# Search Published articles for 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. ## Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents DevFeed: [Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents]() Original publisher: [Read original article]() Author: Charlie Klein; Bryan Smith Published: 2026-09-17T17:00:00Z Content type: opinion Language: en Sources: [Databricks]() Topics: [Web](), [Software](), [Claude Code](), [codex](), [API](), [Model Context Protocol](), [databricks]() Tags: [agent](), [api](), [claude-code](), [codex](), [databricks](), [databricks-ai](), [llms](), [mcp](), [orchestration](), [platform](), [product](), [search](), [web]() ### AI overview The article describes an agent that combines company data in Databricks with web-based market signals. Its enrichment logic is rebuilt across Claude Code, Codex, and a direct model API workflow because each provides different tools, search behavior, and configuration requirements. The article argues that inconsistent web search and the lack of a shared layer make reliable account enrichment difficult. ### Source excerpt An agent that needs the outside worldAn engineer at a software company is building... ## Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes DevFeed: [Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes]() Original publisher: [Read original article]() Author: Adam Malek Published: 2026-09-17T12:39:40Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [code search](), [Parsing](), [jetbrains](), [Large Language Model]() Tags: [agentic-ai](), [ai](), [code-search](), [jetbrains](), [llm](), [llm-agents](), [parsing](), [rag](), [search](), [semantic]() ### AI overview Part 1 of a developer diary explains how JetBrains built a RAG pipeline for semantic code search, covering parsing, chunking, and vectorization. The pipeline is intended to give LLM agents precise, citable evidence from real repositories and retrieve code by meaning rather than exact keywords. ### Source excerpt Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was JetBrains Context. We got it working, we got it [...] ## 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. ## Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving DevFeed: [Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-16T06:06:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [quantization](), [AI search](), [webgpu]() Tags: [algorithms](), [architecture-design](), [development](), [infrastructure](), [news](), [optimization](), [performance](), [pinterest-search](), [quantization](), [search](), [ssd](), [vector]() ### AI overview Pinterest Engineering enhanced its Manas distributed search platform with scalar and product quantization, SSD-based serving, and late-interaction retrieval. The reported evaluations describe trade-offs among index size, recall, throughput, latency, and serving cost. ### Source excerpt Pinterest Engineering has enhanced its Manas search platform to manage vast data, improving efficiency in search and discovery functions. By applying Scalar and Product Quantization, memory usage decreased significantly while maintaining high recall rates. The platform utilizes SSDs for optimized performance, and it is transitioning to multi-vector models for refined relevance matching. By Olimpiu Pop ## Integrating Java API Documentation with Hugo Using a Java 25 Doclet DevFeed: [Integrating Java API Documentation with Hugo Using a Java 25 Doclet]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [Hugo](), [Java](), [Markdown](), [Documentation]() Tags: [documentation](), [hugo](), [java](), [markdown](), [search]() ### AI overview A Java 25 doclet converts Codename One API models and Markdown documentation comments into Hugo content. This integrates API pages with the site's existing theme and search while preserving member links and an offline Javadoc archive. ### Source excerpt A Java 25 doclet turns the Codename One API model and Markdown comments into Hugo content. The site gains integrated search and theming while preserving member links and the offline Javadoc archive. ## Introducing TIN: full-text search for Postgres DevFeed: [Introducing TIN: full-text search for Postgres]() Original publisher: [Read original article]() Author: Patrick Reynolds Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [benchmarking]() Tags: [backups](), [benchmark](), [benchmarks](), [bm25](), [engineering](), [full-text-search](), [index](), [performance](), [postgres](), [reddit](), [replication](), [search](), [text](), [wikipedia]() ### AI overview PlanetScale announces TIN, a full-text search extension for Postgres and Neki databases. The article describes supported query and matching features, transaction and update behavior, and benchmark workloads and corpora used to assess performance. ### Source excerpt TIN is a fast, full-featured, full-text search index for Postgres ## 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 ## Cloudflare adds controls to allow search indexing while disallowing AI training DevFeed: [Cloudflare adds controls to allow search indexing while disallowing AI training]() Original publisher: [Read original article]() Author: Bryan Becker Published: 2026-09-15T13:00:00Z Content type: release Language: en Sources: [Cloudflare Blog]() Topics: [Cloudflare](), [Artificial Intelligence](), [Crawler](), [Google](), [Microsoft]() Tags: [ai](), [ai-bots](), [blocking](), [bot-management](), [bots](), [cloudflare](), [content](), [google](), [internet](), [microsoft](), [network-services](), [product-news](), [search](), [security]() ### AI overview Cloudflare announces a Disallow AI Training setting that lets website owners remain indexed in search while refusing AI training by mixed-use crawlers. Apple, Google, and Microsoft honor or have committed to honor the setting. ### Source excerpt Cloudflare is giving site owners a way to stay discoverable while disallowing AI training. New controls and an Accountable designation establish a shared model with Apple, Google, and Microsoft. ## Search results are sending people to fake Bitrefill checkouts DevFeed: [Search results are sending people to fake Bitrefill checkouts]() Original publisher: [Read original article]() Author: Stefan Dasic Published: 2026-09-15T08:40:22Z Content type: news Language: en Sources: [Malwarebytes]() Topics: [Cryptocurrency](), [Bitcoin](), [Website]() Tags: [bitcoin](), [cryptocurrency](), [fraud](), [payments](), [phishing](), [qr-code](), [scam](), [scams](), [search](), [threat-intel]() ### AI overview Fake Bitrefill checkout pages are appearing in search results and copying the company's branding and payment flow. They persuade victims to send cryptocurrency to scammer-controlled addresses, with no goods delivered and little chance of recovering the payment. ### Source excerpt Fake Bitrefill checkout pages are appearing in search results and tricking people into sending cryptocurrency directly to scammers. ## Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack DevFeed: [Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack]() Original publisher: [Read original article]() Author: Jonas Kim Published: 2026-09-14T16:55:58Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Graphs](), [Amazon Bedrock](), [Amazon OpenSearch Service](), [Amazon Web Services]() Tags: [amazon-bedrock](), [amazon-opensearch-service](), [aws](), [knowledge-graph](), [open-source](), [rag](), [retrieval](), [search](), [vector-search]() ### AI overview This article presents a unified knowledge-graph RAG stack that brings Microsoft GraphRAG and HKUDS LightRAG together on Amazon Bedrock, Amazon Neptune, and Amazon OpenSearch Service. The shared stack supports common ingestion, indexing, caching, and multilingual handling while allowing the retrieval methodology to be selected per query. ### Source excerpt Picture a compliance analyst staring at a few thousand contracts, amendments, and internal memos, trying to answer one question that sounds straightforward: "Which of our obligations are exposed if this one milestone slips?" The answer isn't written in any single document. It's stitched across three -- a master agreement that ties a payment to a [...] ## 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. ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## Perplexity trusts GPT-6 Astra with end-to-end systems DevFeed: [Perplexity trusts GPT-6 Astra with end-to-end systems]() Original publisher: [Read original article]() Published: 2026-09-12T11:14:21.124044Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Language models]() Tags: [api](), [code](), [gpt](), [monitor](), [production](), [search](), [systems](), [testing](), [workflow]() ### AI overview Perplexity describes using GPT-6 Astra to write code and communications, modify and monitor production software, and create end-to-end tests with simulated service responses. ### Source excerpt Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models. ## Evolving Pinterest's Embedding Retrieval Platform DevFeed: [Evolving Pinterest's Embedding Retrieval Platform]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-11T15:01:03Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [Architecture & Design](), [Algorithms](), [Scalability](), [Latency](), [IO]() Tags: [ann](), [cost](), [embedding](), [embeddings](), [engineering](), [infrastructure](), [latency](), [memory](), [models](), [pinterest](), [platform](), [quantization](), [real-time](), [retrieval](), [scalability](), [search]() ### AI overview Pinterest describes evolving its Manas embedding-retrieval platform to address the cost, scale, and flexibility challenges of serving billions of embeddings. The excerpt covers ANN search, vector quantization, and SSD-based serving. ### Source excerpt Authors: Bowen Zhou | Staff Software Engineer; Shan Gao | Senior Software Engineer; Jingwen Hu | Software Engineer II; Wenjiang Chu | Staff Software Engineer The Billion-Embedding Challenge At Pinterest, the "signal" is our lifeblood. Whether it's a home decor enthusiast finding the perfect rug or a fashion seeker discovering a new aesthetic, our discovery engine relies on understanding deep semantic relationships to help our users find inspirations. Over the last few years, the explosive growth of embedding-based retrieval has fundamentally transformed how we surface these signals -- and at the heart of that transformation is Manas, Pinterest's in-house distributed search platform. Embedding Retrieval is one of the core capabilities of Manas, supporting multiple approximate nearest neighbor search algorithms, hybrid queries with both token and embedding clauses, as well as real-time updates to ensure fresh contents become searchable within seconds. Deployed on over 80 clusters and serving billions of embeddings, Manas embedding retrieval powers all major product surfaces at Pinterest including Home Feed, Search, Related Pins, Ads, and Notifications. However, as our corpus scales toward tens of billions of embeddings and our models capture increasingly complex interactions, we face mounting challenges around cost efficiency, scalability, and flexibility. On the infrastructure side, traditional ANN algorithms like HNSW are notoriously memory-hungry -- they require the entire index to reside in RAM to maintain low query latency, making cost grow linearly with corpus size. On the modeling side, the classic two-tower retrieval paradigm is too restrictive: it reduces each candidate to a single embedding and scores relevance through a simple dot product, leaving little room to express richer, context-dependent notions of similarity. To tackle these challenges, our team has been evolving Manas's embedding retrieval stack across three fronts: Quantization. We reduce the memor ## How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation DevFeed: [How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation]() Original publisher: [Read original article]() Author: Claudio Masolo Published: 2026-09-11T10:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [recommendation systems](), [Large Language Model](), [Language models](), [sglang](), [Latency]() Tags: [agentic-ai-architecture](), [ai](), [ai-ml-data-engineering](), [artificial-intelligence](), [large-language-models](), [latency](), [liger](), [linkedin](), [linkedin-ai-multi-teacher](), [llms](), [ml-data-engineering](), [news](), [search](), [sglang](), [training]() ### AI overview LinkedIn describes a multi-teacher distillation pipeline for AI-powered job search that trains a 0.6B-parameter ranking model. The article focuses on SGLang-based teacher serving, online and offline distillation, and training optimizations reported to produce roughly an eightfold speedup. ### Source excerpt LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model. By Claudio Masolo ## Debugging our AI search assistant with agent tracing DevFeed: [Debugging our AI search assistant with agent tracing]() Original publisher: [Read original article]() Author: Dominik Buszowiecki; Shaun Kaasten Published: 2026-09-11T09:00:00Z Content type: article Language: en Sources: [Sentry Blog]() Topics: [AI search](), [agent observability](), [debugging](), [tracing](), [Large Language Model]() Tags: [agent](), [ai](), [api](), [debugging](), [errors](), [eval](), [llm](), [search](), [sentry](), [tracing]() ### AI overview Sentry engineers describe how they debugged the Search Query Assistant, which converts natural-language prompts into Sentry Syntax queries. They used evals for performance measurement and AI Conversation tracing to investigate failures, including a bug involving custom numerical attributes that caused queries to return no results. ### Source excerpt See how Sentry engineers used AI Conversations to debug a natural language search assistant and fix a tricky query generation bug. ## Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0 DevFeed: [Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0]() Original publisher: [Read original article]() Author: Eric Kim Published: 2026-09-10T21:15:39Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI search](), [Retrieval Augmented Generation (RAG)](), [Amazon S3](), [AWS IAM]() Tags: [amazon-bedrock](), [amazon-bedrock-knowledge-bases](), [announcements](), [audio](), [embedding](), [how-to](), [images](), [intermediate-200](), [multimodal](), [rag](), [s3](), [search](), [video]() ### AI overview A walkthrough for building an Amazon Bedrock Knowledge Base with TwelveLabs Marengo Embed 3.0 to perform natural-language semantic search across video, images, and audio. ### Source excerpt TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media. ## GPT Images 2.5 promises edits that leave the rest of your image alone DevFeed: [GPT Images 2.5 promises edits that leave the rest of your image alone]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-10T19:46:32Z Content type: news Language: en Sources: [The New Stack]() Topics: [Artificial Intelligence](), [Latency]() Tags: [ai](), [applications](), [cost](), [generation](), [gpt](), [images](), [latency](), [model](), [models](), [openai](), [production](), [prototyping](), [search](), [workflows]() ### AI overview OpenAI's GPT Images 2.5 introduces two image-editing models: Flare, optimized for speed and lower latency, and Sunburst, designed for greater precision and control. Both have the same listed token rates, but OpenAI does not explain their actual token consumption or comparative per-image costs. ### Source excerpt When OpenAI launched GPT Images 2.5 this week, the company promised better results for a common editing task: changing one The post GPT Images 2.5 promises edits that leave the rest of your image alone appeared first on The New Stack. ## How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules DevFeed: [How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules]() Original publisher: [Read original article]() Published: 2026-09-10T16:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Deep neural networks](), [datasets](), [Code]() Tags: [ai](), [ai-models](), [antibiotics](), [applied-ai](), [chatgpt](), [code](), [codex](), [datasets](), [deep-learning](), [search]() ### AI overview César de la Fuente's lab uses deep-learning models, ChatGPT, and Codex to search genome and protein datasets for antimicrobial candidates that could help fight drug-resistant infections. ### Source excerpt César de la Fuente's lab uses Codex and ChatGPT to search living and extinct genomes for antimicrobial candidates to fight drug-resistant infections. ## 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. ## Three principles for building a vector platform at Thumbtack DevFeed: [Three principles for building a vector platform at Thumbtack]() Original publisher: [Read original article]() Author: John Zhu Published: 2026-09-10T15:45:00Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Embeddings](), [AI search](), [Database](), [data](), [Machine learning](), [API]() Tags: [api](), [data](), [embeddings](), [engineering](), [etl](), [infrastructure](), [machine-learning](), [ml-platform](), [pgvector](), [retrieval](), [search](), [vector](), [vector-search]() ### AI overview This article explains how Thumbtack built a vector platform that lets ML engineers deploy production vector search without managing database access, custom ETL, or query services. It describes three guiding principles: reuse existing infrastructure, treat embeddings as data, and reduce adoption costs for future teams. ### Source excerpt Reusing what we already had, treating embeddings as data, and lowering the next team's cost Today, an ML engineer at Thumbtack can stand up production vector search without negotiating database access, building a custom ETL, or writing a query service. The team brings their choice of embedding model, the data, and the query; the platform handles what connects them. It took several iterations to get to this point. In this post we'll walk through how we got there and the three principles that shaped what we built. A vector database stores high-dimensional numeric arrays (embeddings) and serves nearest-neighbor queries against them. It's how an ML system asks "what's most similar to this?" instead of "what matches this exact key?" The shift from exact lookup to semantic retrieval is what makes vectors useful: a search can return results that mean the same thing, not just results that spell the same. At Thumbtack, embeddings sit between the models that produce them and the services that consume them: language models for text, multimodal models for images, retrieval models for ranking. The platform we describe here is where those embeddings live and how teams reach for them when they need to. Three principles shaped what we built. Reuse what we have: extend the infrastructure we already run rather than stand up a new system. Treat embeddings as data: flow them through the same pipelines that move every other dataset at the company. Lower the next team's cost: make the platform easier to adopt than to work around. Each principle shaped one layer of the system, and together they took vector search from a one-off project to a platform that any team can build on. Architecture at a glance The platform has four moving parts: where embeddings come from, how they reach the database, where they live, and how consumers query them. Each is a layer, and together they form a pipeline that produces vectors and serves similarity searches as a typed API call. The diagram below traces a ## Tako Search is free on AI Gateway through September 30 DevFeed: [Tako Search is free on AI Gateway through September 30]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [vercel ai sdk](), [SDKs](), [API](), [Web](), [data]() Tags: [ai](), [ai-gateway](), [api](), [configuration](), [documentation](), [free](), [integration](), [playground](), [sdk](), [search](), [tools](), [web]() ### AI overview This changelog announces that Tako Search is free on AI Gateway through September 30. It enables AI models to search curated data and the live web, filter results, and provide current answers with citations and visualizations. Tako Search works with any model on AI Gateway and does not require a separate Tako account or API key. ### Source excerpt Tako Search is free exclusively on AI Gateway through September 30. It lets AI models search Tako's curated data and the live web, filter web results by domain or publication date, and use the results to answer questions with current information, citations, and visualizations. After September 30, searches are billed at standard rates. The same integration works with any model on AI Gateway, so you can switch models without changing your search setup. You also don't need a separate Tako account or API key. To use Tako Search with the AI SDK, add gateway.tools.takoSearch() to a generateText or streamText request. The model can then call it when it needs current information: Try Tako Search in the AI Gateway playground. See the web search documentation for configuration and search options. Read more ## LibreOffice Base survey results DevFeed: [LibreOffice Base survey results]() Original publisher: [Read original article]() Author: jzb Published: 2026-09-09T17:07:42Z Content type: news Language: en Sources: [LWN.net]() Topics: [User interface design]() Tags: [blog](), [bug](), [database](), [design](), [enterprise](), [erp](), [java](), [linux](), [media](), [performance](), [search](), [user-experience](), [workflow]() ### AI overview A survey of LibreOffice Base users highlights requests for a cleaner interface, more capable or simpler workflows, stronger search, bug fixes, stability, performance, and improvements to queries, forms, and reports. ### Source excerpt Heiko Tietze has published a blog post summarizing the results of a recent survey about the use of LibreOffice's database application, Base. 455 people participated in the survey, including more than 330 who use Base on Linux, with use cases ranging from maintaining records of personal media such as CDs or DVDs to use enterprise-resource planning (ERP) and finance. Of course, users had many ideas how to improve the application: The majority asks for improvements to the user interface with less clutter and a more attractive design. The workflow and user experience should become either simplified or more powerful, depending on the expertise and the scenario. For example, an elaborate search function is something that many people expect. [...] Almost the same number of answers requests bug fixes, improvements to stability, and better performance. Issues with queries, forms, and reports were mentioned equally often. In this regard, many replies suggest to remove the Java dependencies. ## Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency DevFeed: [Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency]() Original publisher: [Read original article]() Author: Cindy Wang; Cheng Li; Jialu Liu; Sean Kulinski; Arnav Singhvi; Wen Sun; Michael Bendersky Published: 2026-09-09T13:30:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Latency](), [Low Latency](), [data]() Tags: [agents](), [announcements](), [benchmarks](), [cost](), [data](), [databricks](), [enterprise](), [latency](), [low-latency](), [performance](), [platform](), [retrieval](), [search](), [speed](), [third-party]() ### AI overview Databricks introduces Adaptive Instructed-Retriever, a retrieval model that combines fast parallel search with sequential multi-step search for harder enterprise queries. It adaptively spends extra computation only when useful, achieving comparable quality to leading third-party models at twice lower latency while improving over single-step retrieval on reported benchmarks. ### Source excerpt Effective enterprise data agents require search that is both accurate and fast. Earlier... [Next page]()