# AI search Published articles for AI 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. ## Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train DevFeed: [Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train]() Original publisher: [Read original article]() Published: 2026-09-15T20:00:35Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI search](), [Reinforcement learning](), [Artificial Intelligence](), [Google](), [LLMs](), [Algorithms & Theory]() Tags: [ai](), [ai-search](), [algorithms-theory](), [data-mining-modeling](), [diffusion](), [generative-ai](), [google](), [icml](), [icml-2026](), [reinforcement-learning](), [research](), [retrieval](), [rl](), [search]() ### AI overview Google Research presents Retrieve-for-Train, a framework that uses offline reinforcement learning to compile reward-aligned query fan-outs into training data for a lightweight diffusion retriever. The approach is intended to produce diverse, complementary, and coherent search-result sets in a single inference pass, reducing reliance on expensive inference-time reasoning. ### Source excerpt Algorithms & Theory ## Открываем претрейн Alice AI Search: как устроена модель быстрых ответов Алисы на Поиске DevFeed: [Открываем претрейн Alice AI Search: как устроена модель быстрых ответов Алисы на Поиске]() Original publisher: [Read original article]() Author: pet67 (Яндекс) Published: 2026-09-11T06:05:13Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Artificial Intelligence](), [AI search](), [Transformers](), [Mixture of Experts (MoE)](), [Large Language Model](), [hugging face]() Tags: [agentic](), [ai](), [ai-search](), [alice-ai](), [hugging-face](), [llm](), [ml](), [moe](), [rl](), [tag-178bc8f01f24](), [tag-4004cf5948d3](), [tag-61cd5a476b1d](), [tag-d89cae10e887](), [tag-e6d9cc1f0757](), [transformers]() ### AI overview This developer article explains the Alice AI Search pipeline for generating fast answers, including its search and context-processing stages, shorter information contexts, a sparse Mixture-of-Experts architecture combined with an Encoder-Decoder, and online reinforcement learning from user behavior signals. It also announces the open release of the Alice AI-T5-35B-A0.6B Base model, with external inference available through Hugging Face Transformers while optimized production inference remains internal to Yandex. ### Source excerpt Быстрый ответ Алисы AI -- это самый массовый генеративный продукт Яндекса и первое соприкосновение с Алисой для пользователей Поиска. Даже в час пиковой нагрузки пользователь должен получить лаконичный ответ за считаные секунды. Для этого мы, команда Alice AI Search, адаптируем весь пайплайн быстрых ответов -- от собственного претрейна с кастомной архитектурой до онлайн-rl-обучения на поведенческие сигналы пользователей. В статье разберём, как устроен генеративный ответ в Поиске, и расскажем про основные улучшения июньского релиза: как мы ускорили ответы за счёт коротких инфоконтекстов, зачем совместили Encoder-Decoder с разреженной MoE-архитектурой и как обучение на реальных пользовательских сигналах повлияло на качество и использование продукта. Кроме того, мы выложили в открытый доступ обученную с нуля модель Alice AI-T5-35B-A0.6B Base с тем ограничением, что внешним пользователям доступен инференс через Hugging Face Transformers, а оптимизированный production-инференс пока доступен только внутри Яндекса. Читать далее ## 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. ## Elastic and Tencent Cloud deepen strategic partnership to power AI-era search foundation DevFeed: [Elastic and Tencent Cloud deepen strategic partnership to power AI-era search foundation]() Original publisher: [Read original article]() Author: Yuki Jiang Published: 2026-09-03T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [AI search](), [elasticsearch](), [Large Language Model]() Tags: [ai-search](), [cloud](), [elasticsearch](), [large-language-models](), [partnership](), [platform-elasticsearch]() ### AI overview Elastic and Tencent Cloud expanded their collaboration and launched Tencent Cloud Elasticsearch Service Enterprise Edition, positioning AI search, model integration, and agent connectivity for enterprise data retrieval. ### Source excerpt Tencent Cloud and Elastic announced an expanded strategic collaboration in Shenzhen in August 2026. The companies will deepen their technical and product collaboration to help enterprises establish dedicated AI search for the AI era. ## How SLED can win the cybersecurity race with agentic AI DevFeed: [How SLED can win the cybersecurity race with agentic AI]() Original publisher: [Read original article]() Author: Bobby Suber Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Security Attacks](), [AI Bots](), [Language models]() Tags: [agentic-ai](), [agentic-soc](), [ai-agents](), [ai-search](), [attacks](), [cybersecurity](), [large-language-models-llms](), [public-sector-education-non-profit-government](), [security]() ### AI overview The article describes how AI-enabled attacks are pressuring state, local, and education security teams, and presents an agentic SOC as a way to accelerate investigations by correlating alerts, gathering context, and preparing response plans. ### Source excerpt AI-accelerated attacks are outpacing SLED security teams. Learn how state, local, and education organizations are closing the gap by fixing their data foundation first. ## How to structure a page for AEO: A guide to improving AI search DevFeed: [How to structure a page for AEO: A guide to improving AI search]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-07-27T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [AI search](), [Search engine optimization (SEO)](), [Artificial Intelligence](), [ChatGPT](), [Claude]() Tags: [ai-search](), [content](), [guide](), [how-to](), [search](), [seo](), [strategy]() ### AI overview A practical guide to structuring web pages for answer engine optimization (AEO), focusing on direct answers, question-led headings, scannable formats, concise paragraphs, trust signals, and a pyramid structure that helps AI systems extract and surface information. ### Source excerpt Learn how to structure a page for AEO with clear answers, pyramid structures, and concise language, as well as the most common mistakes to avoid. ## How Searchable ships customer-requested features in 30 minutes on Vercel DevFeed: [How Searchable ships customer-requested features in 30 minutes on Vercel]() Original publisher: [Read original article]() Author: Susan Aziz Published: 2026-07-21T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [AI Chat](), [AI Bots]() Tags: [agent](), [ai](), [ai-gateway](), [ai-search](), [analytics](), [api](), [api-keys](), [chatgpt](), [claude](), [development](), [features](), [model](), [models](), [sdks](), [search](), [vercel]() ### AI overview Searchable uses Vercel's AI SDK and AI Gateway to test models without rotating SDKs or API keys. The company says this has increased development velocity by two to five times and enabled some customer-requested features to ship within 30 minutes. ### Source excerpt Searchable on Vercel 5x increase in development velocity 100+ billion tokens processed Customer-requested features shipped in as little as 30 minutes Zero model SDK implementation or API key rotation with AI Gateway Searchable helps brands track and improve how they appear across AI search engines like ChatGPT, Perplexity, and Claude, pairing visibility analytics with an agent that guides users on what to do next. The Searchable team builds on Vercel's AI SDK and AI Gateway to test new models without rotating API keys or SDKs. As a result, development now moves 2 to 5x faster. Read more ## How to choose prompts to track for better AI visibility DevFeed: [How to choose prompts to track for better AI visibility]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-07-05T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [AI search](), [Search engine optimization (SEO)](), [Large Language Model](), [A/B Testing](), [ChatGPT]() Tags: [ai-search](), [article](), [how-to](), [learning](), [llm](), [marketing](), [seo](), [strategy]() ### AI overview This tutorial explains how to choose and track prompts that audiences use in AI search. It recommends focusing on open-ended, action-oriented, and repeatable questions that reflect different stages of the customer journey, helping marketers improve visibility in AI-generated answers. ### Source excerpt Learn how to choose prompts to track that help optimize your marketing and bring in relevant searchers from AI tools like ChatGPT, Gemini, and Perplexity. ## Google AI Search Liability, Internet Infrastructure, and the GenAI-Driven Memory Market DevFeed: [Google AI Search Liability, Internet Infrastructure, and the GenAI-Driven Memory Market]() Original publisher: [Read original article]() Author: Russ Published: 2026-06-29T12:40:41Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [AI search](), [Google](), [genai](), [Internet](), [Cloud]() Tags: [ai-search](), [cloud](), [genai](), [google](), [infrastructure](), [internet](), [networks](), [search](), [worth-reading]() ### AI overview This developer-oriented reading roundup discusses a German court ruling holding Google liable for its AI search summaries, the distribution of CDN, cloud, and content-provider capacity across Internet Exchange Points, and how the GenAI boom has changed the memory market. ### Source excerpt It's no longer just about your IP address or the specific endpoint you think you're connecting to, it's about your location and which intermediary services can most effectively handle your request. Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like "users can check for themselves," and that they generally know "that information generated with AI should not be blindly trusted," the court held that the AI's summaries are reflections of the company and "above all an expression of Google's business activities." The distribution of Content Delivery Networks (CDN), cloud and content provider capacity across Internet Exchange Points (IXPs) provides a fascinating lens into the physical infrastructure of the Internet and public peering. The memory market - by which we mean dynamic main memory as well as flash persistent memory - has been utterly and perhaps forever changed by the GenAI boom. These days you could be excused by suspecting that the world has gone AI-mad, and if you were at the NANOG meeting your suspicions would've only been confirmed! ## Schema markup for SEO: What it is and how it works DevFeed: [Schema markup for SEO: What it is and how it works]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-06-24T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [Structured-data](), [Search engine optimization (SEO)](), [Website](), [AI search]() Tags: [ai-search](), [schema](), [search](), [search-engines](), [seo](), [strategy](), [web]() ### AI overview This tutorial explains schema markup, also called structured data, and how to add relevant Schema.org properties to web pages. It describes how structured data can support rich search results, SEO visibility, user experience, and discovery in AI search, while noting that it does not guarantee higher rankings. ### Source excerpt Learn how schema markup for SEO can drive more traffic to your site by giving search engines the right information for rich search results. ## Your robots.txt Says Yes. Your Firewall Says 403. DevFeed: [Your robots.txt Says Yes. Your Firewall Says 403.]() Original publisher: [Read original article]() Author: Brent Leekley Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [brent.leekley.me blog]() Topics: [Cloudflare](), [AI Bots](), [AI search](), [Crawler](), [Firewall]() Tags: [403](), [aeo](), [ai-bots](), [ai-crawl-control](), [ai-crawlers](), [ai-search](), [ai-visibility](), [blocking](), [bot-management](), [chatgpt-user](), [cloudflare](), [firewall](), [gptbot](), [oai-searchbot](), [perplexity](), [robots](), [robots-txt]() ### AI overview A field note explains how a Cloudflare AI-bot blocking setting returned 403 responses to all AI agents even though the site's robots.txt allowed AI search and blocked training crawlers. It distinguishes training crawlers, search indexers, and user-triggered fetchers, and recommends auditing enforcement at the firewall layer. ### Source excerpt A client's robots.txt welcomed AI search and blocked training crawlers, but Cloudflare's blunt AI-bot toggle was returning 403 to every AI agent at the edge. How the block was found, the AI Crawl Control fix, and why you should audit enforcement, not intent. ## Beyond the Click: 5 Counter-Intuitive Truths About the New Era of AI Search DevFeed: [Beyond the Click: 5 Counter-Intuitive Truths About the New Era of AI Search]() Original publisher: [Read original article]() Author: Brent Leekley Published: 2026-06-07T00:00:00Z Content type: opinion Language: en Sources: [brent.leekley.me blog]() Topics: [AI search](), [Search engine optimization (SEO)](), [Artificial Intelligence](), [Large Language Model](), [observability](), [anthropic]() Tags: [aeo](), [agentic-ai](), [ai-search](), [answer-engine-optimization](), [anthropic](), [brand-visibility](), [chatgpt](), [llm](), [observability](), [seo](), [zero-click]() ### AI overview This field note argues that AI-driven, zero-click search is shifting growth strategy from traditional SEO toward Answer Engine Optimization. It discusses the Mirror Effect, in which large language models form a brand narrative from the broader web rather than only a company website, and argues that reputation management, authentic content, and observability are increasingly important for brand visibility. ### Source excerpt The shift from SEO to AEO: the Mirror Effect, social signals, productivity arbitrage, the authenticity paradox, and agentic AI. Five counter-intuitive truths about brand visibility in a zero-click world. ## Dodo Digest: The Problem Isn't AI Search. It's Rushed Products. DevFeed: [Dodo Digest: The Problem Isn't AI Search. It's Rushed Products.]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-05-31T00:00:00Z Content type: opinion Language: en Sources: [Dodo Payments Blog]() Topics: [AI search](), [Artificial Intelligence](), [Google](), [DuckDuckGo]() Tags: [ai](), [ai-search](), [alternatives](), [google](), [newsletter](), [quality](), [search](), [trust]() ### AI overview The article argues that backlash against Google's AI search reflects rushed product development rather than rejection of AI itself. It uses confusing search responses as an example and emphasizes quality, trust, and reliability as long-term advantages. ### Source excerpt Google's AI search is facing backlash as users question response quality. The real risk isn't AI itself, it's shipping products faster than they're ready. Plus, we shipped Dodo Payments v1.99.0 with a major product revamp. ## How ChatFeatured migrated from PlanetScale Postgres to Postgres Managed by ClickHouse to power AI brand discovery DevFeed: [How ChatFeatured migrated from PlanetScale Postgres to Postgres Managed by ClickHouse to power AI brand discovery]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-18T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [AI search](), [AI Models](), [AI Agent](), [Database](), [ChatGPT](), [Claude Code](), [cursor]() Tags: [ai-agent](), [ai-models](), [ai-search](), [chatgpt](), [claude-code](), [clickhouse](), [cursor](), [database]() ### AI overview ChatFeatured migrated from PlanetScale Postgres to Postgres managed by ClickHouse to support transactional workloads and complex analytics for its AI brand-discovery platform. The article reports that analytics query times fell from 2.5 minutes to under one second, while the platform's embedded AEO strategist analyzes sources cited by AI models and helps marketers create content to improve brand visibility. ### Source excerpt How ChatFeatured cut analytics query times from 2.5 minutes to under a second by migrating from PlanetScale Postgres to Postgres managed by ClickHouse -- in just 30 minutes. ## Scaling a Vespa Application: Feeding Fast and Furiously DevFeed: [Scaling a Vespa Application: Feeding Fast and Furiously]() Original publisher: [Read original article]() Author: Kai Borgen Published: 2026-04-28T00:00:00Z Content type: tutorial Language: en Sources: [Vespa Blog]() Topics: [AI search](), [Deployment](), [datasets](), [information retrieval](), [Cloud](), [Homebrew](), [Command-line interface](), [onnx](), [optimum](), [XML]() Tags: [ai](), [ai-search](), [cli](), [cloud](), [dataset](), [genai](), [how-to](), [information-retrieval](), [install](), [metrics](), [onnx](), [optimum](), [performance](), [rag](), [ranking](), [tensors](), [tutorial]() ### AI overview This tutorial demonstrates how to scale a Vespa application while feeding the full MS_marco passages dataset. It covers preparing the dataset, configuring access, deploying a sample application, and using scaling and metrics to improve feed throughput and performance. ### Source excerpt A tutorial on how to scale the resources in a Vespa application to increase feed throughput. Using the metrics dashboard for informed and optimised scaling. ## Building an MCP Clothing Search Server with Azure AI Search and an Image Slideshow DevFeed: [Building an MCP Clothing Search Server with Azure AI Search and an Image Slideshow]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2026-03-13T05:49:00Z Content type: tutorial Language: en Sources: [Pamela Fox]() Topics: [Model Context Protocol](), [MCP Server](), [AI search](), [Azure](), [Embeddings](), [vs-code]() Tags: [ai-search](), [azure](), [copilot](), [embeddings](), [image](), [images](), [javascript](), [mcp](), [mcp-server](), [model-context-protocol](), [python](), [vs]() ### AI overview This tutorial explains how to build a closet MCP server that searches clothing from a user query and presents matching items as images. It uses FastMCP, Azure AI Search hybrid retrieval, multimodal embeddings, LLM-generated image descriptions, and an MCP app with a JavaScript-powered slideshow. ### Source excerpt When I was a kid, one of my first Java applets was a UI for choosing outfits by mixing and matching different articles of clothing. Now, with the advent of agents and MCP, I realized that I could make a modern, more dynamic version: an MCP server that can find relevant clothing based off a user query, and render matching clothing as a slideshow. Let's walk through the experience and code powering it. Searching for relevant clothing After connecting VS Code to my closet MCP server, I ask a query like: i am presenting at PyAI about MCP, do I have MCP themed clothing? show me the best option. GitHub Copilot decides that it can use the closet MCP server to answer that question, and it calls the image_search tool with these arguments: { "query": "MCP Model Context Protocol themed clothing", "max_results": 5 } The tool call returns a mix of binary files - thumbnails for each matching article of clothing, and structured data- a JSON containing filename, display name, and description for each article. { "results": [ { "filename": "IMG_3234.jpg", "display_name": "IMG_3234.jpg", "description": "The image shows a black sleeveless dress hanging on a white hanger against a plain wall. The dress has a printed text on the front that reads: \"YOU DOWN WITH MCP? Yeah, you know me!\" The first line is in large white uppercase letters, and the second line is in smaller pink cursive letters. The dress has a fitted top and a flared skirt." },... Here's what that looks like in the GitHub Copilot chat interface. Notice that Copilot attaches the images, so I can actually click on them to see each result directly in VS Code, as if they were a file in the workspace. Now let's look at the code powering that tool call. I built the server using FastMCP, so I declare my tools by wrapping functions in mcp.tool() decorator and annotating the arguments with types and helpful descriptions. Inside the function, I use Azure AI Search with hybrid retrieval on both the text query and the query's vector, ## Building AI Search on Heroku DevFeed: [Building AI Search on Heroku]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2026-01-29T17:50:08Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Heroku](), [AI search](), [Retrieval-Augmented Generation](), [Claude]() Tags: [ai](), [ai-search](), [building](), [engineering](), [heroku](), [heroku-ai](), [managed-inference-and-agents](), [news](), [pgvector](), [rag](), [retrieval](), [retrieval-augmented-generation](), [search](), [vector-search]() ### AI overview This article presents a Heroku reference architecture for production-grade enterprise AI search using two-stage retrieval. It combines vector search with semantic reranking so documents are scored for whether they answer the query, then uses Claude to generate an answer. ### Source excerpt If you've built a RAG (Retrieval Augmented Generation) system, you've probably hit this wall: your vector search returns 20 documents that are semantically similar to the query, but half of them don't actually answer it. A user asks "how do I handle authentication errors?" and gets back documentation about authentication, errors, and error handling in [...] The post Building AI Search on Heroku appeared first on Heroku. ## Scrunch ships AI search analytics from decision to production in 48 hours with Tinybird DevFeed: [Scrunch ships AI search analytics from decision to production in 48 hours with Tinybird]() Original publisher: [Read original article]() Author: Tinybird Published: 2025-10-24T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [AI search](), [Optimization](), [real-time]() Tags: [agent](), [ai](), [ai-search](), [analytics](), [cost](), [customer-stories](), [optimization](), [production](), [real-time](), [search](), [startup]() ### AI overview A case study of Scrunch, an AI search optimization startup, describes how it built real-time Agent Traffic features with Tinybird in 48 hours while achieving cost predictability. ### Source excerpt Learn how the AI search optimization startup built real-time Agent Traffic features in just 48 hours, achieving 100% cost predictability while tripling in size. ## Baidu и AI Search Paradigm: мультиагентная структура для интеллектуального поиска информации DevFeed: [Baidu и AI Search Paradigm: мультиагентная структура для интеллектуального поиска информации]() Original publisher: [Read original article]() Author: redmadrobot (red\_mad\_robot) Published: 2025-10-14T18:36:48Z Content type: article Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [AI search](), [Artificial Intelligence](), [Large Language Model](), [information retrieval](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-search](), [executor](), [information-retrieval](), [llm](), [rag](), [search](), [tag-d89cae10e887](), [tag-e1c0c11ccefc]() ### AI overview The article analyzes Baidu's AI Search Paradigm, a proposed multi-agent architecture for intelligent search built on large language models. It explains how specialized agents coordinate planning, tool use, execution, and answer writing, and contrasts this approach with classical information retrieval, semantic search, learning-to-rank, and RAG systems. ### Source excerpt Аналитический центр red_mad_robot продолжает разбирать ключевые исследования в сфере интеллектуальных систем и генеративного поиска. На этот раз рассказываем про архитектуру AI Search Paradigm от Baidu -- новой системы интеллектуального поиска, построенной на LLM и мультиагентных методах. Читать далее ## Laravel Keeps Teams Lean. The Artisan of the Day Is Steve Beyatte. DevFeed: [Laravel Keeps Teams Lean. The Artisan of the Day Is Steve Beyatte.]() Original publisher: [Read original article]() Author: Ana Tavares Published: 2025-09-23T15:28:05Z Content type: article Language: en Sources: [Laravel Blog]() Topics: [Laravel](), [D](), [Next.js](), [AI search]() Tags: [ai-search](), [efficiency](), [engineering](), [laravel](), [next-js](), [revenue](), [rust](), [startups]() ### AI overview Steve Beyatte describes how Laravel and Laravel Backpack helped him build and scale lean startups. His company Funden reached $100,000 in monthly revenue within its first year and was acquired for more than seven figures, while Press Ranger is applying the same fast, low-overhead approach. ### Source excerpt Steve Beyatte shares how Laravel powers his lean startups, scaling to seven-figure exits and $1M revenue faster than big engineering teams. ## Mintlify acquires Trieve to improve RAG search in documentation DevFeed: [Mintlify acquires Trieve to improve RAG search in documentation]() Original publisher: [Read original article]() Author: Han Wang Published: 2025-07-24T00:00:00Z Content type: release Language: en Sources: [Mintlify Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [AI search](), [Documentation]() Tags: [ai](), [ai-search](), [ai-trends](), [announce](), [documentation](), [rag](), [search]() ### AI overview Mintlify has acquired Trieve, a provider of retrieval-augmented generation infrastructure, to enhance AI search and chat experiences in documentation. The companies aim to provide faster, more accurate, and context-aware answers at scale. ### Source excerpt We're excited to announce that Mintlify has acquired Trieve, a provider of retrieval-augmented generation (RAG) infrastructure designed to power quality AI search and chat experiences. ## Postgres for Everything: Why Vecstore Replaced Pinecone and RDS with Neon DevFeed: [Postgres for Everything: Why Vecstore Replaced Pinecone and RDS with Neon]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-07-17T15:30:28Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [AI search](), [SDKs]() Tags: [ai](), [ai-search](), [case-studies](), [database](), [developer-experience](), [embedding](), [latency](), [postgres](), [rust](), [vector]() ### AI overview Vecstore replaced separate Pinecone and RDS services with Neon for its AI search platform. The article reports lower latency, reduced operational complexity, and improved multi-region developer experience. ### Source excerpt "We replaced both Pinecone and RDS with Neon, and latency dropped from 200ms to 80ms with a much simpler setup. Neon also gave us a smoother developer experience across multiple regions. It just works" (Giorgi Kenchadze, Founder & CEO at Vecstore) Vectstore is a new AI search pla... ## Behind trivago's Smart AI Search: From Concept to Reality DevFeed: [Behind trivago's Smart AI Search: From Concept to Reality]() Original publisher: [Read original article]() Author: Sören Weber Senior Product Manager @ trivago; Core Product; AI Linkedin profile Published: 2024-12-17T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [AI search](), [Generative AI](), [Large Language Model](), [Google](), [Development](), [User experience (UX)]() Tags: [ai](), [ai-search](), [artificial-intelligence](), [core](), [engineering](), [generative](), [generative-ai](), [google](), [industry](), [integration](), [interface](), [interview](), [language-models](), [machine-learning](), [search](), [smart](), [ux-design]() ### AI overview An interview with trivago product manager Sören Weber describes the development of Smart AI Search, a natural-language hotel search feature. The article covers the team's motivations, use of generative AI and LLMs, early prototypes, collaboration with Google, and integration challenges involving the existing search experience and UX design. ### Source excerpt Are you passionate about AI and its potential to transform the travel industry? Then dive into our interview with Sören Weber, Senior Product Manager at trivago, as he reveals the journey from c... ## Artificial Intelligence APIs with Python DevFeed: [Artificial Intelligence APIs with Python]() Original publisher: [Read original article]() Published: 2024-11-16T00:00:00Z Content type: article Language: en Sources: [Kodeco | High quality programming tutorials: iOS, Android, Swift, Kotlin, Unity, and more]() Topics: [Python](), [AI Development](), [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence](), [OpenAI](), [Google](), [Azure](), [LangChain](), [AI search](), [Programming]() Tags: [ai](), [ai-apis](), [ai-development](), [ai-search](), [artificial-intelligence](), [azure](), [developers](), [google](), [langchain](), [openai](), [program](), [python](), [rag](), [retrieval-augmented-generation-rag]() ### AI overview A Kodeco program that teaches developers to integrate AI services into their workflows using Python. The curriculum covers Python for AI, text generation with OpenAI and Google Gemini, multimodal integration, Retrieval-Augmented Generation with LangChain and Azure AI Search, and AI Agents with LangGraph. ### Source excerpt This program is designed to equip you with the skills necessary to integrate AI services into your development workflow. It covers a wide range of topics, from basic Python programming for AI to advanced concepts like Retrieval-Augmented Generation (RAG) and AI Agents. You'll gain hands-on experience with popular AI platforms such as OpenAI, Google Gemini, and Azure AI Services. [Next page]()