# discovery Published articles for discovery. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Museas & Astronoma On Apple Vision Pro Support Curiosity & Discovery DevFeed: [Museas & Astronoma On Apple Vision Pro Support Curiosity & Discovery]() Original publisher: [Read original article]() Author: Laura Mingail Published: 2026-09-07T23:28:57Z Content type: article Language: en Sources: [UploadVR]() Topics: [3D](), [App]() Tags: [3d](), [apple](), [apps](), [art](), [design](), [discovery](), [education](), [exploration](), [interview](), [learning](), [science](), [technologies](), [vision]() ### AI overview An interview with Miguel Garcia Gonzalez examines how the Apple Vision Pro apps Museas and Astronoma use immersive design, real-world source material, and selective 3D presentation to encourage curiosity and exploration of art and science. ### Source excerpt What makes exploring art and science in spatial apps so compelling? We look at Apple Vision Pro apps Museas and Astronoma to learn how to design for curiosity and discovery. ## What is MCP authorization? How OAuth works for AI agents DevFeed: [What is MCP authorization? How OAuth works for AI agents]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [Model Context Protocol (MCP)](), [OAuth](), [MCP Server](), [AI Agent](), [Authorization](), [Authentication]() Tags: [agents](), [authentication](), [authorization](), [discovery](), [mcp](), [oauth](), [scopes](), [token]() ### AI overview This tutorial explains MCP authorization as an OAuth 2.1 flow for allowing AI agents to call protected MCP servers on a user's behalf. It covers the roles of the MCP server, client, and authorization server; audience-bound tokens; discovery; deprecated Dynamic Client Registration; and step-up authorization for additional scopes. ### Source excerpt MCP authorization is the OAuth 2.1 flow that lets an AI agent call a protected MCP server on a user's behalf. Here is how it works, step by step, under the 2026-07-28 spec. ## Shifting readiness left: What AI can (and can't) do for organisational readiness DevFeed: [Shifting readiness left: What AI can (and can't) do for organisational readiness]() Original publisher: [Read original article]() Author: Nel Mathams Published: 2026-07-27T14:48:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [data]() Tags: [ai](), [artificial-intelligence](), [building-shared-understanding](), [delivery](), [discovery](), [ethics](), [funding](), [governance](), [institute-for-government](), [objectives](), [organisational-goals](), [organisational-readiness](), [shift-left](), [transformation]() ### AI overview This opinion article argues that organisational readiness depends on shared understanding rather than only governance, funding, or delivery structures. It examines how AI can help align goals, surface hidden assumptions, and accelerate discovery while keeping human judgement in control, including ethical considerations around data. ### Source excerpt Organisational readiness is often treated as a matter of governance, funding and delivery structures. In this post, I argue that true readiness is about building shared understanding, and explore how AI can help organisations align on goals, surface hidden assumptions and accelerate discovery work, while keeping human judgement firmly in control. ## Product Discovery for Product Managers in 2026 DevFeed: [Product Discovery for Product Managers in 2026]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-07-22T20:01:35Z Content type: tutorial Language: en Sources: [The Product Compass]() Topics: [Product Management](), [Usability](), [Users]() Tags: [2026](), [discovery](), [guide](), [product-management](), [risk]() ### AI overview This guide explains why product discovery remains important for product managers in 2026, even as shipping becomes faster and cheaper. It focuses on validating ideas, managing product risks, and deciding what to build before committing resources. ### Source excerpt You can ship an idea the same day you have it. That's why discovery matters more, not less. What changed, what didn't, and what to learn. ## IBM commits $50M in quantum access for US Genesis Mission DevFeed: [IBM commits $50M in quantum access for US Genesis Mission]() Original publisher: [Read original article]() Published: 2026-07-22T20:00:00Z Content type: release Language: en Sources: [IBM Research]() Topics: [ibm](), [Artificial Intelligence](), [Quantum Computing](), [High-Performance Computing](), [Supercomputing]() Tags: [accelerate](), [ai](), [department-of-energy](), [discovery](), [government](), [high-performance-computing](), [ibm](), [news](), [project](), [quantum](), [supercomputing](), [us]() ### AI overview IBM says a project was selected by the U.S. Department of Energy's Genesis Mission to accelerate AI-driven scientific discovery and will contribute up to $50 million in quantum system access. The mission combines AI, quantum computing, supercomputing, and scientific instruments. ### Source excerpt An IBM project was also selected to accelerate AI-driven quantum application discovery. ## Harness IDP AI Asset Catalog Organizes and Governs Prompts, Skills, Agents, Plugins, and Commands DevFeed: [Harness IDP AI Asset Catalog Organizes and Governs Prompts, Skills, Agents, Plugins, and Commands]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-07-21T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [internal developer portal](), [shadow AI](), [Platform Engineering](), [Generative AI](), [Security]() Tags: [ai](), [catalog](), [discovery](), [git](), [governance](), [idp](), [platform-engineering](), [search](), [security](), [shadow-ai]() ### AI overview The article presents Harness IDP's AI Asset Catalog, which brings prompts, skills, agents, plugins, and custom commands into a governed software catalog. It describes Git-based discovery, semantic search, ownership and lineage mapping, and automated scorecards for risk and compliance. ### Source excerpt Harness IDP AI Asset Catalog helps teams discover, govern, and reuse AI assets with automated Git discovery, scorecards, and semantic search. | Blog ## Помочь пользователю открыть новое: как мы боролись с замкнутым кругом рекомендаций в Яндекс Лавке DevFeed: [Помочь пользователю открыть новое: как мы боролись с замкнутым кругом рекомендаций в Яндекс Лавке]() Original publisher: [Read original article]() Author: ramilboiarchenkov (Яндекс) Published: 2026-07-07T07:02:47Z Content type: tutorial Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Machine learning]() Tags: [discovery](), [exploration](), [machine-learning](), [ml](), [tag-4004cf5948d3](), [tag-b0a411324cb6](), [tag-b2cbb9058e3c]() ### AI overview This article explains how the Yandex Lavka team addressed the feedback loop in recommendation systems, which can overemphasize familiar products and limit discovery. It describes personalized exploration of unfamiliar products and discusses calibrating the probability and aggressiveness of that exploration for each user. ### Source excerpt Хорошая рекомендательная система быстро учится угадывать, что вы положите в корзину. И чем точнее она угадывает, тем реже показывает что-то незнакомое: ведь выгоднее предлагать проверенное. Со временем система замыкается на привычках человека и перестаёт показывать ему хоть что-то за их пределами. Беда в том, что интересы меняются, а система просто так этого не замечает. Изменить ситуацию, как правило, удаётся лишь ценой краткосрочных потерь: стоит добавить в выдачу незнакомые товары, и объём ближайших покупок неизбежно начинает снижаться. Меня зовут Рамиль Боярченков, я занимаюсь машинным обучением в команде Яндекс Лавки. Расскажу, как мы собрали механизм, который подмешивает незнакомые товары персонально -- тем, кто к ним расположен, -- и с какой вероятностью это делать для каждого пользователя. По пути разберу, как мы калибровали "агрессивность" exploration и что получилось в итоге. Читать далее ## Security Baked Into the JVM: why fork Apache River and OpenJDK? DevFeed: [Security Baked Into the JVM: why fork Apache River and OpenJDK?]() Original publisher: [Read original article]() Author: Peter Firmstone Published: 2026-06-28T00:00:00Z Content type: opinion Language: en Sources: [Nicolas Fränkel]() Topics: [Security](), [Java](), [openjdk](), [Architecture & Design](), [Remote Procedure Call (RPC)](), [Networks]() Tags: [authorization](), [dirtychai](), [discovery](), [distributed](), [ipv6](), [java](), [jgdms](), [jini](), [jvm](), [lock-free](), [microservices](), [openjdk](), [rpc](), [security](), [self-healing](), [technical]() ### AI overview This article introduces DirtyChai, a community fork of OpenJDK that restores Java authorization infrastructure, and JGDMS, a security-hardened fork of Apache River for dynamically discoverable microservices over IPv6. It argues that distributed systems require security beyond network firewalls and outlines the projects' complementary roles, including authorization, service discovery, hardened deserialization, transport security, proxy trust verification, and codebase safety checks. ### Source excerpt The more distributed a system, the harder it is to secure. Code crosses JVM boundaries. Objects are serialized across trust boundaries. Third-party proxies run inside your process. The usual answer is a network firewall. It helps, but it operates at the wrong level. Java 17 deprecated the SecurityManager, Java 24 put the final nail in its coffin. Most developers didn't notice. ## From Weeks to Hours: How Claude Design Compresses Product Discovery DevFeed: [From Weeks to Hours: How Claude Design Compresses Product Discovery]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-05-04T20:15:15Z Content type: opinion Language: en Sources: [The Product Compass]() Topics: [Claude](), [Code]() Tags: [claude](), [design](), [discovery](), [product](), [production](), [prototype]() ### AI overview The article presents Claude Design as a way to compress product discovery from weeks to hours, move from design to shipped code in days, and address five remaining gaps. The claims are described as tested on real production code rather than a demo. ### Source excerpt Idea to prototype in hours. Design to shipped code in days. Five gaps still bite. Tested on real production code, not a demo. ## Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge DevFeed: [Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge]() Original publisher: [Read original article]() Author: Shubhojeet Sarkar; Shengbo Guo; Guohao Zhang; Woon Jo; Laura Vig Published: 2026-04-21T16:00:19Z Content type: article Language: en Sources: [Meta ML Applications]() Topics: [Meta](), [systems]() Tags: [architecture](), [automated](), [consensus](), [discovery](), [engineering](), [facebook](), [improvements](), [meta](), [ml-applications](), [model](), [retrieval](), [search](), [validation]() ### AI overview Meta describes a modernization of Facebook Groups Search using hybrid retrieval and automated model-based evaluation. The article explains how the changes address discovery, content consumption, and validation challenges in community search, while reporting improvements in engagement and relevance without increased error rates. ### Source excerpt We've fundamentally transformed Facebook Groups Search to help people more reliably discover, sort through, and validate community content that's most relevant to them. We've adopted a new hybrid retrieval architecture and implemented automated model-based evaluation to address the major friction points people experience when searching community content. Under this new framework, we've made tangible improvements [...] Read More... The post Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge appeared first on Engineering at Meta. ## Google Workspace's continuous approach to mitigating indirect prompt injections DevFeed: [Google Workspace's continuous approach to mitigating indirect prompt injections]() Original publisher: [Read original article]() Author: Kimberly Samra (noreply@blogger.com) Published: 2026-04-02T16:00:00Z Content type: opinion Language: en Sources: [Google Online Security]() Topics: [prompt injection](), [Google](), [Security](), [Adversarial attacks](), [Machine Learning, Security Attacks](), [Machine learning](), [Artificial Intelligence](), [Google AI]() Tags: [ai](), [attacks](), [automated](), [discovery](), [google](), [llms](), [machine-learning](), [none](), [prompt](), [prompt-injection](), [red-teaming](), [research](), [safety](), [security](), [testing]() ### AI overview Google describes its ongoing approach to mitigating indirect prompt injection attacks against Workspace with Gemini. The approach includes discovering new attack vectors, human and automated red-teaming, and collaboration with external researchers through the Google AI Vulnerability Rewards Program. ### Source excerpt Posted by Adam Gavish, Google GenAI Security Team Indirect prompt injection (IPI) is an evolving threat vector targeting users of complex AI applications with multiple data sources, such as Workspace with Gemini. This technique enables the attacker to influence the behavior of an LLM by injecting malicious instructions into the data or tools used by the LLM as it completes the user's query. This may even be possible without any input directly from the user. IPI is not the kind of technical problem you "solve" and move on. Sophisticated LLMs with increasing use of agentic automation combined with a wide range of content create an ultra-dynamic and evolving playground for adversarial attacks. That's why Google takes a sophisticated and comprehensive approach to these attacks. We're continuously improving LLM resistance to IPI attacks and launching AI application capabilities with ever-improving defenses. Staying ahead of the latest indirect prompt injection attacks is critical to our mission of securing Workspace with Gemini. In our previous blog "Mitigating prompt injection attacks with a layered defense strategy", we reviewed the layered architecture of our IPI defenses. In this blog, we'll share more detail on the continuous approach we take to improve these defenses and to solve for new attacks. New attack discovery By proactively discovering and cataloging new attack vectors through internal and external programs, we can identify vulnerabilities and deploy robust defenses ahead of adversarial activity. Human Red-Teaming Human Red-Teaming uses adversarial simulations to uncover security and safety vulnerabilities. Specialized teams execute attacks based on realistic user profiles to exploit weaknesses, coordinating with product teams to resolve identified issues. Automated Red-Teaming Automated Red-Teaming is done via dynamic, machine-learning-driven frameworks to stress-test environments. By algorithmically generating and iterating on attack payloads, we can mimi ## Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs DevFeed: [Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs]() Original publisher: [Read original article]() Author: Moein Hasani Published: 2026-02-26T18:55:35Z Content type: article Language: en Sources: [Instacart]() Topics: [recommendation systems](), [LLMs](), [Generative AI](), [personalization](), [User Experience](), [Artificial Intelligence]() Tags: [ai](), [discovery](), [generative-ai](), [instacart](), [large-language-models](), [llm](), [llms](), [machine-learning](), [personalization](), [recommendation-systems](), [recommendations](), [recommender-systems](), [systems](), [user-experience]() ### AI overview Instacart describes its early effort to use large language models in the Shopping Hub, an app surface for personalized product discovery. The article covers an AI-native platform for content generation, evaluation, and retrieval, and reports that generative models show promise for improving recommendations at scale. ### Source excerpt Key Contributors: Moein Hasani, Hamidreza Shahidi, Trace Levinson, Guanghua Shu Introduction At Instacart, we are laser-focused on improving the user experience by making shopping feel easy, engaging, and personalized. Our discovery surfaces play a central role in bringing this to life. Alongside explicit Search intents, discovery is our opportunity to meet customers' implicit needs, presenting them with the most relevant and inspiring content we have to offer. The main discovery surface within the Instacart app, referred to here as the "Shopping Hub", is one of the most critical in this regard. This is the surface a customer lands on within the Instacart app after selecting their desired retailer, guiding them along their entire journey. What users see here shapes not just what they buy, but how intuitive and enjoyable their experience feels. Given its importance, our team runs dozens of Shopping Hub experiments per year, constantly evaluating new ways to enrich the discovery experience. Historically, these experiments have been constrained by static content libraries feeding our recommendation systems. With the rapid advancement of generative AI, a critical opportunity began to emerge: rather than incrementally improving a swath of legacy systems, could we leverage LLMs to rethink how content shows up for a user from the ground up? Which new primitives could we build to uplevel quality, personalization, and cohesion across the page? This blog post walks through our early journey to answer these questions. By investing in a new AI-native platform for content generation, evaluation, and retrieval, we have found generative models to show real promise in improving recommendations at scale. Below, we highlight the approach we took in developing this platform, a few key learnings so far, and where we're most bullish moving forward. Limitations of Traditional Recommendation Engines Our Shopping Hub page is constructed from multiple subcomponents called placements. Each p ## Kubo 0.39.0 makes the DHT Sweep provider the default DevFeed: [Kubo 0.39.0 makes the DHT Sweep provider the default]() Original publisher: [Read original article]() Author: Ipfs Published: 2025-11-27T00:00:00Z Content type: release Language: en Sources: [IPFS]() Topics: [IPFS](), [Self-hosted](), [Networks]() Tags: [discovery](), [ipfs](), [network](), [release](), [release-notes](), [self-hosting]() ### AI overview Kubo 0.39.0 makes the Amino DHT Sweep provider the default, adds immediate root CID providing, and improves persistence, recovery, alerts, and monitoring for IPFS nodes. ### Source excerpt Just released: Kubo 0.39.0! ## Google DeepMind supports U.S. Department of Energy on Genesis: a national mission to accelerate innovation and scientific discovery DevFeed: [Google DeepMind supports U.S. Department of Energy on Genesis: a national mission to accelerate innovation and scientific discovery]() Original publisher: [Read original article]() Author: Pushmeet Kohli; Tom Lue Published: 2025-11-24T14:12:03Z Content type: news Language: en Sources: [Google DeepMind News]() Topics: [AI for science](), [Artificial Intelligence](), [Google](), [Frontier AI](), [Google Cloud Platform (GCP)]() Tags: [ai](), [department-of-energy](), [discovery](), [energy](), [frontier-ai](), [google](), [google-cloud](), [innovation](), [models](), [multi-agent](), [partnership](), [plasma](), [productivity](), [research](), [science]() ### AI overview Google DeepMind and the U.S. Department of Energy are partnering through the Genesis Mission to accelerate scientific discovery with advanced AI and computing. Google DeepMind will provide scientists at all 17 DOE National Laboratories access to frontier AI for Science models and agentic tools, beginning with AI co-scientist on Google Cloud. ### Source excerpt Google DeepMind and the DOE partner on Genesis, a new effort to accelerate science with AI. ## AI progress and recommendations DevFeed: [AI progress and recommendations]() Original publisher: [Read original article]() Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [AI Strategy](), [Software Engineering]() Tags: [2026](), [ai](), [chatbots](), [company](), [cost](), [discovery](), [future](), [recommendations](), [research](), [safety]() ### AI overview The article describes rapid advances in AI, including systems that can outperform top human performance in difficult intellectual competitions and increasingly support software engineering and knowledge discovery. It discusses falling costs, expected capability gains through 2026 and beyond, societal adaptation, safety, and the possibility of a better future. ### Source excerpt AI is advancing fast. We have the chance to shape its progress--toward discovery, safety, and a better future for everyone. ## Accelerating the magic cycle of research breakthroughs and real-world applications DevFeed: [Accelerating the magic cycle of research breakthroughs and real-world applications]() Original publisher: [Read original article]() Published: 2025-10-31T07:40:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Google](), [Artificial Intelligence](), [LLMs](), [data](), [datasets]() Tags: [climate-sustainability](), [discovery](), [earth-ai](), [generative-ai](), [geospatial](), [google](), [health-bioscience](), [innovation](), [llms](), [models](), [quantum](), [research](), [sustainability]() ### AI overview Google Research describes how advances in AI models, agentic tools, and open platforms are accelerating a cycle between scientific research and real-world applications. The article highlights Earth AI, including geospatial models and an LLM-powered reasoning agent that works across imagery, population, environmental data, and multiple datasets. ### Source excerpt Climate & Sustainability ## Consensus accelerates research with GPT-5 and Responses API DevFeed: [Consensus accelerates research with GPT-5 and Responses API]() Original publisher: [Read original article]() Published: 2025-10-23T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [API](), [OpenAI](), [Graphs](), [Library]() Tags: [agents](), [analysis](), [api](), [discovery](), [gpt](), [graph](), [hallucinations](), [library](), [multi-agent](), [openai](), [research](), [responses](), [search](), [workflow]() ### AI overview Consensus built Scholar Agent, a multi-agent research assistant powered by GPT-5 and the Responses API. The system plans research questions, searches scientific papers and citation data, reads and interprets papers, and synthesizes cited findings. Its narrow agent roles are intended to improve precision and reduce hallucinations, shortening research tasks from weeks to minutes. ### Source excerpt Consensus uses GPT-5 and OpenAI's Responses API to power a multi-agent research assistant that reads, analyzes, and synthesizes evidence in minutes--helping over 8 million researchers accelerate scientific discovery. ## Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery DevFeed: [Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery]() Original publisher: [Read original article]() Author: Inês Soveral Published: 2025-09-09T15:01:44Z Content type: article Language: en Sources: [OLX]() Topics: [AI search](), [Query (disambiguation)]() Tags: [article](), [classifieds](), [discovery](), [hybrid-search](), [retrieval](), [search](), [search-engines](), [vector-search]() ### AI overview OLX describes its transition from keyword matching to hybrid search, combining keyword and vector retrieval for classifieds discovery. The article explains the motivations, implementation challenges, solutions, and observed benefits, including improved handling of vague, misspelled, or differently phrased queries. ### Source excerpt Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery In the midst of a fast-paced technological revolution, where user expectations grow increasingly sophisticated, the quality of search in the classifieds space has never been more critical. In fact, users expect the search box to understand what they are looking for and produce relevant search results; if this is not the case, they will easily move on to any competitor who provides this experience. At OLX, our search system traditionally relied on keyword matching between user queries and ad titles and descriptions -- a straightforward but rigid approach. While functional, it often led to low recall or even zero results pages (ZRPs). To mitigate this, we gradually developed an extension chain logic -- which will be explained in detail in later sections -- to address specific edge cases and expand recall. Over time, however, this logic became increasingly complex and difficult to improve upon, calling for a disruptive solution to further evolve our search system. In this article, we share why and how we transitioned to Hybrid Search as a retrieval strategy for our double-sided marketplace. We walk through the key changes required to support semantic search, highlight the challenges we faced, and detail the solutions put in place to overcome them. Finally, we reflect on the tangible improvements and practical benefits observed after this shift. What is Hybrid Search and what value does it bring? Despite its lack of flexibility, keyword matching remains highly effective in e-commerce. It returns results that exactly match the user's query terms, offering clear traceability and helping users understand why specific ads appear. This is particularly useful when users know precisely what they're looking for, and when combined with structured filters, a feature of the OLX marketplace. However, keyword matching falls short when queries are vague, misspelled, or phrased differently from how sellers descri ## Finding End-to-End Paths: Topology and Endpoints DevFeed: [Finding End-to-End Paths: Topology and Endpoints]() Original publisher: [Read original article]() Published: 2025-06-06T05:51:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [networking](), [Routing (disambiguation)](), [Network design]() Tags: [arp](), [bridging](), [dhcp](), [discovery](), [ipv4](), [ipv6](), [network](), [networking-fundamentals](), [router](), [routing]() ### AI overview This article explains how networks discover topology and endpoints, build forwarding tables, and adapt end-to-end paths when links or nodes fail. It contrasts complete topology views in link-state protocols with local-neighbor knowledge in distance-vector protocols, and describes learning mechanisms including bridging, ARP, DHCP, and ICMPv6 Neighbor Discovery. ### Source excerpt We know there are three main ways to move packets across a network. However, before we can start forwarding packets, someone has to populate the forwarding tables in the intermediate devices or build the sequence of nodes to traverse in source routing. Usually, whoever is responsible for the contents of the forwarding tables must first discover the network topology. Let's start there, using the following network diagram to illustrate the discussion. Read more ... ## Using Kanban Metrics and Reports to Identify Team Improvement Opportunities DevFeed: [Using Kanban Metrics and Reports to Identify Team Improvement Opportunities]() Original publisher: [Read original article]() Author: dennikolaev (hh.ru) Published: 2025-02-21T14:00:30Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [Kanban](), [jira]() Tags: [cycle-time](), [datadriven](), [delivery](), [discovery](), [jira](), [kanban](), [lead-time](), [tag-22e9d0410dcb](), [tag-694195a2e09b](), [tag-8720e37629f7](), [tag-add1155413d1](), [tag-b92bf5906bbd](), [tag-e7d8489b7f64]() ### AI overview A project manager at hh.ru explains how to use Kanban-oriented metrics and reports to analyze team efficiency and identify improvement opportunities. The article covers lead time, cycle time, percentiles, classes of service, and reports from an internally customized Jira-based system. ### Source excerpt "Как и на какие метрики смотреть в поисках зоны роста команды?" -- на эту тему менеджеры проектов в нашей компании задумываются регулярно, так как менеджеры отвечают в том числе и за эффективность процессов. Правильный ответ на вопрос будет зависеть от контекста команды, заказчиков, продукта, рабочего процесса и много чего еще. Но можно выделить ряд метрик и отчетов, построенных на этих метриках, которые будут универсальны, и которые можно брать за основу для анализа. А поскольку hh.ru -- компания, которая несколько последних лет применяет Kanban-метод, то и метрики ниже будут сильно пересекаться с тем, что этот метод предлагает. Меня зовут Денис Николаев, и я менеджер проектов в hh. В этой статье покажу, на какие основные отчеты смотрю я. Также добавлю примеры того, на что обращать внимание в этих отчетах, чтобы в дальнейшем работать над повышением эффективности. Читать далее ## Ep. 4: Mastering Kubernetes Networking: Essential Insights for Efficient Deployment DevFeed: [Ep. 4: Mastering Kubernetes Networking: Essential Insights for Efficient Deployment]() Original publisher: [Read original article]() Published: 2024-06-03T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Kubernetes networking](), [Kubernetes](), [Deployment](), [Routing (disambiguation)](), [Go Language](), [configuration]() Tags: [clusters](), [configuration](), [deployment](), [discovery](), [go](), [kubernetes](), [kubernetes-clusters](), [kubernetes-networking](), [networking](), [routing](), [yaml]() ### AI overview This video tutorial explains how to configure networking in Kubernetes clusters for Go applications. It covers service definitions, internal and external traffic routing, namespaces, ports, DNS-based service discovery, and configuration patching with Kustomize. ### Source excerpt Introduction: In this detailed discussion, Bill delves into the critical aspects of networking within Kubernetes clusters, emphasizing the necessity of properly defining services to manage internal and external communication effectively. Learn the critical role of service definitions in Kubernetes for managing internal and external communication, ensuring your Go applications are accessible and networked correctly within the cluster. Gain insights into configuring Kubernetes environments to handle traffic routing and service discovery, which is essential for deploying scalable and maintainable Go applications. ## Espressif Thread Border Router DevFeed: [Espressif Thread Border Router]() Original publisher: [Read original article]() Author: John Lee Published: 2023-06-14T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [Espressif](), [networking](), [ESP-IDF](), [Matter]() Tags: [announce](), [blog](), [border-router](), [development-kit](), [discovery](), [esp-idf](), [esp32](), [espressif](), [ethernet](), [iot](), [ipv4](), [matter](), [multicast](), [open-thread](), [thread](), [wi-fi]() ### AI overview Espressif's Thread Border Router solution received Thread Group certification, and its accompanying development kit was officially released. The article describes its ESP-IDF-based architecture, networking components, and features including IPv6 connectivity, service discovery, multicast forwarding, and NAT64. ### Source excerpt We are glad to announce that the Espressif Thread Border Router (ESP Thread BR) solution has received certification from the Thread Group, and the accompanying development kit has now been officially released. This blog post will delve into the technical aspects of the solution and explore the benefits it offers, facilitating faster time-to-market for our customers' products. ## How continuous product discovery works for us DevFeed: [How continuous product discovery works for us]() Original publisher: [Read original article]() Author: Sören Weber Senior Product Manager @ trivago; Core Product; AI Linkedin profile Published: 2023-02-01T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Product Management](), [Self-organizing Team](), [Agile](), [User experience (UX)](), [Development]() Tags: [cross-functional-teams](), [delivery](), [development](), [discovery](), [driving](), [engineering](), [good-practices](), [product-management](), [research](), [scope](), [software](), [strategy]() ### AI overview A trivago product manager explains continuous product discovery, distinguishing it from product delivery and describing how user research, solution ideation, and solution testing help teams decide what to build. The article also discusses trivago's adoption of Teresa Torres's continuous discovery framework alongside cross-functional teams, outcome-driven OKRs, and agile software development. ### Source excerpt Hello, I am a product manager here at trivago. I have worked on different parts of the product such as apps, alternative accommodations, landing pages, and search & flow. We work in cross-fu... ## @TwitterDev x @Charmcli: Developer Tools discovery session DevFeed: [@TwitterDev x @Charmcli: Developer Tools discovery session]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2022-05-19T00:00:00Z Content type: news Language: en Sources: [Carlos Becker]() Topics: [dev-tools](), [Tooling]() Tags: [developer-tools](), [discovery](), [live](), [space](), [tools](), [twitter]() ### AI overview A Twitter Spaces session involving Twitter and Charm participants discussed favorite developer tools. The session was not recorded. ### Source excerpt Twitter Spaces with Twitter and Charm folks, discussing favorite developer tools. [Next page]()