# Embeddings Embeddings are vector representations of data in a lower-dimensional space that capture semantic relationships and support machine-learning and similarity-search applications. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Data Engineering Weekly #287 DevFeed: [Data Engineering Weekly #287]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-14T02:52:23Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Kafka](), [data observability](), [Embeddings](), [Multi-tenancy](), [Event-Streaming](), [OpenAI](), [Library](), [Code]() Tags: [architecture](), [architectures](), [data-engineering](), [data-quality](), [embedding](), [engineering](), [kafka](), [multi-tenancy](), [object-storage](), [observability](), [openai](), [streaming](), [vector]() ### AI overview Data Engineering Weekly #287 covers building data platforms from scratch, including composable architectures, data quality, and observability. It also previews talks on governed machine-executable ontologies for marketing activation and fair, order-preserving Kafka consumption for many tenants. The issue links to OpenAI's storage platform scaling for ChatGPT and Pinterest's embedding retrieval platform. ### Source excerpt The Weekly Data Engineering Newsletter ## Enterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU DevFeed: [Enterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU]() Original publisher: [Read original article]() Author: Anthony Su; Injae Kwak Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Embeddings](), [vllm](), [Inference](), [long-context](), [autoscaling](), [Kubernetes](), [Cloud](), [Google](), [Artificial Intelligence](), [model-deployment](), [Open Source]() Tags: [autoscaling](), [embedding](), [github](), [gpu](), [inference](), [kubernetes](), [long-context](), [multimodal](), [open-source](), [retrieval](), [tpu](), [vllm]() ### AI overview This article explains how Google Cloud integrated native TPU support into vLLM to serve long-context, multimodal embedding models at production scale. It describes elastic TPU provisioning with Google Kubernetes Engine, autoscaling across accelerator types, and TPU-specific optimizations for long sequences and chunked prefill. The resulting setup is designed to preserve numerical parity with GPU reference baselines while supporting high-throughput semantic retrieval applications. ### Source excerpt Google Cloud has natively integrated TPU support into the vLLM serving engine, allowing developers to elastically scale high-demand embedding pipelines using Google Kubernetes Engine (GKE). To handle massive 15K+ token contexts for models like Qwen3-Embedding-8B, the engineering team implemented TPU-specific optimizations such as hardware-safe tensor alignment, JAX/XLA compilation pre-warming, and a hybrid StepPool architecture for chunked prefill management. These enhancements achieve near-perfect numerical parity with reference GPU baselines, and developers can immediately leverage the open-sourced setup recipes on the AI-Hypercomputer GitHub to build their own high-throughput semantic retrieval applications. ## Caching Responses for Repeated Questions in Production LLM Applications DevFeed: [Caching Responses for Repeated Questions in Production LLM Applications]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-10T21:34:26Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Caching](), [Redis](), [Embeddings](), [Large Language Model](), [Inference]() Tags: [cache](), [caching](), [embeddings](), [inference](), [llm](), [redis]() ### AI overview Production LLM applications can reduce repeated model calls by caching generated responses and matching new questions to prior questions using embeddings. The article discusses Redis LangCache, including similarity thresholds, expiration, data isolation, monitoring, and an example where a cache hit was roughly 6x faster than direct inference. ### Source excerpt What production agents must preserve between turns, and what they must reset. ## 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 ## Build a support chatbot with Appwrite Functions and VectorsDB DevFeed: [Build a support chatbot with Appwrite Functions and VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Chat Bot](), [Retrieval-Augmented Generation](), [Embeddings](), [React](), [SDKs](), [Node.js]() Tags: [api](), [build](), [chat](), [deploy](), [embedding](), [functions](), [node-js](), [rag](), [react](), [retrieval](), [support](), [tutorials]() ### AI overview This tutorial builds Harbor Help, a support chatbot for a fictional project-management product. It uses Appwrite VectorsDB to store help-center article embeddings, an Appwrite Function to retrieve relevant excerpts and generate answers through OpenRouter, and Appwrite Sites to host a React interface. The chatbot uses retrieval-augmented generation and can refuse questions outside the help center. ### Source excerpt Seed a help center, retrieve relevant articles with VectorsDB, and answer customer questions through an Appwrite Function. Deploy the chat interface on Appwrite Sites. ## When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-09T20:31:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Optimization](), [model-deployment](), [multimodal](), [Dynamo](), [Inference Performance](), [Embeddings](), [Mixture of Experts (MoE)](), [GPU](), [Open Source](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [computer-vision-video-analytics](), [developer-tools-techniques](), [dynamo](), [embeddings](), [gpu](), [inference](), [low-latency-inference](), [mixture-of-experts-moe](), [model-serving](), [multimodal](), [nvfp4](), [open-source](), [optimization]() ### AI overview This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time. ### Source excerpt Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill... ## 5 Embedding Compression Techniques DevFeed: [5 Embedding Compression Techniques]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-04T20:51:57Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Compression](), [Embeddings](), [quantization](), [Inference]() Tags: [compression](), [embedding](), [inference](), [quantization](), [vector]() ### AI overview A tutorial explaining five embedding compression techniques: PCA, Matryoshka Representation Learning, scalar quantization, binary quantization, and Product Quantization. It describes how they reduce dimensions or per-value precision and how rescoring can improve ranking after compressed retrieval. ### Source excerpt ...explained visually. ## Build a memory MCP server on Appwrite DevFeed: [Build a memory MCP server on Appwrite]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Model Context Protocol](), [MCP Server](), [OAuth 2.0](), [Serverless](), [Databases](), [Embeddings](), [Claude Code](), [web applications]() Tags: [ai](), [ai-assistants](), [ai-tools](), [api](), [claude](), [claude-code](), [embeddings](), [mcp](), [mcp-server](), [memory](), [net](), [oauth2](), [sdk](), [serverless](), [tutorial](), [tutorials]() ### AI overview This tutorial builds Recall, a small persistent-memory app on Appwrite. It hosts a stateless remote MCP server in an Appwrite Function, stores embedded memories in VectorsDB for similarity search, and uses OAuth2 for user-authorized access. It also explains connecting Claude Code to the server. ### Source excerpt Give your AI tools a shared, persistent memory. Host a stateless MCP server on Appwrite Functions, store memories in VectorsDB, and protect it with your project's OAuth2 server. ## How Embedding Models Affect Retrieval-Augmented Generation Quality DevFeed: [How Embedding Models Affect Retrieval-Augmented Generation Quality]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-02T15:31:12Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Embeddings](), [Retrieval Augmented Generation (RAG)](), [Language models](), [Artificial Intelligence]() Tags: [embedding](), [embeddings](), [model](), [rag](), [retrieval-augmented-generation]() ### AI overview This article explains how embedding models support retrieval-augmented generation by translating words into vectors and controlling the search for relevant information before an answer is generated. It discusses why poor retrieval can produce incorrect answers even when the language model is capable, and outlines considerations for evaluating and selecting embedding models. ### Source excerpt In this article, we're going to look at how this embedding model works in an RAG setup and what makes it such a critical part of the system. ## 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. ## MAPS: Netflix's Multimodal Asset Personalization at Scale DevFeed: [MAPS: Netflix's Multimodal Asset Personalization at Scale]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-08-28T16:01:02Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [personalization](), [Embeddings](), [multimodal](), [recommendation systems](), [data](), [A/B Testing]() Tags: [data](), [embedding](), [embeddings](), [machine-learning](), [model](), [multimodal](), [netflix](), [personalization](), [production](), [testing]() ### AI overview Netflix describes MAPS, a multimodal asset-personalization system that uses embeddings from artwork and video previews to address cold-start problems and personalize recommendations soon after a title launches. ### Source excerpt By Emma Yanyang Kong, Aditya Deshpande, Asad Abbasi, Bowei Yan, David Fagnan, Ashish Rastogi, Dhaval Patel, Ray Zhang Introduction The Netflix experience is a journey of discovery. Every visual cue, from the artwork on a title to the video previews that autoplay while you browse, is there to connect you with a story you will love. We call these visual cues assets, and choosing the right one for each member is a personalization problem of its own. But which image or video preview of Squid Game should we show you? And what do we do right after a title launches, when there's far too little interaction data to know which asset we should recommend to each member? For years, our models answered the first question well and the second poorly. They learned which assets members interacted with, but treated every asset as an opaque ID, blind to what was actually in the artwork or video preview. Right after a title launched, its assets had no history, so we dialed up exploration on its assets to gather interaction data, and otherwise fell back to popularity heuristics that ignore your taste. Only once enough interactions had piled up could personalization take over. This is the classic cold-start problem. This post shares how multimodal embeddings let our models see and hear the assets they recommend, so personalization can kick in far sooner, close to a title's launch. Because a new asset arrives with its embedding the model already understands, that embedding carries member taste signals from related assets immediately. Consequently, the model needs far less interaction history before it can personalize. We cover three production systems, artwork personalization, query-aware artwork ranking, and video preview personalization, plus a cheap trick for choosing new embeddings before committing to full end-to-end integration and A/B testing. Artwork Personalization A single image is often a member's first touchpoint with a title, so we create a diverse set of artworks for each tit ## Scaling Conditional Learned Retrieval for Pinterest Home Feed DevFeed: [Scaling Conditional Learned Retrieval for Pinterest Home Feed]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-08-26T14:01:05Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [recommendation systems](), [Embeddings](), [Machine Learning & Artificial Intelligence]() Tags: [blog](), [embedding](), [eng-culture](), [engineering](), [infrastructure](), [pinner-experience](), [pinterest](), [ranking](), [retrieval]() ### AI overview Pinterest describes scaling Conditional Learned Retrieval for home-feed candidate generation. The approach extends two-tower retrieval with explicit contexts that produce multiple user embeddings for different interests and retrieval use cases. ### Source excerpt Devin Kreuzer | Sr. Machine Learning Engineer; Yichi Wang | Machine Learning Engineer I; Sujan Reddy Ale | Machine Learning Engineer I; Zelun Wang | Sr. Machine Learning Engineer; Hongtao Lin | Sr. Machine Learning Engineer; Piyush Maheshwari | Staff Machine Learning Engineer Pinterest home feed candidate generation is a large-scale User-to-Pin retrieval problem. A common approach is a two-tower model: a user tower encodes the user, an item tower encodes candidate Pins, and approximate nearest neighbor search retrieves Pins close to the user embedding. But Pinterest users often have multiple intentions at once -- planning a renovation, saving recipes, exploring fashion, or organizing travel ideas. A single retrieval embedding can struggle to capture this diversity. Conditional Learned Retrieval, or CLR, extends the two-tower setup by conditioning the user tower on an explicit retrieval context. Instead of producing only one user embedding, CLR can generate condition-aware embeddings that reflect different aspects of a user's interests while still grounding retrieval in the user's overall behavior. Prior Pinterest work studied this formulation in two settings. The RecSys'24 paper: Bootstrapping Conditional Retrieval for User-to-Item Recommendations described how to bootstrap conditional retrieval by constructing training data for (user, condition) -> item retrieval from existing user-item and condition signals, and applied it to interest-based notifications. The KDD'25 paper: Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest placed Conditional Retrieval within a broader multi-embedding retrieval framework for home feed, where explicit interest conditions complement implicit interests extracted from user behavior. In this blog, we describe how CLR evolved from early interest-conditioned retrieval into a broader retrieval system for Pinterest home feed. We focus on three areas: expanding CLR to support more retrieval us ## The Real Python Podcast - Episode #308: Navigating Silent Failures in AI: Strategies for Effective Oversight DevFeed: [The Real Python Podcast - Episode #308: Navigating Silent Failures in AI: Strategies for Effective Oversight]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-21T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Artificial Intelligence](), [Large Language Model](), [AI-assisted coding](), [LLMs](), [Command-line interface](), [audit trail](), [Embeddings](), [Markdown](), [Script](), [Python]() Tags: [agentic-ai](), [agents](), [ai](), [api](), [audit-trail](), [cli](), [coding-agents](), [embeddings](), [gemini](), [markdown](), [opencode](), [tokens]() ### AI overview This podcast episode examines why AI systems can fail silently and how to improve oversight through context, checklists, validation, hooks, skills, scripts, and audit trails. It discusses document parsing problems such as unsupported formats, dropped attachments, and silent truncation, along with coding agents, CLI tools, embeddings, model selection, and token usage. The episode also highlights a course on AI-assisted Python coding with OpenCode and Gemini. ### Source excerpt Why do AI systems silently fail? How can you set up a system that produces results while also reviewing and validating the work? This week on the show, Calvin Hendryx-Parker returns to discuss his recent talk "Orchestrate Agentic AI: Context, Checklists, and No-Miss Reviews." ## 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. ## Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost DevFeed: [Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost]() Original publisher: [Read original article]() Author: Cody Mazza-Anthony Published: 2026-08-19T14:32:58Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Compression](), [Large Language Model](), [Embeddings](), [Latency](), [Inference](), [Inference Performance](), [Low-Latency Inference](), [Post-training optimization](), [GraphQL](), [model-deployment]() Tags: [compression](), [embedding](), [inference](), [latency](), [llm](), [model](), [tokens]() ### AI overview Gisting compresses an LLM agent's system prompt into learned gist tokens, preserving prediction quality while reducing inference latency, increasing throughput, and lowering GPU requirements. ### Source excerpt Gisting compresses context into a set of learned tokens, preserving its quality while making the model faster and cheaper. ## Scale pgvector with binary quantization on Amazon Aurora PostgreSQL DevFeed: [Scale pgvector with binary quantization on Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Steve Dille Published: 2026-08-18T16:37:22Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [quantization](), [AI search](), [Compression](), [Embeddings](), [Cache](), [Latency](), [datasets](), [configuration](), [postgresql clusters](), [Database]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [cache](), [compression](), [datasets](), [embeddings](), [latency](), [postgresql](), [postgresql-compatible](), [quantization](), [rds-for-postgresql](), [search](), [technical-how-to](), [validation](), [vector]() ### AI overview This practical guide explains how to use binary quantization with reranking in pgvector to scale HNSW vector search on Amazon Aurora PostgreSQL. It covers index-size reduction, performance and recall tradeoffs, sizing, validation, and suitable operating conditions for datasets ranging from 5 million to 100 million vectors. ### Source excerpt Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best. ## Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers DevFeed: [Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers]() Original publisher: [Read original article]() Author: Tom Aarsen; Antoine Chaffin; Raphael Sourty Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [sentence-transformers](), [Embeddings](), [Open Source]() Tags: [community](), [embedding](), [embeddings](), [guide](), [multimodal](), [nlp](), [open-source](), [retrieval](), [search](), [sentence-transformers](), [transformers](), [vector]() ### AI overview This article explains multi-vector, or late-interaction, embedding models with Sentence Transformers. It covers token-level representations, MaxSim scoring, retrieval over text and page images, integration with search systems, and index-size tradeoffs. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Vector search database: news & 2026 guide DevFeed: [Vector search database: news & 2026 guide]() Original publisher: [Read original article]() Author: Simran Regmi Published: 2026-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Database](), [AI search](), [Embeddings](), [ann](), [Large Language Model](), [Algorithms]() Tags: [ann](), [database](), [embeddings](), [guide](), [latency](), [llm](), [search](), [tech-de](), [vector]() ### AI overview This guide explains how vector search databases store embeddings and retrieve semantically similar items for LLM-powered applications. It covers vector spaces, dimensionality, similarity measures, traditional indexes, the curse of dimensionality, and approximate nearest neighbor search, including HNSW. ### Source excerpt If you've built anything on top of an LLM in the past couple of years, you may have hit the wall many builders hit: the model writes fluently but has no view into your data. A vector search database helps close that gap. It stores vector embeddings an... ## Elastic community newsletter -- August 2026 DevFeed: [Elastic community newsletter -- August 2026]() Original publisher: [Read original article]() Author: Elastic DevRel team Published: 2026-08-13T00:00:00Z Content type: news Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [devrel](), [dashboards](), [Embeddings](), [multimodal](), [quantization](), [AI search](), [tracing]() Tags: [audio](), [dashboards](), [devrel](), [elasticsearch](), [embeddings](), [images](), [multimodal](), [pdf](), [quantization](), [tracing](), [video]() ### AI overview The Elastic community newsletter covers Elasticsearch 9.5, including native PromQL, the Dashboards API, Columnar Mode, vector search improvements, multimodal semantic search, and Elastic Agent Builder tracing in technical preview. ### Source excerpt In this newsletter, we cover version 9.5 of Elasticsearch, the latest blogs and videos, and upcoming events. ## Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis DevFeed: [Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis]() Original publisher: [Read original article]() Author: Kyle Wiggers Published: 2026-08-12T16:14:36Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Embeddings](), [foundation-models](), [Open Source](), [API](), [Fine-tuning](), [LLM evaluation / benchmarking]() Tags: [api](), [benchmarking](), [data](), [embeddings](), [fine-tuning](), [foundation-models](), [models](), [open-source]() ### AI overview OlmoEarth Studio now supports computing and exporting embedding vectors from open source OlmoEarth foundation models. Users can configure geographic area, time range, encoder, resolution, and imagery sources through the Studio UI or API, then download the results as Cloud-Optimized GeoTIFFs for downstream analysis such as similarity search, segmentation, and exploration. ### Source excerpt Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis OlmoEarth Studio, our platform for building Earth observation models, now lets you compute and export embedding vectors--compact numerical representations of Earth-observation data produced by our open source OlmoEarth foundation models. ## Chaining Activities -- from text to vectors DevFeed: [Chaining Activities -- from text to vectors]() Original publisher: [Read original article]() Author: Houman Kargaran Published: 2026-08-12T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [data](), [Embeddings](), [on-prem](), [pii](), [Redis](), [retry](), [Sequences]() Tags: [article](), [community](), [data-pipeline](), [embeddings](), [on-prem](), [pii](), [redis](), [retries](), [storage]() ### AI overview This guest post explains a Temporal workflow that chains discrete Activities to process complaints while maintaining a PII boundary. It uses an on-premises all-MiniLM-L6-v2 model for embeddings, caches vectors in Redis, stores them externally, and relies on deterministic workflow sequencing and retry policies. ### Source excerpt Learn how to build a durable, PII-conscious data pipeline with Temporal using on-prem embeddings, idempotent Activities, external vector storage, and retries. ## How we selected the next vector database at Booking.com DevFeed: [How we selected the next vector database at Booking.com]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2026-08-11T10:31:50Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Database](), [Retrieval Augmented Generation (RAG)](), [Embeddings](), [Back end](), [opensearch](), [AI search]() Tags: [ai](), [database](), [embeddings](), [featured](), [genai](), [hybrid-search](), [retrieval-augmented-generation](), [retrieval-augmented-generation-rag](), [semantic](), [vector-database]() ### AI overview Booking.com explains why selecting a vector database became an infrastructure decision as embeddings and vector search expanded across its machine learning and GenAI systems. The article describes diverse functional and operational requirements, including hybrid search, multi-vector support, capacity, request rates, metadata filtering, and concurrency, and introduces OpenSearch as the initial choice. ### Source excerpt This work was done in collaboration with Klaus Schaefers. Over the past few years, embeddings and vector search have become an important capability in many of our machine learning and GenAI systems at Booking.com. We initially started with a handful of use cases and experiments, and later this capability has grown into shared infrastructure that powers similarity search, semantic filtering, and retrieval-augmented generation (RAG). We used to treat vector search as a backend implementation detail, but today it directly drives the user experience. The real win isn't only speed but also the context. Expanding the variety of domain data we can retrieve efficiently gives our system the depth of context it needs to deliver accurate, and personalized experiences across the platform. This makes selecting the underlying vector database an infrastructure decision similar to choosing a primary datastore or message queue. It has to be predictable and scalable. As more teams started using our vector store, we began seeing highly diverse functional and operational requirements across different use cases. Some teams needed advanced capabilities like hybrid search or multi-vector support, while others demanded larger vector capacities and higher RPS metrics. These architectural needs ultimately brought us to a point where we needed to reassess whether our current setup could support this next phase of growth. Context: how embeddings fit into our stack Embeddings are vectors: fixed-length arrays of numbers produced by a model to represent an item (text, image, etc.). Each vector can be seen as a point in a high-dimensional space, where distance (or similarity) between points approximates semantic relatedness. By searching for the nearest vectors to a query vector, we retrieve items that are semantically "similar". This simple mechanism enables a wide range of use cases for us due its ability to do semantic similarity search. RAG-based use cases are the most well known examples. Ano ## AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) DevFeed: [AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-10T15:45:03Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Amazon Web Services](), [Artificial Intelligence](), [Amazon Bedrock AgentCore](), [Amazon DynamoDB](), [AWS Transform](), [Embeddings](), [Containers](), [Serverless](), [Mainframe](), [OpenAI]() Tags: [ai](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-dynamodb](), [applications](), [aws](), [aws-lambda](), [aws-transform](), [bedrock](), [containers](), [embeddings](), [kiro](), [modernization](), [news](), [open-source](), [product](), [serverless](), [week-in-review]() ### AI overview This AWS Weekly Roundup covers the AWS Heroes Summit and selected launches, including web search for OpenAI models in Amazon Bedrock, dedicated runtime instances for agents in Amazon Bedrock AgentCore, vector search in Amazon DynamoDB, and general availability of AWS Transform continuous modernization. ### Source excerpt Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community. The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions [...] [Next page]()