# Google Search Published articles for Google Search. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Cloudflare Adds Setting to Block AI Training Crawlers While Allowing Search Crawlers DevFeed: [Cloudflare Adds Setting to Block AI Training Crawlers While Allowing Search Crawlers]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-16T17:18:57Z Content type: news Language: en Sources: [Web Designer Depot]() Topics: [Cloudflare](), [AI Bots](), [Crawler](), [Google Search](), [Artificial Intelligence]() Tags: [ai](), [ai-crawlers](), [ai-tech](), [ai-training](), [anthropic](), [artificial-intelligence](), [cloudflare](), [content-protection](), [future-of-the-web](), [google-extended](), [google-search](), [googlebot](), [openai](), [publishers](), [robots-txt](), [search](), [search-engines](), [web](), [web-design](), [web-development](), [web-publishing](), [web-scraping](), [website-traffic]() ### AI overview Cloudflare launched a Disallow AI Training setting that lets website owners allow traditional search crawlers while blocking training-only crawlers from companies including Amazon, Anthropic, Meta, and OpenAI. The article notes that robots.txt depends on crawler compliance and that blocking Google-Extended does not remove content from Google Search features such as AI Overviews or AI Mode. ### Source excerpt Cloudflare just gave website owners a new weapon against AI crawlers: keep the search traffic, block the AI training. After years of watching bots consume the web's content, publishers finally have an easier way to tell AI companies where to go. ## Google's new search redirects make links harder to check before you click DevFeed: [Google's new search redirects make links harder to check before you click]() Original publisher: [Read original article]() Author: Pieter Arntz Published: 2026-09-14T14:17:44Z Content type: news Language: en Sources: [Malwarebytes]() Topics: [Google](), [Google Search](), [Search engine optimization (SEO)](), [Security](), [Accessibility](), [Reddit]() Tags: [accessibility](), [data](), [google](), [google-search](), [goto-url](), [news](), [reddit](), [search](), [security](), [seo](), [serp]() ### AI overview Google is routing some search result links through opaque, Google-specific redirects instead of linking directly to destinations. The change may make bulk URL extraction harder, but it also limits legitimate tools and makes it more difficult for users to verify a link's destination by hovering before clicking. ### Source excerpt Google says its new opaque redirects tackle evolving abuse, but they also prevent users from checking a result's destination by hovering over it. ## Google Is Testing a Search Bar That Works Outside Chrome DevFeed: [Google Is Testing a Search Bar That Works Outside Chrome]() Original publisher: [Read original article]() Author: Simon Sterne Published: 2026-09-10T16:00:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Chrome](), [Google](), [Chromium](), [Artificial Intelligence]() Tags: [ai](), [ai-assistants](), [ai-search](), [ai-tech](), [artificial-intelligence](), [browser](), [browser-design](), [chrome](), [chrome-canary](), [chromium](), [everywhere-omnibox](), [future-of-browsers](), [future-of-search](), [gemini](), [gemini-in-chrome](), [google](), [google-chrome](), [google-search](), [project-loom](), [search](), [search-technology](), [ui-design](), [ux-design](), [web-browsers](), [web-design]() ### AI overview Google is testing Project Loom, an experimental floating Search bar that can appear over other Windows apps instead of remaining inside Chrome. The feature is unfinished, with screen sharing, Lens, and AI Mode controls reportedly not yet working. ### Source excerpt Google is quietly testing a way to bring Search outside Chrome and directly on top of whatever app you're using. It's called Project Loom, and this little floating search box could hint at a much bigger future where Google follows you around your desktop. ## Build an AI Agent with Real-Time Web Search in JavaScript DevFeed: [Build an AI Agent with Real-Time Web Search in JavaScript]() Original publisher: [Read original article]() Author: Amit Merchant Published: 2026-08-14T00:00:00Z Content type: tutorial Language: en Sources: [Amit Merchant]() Topics: [AI Agent](), [JavaScript](), [real-time](), [Web](), [Large Language Model](), [Google Search]() Tags: [ai-agent](), [google-search](), [javascript](), [large-language-models-llms](), [programming](), [real-time](), [tutorial](), [web]() ### AI overview This tutorial explains how to build a small JavaScript AI agent that decides when it needs web search, retrieves Google search results through SearchApi, and uses those results to formulate an answer. It also explains tool calling and the agent loop. ### Source excerpt Large language models (LLMs) are great at answering questions, but they have an important limitation: they don't inherently have access to what's happening on the web right now. ## Why Your Favorite Websites Look Like 2005 (And Why You Secretly Love It) DevFeed: [Why Your Favorite Websites Look Like 2005 (And Why You Secretly Love It)]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-07-28T11:30:00Z Content type: opinion Language: en Sources: [Web Designer Depot]() Topics: [Google](), [amazon](), [Web Development](), [Latency](), [JavaScript](), [Font]() Tags: [amazon](), [amazon-design](), [cognitive-load](), [complexity](), [conversion-rate-optimization](), [cost](), [customer-trust](), [data](), [design](), [design-psychology](), [digital-accessibility](), [digital-marketing](), [fonts](), [functional-design](), [google](), [google-search](), [human-centric-design](), [information-density](), [javascript](), [latency](), [minimalism](), [speed](), [ui](), [user-experience](), [user-interface](), [ux-strategy](), [ux-usability](), [visual-hierarchy](), [walmart-ecommerce](), [web-design](), [web-performance](), [website-usability]() ### AI overview This opinion argues that the seemingly dated interfaces of Google, Amazon, and Walmart succeed because they reduce cognitive load, make tasks predictable, and prioritize speed over visual complexity. It contrasts these designs with heavily animated, asset-intensive websites that can delay interaction and frustrate users. ### Source excerpt Why are the world's most powerful websites still stuck in the early 2000s? While boutique brands chase "sleek" designs that only end up frustrating users, giants like Amazon and Google have realized that "ugly" is actually a superpower for trust and speed. ## Your agent wants to search like a 2010 quant DevFeed: [Your agent wants to search like a 2010 quant]() Original publisher: [Read original article]() Author: Jon Bratseth Published: 2026-07-07T00:00:00Z Content type: opinion Language: en Sources: [Vespa Blog]() Topics: [AI search](), [information retrieval](), [Language models](), [Google Search]() Tags: [agents](), [ai](), [ai-agents](), [bm25](), [genai](), [google-search](), [hybrid-search](), [information-retrieval](), [rag](), [ranking](), [search]() ### AI overview The article argues that AI agents should retrieve information with more control and sophistication than ordinary human search users. It describes a progression from vector retrieval to hybrid search using methods such as BM25 and machine-learned ranking, and presents search as code as a possible next stage. ### Source excerpt The idea of empowering AI agents to retrieve information like a professional is going mainstream. ## Building Which-Fuji: A Side Project for Fujifilm Camera Enthusiasts DevFeed: [Building Which-Fuji: A Side Project for Fujifilm Camera Enthusiasts]() Original publisher: [Read original article]() Author: masnun Published: 2026-06-28T06:11:45Z Content type: opinion Language: en Sources: [Abu Ashraf Masnun]() Topics: [Website](), [Web](), [Cloudflare](), [Search engine optimization (SEO)](), [Google Search](), [Structured-data](), [Content Management System]() Tags: [building](), [camera](), [cloudflare](), [cloudflare-pages](), [google-search](), [project](), [robots](), [seo](), [side-project](), [uncategorized]() ### AI overview The article presents Which-Fuji, a small website for helping people choose a Fujifilm camera. It describes the site's browse page, quiz, camera reviews, and longer-form articles, along with the author's reasons for building it. The project uses a static-first architecture on Cloudflare Pages and explores structured data, sitemaps, robots policies, and discovery in Google Search. ### Source excerpt I've been quietly working on a small side project for the last few weeks, and it's finally at a point where I can share it properly. Which-Fuji is a tiny website I built to help people figure out which Fujifilm camera to buy. That's it. No reviews, no affiliate spam, no walls of text -- just [...] The post Building Which-Fuji: A Side Project for Fujifilm Camera Enthusiasts appeared first on Abu Ashraf Masnun. ## Re-autoresearching MSMARCO BM25, on Vespa DevFeed: [Re-autoresearching MSMARCO BM25, on Vespa]() Original publisher: [Read original article]() Author: andreer thomas Published: 2026-05-29T00:00:00Z Content type: article Language: en Sources: [Vespa Blog]() Topics: [Benchmark](), [dataset](), [Python](), [Large Language Model](), [generalization in machine learning](), [pandas](), [Google Search]() Tags: [benchmark](), [bm25](), [dataset](), [evaluation](), [experiment](), [google-search](), [information-retrieval](), [openai](), [pandas](), [python](), [ranking](), [retrieval](), [zero-shot]() ### AI overview This article reproduces an MSMARCO BM25 autoresearch experiment in Vespa. It compares LLM-driven Python reranking with an approach restricted to existing Vespa rank features and reports a comparable improvement on a 650,000-passage subset, with better generalization to the full dataset. ### Source excerpt BM25 is having a moment. We reproduce Doug Turnbull's MSMARCO autoresearch experiment in Vespa and get a comparable MRR@10 lift from existing rank features -- with twice the generalization to full MSMARCO. ## Google's shift toward AI-first search increases the importance of AEO DevFeed: [Google's shift toward AI-first search increases the importance of AEO]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-05-21T00:00:00Z Content type: opinion Language: en Sources: [Dodo Payments Blog]() Topics: [Google Search](), [Search engine optimization (SEO)](), [Google](), [Artificial Intelligence](), [Large Language Model](), [Website]() Tags: [ai](), [google](), [google-search](), [llms](), [newsletter](), [opinions](), [seo](), [web]() ### AI overview The article argues that Google's integration of Gemini is shifting search from link-based discovery toward conversational, AI-mediated answers. It says SEO alone may no longer be sufficient and that Answer Engine Optimization (AEO), structured information, semantic clarity, and contextual relevance are becoming more important for visibility. It also introduces DualMark, an open-source project intended to make infrastructure more readable to AI systems. ### Source excerpt Google is moving deeper into AI-first search with Gemini, and the discovery layer of the internet is shifting from SEO to AEO. Here's why AEO matters and how we open-sourced DualMark to help. ## Build with Nano Banana Pro, our Gemini 3 Pro Image model DevFeed: [Build with Nano Banana Pro, our Gemini 3 Pro Image model]() Original publisher: [Read original article]() Author: Alisa Fortin Published: 2025-11-20T15:11:14Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [text-to-image](), [Artificial Intelligence](), [API](), [multimodal](), [Google Search](), [Figma]() Tags: [4k](), [ai](), [ai-studio](), [api](), [color-grading](), [demo](), [figma](), [gemini](), [generation](), [google-search](), [image](), [model](), [multimodal](), [none](), [product-design]() ### AI overview The article announces Nano Banana Pro, also called Gemini 3 Pro Image, a higher-fidelity image generation and editing model built on Gemini 3 Pro. It describes developer access through the Gemini API in Google AI Studio and Vertex AI, with multimodal capabilities, Google Search grounding, improved text rendering, precise image controls, high-resolution outputs, and support for compositing multiple reference elements. The model is also being integrated into developer and creative platforms for UI mockups and visual asset creation. ### Source excerpt Nano Banana Pro, or Gemini 3 Pro Image, is our most advanced image generation and editing model. ## Introducing Nano Banana Pro DevFeed: [Introducing Nano Banana Pro]() Original publisher: [Read original article]() Author: Naina Raisinghani Published: 2025-11-20T15:05:02Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google](), [data](), [Google Search]() Tags: [creativity](), [data](), [design](), [fonts](), [gemini](), [generate](), [generation](), [google](), [google-search](), [image](), [images](), [model](), [none](), [prototypes](), [real-time](), [reasoning](), [search]() ### AI overview Google DeepMind introduces Nano Banana Pro, also called the Gemini 3 Pro Image model, for image generation and editing. It uses advanced reasoning, real-world knowledge, and Google Search to create context-rich visuals, infographics, diagrams, prototypes, and images containing legible multilingual text. ### Source excerpt Nano Banana Pro is our new image generation and editing model from Google DeepMind. ## Gemini 2.5 Flash-Lite is now ready for scaled production use DevFeed: [Gemini 2.5 Flash-Lite is now ready for scaled production use]() Original publisher: [Read original article]() Author: Logan Kilpatrick; Zach Gleicher Published: 2025-10-25T17:34:32Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [long-context](), [Latency](), [multimodal](), [Google Search]() Tags: [ai](), [cost](), [gemini](), [google-search](), [latency](), [launch](), [model](), [multimodal](), [production](), [speed](), [token]() ### AI overview Google DeepMind announces the stable release of Gemini 2.5 Flash-Lite for scaled production use. It is positioned as the fastest and lowest-cost model in the Gemini 2.5 family, with optional native reasoning, lower latency, reduced audio-input pricing, and support for a 1 million-token context window, controllable thinking budgets, multimodal understanding, and tools including Google Search grounding, code execution, and URL context. ### Source excerpt Gemini 2.5 Flash-Lite, previously in preview, is now stable and generally available. This cost-efficient model provides high quality in a small size, and includes 2.5 family features like a 1 million-token context window and multimodality. ## Grounding with Google Search in the Firebase AI Logic client SDKs DevFeed: [Grounding with Google Search in the Firebase AI Logic client SDKs]() Original publisher: [Read original article]() Author: Ankita Saxena; Daniel La Rocque Published: 2025-07-28T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Search](), [SDKs](), [Artificial Intelligence](), [Mobile](), [Web]() Tags: [ai](), [ai-logic](), [android](), [firebase](), [flutter](), [gemini](), [google](), [google-search](), [grounding](), [ios](), [javascript](), [mobile](), [news](), [react](), [sdks](), [search](), [tutorials](), [unity](), [updates](), [vertex-ai](), [web]() ### AI overview Firebase announces Grounding with Google Search for its AI Logic client-side SDKs. The feature lets Gemini search current web content before generating responses, enabling mobile and web applications to use more up-to-date information. ### Source excerpt News, tutorials, and updates from the Firebase team. ## High-Precision Responses with Genkit's Google Search Integration DevFeed: [High-Precision Responses with Genkit's Google Search Integration]() Original publisher: [Read original article]() Author: tanabee Published: 2024-10-21T18:37:56Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Google Search](), [Google](), [Google Cloud Platform (GCP)](), [Artificial Intelligence]() Tags: [ai](), [ai-api](), [cloud](), [code](), [gemini](), [genkit](), [github](), [google](), [google-search](), [grounding](), [how-to](), [integration]() ### AI overview This tutorial explains how to integrate Google Search with Genkit 0.5.9 so applications can use current information, including details from specific domains, when generating responses. It covers enabling Vertex AI, configuring a Google Cloud project and local environment, initializing a Genkit project, and running the Genkit Developer UI. ### Source excerpt aGenkit With the release of Genkit version 0.5.9, it is now possible to leverage Google Search directly through Genkit. By using Google Search, Genkit can access the latest information, including niche details from specific domains, leading to more accurate responses. This article explains how to set up Google Search integration for Genkit. Enabling Vertex AI To use the Google Search feature, you need a Google Cloud project with Vertex AI enabled. Follow these steps to enable Vertex AI and link your local environment with Google Cloud: 1. In the Cloud console, Enable the Vertex AI API for your project. 2. Set environment variables: export GCLOUD_PROJECT= export GCLOUD_LOCATION=us-central1 3. Authenticate with gcloud: gcloud auth application-default loginInitializing a Genkit Project Next, initialize your Genkit project. % npm init -y % npm i -D genkit-cli % npm i genkit @genkit-ai/googleai @genkit-ai/vertexai % mkdir src && touch src/index.ts Paste the following initialization code into the src/index.ts file. import { genkit, z } from 'genkit'; import { vertexAI } from '@genkit-ai/vertexai'; import { gemini15Flash } from '@genkit-ai/vertexai'; const ai = genkit({ model: gemini15Flash, plugins: [vertexAI({ location: 'us-central1' })], }); export const mainFlow = ai.defineFlow( { name: 'mainFlow', inputSchema: z.string(), outputSchema: z.string(), }, async (prompt) => { const { text } = await ai.generate(prompt); return text; } ); ai.startFlowServer({ flows: [mainFlow] });Enabling Google Search To enable Google Search, specify `googleSearchRetrieval` with `withConfig` for the `gemini15Flash` model, as shown in the following code. const ai = genkit({ model: gemini15Flash.withConfig({ googleSearchRetrieval: {}}), plugins: [vertexAI({ location: 'us-central1' })], });Run locally Launch the Genkit Developer UI with the following command: % npx genkit start -- npx tsx --watch src/index.ts Try asking about the current weather in Tokyo. Ask about Tokyo's weat ## AI Power Plays: Partnerships and Rivals 🤝🚀 - Air Around AI (a3) #9 DevFeed: [AI Power Plays: Partnerships and Rivals 🤝🚀 - Air Around AI (a3) #9]() Original publisher: [Read original article]() Author: Pradeep Kumar Published: 2024-07-29T14:30:58Z Content type: article Language: en Sources: [Air Around AI]() Topics: [Artificial Intelligence](), [OpenAI](), [Microsoft](), [Google](), [Google Search](), [GPU](), [llama](), [Meta](), [Amazon Web Services](), [Large Language Model](), [text-to-image]() Tags: [ai](), [aws](), [benchmarks](), [google](), [google-search](), [gpu](), [llama](), [llms](), [meta](), [microsoft](), [openai]() ### AI overview This article surveys recent AI industry developments, including the Microsoft-OpenAI partnership, SearchGPT, Meta's Llama 3.1, Mistral Large 2, Adobe Firefly 3, and Google's Gemini 1.5 Flash update. It also discusses competition among major companies and the computing demands of larger models. ### Source excerpt Meta got Llama 3.1, Mistral brings Large 2, OpenAI flexes SearchGPT, Adobe gifts Firefly, Google offers faster Gemini, xAI promises largest supercomputer, CNN Explorer ## Accelerating theblueground.com: Five Core Web Vitals improvements DevFeed: [Accelerating theblueground.com: Five Core Web Vitals improvements]() Original publisher: [Read original article]() Author: Dimitris Zotos Published: 2023-12-22T10:10:37Z Content type: article Language: en Sources: [Blueground Engineering blog]() Topics: [Core Web Vitals](), [Optimization](), [User Experience](), [Google Search](), [Mobile](), [Web]() Tags: [core-web-vitals](), [google-search](), [interaction-to-next-paint](), [mobile](), [optimization](), [performance](), [seo](), [user-experience](), [web]() ### AI overview A Blueground case study describes efforts to improve theblueground.com's Core Web Vitals, focusing on website loading performance and Largest Contentful Paint. The article explains the roles of LCP, FID, and CLS, notes Google's planned replacement of FID with Interaction to Next Paint, and reports an initial mobile LCP of 2.7-3 seconds alongside low impressions in Google Search Console. ### Source excerpt In the realm of modern hospitality, Blueground stands out by providing fully furnished apartments that redefine travel. However, beyond the sophistication of our spaces, a pivotal factor comes into play -- our website's load speed performance. Serving thousands of daily users requires a digital experience that matches the ## How Core Web Vitals saved users 10,000 years of waiting for web pages to load DevFeed: [How Core Web Vitals saved users 10,000 years of waiting for web pages to load]() Original publisher: [Read original article]() Author: Chromium Blog (noreply@blogger.com) Published: 2023-11-07T17:03:00Z Content type: article Language: en Sources: [Chromium Blog]() Topics: [Core Web Vitals](), [Chrome](), [Web](), [Google](), [User Experience](), [Google Search]() Tags: [chrome](), [core-web-vitals](), [ecosystem](), [google](), [google-search](), [performance](), [the-fast-and-the-curious](), [user-experience](), [web](), [web-performance]() ### AI overview This article explains how Core Web Vitals helped improve web performance for Chrome users on desktop and Android, reportedly saving more than 10,000 years of page-loading time in 2023. It describes how shared metrics, developer guidance, and collaboration across Chrome, Search, and the web ecosystem contributed to faster, more responsive pages. ### Source excerpt Today's The Fast and the Curious post explores how Core Web Vitals saved Chrome users more than 10,000 Years of waiting for web pages to load in 2023 (across Chrome desktop and Android) by quantifying the experience of sites and identifying opportunities to make improvements. In 2020, we introduced Web Vitals - essential quality signals for webpages to ensure a better user experience. Since then, there has been a massive leap in web performance made possible by our work on Core Web Vitals (CWV) and its broader impact on the web. Today, over 40% of sites pass all of the CWV metrics, leading to pages that load and respond to interactions more quickly. Here's a closer look at the journey to help improve the performance for sites and some specific work done in the browser and the ecosystem to enable this achievement. Chrome's Quest for Speed The very essence of the web lies in its ability to provide information and services efficiently and rapidly. This principle is at the heart of Google's business and drives our work on Chrome. However, we noticed an issue with sites over a long time horizon. Even if slow sites improved their performance for a while, it would often decline over time. No matter how fast Google Search might be, the user experience would be subpar if the pages found were slow to load. We could not help these sites improve their performance directly, but we wanted users to have a great experience when they moved from Google Search to the individual sites. To tackle the challenge of improving the user experience while simultaneously providing unified guidance to developers, teams from Search and Chrome collaborated to address the issue of slow web pages. Defining the Fast Web We examined millions of pages to define a public standard for a fast, user-friendly web page (initially published in The Science Behind Web Vitals). We published our specifications and data to the open ecosystem and took note of the feedback we received. The introduction of CWV metric ## SEO Myths: Top 5 Sitemap Myths Demystified DevFeed: [SEO Myths: Top 5 Sitemap Myths Demystified]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2023-10-30T20:03:00Z Content type: article Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Search engine optimization (SEO)](), [Google Search](), [Crawler]() Tags: [2005](), [article](), [demystify](), [google-search](), [insights](), [misconceptions](), [myths](), [seo](), [sitemaps](), [surrounding](), [we-ll]() ### AI overview This article examines five common myths about sitemaps and explains that they are optional signals search engines may use, not directives. It also describes situations where a sitemap may help, such as large or new sites and sites with rich media. ### Source excerpt Sitemaps have been around since 2005, but there are still many myths and misconceptions surrounding them. We'll demystify five SEO myths in this article. ## Typeahead Search at Nextdoor DevFeed: [Typeahead Search at Nextdoor]() Original publisher: [Read original article]() Author: Jerry Tian Published: 2022-07-06T19:49:06Z Content type: tutorial Language: en Sources: [Nextdoor]() Topics: [Latency](), [API](), [Low Latency](), [Network](), [Requirements](), [User Experience](), [Google Search]() Tags: [apis](), [autocomplete](), [engineering](), [geohash](), [google-search](), [latency](), [low-latency](), [network](), [requirements](), [search](), [system-design-project](), [user-experience]() ### AI overview This article explains how Nextdoor built a proximity-based typeahead search service for businesses, users, and keywords. It describes the service's focus on geographic relevance, low latency, horizontal scalability, extensibility, and high-throughput indexing. ### Source excerpt Background In a thriving community, people are connected to their friends and local businesses. Nextdoor is the hyperlocal platform that mirrors these offline relationships. Every day, through active discussions on the platform, new relationships are formed and existing ones strengthened. For example, a Nextdoor user can create a post like "I really like @XYZ cafe. @John is a hard working business owner and we should all support him by buying a cup of delicious latte!" Here, the post is created by at-mentioning (via the @ symbol) nearby businesses and users. From this post, users in the neighborhood can contribute by at-mentioning others to be part of the comment threads. As a result, John's cafe thrives and acts as a neighborhood hub where new friends are made. Every month, millions of these mentions are created in various discussions (including lost dogs!). In addition to posts and comments, a user can type into the search box and see, among other things, nearby users and businesses. All these features are powered by the same autocomplete service -- a set of APIs to ingest data and handle typeahead search of different entity types (businesses, users, keywords etc) on Nextdoor. This post focuses on how we built a proximity-based typeahead service to power typeahead use cases at Nextdoor. Proximity-Based Typeahead Search as a Service Any good search experience can be boiled down to two core components: Relevance: Given a search query, whether the user sees relevant results or not. As a hyperlocal social network, relevancy is heavily weighted by geo proximity. 2. Low latency. Google Search found that a 400 millisecond delay resulted in a -0.59% change in searches/user. What's more, even after the delay was removed, these users still had -0.21% fewer searches, indicating that a slower user experience affects long term behavior. For a good autocomplete experience, as users type, relevant results should show up instantaneously. To meet the product requirements, we set ou ## Privacy-focused alternatives to mainstream Big Tech services DevFeed: [Privacy-focused alternatives to mainstream Big Tech services]() Original publisher: [Read original article]() Published: 2020-11-08T00:00:00Z Content type: opinion Language: en Sources: [Markus Hatvan]() Topics: [online privacy](), [Security, Privacy and Abuse Prevention](), [DuckDuckGo](), [Google Search](), [Open Source]() Tags: [alternatives](), [data-privacy](), [google](), [google-search](), [online-privacy](), [privacy]() ### AI overview This opinion article discusses switching from mainstream Big Tech services to privacy-focused alternatives. It explains concerns about personal-data collection, tracking, and corporate concentration, and presents the author's experience balancing privacy, convenience, user experience, and service quality. Google Search alternatives, including DuckDuckGo, are discussed as an example. ### Source excerpt Regular scandals of data leaks by big corporate companies and concerns about misuse of personal information through illegal data collection practices leads to internet users switching away from mainstream products. ## An experience with Daimler's vulnerability reporting program DevFeed: [An experience with Daimler's vulnerability reporting program]() Original publisher: [Read original article]() Author: Eaton Published: 2019-12-19T19:13:31Z Content type: opinion Language: en Sources: [Eaton Works Feed]() Topics: [vulnerability](), [Website](), [Google Search](), [pdf](), [Query (disambiguation)]() Tags: [enumeration](), [google-search](), [pdf](), [query](), [reporting](), [script](), [vulnerability](), [website]() ### AI overview A firsthand account of finding sensitive and confidential documents indexed on Mercedes-Benz USA's Dealer Help Center website. The article describes how varying PDF ID numbers exposed additional documents through an unsecured endpoint and characterizes this as an enumeration attack, while noting that no customer data was found or believed to be at risk. ### Source excerpt Reporting sensitive content exposure on an MBUSA website to Daimler. ## What you see is what you get! DevFeed: [What you see is what you get!]() Original publisher: [Read original article]() Published: 2018-07-19T00:00:00Z Content type: article Language: en Sources: [HackerEarth]() Topics: [Django](), [Markdown](), [Open Source](), [Stack Overflow](), [Google Search]() Tags: [comparison](), [developer](), [django](), [google-search](), [markdown](), [open-source](), [search](), [software]() ### AI overview HackerEarth replaced a Markdown editor in its Recruiter Dashboard with CKEditor 4, a WYSIWYG rich-text editor, to make content creation easier for recruiters. The article explains the reasons for choosing CKEditor over TinyMCE and describes its integration into the Django-based Recruit platform. ### Source excerpt Introduction HackerEarth has grown into a platform that serves a huge number of customers for technical assessment. To make this possible, we try our best to make the platform as easy-to-use as it can get. At several places in our Recruiter Dashboard, we used to have a Markdown editor to allow users to edit free text. There have been multiple times when many of our recruiters have struggled to create content using the Markdown editor. They need not to worry anymore. After many such requests to improve this, we came up with a fix. Say hello to CKEditor (version 4)--The well-known WYSIWYG, Rich Text editor. Why CKEditor? In the battle of the titans (of WYSIWYG editing) between CKEditor and TinyMCE, we decided to go with CKEditor because of the following reasons: It has a huge community of active developers. The strength of the community around an open source project is strongly related to the project's success. As compared to TinyMCE, it provides better support for the following: Multiple languages Source editing Tables Image and media handling etc. It was designed with modularity in mind which allows you to go much deeper if you're a developer. It is doing much better as compared to TinyMCE. One of the easy tricks while surveying software is to compare how alternatives are doing on Google and Stack Overflow trends. Google search comparison (past 5 years) Number of Stack Overflow questions asked Integration The integration of WYSIWYG editor across HackerEarth's Recruit platform is broadly divided into three steps: Adding the Django CKEditor package As the Recruiter dashboard is written entirely in Django, we decided to integrate CKEditor using the django-ckeditor package. CKEditor provides a huge list of out-of-the-box functionalities. Thinking from the perspective of recruiters and problem setters, we decided to opt for a few of them only. The Django CKEditor package reads the configuration from the settings.py file. Here is the snapshot of what the CKEditor configura ## Where is the best place to get help with Firebase? DevFeed: [Where is the best place to get help with Firebase?]() Original publisher: [Read original article]() Author: Doug Stevenson Published: 2018-02-23T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Search](), [Stack Overflow](), [Code](), [Programming]() Tags: [best-practices](), [code](), [community](), [firebase](), [google-search](), [how-to](), [programming](), [tutorials]() ### AI overview This Firebase team tutorial explains where to seek answers to Firebase questions. It recommends starting with Google Search, then choosing among relevant forums such as Stack Overflow, and provides advice for writing clear, well-formatted questions and sharing code and complete error details. ### Source excerpt News, tutorials, and updates from the Firebase team. ## TIP: Search the Website DevFeed: [TIP: Search the Website]() Original publisher: [Read original article]() Author: Shai Almog Published: 2016-12-11T00:00:00Z Content type: tutorial Language: en Sources: [CodeName One]() Topics: [Website](), [Google Search](), [browser](), [pdf](), [Stack Overflow](), [Google Groups](), [Cloudflare]() Tags: [google](), [google-search](), [guide](), [index](), [js](), [pdf](), [search](), [server](), [tool](), [tools]() ### AI overview The article explains workarounds for searching a website that lacks built-in search, including Google site search, a JavaDoc index, a searchable developer-guide PDF, Stack Overflow, and Google Groups. It also discusses challenges in implementing static-site search. ### Source excerpt A frequent complaint we get is the lack of a search feature on the site and we get that. It's frustrating to us too. We'd like to add it but are still looking at the "right way" to do it which I'll discuss at more length below but for now I'd like to discuss a couple of relatively simple workarounds. The Current Workarounds Google Site Search We can just type site:codenameone.com followed by your query into google when searching to search within the site e.g. like this https://www.google.co.il/search?q=site%3Acodenameone.com+Button [Next page]()