# genai Published articles for genai. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck DevFeed: [Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-15T17:23:54Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Data Infrastructure](), [AI Infrastructure](), [idc](), [Inference](), [Retrieval-Augmented Generation](), [synthetic-data]() Tags: [ai](), [compute](), [data-infrastructure](), [datasets](), [enterprise](), [genai](), [hdd](), [idc](), [inference](), [reports](), [research](), [retrieval](), [storage](), [synthetic-data]() ### AI overview Seagate and WD published separate studies indicating that AI is increasing enterprise storage requirements and extending data retention. Although their headline percentages differ because they asked different questions, both reports point to storage becoming a larger part of AI infrastructure planning alongside growing archive and retrieval needs. ### Source excerpt Seagate and WD published separate AI storage studies within days of each other; the headline numbers: Seagate says 99% of enterprises expect AI to increase their storage requirements over the next three years, while WD's IDC research puts the comparable figure at 74%. Read the fine print, and both reports land in the same directional The post Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck appeared first on StorageReview.com. ## AI Can Help Write an Article, but It Can't Stand Behind It DevFeed: [AI Can Help Write an Article, but It Can't Stand Behind It]() Original publisher: [Read original article]() Author: Raluca Budiu Published: 2026-09-11T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [Artificial Intelligence](), [Generative AI](), [User experience (UX)](), [Microsoft]() Tags: [ai](), [ai-authored](), [ai-content](), [ai-editing](), [ai-writing](), [article](), [copilot](), [genai](), [generative-ai](), [microsoft](), [ux](), [writing]() ### AI overview The article explains that NN/G uses AI during its editorial process for writing clarity, formatting, and critique, while humans retain editorial judgment and responsibility for published content. It describes a rigorous review cycle involving topic selection, multiple revisions, and checks for logical coherence, UX accuracy, and copy. AI tools such as Copilot in Microsoft Word and Grammarly help make writing more concise and clear, especially for authors who are not professional writers or speak English as a second language. ### Source excerpt NN/G uses AI for clarity, formatting, and critique, but humans retain editorial judgment and responsibility for every article. ## EA is using genAI for commentator voiceover in NHL 27 DevFeed: [EA is using genAI for commentator voiceover in NHL 27]() Original publisher: [Read original article]() Author: Diego Argüello Published: 2026-09-10T17:18:45Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [genai](), [Artificial Intelligence](), [Sports](), [simulator]() Tags: [ai](), [gaming](), [genai](), [simulator](), [sports]() ### AI overview EA says NHL 27 uses AI voice technology to expand commentary recordings made with John Buccigross and Darren Pang, with their consent and active collaboration. The article also reports earlier claims about the technology and notes that AI-generated lines can require correction. ### Source excerpt EA says it's using the tech to expand on recording sessions with John Buccigross and Darren Pang's 'full consent and active collaboration.' ## Building Reproducible AI Evaluation Workflows with Docker Sandboxes DevFeed: [Building Reproducible AI Evaluation Workflows with Docker Sandboxes]() Original publisher: [Read original article]() Author: Jennifer Kohl Published: 2026-09-02T13:00:00Z Content type: tutorial Language: en Sources: [Docker]() Topics: [ai observability](), [Human-AI evaluation]() Tags: [ai](), [ai-agent](), [ai-ml](), [ai-models](), [benchmarks](), [claude](), [community](), [docker](), [docker-sandboxes](), [evaluation](), [genai](), [json](), [open-source](), [python](), [sandbox](), [sandboxes](), [workflow](), [workflows]() ### AI overview The article presents an open-source Docker Sandboxes Mixin Kit for making AI evaluation workflows reproducible. It runs configured commands in a consistent environment and records structured results and runtime evidence, without executing models or generating evaluation judgments itself. ### Source excerpt Learn how Docker Sandboxes can make AI evaluation workflows more reproducible with consistent execution, structured artifacts, and runtime evidence. ## Track LLM API Costs with genai-prices DevFeed: [Track LLM API Costs with genai-prices]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-09-01T11:16:32Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Large Language Model](), [API](), [Inference](), [Library](), [Python](), [Binance]() Tags: [api](), [cost](), [example](), [genai](), [inference](), [install](), [library](), [llm](), [openai](), [pricing](), [python](), [report](), [request](), [tips](), [token](), [uv]() ### AI overview A tutorial introducing the genai-prices Python library for estimating the cost of calling LLM inference APIs. It explains that the library can avoid maintaining a custom pricing table and can report token counts and cost for each request, with an OpenAI installation and usage example. ### Source excerpt Tip: Use the genai-prices Python library to calculate the estimated cost of calling LLM inference APIs. This avoids maintaining your own pricing table and lets you report token counts and cost for each request. Below is an example: # Install: uv add openai genai-prices from openai import OpenAI from ## Worth Reading 082926 DevFeed: [Worth Reading 082926]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-29T14:47:35Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [cdnjs](), [ChatGPT](), [genai](), [data centers](), [DNSSEC]() Tags: [chatgpt](), [data-centers](), [dnssec](), [genai](), [technology](), [worth-reading]() ### AI overview A developer-oriented reading roundup covering content delivery networks, ChatGPT and scaling toward artificial general intelligence, high-density data-center builds, GenAI-assisted medical image analysis, and the practical difficulty of maintaining DNSSEC. ### Source excerpt The term "content delivery network" reflects the technology's original value proposition. Sam Altman released ChatGPT for free in late 2022. Many users fell in love both with it and Altman's argument that artificial general intelligence could be achieved through scaling. T If you work around data centers and high-density builds, you have probably heard the term "Multi-Core Fiber" thrown around. Ask yourself these questions, assuming you have a serious medical condition and your doctors are going to be using GenAI to scan your images and test results to discover the breadth and depth of your condition and to recommend treatment. Maybe adoption is low not because people don't believe DNSSEC is useful, but because turning it on and keeping it on is still genuinely harder than it should be. ## Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI DevFeed: [Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI]() Original publisher: [Read original article]() Author: Kostiantyn Okhrimenko Published: 2026-08-27T11:15:58Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [semantic-layer](), [genai](), [analytics stack](), [Large Language Model](), [Artificial Intelligence](), [data](), [dashboards](), [Query (disambiguation)]() Tags: [agent](), [ai](), [ai-tools](), [analytics-stack](), [bi-tools](), [dashboards](), [data](), [genai](), [real-time](), [self-service](), [self-serving-analytics](), [semantic-layer](), [sql]() ### AI overview The article examines how GenAI-driven natural-language interfaces can help stakeholders obtain trusted data answers without repeatedly interrupting data and engineering teams. It argues that a robust semantic layer is necessary to make self-service analytics reliable and precise. ### Source excerpt When an urgent request for a report or dashboard arrives, often just before an executive meeting, data and engineering teams must drop planned work to respond. One request may be reasonable, but repeated interruptions come at a cost: important work, such as scaling infrastructure, improving reliability, models optimization, gets pushed back, while quick, one-off dashboards become more technical debt to maintain. For managers and other decision-makers, the need is real: they require reliable data to make decisions quickly. But getting an answer often depends on someone who knows SQL, understands the data structure, and has time to help. When those people are already busy, the question waits, even when the answer is sitting in the data warehouse. By the time the report is ready, the decision window may have passed. This is not just a prioritization issue. We need a better way for people to get trusted answers quickly without constantly pulling teams away from building and improving the data platform. All of the above can be illustrated by the image: Image 1: Typical reporting circleWhat we will talk about The explosion of GenAI over the last few years has shifted the focus for the modern analytics stack. We are evolving beyond traditional Data Democratization, which often gave teams access to complex pre-AI tools without clear governance, toward natural language data interaction: asking questions in plain English -- Talk to your data concept. In the traditional stack, the "interface" to data was either a dashboard or a SQL editor. This created a high barrier to entry that caused the friction. By properly architecting and utilizing GenAI-driven tools, we can finally bridge the gap between intent and insight. Talk to your data is a self-serve ecosystem where any stakeholder can bypass the traditional ticketing queue and, instead of waiting for an engineer to interpret a requirement and translate it into a query, the user engages with a specialised agent. The challenge, h ## Durable Digest: August highlights DevFeed: [Durable Digest: August highlights]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-08-27T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Cloud](), [AI Development](), [AWS Lambda](), [Workers](), [autoscaling](), [Amazon Web Services](), [AI Agent](), [AWS IAM](), [SDKs]() Tags: [ai](), [ai-agent](), [announcements](), [autoscaling](), [aws](), [aws-lambda](), [cloud](), [distributed](), [durability](), [genai](), [google](), [iam](), [infrastructure](), [workers]() ### AI overview Temporal's August 2026 Durable Digest highlights new and preview features for running Workers on AWS Lambda, organizing Temporal Cloud resources, invoking Nexus Operations, and building durable AI applications. ### Source excerpt Highlights this month include new ways to run Workers without managing infrastructure, organize Temporal Cloud resources and build more durable AI applications. ## 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 ## The Human Context Advantage: Takeaways from the Ai4 Keynote with Andrew Ng, Geoffrey Hinton, and Fei-Fei Li DevFeed: [The Human Context Advantage: Takeaways from the Ai4 Keynote with Andrew Ng, Geoffrey Hinton, and Fei-Fei Li]() Original publisher: [Read original article]() Author: Bonnie Chase Published: 2026-08-10T00:00:00Z Content type: opinion Language: en Sources: [Vespa Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [systems]() Tags: [agents](), [ai](), [business](), [developers](), [education](), [future-of-work](), [genai](), [jobs](), [keynote](), [rag](), [work]() ### AI overview The article reflects on an Ai4 keynote featuring Andrew Ng, Geoffrey Hinton, and Fei-Fei Li. It argues that humans retain a context advantage because they understand organizational goals, customers, relationships, constraints, and unwritten rules that current AI systems lack. The article suggests that enterprise AI success will depend on delivering relevant context at the right time, while AI changes jobs by automating tasks and broadening developers' responsibilities rather than simply eliminating work. ### Source excerpt From jobs and education to open models and AI infrastructure, the AI4 keynote made one thing clear: competitive advantage won't come from AI alone, but from connecting it to the right context. ## Data Engineering Weekly #281 DevFeed: [Data Engineering Weekly #281]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-03T12:34:40Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Loop Engineering](), [Artificial Intelligence](), [Large Language Model](), [post-training]() Tags: [agent-harness](), [ai](), [data-engineering](), [feature-engineering](), [genai](), [netflix](), [newsletter](), [post-training](), [weekly]() ### AI overview Data Engineering Weekly #281 covers building data platforms, emerging approaches to AI workflow architecture, data modernization, Netflix's GenRec recommendation system, AI infrastructure modernization, and evaluation practices for generative AI at scale. ### Source excerpt The Weekly Data Engineering Newsletter ## Focus on the Feature, Not the Fixture: GenAI powered GraphQL mocks DevFeed: [Focus on the Feature, Not the Fixture: GenAI powered GraphQL mocks]() Original publisher: [Read original article]() Author: Samuel Vazquez Published: 2026-07-31T11:01:02Z Content type: article Language: en Sources: [Expedia]() Topics: [GraphQL](), [genai](), [Large Language Model](), [API](), [Command-line interface]() Tags: [ai](), [api](), [artificial-intelligence](), [cli](), [developer](), [genai](), [graphql](), [llm](), [rust](), [schema](), [software-engineering](), [tools]() ### AI overview The article presents mockql-rs, a CLI that combines a GraphQL schema, query annotations, hints, and an LLM to generate contextual mock responses. It argues that GraphQL constrains the response shape while the LLM fills only annotated fields, reducing handwritten fixtures and allowing real and mocked fields to coexist. ### Source excerpt Expedia Group Technology -- EngineeringCombine a GraphQL schema, hints and a LLM to generate contextual GraphQL mock responsesPhoto by Samuel Vazquez somewhere in New Zealand A product developer on our navigation header team spent an afternoon hand-typing a 200-line GraphQL JSON mock response so they could keep building the UI while a resolver was pending to be implemented. The schema changed the next morning. We threw the mock away. That is the boring tax on every GraphQL prototype: mocks that drift, fixtures that rot, frontends blocked on backends, and demos slipping because nobody wanted to update the same mock data again. Mock data is just data. It should not be the most expensive part of trying an idea. GenAI + GraphQL: a match made in heaven Most tools that promise "AI generates an API" share the same flaw: the model invents a shape from scratch, and you spend the afternoon reshaping its output to match your actual types. GraphQL flips that. The selection set is the spec. The schema is the type system. Hand an LLM a query and it already knows the exact JSON it must return, field by field, type by type. That's the unlock: LLMs are bad at inventing shapes and great at filling them in, and GraphQL hands them a bounded shape for free. mockql-rs is what happens when you take that pairing seriously: mark what you want mocked directly in the query with a "@mock" directive: query LoyaltyRewards @mock { loyaltyRewards { heading @mock(hint: "Platinum member") { text } subtitles @mock(hint: "At least 4 items") { text theme } } } Annotate the field, add a hint, keep building. mockql-rs parses the operation, validates it against the schema, and prompts an LLM to fill in only the fields you annotated with "@mock". { "data": { "loyaltyRewards": { "heading": { "text": "Welcome back, Platinum Member" }, "subtitles": [ { "text": "2,450 points until your next reward", "theme": "HIGHLIGHT" }, { "text": "3 nights earned this quarter", "theme": "STANDARD" }, { "text": "Breakfast inc ## Open-Source Projects Push Back on AI Contributions as AI Deployment and Sustainability Concerns Grow DevFeed: [Open-Source Projects Push Back on AI Contributions as AI Deployment and Sustainability Concerns Grow]() Original publisher: [Read original article]() Author: Sergio Visinoni Published: 2026-07-31T10:07:56Z Content type: opinion Language: en Sources: [Sudo Make Me a CTO]() Topics: [Open Source](), [Artificial Intelligence](), [genai]() Tags: [ai](), [enshittification](), [genai](), [open-source](), [something-big-is-happening]() ### AI overview This issue discusses open-source projects rejecting AI-generated contributions, questionable corporate AI deployments, and the environmental costs of generative AI and data-center expansion. It also includes positive developments intended to show that constructive action remains possible. ### Source excerpt Open-source projects increasingly pushing back on AI, the enshittification continues and everybody is so fixated about deploying more AI that nobody is asking for, while the planet is literally burning. ## GenRec: Towards LLM-Native Recommendation at Netflix DevFeed: [GenRec: Towards LLM-Native Recommendation at Netflix]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-07-30T20:10:15Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [recommendation systems](), [Large Language Model](), [LLM Techniques](), [personalization](), [vllm]() Tags: [data](), [feature-engineering](), [genai](), [large-language-models](), [large-language-models-llms](), [llm](), [llms](), [netflix](), [personalization](), [ranking](), [recommendation-system](), [vllm]() ### AI overview Netflix presents GenRec, an LLM-backed recommendation ranker trained on Netflix-specific data and objectives. It converts user histories, item metadata, and context into text, uses a catalog-aware scoring head, aligns recommendations with long-term member value and business goals, and runs in prefill-only mode on Netflix's LLM serving stack. In a large-scale A/B test, GenRec improved short- and long-term online metrics while using fewer labeled examples and input signals than a mature production ranker. ### Source excerpt Authors: Ying Li, Arjun Rao, Shradha Sehgal Introduction Recommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand-crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interactions, and multi-task objectives. This stack has evolved over many years to support diverse content types (movies, series, games, live, podcasts) and product surfaces, but its complexity makes it costly to onboard new use cases: adding a content type or surface can require significant feature engineering, architecture change, infrastructure work, and experimentation. At the same time, large language models (LLMs) are changing how we think about recommendation, as shown by recent work such as PLUM, GLIDE, and OneRec-Think. Their broad world knowledge and strong language understanding make it possible to represent user histories and item metadata directly as text, capture rich relationships in a shared semantic space, and steer recommendations via natural-language prompts. However, off-the-shelf LLMs are still far from production-ready recommenders: they often over-recommend globally popular content, hallucinate out-of-catalog items, ignore business constraints, and provide only limited personalization. To address this, we built GenRec, an LLM-backed recommendation ranker that post-trains an internal foundation LLM on Netflix-specific data and objectives. GenRec shows that an LLM-based ranker can match or exceed a mature production system while relying on far fewer labeled examples and input signals. Figure 1: GenRec pipeline. Raw logs of user history, item metadata, and context are transformed via context engineering into natural-language prompts and fed into the GenRec, which runs on vLLM in prefill-only mode and outputs scores for each catalog item, yielding a recommendation ranking. At a high level, GenRec: Verbalizes user histories, item metadata, and context as text ## Eval-driven development: Lessons from evaluating GenAI at scale DevFeed: [Eval-driven development: Lessons from evaluating GenAI at scale]() Original publisher: [Read original article]() Author: Rohit Girme Published: 2026-07-28T17:01:03Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Generative AI](), [Large Language Model](), [Software Engineering](), [Artificial Intelligence](), [Tooling]() Tags: [agentic-ai](), [ai](), [article](), [development](), [engineering](), [eval](), [evals](), [evaluation](), [genai](), [generation](), [generative-ai](), [llm](), [software](), [software-testing](), [testing](), [tooling](), [workflows]() ### AI overview This article presents eval-driven development as a core engineering discipline for trustworthy Generative AI products. It explains why evaluating LLM systems is difficult, including non-deterministic outputs, subjective correctness, AI-based evaluation risks, and failures across retrieval, reasoning, tool calls, and generation. It shares foundational evaluation practices and cautions that teams should plan evaluation early and ground success criteria in their data. ### Source excerpt How Airbnb teams build trustworthy Generative AI products by treating evaluation as a first-class engineering discipline; not an afterthought.Nestled into the lush hillside, this stunning modern retreat features striking natural wood architecture, terraced balconies, and a serene landscape. By: Rohit Girme, Dan Miller, Mia Zhao, Lifan Yang, Clint Kelly Introduction Generative AI breaks a lot of the assumptions that used to hold true for software testing. Unlike traditional software, LLM outputs are non-deterministic, and "correct" is subjective. Because so much judgment is involved, you often need an AI to evaluate an AI, which introduces its own potential failure modes. Making matters more complicated, a single interaction with an LLM can chain retrieval, reasoning, tool calls, and generation, each of which can fail independently. At Airbnb, we build LLM-powered features across our product, with recent launches including review highlights, AI customer support, smart communication features for guests and hosts, and more. Behind the scenes, we also use AI to help us spot trends and understand what's working, guiding where we improve the product next. Each product team may have its own evaluation criteria, process, workflows, etc. However, these are built on top of some common foundations and principles. An infrastructure team provides tooling and best practices, incorporating learnings across domains so that they are shared with everyone building products at Airbnb. In this article, we wanted to share some of these best practices and learnings with the broader engineering community. Please note that the recommendations here are not intended to be prescriptive; there is no one-size-fits all approach when it comes to running evals. 1. Foundation Evaluating LLM-based systems is challenging work, and this should be planned for at the outset. Without a deliberate strategy, three things tend to happen: False confidence: A generic "helpfulness" metric scores well, you ship, ## Инференс LLM: от KV-кэша до продакшен-деплоя DevFeed: [Инференс LLM: от KV-кэша до продакшен-деплоя]() Original publisher: [Read original article]() Author: a\_ryzhov (hh.ru, Конференции Олега Бунина (Онтико)) Published: 2026-07-27T05:30:45Z Content type: tutorial Language: ru Sources: [HeadHunter RU]() Topics: [Large Language Model](), [Caching](), [on-prem](), [genai](), [MLOps](), [sglang](), [vllm]() Tags: [2026](), [compute](), [genai](), [http](), [inference](), [kv-cache](), [llm](), [mlops](), [on-prem](), [sglang](), [sram](), [vllm]() ### AI overview This Russian-language developer article explains how LLM inference behaves in on-premises production environments in 2026. It argues that GPU memory management is the main efficiency constraint, describes how KV caching shifts decoding from compute-bound to memory-bandwidth-bound work, and introduces vLLM and SGLang as ways to address the problem. ### Source excerpt Привет! Я Саша Рыжов, MLOps-инженер в hh.ru, уже три года занимаюсь развитием инфраструктуры для искусственного интеллекта. Компании, которые развивают GenAI, рано или поздно приходят к задачам по запуску LLM на собственном железе. В статье я расскажу, как обстоят дела с движками инференса в 2026 году и как запустить on-prem-прод и не изобрести при этом велосипед. Читать далее ## Build intelligent Android apps: Introduction to Jetpacker DevFeed: [Build intelligent Android apps: Introduction to Jetpacker]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-07-21T13:00:00Z Content type: tutorial Language: en Sources: [Android Developers Blog]() Topics: [android-development](), [Mobile](), [On-device AI](), [Inference](), [genai](), [Open Source](), [App](), [ui](), [Google]() Tags: [android](), [android-development](), [apps](), [genai](), [google](), [inference](), [on-device](), [open-source](), [ui]() ### AI overview This introduction to a technical blog series presents Jetpacker, an open-source Android showcase app built for Google I/O. It outlines choices involving on-device, cloud, and hybrid inference, Android system integration, and agentic flows, with later posts promising implementation guidance and code examples. ### Source excerpt Posted by Jolanda Verhoef, Senior Developer Relations Engineer, Android Developer Relations Building GenAI features in your app usually means navigating through various models, APIs and architecture choices: Execution location: Where does your model run? On device, in the cloud, or both? Complexity: How complex is your setup? Are you doing a single inference call or do you need a more agentic flow? In-app or Android System: Should your feature be built into your Android app or does it fit better as an Android system integration? In this blog post series we'll navigate these choices with you. We will take you along on a journey, starting with a basic mobile app and transforming it into a personalized, intelligent, and agentic experience. Jetpacker: a demo travel app Jetpacker is a technical showcase app that our team built from the ground up for this year's Google I/O (built using Antigravity). At its core, Jetpacker helps users plan, explore, and enjoy their next big adventure. It shows an overview of your trips, the itinerary of each trip, and details of each event on that trip. Of course following all best practices of Android development, including a beautifully expressive Material UI design. And best of all? It's fully open source! Today we are publishing a series of technical blog posts diving deep into each of these features. We'll provide detailed implementation steps, code snippets, and architectural insights to help you build your own intelligent Android applications. On-device intelligence On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes Using an on-device model comes with no additional cloud inference costs, means you don't have to worry about internet connectivity, and lets users be confident that private information will be processed locally, on the device, without any of their data being sent to the cloud. In Jetpacker, we chose on-device inference for three of our features: The trip overview feature tra ## Build intelligent Android apps: Introduction to Jetpacker DevFeed: [Build intelligent Android apps: Introduction to Jetpacker]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-07-21T13:00:00Z Content type: article Language: en Sources: [Android Developers Blog](), [Android Developers Blog]() Topics: [AI Bots](), [User interface design]() Tags: [agentic](), [android](), [apis](), [cloud](), [genai](), [inference](), [on-device](), [orchestration]() ### AI overview An introduction to the Jetpacker technical showcase and a blog series about building intelligent Android applications with on-device, cloud, hybrid, and agentic AI approaches. ### Source excerpt Posted by Jolanda Verhoef, Senior Developer Relations Engineer, Android Developer Relations Building GenAI features in your app usually means navigating through various models, APIs and architecture choices: Execution location: Where does your model run? On device, in the cloud, or both? Complexity: How complex is your setup? Are you doing a single inference call or do you need a more agentic flow? In-app or Android System: Should your feature be built into your Android app or does it fit better as an Android system integration? In this blog post series we'll navigate these choices with you. We will take you along on a journey, starting with a basic mobile app and transforming it into a personalized, intelligent, and agentic experience. Jetpacker: a demo travel app Jetpacker is a technical showcase app that our team built from the ground up for this year's Google I/O (built using Antigravity). At its core, Jetpacker helps users plan, explore, and enjoy their next big adventure. It shows an overview of your trips, the itinerary of each trip, and details of each event on that trip. Of course following all best practices of Android development, including a beautifully expressive Material UI design. And best of all? It's fully open source! Today we are publishing a series of technical blog posts diving deep into each of these features. We'll provide detailed implementation steps, code snippets, and architectural insights to help you build your own intelligent Android applications. On-device intelligence On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes Using an on-device model comes with no additional cloud inference costs, means you don't have to worry about internet connectivity, and lets users be confident that private information will be processed locally, on the device, without any of their data being sent to the cloud. In Jetpacker, we chose on-device inference for three of our features: The trip overview feature tra ## CEO Rowan Trollope's organizational announcement to Redis employees DevFeed: [CEO Rowan Trollope's organizational announcement to Redis employees]() Original publisher: [Read original article]() Author: Rowan Trollope Published: 2026-07-15T00:00:00Z Content type: news Language: en Sources: [Redis Blog]() Topics: [Redis](), [genai](), [Artificial Intelligence](), [Software]() Tags: [ai](), [announcement](), [announcements](), [company](), [developers](), [engineering](), [genai](), [organizational](), [workflows]() ### AI overview Redis CEO Rowan Trollope announces an organizational change involving the reduction of approximately 200 roles globally and a realignment of roles, teams, and priorities. The announcement attributes the changes to rapidly evolving customer and developer needs, particularly around GenAI, agents, memory, context, real-time data, and intelligent applications. Redis is also changing its internal workflows, with smaller teams and streamlined processes in Product and Engineering. ### Source excerpt Today, we are announcing an organizational change at Redis, including a reduction of approximately 200 roles globally and a realignment of roles, teams, and priorities across the company. This is a difficult decision because it affects people who hav... ## Your agent wants to search like a 2010 quant DevFeed: [Your agent wants to search like a 2010 quant]() Original publisher: [Read original article]() Author: Jon Bratseth Published: 2026-07-07T00:00:00Z Content type: opinion Language: en Sources: [Vespa Blog]() Topics: [AI search](), [information retrieval](), [Language models](), [Google Search]() Tags: [agents](), [ai](), [ai-agents](), [bm25](), [genai](), [google-search](), [hybrid-search](), [information-retrieval](), [rag](), [ranking](), [search]() ### AI overview The article argues that AI agents should retrieve information with more control and sophistication than ordinary human search users. It describes a progression from vector retrieval to hybrid search using methods such as BM25 and machine-learned ranking, and presents search as code as a possible next stage. ### Source excerpt The idea of empowering AI agents to retrieve information like a professional is going mainstream. ## Google AI Search Liability, Internet Infrastructure, and the GenAI-Driven Memory Market DevFeed: [Google AI Search Liability, Internet Infrastructure, and the GenAI-Driven Memory Market]() Original publisher: [Read original article]() Author: Russ Published: 2026-06-29T12:40:41Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [AI search](), [Google](), [genai](), [Internet](), [Cloud]() Tags: [ai-search](), [cloud](), [genai](), [google](), [infrastructure](), [internet](), [networks](), [search](), [worth-reading]() ### AI overview This developer-oriented reading roundup discusses a German court ruling holding Google liable for its AI search summaries, the distribution of CDN, cloud, and content-provider capacity across Internet Exchange Points, and how the GenAI boom has changed the memory market. ### Source excerpt It's no longer just about your IP address or the specific endpoint you think you're connecting to, it's about your location and which intermediary services can most effectively handle your request. Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like "users can check for themselves," and that they generally know "that information generated with AI should not be blindly trusted," the court held that the AI's summaries are reflections of the company and "above all an expression of Google's business activities." The distribution of Content Delivery Networks (CDN), cloud and content provider capacity across Internet Exchange Points (IXPs) provides a fascinating lens into the physical infrastructure of the Internet and public peering. The memory market - by which we mean dynamic main memory as well as flash persistent memory - has been utterly and perhaps forever changed by the GenAI boom. These days you could be excused by suspecting that the world has gone AI-mad, and if you were at the NANOG meeting your suspicions would've only been confirmed! ## DCD: доменно-ориентированная архитектура для построения RAG-систем DevFeed: [DCD: доменно-ориентированная архитектура для построения RAG-систем]() Original publisher: [Read original article]() Author: redmadrobot (red\_mad\_robot) Published: 2026-06-18T09:42:21Z Content type: article Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Retrieval Augmented Generation (RAG)]() Tags: [collection](), [domain](), [genai](), [llm](), [nlp](), [rag](), [tag-1cd610c0e518](), [tag-68e701e78517](), [tag-7bc388df28ed](), [tag-d89cae10e887]() ### AI overview The article introduces DCD (Domain-Collection-Document), a hierarchical architecture for organizing knowledge and processing queries in RAG systems. It describes domain-based search restriction, multi-stage routing, structured model outputs, smart chunking, hybrid search, and validation mechanisms intended to improve retrieval and generation quality in heterogeneous document collections. ### Source excerpt Привет! Это Роботы. Недавно мы выпустили статью на arXiv, где представили архитектурный подход DCD (Domain-Collection-Document) для структурирования пространства знаний и обработки запросов в RAG-системах. Мы провели подробные эксперименты, оценили работу подхода на собственном бенчмарке и показали, как он влияет на качество поиска и генерации в сравнении с другими подобными методами. А теперь хотим здесь рассказать о ключевых идеях, лежащих в основе DCD Design. Читать далее ## What to consider before asking an AI chatbot for health advice DevFeed: [What to consider before asking an AI chatbot for health advice]() Original publisher: [Read original article]() Author: Phil Muncaster Published: 2026-05-27T08:50:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Artificial Intelligence](), [Chat Bot](), [Generative AI](), [online privacy](), [Security](), [Large Language Model](), [ChatGPT](), [Microsoft](), [amazon]() Tags: [ai](), [amazon](), [chatgpt](), [data](), [genai](), [generative-ai](), [hallucinations](), [llm](), [llms](), [microsoft](), [privacy](), [security]() ### AI overview This article examines the risks of using AI chatbots for health advice, including hallucinations, inconsistent answers, incorrect guidance, and exposure of sensitive personal information. It explains why people turn to chatbots for medical questions and why these tools should not replace physicians. ### Source excerpt Using chatbots for medical advice could elicit hallucinations and even expose you to security and privacy risks. Here's what's at stake and how to stay safe. ## CockroachDB + Memori Labs: Keeping Agent Context Alive DevFeed: [CockroachDB + Memori Labs: Keeping Agent Context Alive]() Original publisher: [Read original article]() Author: Harsh Shah Published: 2026-05-12T00:00:00Z Content type: opinion Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [context](), [Artificial Intelligence](), [genai](), [Retrieval Augmented Generation (RAG)](), [Embeddings]() Tags: [ai](), [cockroachdb](), [context](), [embeddings](), [genai](), [rag](), [sql]() ### AI overview This article presents Memori Labs as a durable memory layer for agentic and GenAI applications, using CockroachDB as a Postgres-compatible system of record. It explains that persistent, governed memory can preserve context across interactions, improve retrieval, reduce repeated prompt context, and help control latency and token costs. ### Source excerpt Agents get better when they learn from interactions over time. [Next page]()