# AI search A computing approach that applies artificial intelligence to retrieve, rank, and generate responses from indexed information. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an Internal Developer Platform with Artificial Intelligence DevFeed: [Building an Internal Developer Platform with Artificial Intelligence]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-17T11:11:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [internal developer platform](), [AI Development](), [AI search](), [agent observability](), [OpenTelemetry](), [telemetry]() Tags: [agent](), [agents](), [artificial-intelligence](), [culture-methods](), [developer-experience](), [developer-platform](), [distributed-tracing](), [guardrails](), [internal-developer-platform](), [logging](), [logs](), [metrics](), [news](), [opentelemetry](), [platform-artificial-intelligence](), [platform-engineering](), [prometheus](), [traces]() ### AI overview This news article covers a KubeCon presentation about using AI agents as an internal developer platform. It discusses semantic search across sources such as Git, Slack, Jira, repositories, pull requests, and wiki pages; guardrails for controlling actions; and logs, metrics, and traces for understanding agent behavior. The speakers also describe OpenTelemetry conventions for GenAI and related observability tools. ### Source excerpt Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior. By Ben Linders ## 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 ## 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 ## 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. ## AI Functions in ClickHouse: Upgrade your SQL to the AI age DevFeed: [AI Functions in ClickHouse: Upgrade your SQL to the AI age]() Original publisher: [Read original article]() Author: Andriy Yakovlev; George Larionov Published: 2026-09-11T12:49:32Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Large Language Model](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Databases]() Tags: [ai](), [classification](), [clickhouse](), [embeddings](), [llm](), [rag](), [sql]() ### AI overview ClickHouse introduces beta AI Functions that invoke LLM and embedding providers directly from SQL for tasks including classification, extraction, generation, translation, filtering, redaction, embeddings, and semantic similarity. ### Source excerpt Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls--all directly from SQL. ## 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. ## 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 ## CERN PGDay 2027: Announcement and CfP DevFeed: [CERN PGDay 2027: Announcement and CfP]() Original publisher: [Read original article]() Author: Swiss PostgreSQL Users Group Published: 2026-09-11T00:00:00Z Content type: news Language: en Sources: [PostgreSQL news]() Topics: [Databases](), [Machine learning](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [announcement](), [community](), [conference](), [database](), [postgresql]() ### AI overview CERN PGDay 2027 is an announcement and call for papers for a single-day PostgreSQL community conference at CERN in Geneva. Proposed talk themes include large-scale database performance, AI and vector-search workloads, security, and PostgreSQL extensibility. ### Source excerpt CERN PGDay 2027 Date: Friday, February 12, 2027 Location: CERN Campus, Geneva, Switzerland / Suisse Romande Language: English Organizers: Swiss PostgreSQL Users Group (SwissPUG) & CERN Format: Single-track (6-7 sessions) followed by networking social event Homepage: swisspug.org/cern-pgday-2027 About the Event Continuing in the line of work of the past editions, CERN PGDay 2027 returns as the annual gathering for PostgreSQL users, developers, and enthusiasts across Suisse Romande (western Switzerland) and the neighboring French border regions. Co-organized by CERN and SwissPUG, this single-day community conference offers a premier opportunity to network, share practical experiences, and explore the future of the world's most advanced open-source database. The event takes place in the unique, international environment of Geneva--a hub for major scientific institutions, non-governmental organizations, and financial and tech enterprises. Format & Venue Single-Track Schedule: The program consists of a single track featuring 6 to 7 technical sessions presented entirely in English. Social Event & Networking: Following the technical presentations, a social event will take place to facilitate community networking, discussions, and collaboration in the inspiring atmosphere of CERN. CERN Visits: Attendees are encouraged to arrange their schedules before or after the event to take advantage of the conference location and visit CERN, the European Organization for Nuclear Research. Call for Papers (CfP) Speaker proposals for CERN PGDay 2027 can be entered online via Indico. Call for Papers Closes: November 8, 2026 (23:59 CET) Call for Sponsors (CfS) Sponsors proposals for CERN PGDay 2027 are welcome. Please find the contract with all details online. Key Themes We welcome talk proposals covering broader PostgreSQL and community trends and localized enterprise & scientific topics: High-Performance Science & Big Data: Managing massive datasets, extreme write throughput, partitioning ## 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. ## 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 ## Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3 DevFeed: [Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-09-08T21:32:47Z Content type: release Language: en Sources: [ScyllaDB]() Topics: [releases](), [Amazon S3](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [product](), [rag](), [release](), [s3](), [search](), [storage]() ### AI overview ScyllaDB 2026.3 adds full-text search, a preview object-storage backend, OCI integration, and experimental table and migration capabilities. It also introduces large-data guardrails and cluster-wide restoration from object-storage backups. ### Source excerpt new updates should help you move even more workloads to ScyllaDB, at a fraction of the cost. ## See you at Elastic{ON} 2026 DevFeed: [See you at Elastic{ON} 2026]() Original publisher: [Read original article]() Author: Dan Courcy Published: 2026-09-08T00:00:00Z Content type: news Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [AI search](), [telemetry]() Tags: [ai](), [ai-agent](), [developer](), [elasticsearch](), [event](), [logging](), [metrics](), [observability](), [platform-observability-search-security](), [production](), [search](), [sre]() ### AI overview Elastic announces its Elastic{ON} 2026 global event tour, with sessions for developers on scaling AI agents with Elasticsearch and for SREs on observability, logging, metrics, and AI-assisted incident response. ### Source excerpt Elastic{ON} kicks off this September with seven stops in cities around the world. Elastic's CEO Ash Kulkarni will be appearing at stops across the tour, opening keynotes with a candid conversation about why your data has never been more valuable. ## Laravel August product updates DevFeed: [Laravel August product updates]() Original publisher: [Read original article]() Author: Laravel Team Published: 2026-09-03T20:30:00Z Content type: release Language: en Sources: [Laravel Blog]() Topics: [cloud-infrastructure](), [Filesystems](), [AI search](), [Database](), [Authorization](), [changelog]() Tags: [api](), [changelog](), [cloud](), [compute](), [database](), [filesystems](), [laravel](), [product](), [search](), [ubuntu](), [updates](), [vector]() ### AI overview Laravel's August update announces framework, Cloud, and Forge changes, including read-through filesystems, semantic search, vector support, managed queue options, scoped API tokens, and Ubuntu 26.04 rollout. ### Source excerpt Here's everything that shipped in August. ## 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. ## Announcing VectorsDB: Similarity search as a first-class Appwrite database DevFeed: [Announcing VectorsDB: Similarity search as a first-class Appwrite database]() Original publisher: [Read original article]() Author: Arnab Chatterjee Published: 2026-09-02T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI search](), [Databases](), [Embeddings]() Tags: [ai](), [announcements](), [databases](), [embeddings](), [product](), [search](), [vector]() ### AI overview Appwrite announces VectorsDB, a first-class database for storing and searching vectors. It supports built-in text embedding generation, externally generated embeddings, similarity ranking, metadata, permissions, queries, pagination, ordering, transactions, bulk operations, and CSV import and export. ### Source excerpt Store embeddings, generate them from text with built-in models, and rank documents by similarity without adding a separate vector service to your stack. ## Build a help center with semantic search using Appwrite VectorsDB DevFeed: [Build a help center with semantic search using Appwrite VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI search](), [Embeddings](), [Front end](), [Node.js]() Tags: [api](), [app](), [build](), [embeddings](), [front-end](), [node-js](), [search](), [tutorial](), [tutorials](), [vector-search]() ### AI overview This tutorial shows how to build a help center with semantic search using Appwrite VectorsDB and a TanStack Start front end. It covers creating a vector collection, embedding help articles, and ranking answers by meaning. ### Source excerpt Create a vector collection in the Appwrite Console, embed help articles as they are published, and rank answers by meaning in a TanStack Start app. ## Four things you can build with Appwrite VectorsDB DevFeed: [Four things you can build with Appwrite VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI search](), [Databases]() Tags: [ai](), [cache](), [database](), [databases](), [embedding](), [history](), [search](), [vector-search]() ### AI overview This tutorial explains how Appwrite VectorsDB uses vector embeddings to find documents by meaning rather than exact keywords. It demonstrates help-article search, filtered search, recommendations from user history, and an answer cache, including a 256-number vector approach used in the examples. ### Source excerpt Appwrite VectorsDB finds documents by meaning, not by words. This post builds four features with it: a help article search, a filtered search, a recommendation from user history, and an answer cache. ## Polimill builds Japan's next-generation public AI infrastructure DevFeed: [Polimill builds Japan's next-generation public AI infrastructure]() Original publisher: [Read original article]() Published: 2026-08-31T07:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [AI search]() Tags: [ai-infrastructure](), [ai-platform](), [codex](), [data](), [generative-ai](), [government](), [gpt](), [japan](), [openai](), [platform](), [productivity](), [public-ai](), [public-sector](), [search](), [workflows]() ### AI overview Polimill's QommonsAI uses OpenAI GPT models to organize and search municipal administrative knowledge across Japan, supporting public-sector workflows and development productivity. ### Source excerpt Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development. ## CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector DevFeed: [CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector]() Original publisher: [Read original article]() Author: Anisa Dean Published: 2026-08-26T16:40:00Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [AI search](), [tenant data protection]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [architecture](), [compliance](), [customer-solutions](), [embedding](), [postgresql](), [postgresql-compatible](), [production](), [scale](), [search]() ### AI overview CORTO describes scaling legal semantic search on Amazon Aurora PostgreSQL with pgvector for billions of documents and vectors. The article focuses on embedding choices, multi-tenant isolation, cost efficiency, and sub-second query performance. ### Source excerpt How CORTO scaled Amazon Aurora PostgreSQL with pgvector to 7.6 billion vectors and 2.5 billion documents in production, delivering sub-second legal search for 10,000+ law firms at 75% lower storage cost. ## Forging India's Digital Future: Elastic{ON} Comes to Mumbai DevFeed: [Forging India's Digital Future: Elastic{ON} Comes to Mumbai]() Original publisher: [Read original article]() Author: Atul Ahuja Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [AI search](), [telemetry]() Tags: [agentic-ai](), [agentic-ai-cybersecurity](), [ai-agents](), [cybersecurity](), [data](), [disaster-recovery](), [india](), [observability](), [observability-security-search](), [resilience](), [search](), [security]() ### AI overview Elastic{ON} Mumbai will cover search, full-stack observability, security, and the use of enterprise data to ground agentic AI. The article also discusses regulatory expectations for unified, real-time technology visibility, resilience, and evidence production. ### Source excerpt Join us at Elastic{ON} Mumbai to explore search, full-stack observability, and security, and how Elastic acts as the context engine that grounds agentic AI in your enterprise data, transforming regulatory mandates and data into an AI advantage. ## Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers DevFeed: [Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers]() Original publisher: [Read original article]() Author: Tom Aarsen Published: 2026-08-26T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [sentence-transformers](), [AI search](), [AI, ML & Data Engineering]() Tags: [community](), [datasets](), [embedding](), [embeddings](), [evaluation](), [guide](), [nlp](), [open-source](), [retrieval](), [sentence-transformers](), [training]() ### AI overview A tutorial on finetuning multi-vector embedding models with Sentence Transformers. It explains late-interaction token-level retrieval, training components, and domain-specific retrieval improvements. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code DevFeed: [How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code]() Original publisher: [Read original article]() Author: Niels Rogge Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI search](), [inference-endpoints](), [jobs](), [hugging face](), [Embeddings](), [PostgreSQL](), [Retrieval Augmented Generation (RAG)]() Tags: [database](), [embeddings](), [hugging-face](), [inference](), [inference-endpoints](), [jobs](), [latency](), [rag](), [research](), [search](), [storage]() ### AI overview This article explains how Papers with Code uses hybrid search to find research papers through exact keyword matching and semantic vector search. The production system combines PostgreSQL full-text search, pgvector embeddings, reciprocal rank fusion, and Hugging Face Jobs, Storage Buckets, and Inference Endpoints. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The search multiplier: Driving revenue, productivity, and AI at scale DevFeed: [The search multiplier: Driving revenue, productivity, and AI at scale]() Original publisher: [Read original article]() Author: Nicole Volk Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [AI search](), [AI Infrastructure](), [Generative AI](), [idc](), [Resilience]() Tags: [accelerate](), [agentic-ai-cloud-search-context-engineering-customer-experience-deployment-end-user-experience](), [ai](), [ai-infrastructure](), [analysis](), [business-value](), [customer-story-elasticsearch-workplace-search](), [elasticsearch](), [elasticsearch-platform](), [financial-services](), [government](), [healthcare](), [resilience](), [retail](), [retrieval](), [search]() ### AI overview An IDC study of 11 large enterprises reports that the Elasticsearch Platform, used as a search and retrieval foundation, helped organizations deliver AI products faster while improving revenue, productivity, search relevance, and operational resilience. The study reports a 517% three-year ROI, $13.4 million in annual benefits per organization, and an 11-month payback period. ### Source excerpt An independent IDC study found organizations deploying the Elasticsearch Platform for enterprise search and agentic AI deliver AI products faster, drive higher revenue, boost productivity, and strengthen operational resilience across the business. ## Algolia joins the Vercel Marketplace DevFeed: [Algolia joins the Vercel Marketplace]() Original publisher: [Read original article]() Author: Sam Halstead Published: 2026-08-19T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI search](), [AI-assisted coding]() Tags: [agent-skills](), [api](), [cli](), [search](), [vercel]() ### AI overview Algolia can now be provisioned and managed through the Vercel Marketplace, with credentials added to project environment variables. The integration supports indexed search, crawling Vercel domains, InstantSearch interfaces, recommendations, AI ranking, and agent skills for API use. ### Source excerpt You can now provision and manage Algolia directly from the Vercel Marketplace. Algolia is a hosted search platform. You send it your content, it builds an index, and your app queries that index instead of your database. Results come back in milliseconds, with typo tolerance, synonyms, and facet counts built in. Key capabilities: Index product catalogs, documentation, or any other content Add recommendations and AI ranking to surface the most relevant results Point the Algolia Crawler at a Vercel domain to index your site automatically Build search interfaces with Algolia's InstantSearch libraries Installing adds your Algolia credentials to your project's environment variables; redeploy to pick them up. Algolia's agent skills install too, so your coding agent knows how to use the API out of the box. Install Algolia on the Vercel Marketplace or with the Vercel CLI: vc i algolia. Read more [Next page]()