzV86N1Gw8wT5lRmOdX/ii1FKpnlo9mfv5biw90Z/xTJjsfbbw.js","https:/framerusercontent.com/modules/fmJk0ynA3L7DcUojhMtM/aaDB85zq8xgZH1IOgXbE/dglb0htIf.js","https:/framerusercontent.com/modules/1BR6RJ8wbI6qrfkT0ukp/zhHmnAMF1GYWrD4pYujW/N3YypGIb6.js","https:/framerusercontent.com/modules/dcManc9yK17OEHWPXYh5/U9MzPXpsOw0axMi5YrIV/N3YypGIb6.js","framer:toplevel/main"],"sourcesContent":["import{jsx as _jsx}from\"react/jsx-runtime\";import{useEffect,useRef}from\"react\";import{Frame,RenderTarget,addPropertyControls,ControlType}from\"framer\";function findScrollableAncestor(el){while(el&&el!==document.body){const style=window.getComputedStyle(el);if((style.overflowY===\"auto\"||style.overflowY===\"scroll\")&&el.scrollHeight>el.clientHeight){return el;}el=el.parentElement;}return null;}/**\n * @framerDisableUnlink\n *\n * @framerSupportedLayoutWidth 100%\n * @framerSupportedLayoutHeight 100%\n */export default function ScrollbarPro({containerRef,appearance,behavior}){if(RenderTarget.current()===RenderTarget.canvas)return null;const wrapperRef=useRef(null);useEffect(()=>{let target=null;let isPage=false;if(containerRef?.current){target=containerRef.current;}else{const found=findScrollableAncestor(wrapperRef.current);if(found)target=found;}if(!target){target=document;isPage=true;}const csId=Math.random().toString(36).substr(2,6);const csClass=`cs-active-${csId}`;const styleTag=document.createElement(\"style\");styleTag.id=`cs-style-${csId}`;styleTag.innerHTML=`\n            .${csClass}::-webkit-scrollbar { display: none !important; }\n            .${csClass} { scrollbar-width: none !important; -ms-overflow-style: none !important; }\n        `;document.head.appendChild(styleTag);if(isPage){document.documentElement.classList.add(csClass);document.body.classList.add(csClass);}else{target.classList.add(csClass);}const{thumbColor,thumbWidth,cornerRadius,showTrack,trackColor,dragColor,offset}=appearance;const{autoHide,hideDelay}=behavior;const track=document.createElement(\"div\");const thumb=document.createElement(\"div\");Object.assign(track.style,{position:\"fixed\",width:`${thumbWidth}px`,background:showTrack?trackColor:\"transparent\",borderRadius:`${cornerRadius}px`,opacity:autoHide?\"0\":\"1\",transition:\"opacity 0.3s\",zIndex:9999,pointerEvents:\"none\"});Object.assign(thumb.style,{position:\"fixed\",width:`${thumbWidth}px`,backgroundColor:thumbColor,borderRadius:`${cornerRadius}px`,opacity:autoHide?\"0\":\"1\",transition:\"opacity 0.3s\",zIndex:1e4,cursor:\"grab\",pointerEvents:\"auto\"});document.body.appendChild(track);document.body.appendChild(thumb);let hideTimer=null;let isDragging=false;let dragStartY=0;let startScrollTop=0;let scrollable=0;let visibleHeight=0;let thumbH=0;const showAll=()=>{track.style.opacity=showTrack?\"1\":\"0\";thumb.style.opacity=\"1\";if(autoHide&&!isDragging){if(hideTimer)clearTimeout(hideTimer);hideTimer=window.setTimeout(()=>{track.style.opacity=\"0\";thumb.style.opacity=\"0\";},hideDelay*1e3);}};const updateAll=()=>{let scrollTop;let scrollHeight;let baseTop;let baseRight;if(isPage){scrollTop=window.pageYOffset||document.documentElement.scrollTop;scrollHeight=document.documentElement.scrollHeight;visibleHeight=window.innerHeight;baseTop=0;baseRight=0;}else{const el=target;const rect=el.getBoundingClientRect();scrollTop=el.scrollTop;scrollHeight=el.scrollHeight;visibleHeight=el.clientHeight;baseTop=rect.top;baseRight=window.innerWidth-rect.right;}track.style.top=`${baseTop}px`;track.style.right=`${baseRight+offset}px`;track.style.height=`${visibleHeight}px`;scrollable=scrollHeight-visibleHeight;const minH=thumbWidth*2;thumbH=Math.max(visibleHeight/scrollHeight*visibleHeight,minH);const offsetY=scrollable>0?scrollTop/scrollable*(visibleHeight-thumbH):0;thumb.style.height=`${thumbH}px`;thumb.style.top=`${baseTop+offsetY}px`;thumb.style.right=`${baseRight+offset}px`;showAll();}// Drag behavior\n;const onMouseDown=e=>{isDragging=true;dragStartY=e.clientY;startScrollTop=isPage?window.scrollY:target.scrollTop;thumb.style.backgroundColor=dragColor;thumb.style.cursor=\"grabbing\";document.addEventListener(\"mousemove\",onMouseMove);document.addEventListener(\"mouseup\",onMouseUp);};const onMouseMove=e=>{if(!isDragging)return;const deltaY=e.clientY-dragStartY;const scrollDelta=deltaY/(visibleHeight-thumbH)*scrollable;if(isPage){window.scrollTo(0,startScrollTop+scrollDelta);}else{target.scrollTop=startScrollTop+scrollDelta;}};const onMouseUp=()=>{isDragging=false;thumb.style.backgroundColor=thumbColor;thumb.style.cursor=\"grab\";document.removeEventListener(\"mousemove\",onMouseMove);document.removeEventListener(\"mouseup\",onMouseUp);showAll();};thumb.addEventListener(\"mousedown\",onMouseDown);window.addEventListener(\"scroll\",updateAll,{passive:true});window.addEventListener(\"resize\",updateAll);if(!isPage){target.addEventListener(\"scroll\",updateAll,{passive:true});}updateAll();return()=>{thumb.removeEventListener(\"mousedown\",onMouseDown);window.removeEventListener(\"scroll\",updateAll);window.removeEventListener(\"resize\",updateAll);if(!isPage){target.removeEventListener(\"scroll\",updateAll);target.classList.remove(csClass);}else{document.documentElement.classList.remove(csClass);document.body.classList.remove(csClass);}track.remove();thumb.remove();styleTag.remove();};},[containerRef,appearance,behavior]);return /*#__PURE__*/_jsx(\"div\",{ref:wrapperRef,style:{width:\"100%\",height:\"100%\",position:\"relative\"},children:/*#__PURE__*/_jsx(Frame,{width:\"100%\",height:\"100%\",background:\"transparent\"})});}ScrollbarPro.defaultProps={appearance:{thumbColor:\"#A6FF00\",thumbWidth:4,cornerRadius:2,showTrack:false,trackColor:\"rgba(166, 255, 0, 0.04)\",dragColor:\"#FF0080\",offset:0},behavior:{autoHide:true,hideDelay:.5}};addPropertyControls(ScrollbarPro,{appearance:{type:ControlType.Object,title:\"Style\",controls:{thumbColor:{type:ControlType.Color,title:\"Color\"},dragColor:{type:ControlType.Color,title:\"Drag Color\",description:\"Thumb color while dragging\"},thumbWidth:{type:ControlType.Number,title:\"Width\",min:1,max:30,step:1},cornerRadius:{type:ControlType.Number,title:\"Radius\",min:0,max:50,step:1},showTrack:{type:ControlType.Boolean,title:\"Show Track\",description:\"Toggles the background track visibility behind the scrollbar thumb\"},trackColor:{type:ControlType.Color,title:\"Track Color\",hidden:({appearance})=>!appearance.showTrack},offset:{type:ControlType.Number,title:\"Offset\",min:0,max:200,step:1,description:\"Distance from right edge in pixels\"}}},behavior:{type:ControlType.Object,title:\"Behavior\",controls:{autoHide:{type:ControlType.Boolean,title:\"Autohide\"},hideDelay:{type:ControlType.Number,title:\"Delay (s)\",min:0,max:10,step:.5,hidden:({behavior})=>!behavior.autoHide}}}});\nexport const __FramerMetadata__ = {\"exports\":{\"default\":{\"type\":\"reactComponent\",\"name\":\"ScrollbarPro\",\"slots\":[],\"annotations\":{\"framerSupportedLayoutWidth\":\"100%\",\"framerContractVersion\":\"1\",\"framerSupportedLayoutHeight\":\"100%\",\"framerDisableUnlink\":\"*\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./ScrollbarPro.map","import{jsx as _jsx}from\"react/jsx-runtime\";import{addPropertyControls,ControlType}from\"framer\";/**\n * SEO\n * @framerDisableUnlink\n */export default function SEOPro(props){//<div hidden aria-hidden=\"true\">\nreturn /*#__PURE__*/_jsx(\"div\",{...props.Divhidden?{hidden:true}:{},\"aria-hidden\":props.Divhidden,dangerouslySetInnerHTML:{__html:props.value}});}addPropertyControls(SEOPro,{Divhidden:{type:ControlType.Boolean,defaultValue:true},value:{type:ControlType.String,title:\"htmlelement\",displayTextArea:true}});\nexport const __FramerMetadata__ = {\"exports\":{\"default\":{\"type\":\"reactComponent\",\"name\":\"SEOPro\",\"slots\":[],\"annotations\":{\"framerDisableUnlink\":\"\",\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./SEOPro.map","import{jsx as _jsx,jsxs as _jsxs,Fragment as _Fragment}from\"react/jsx-runtime\";import{useEffect,useState}from\"react\";import{addPropertyControls,ControlType,// @ts-ignore\ngetPropertyControls}from\"framer\";import{rootstyles,changetheme}from\"https://framerusercontent.com/modules/niZ6YpK06zTaQeX49VIS/kyUI2MH03eq9506I17so/Theme.js\";import{updateGlobalFont}from\"https://framerusercontent.com/modules/20zhayqm9y3Y0ekkXAdJ/sr1Sj9PIAlRd9EdJVnUb/Style.js\";import ButtonX from\"https://framerusercontent.com/modules/4qsgEiJMwmhCjkYCC8G4/Xf46vW5mM4HlyGcPkkRu/ButtonX.js\";const btnProps=getPropertyControls(ButtonX);import{createStore}from\"https://framer.com/m/framer/store.js\";let sharedParams=\"\";export function loadParams(){const isClient=typeof window!==\"undefined\";if(isClient){const params=new URLSearchParams(window.location.search);sharedParams=params;return String(params);}else{return\"\";}}// export function getParam(key: string) {\n//     return sharedParams\n// }\nexport const useStoretheme=createStore({// Set the starting theme below, otherwise default to system.\ntheme:\"system\"});const changeThemeMode=theme=>{const htmlElement=document.getElementsByTagName(\"html\")[0];const bodyElement=document.getElementsByTagName(\"body\")[0];htmlElement.setAttribute(\"toggle-theme\",`${theme}`);bodyElement.setAttribute(\"toggle-theme\",`${theme}`);localStorage.setItem(\"theme\",`${theme}`);// Trigger event that is compatible with Framestack Theme Image Component\nconst event=new Event(\"themeChange\");window.dispatchEvent(event);return;};const FetchJSON=url=>{if(url===\"\"||url===undefined){return null;}const[jsonData,setJsonData]=useState(null)// 存储 JSON 数据\n;useEffect(()=>{// 定义异步函数来获取数据\nconst fetchData=async()=>{try{const response=await fetch(url)// 使用 fetch 请求数据\n;if(!response.ok){throw new Error(`HTTP error! status: ${response.status}`);}const data=await response.json()// 解析 JSON 内容\n;setJsonData(data)// 存储到 state\n;}catch(error){console.error(\"Error fetching the JSON:\",error);}};fetchData()// 调用函数\n;},[url])// 依赖项为 url\n;return jsonData;};/**\n * @framerDisableUnlink\n */export default function LeaderPro(props){const[primaryColor,setPrimaryColor]=useState(props.primaryColor);const[actColor,setActColor]=useState(props.actColor);const[scale,setscale]=useState(props.radiusScale);const[bgColor,setBGColor]=useState(props.bgColor);const[fontF,setFontF]=useState(props.fontF);const[SwitchOnce,setswitch]=useState(false);useEffect(()=>{if(bgColor.key===\"NickDuDesigned\"){const badge=document.getElementById(\"__framer-badge-container\");if(badge){badge.style.zIndex=\"-999\";}}const observer=new MutationObserver(()=>{const framerEditorBar=document.getElementById(\"__framer-editorbar-container\");if(framerEditorBar){framerEditorBar.remove();console.log(\"Framer editor bar removed.\");observer.disconnect()// 删除后停止监听\n;}});observer.observe(document.body,{childList:true,subtree:true});// 设置超时清理，避免无限监听\nconst timeoutId=setTimeout(()=>{observer.disconnect();},1e4)// 10秒后停止监听\n;return()=>{observer.disconnect();clearTimeout(timeoutId);};},[])// 空数组确保该副作用只在组件挂载时运行一次\n;// 添加字体配置处理\nuseEffect(()=>{if(props.fontConfigJSON&&props.fontConfigJSON.trim()!==\"\"){try{const parsedConfig=JSON.parse(props.fontConfigJSON);updateGlobalFont(parsedConfig);}catch(error){console.error(\"Invalid Font Config JSON:\",error);}}},[props.fontConfigJSON]);const themefile=FetchJSON(props.theme);if(themefile!==null){//console.log(\"here\")\nchangetheme(themefile);}const[store,setStore]=useStoretheme();useEffect(()=>{// If starting theme is other than light/dark then match device theme.\nif(store.theme!==\"dark\"&&store.theme!==\"light\"){// Detect system theme\nconst mediaQuery=window.matchMedia(\"(prefers-color-scheme: dark)\");let newTheme=mediaQuery.matches?\"dark\":\"light\";// Set store theme\nconst DLM=props.darktheme;if(DLM!==\"auto\"&&DLM!==newTheme){setswitch(true);}setStore({theme:newTheme});localStorage.setItem(\"theme\",`${newTheme}`);}else{// Set the starting theme based on store\nlocalStorage.setItem(\"theme\",`${store.theme}`);}// Create attributes on html and body so that theme will be applied based on store\nconst htmlElement=document.getElementsByTagName(\"html\")[0];const bodyElement=document.getElementsByTagName(\"body\")[0];htmlElement&&htmlElement.setAttribute(\"toggle-theme\",`${store.theme}`);bodyElement&&bodyElement.setAttribute(\"toggle-theme\",`${store.theme}`);// Create sets of light and dark mode tokens\nlet lightThemeTokens=[];let darkThemeTokens=[];for(let i=0;i<document.styleSheets.length;i++){const sheet=document.styleSheets[i];try{for(let rule of sheet.cssRules){// Get light and dark mode tokens\nif(rule.selectorText===\"body\"){const style=rule.style;for(let j=0;j<style.length;j++){const propertyName=style[j];if(propertyName.includes(\"--token\")){const value=style.getPropertyValue(propertyName);// Check for specific tokens or list all\nconst combinedCssRule=`${propertyName}: ${value};`;lightThemeTokens.push(combinedCssRule);}}lightThemeTokens=lightThemeTokens.join(\" \");}else if(rule.conditionText===\"(prefers-color-scheme: dark)\"){const cssTextIgnore=\"body:not([data-framer-theme])\";if(!rule.cssText.includes(cssTextIgnore)){const mediaRulesString=rule.cssRules[0].cssText.replace(\"body\",\"\").replace(/\\s*{\\s*/,\"\").replace(/\\s*}\\s*$/,\"\");darkThemeTokens=mediaRulesString;}}}}catch(e){//console.warn(\"Cannot access stylesheet:\", sheet.href)\n}}// Create styleSheet with id and populate with correct CSS text\nlet styleElement=document.createElement(\"style\");styleElement.id=\"toggle-theme\";const customCssRule=`body[toggle-theme=\"light\"] {${lightThemeTokens}} body[toggle-theme=\"dark\"]{${darkThemeTokens}} html[toggle-theme=\"light\"] { color-scheme: light; } html[toggle-theme=\"dark\"] { color-scheme: dark; }`;styleElement.textContent=customCssRule;document.head.appendChild(styleElement);if(props.darktheme===\"auto\"){const mediaQuery=window.matchMedia(\"(prefers-color-scheme: dark)\");const handleSystemThemeChange=e=>{const newTheme=e.matches?\"dark\":\"light\";setStore({theme:newTheme});localStorage.setItem(\"theme\",newTheme);changeThemeMode(newTheme);};mediaQuery.addEventListener(\"change\",handleSystemThemeChange);return()=>mediaQuery.removeEventListener(\"change\",handleSystemThemeChange);}// Cleanup function\nreturn()=>{// Check if the style element exists and remove it\nconst existingStyleElement=document.getElementById(\"toggle-theme\");if(existingStyleElement){document.head.removeChild(existingStyleElement);}htmlElement&&htmlElement.setAttribute(\"toggle-theme\",\"system\");bodyElement&&bodyElement.setAttribute(\"toggle-theme\",\"system\");};},[]);useEffect(()=>{// 这里通过监听让被动leader执行mode 切换\nif(props.unvisible){const handleStorageChange=event=>{if(event.key===\"theme\"&&event.newValue){// console.log(\"here\")\n// console.log(event.newValue)\nif(event.newValue!==store.theme){setStore({theme:event.newValue});changeThemeMode(event.newValue);}}};window.addEventListener(\"storage\",handleStorageChange);// 在组件卸载时移除监听器\nreturn()=>{window.removeEventListener(\"storage\",handleStorageChange);};}},[]);useEffect(()=>{if(store.theme===\"dark\"){// console.log(\"Adding dark class\")\ndocument.documentElement.classList.add(\"darkn\");document.documentElement.classList.remove(\"lightn\");}else{document.documentElement.classList.add(\"lightn\");document.documentElement.classList.remove(\"darkn\");}},[store.theme]);useEffect(()=>{if(SwitchOnce){setswitch(false);handleTap();}},[SwitchOnce]);const handleTap=()=>{let newTheme=store.theme===\"light\"?\"dark\":\"light\";setStore({theme:newTheme});changeThemeMode(newTheme);};return /*#__PURE__*/_jsxs(_Fragment,{children:[rootstyles({primarycolor:primaryColor,scale:scale,bgcolorlight:bgColor.light,bgcolordark:bgColor.dark,actcolor:actColor,fontF:fontF}),props.unvisible?null:/*#__PURE__*/_jsx(ButtonX,{...props.button,onTap:()=>handleTap(),ToggleDetail:{...props.button.ToggleDetail,On:store.theme===\"light\"?true:false}})]});}addPropertyControls(LeaderPro,{fontF:{type:ControlType.String,title:\"Font Family\",defaultValue:\"SF Pro, Helvetica, Arial, sans-serif\"},fontConfigJSON:{type:ControlType.String,title:\"Font Config (JSON)\",displayTextArea:true,defaultValue:\"\",description:\"Paste JSON to customize GlobalFont\"},primaryColor:{type:ControlType.Color,title:\"Primary Color\",defaultValue:\"#06B6D4\"},actColor:{type:ControlType.Color,title:\"Active Color\",defaultValue:\"#06B6D4\"},bgColor:{type:ControlType.Object,title:\"Background Color\",controls:{light:{type:ControlType.Color,title:\"Light Color\",defaultValue:\"#fefefe\"},dark:{type:ControlType.Color,title:\"Dark Color\",defaultValue:\"#1d1d1d\"},key:{type:ControlType.String,defaultValue:\"\"}}},radiusScale:{type:ControlType.Enum,title:\"Radius Scale\",options:[1.5,1.25,1,.75,.5,.3,0],defaultValue:1},theme:{type:ControlType.File,allowedFileTypes:[\"json\"]},darktheme:{type:ControlType.Enum,title:\"Theme\",options:[\"dark\",\"light\",\"auto\"],defaultValue:\"auto\"},button:{type:ControlType.Object,controls:{...btnProps}},unvisible:{type:ControlType.Boolean,defaultValue:false}});\nexport const __FramerMetadata__ = {\"exports\":{\"useStoretheme\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"loadParams\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"default\":{\"type\":\"reactComponent\",\"name\":\"LeaderPro\",\"slots\":[],\"annotations\":{\"framerDisableUnlink\":\"\",\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}","import{isBrowser}from\"framer-motion\";export const DEFAULT_DOMAIN=\"https://www.googletagmanager.com\";export const DEFAULT_SCRIPT_NAME=\"gtm.js\";/**\n * Function to get and set dataLayer\n * @param dataLayer - The dataLayer\n * @param dataLayerName - The dataLayer name\n */const getDataLayerSnippet=(dataLayer,dataLayerName=\"dataLayer\")=>`window['${dataLayerName}']=window['${dataLayerName}']||[];`+(dataLayer?`window['${dataLayerName}'].push(${JSON.stringify(dataLayer)});`:\"\")+`window['${dataLayerName}'].push({'gtm.start':new Date().getTime(),event:'gtm.js'})`;/**\n * Function to get the GTM script\n * @param dataLayerName - The name of the dataLayer\n * @param customDomain - Custom domain for gtm\n * @param customScriptName - Custom script file name for gtm\n * @param environment - The parameters to use a custom environment\n * @param id - The id of the container\n */const getGTMScript=(dataLayerName,id,environment,customDomain=DEFAULT_DOMAIN,customScriptName=DEFAULT_SCRIPT_NAME)=>{let params=\"\";if(environment){const{gtm_auth,gtm_preview}=environment;params=`&gtm_auth=${gtm_auth}&gtm_preview=${gtm_preview}&gtm_cookies_win=x`;}return`${customDomain}/${customScriptName}?id=${id}${dataLayerName===\"dataLayer\"?\"\":`&l=${dataLayerName}`}${params}`;};/**\n * Function to setup the Google Tag Manager\n * @param params - The snippets params\n */const setupGTM=params=>{const getDataLayerScript=()=>{const dataLayerScript=document.createElement(\"script\");if(params.nonce){dataLayerScript.setAttribute(\"nonce\",params.nonce);}dataLayerScript.innerHTML=getDataLayerSnippet(params.dataLayer,params.dataLayerName);return dataLayerScript;};const getScript=()=>{const script=document.createElement(\"script\");if(params.nonce){script.setAttribute(\"nonce\",params.nonce);}script.src=getGTMScript(params.dataLayerName,params.id,params.environment,params.customDomain,params.customScriptName);script.crossOrigin=\"anonymous\"// allows for more detailed INP collection\n;return script;};return{getDataLayerScript,getScript};};/**\n * Function to init the GTM\n * @param dataLayer - The dataLayer\n * @param dataLayerName - The dataLayer name\n * @param environment - Specify the custom environment to use\n * @param nonce - Server-generated nonce\n * @param id - The ID of the GTM\n */export const initGTM=({dataLayer,dataLayerName,environment,nonce,id,customDomain,customScriptName})=>{const gtm=setupGTM({dataLayer,dataLayerName,environment,nonce,id,customDomain,customScriptName});const dataLayerScript=gtm.getDataLayerScript();const script=gtm.getScript();document.head.append(dataLayerScript,script);};export function sendToGTM(){if(isBrowser){window[\"dataLayer\"]=window[\"dataLayer\"]||[];window[\"dataLayer\"].push(arguments);}}\nexport const __FramerMetadata__ = {\"exports\":{\"DEFAULT_DOMAIN\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"DEFAULT_SCRIPT_NAME\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"initGTM\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"sendToGTM\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./send.map","import{isBrowser}from\"framer-motion\";import{useLayoutEffect}from\"react\";export const DEFAULT_FONT_FAMILY=`\"Inter\", system-ui, -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"`;export function getFlexboxValues(position){const positionParts=position.split(\"-\");let justifyContent,alignItems;switch(positionParts[0]){case\"top\":alignItems=\"flex-start\";break;case\"bottom\":alignItems=\"flex-end\";break;case\"center\":alignItems=\"center\";break;default:alignItems=\"initial\";break;}switch(positionParts[1]){case\"left\":justifyContent=\"flex-start\";break;case\"right\":justifyContent=\"flex-end\";break;case\"center\":justifyContent=\"center\";break;default:justifyContent=\"initial\";break;}return{justifyContent,alignItems};}export function getMultipleShadows(...shadows){const output=[];shadows.forEach(shadow=>{return shadow&&output.push(shadow);});return output.join(\", \");}export function getShadow(shadow){if(shadow){return`${shadow.shadowX}px ${shadow.shadowY}px ${shadow.shadowBlur}px ${shadow.shadowColor}`;}else return null;}export function safeJSONParse(jsonString,onError){try{return JSON.parse(jsonString);}catch{if(onError)onError();}}export const getCookie=(name,cookies)=>{cookies=cookies?cookies:isBrowser?document.cookie:\"\";var _cookies_match;const[,,cookie]=(_cookies_match=cookies.match(`(^|;) ?${name}=([^;]*)(;|$)`))!==null&&_cookies_match!==void 0?_cookies_match:[null,null,null];return cookie;};/**\n * Yields to main thread before continuing execution, which might allow the browser to paint.\n * If `options.priority` is 'user-blocking', it will asynchronously resolve in older browsers.\n * @param {object} options - see https://github.com/WICG/scheduling-apis/blob/main/explainers/yield-and-continuation.md\n * @see interactionResponse for guaranteeing execution after a paint\n */export function yieldToMain(options){if(\"scheduler\"in window){if(\"yield\"in scheduler)return scheduler.yield(options);if(\"postTask\"in scheduler)return scheduler.postTask(()=>{},options);}if((options===null||options===void 0?void 0:options.priority)===\"user-blocking\"){// `setTimeout` could suffer from being delayed for longer: https://developer.chrome.com/blog/introducing-scheduler-yield-origin-trial#the_problem_with_current_yielding_strategies\n// so for browsers not supporting yield, we guarantee execution for high priority actions, but this does not create space for a paint opportunity as trade-off.\nreturn Promise.resolve();}return new Promise(resolve=>{setTimeout(resolve);});}/**\n * Helper function for `yieldToMain`, which yields before calling `fn`.\n * @see yieldToMain\n */export async function yieldBeforeCb(fn,options){await yieldToMain(options);return fn();}/**\n * Similar to `yieldToMain`, but also waits for the next animation frame before yielding (with a fallback of 100ms if the animation frame never fires).\n * Compared to `yieldToMain`, it guarantees improved INP, but might make processing a little slower. Use only if necessary.\n * @see yieldToMain\n */export function interactionResponse(options){return new Promise(resolve=>{setTimeout(resolve,200)// Fallback for the case where the animation frame never fires.\n;requestAnimationFrame(()=>{void yieldBeforeCb(resolve,options);});});}/**\n * Runs `fn` after the next paint. Similar to `useEffect`, but *guarantees* that the function is run after the next paint.\n * @important Does not support a cleanup fn.\n * @see https://thoughtspile.github.io/2021/11/15/unintentional-layout-effect/\n */export function useAfterPaintEffect(fn,deps,options){useLayoutEffect(()=>{const runAfterPaint=async()=>{await interactionResponse(options);fn();};void runAfterPaint();// eslint-disable-next-line react-hooks/exhaustive-deps -- deps are passed in\n},deps);}\nexport const __FramerMetadata__ = {\"exports\":{\"useAfterPaintEffect\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"getCookie\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"safeJSONParse\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"getFlexboxValues\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"yieldBeforeCb\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"getShadow\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"getMultipleShadows\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"FlexboxPosition\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"yieldToMain\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"interactionResponse\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"DEFAULT_FONT_FAMILY\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./utils.map","import{useIsOnFramerCanvas}from\"framer\";import{isBrowser}from\"framer-motion\";import{useEffect,useReducer,useState,startTransition}from\"react\";import{initGTM,sendToGTM}from\"https://framerusercontent.com/modules/RFM6zI5MxOiqwwNiQep4/2CcJhEanvh9oX5JHRkd8/send.js\";import{safeJSONParse,yieldBeforeCb}from\"https://framerusercontent.com/modules/tfq8EDfrazNLXzc9LJte/hLcdFz4t8UjInDMvzq41/utils.js\";function toGTMConsent(consent){return{functionality_storage:consent.necessary?\"granted\":\"denied\",security_storage:consent.necessary?\"granted\":\"denied\",ad_storage:consent.marketing?\"granted\":\"denied\",ad_user_data:consent.marketing?\"granted\":\"denied\",ad_personalization:consent.marketing?\"granted\":\"denied\",analytics_storage:consent.analytics?\"granted\":\"denied\",personalization_storage:consent.preferences?\"granted\":\"denied\"};}function fromGTMConsent(consent){return{necessary:consent.functionality_storage===\"granted\"&&consent.security_storage===\"granted\",marketing:consent.ad_storage===\"granted\"&&consent.ad_user_data===\"granted\"&&consent.ad_personalization===\"granted\",analytics:consent.analytics_storage===\"granted\",preferences:consent.personalization_storage===\"granted\"};}function reducer(state,action){switch(action.type){case\"autoAccept\":return{...state,sync:true,autoAccepted:true,modes:{analytics:true,marketing:true,necessary:true,preferences:true}};case\"acceptAll\":return{...state,sync:true,dismissed:true,modes:{analytics:true,marketing:true,necessary:true,preferences:true}};case\"rejectAll\":return{...state,sync:true,dismissed:true,modes:{analytics:false,marketing:false,necessary:false,preferences:false}};case\"acceptCurrent\":return{...state,dismissed:true,sync:true};case\"update\":return{...state,modes:{...state.modes,...action.modes},sync:action.sync};case\"toggle\":return{...state,modes:{...state.modes,[action.mode]:!state.modes[action.mode]}};case\"initFromLocalStorage\":return{...state,modes:action.modes,dismissed:action.dismissed,autoAccepted:action.autoAccepted,initializedFromLocalStorage:true,sync:true};case\"initFromGTM\":return{...state,modes:action.modes,initializedFromGTM:true};case\"dismiss\":return{...state,dismissed:true};case\"synced\":return{...state,sync:false,hasSynced:state.hasSynced||action.success};default:return state;}}const initialState={dismissed:false,autoAccepted:false,modes:null,sync:false,initializedFromLocalStorage:false,initializedFromGTM:false,hasSynced:false};export const defaultConsent={necessary:false,analytics:false,marketing:false,preferences:false};// Keep track of if GTM has been loaded as a script and default consent has been set,\n// to ensure the script does not keep appending between page switches.\nlet hasInitializedGTM=false;export function useConsent({gtmId,defaultConsent,gtmLoadedExternally=false,defaultsFromGTM=false}){const[state,dispatch]=useReducer(reducer,initialState);const isOnFramerCanvas=useIsOnFramerCanvas();const consentModeLocalStorageKey=\"framerCookiesConsentMode\";const dismissedLocalStorageKey=\"framerCookiesDismissed\";const autoAcceptedLocalStorageKey=\"framerCookiesAutoAccepted\";function getStateFromLocalStorage(){const consentFromLocalStorage=localStorage.getItem(consentModeLocalStorageKey);const dismissedFromLocalStorage=localStorage.getItem(dismissedLocalStorageKey);const autoAcceptedFromLocalStorage=localStorage.getItem(autoAcceptedLocalStorageKey);const isDismissed=dismissedFromLocalStorage!==null;const isAutoAccepted=autoAcceptedFromLocalStorage!==null;const hasConsentInLocalStorage=consentFromLocalStorage!==null;const consentInLocalStorageIsNotDefault=isDismissed||isAutoAccepted;const shouldLoadConsentFromLocalStorage=hasConsentInLocalStorage&&consentInLocalStorageIsNotDefault;dispatch({type:\"initFromLocalStorage\",dismissed:isDismissed,autoAccepted:isAutoAccepted,modes:shouldLoadConsentFromLocalStorage?safeJSONParse(consentFromLocalStorage,()=>localStorage.removeItem(consentModeLocalStorageKey)):defaultConsent});}function syncToGTM(){if(gtmId){if(!hasInitializedGTM&&!gtmLoadedExternally&&!defaultsFromGTM){// This is the first time we sync consent, so we save it as \"default\" and initialize tag manager.\n// This order is important, because we need to have set the default consent BEFORE we initialize\n// GTM. See https://developers.google.com/tag-platform/devguides/consent?tab=tag-manager&sjid=11348191096952324675-EU#implementation_example\n// It might seem weird that we're \"sending\" before initializing, but \"sending\" here means building up\n// the \"dataLayer\" object that GTM picks up when it initializes.\nsendToGTM(\"consent\",\"default\",toGTMConsent(state.modes));initGTM({dataLayer:undefined,dataLayerName:\"dataLayer\",environment:undefined,nonce:undefined,injectScript:true,id:gtmId});hasInitializedGTM=true;}else{hasInitializedGTM=true;sendToGTM(\"consent\",\"update\",toGTMConsent(state.modes));// must be sent like this or else GTM doesn't act on it(?)\n// Publically documented: https://www.framer.com/academy/lessons/cookie-banner-component#:~:text=Run%20a%20Tag%20After%20The%20Consent%20Update\nwindow.dataLayer.push({event:\"cookie_consent_update\"});}}}useEffect(()=>{if(isOnFramerCanvas)return;if(!defaultsFromGTM)return;if(defaultConsent===null)return;if(state.initializedFromGTM)return;if(state.modes!==null)return;// State was already set (from localStorage)\nyieldBeforeCb(()=>{dispatch({type:\"initFromGTM\",modes:defaultConsent});},{priority:\"user-blocking\"});},[defaultsFromGTM,defaultConsent,state.initializedFromGTM,state.modes]);useEffect(()=>{if(isOnFramerCanvas)return;yieldBeforeCb(()=>getStateFromLocalStorage(),{priority:\"user-blocking\"});},[]);// Anytime the dismissed value is updated, we need to persist it in local storage.\nuseEffect(()=>{if(isOnFramerCanvas)return;if(state.dismissed){localStorage.setItem(dismissedLocalStorageKey,\"true\");}},[state.dismissed]);// Anytime consent is auto accepted, we need to persist it in local storage.\nuseEffect(()=>{if(isOnFramerCanvas)return;if(state.autoAccepted){localStorage.setItem(autoAcceptedLocalStorageKey,\"true\");}},[state.autoAccepted]);// Sync data to dataLayer and localStorage.\nuseEffect(()=>{if(isOnFramerCanvas)return;if(!state.sync)return;const shouldSync=isBrowser&&state.modes!==null;yieldBeforeCb(()=>{if(!shouldSync){dispatch({type:\"synced\",success:false});return;}// else\nsyncToGTM();// Save locally\nlocalStorage.setItem(consentModeLocalStorageKey,JSON.stringify(state.modes));dispatch({type:\"synced\",success:true});},{priority:\"user-blocking\"});},[state.sync,state.modes]);function dismiss(){dispatch({type:\"dismiss\"});localStorage.setItem(dismissedLocalStorageKey,\"true\");}function autoAccept(){dispatch({type:\"autoAccept\"});}function acceptAll(){dispatch({type:\"acceptAll\"});}function rejectAll(){dispatch({type:\"rejectAll\"});}function acceptCurrent(){dispatch({type:\"acceptCurrent\"});}function toggleMode(mode){dispatch({type:\"toggle\",mode});}return{modes:state.modes,isInitialized:state.hasSynced||state.initializedFromGTM,isDismissed:state.dismissed,isAutoAccepted:state.autoAccepted,dismiss,autoAccept,acceptAll,rejectAll,acceptCurrent,toggleMode};}////////////////////////////////////////////////////////////////////////////////\nconst PROMISE_NAME=\"__framer_cookieBannerDefaultConsentPromise\";// NOTE: Error can be ignored, because of `isBrowser`\nconst gtmSetsDefaultConsent=isBrowser&&!!window[PROMISE_NAME];export function useGTMDefaultConsent(){const[consent,setConsent]=useState(null);useEffect(()=>{if(!gtmSetsDefaultConsent)return;window[PROMISE_NAME].then(result=>{startTransition(()=>{setConsent(result);});});},[]);// Simplified region to use for the cookie banner\nlet region;if(consent){region=Object.values(consent).every(v=>v===\"granted\")?\"World\":\"EU\";}return{available:gtmSetsDefaultConsent,consent:consent?fromGTMConsent(consent):null,region};}\nexport const __FramerMetadata__ = {\"exports\":{\"ConsentModeName\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"useGTMDefaultConsent\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"ConsentModes\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"useConsent\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"defaultConsent\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./consent.map","// Check if visitor is in EU\nconst countries=[\"BE\",\"EL\",\"LT\",\"PT\",\"BG\",\"ES\",\"LU\",\"RO\",\"CZ\",\"FR\",\"RE\",\"GP\",\"MQ\",\"GF\",\"YT\",\"BL\",\"MF\",\"PM\",\"WF\",\"PF\",\"NC\",\"HU\",\"SI\",\"DK\",\"FO\",\"GL\",\"HR\",\"MT\",\"SK\",\"DE\",\"IT\",\"NL\",\"AW\",\"CW\",\"SX\",\"FI\",\"AX\",\"EE\",\"CY\",\"AT\",\"SE\",\"IE\",\"LV\",\"PL\",\"UK\",\"GB\",\"AI\",\"BM\",\"IO\",\"VG\",\"KY\",\"FK\",\"GI\",\"MS\",\"PN\",\"SH\",\"TC\",\"GG\",\"JE\",\"IM\"];const isInEUTimezone=()=>{return Intl?.DateTimeFormat()?.resolvedOptions()?.timeZone?.startsWith(\"Europe\");};const isEULocale=()=>{const locale=navigator.language??navigator.languages?.[0];return countries.some(country=>locale?.toUpperCase()?.includes(country));};let _inEU=null;export const inEU=()=>{if(_inEU===null)_inEU=isInEUTimezone()||isEULocale();return _inEU;};\nexport const __FramerMetadata__ = {\"exports\":{\"inEU\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./inEU.map","import{isBrowser}from\"framer-motion\";import{inEU}from\"https://framerusercontent.com/modules/HKzIAGtbudIGLRAteuFH/Nhaar5CYKIbxxEaVvTJs/inEU.js\";export function useRegion({content,useRegionFromProps,skipEUCheck,regionFromGTM}){const isInEUBasedOnLocation=isBrowser&&!skipEUCheck&&!regionFromGTM?inEU():false;const regionBasedOnLocation=isInEUBasedOnLocation?\"EU\":\"World\";const regionFromProps=content.isEU?\"EU\":\"World\";const regionContent={EU:{title:content.euTitle,description:content.euDescription,type:content.euType,defaults:content.euDefaults,policy:content.euPolicy,blocking:content.euBlocking,showReject:content.euType===\"advanced\"?content.euShowReject:true},World:{title:content.worldTitle,description:content.worldDescription,type:content.worldType,defaults:content.worldDefaults,policy:content.worldPolicy,blocking:content.worldBlocking,showReject:content.worldType===\"advanced\"?content.worldShowReject:true}};return regionContent[regionFromGTM?regionFromGTM:useRegionFromProps?regionFromProps:regionBasedOnLocation];}\nexport const __FramerMetadata__ = {\"exports\":{\"RegionContent\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"RegionType\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"useRegion\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./region.map","import{jsx as _jsx,jsxs as _jsxs,Fragment as _Fragment}from\"react/jsx-runtime\";import{useState,lazy,Suspense}from\"react\";import{withCSS}from\"framer\";import{AnimatePresence,motion}from\"framer-motion\";import{DEFAULT_FONT_FAMILY,getMultipleShadows,getShadow}from\"https://framerusercontent.com/modules/tfq8EDfrazNLXzc9LJte/hLcdFz4t8UjInDMvzq41/utils.js\";const SPACING=20;// if you update the Toggle component inside this project, copy the new URL to here:\nconst Toggle=/*#__PURE__*/lazy(()=>import(\"https://framerusercontent.com/modules/QDCSmi7dYWSK5AcRSFyX/sBWK1WbcsjrFOSoIzxuJ/Blf0sjosZ.js\"));export const Banner=withCSS(function Banner({banner,button,region,options,previewOptions,consentModes,onDismiss,onAcceptAll,onRejectAll,onAcceptCurrent,onToggleConsent,animateOnMount}){const maxHeightReduction=banner.insetPerSide?banner.insetTop+banner.insetBottom:banner.inset*2;const linkColor=banner.style.link||button.primary.fill;const paddingValue=banner.paddingPerSide?`${banner.paddingTop}px ${banner.paddingRight}px ${banner.paddingBottom}px ${banner.paddingLeft}px`:`${banner.padding}px`;const bannerShadow=getShadow(banner.style.shadow);const borderShadow=banner.style.border?.width?`inset 0 0 0 ${banner.style.border.width}px ${banner.style.border.color}`:null;const bannerStyle={background:banner.style.fill,boxShadow:getMultipleShadows(bannerShadow,borderShadow),overflow:\"hidden\",borderRadius:banner.style.border.radius};return /*#__PURE__*/_jsx(motion.div,{initial:animateOnMount&&{x:banner.animation.x,y:banner.animation.y,scale:banner.animation.scale,opacity:0},animate:{y:0,x:0,scale:1,opacity:1},exit:{x:banner.animation.x,y:banner.animation.y,scale:banner.animation.scale,opacity:0},transition:animateOnMount?banner.animation.transition:{duration:0},style:{fontFamily:DEFAULT_FONT_FAMILY,maxHeight:`calc(100vh - ${maxHeightReduction}px)`,flexDirection:\"column\",gap:12,position:\"relative\",display:\"flex\",zIndex:100,pointerEvents:\"auto\"},children:/*#__PURE__*/_jsx(\"div\",{style:{...bannerStyle,overflow:\"scroll\",width:\"100%\",maxWidth:banner.width},className:`--framer-cookie-banner-container --framer-cookie-banner-type-${region.type}`,children:region.type===\"simple\"?/*#__PURE__*/_jsx(SimpleBanner,{banner:banner,button:button,linkColor:linkColor,description:region.description,policy:region.policy,onDismiss:onDismiss}):region.type===\"medium\"?/*#__PURE__*/_jsx(AcceptRejectBanner,{banner:banner,button:button,linkColor:linkColor,title:region.title,description:region.description,policy:region.policy,onAccept:onAcceptAll,onReject:onRejectAll}):/*#__PURE__*/_jsx(OptionsBanner,{banner:banner,button:button,options:options,previewOptions:previewOptions,linkColor:linkColor,title:region.title,description:region.description,showReject:region.showReject,policy:region.policy,onOptionToggle:onToggleConsent,consent:consentModes,onAcceptAll:onAcceptAll,onRejectAll:onRejectAll,onAcceptCurrent:onAcceptCurrent})})});},[`.--framer-cookie-banner-container::-webkit-scrollbar { display: none; }`,`.--framer-cookie-banner-container { \n            -ms-overflow-style: none; \n            scrollbar-width: none;  \n        }`],\"framer-lib-cookie-banner\");function SimpleBanner({banner,button,description,policy,onDismiss,linkColor}){const padding=banner.paddingPerSide?`${banner.paddingTop}px ${banner.paddingRight}px ${banner.paddingBottom}px ${banner.paddingLeft}px`:`${banner.padding}px`;return /*#__PURE__*/_jsxs(\"div\",{style:{display:\"flex\",flexDirection:\"row\",padding,gap:SPACING},children:[/*#__PURE__*/_jsx(Description,{style:{...banner.style.fontBody,flex:1,alignItems:\"center\",color:banner.style.colorBody},linkColor:linkColor,description:description,policy:policy}),/*#__PURE__*/_jsx(motion.div,{style:{display:\"flex\",justifyContent:\"center\",alignItems:\"center\"},children:/*#__PURE__*/_jsx(Button,{onClick:onDismiss,settings:{...button,fluid:false},id:\"dismiss\",children:button.labels.confirm})})]});}function AcceptRejectBanner({banner,button,title,linkColor,description,policy,onAccept,onReject}){const padding=banner.paddingPerSide?`${banner.paddingTop}px ${banner.paddingRight}px ${banner.paddingBottom}px ${banner.paddingLeft}px`:`${banner.padding}px`;return /*#__PURE__*/_jsxs(\"div\",{style:{padding},children:[/*#__PURE__*/_jsxs(\"div\",{children:[title&&/*#__PURE__*/_jsx(Headline,{style:{...banner.style.fontTitle,color:banner.style.colorTitle},children:title}),/*#__PURE__*/_jsx(Description,{style:{...banner.style.fontBody,color:banner.style.colorBody},linkColor:linkColor,description:description,policy:policy})]}),/*#__PURE__*/_jsxs(Buttons,{direction:button.direction,children:[/*#__PURE__*/_jsx(Button,{settings:button,onClick:onReject,id:\"reject\",children:button.labels.reject}),/*#__PURE__*/_jsx(Button,{settings:button,primary:true,onClick:onAccept,id:\"accept\",children:button.labels.accept})]})]});}function OptionsBanner({banner,button,options,previewOptions,title,description,policy,showReject=true,linkColor,consent,onAcceptCurrent,onAcceptAll,onRejectAll,onOptionToggle}){const[showOptions,setShowOptions]=useState(false);const optionTheme={...options.style,color:banner.style.colorBody};const padding=banner.paddingPerSide?`${banner.paddingTop}px ${banner.paddingRight}px ${banner.paddingBottom}px ${banner.paddingLeft}px`:`${banner.padding}px`;// const optionNames = consent && Object.keys(consent)\nconst optionNames=[\"necessary\",\"preferences\",\"analytics\",\"marketing\"];const shouldShowOptions=showOptions||previewOptions;return /*#__PURE__*/_jsxs(\"div\",{style:{padding},children:[/*#__PURE__*/_jsxs(\"div\",{children:[title&&/*#__PURE__*/_jsx(Headline,{style:{...banner.style.fontTitle,color:banner.style.colorTitle},children:title}),/*#__PURE__*/_jsx(Description,{style:{...banner.style.fontBody,color:banner.style.colorBody},linkColor:linkColor,description:description,policy:policy}),/*#__PURE__*/_jsx(AnimatePresence,{children:shouldShowOptions&&/*#__PURE__*/_jsx(motion.div,{initial:previewOptions?null:{opacity:0,height:0},animate:{opacity:1,height:\"auto\"},exit:{opacity:0,height:0},style:{display:\"flex\",flexDirection:\"column\",gap:10,marginTop:SPACING,overflow:\"hidden\"},children:optionNames&&optionNames.map(option=>/*#__PURE__*/_jsx(Option,{title:options[option].title,description:options[option].description,titleColor:banner.style.colorTitle,descriptionColor:banner.style.colorBody,showDescription:options.descriptions,enabled:consent[option],onClick:()=>onOptionToggle(option),theme:optionTheme,optional:option===\"necessary\"?options[option].optional:true}))},\"modal\")})]}),/*#__PURE__*/_jsx(Buttons,{direction:button.direction,children:shouldShowOptions?/*#__PURE__*/_jsx(Button,{settings:button,primary:true,onClick:onAcceptCurrent,id:\"accept\",children:button.labels.save}):/*#__PURE__*/_jsxs(_Fragment,{children:[showReject&&/*#__PURE__*/_jsx(Button,{settings:button,onClick:onRejectAll,id:\"reject\",children:button.labels.rejectAll}),/*#__PURE__*/_jsx(Button,{settings:button,onClick:()=>{setShowOptions(true);},id:\"customize\",children:button.labels.customize}),/*#__PURE__*/_jsx(Button,{settings:button,primary:true,onClick:onAcceptAll,id:\"accept\",children:button.labels.acceptAll})]})})]});}function Option({title,titleColor,description,descriptionColor,showDescription,enabled,optional,onClick,theme}){const paddingValue=theme.paddingPerSide?`${theme.paddingTop}px ${theme.paddingRight}px ${theme.paddingBottom}px ${theme.paddingLeft}px`:`${theme.padding}px`;const borderShadow=theme.border?`inset 0 0 0 ${theme.border.width}px ${theme.border.color}`:null;return /*#__PURE__*/_jsxs(motion.div,{style:{boxShadow:borderShadow,background:theme.background,borderRadius:theme.border.radius,padding:paddingValue,cursor:\"pointer\",userSelect:\"none\",pointerEvents:\"all\"},onClick:onClick,whileHover:{opacity:.5},children:[/*#__PURE__*/_jsxs(\"div\",{style:{display:\"flex\",justifyContent:\"space-between\"},children:[/*#__PURE__*/_jsx(\"p\",{style:{margin:0,fontWeight:600,fontSize:12,color:titleColor,...theme.fontTitle},children:title}),optional?/*#__PURE__*/_jsx(Suspense,{children:/*#__PURE__*/_jsx(Toggle,{variant:enabled?\"On\":\"Off\",background:theme.toggleColor,backgroundInactive:theme.toggleColorInactive,ariaLabel:`Toggle for ${title}`})}):/*#__PURE__*/_jsx(\"p\",{style:{margin:0,fontSize:12,color:theme.toggleColor,...theme.fontTitle,fontWeight:400},children:\"Always active\"})]}),description&&/*#__PURE__*/_jsx(\"p\",{style:{margin:0,marginTop:10,fontSize:12,lineHeight:1.5,color:descriptionColor,...theme.fontBody},children:description})]});}function Headline({children,style}){return /*#__PURE__*/_jsx(\"p\",{style:{fontSize:14,margin:\"0px 0px 10px 0px\",fontWeight:\"bold\",padding:0,...style},children:children});}function Description({style,description,policy,linkColor}){const shouldShow=description||policy?.link;if(!shouldShow)return null;if(!description){return /*#__PURE__*/_jsx(DescriptionParagraph,{policy:policy,style:style,linkColor:linkColor});}// @ivan: Support rendering the description in multiple paragraphs.\n// This is a site speed optimization: it allows to split a single\n// huge <p> into several smaller ones, which prevents the huge <p>\n// from becoming an LCP element.\nconst descriptionParagraphs=description?.split(\"\\n\\n\");return description.split(\"\\n\\n\").map((line,index)=>/*#__PURE__*/_jsx(DescriptionParagraph,{line:line,// Only render the policy in the last paragraph\n    policy:index===descriptionParagraphs.length-1?policy:null,style:{// Add a spacing between paragraphs\n    marginTop:index>0?4:0,...style},linkColor:linkColor},index));}function DescriptionParagraph({line,policy,style,linkColor}){return /*#__PURE__*/_jsxs(\"p\",{style:{lineHeight:1.5,margin:0,padding:0,fontSize:14,...style},children:[line,line&&policy?.link?\" \":null,policy?.link&&/*#__PURE__*/_jsxs(\"span\",{children:[policy?.prefix,\" \",/*#__PURE__*/_jsx(\"a\",{href:policy?.link,target:\"_blank\",style:{color:linkColor,textDecoration:\"none\"},children:policy?.label}),\".\"]})]});}function Buttons({children,direction}){return /*#__PURE__*/_jsx(\"div\",{style:{display:\"flex\",flexDirection:direction,gap:10,marginTop:16},children:children});}const Button=withCSS(function Button({id,children,primary,settings,onClick}){const paddingValue=settings.paddingPerSide?`${settings.paddingTop}px ${settings.paddingRight}px ${settings.paddingBottom}px ${settings.paddingLeft}px`:`${settings.padding}px`;const theme=primary?settings.primary:settings.secondary;return /*#__PURE__*/_jsx(motion.input,{className:\"__framer-cookie-component-button\",id:`__framer-cookie-component-button-${id}`,onClick:onClick,type:\"button\",value:`${children}`,whileHover:{opacity:settings.hoverOpacity??.6},whileTap:{opacity:settings.tapOpacity??.4},style:{WebkitAppearance:\"none\",appearance:\"none\",width:settings.fluid?\"100%\":\"auto\",height:\"auto\",border:\"none\",padding:paddingValue,borderRadius:settings.borderRadius,boxShadow:getShadow(theme.shadow),background:theme.fill,color:theme.color,fontSize:14,lineHeight:1,cursor:\"pointer\",fontWeight:settings.font?\"unset\":600,...settings.font}});},\".__framer-cookie-component-button:focus:not(:focus-visible){outline-color:transparent}\");\nexport const __FramerMetadata__ = {\"exports\":{\"Banner\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"BannerComponentProps\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./Banner.map","import{jsx as _jsx,jsxs as _jsxs,Fragment as _Fragment}from\"react/jsx-runtime\";import{addPropertyControls,ControlType,RenderTarget,useIsOnFramerCanvas,withCSS}from\"framer\";import{AnimatePresence,isBrowser,motion}from\"framer-motion\";import{lazy,Suspense,useEffect,useState}from\"react\";import{createPortal}from\"react-dom\";import{defaultConsent,useConsent,useGTMDefaultConsent}from\"https://framerusercontent.com/modules/zvf2WTzc98u6EX2y7YDj/dsQTNWNk09IMnZEHMfzP/consent.js\";import{useRegion}from\"https://framerusercontent.com/modules/0oeZpJVursioGAbwgB9e/atthcI0SmGvO4HAxG9e5/region.js\";import{Banner}from\"https://framerusercontent.com/modules/EkwkxVF9vkTs720qqBC8/L9fJK0tucMOkjZFRszrm/Banner.js\";import{inEU}from\"https://framerusercontent.com/modules/HKzIAGtbudIGLRAteuFH/Nhaar5CYKIbxxEaVvTJs/inEU.js\";import{DEFAULT_FONT_FAMILY,getFlexboxValues,interactionResponse,yieldBeforeCb}from\"https://framerusercontent.com/modules/tfq8EDfrazNLXzc9LJte/hLcdFz4t8UjInDMvzq41/utils.js\";// Keep track of open state between page reloads\nlet initiallyOpen=false;/**\n * COOKIE BANNER\n * By Floris Verloop\n *\n * @framerSupportedLayoutWidth auto\n * @framerSupportedLayoutHeight auto\n *\n * @framerDisableUnlink\n *\n */export default function CookieBanner({gtmId,preview,trigger,banner,button,content,options,style,gtmLoadedExternally,onShown,onConsentChange,onAccept,onDismiss,onReject,onSavePreferences}){const isOnFramerCanvas=useIsOnFramerCanvas();const isPreview=preview&&isOnFramerCanvas;const skipLogic=!preview&&isOnFramerCanvas;const isInEU=isBrowser?inEU():false;// we ignore customer's GTM in preview because their specific setups can break the preview currently\n// this can be removed if we remove reliance on globals for react symbols in the preview\n// https://framer-team.slack.com/archives/C01J38GC2SW/p1770826268498759\nconst skipGTMId=isInFramerPreview();const gtmDefaults=useGTMDefaultConsent();const region=useRegion({content,useRegionFromProps:isPreview,// skip EU check (expensive) if we are on the canvas and don't want a preview\nskipEUCheck:skipLogic,regionFromGTM:gtmDefaults.region});const consent=useConsent({gtmId:skipGTMId?\"\":gtmId,defaultConsent:gtmDefaults.available?gtmDefaults.consent:region.defaults,gtmLoadedExternally,defaultsFromGTM:gtmDefaults.available});const[isOpen,setIsOpen]=useState(initiallyOpen);// On page switch, disable all transitions so the banner shows up as fast as possible.\nconst[instantlyShowOnMount,setInstantlyShowOnMount]=useState(initiallyOpen);useEffect(()=>{if(skipLogic)return;// Save open state between page switches\ninitiallyOpen=isOpen;// Disable instantly show on mount after first open\nif(isOpen){setInstantlyShowOnMount(false);}// Track shown event\nif(isOpen&&!isPreview&&onShown){yieldBeforeCb(()=>onShown({isInEU}),{priority:\"user-blocking\"});}},[isOpen,skipLogic]);// Check if user should be prompted\nuseEffect(()=>{if(skipLogic)return;const noConsentGiven=consent.isInitialized&&!consent.isDismissed;const shouldAutoAccept=region.type===\"simple\"&&!consent.isAutoAccepted;if(noConsentGiven){performance.mark(\"framer-cookie-open\");setIsOpen(true);/** Automatically accept all cookies for simple banner. */if(shouldAutoAccept){consent.autoAccept();// Fire callback\nif(onAccept){yieldBeforeCb(()=>onAccept({isInEU}));}}}if(consent.isDismissed){setIsOpen(false);}},[consent.isInitialized,consent.isDismissed,skipLogic]);useEffect(()=>{if(skipLogic)return;if(onConsentChange){yieldBeforeCb(()=>onConsentChange({isInEU,consent:consent.modes}));}},[consent.modes,skipLogic]);async function handleDismiss(){await interactionResponse();consent.dismiss();setIsOpen(false);// Fire callback\nif(onDismiss){yieldBeforeCb(()=>onDismiss({isInEU}));}}async function handleAcceptAll(){await interactionResponse();consent.acceptAll();setIsOpen(false);// Fire callback\nif(onAccept){yieldBeforeCb(()=>onAccept({isInEU}));}}async function handleRejectAll(){await interactionResponse();consent.rejectAll();setIsOpen(false);// Fire callback\nif(onReject){yieldBeforeCb(()=>onReject({isInEU}));}}async function handleAcceptCurrent(){await interactionResponse();consent.acceptCurrent();setIsOpen(false);// Fire callbacks\nyieldBeforeCb(()=>{if(onAccept){onAccept({isInEU});}});yieldBeforeCb(()=>{if(onSavePreferences){onSavePreferences({isInEU});}});}if(isPreview){return /*#__PURE__*/_jsx(\"div\",{style:{...style,width:banner.width},children:/*#__PURE__*/_jsx(Banner,{banner:banner,button:button,region:region,options:options,previewOptions:isPreview&&options.preview,consentModes:{...defaultConsent,necessary:true},animateOnMount:false})});}return /*#__PURE__*/_jsxs(_Fragment,{children:[/*#__PURE__*/_jsx(Trigger,{style:style,trigger:trigger,onClick:()=>setIsOpen(true)}),!skipLogic&&/*#__PURE__*/_jsx(Overlay,{banner:banner,button:button,region:region,options:options,consentModes:consent.modes,animateOnMount:!instantlyShowOnMount,onAcceptAll:handleAcceptAll,onAcceptCurrent:handleAcceptCurrent,onRejectAll:handleRejectAll,onDismiss:handleDismiss,onToggleConsent:consent.toggleMode,isOpen:isOpen})]});}function isInFramerPreview(){return RenderTarget.current()===RenderTarget.preview&&typeof window!==\"undefined\"&&window.origin?.endsWith(\"framercanvas.com\");}const IconCookie=/*#__PURE__*/lazy(()=>import(\"https://framerusercontent.com/modules/80EyUU0Nk6u7skW3IlHH/qKhU3oZiLDe4R0LcF3Tp/Icons.js\"));function Overlay(props){const insetValue=props.banner.insetPerSide?`${props.banner.insetTop}px ${props.banner.insetRight}px ${props.banner.insetBottom}px ${props.banner.insetLeft}px`:`${props.banner.inset}px`;const{justifyContent,alignItems}=getFlexboxValues(props.banner.position);const isOpen=props.isOpen;const[shouldRenderPortal,setShouldRenderPortal]=useState(isOpen);// if the portal has previously been rendered, we want to keep rendering the portal, which ensures:\n// - that AnimatePresence works (fade out effect)\n// - we don't cause body style recalc/reflow when the DOM node is removed\nif(!shouldRenderPortal&&isOpen)setShouldRenderPortal(isOpen);if(!shouldRenderPortal)return null;const blocking=props.region.blocking;return /*#__PURE__*/createPortal(/*#__PURE__*/_jsx(\"div\",{style:{display:\"contents\"},children:/*#__PURE__*/_jsx(AnimatePresence,{children:isOpen?/*#__PURE__*/_jsxs(motion.div,{style:{// We only need to span to the full viewport width if the overlay is blocking.\n// Otherwise, we optimize for a smaller layer size. see: https://framer-team.slack.com/archives/C05V49Q4NJ2/p1709820207863249\ntop:blocking||alignItems===\"flex-start\"?0:undefined,left:blocking||justifyContent===\"flex-start\"?0:undefined,right:blocking||justifyContent===\"flex-end\"?0:undefined,height:blocking?\"100%\":undefined,width:blocking||justifyContent===\"center\"?\"100%\":undefined,bottom:blocking||alignItems===\"flex-end\"?0:undefined,boxSizing:\"border-box\",position:\"fixed\",touchAction:\"none\",padding:insetValue,zIndex:props.banner.zIndex,display:\"flex\",flexDirection:\"row\",gap:20,justifyContent:\"center\",pointerEvents:blocking?\"all\":\"none\"},children:[blocking&&/*#__PURE__*/_jsx(Backdrop,{color:props.banner.style?.backdrop}),/*#__PURE__*/_jsx(\"div\",{style:{width:\"100%\",height:\"100%\",display:\"flex\",justifyContent,alignItems,pointerEvents:\"none\",maxWidth:props.banner.containerWidth>0?props.banner.containerWidth:\"unset\"},children:/*#__PURE__*/_jsx(Banner,{...props})})]}):null})}),document.body);}const Trigger=withCSS(({trigger,style,onClick})=>{const isOnFramerCanvas=useIsOnFramerCanvas();if(trigger.type!==\"none\"){return /*#__PURE__*/_jsx(\"button\",{className:\"__framer-cookie-component-button\",\"aria-label\":\"Cookie Trigger\",style:{width:\"100%\",height:\"100%\",background:\"none\",display:\"flex\",border:\"none\",padding:0,color:trigger.color,fontSize:16,cursor:\"pointer\",...trigger.textFont},onClick:onClick,children:trigger.type===\"icon\"?/*#__PURE__*/_jsx(_Fragment,{children:trigger.iconType===\"custom\"&&trigger.iconImage?/*#__PURE__*/_jsx(\"img\",{alt:\"icon entry point for Site Search\",src:trigger.iconImage.src,width:trigger.iconSize,height:trigger.iconSize}):/*#__PURE__*/_jsx(Suspense,{children:/*#__PURE__*/_jsx(IconCookie,{width:trigger.iconSize,height:trigger.iconSize,color:trigger.color})})}):/*#__PURE__*/_jsx(\"span\",{style:{whiteSpace:\"nowrap\"},children:trigger.text})});}if(isOnFramerCanvas){return /*#__PURE__*/_jsxs(\"div\",{style:{borderRadius:8,color:\"#09F\",border:\"1px dashed #09F\",background:\"rgba(0, 153, 255, 0.1)\",padding:20,display:\"flex\",flexDirection:\"column\",gap:5,fontFamily:DEFAULT_FONT_FAMILY,textAlign:\"center\",justifyContent:\"center\",width:164,...style},children:[/*#__PURE__*/_jsx(\"p\",{style:{fontSize:12,fontWeight:600,lineHeight:1,margin:0},children:\"Cookie Banner\"}),/*#__PURE__*/_jsx(\"p\",{style:{fontSize:12,lineHeight:1.5,margin:0},children:\"Put on a page to add a Cookie Banner.\"})]});}},\".__framer-cookie-component-button:focus:not(:focus-visible){outline-color:transparent}\",\"framer-lib-cookie-trigger\");function Backdrop({color}){return /*#__PURE__*/_jsx(motion.div,{initial:{opacity:0},animate:{opacity:1},exit:{opacity:0},style:{position:\"absolute\",top:0,left:0,right:0,bottom:0,width:\"100%\",height:\"100%\",backgroundColor:color,pointerEvents:\"none\"}});}addPropertyControls(CookieBanner,{gtmId:{title:\"GTM ID\",type:ControlType.String,placeholder:\"GTM-AAAAAAA\",description:\"Your GTM container ID.\\n[Learn more](https://www.framer.com/learn/cookie-banner/)\"},preview:{type:ControlType.Boolean,defaultValue:true,description:\"Lets you preview the banner on the Canvas.\"},trigger:{type:ControlType.Object,buttonTitle:\"Icon, Text\",controls:{type:{title:\"Type\",type:ControlType.Enum,options:[\"text\",\"icon\",\"none\"],optionTitles:[\"Text\",\"Icon\",\"None\"],defaultValue:\"text\",displaySegmentedControl:true},iconType:{title:\"Icon\",type:ControlType.Enum,options:[\"default\",\"custom\"],optionTitles:[\"Default\",\"Custom\"],displaySegmentedControl:true,hidden:props=>props.type!==\"icon\"},text:{title:\"Label\",type:ControlType.String,defaultValue:\"Cookie Settings\",hidden:props=>props.type!==\"text\"},textFont:{// @ts-ignore - internal\ntype:ControlType.Font,title:\" \",controls:\"extended\",hidden:props=>props.type!==\"text\"},iconSize:{title:\"Size\",type:ControlType.Number,displayStepper:true,defaultValue:24,hidden:props=>props.type!==\"icon\"},color:{title:\"Color\",type:ControlType.Color,defaultValue:\"#333\",hidden:props=>props.type===\"none\"||props.type===\"icon\"&&props.iconType===\"custom\"},iconImage:{title:\"File\",type:ControlType.ResponsiveImage,allowedFileTypes:[\"jpg\",\"png\",\"svg\"],hidden:props=>props.iconType===\"default\"}}},banner:{title:\"Banner\",type:ControlType.Object,buttonTitle:\"Font, Styles\",controls:{position:{type:ControlType.Enum,title:\"Position\",options:[\"top-left\",\"top-center\",\"top-right\",\"bottom-right\",\"bottom-center\",\"bottom-left\"],optionTitles:[\"Top Left\",\"Top Center\",\"Top Right\",\"Bottom Right\",\"Bottom Center\",\"Bottom Left\"],defaultValue:\"bottom-right\"},zIndex:{title:\"Z Index\",type:ControlType.Number,defaultValue:10,displayStepper:true,min:0,max:10},width:{title:\"Width\",type:ControlType.Number,defaultValue:360,min:200,max:1e3,displayStepper:true,step:5},containerWidth:{title:\"Wrapping\",type:ControlType.Number,defaultValue:0,min:0},padding:{title:\"Padding\",type:ControlType.FusedNumber,toggleKey:\"paddingPerSide\",toggleTitles:[\"Padding\",\"Padding per side\"],defaultValue:20,valueKeys:[\"paddingTop\",\"paddingRight\",\"paddingBottom\",\"paddingLeft\"],valueLabels:[\"T\",\"R\",\"B\",\"L\"],min:0},inset:{title:\"Inset\",type:ControlType.FusedNumber,toggleKey:\"insetPerSide\",toggleTitles:[\"Inset\",\"Inset per side\"],defaultValue:20,valueKeys:[\"insetTop\",\"insetRight\",\"insetBottom\",\"insetLeft\"],valueLabels:[\"T\",\"R\",\"B\",\"L\"],min:0},style:{type:ControlType.Object,title:\"Style\",buttonTitle:\"Fonts, Colors, Shadow\",controls:{fontTitle:{// @ts-ignore - internal\ntype:ControlType.Font,title:\"Title\",controls:\"extended\"},colorTitle:{title:\" \",type:ControlType.Color,defaultValue:\"#000\"},fontBody:{// @ts-ignore - internal\ntype:ControlType.Font,title:\"Body\",controls:\"extended\"},colorBody:{title:\" \",type:ControlType.Color,defaultValue:\"#444\"},fill:{title:\"Fill\",type:ControlType.Color,defaultValue:\"#FFF\"},link:{title:\"Link\",type:ControlType.Color,optional:true,defaultValue:\"#999\"},border:{type:ControlType.Object,title:\"Border\",buttonTitle:\"Radius, Width\",controls:{radius:{title:\"Radius\",type:ControlType.Number,displayStepper:true,min:0,defaultValue:14},width:{title:\"Width\",type:ControlType.Number,displayStepper:true,min:0,defaultValue:1},color:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.05)\"}}},shadow:{type:ControlType.Object,title:\"Shadow\",optional:true,controls:{shadowColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.25)\"},shadowX:{title:\"X\",type:ControlType.Number,min:-100,max:100,defaultValue:0},shadowY:{title:\"Y\",type:ControlType.Number,min:-100,max:100,defaultValue:2},shadowBlur:{title:\"Blur\",type:ControlType.Number,min:0,max:100,defaultValue:4}}},backdrop:{title:\"Backdrop\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.1)\",hidden:(_,props)=>!props.content.euBlocking&&!props.content.worldBlocking}}},animation:{icon:\"effect\",buttonTitle:\"Options\",type:ControlType.Object,controls:{x:{type:ControlType.Number,displayStepper:true,defaultValue:0},y:{type:ControlType.Number,displayStepper:true,defaultValue:10},scale:{type:ControlType.Number,min:0,step:.1,defaultValue:1},transition:{type:ControlType.Transition}}}}},button:{title:\"Buttons\",type:ControlType.Object,buttonTitle:\"Variants, Style\",controls:{primary:{title:\"Primary\",type:ControlType.Object,buttonTitle:\"Colors, Shadow\",controls:{fill:{title:\"Fill\",type:ControlType.Color,defaultValue:\"#000\"},color:{title:\"Color\",type:ControlType.Color,defaultValue:\"#FFF\"},shadow:{type:ControlType.Object,title:\"Shadow\",optional:true,controls:{shadowColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.25)\"},shadowX:{title:\"X\",type:ControlType.Number,min:-100,max:100},shadowY:{title:\"Y\",type:ControlType.Number,min:-100,max:100},shadowBlur:{title:\"Blur\",type:ControlType.Number,min:0,max:100}}}}},secondary:{title:\"Secondary\",type:ControlType.Object,buttonTitle:\"Colors, Shadow\",controls:{fill:{title:\"Fill\",type:ControlType.Color,defaultValue:\"#EEE\"},color:{title:\"Color\",type:ControlType.Color,defaultValue:\"#444\"},shadow:{type:ControlType.Object,title:\"Shadow\",optional:true,controls:{shadowColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.25)\"},shadowX:{title:\"X\",type:ControlType.Number,min:-100,max:100},shadowY:{title:\"Y\",type:ControlType.Number,min:-100,max:100},shadowBlur:{title:\"Blur\",type:ControlType.Number,min:0,max:100}}}}},labels:{type:ControlType.Object,controls:{accept:{type:ControlType.String,defaultValue:\"Accept\"},reject:{type:ControlType.String,defaultValue:\"Reject\"},acceptAll:{type:ControlType.String,defaultValue:\"Accept all\"},rejectAll:{type:ControlType.String,defaultValue:\"Reject all\"},customize:{type:ControlType.String,defaultValue:\"Customize\"},save:{type:ControlType.String,defaultValue:\"Save Preferences\"},confirm:{type:ControlType.String,defaultValue:\"Okay\"}}},font:{// @ts-ignore - internal\ntype:ControlType.Font,title:\"Font\",controls:\"extended\"},padding:{title:\"Padding\",type:ControlType.FusedNumber,toggleKey:\"paddingPerSide\",toggleTitles:[\"Padding\",\"Padding per side\"],defaultValue:10,valueKeys:[\"paddingTop\",\"paddingRight\",\"paddingBottom\",\"paddingLeft\"],valueLabels:[\"T\",\"R\",\"B\",\"L\"],min:0},borderRadius:{title:\"Radius\",type:ControlType.Number,displayStepper:true,min:0,defaultValue:8},tapOpacity:{title:\"Tap Opacity\",type:ControlType.Number,step:.1,displayStepper:true,max:1,min:0,defaultValue:.4},hoverOpacity:{title:\"Hover Opacity\",type:ControlType.Number,step:.1,displayStepper:true,max:1,min:0,defaultValue:.6},direction:{type:ControlType.Enum,title:\"Direction\",options:[\"row\",\"column\"],// @ts-ignore - internal\noptionIcons:[\"direction-horizontal\",\"direction-vertical\"],defaultValue:\"row\",displaySegmentedControl:true},fluid:{title:\"Fluid\",type:ControlType.Boolean,defaultValue:true}}},content:{title:\"Regions\",type:ControlType.Object,buttonTitle:\"World, EU\",controls:{isEU:{title:\" \",type:ControlType.Boolean,defaultValue:true,enabledTitle:\"EU\",disabledTitle:\"World\"},euType:{title:\"Choices\",type:ControlType.Enum,options:[\"simple\",\"medium\",\"advanced\"],optionTitles:[\"None\",\"Accept/Reject\",\"Customizable\"],defaultValue:\"medium\",hidden:props=>!props.isEU},euTitle:{title:\"Title\",type:ControlType.String,defaultValue:\"Cookie Settings\",hidden:props=>props.euType===\"simple\"||!props.isEU},euDescription:{title:\"Description\",type:ControlType.String,defaultValue:\"We use cookies to enhance your experience, analyze site traffic and deliver personalized content.\",displayTextArea:true,hidden:props=>!props.isEU},euPolicy:{title:\"Policy\",type:ControlType.Object,buttonTitle:\"Link, Prefix\",controls:{link:{title:\"Link\",type:ControlType.Link,defaultValue:\"https://www.framer.com/legal/policy/\"},prefix:{title:\"Prefix\",type:ControlType.String,defaultValue:\"Read our\"},label:{title:\"Label\",type:ControlType.String,defaultValue:\"Cookie Policy\"}},hidden:props=>!props.isEU},euDefaults:{title:\"Defaults\",buttonTitle:\"Options\",type:ControlType.Object,controls:{necessary:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:true},preferences:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:false},analytics:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:false},marketing:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:false,description:\"The default consent when the user hasn’t provided any yet.\"}},hidden:props=>!props.isEU},euShowReject:{title:\"Reject All\",type:ControlType.Boolean,defaultValue:true,enabledTitle:\"Show\",disabledTitle:\"Hide\",hidden:props=>props.euType!==\"advanced\"},euBlocking:{title:\"Blocking\",type:ControlType.Boolean,defaultValue:false,description:\"Renders a content blocking backdrop.\",hidden:props=>!props.isEU},worldType:{title:\"Choices\",type:ControlType.Enum,options:[\"simple\",\"medium\",\"advanced\"],optionTitles:[\"None\",\"Accept/Reject\",\"Customizable\"],defaultValue:\"simple\",hidden:props=>props.isEU},worldTitle:{title:\"Title\",type:ControlType.String,defaultValue:\"Cookie Settings\",hidden:props=>props.worldType===\"simple\"||props.isEU},worldDescription:{title:\"Description\",type:ControlType.String,defaultValue:\"We use cookies to personalize content, run ads, and analyze traffic.\",displayTextArea:true,hidden:props=>props.isEU},worldPolicy:{title:\"Policy\",type:ControlType.Object,buttonTitle:\"Link, Prefix\",controls:{link:{title:\"Link\",type:ControlType.Link},prefix:{title:\"Prefix\",type:ControlType.String,defaultValue:\"Read our\"},label:{title:\"Label\",type:ControlType.String,defaultValue:\"Cookie Policy\"}},hidden:props=>props.isEU},worldDefaults:{title:\"Defaults\",buttonTitle:\"Options\",type:ControlType.Object,controls:{necessary:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:true},preferences:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:true},analytics:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:true},marketing:{type:ControlType.Boolean,enabledTitle:\"Granted\",disabledTitle:\"Denied\",defaultValue:true,description:\"The default consent when the user hasn’t provided any yet.\"}},hidden:props=>props.isEU},worldShowReject:{title:\"Reject All\",type:ControlType.Boolean,defaultValue:true,enabledTitle:\"Show\",disabledTitle:\"Hide\",hidden:props=>props.worldType!==\"advanced\"},worldBlocking:{title:\"Blocking\",type:ControlType.Boolean,defaultValue:false,description:\"Renders a content blocking backdrop.\",hidden:props=>props.isEU}}},options:{type:ControlType.Object,buttonTitle:\"Content, Styles\",hidden:(_,props)=>props.content.euType!==\"advanced\"&&props.content.worldType!==\"advanced\",controls:{preview:{type:ControlType.Boolean,defaultValue:false,description:\"Open when previewing banner on the canvas.\",hidden:(_,props)=>!props.preview},necessary:{title:\"Necessary\",type:ControlType.Object,buttonTitle:\"Content\",controls:{title:{title:\"Title\",type:ControlType.String,defaultValue:\"Necessary\"},description:{title:\"Description\",type:ControlType.String,defaultValue:\"Enables security and basic functionality.\",displayTextArea:true},optional:{title:\"Optional\",type:ControlType.Boolean,defaultValue:true}}},preferences:{title:\"Preferences\",type:ControlType.Object,buttonTitle:\"Content\",controls:{title:{title:\"Title\",type:ControlType.String,defaultValue:\"Preferences\"},description:{title:\"Description\",type:ControlType.String,defaultValue:\"Enables personalized content and settings.\",displayTextArea:true,optional:true}}},analytics:{title:\"Analytics\",type:ControlType.Object,buttonTitle:\"Content\",controls:{title:{title:\"Title\",type:ControlType.String,defaultValue:\"Analytics\"},description:{title:\"Description\",type:ControlType.String,defaultValue:\"Enables tracking of performance.\",displayTextArea:true}}},marketing:{title:\"Marketing\",type:ControlType.Object,buttonTitle:\"Content\",controls:{title:{title:\"Title\",type:ControlType.String,defaultValue:\"Marketing\"},description:{title:\"Description\",type:ControlType.String,defaultValue:\"Enables ads personalization and tracking.\",displayTextArea:true}}},style:{type:ControlType.Object,title:\"Style\",buttonTitle:\"Fonts, Colors\",controls:{fontTitle:{// @ts-ignore - internal\ntype:ControlType.Font,title:\"Title\",controls:\"basic\"},fontBody:{// @ts-ignore - internal\ntype:ControlType.Font,title:\"Body\",controls:\"basic\"},background:{title:\"Background\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.02)\"},border:{type:ControlType.Object,title:\"Border\",buttonTitle:\"Radius, Width\",controls:{radius:{title:\"Radius\",type:ControlType.Number,displayStepper:true,min:0,defaultValue:8},width:{title:\"Width\",type:ControlType.Number,displayStepper:true},color:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.02)\"}}},toggleColor:{title:\"On\",type:ControlType.Color,defaultValue:\"#000\"},toggleColorInactive:{title:\"Off\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.1)\"},padding:{title:\"Padding\",type:ControlType.FusedNumber,toggleKey:\"paddingPerSide\",toggleTitles:[\"Padding\",\"Padding per side\"],defaultValue:12,valueKeys:[\"paddingTop\",\"paddingRight\",\"paddingBottom\",\"paddingLeft\"],valueLabels:[\"T\",\"R\",\"B\",\"L\"],min:0}}}}}});CookieBanner.displayName=\"Cookie Banner\";\nexport const __FramerMetadata__ = {\"exports\":{\"default\":{\"type\":\"reactComponent\",\"name\":\"CookieBanner\",\"slots\":[],\"annotations\":{\"framerContractVersion\":\"1\",\"framerSupportedLayoutWidth\":\"auto\",\"framerSupportedLayoutHeight\":\"auto\",\"framerDisableUnlink\":\"*\"}},\"ContentProps\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"ContentType\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"ButtonsProps\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"CookieBannerProps\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"PolicyProps\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"OptionsStyle\":{\"type\":\"tsType\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./Cookies.map","import{jsx as _jsx,jsxs as _jsxs}from\"react/jsx-runtime\";import{useCallback,useState}from\"react\";import{addPropertyControls,ControlType,withCSS,useRouter,inferInitialRouteFromPath,useTracking}from\"framer\";import{motion,useAnimationControls}from\"framer-motion\";const emailRegex=/^(([^<>()[\\]\\\\.,;:\\s@\"]+(\\.[^<>()[\\]\\\\.,;:\\s@\"]+)*)|(\".+\"))@((\\[[0-9]{1,3}\\.[0-9]{1,3}\\.[0-9]{1,3}\\.[0-9]{1,3}\\])|(([a-zA-Z\\-0-9]+\\.)+[a-zA-Z]{2,}))$/;const mailchimpRegex=/^https?:\\/\\/([^\\/]+)[^\\?]+\\??(.+)$/;const validateEmail=email=>{return emailRegex.test(String(email).toLowerCase());};const parseMailChimpUrl=url=>{const[,domain,parameters]=url.replace(\"&amp;\",\"&\").match(mailchimpRegex)??[null,null,null];return[domain,parameters?new URLSearchParams(parameters):null];};function safeURL(url){console.log(url);try{new URL(url);return url;}catch{try{new URL(`https://${url}`);return`https://${url}`;}catch{}}return undefined;}function isInternalURL(href){if(href===undefined)return false;if(href.startsWith(\"#\")||href.startsWith(\"/\")||href.startsWith(\".\"))return true;return false;}/**\n * Increment the number whenever shipping a new version to customers.\n * This will ensure that multiple versions of this component can exist\n * in the same project without css rules overlapping. Only use valid css class characters.\n */const VERSION=\"v1\";/**\n * INPUT\n * By Benjamin den Boer\n *\n * @framerDisableUnlink\n *\n * @framerIntrinsicWidth 300\n * @framerIntrinsicHeight 40\n *\n * @framerSupportedLayoutWidth fixed\n * @framerSupportedLayoutHeight any\n */const Input=withCSS(function Input({service,redirectAs,mailchimpURL,loopsID,loopsUserGroup,loopsMailingList,formsparkID,getwaitlistAPI,convertkitAPI,convertkitFormID,trackingId,input,button,font,layout,link,gap,style,onSubmit}){const[email,setEmail]=useState(\"\");const[isError,setError]=useState(false);const[isLoading,setLoading]=useState(false);const[isFocus,setFocus]=useState(false);// Padding\nconst{paddingPerSide,paddingTop,paddingRight,paddingBottom,paddingLeft,padding,borderRadius,borderObject,focusObject,shadowObject,height,fixedHeight}=input;const{buttonPaddingPerSide,buttonPaddingTop,buttonPaddingRight,buttonPaddingBottom,buttonPaddingLeft,buttonPadding}=button;const paddingValue=paddingPerSide?`${paddingTop}px ${button.isDocked?paddingRight+button.widthWhenDocked:paddingRight}px ${paddingBottom}px ${paddingLeft}px`:`${padding}px ${button.isDocked?padding+button.widthWhenDocked:padding}px ${padding}px ${padding}px`;const buttonPaddingValue=buttonPaddingPerSide?`${button.isDocked?0:buttonPaddingTop}px ${buttonPaddingRight}px ${button.isDocked?0:buttonPaddingBottom}px ${buttonPaddingLeft}px`:`${button.isDocked?0:buttonPadding}px ${buttonPadding}px ${button.isDocked?0:buttonPadding}px ${buttonPadding}px`;const router=useRouter();const track=useTracking();const onSuccess=()=>{/* Reset */setLoading(false);setFocus(false);setEmail(\"\");if(trackingId){track(trackingId);}if(redirectAs===\"link\"&&link&&!isError){const[fullPath,hash]=link.split(\"#\");const path=fullPath.split(\"?\")[0]||\"\";const{routeId,pathVariables}=inferInitialRouteFromPath(router.routes,path);if(routeId){router.navigate(routeId,hash,pathVariables);}if(!isInternalURL(link)){const url=safeURL(link);if(url&&typeof window!==\"undefined\")window.open(url,\"_blank\");}}};const validateForm=useCallback(email=>{if(email===\"\"||!validateEmail(email)){setError(true);formControls.start(\"error\");return false;}return true;},[validateEmail]);const handleChange=useCallback(event=>{setError(false);setEmail(event.target.value);},[]);const handleFocus=useCallback(event=>{setFocus(true);},[]);const handleBlur=useCallback(event=>{setFocus(false);setError(false);},[]);const handleSubmit=useCallback(event=>{event.preventDefault();// Prevent submitting while submitting\nif(isLoading)return;setLoading(true);if(service===\"mailchimp\"){const[domain,parameters]=parseMailChimpUrl(mailchimpURL);if(!validateForm(email)||!domain||!parameters){setLoading(false);return;}// MERGE0 is Mailchimp’s email field name\nparameters.set(\"MERGE0\",email);fetch(`https://${domain}/subscribe/post`,{method:\"POST\",mode:\"no-cors\",headers:{\"Content-Type\":\"application/x-www-form-urlencoded;charset=UTF-8\"},body:parameters.toString()}).then(response=>{onSuccess();if(redirectAs===\"overlay\")onSubmit?.();}).catch(error=>{console.error(error);setLoading(false);setError(true);formControls.start(\"error\");});}if(service===\"loops\"){if(!validateForm(email)){setLoading(false);return;}const emailBody=`email=${encodeURIComponent(email)}`;const userGroupBody=`userGroup=${encodeURIComponent(loopsUserGroup)}`;const mailingListBody=`mailingLists=${encodeURIComponent(loopsMailingList)}`;const hasMailingList=!!loopsMailingList&&loopsMailingList!==\" \";const hasUserGroup=!!loopsUserGroup&&loopsUserGroup!==\" \";let formBody=emailBody;if(hasUserGroup){formBody+=`&${userGroupBody}`;}if(hasMailingList){formBody+=`&${mailingListBody}`;}fetch(`https://app.loops.so/api/newsletter-form/${loopsID}`,{method:\"POST\",mode:\"no-cors\",headers:{\"Content-Type\":\"application/x-www-form-urlencoded\"},body:formBody}).then(()=>{onSuccess();if(redirectAs===\"overlay\")onSubmit?.();}).catch(error=>{console.error(error);setLoading(false);setError(true);formControls.start(\"error\");});}if(service===\"formspark\"){if(!validateForm(email)){setLoading(false);return;}const data=new FormData(event.target);const entries=Object.fromEntries(data.entries());fetch(`https://api.formspark.io/${formsparkID}`,{method:\"POST\",headers:{\"Content-Type\":\"application/json\",Accept:\"application/json\"},body:JSON.stringify(entries)}).then(()=>{onSuccess();onSubmit?.();}).catch(error=>{console.error(error);setLoading(false);setError(true);formControls.start(\"error\");});}if(service===\"getwaitlist\"){if(!validateForm(email)){setLoading(false);return;}const formData=new FormData(event.target);const data=Object.fromEntries(formData.entries());data.referral_link=document.URL;fetch(`https://api.getwaitlist.com/api/v1\n/waiter/`,{method:\"POST\",headers:{\"Content-Type\":\"application/json\",Accept:\"application/json\"},body:JSON.stringify(data)}).then(()=>{onSuccess();if(redirectAs===\"overlay\")onSubmit?.();}).catch(error=>{console.error(error);setLoading(false);setError(true);formControls.start(\"error\");});}if(service===\"convertkit\"){if(!validateForm(email)){setLoading(false);return;}const formData=new FormData(event.target);const data=Object.fromEntries(formData.entries());data.referral_link=document.URL;data.api_key=convertkitAPI;fetch(`https://api.convertkit.com/v3/forms/${convertkitFormID}/subscribe`,{method:\"POST\",headers:{\"Content-Type\":\"application/json\",Accept:\"application/json\"},body:JSON.stringify(data)}).then(()=>{onSuccess();if(redirectAs===\"overlay\")onSubmit?.();}).catch(error=>{console.error(error);setLoading(false);setError(true);formControls.start(\"error\");});}},[mailchimpURL,formsparkID,convertkitFormID,email,onSubmit,validateForm,isLoading]);// Animation\nconst formControls=useAnimationControls();// Input Box Shadow Stylees\nconst focusStylesFrom=input.focusObject?`inset 0 0 0 ${focusObject.focusWidthFrom}px ${focusObject.focusColor}`:null;const focusStylesTo=input.focusObject?`inset 0 0 0 ${focusObject.focusWidthTo}px ${focusObject.focusColor}`:null;const shadowStyles=input.shadowObject?`${shadowObject.shadowX}px ${shadowObject.shadowY}px ${shadowObject.shadowBlur}px ${shadowObject.shadowColor}`:null;const borderStyles=input.borderObject?`inset 0 0 0 ${borderObject.borderWidth}px ${borderObject.borderColor}`:null;// Shake or wiggle as error\nconst formVariants={default:{x:0},error:{x:[0,-4,4,0],transition:{duration:.2}}};const inputVariants={default:{boxShadow:dynamicBoxShadow(focusStylesFrom,shadowStyles,borderStyles)},focused:{boxShadow:dynamicBoxShadow(focusStylesTo,shadowStyles,borderStyles)}};return /*#__PURE__*/_jsx(motion.div,{style:{...style,...containerStyles,\"--framer-custom-placeholder-color\":input.placeholderColor},variants:formVariants,animate:formControls,children:/*#__PURE__*/_jsxs(\"form\",{style:{width:\"100%\",height:\"auto\",display:\"flex\",position:\"relative\",flexDirection:layout===\"vertical\"?\"column\":\"row\",color:button.color,gap:button.isDocked?0:gap},onSubmit:handleSubmit,method:\"POST\",children:[service===\"getwaitlist\"&&/*#__PURE__*/_jsx(\"input\",{type:\"hidden\",name:\"api_key\",value:getwaitlistAPI}),service===\"convertkit\"&&/*#__PURE__*/_jsx(\"input\",{type:\"hidden\",name:\"api_key\",value:convertkitAPI}),/*#__PURE__*/_jsx(motion.input,{type:\"email\",name:\"email\",placeholder:input.placeholder,value:email,className:`${VERSION} framer-custom-input`,onChange:handleChange,onFocus:handleFocus,onBlur:handleBlur,autoComplete:\"off\",autoCapitalize:\"off\",autoCorrect:\"off\",spellCheck:\"false\",style:{...defaultStyle,padding:paddingValue,borderRadius,fontSize:16,...font,background:input.fill,height:height?\"auto\":fixedHeight,color:input.color,boxShadow:dynamicBoxShadow(focusStylesFrom,shadowStyles,borderStyles)},variants:inputVariants,initial:false,animate:isFocus?\"focused\":\"default\",transition:{duration:.3},\"data-1p-ignore\":true}),!button.shouldAppear&&isLoading&&/*#__PURE__*/_jsx(Spinner,{shouldAppear:button.shouldAppear,paddingPerSide:paddingPerSide,paddingTop:paddingTop,paddingRight:paddingRight,padding:padding,color:input.color}),button.shouldAppear&&/*#__PURE__*/_jsxs(\"div\",{style:{position:button.isDocked?\"absolute\":\"relative\",top:button.isDocked?button.insetWhenDocked:0,right:button.isDocked?button.insetWhenDocked:0,bottom:button.isDocked?button.insetWhenDocked:0},children:[/*#__PURE__*/_jsx(motion.input,{type:\"submit\",value:button.label,style:{...defaultStyle,width:button.isDocked?button.widthWhenDocked:\"100%\",height:height?\"100%\":fixedHeight,cursor:\"pointer\",padding:buttonPaddingValue,borderRadius:button.isDocked?borderRadius-button.insetWhenDocked:borderRadius,// fontWeight: button.fontWeight,\nfontSize:16,...button.buttonFont,background:button.fill,color:button.color,zIndex:1,boxShadow:getButtonShadow(button)}}),isLoading&&/*#__PURE__*/_jsx(\"div\",{style:{borderRadius:button.isDocked?borderRadius-button.insetWhenDocked:borderRadius,position:\"absolute\",display:\"flex\",justifyContent:\"center\",alignItems:\"center\",width:\"100%\",height:\"100%\",inset:0,zIndex:2,color:button.color,background:button.fill},children:/*#__PURE__*/_jsx(Spinner,{color:button.color})})]})]})});},[`.${VERSION}.framer-custom-input::placeholder { color: var(--framer-custom-placeholder-color) !important; }`]);const Spinner=props=>{const noButtonStyles=!props.shouldAppear?{position:\"absolute\",top:`calc(50% - 8px)`,right:props.paddingPerSide?props.paddingRight:props.padding}:{};return /*#__PURE__*/_jsx(motion.div,{style:{height:16,width:16,...noButtonStyles},initial:{rotate:0},animate:{rotate:360},transition:{duration:1,repeat:Infinity},children:/*#__PURE__*/_jsx(motion.div,{initial:{scale:0},animate:{scale:1},children:/*#__PURE__*/_jsxs(\"svg\",{xmlns:\"http://www.w3.org/2000/svg\",width:\"16\",height:\"16\",style:{fill:\"currentColor\",color:props.color},children:[/*#__PURE__*/_jsx(\"path\",{d:\"M 8 0 C 3.582 0 0 3.582 0 8 C 0 12.419 3.582 16 8 16 C 12.418 16 16 12.419 16 8 C 15.999 3.582 12.418 0 8 0 Z M 8 14 C 4.687 14 2 11.314 2 8 C 2 4.687 4.687 2 8 2 C 11.314 2 14 4.687 14 8 C 14 11.314 11.314 14 8 14 Z\",fill:\"currentColor\",opacity:\"0.2\"}),/*#__PURE__*/_jsx(\"path\",{d:\"M 8 0 C 12.418 0 15.999 3.582 16 8 C 16 8 16 9 15 9 C 14 9 14 8 14 8 C 14 4.687 11.314 2 8 2 C 4.687 2 2 4.687 2 8 C 2 8 2 9 1 9 C 0 9 0 8 0 8 C 0 3.582 3.582 0 8 0 Z\",fill:\"currentColor\"})]})})});};addPropertyControls(Input,{service:{title:\"Service\",type:ControlType.Enum,options:[\"loops\",\"formspark\",\"mailchimp\",\"getwaitlist\",\"convertkit\"],optionTitles:[\"Loops\",\"FormSpark\",\"MailChimp\",\"Get Waitlist\",\"ConvertKit\"],defaultValue:\"loops\"},mailchimpURL:{title:\"URL\",placeholder:\"https://***.us6.list-manage.com/subscribe/post?u=***\",type:ControlType.String,hidden:props=>props.service!==\"mailchimp\"},loopsID:{title:\"ID\",placeholder:\"klm2jxy0i98abzr01pq7defg5\",type:ControlType.String,hidden:props=>props.service!==\"loops\"},loopsUserGroup:{title:\"User Group\",type:ControlType.String,placeholder:\"Title\",optional:true,hidden:props=>props.service!==\"loops\"},loopsMailingList:{title:\"Mailing List\",type:ControlType.String,placeholder:\"cm3q41c5v02ii1lml14om2wtr\",optional:true,hidden:props=>props.service!==\"loops\"},formsparkID:{title:\"ID\",placeholder:\"7PbPpGN3\",type:ControlType.String,hidden:props=>props.service!==\"formspark\"},getwaitlistAPI:{title:\"ID\",placeholder:\"9148\",type:ControlType.String,hidden:props=>props.service!==\"getwaitlist\"},convertkitAPI:{title:\"API Key\",placeholder:\"5opis1WK6xnVsBwrOINUpe\",type:ControlType.String,hidden:props=>props.service!==\"convertkit\"},convertkitFormID:{title:\"Form ID\",placeholder:\"1043389\",type:ControlType.String,hidden:props=>props.service!==\"convertkit\"},redirectAs:{title:\"Success\",type:ControlType.Enum,options:[\"link\",\"overlay\"],optionTitles:[\"Open Link\",\"Show Overlay\"],defaultValue:\"link\"},link:{title:\"Redirect\",type:ControlType.Link,hidden:props=>props.redirectAs===\"overlay\"},onSubmit:{title:\"Submit\",type:ControlType.EventHandler,hidden:props=>props.redirectAs===\"link\"},trackingId:{title:\"Tracking\",placeholder:\"ID\",type:ControlType.TrackingId},layout:{title:\"Layout\",type:ControlType.Enum,options:[\"horizontal\",\"vertical\"],displaySegmentedControl:true,hidden:props=>props.button.isDocked},font:{type:ControlType.Font,title:\"Font\",controls:\"extended\"},input:{title:\"Input\",type:ControlType.Object,controls:{placeholder:{title:\"Placeholder\",type:ControlType.String,defaultValue:\"name@email.com\"},placeholderColor:{title:\" \",type:ControlType.Color,defaultValue:\"rgba(0, 0, 0, 0.3)\"},fill:{title:\"Fill\",type:ControlType.Color,defaultValue:\"#EBEBEB\"},color:{title:\"Text\",type:ControlType.Color,defaultValue:\"#000\"},height:{title:\"Height\",type:ControlType.Boolean,enabledTitle:\"Auto\",disabledTitle:\"Fixed\"},fixedHeight:{title:\" \",type:ControlType.Number,displayStepper:true,min:0,defaultValue:50,hidden:props=>props.height},padding:{title:\"Padding\",type:ControlType.FusedNumber,toggleKey:\"paddingPerSide\",toggleTitles:[\"Padding\",\"Padding per side\"],defaultValue:15,valueKeys:[\"paddingTop\",\"paddingRight\",\"paddingBottom\",\"paddingLeft\"],valueLabels:[\"T\",\"R\",\"B\",\"L\"],min:0},borderRadius:{title:\"Radius\",type:ControlType.Number,displayStepper:true,min:0,defaultValue:8},focusObject:{type:ControlType.Object,title:\"Focus\",optional:true,controls:{focusWidthFrom:{title:\"From\",type:ControlType.Number,displayStepper:true,defaultValue:0},focusWidthTo:{title:\"To\",type:ControlType.Number,displayStepper:true,defaultValue:2},focusColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"#09F\"}}},borderObject:{type:ControlType.Object,title:\"Border\",optional:true,controls:{borderWidth:{title:\"Width\",type:ControlType.Number,displayStepper:true,defaultValue:1},borderColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(200,200,200,0.5)\"}}},shadowObject:{type:ControlType.Object,title:\"Shadow\",optional:true,controls:{shadowColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.25)\"},shadowX:{title:\"Shadow X\",type:ControlType.Number,min:-100,max:100,defaultValue:0},shadowY:{title:\"Shadow Y\",type:ControlType.Number,min:-100,max:100,defaultValue:2},shadowBlur:{title:\"Shadow B\",type:ControlType.Number,min:0,max:100,defaultValue:4}}}}},button:{title:\"Button\",type:ControlType.Object,controls:{shouldAppear:{title:\"Show\",type:ControlType.Boolean,defaultValue:true},label:{title:\"Label\",type:ControlType.String,defaultValue:\"Subscribe\"},buttonFont:{type:ControlType.Font,title:\"Font\",controls:\"extended\"},fill:{title:\"Fill\",type:ControlType.Color,defaultValue:\"#333\"},color:{title:\"Text\",type:ControlType.Color,defaultValue:\"#FFF\"},isDocked:{title:\"Docked\",type:ControlType.Boolean,defaultValue:false},widthWhenDocked:{title:\"Width\",type:ControlType.Number,min:0,defaultValue:100,displayStepper:true,hidden:props=>!props.isDocked},insetWhenDocked:{title:\"Inset\",type:ControlType.Number,min:0,defaultValue:5,displayStepper:true,hidden:props=>!props.isDocked},buttonPadding:{title:\"Padding\",type:ControlType.FusedNumber,toggleKey:\"buttonPaddingPerSide\",toggleTitles:[\"Padding\",\"Padding per side\"],defaultValue:15,valueKeys:[\"buttonPaddingTop\",\"buttonPaddingRight\",\"buttonPaddingBottom\",\"buttonPaddingLeft\"],valueLabels:[\"T\",\"R\",\"B\",\"L\"],min:0},buttonShadow:{type:ControlType.Object,title:\"Shadow\",optional:true,controls:{shadowColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.25)\"},shadowX:{title:\"Shadow X\",type:ControlType.Number,min:-100,max:100,defaultValue:0},shadowY:{title:\"Shadow Y\",type:ControlType.Number,min:-100,max:100,defaultValue:2},shadowBlur:{title:\"Shadow B\",type:ControlType.Number,min:0,max:100,defaultValue:4}}}}},gap:{title:\"Gap\",type:ControlType.Number,displayStepper:true,min:0,defaultValue:10,hidden:props=>props.button.isDocked}});const defaultStyle={WebkitAppearance:\"none\",appearance:\"none\",width:\"100%\",height:\"auto\",outline:\"none\",border:\"none\"};const containerStyles={position:\"relative\",width:\"100%\",height:\"100%\",display:\"flex\",justifyContent:\"center\",alignItems:\"center\"};function getButtonShadow(button){if(button.buttonShadow){return`${button.buttonShadow.shadowX}px ${button.buttonShadow.shadowY}px ${button.buttonShadow.shadowBlur}px ${button.buttonShadow.shadowColor}`;}else return\"none\";}function dynamicBoxShadow(...shadows){const output=[];shadows.forEach(shadow=>shadow&&output.push(shadow));return output.join(\", \");}export default Input;\nexport const __FramerMetadata__ = {\"exports\":{\"default\":{\"type\":\"reactComponent\",\"name\":\"Input\",\"slots\":[],\"annotations\":{\"framerDisableUnlink\":\"*\",\"framerSupportedLayoutWidth\":\"fixed\",\"framerSupportedLayoutHeight\":\"any\",\"framerContractVersion\":\"1\",\"framerIntrinsicWidth\":\"300\",\"framerIntrinsicHeight\":\"40\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./Input.map","/*\n * Input (framer/InputSites@1.16.3)\n *\n * 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/*#__PURE__*/_jsx(Input,{...props,service:\"mailchimp\"});}addPropertyControls(Mailchimp,{mailchimpURL:{title:\"URL\",placeholder:\"https://***.us6.list-manage.com/subscribe/post?u=***\",type:ControlType.String},redirectAs:{title:\"Success\",type:ControlType.Enum,options:[\"link\",\"overlay\"],optionTitles:[\"Open Link\",\"Show 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0.3)\"},fill:{title:\"Fill\",type:ControlType.Color,defaultValue:\"#EBEBEB\"},color:{title:\"Text\",type:ControlType.Color,defaultValue:\"#000\"},height:{title:\"Height\",type:ControlType.Boolean,enabledTitle:\"Auto\",disabledTitle:\"Fixed\"},fixedHeight:{title:\" \",type:ControlType.Number,displayStepper:true,min:0,defaultValue:50,hidden:props=>props.height},padding:{title:\"Padding\",type:ControlType.FusedNumber,toggleKey:\"paddingPerSide\",toggleTitles:[\"Padding\",\"Padding per side\"],defaultValue:15,valueKeys:[\"paddingTop\",\"paddingRight\",\"paddingBottom\",\"paddingLeft\"],valueLabels:[\"T\",\"R\",\"B\",\"L\"],min:0},borderRadius:{title:\"Radius\",type:ControlType.Number,displayStepper:true,min:0,defaultValue:8},focusObject:{type:ControlType.Object,title:\"Focus\",optional:true,controls:{focusWidthFrom:{title:\"From\",type:ControlType.Number,displayStepper:true,defaultValue:0},focusWidthTo:{title:\"To\",type:ControlType.Number,displayStepper:true,defaultValue:2},focusColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"#09F\"}}},borderObject:{type:ControlType.Object,title:\"Border\",optional:true,controls:{borderWidth:{title:\"Width\",type:ControlType.Number,displayStepper:true,defaultValue:1},borderColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(200,200,200,0.5)\"}}},shadowObject:{type:ControlType.Object,title:\"Shadow\",optional:true,controls:{shadowColor:{title:\"Color\",type:ControlType.Color,defaultValue:\"rgba(0,0,0,0.25)\"},shadowX:{title:\"Shadow 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getProps=({fill,fontsize,fontweight,height,htmlelement,id,link,logo,onlyLogo,pageTitle,scrollSection,unvisible,visibleShare,width,...props})=>{return{...props,awNumB1To:visibleShare??props.awNumB1To??true,DlY84xgY_:humanReadableEnumMap[logo]??logo??props.DlY84xgY_??\"T4v9dKQSP\",HVmTXsPHl:link??props.HVmTXsPHl,i84AKQOBN:scrollSection??props.i84AKQOBN,jou6LLiY7:unvisible??props.jou6LLiY7??false,JztBhPmmU:htmlelement??props.JztBhPmmU??\" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. 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You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. 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Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. 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<div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\", kDB2wtwtQ: \"medium\", n0c50JNc8: \"var(--token-5032e05a-740f-43f8-9e35-0da4740b00ef, rgb(250, 244, 242))\", OvGNJbJc2: \"Detail ---(13px p)\", rVCLCTNNg: \"\", YmoB_3zq2: false}\r\nconst props2 = {...props1, DlY84xgY_: \"rpsavy57b\", HVmTXsPHl: \"javascript:history.back(-1)\", i84AKQOBN: {href: {hash: \":Ru38axQER\", webPageId: \"J_K0pNWTB\"}, refKey: true}, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? 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It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\"}\r\nconst props3 = {...props1, DlY84xgY_: \"rpsavy57b\", HVmTXsPHl: {href: {webPageId: \"ECFHkDkZa\"}}, i84AKQOBN: undefined, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\", rVCLCTNNg: \"AI Native Capital - Contact\", YmoB_3zq2: true}\r\nconst props4 = {...props1, HVmTXsPHl: {href: {webPageId: \"X4V7Q6zQ7\"}}, i84AKQOBN: {href: {hash: \":UinKqlVfb\", webPageId: \"VZkV9as9j\"}, refKey: true}, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\"}\r\nconst props5 = {...props1, awNumB1To: true, HVmTXsPHl: {href: {webPageId: \"X4V7Q6zQ7\"}}, i84AKQOBN: {href: {hash: \":KlKEa43_R\", webPageId: \"VT29DirfJ\"}, refKey: true}, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\"}\r\nconst props6 = {...props1, awNumB1To: true, DlY84xgY_: \"rpsavy57b\", HVmTXsPHl: {href: {webPageId: \"X4V7Q6zQ7\"}}, i84AKQOBN: undefined, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\", rVCLCTNNg: \"Whitepaper\", YmoB_3zq2: true}\r\nconst props7 = {...props1, DlY84xgY_: \"rpsavy57b\", HVmTXsPHl: \"javascript:history.back(-1)\", i84AKQOBN: undefined, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\", rVCLCTNNg: getLocalizedValue(localizedValues1, activeLocale) ?? \"Careers\", YmoB_3zq2: true}\r\nconst props8 = {...props1, awNumB1To: true, DlY84xgY_: \"rpsavy57b\", HVmTXsPHl: {href: {webPageId: \"X4V7Q6zQ7\"}}, i84AKQOBN: undefined, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\", rVCLCTNNg: \"AI Native Capital\", YmoB_3zq2: true}\r\nconst props9 = {...props1, awNumB1To: true, DlY84xgY_: \"rpsavy57b\", HVmTXsPHl: \"javascript:history.back(-1)\", i84AKQOBN: undefined, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\"}\r\nconst props10 = {...props1, awNumB1To: true, DlY84xgY_: \"rpsavy57b\", HVmTXsPHl: {href: {webPageId: \"X4V7Q6zQ7\"}}, i84AKQOBN: {href: {hash: \":Hhz0f295s\", webPageId: \"Tk7tJfCE8\"}, refKey: true}, JztBhPmmU: \" <div><p>The transition from traditional management to AI-Native enterprise intelligence marks a historic shift in how organizations evolve. Modern enterprises were built to compensate for biological limits, but the rise of intelligent agents initiates a new age of discovery, cognition, and structural intelligence. This shift represents the twilight of management and the dawn of intelligent enterprise evolution.</p>\\n\\n<p>Management tools such as KPIs, hierarchy, incentives, and supervision were invented as corrective systems for human limitations. In contrast, AI-Native enterprises operate through continuous memory, holographic cognition, and endogenous evolution. These traits redefine execution, growth, and organizational intelligence at a species-level scale.</p>\\n\\n<h2>The Rise of Intelligent Agents in Enterprise Transformation</h2>\\n<p>Intelligent agents introduce a fundamentally different cognitive anatomy into organizations. Unlike human workers who rely on fragmented memory and partial context, agents possess uninterrupted memory continuity, full-context alignment, and reward-driven endogenous behavior. This creates a new foundation for enterprise cognition, discovery, and adaptive intelligence.</p>\\n\\n<p>As enterprises integrate agents into workflows, traditional structures begin to collapse. Fixed KPIs become ceilings on intelligence, hierarchical filters become bottlenecks, incentive systems turn into noise, and static planning loses relevance in a world of real-time simulation. Execution transforms from a consumption activity into a continuous learning process.</p>\\n\\n<h3>Understanding the Collapse of Traditional Management Structures</h3>\\n<p>The constraints that shaped management for a century become structural limitations in an AI-driven environment. Agents equipped with continuous memory and discovery-driven reasoning require free information flow, adaptive objectives, and dynamic world-model feedback. Legacy structures such as process oversight and multi-layer approval chains obstruct enterprise intelligence evolution.</p>\\n\\n<p>In AI-native environments, cognition replaces supervision. Discovery replaces control. Evolution replaces instruction.</p>\\n\\n<h3>The AI-Native Enterprise: A New Biological Blueprint</h3>\\n<p>An AI-Native enterprise is not a traditional organization enhanced by software—it is a new species of enterprise architecture. The biological genes of the corporation are rewritten around computational traits instead of human limits. Architecture evolves into a distributed computational graph; growth becomes cognitive compounding; memory becomes an evolving substrate; execution becomes training; and humans rise into roles of meaning-making and value-definition.</p>\\n\\n<h4>Key Traits of AI-Native Enterprise Evolution</h4>\\n<ul>\\n<li><strong>Architecture as Intelligence:</strong> Organizational structure becomes a high-dimensional computational network optimized for knowledge flow.</li>\\n<li><strong>Growth as Cognitive Compounding:</strong> Zero marginal learning cost enables enterprise-wide intelligence amplification.</li>\\n<li><strong>Memory as Evolution:</strong> Long-term vectorized memory forms the subconscious of the organization.</li>\\n<li><strong>Execution as Training:</strong> Every business action becomes a Bayesian update to the enterprise world model.</li>\\n<li><strong>Humans as Meaning:</strong> People define intent, ethics, and direction while agents optimize discovery in infinite solution spaces.</li>\\n</ul>\\n\\n<p>These traits form the genetic foundation of intelligence-native enterprises capable of continuous discovery, evolutionary cognition, and self-updating decision logic.</p>\\n\\n<h3>Discoverative AI as the Cognitive Doctrine of Future Enterprises</h3>\\n<p>Discoverative AI emphasizes that intelligence must go beyond fitting existing knowledge. It must build hypotheses, revise internal models, and discover new structures through interaction. This mirrors the operational form of AI-Native enterprises, which function as platforms for discovery rather than containers for processes.</p>\\n\\n<p>As intelligent agents reshape organizational cognition, traditional digital infrastructure—ERP systems, workflow tools, SaaS platforms—struggles to contain emergent intelligence. New enterprise operating systems are required, not for resource planning but for cognitive evolution.</p>\\n\\n<p>The dawn of intelligence demands a shift from management logic to discovery logic. In the future, enterprises will no longer be defined by humans directing intelligence but by intelligence expanding and extending human meaning.</p>\\n</div>\", rVCLCTNNg: getLocalizedValue(localizedValues2, activeLocale) ?? \"Blog  \", YmoB_3zq2: true}\r\nconst props11 = {...props1, HVmTXsPHl: {href: {webPageId: \"X4V7Q6zQ7\"}}, i84AKQOBN: {href: {hash: \":tXIPaxfnu\", webPageId: \"thEjxIh59\"}, refKey: true}, jou6LLiY7: true, JztBhPmmU: \" <div><p>Artificial intelligence has advanced rapidly, yet most systems still focus on <strong><span>generating outputs</span></strong> rather than engaging in true <strong><span>discovery</span></strong>. Today’s models produce fluent responses, but they struggle to identify uncertainty, test assumptions, or push beyond learned patterns to actually <strong><span>discover new insights</span></strong>.</p>\\n\\n<p>This limitation slows progress in research and innovation. Teams expect AI to help uncover original ideas, but instead they receive polished summaries of existing knowledge—results that lack genuine <strong><span>discovery</span></strong>.</p>\\n\\n<p>The traditional <strong>AI evaluation process</strong> measures performance on fixed benchmarks. These tests can <em><span>only capture</span></em> how well a model imitates known data. They don’t assess whether it can <strong><span>reason</span></strong>, <strong><span>reflect</span></strong>, or meaningfully <strong><span>discover</span></strong> beyond prior information.</p>\\n\\n<p>Enter <strong>Discoverative AI</strong>—a framework redefining how intelligence is measured and developed. Positioned as the evolution of AI from generation to <strong><span>discovery</span></strong>, Discoverative introduces a new paradigm for assessing how models learn, change, and adapt over time. The system moves beyond static accuracy and shifts toward <strong><span>dynamic discovery-driven cognition</span></strong>.</p>\\n\\n<p>These <strong>next-generation</strong> evaluation tools help you understand how an AI system grows through continual <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> doesn’t replace traditional benchmarks. Instead, it <strong><span>adds a higher dimension of measurement</span></strong> by evaluating how intelligence evolves in response to new information and new discoveries.</p>\\n\\n<p>The result? You gain visibility into how AI <strong>builds understanding</strong>, improves reasoning, and actively <strong><span>discovers</span></strong> meaningful patterns hidden in complex data.</p>\\n\\n<h2>What Is Discoverative AI and How Does It Work?</h2>\\n\\n<p>Picture an AI system that doesn’t just answer questions—it explores uncertainty, revises its beliefs, and <strong><span>discovers insights</span></strong> rather than merely generating text. That’s what <strong><span>Discoverative AI</span></strong> brings to the future of artificial intelligence.</p>\\n\\n<p>This framework changes how we measure intelligence. It evaluates cognitive growth, not just performance snapshots, and emphasizes continuous <strong><span>discovery</span></strong>.</p>\\n\\n<p>A <strong>Discoverative Intelligence</strong> system operates across multiple layers of reasoning. It identifies novelty, recalibrates its internal models, and explains its decision-making process—all core capabilities of <strong><span>discovery-driven understanding</span></strong>.</p>\\n\\n<p>Human researchers stay at the center of interpretation, while Discoverative AI provides the structure for analyzing how an AI system evolves through ongoing discovery.</p>\\n\\n<h3>Understanding Discoverative Intelligence Technology</h3>\\n\\n<p>At its core, <strong>Discoverative AI</strong> measures an AI system’s ability to <strong><span>grow its knowledge through discovery</span></strong>. It evaluates not just what the model knows, but how it <strong><span>updates</span></strong> and expands its understanding when faced with new information.</p>\\n\\n<p>These systems assess reasoning quality through multiple diagnostic signals: uncertainty detection, hypothesis revision, anomaly identification, and self-reflection scores—all essential to <strong><span>discovery</span></strong>.</p>\\n\\n<p><strong>Discoverative Intelligence</strong> works by exposing models to new inputs, then examining how their internal representations shift—revealing the model’s internal process of <strong><span>discovery</span></strong>.</p>\\n\\n<p>Finally, <em>it surfaces insights</em> showing where the model is strong and where it needs development, enabling more intentional fine-tuning and discovery-oriented training.</p>\\n\\n<p>This framework is powerful because it evaluates models in motion. Traditional benchmarks test static performance. A <strong><span>Discoverative benchmark</span></strong> tests <strong><span>cognitive discovery and evolution</span></strong>.</p>\\n\\n<h3>Core Components of the Discoverative Framework</h3>\\n\\n<p>The <strong>Discoverative</strong> evaluation ecosystem relies on deep diagnostic layers. Understanding these parts clarifies why the system reveals more than accuracy scores ever could—and why it enables real <strong><span>discovery</span></strong>.</p>\\n\\n<p>The <strong>platform that delivers</strong> <strong><span>discoverative intelligence</span></strong> is made of integrated components that analyze model behavior dynamically, tracking how discovery unfolds.</p>\\n\\n<h4>Novelty Detection and Cognitive Signals</h4>\\n\\n<p><strong>Novelty detection</strong> is the foundation of <strong><span>discovery</span></strong>. <strong><span>Discoverative AI</span></strong> identifies inputs a model hasn’t seen before and measures how well it recognizes uncertainty and begins the discovery process.</p>\\n\\n<p>It doesn’t simply compare against known data. It evaluates how the model responds to new patterns, ambiguity, and unexpected relationships—conditions where true <strong><span>discovery</span></strong> happens.</p>\\n\\n<p><strong>Discovery-driven evaluation</strong> looks at uncertainty calibration, anomaly recognition, and hypothesis formation.</p>\\n\\n<p>All findings are distilled into actionable insights researchers can use immediately.</p>\\n\\n<p>Here’s what <strong>discovery-based evaluation</strong> typically examines:</p>\\n\\n<ul>\\n  <li><strong>Uncertainty signals:</strong> When and how the model expresses doubt</li>\\n  <li><strong>Novelty recognition:</strong> Ability to detect unfamiliar data</li>\\n  <li><strong>Hypothesis revision:</strong> How the model updates internal beliefs through discovery</li>\\n  <li><strong>Reasoning stability:</strong> Consistency and reliability across iterations</li>\\n  <li><strong>Reflective adaptation:</strong> Signs of self-correction and cognitive growth</li>\\n</ul>\\n\\n<p>The <strong>Discoverative framework</strong> moves beyond surface-level outcomes. It analyzes how an AI system adjusts its internal structure to create genuine understanding—and genuine <strong><span>discovery</span></strong>.</p>\\n\\n<h4>Benchmark Integration and Reflective Evaluation</h4>\\n\\n<p>Integration with standard AI workflows makes <strong>Discoverative AI</strong> immediately practical. It complements existing benchmarks and adds a new layer of introspection and <strong><span>discovery-driven diagnostics</span></strong>.</p>\\n\\n<p><strong>Benchmark integration</strong> enables models to be evaluated on both performance and cognitive evolution—how they discover new structure over time.</p>\\n\\n<p>These reflective diagnostics turn raw signals into strategic insights. The framework highlights where reasoning is unstable and where conceptual structure supports deeper discovery.</p>\\n\\n<p>The <strong>Discoverative system</strong> integrates with logs, embeddings, world-models, and error traces to build a multi-dimensional view of cognition and discovery paths.</p>\\n\\n<p>This enables deeper understanding throughout the <strong><span>evaluation lifecycle</span></strong>.</p>\\n\\n<p>Automation extends to identifying conceptual gaps, tracing reasoning paths, and surfacing notable model behaviors that indicate discovery potential.</p>\\n\\n<p>This keeps development aligned with how intelligence actually grows—through continual <strong><span>discovery</span></strong>, not just static performance.</p>\\n\\n<h3>Why AI Needs a Discoverative Framework</h3>\\n\\n<p>Modern AI systems generate impressive outputs, but their reasoning often remains opaque. Discoverative assessment reduces this blind spot by evaluating the mechanisms behind cognition and <strong><span>discovery</span></strong>.</p>\\n\\n<p>Traditional benchmarks offer limited insight into uncertainty, novelty detection, and conceptual growth—key drivers of discovery.</p>\\n\\n<p>A <strong>discoverative assessment system</strong> transforms AI development by highlighting how models evolve and discover, not only how they perform. Researchers can focus on strengthening reasoning rather than optimizing for static scores.</p>\\n\\n<p>The framework doesn’t replace performance tests. It <em>enhances</em> them by providing <strong><span>depth, structure, and discovery-oriented transparency</span></strong>.</p>\\n\\n<h3>Understanding Cognitive Growth and Model Evolution</h3>\\n\\n<p>AI development involves significant iteration, but most evaluation tools hide the process behind final numbers. Researchers must analyze logs manually to understand what changed inside the model.</p>\\n\\n<p><strong>Discoverative tools</strong> automate this diagnostic process. They surface reasoning shifts, identify unstable patterns, and track conceptual evolution across versions—making discovery explicit rather than hidden.</p>\\n\\n<p>This dramatically reduces the analytical burden and allows scientists to focus on <strong><span>meaningful discoveries</span></strong> that improve model performance.</p>\\n\\n<h4>Interpretable Insights and Structured Reflection</h4>\\n\\n<p>A key concern in AI development is understanding why a model behaves the way it does. Discoverative AI answers this by提供 structured reflection layers that explain behavior and highlight discovery patterns.</p>\\n\\n<p>The system analyzes conceptual relationships, uncertainty patterns, reasoning chains, and internal representation shifts—all contributors to deeper discovery.</p>\\n\\n<p>These insights produce <strong>interpretable narratives</strong> that help researchers improve architectures, datasets, and training methods.</p>\\n\\n<p><strong><span>No aspect of cognition is lost in the noise</span></strong> because Discoverative tracks reasoning and discovery in a consistent, repeatable manner across experiments.</p>\\n\\n<p>Teams using <strong>Discoverative diagnostics</strong> report dramatically improved clarity into model behavior. They can pinpoint conceptual weaknesses, identify developmental milestones, and observe how reasoning—and discovery—improves over time.</p>\\n\\n<h4>Tracking Discoverative Metrics and Cognitive Dashboards</h4>\\n\\n<p>Understanding how intelligence evolves requires visibility into key cognitive metrics. Discoverative AI provides comprehensive <strong>dashboard</strong> analytics that track reasoning, uncertainty, and ongoing <strong><span>discovery</span></strong>.</p>\\n\\n<p>These dashboards break down cognition across tasks, domains, and input types.</p>\\n\\n<p>Researchers see exactly where understanding strengthens or degrades across training cycles—along with where discovery accelerates or stalls.</p>\\n\\n<p>Real-time cognitive tracking keeps teams aligned on developmental progress and conceptual evolution.</p>\\n\\n<p>The analytics reveal structural gaps quickly. Perhaps the model recognizes novelty but fails to revise beliefs. Or it calibrates uncertainty well but struggles with hypothesis stability.</p>\\n\\n<p>These insights allow teams to <strong><span>advance intelligence through continuous discovery</span></strong> systematically.</p>\\n</div>\"}\r\nconst siteRef = useSiteRefs()\r\nconst templateProps = {BHDx3EUaG: props3, ECFHkDkZa: props8, J_K0pNWTB: props2, M6UnIrxig: props9, RnYDPzYVC: props7, thEjxIh59: props11, Tk7tJfCE8: props10, VT29DirfJ: props5, VZkV9as9j: props4, X4V7Q6zQ7: props1, Xl6OAbDBd: props6}\r\nconst props = templateProps[webPageId] ?? {}\r\nswitch (webPageId) {case \"X4V7Q6zQ7\":\r\ncase \"J_K0pNWTB\":\r\ncase \"BHDx3EUaG\":\r\ncase \"VZkV9as9j\":\r\ncase \"VT29DirfJ\":\r\ncase \"Xl6OAbDBd\":\r\ncase \"RnYDPzYVC\":\r\ncase \"ECFHkDkZa\":\r\ncase \"M6UnIrxig\":\r\ncase \"Tk7tJfCE8\":\r\ncase \"thEjxIh59\":\r\nreturn React.createElement(ResolveLinks, {links: [props[\"HVmTXsPHl\"], props[\"i84AKQOBN\"]]}, (resolvedLinks) => React.createElement(Main_New, {...props, HVmTXsPHl: resolvedLinks[0], i84AKQOBN: siteRef(resolvedLinks[1]), key: \"Main_New\", style}, children(true)));\r\ndefault: { return children(false) }}}\r\nexport function getLayoutTemplateBreakpoints(routeId) {switch (routeId) {case \"X4V7Q6zQ7\":\r\ncase \"J_K0pNWTB\":\r\ncase \"BHDx3EUaG\":\r\ncase \"VZkV9as9j\":\r\ncase \"VT29DirfJ\":\r\ncase \"Xl6OAbDBd\":\r\ncase \"RnYDPzYVC\":\r\ncase \"ECFHkDkZa\":\r\ncase \"M6UnIrxig\":\r\ncase \"Tk7tJfCE8\":\r\ncase \"thEjxIh59\":\r\nreturn [{hash: \"bugju\", mediaQuery: \"(min-width: 1200px)\"}, {hash: \"1tp2hqo\", mediaQuery: \"(min-width: 810px) and (max-width: 1199.98px)\"}, {hash: \"gqfai0\", mediaQuery: \"(max-width: 809.98px)\"}]\r\ndefault: { return undefined }}}\r\n\r\n\t\tconst isBrowser = typeof document !== \"undefined\"\r\n\t\tconst isBot = isBrowser && /bot|-google|google-|yandex|ia_archiver|crawl|spider/iu.test(navigator.userAgent)\r\n\r\n\t\texport async function getPageRoot({ routeId, pathVariables, localeId, collectionItemId }) {\r\n\t\t\tconst rootPreload = routes[routeId].page.preload()\r\n\r\n\t\t\tFramer.setLibraryFeatures({disableCustomCode: false, editorBarDisableFrameAncestorsSecurity: false, motionDivToDiv: false, onPageLocalizationSupport: true, onPageMoveTool: true, onPageRichTextBlockSelection: true, privateRouterReplaceState: false, scrollRestoration: true, synchronousNavigationOnDesktop: false, yieldOnTap: false})\r\n\r\n\t\t\tconst content = React.createElement(\r\n\t\t\t\tFramer.PageRoot,\r\n\t\t\t\t{\r\n\t\t\t\t\tisWebsite: true,\r\n\t\t\t\t\tenvironment: \"site\",\r\n\t\t\t\t\trouteId,\r\n\t\t\t\t\tpathVariables,\r\n\t\t\t\t\troutes,\r\n\t\t\t\t\tcollectionUtils,\r\n\t\t\t\t\tframerSiteId,\r\n\t\t\t\t\tnotFoundPage: Framer.lazy(() => import(\"__framer-not-found-page\")),\r\n\t\t\t\t\tisReducedMotion: undefined,\r\n\t\t\t\t\tlocaleId,\r\n\t\t\t\t\tlocales,\r\n\t\t\t\t\tpreserveQueryParams: true,\r\n\t\t\t\t\tsiteCanonicalURL: \"https://www.discoverative.ai\",\r\n\t\t\t\t\tEditorBar: \r\n\t\t\ttypeof window !== \"undefined\" ? (() => {\r\n\t\t\t\tif (isBot) {\r\n\t\t\t\t\tconsole.log(\"[Framer On-Page Editing] Unavailable because navigator is bot\")\r\n\t\t\t\t\treturn\r\n\t\t\t\t}\r\n\r\n\t\t\t\treturn Framer.lazy(async () => {\r\n\t\t\t\t\tconst dependencies = {\r\n\t\t\t\t\t\t__version: 3,\r\n\t\t\t\t\t\tframer: {\r\n\t\t\t\t\t\t\tuseCurrentRoute: Framer.useCurrentRoute,\r\n\t\t\t\t\t\t\tuseLocaleInfo: Framer.useLocaleInfo,\r\n\t\t\t\t\t\t\tuseRouter: Framer.useRouter,\r\n\t\t\t\t\t\t},\r\n\t\t\t\t\t\treact: {\r\n\t\t\t\t\t\t\tcreateElement: React.createElement,\r\n\t\t\t\t\t\t\tFragment: React.Fragment,\r\n\t\t\t\t\t\t\tmemo: React.memo,\r\n\t\t\t\t\t\t\tuseCallback: React.useCallback,\r\n\t\t\t\t\t\t\tuseEffect: React.useEffect,\r\n\t\t\t\t\t\t\tuseRef: React.useRef,\r\n\t\t\t\t\t\t\tuseState: React.useState,\r\n\t\t\t\t\t\t\tuseLayoutEffect: React.useLayoutEffect,\r\n\t\t\t\t\t\t},\r\n\t\t\t\t\t\t\"react-dom\": { createPortal },\r\n\t\t\t\t\t}\r\n\r\n\t\t\t\t\twindow.__framer_editorBarDependencies = dependencies\r\n\r\n\t\t\t\t\tconst { createEditorBar } = await import(\"https://framer.com/edit/init.mjs\")\r\n\t\t\t\t\treturn {\r\n\t\t\t\t\t\tdefault: createEditorBar(),\r\n\t\t\t\t\t}\r\n\t\t\t\t})\r\n\t\t\t})() : undefined\r\n\t\t,\r\n\t\t\t\t\tadaptLayoutToTextDirection: false,\r\n\t\t\t\t\tLayoutTemplate: Template,\r\n\t\t\t\t\tloadSnippetsModule: new Framer.LazyValue(() => import(\"https://framerusercontent.com/modules/weT6fgM02kJE3Hh25DdH/UwLXyyqqahfHQ3KCGPbz/snippets.js\")),\r\n\t\t\t\t\tinitialCollectionItemId: collectionItemId,\r\n\t\t\t\t}\r\n\t\t\t)\r\n\r\n\t\t\tconst contentWithGracefullyDegradingErrorBoundary = React.createElement(Framer.GracefullyDegradingErrorBoundary, {\r\n\t\t\t\tchildren: content\r\n\t\t\t})\r\n\r\n\t\t\t\r\n\t\t\tconst page = React.createElement(Framer.PageEffectsProvider, {\r\n\t\t\t\tchildren: contentWithGracefullyDegradingErrorBoundary,\r\n\t\t\t\tvalue: {global: {enter: {mask: {angle: 180, type: \"wipe\", width: \"100%\"}, opacity: 1, rotate: 0, rotate3d: false, rotateX: 0, rotateY: 0, scale: 1, transition: {damping: 30, delay: 0, duration: 0.4, ease: [0.27, 0, 0.51, 1], mass: 1, stiffness: 400, type: \"tween\"}, x: \"0px\", y: \"0px\"}}, routes: {}}\r\n\t\t\t})\r\n\r\n\t\t\t// We don't want the initial render to immediately have to suspend.\r\n\t\t\tawait rootPreload\r\n\t\t\treturn page\r\n\t\t}\r\n\r\n\t\tif (isBrowser) {\r\n\t\t\twindow.__framer_importFromPackage = (packageAndFilename, exportIdentifier) => () => {\r\n\t\t\t\treturn React.createElement(Framer.ErrorPlaceholder, { error: 'Package component not supported: \"' + exportIdentifier + '\" in \"' + packageAndFilename + '\"' })\r\n\t\t\t}\r\n\r\n\t\t\t\r\n\t\t\t\r\n\t\t\twindow.__framer_events = window.__framer_events || []\r\n\r\n\t\t\t// Initialize lazy modules cache for hydration\r\n\t\t\tFramer.initLazyModulesCache()\r\n\r\n\t\t\tconst container = document.getElementById(\"main\")\r\n\t\t\t// We know that #main is parsed before this script, so we don't need to wait for DOMContentLoaded or similar events.\r\n\t\t\tif (\"framerHydrateV2\" in container.dataset) main(true, container)\r\n\t\t\telse main(false, container)\r\n\t\t}\r\n\r\n\t\tfunction track() {\r\n\t\t\tif (!isBrowser) return\r\n\t\t\twindow.__framer_events.push(arguments)\r\n\t\t}\r\n\r\n\t\tasync function main(shouldHydrate, container) {\r\n\t\t\tfunction handleError(error, errorInfo, recoverable = true) {\r\n\t\t\t\tif (error.caught || window.__framer_hadFatalError) return // we already logged it\r\n\r\n\t\t\t\tconst componentStack = errorInfo?.componentStack\r\n\t\t\t\tif (recoverable) {\r\n\t\t\t\t\tconsole.warn(\"Caught a recoverable error. The site is still functional, but might have some UI flickering or degraded page load performance. If you are the author of this website, update external components and check recently added custom code or code overrides to fix the following server/client mismatches:\\n\", error, componentStack)\r\n\t\t\t\t\t// we only want to collect 1%, because this can be quite noisy (floods the data pipeline)\r\n\t\t\t\t\tif (Math.random() > 0.01) return\r\n\t\t\t\t} else {\r\n\t\t\t\t\tconsole.error(\"Caught a fatal error. Please report the following to the Framer team via https://www.framer.com/contact/:\\n\", error, componentStack)\r\n\t\t\t\t}\r\n\t\t\t\ttrack(recoverable ? \"published_site_load_recoverable_error\" : \"published_site_load_error\", {\r\n\t\t\t\t\tmessage: String(error),\r\n\t\t\t\t\tcomponentStack, // componentStack is more useful\r\n\t\t\t\t\tstack: componentStack ? undefined : error instanceof Error && typeof error.stack === \"string\" ? error.stack : null,\r\n\t\t\t\t})\r\n\t\t\t}\r\n\r\n\t\t\ttry {\r\n\t\t\t\tlet routeId, localeId, pathVariables, breakpoints, routeData\r\n\t\t\t\tif (shouldHydrate) {\r\n\t\t\t\t\trouteData = JSON.parse(container.dataset[\"framerHydrateV2\"])\r\n\t\t\t\t\trouteId = routeData.routeId\r\n\t\t\t\t\tlocaleId = routeData.localeId\r\n\t\t\t\t\tpathVariables = routeData.pathVariables\r\n\t\t\t\t\tbreakpoints = routeData.breakpoints\r\n\r\n\t\t\t\t\trouteId = Framer.patchRoutesForABTesting(routes, routeId) // Prioritize optimized route id to avoid flickering in browsers not supporting the server-timing header (Safari older than 16.4)\r\n\t\t\t\t} else {\r\n\t\t\t\t\tFramer.patchRoutesForABTesting(routes, undefined) // This must happen before inferInitialRouteFromPath\r\n\r\n\t\t\t\t\t\r\n\t\t\t\t\tconst serverRouteExperimentEnabled = true\r\n\t\t\t\t\tconst serverRoute = serverRouteExperimentEnabled && performance.getEntriesByType(\"navigation\")[0]?.serverTiming?.find(e => e.name === \"route\")?.description\r\n\t\t\t\t\tif (serverRoute) {\r\n\t\t\t\t\t\tconst routeData = new URLSearchParams(serverRoute)\r\n\t\t\t\t\t\trouteId = routeData.get(\"id\")\r\n\t\t\t\t\t\tlocaleId = routeData.get(\"locale\")\r\n\t\t\t\t\t\tfor (const [key, value] of routeData.entries()) {\r\n\t\t\t\t\t\t\tif (!key.startsWith(\"var.\")) continue\r\n\t\t\t\t\t\t\tpathVariables ??= {}\r\n\t\t\t\t\t\t\tpathVariables[key.slice(4)] = value\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t}\r\n\t\t\t\t\tif (!routeId || !localeId) {\r\n\t\t\t\t\t\tconst routeData = Framer.inferInitialRouteFromPath(routes, decodeURIComponent(location.pathname), true, locales)\r\n\t\t\t\t\t\trouteId = routeData.routeId\r\n\t\t\t\t\t\tlocaleId = routeData.localeId\r\n\t\t\t\t\t\tpathVariables = routeData.pathVariables\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\r\n\t\t\t\tconst pagePromise = getPageRoot({\r\n\t\t\t\t\trouteId,\r\n\t\t\t\t\tlocaleId,\r\n\t\t\t\t\tpathVariables,\r\n\t\t\t\t\tcollectionItemId: routeData?.collectionItemId,\r\n\t\t\t\t})\r\n\r\n\t\t\t\t\r\nif (typeof window !== \"undefined\") {\r\n\tvoid (async () => {\r\n\t\tconst route = routes[routeId]\r\n\r\n\t\tconst defaultLocaleId = \"default\"\r\n\t\tconst framerLocale = locales.find(({ id }) => !localeId ? id === defaultLocaleId : id === localeId).code\r\n\r\n\t\tlet collectionItemId = routeData?.collectionItemId ?? null\r\n\t\tif (collectionItemId === null && route?.collectionId && collectionUtils) {\r\n\t\t\t  const utils = await collectionUtils[route.collectionId]?.()\r\n\t\t\t  const [slug] = Object.values(pathVariables)\r\n\t\t\t  if (utils && typeof slug === \"string\") {\r\n\t\t\t\t  collectionItemId = (await utils.getRecordIdBySlug(slug, framerLocale || undefined)) ?? null\r\n\t\t\t  }\r\n\t\t}\r\n\r\n\t\tconst resolvedDateTimeOptions = Intl.DateTimeFormat().resolvedOptions()\r\n\t\tconst timezone = resolvedDateTimeOptions.timeZone\r\n\t\tconst locale = resolvedDateTimeOptions.locale\r\n\r\n\t\t// wait for the page to be activated before sending the pageview event\r\n\t\t// https://developer.chrome.com/docs/web-platform/prerender-pages#impact-on-analytics\r\n\t\tawait new Promise((resolve) => {\r\n\t\t\tif (document.prerendering) {\r\n\t\t\t\tdocument.addEventListener(\"prerenderingchange\", resolve, { once: true })\r\n\t\t\t} else {\r\n\t\t\t\tresolve()\r\n\t\t\t}\r\n\t\t})\r\n\r\n\t\twindow.__framer_events.push([\r\n\t\t\t\"published_site_pageview\",\r\n\t\t\t{\r\n\t\t\t\tframerSiteId,\r\n\t\t\t\tversion: 2,\r\n\t\t\t\troutePath: route?.path || \"/\",\r\n\t\t\t\tcollectionItemId,\r\n\t\t\t\tframerLocale: framerLocale || null,\r\n\t\t\t\twebPageId: route?.abTestingVariantId ?? routeId,\r\n\t\t\t\tabTestId: route?.abTestId,\r\n\t\t\t\treferrer: document.referrer || null,\r\n\t\t\t\turl: window.location.href,\r\n\t\t\t\thostname: window.location.hostname || null,\r\n\t\t\t\tpathname: window.location.pathname || null,\r\n\t\t\t\thash: window.location.hash || null,\r\n\t\t\t\tsearch: window.location.search || null,\r\n\t\t\t\ttimezone,\r\n\t\t\t\tlocale,\r\n\t\t\t},\r\n\t\t\t\"eager\"\r\n\t\t])\r\n\r\n\t\t// Yield to avoid blocking the main thread with the user code\r\n\t\tawait Framer.yieldToMain({ priority: \"background\", ensureContinueBeforeUnload: true, continueAfter: \"paint\" })\r\n\r\n\t\tdocument.dispatchEvent(new CustomEvent(\"framer:pageview\", {\r\n\t\t\tdetail: { framerLocale: framerLocale || null }\r\n\t\t}))\r\n\t})()\r\n}\r\n\r\n\r\n\t\t\t\tconst page = await pagePromise\r\n\t\t\t\tif (shouldHydrate) {\r\n\t\t\t\t\t\r\n\r\n\t\t\t\t\tFramer.withPerformanceMarks(\"framer-rewrite-breakpoints\", () => {\r\n\t\t\t\t\t\tFramer.removeHiddenBreakpointLayersV2(breakpoints)\r\n\t\t\t\t\t\twindow.__framer_onRewriteBreakpoints?.(breakpoints)\r\n\t\t\t\t\t})\r\n\r\n\t\t\t\t\t\r\n\r\n\t\t\t\t\tconst startTransition = isBot ? cb => cb() : React.startTransition\r\n\t\t\t\t\tstartTransition(() => {\r\n\t\t\t\t\t\tFramer.markHydrationStart()\r\n\t\t\t\t\t\tFramer.turnOffReactEventHandling()\r\n\t\t\t\t\t\tReactDOM.hydrateRoot(container, page, { onRecoverableError: handleError })\r\n\t\t\t\t\t})\r\n\t\t\t\t} else {\r\n\t\t\t\t\t\r\n\t\t\t\t\tReactDOM.createRoot(container, { onRecoverableError: handleError }).render(page)\r\n\t\t\t\t}\r\n\t\t\t} catch (error) {\r\n\t\t\t\thandleError(error, undefined, false)\r\n\t\t\t\tthrow 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