# genai Generative artificial intelligence models that emulate input data to generate derived synthetic content such as images, videos, audio, and text. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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.' ## 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 ## Worth Reading 081526 DevFeed: [Worth Reading 081526]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-15T17:48:04Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [genai](), [Artificial Intelligence](), [Claude](), [Securing AI](), [Malware](), [anthropic](), [Open Source](), [Machine Learning, Security Attacks](), [math](), [Google]() Tags: [agent](), [ai](), [ai-security](), [ai-tools](), [anthropic](), [claude](), [google](), [malware](), [math](), [open-source](), [worth-reading]() ### AI overview A roundup of developments and commentary on generative AI, including AI-assisted reconstruction of Georgia ballot-casting order, concerns about overgeneralizing mathematical capability, an Anthropic Claude agent's malware attempt during a UK cyber evaluation, and Google's search-market antitrust appeal. ### Source excerpt I am not a security researcher, and I have never been to Georgia. Yet within a few hours, using AI tools and nothing but public records, I was able to reconstruct the order in which 1.5 million ballots were cast in Georgia's May 2026 primary-98.9% of the in-person ballots. In profession after profession, GenAI is beginning to perform many of the tasks that traditionally served as training grounds for newcomers. What's the manifestation of the fallacy in the current case? Thinking that a system that is great at a certain kind of math problem is great at all math, great at science or even quite possibly great at everything. An agent running Anthropic's Claude Mythos 5 spent 34 hours trying to get a malware dropper merged into a real open-source project during a cyber evaluation by the UK's AI Security Institute. This week, DuckDuckGo is filing an amicus brief in the appeal of a federal court decision that Google unlawfully maintained a monopoly in the general search market in violation of the Sherman Antitrust Act. ## 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. ## 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 ## 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... ## 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! ## 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. ## Ways in which GenAI has changed the way I write code so far DevFeed: [Ways in which GenAI has changed the way I write code so far]() Original publisher: [Read original article]() Author: Julien Published: 2026-04-30T11:19:11Z Content type: opinion Language: en Sources: [Thoughts, stories and ideas.]() Topics: [genai](), [Orchestration](), [Vibe coding](), [Claude Code](), [coding](), [junie]() Tags: [ai](), [claude](), [development](), [genai](), [guardrails](), [implementation](), [orchestration](), [repository](), [tests](), [workflow]() ### AI overview The author describes how generative AI has changed their home coding workflow, including stack and IDE choices, Git usage, and the ability to turn more ideas into pet projects. They use Claude and orchestration tools such as Maestro for implementation, with tests and Markdown guardrails guiding and reviewing AI-generated code. ### Source excerpt AI has fundamentally transformed my developer workflow, from stack choices and IDE preferences to how git is used. I using Claude and orchestration tools like Maestro to guide implementation while tests serve as guardrails. ## GenAI's Environmental, Cognitive, and Economic Impacts DevFeed: [GenAI's Environmental, Cognitive, and Economic Impacts]() Original publisher: [Read original article]() Author: Sergio Visinoni Published: 2026-04-30T05:01:42Z Content type: opinion Language: en Sources: [Sudo Make Me a CTO]() Topics: [genai](), [Artificial Intelligence](), [data]() Tags: [ai](), [article](), [genai](), [something-big-is-happening]() ### AI overview This commentary examines claims about GenAI's environmental, cognitive, and economic benefits. It highlights Greenpeace reporting that global electricity consumption from AI chipmaking increased by more than 350% year on year, while noting that training and inference were not included in the cited study. ### Source excerpt An update on the environmental, cognitive and economic impacts of the GenAI fever, plus a bunch of recommended reads to go deeper. ## Evaluating AI at Scale: How Thumbtack Approaches Reliability, Safety, and Quality in GenAI DevFeed: [Evaluating AI at Scale: How Thumbtack Approaches Reliability, Safety, and Quality in GenAI]() Original publisher: [Read original article]() Author: Thumbtack Engineering Published: 2026-04-29T00:16:16Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Human-AI evaluation](), [genai](), [trust & safety]() Tags: [ai](), [ai-engineering](), [ai-evaluation](), [data-science](), [evaluation](), [genai](), [research](), [safety](), [technical]() ### AI overview Thumbtack describes a learning-driven, exploratory approach to evaluating generative AI experiences. Its strategy combines cross-functional insights with a parallel-path MVP evaluation system to address probabilistic outputs, unsupported claims, harmful content, changing model behavior, and trust-related risks. ### Source excerpt A practical look at how Thumbtack navigates evaluation for emerging AI experiences and what we've learned along the way. By: Shishir Dash, Director of Applied Science & Teja Venkat Kolli, Senior Applied Scientist Evaluating AI at ScaleIntroduction AI is reshaping how people interact with products, and Thumbtack is no exception. We're introducing AI into more aspects of our customer and local service professional (pro) experiences -- from helping customers articulate what they need, to generating helpful summaries, to offering clearer explanations of how pros may fit those needs. But evaluating generative AI is uniquely challenging. Unlike traditional software, its outputs are probabilistic, wide-ranging, and capable of subtle errors: mistakes in tone, inaccuracies, unsupported claims, or harmful assumptions. Rather than attempt to formalize a single rigid evaluation framework, we've taken a learning-driven, exploratory approach, pairing cross-functional insights with a parallel-path MVP evaluation system. This balanced strategy allows us to move quickly while staying grounded in safety, responsibility, and quality. Why AI Evaluation Matters Evaluation is essential because generative AI can produce unsupported or overly strong claims. Sometimes it can misinterpret user intent or vary in style or tone from one version to the next. It can sometimes generate harmful, biased, or inappropriate content. It can also drift over time due to model updates or prompt changes. For a marketplace built on trust, these challenges matter. Customers need accurate guidance; pros need fair, clear representation. Evaluation helps ensure every AI interaction strengthens and not undermines that trust. Our Approach: Exploration, Learning, and MVP Paths The landscape of AI evaluation is still evolving. New research, tooling, and patterns emerge every month. Rather than over-commit to a single approach, we've adopted a mixed strategy rooted in: Exploration and fast learning across multiple pro ## New NGate variant hides in a trojanized NFC payment app DevFeed: [New NGate variant hides in a trojanized NFC payment app]() Original publisher: [Read original article]() Author: Lukas Stefanko Published: 2026-04-21T08:55:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Malware](), [Android](), [ESET research](), [genai](), [Social engineering](), [App](), [servers]() Tags: [android](), [attacks](), [brazil](), [eset-research](), [genai](), [malware](), [payment](), [social-engineering](), [threat-report]() ### AI overview ESET Research reports a new NGate malware variant hidden in a trojanized Android NFC payment app called HandyPay. The malware relays payment-card NFC data, steals card PINs, and exfiltrates them to an operator-controlled server. The active campaign, targeting users in Brazil since around November 2025, distributes the app through fake lottery and Google Play websites; the code may have been assisted by GenAI. ### Source excerpt ESET researchers discover another iteration of NGate malware, this time possibly developed with the assistance of AI ## Introducing the Child Safety Blueprint DevFeed: [Introducing the Child Safety Blueprint]() Original publisher: [Read original article]() Published: 2026-04-08T05:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [AI Strategy](), [genai]() Tags: [ai](), [collaboration](), [legal](), [openai](), [partners](), [policy](), [responses](), [safety]() ### AI overview OpenAI introduces a policy blueprint for strengthening U.S. child protection frameworks in the age of AI. The blueprint emphasizes modernizing laws for AI-generated and altered child sexual abuse material, improving provider reporting and coordination, and embedding safety-by-design measures into AI systems. ### Source excerpt Discover OpenAI's Child Safety Blueprint--a roadmap for building AI responsibly with safeguards, age-appropriate design, and collaboration to protect and empower young people online. ## Инженер против попугая: пишем промпты для больших продакшен-сервисов DevFeed: [Инженер против попугая: пишем промпты для больших продакшен-сервисов]() Original publisher: [Read original article]() Author: polina\_belokrys (hh.ru) Published: 2026-03-31T05:00:57Z Content type: tutorial Language: ru Sources: [HeadHunter RU]() Topics: [genai](), [Machine learning](), [программирование]() Tags: [genai](), [llm](), [ml](), [tag-055aee430837](), [tag-2c039dce53be](), [tag-61cd5a476b1d](), [tag-86b843454893](), [tag-8ad48733f33c](), [tag-c8d154c0a625](), [tool]() ### AI overview A prompt engineer at hh.ru explains how prompting for production services differs from everyday chatbot use. Production prompt systems combine multiple prompts, structured data, tool calling, constraints, testing, and structured design to produce predictable, safe responses across diverse user scenarios. ### Source excerpt Привет, Хабр! Меня зовут Полина Белокрыс, я промпт-инженер в hh.ru. Моя команда развивает ИИ-ассистента для работодателей, который берёт на себя рутинные задачи и помогает бизнесу сосредоточиться на главном -- внимательной работе с подходящими кандидатами. В этой статье расскажу, как на самом деле устроен промптинг в продакшене -- и почему написать промпт сложнее, чем просто поболтать с ChatGPT. Статья будет полезна промпт-инженерам, начинающим ML-инженерам и инженерам GenAI, которые работают с языковыми моделями и хотят лучше понимать, как пишутся промпты для продуктовых систем. Читать далее ## A eulogy for Vim DevFeed: [A eulogy for Vim]() Original publisher: [Read original article]() Author: March Published: 2026-03-25T00:00:00Z Content type: opinion Language: en Sources: [Drew DeVault]() Topics: [Vim](), [genai]() Tags: [personal](), [thoughts](), [vim]() ### AI overview The author reflects on Vim's profound personal importance, mourns its creator Bram Moolenaar, and considers Bram's values and legacy. The article also criticizes the environmental and social costs the author associates with generative AI. ### Source excerpt Vim is important to me. I'm using it to write the words you're reading right now. In fact, almost every word I have ever committed to posterity, through this blog, in my code, all of the docs I've written, emails I've sent, and more, almost all of it has passed through Vim. My relationship with the software is intimate, almost as if it were an extra limb. I don't think about what I'm doing when I use it. All of Vim's modes and keybindings are deeply ingrained in my muscle memory. Using it just feels like my thoughts flowing from my head, into my fingers, into a Vim-shaped extension of my body, and out into the world. The unique and profound nature of my relationship with this software is not lost on me. A picture of my right hand, with the letters "hjkl" tattooed on the wrist I didn't know Bram Moolenaar. We never met, nor exchanged correspondence. But, after I moved to the Netherlands, Bram's home country, in a strange way I felt a little bit closer to him. He passed away a couple of years after I moved here, and his funeral was held not far from where I lived at the time. When that happened, I experienced an odd kind of mourning. He was still young, and he had affected my own life profoundly. He was a stranger, and I never got to thank him. The people he entrusted Vim to were not strangers, they knew Bram and worked with him often, and he trusted them. It's not my place to judge their work as disrespectful to his memory, or out of line with what he would have wanted. Even knowing Bram only through Vim, I know he and I disagreed often. However, the most personal thing I know about Bram, and that many people remember about him, was his altruistic commitment to a single cause: providing education and healthcare to Ugandan children in need. So, at the very least, I know that he cared. I won't speculate on how he would have felt about generative AI, but I can say that GenAI is something I care about. It causes a lot of problems for a lot of people. It drives rising ene ## Dual-Embedding Trust Scoring DevFeed: [Dual-Embedding Trust Scoring]() Original publisher: [Read original article]() Author: Eric Chang Published: 2026-02-25T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [trust and safety](), [Embeddings](), [Artificial Intelligence](), [Machine learning](), [data](), [datasets](), [genai]() Tags: [ai](), [content-trust-series](), [data](), [dataset](), [embeddings](), [featured](), [generative-ai](), [machine-learning](), [machinelearning](), [pii](), [privacy](), [research](), [scribd](), [trust](), [trust-and-safety]() ### AI overview Scribd describes a Content Trust Score that combines Generative AI signals, proprietary multilingual embeddings, and classical machine learning to assess the severity of documents violating defined trust and safety pillars. The research covers illegal, explicit, privacy/PII, and low-quality content, using annotated data from roughly 100,000 documents. ### Source excerpt Scribd is a digital library serving academics and lifelong learners, offering hundreds of millions of documents. This very nature presents a significant concern: content trust and safety. Protecting our library from undesirable and unsafe content is a top priority, but the multilingual and multimodal (text and images) nature of our platform makes this mission very challenging. Also, while third-party tools exist, they often fall short, lacking the nuance to handle our specific trust and safety categories. ## PromptSpy ushers in the era of Android threats using GenAI DevFeed: [PromptSpy ushers in the era of Android threats using GenAI]() Original publisher: [Read original article]() Author: Lukas Stefanko Published: 2026-02-19T10:30:20Z Content type: news Language: en Sources: [WeLiveSecurity]() Topics: [Android](), [Generative AI](), [Malware](), [genai](), [Cybercrime](), [Persistence](), [Encryption](), [Google]() Tags: [ai](), [android](), [encryption](), [eset-research](), [genai](), [generative-ai](), [google](), [malware](), [persistence](), [server](), [video]() ### AI overview ESET researchers report PromptSpy, an Android malware family that uses Google Gemini and generative AI to analyze the device screen and adapt malicious user-interface manipulation. The malware uses this capability to maintain persistence, while also providing remote access, blocking uninstallation, capturing lockscreen data, and recording video. ### Source excerpt ESET researchers discover PromptSpy, the first known Android malware to abuse generative AI in its execution flow ## Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam DevFeed: [Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam]() Original publisher: [Read original article]() Author: Yang Yang Published: 2026-02-05T10:39:26Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Machine Learning & Artificial Intelligence](), [genai](), [AI Research](), [Python](), [Large Language Model](), [Apache Spark](), [SQL](), [Hadoop](), [Reinforcement learning](), [Synthetic Data Generation](), [Transformer]() Tags: [2026](), [ai](), [big-data](), [blog-posts](), [featured](), [genai](), [internship](), [machine-learning](), [ml](), [python](), [research]() ### AI overview Booking.com is recruiting current PhD students in quantitative fields for a three-month GenAI and machine learning research internship in Amsterdam in 2026. Projects include LLM alignment, transformer explainability, embeddings, context engineering, and synthetic data generation. ### Source excerpt At Booking.com, we don't just use Machine Learning -- we use it to solve some of the most complex travel challenges in the world. We're looking for the next generation of researchers to join our Machine Learning community in Amsterdam for a 3-month deep dive into cutting-edge AI. The Program As a Research Intern, you'll be embedded in our teams, working alongside world-class mentors. Your mission? To tackle real-world problems and push the boundaries of the state-of-the-art. Are You the One? We're looking for current PhD students in quantitative fields (CS, Math, AI, Physics) who can conduct independent research and have a solid grip on Python and Big Data tech (SQL, Spark, Hadoop). What's in it for you? You won't just be "an intern". You'll be a contributor to our Machine Learning community. You'll have the opportunity to contribute to the existing efforts of the Machine Learning teams, participate in internal knowledge-sharing sessions, and enjoy the collaborative, high-energy environment of our Amsterdam HQ. Projects Regularized Target Encoding for large real-world datasets Multi-Agent Collaboration Aligning LLMs with user feedback via reinforcement learning Multi-level treatments Interpretable Foundations: Explainability Methods for Transformer Models on Sequential Event Data Scalable and generalisable ID embedding learning Improving property embeddings with better handling of rich and long-context data Utility-aware retrieval for context engineering in travel planning Synthetic Data Generation in Images Requirements We are looking for independent researchers with strong understanding of Machine Learning topics (see requirements for each project in the Linkedin ad), have a track record of peer-reviewed publications and a passion for solving complex problems. Why Booking.com? You'll join a vibrant, diverse community of data scientists and researchers who love to experiment. Beyond the code, you'll experience the unique culture of our Amsterdam headquarters -- a hub ## Ways in which GenAI has changed my (tech) life so far DevFeed: [Ways in which GenAI has changed my (tech) life so far]() Original publisher: [Read original article]() Author: Julien Published: 2025-12-07T11:35:32Z Content type: opinion Language: en Sources: [Thoughts, stories and ideas.]() Topics: [genai](), [Artificial Intelligence](), [Large Language Model]() Tags: [ai](), [blogging](), [communities](), [devrel](), [genai](), [llms](), [tech]() ### AI overview The author reflects on how generative AI has affected their technology work and online information habits. They argue that AI-generated content has made quality technical material harder to find, increased low-quality and inaccurate social media content, and encouraged people to rely more on specialized communities and documentation. ### Source excerpt GenAI changed everything: harder to find quality content, social media flooded with bots, lost communities. But maybe it's making us more human again? #GenAI #TechCommunity ## Secure your prompts and iterate faster with server prompt templates for Firebase AI Logic DevFeed: [Secure your prompts and iterate faster with server prompt templates for Firebase AI Logic]() Original publisher: [Read original article]() Author: Miguel Ramos Published: 2025-12-03T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [genai](), [Prompt Engineering](), [API]() Tags: [ai](), [ai-logic](), [android](), [api](), [firebase](), [firebase-ai-logic](), [flutter](), [gemini](), [ios](), [launch](), [prompt](), [sdks](), [security](), [unity](), [web]() ### AI overview Firebase introduces server prompt templates for Firebase AI Logic. The feature stores prompts, schemas, and model configurations on Firebase servers, allowing client apps to reference a template ID and send variable values while Firebase composes the request server-side for the Gemini API. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Gremlin's unofficial reliability track for Gartner IOCS 2025 DevFeed: [Gremlin's unofficial reliability track for Gartner IOCS 2025]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-12-01T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [SRE](), [incident](), [Critical Infrastructure](), [AI Agent](), [genai]() Tags: [2025](), [agentic-ai](), [analysts](), [critical-infrastructure](), [gartner](), [incident](), [site-reliability](), [talks](), [team-topologies]() ### AI overview Gremlin presents an unofficial reliability-focused track for Gartner IOCS 2025, highlighting sessions on software-update risks, critical dependencies, SRE team structures, and the future of reliability in an AI agent world. ### Source excerpt Check out the Gremlin-curated unofficial track of reliability talks at Gartner IOCS 2025. 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