# developer-productivity Published articles for developer-productivity. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Red Hat Developer Hub: Preventing compliance violations with AI coding agents DevFeed: [Red Hat Developer Hub: Preventing compliance violations with AI coding agents]() Original publisher: [Read original article]() Author: Evan Shortiss, Ben Wilcock Published: 2026-09-15T13:17:13Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [internal developer portal](), [AI-assisted coding](), [Backstage](), [Claude Code](), [Command-line interface](), [Model Context Protocol](), [Security]() Tags: [ai-coding-agents](), [ai-inference](), [backstage](), [catalog](), [claude-code](), [cli](), [developer](), [developer-productivity](), [developer-tools](), [mcp](), [platform-engineering](), [security]() ### AI overview This tutorial demonstrates how an AI coding agent connected to Red Hat Developer Hub can query a Backstage software catalog, TechDocs, and software templates before making architectural decisions. Using a simulated insurance-company catalog, the demo shows how this context can help avoid PCI-DSS compliance violations, resolve governance conflicts, and create a traceable decision record. ### Source excerpt AI coding agents can generate entire services, reason about architectures, and scaffold applications in minutes. But ask one to build a service for your organization and you'll quickly notice the gap: it doesn't know your rules. It doesn't know which messaging broker your compliance team mandates, which services already exist in adjacent domains, or which project template is the golden path for your team. The post Red Hat Developer Hub: Preventing compliance violations with AI coding agents appeared first on Red Hat Developer. ## What does DX do? Key features, use cases, and alternatives DevFeed: [What does DX do? Key features, use cases, and alternatives]() Original publisher: [Read original article]() Author: John Crowley Published: 2026-09-09T15:53:52Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [code productivity](), [developer-productivity](), [Developer experience](), [atlassian](), [Git](), [Instrumentation](), [sdlc](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [atlassian](), [developer-experience](), [developer-productivity](), [engineering](), [git](), [instrumentation](), [metrics](), [sdlc](), [survey]() ### AI overview This article explains what DX does as an engineering intelligence platform. DX measures developer productivity, developer experience, delivery data, and AI's impact on codebases using developer surveys and data from Git and Jira. It describes DX's DevEx Cloud, DX Data Cloud, DX AI, and Fabric offerings, along with their research-backed metrics, use cases, limitations, and alternatives. ### Source excerpt What DX does: its engineering intelligence features, survey-based metrics, common use cases, where it stops, and the alternatives ## Red Hat Developer Hub software template authoring with rhdh-templates DevFeed: [Red Hat Developer Hub software template authoring with rhdh-templates]() Original publisher: [Read original article]() Author: Kashish Mittal Published: 2026-09-07T07:01:32Z Content type: tutorial Language: en Sources: [Red Hat Developer]() Topics: [Backstage](), [internal developer portal](), [Nunjucks](), [AI-assisted coding](), [YAML]() Tags: [ai-coding-agents](), [artificial-intelligence](), [backstage](), [developer-portal](), [developer-productivity](), [developer-tools](), [platform-engineering](), [quick-start](), [red-hat](), [software-templates](), [terminal](), [validation]() ### AI overview This tutorial introduces the rhdh-templates skill for building Red Hat Developer Hub and Backstage software templates with an AI coding assistant. It covers Nunjucks conventions, YAML schema checking, reference templates, local offline validation, repository templatization, and optional testing against a Red Hat Developer Hub instance. ### Source excerpt If you are a platform engineer or developer building Red Hat Developer Hub software templates, you know the friction of wrestling with Nunjucks syntax, guessing location.yaml placements, and discovering errors only after rendering in your developer portal. While AI coding agents handle generic YAML, they lack Red Hat Developer Hub and Backstage-specific conventions. The post Red Hat Developer Hub software template authoring with rhdh-templates appeared first on Red Hat Developer. ## Becoming an AI Team DevFeed: [Becoming an AI Team]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-01T15:01:06Z Content type: opinion Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [AI Strategy](), [AI Development](), [code productivity](), [Refactoring]() Tags: [ai](), [developer-productivity](), [engineering](), [engineering-culture](), [infrastructure](), [pinterest](), [refactoring]() ### AI overview The article argues that becoming an AI team requires changing team ownership, planning, operating models, and roles--not merely adding AI tools. It describes AI-assisted automation, analysis, and prototyping as ways to improve infrastructure reliability, cost efficiency, and developer productivity. ### Source excerpt John Grass | Sr. Manager, Engineering A Fundamental Transformation An AI team is fundamentally more than just a group whose members incorporate AI tools into their existing workflows. The journey to becoming an AI team necessitates a fundamental and comprehensive paradigm shift in how the team defines ownership, engages in strategic planning, and, most critically, executes on its core goals and objectives. This transformation is not merely an addition of new technology; it is a restructuring of the team's operating model, philosophy, and individual roles. Becoming an AI team requires a holistic shift: team members transition from routine, manual execution to empowered, AI-augmented strategists and problem-solvers. In an AI-centric environment, every single member is significantly empowered, not only through access to cutting-edge AI tools and sophisticated models but through an expanded scope of responsibility and influence. These new capabilities allow individuals to automate routine tasks, accelerate data analysis, and rapidly prototype solutions, freeing up cognitive resources for higher-level, more strategic thinking. The expectation shifts from simply completing tasks to orchestrating intelligent systems and focusing on solving problems that were previously intractable. Consequently, the very nature of the work for both individual contributors and managers will be fundamentally different from anything that has come before. At Pinterest, this isn't theoretical. Our infrastructure teams sit at the core of a product that serves billions of Pins, boards, ads, and real-time signals. That reality has forced us to treat "becoming an AI team" as an operational necessity, not a side project as we cannot keep scaling reliability, cost efficiency, and developer productivity using only traditional playbooks. AI Team Capabilities The advent of accessible Artificial Intelligence (AI) tools has fundamentally raised the performance ceiling for what development teams can accomp ## CircleCI Smarter Testing: Stop running tests that don't matter DevFeed: [CircleCI Smarter Testing: Stop running tests that don't matter]() Original publisher: [Read original article]() Author: Nathan Fish Published: 2026-08-26T19:00:00Z Content type: article Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Testing](), [CI/CD]() Tags: [accelerate](), [auto-rerun-failed-tests](), [ci-cd](), [circleci](), [circleci-news](), [developer-productivity](), [dynamic-test-splitting](), [engineering-productivity](), [flaky](), [intelligent-test-selection](), [parallel](), [pipeline](), [quality](), [reduce](), [smarter-testing](), [test-impact-analysis](), [testing](), [tests](), [time]() ### AI overview CircleCI describes Smarter Testing, a set of features designed to reduce CI/CD test execution time by skipping tests unaffected by changes, balancing parallel nodes, and retrying flaky tests. The article says early users have seen test runs up to four times faster. ### Source excerpt Testing eats up to half your pipeline time. See how CircleCI Smarter Testing skips unaffected tests, rebalances parallel nodes, and retries flaky tests. ## Measuring IDP Success: Metrics Beyond Tracking DevFeed: [Measuring IDP Success: Metrics Beyond Tracking]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-08-21T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [internal developer portal](), [Developer experience](), [code productivity](), [Platform Engineering]() Tags: [developer-experience](), [developer-productivity](), [idp](), [internal-developer-portal](), [platform](), [privacy](), [surveillance]() ### AI overview This guide explains how to measure Internal Developer Portal success with outcome-based metrics that respect developer privacy. It emphasizes ROI and system-health indicators such as deployment frequency and mean time to recovery, while warning that individual activity tracking can erode trust, distort behavior, and undermine adoption. ### Source excerpt Learn how to measure IDP success with meaningful metrics that respect developer privacy. Discover ROI indicators that matter. Explore now. | Blog ## How dLocal autonomously resolves 45% of engineering tickets with Port - driving 20% performance gains DevFeed: [How dLocal autonomously resolves 45% of engineering tickets with Port - driving 20% performance gains]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T11:33:11Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Agent](), [code productivity](), [sdlc](), [Orchestration](), [Pull Request](), [Deployment]() Tags: [ai-agent](), [ai-agents](), [deployment](), [developer-productivity](), [orchestration](), [review](), [sdlc](), [security]() ### AI overview dLocal built an AI agent on Port's Agentic SDLC Platform to autonomously resolve 45% of engineering tickets end to end, increasing developer productivity by 20%. ### Source excerpt See how dLocal built an AI agent that autonomously resolves 45% of engineering tickets and lifted developer productivity 20% - all on Port's context layer. ## Top 3 agentic workflows for platform engineers in Port DevFeed: [Top 3 agentic workflows for platform engineers in Port]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-07-31T11:56:54Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Platform Engineering](), [AI Development](), [Tech Debt](), [Developer experience](), [developer-productivity](), [software-development]() Tags: [agentic](), [developer-experience](), [developer-productivity](), [platform](), [platform-engineering](), [software-development](), [tech-debt](), [workflows]() ### AI overview This article introduces agentic workflows for platform engineers, describing how AI agents can be integrated into existing software development and platform operations. The supplied excerpt focuses on autonomously resolving non-urgent engineering tasks, such as minor bugs, UX/UI issues, and refactoring, to reduce tech debt and improve developer productivity. ### Source excerpt Learn about the agentic workflows you can build in Port today and how they solve major productivity challenges. ## Top 10 developer productivity tools DevFeed: [Top 10 developer productivity tools]() Original publisher: [Read original article]() Author: Jenna Danoy Published: 2026-07-30T11:29:15Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [developer-productivity](), [Developer experience](), [DevOps](), [internal developer portal](), [Software Engineering]() Tags: [cognitive-load](), [collaboration](), [developer-productivity](), [devops](), [efficiency]() ### AI overview This article presents a list of developer productivity tools intended to help engineering teams ship faster, reduce friction, and improve collaboration. It discusses tool sprawl, developer experience, cognitive load, software engineering intelligence, developer surveys, and governance or task management. ### Source excerpt Explore developer productivity tools that help teams ship faster, reduce friction, and improve collaboration across engineering workflows. ## Developer productivity insights platforms (DPIP) measure dashboards. Your DevEx problem is bigger Than that. DevFeed: [Developer productivity insights platforms (DPIP) measure dashboards. Your DevEx problem is bigger Than that.]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-07-30T11:28:55Z Content type: opinion Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [code productivity](), [devex](), [dashboards](), [developer-productivity](), [Artificial Intelligence](), [DevOps](), [Platform Engineering](), [CI/CD](), [Large Language Model](), [Optimization](), [migration]() Tags: [agentic](), [ai](), [ci-cd](), [dashboards](), [developer-productivity](), [devex](), [devops](), [llm](), [measurements](), [metrics](), [migration](), [optimization](), [platform-engineering]() ### AI overview This developer article argues that Developer Productivity Insight Platforms (DPIP) should extend beyond measuring engineering time, resources, effectiveness, and delivery progress. It says dashboards and metrics are insufficient without workflows and self-service actions that help developers resolve identified problems. The article also highlights platform convergence, CI/CD integrations, agentic solutions, LLM support, resource optimization, and data migration, arguing that enterprise engineering needs an end-to-end platform connecting insight to action. ### Source excerpt Gartner defines DPIP as solutions that provide software engineering leaders with data-driven insights into their teams' use of time, resources, operational effectiveness and progress on deliverables. ## Boost Developer Productivity: 8 Key Questions DevFeed: [Boost Developer Productivity: 8 Key Questions]() Original publisher: [Read original article]() Author: Mridhula Venkat Published: 2026-07-24T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [code productivity](), [dora metrics](), [Developer experience](), [Software Engineering](), [Instrumentation](), [pull-requests]() Tags: [developer-experience](), [developer-productivity](), [dora-metrics](), [efficiency](), [instrumentation](), [metrics](), [productivity](), [pull-requests](), [slack](), [software-engineering]() ### AI overview This article presents eight questions for engineering leaders to assess and improve developer productivity. It argues that story points and commit counts can obscure bottlenecks such as handoff delays, review queues, and context switching, and recommends considering team velocity, deployment frequency, DORA metrics, delivery quality, and business impact. ### Source excerpt Boost developer productivity with 8 essential questions covering metrics, workflows, and team efficiency. Learn how Harness SEI helps. | Blog ## Platform Product Management: AI-enabled vs. AI-native capability stacks DevFeed: [Platform Product Management: AI-enabled vs. AI-native capability stacks]() Original publisher: [Read original article]() Author: Poojitha Marreddy Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Product Management](), [code productivity](), [developer-productivity](), [Artificial Intelligence](), [Infrastructure as code](), [Terraform](), [DevSecOps](), [GitHub](), [Jenkins](), [Kubernetes]() Tags: [agents](), [ai](), [code](), [cognitive-load](), [configuration](), [developer](), [developer-productivity](), [devsecops](), [github](), [iac](), [jenkins](), [kubernetes](), [management](), [platform](), [product](), [productivity](), [terraform]() ### AI overview This article presents platform product management as a way to reduce developer cognitive load and improve delivery velocity. It contrasts AI-enabled capability stacks, which use AI to assist developers, with AI-native stacks, which run AI workloads; both depend on an internal developer platform but require different investments, governance, and success metrics. The article introduces the double diamond framework for discovering, defining, developing, and delivering platform capabilities. ### Source excerpt Platform PM framework: Shift from tool standardization to product thinking. Learn to build AI-enabled vs AI-native stacks to boost developer productivity ## How to measure developer productivity and platform ROI: A complete framework for platform engineers DevFeed: [How to measure developer productivity and platform ROI: A complete framework for platform engineers]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [code productivity](), [dora metrics](), [Developer experience]() Tags: [article](), [business-value](), [deployment](), [developer](), [developer-experience](), [developer-productivity](), [dora-metrics](), [efficiency](), [measurement](), [platform](), [platform-engineering](), [productivity]() ### AI overview A guide for platform engineers and technical leaders on measuring developer productivity and platform ROI. It explains why lines of code, story points, and commit counts fail to capture platform value, then introduces DORA metrics and the SPACE framework as approaches for assessing system-level improvements and developer experience. ### Source excerpt A comprehensive framework for platform engineers and leaders to measure developer productivity and platform ROI. Learn why traditional metrics fail, and how to use DORA and SPACE frameworks for accurate assessment. ## How to create a developer experience survey: The ultimate guide DevFeed: [How to create a developer experience survey: The ultimate guide]() Original publisher: [Read original article]() Author: Sooraj Shah Published: 2026-07-22T12:04:29Z Content type: tutorial Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [devex](), [Developer experience](), [code productivity](), [internal developer portal](), [sdlc](), [Tooling]() Tags: [article](), [developer](), [developer-experience](), [developer-productivity](), [developers](), [development](), [devex](), [guide](), [how-to](), [metrics](), [onboarding](), [productivity](), [research](), [sdlc](), [software](), [tooling]() ### AI overview This guide explains how to create a developer experience survey and use the results to identify challenges, improve onboarding and tooling, reduce SDLC bottlenecks, and prioritize capabilities for a developer portal. It emphasizes combining survey feedback with qualitative and quantitative measures of developer productivity. ### Source excerpt A developer experience survey helps you to know exactly what features and capabilities you need for your platform and portal ## 5 takeaways from the State of Software Delivery Q2 Pulse report DevFeed: [5 takeaways from the State of Software Delivery Q2 Pulse report]() Original publisher: [Read original article]() Author: Jacob Schmitt Published: 2026-07-08T13:00:00Z Content type: release Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Software](), [Development](), [Artificial Intelligence]() Tags: [ci-cd-benchmarks](), [circleci-news](), [cost](), [developer-productivity](), [efficiency](), [merge-efficiency-ratio](), [popular](), [report](), [software-delivery](), [software-delivery-metrics](), [software-delivery-pulse](), [software-delivery-report](), [state-of-software-delivery](), [teams](), [velocity](), [workflows]() ### AI overview CircleCI releases its 2026 State of Software Delivery Q2 Pulse report, based on more than 20 million workflows analyzed in March 2026. The report finds that the gap between high-performing teams and the median team widened, while feature-branch throughput increased and main-branch throughput remained flat. ### Source excerpt The velocity gap is widening. See what 20M+ workflows reveal about merge efficiency, delivery costs, and the teams pulling ahead in the Q2 Pulse report. ## Samsung Electronics brings ChatGPT and Codex to employees DevFeed: [Samsung Electronics brings ChatGPT and Codex to employees]() Original publisher: [Read original article]() Published: 2026-06-21T23:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [codex](), [samsung](), [OpenAI](), [Artificial Intelligence](), [AI Strategy](), [code productivity](), [developer-productivity](), [GitHub](), [coding]() Tags: [ai](), [ai-adoption](), [chatgpt](), [codex](), [company](), [developer-productivity](), [enterprise](), [openai](), [platform](), [productivity](), [samsung](), [security](), [software-development]() ### AI overview Samsung Electronics is deploying ChatGPT Enterprise and Codex to employees in Korea and across its Device eXperience division worldwide. The rollout covers technical and non-technical work, including software development, manufacturing, marketing, product development, information analysis, document drafting, and workflow automation. ### Source excerpt Samsung Electronics deploys ChatGPT Enterprise and Codex to employees worldwide, marking one of OpenAI's largest enterprise AI rollouts. ## What should Coding Challenges become next? DevFeed: [What should Coding Challenges become next?]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-06-20T08:02:12Z Content type: opinion Language: en Sources: [Coding Challenges]() Topics: [coding](), [Software Engineering](), [Artificial Intelligence](), [Generative AI](), [code productivity]() Tags: [ai](), [coding](), [developer-productivity](), [newsletter](), [programming](), [software-engineering]() ### AI overview The Coding Challenges newsletter asks readers what would make it worth reading every week. It invites feedback on coding projects, AI and LLMs, software engineering, architecture, testing, system design, and related developer topics, while also offering public comments, private email replies, or unsubscribing. ### Source excerpt Something needs to change, and I'd like you to help shape it. ## Agentic Testing: Where Agents Fit in the E2E Testing Stack DevFeed: [Agentic Testing: Where Agents Fit in the E2E Testing Stack]() Original publisher: [Read original article]() Author: Sergii Gorbachov Published: 2026-06-11T14:15:28Z Content type: article Language: en Sources: [Engineering at Slack]() Topics: [Playwright](), [Model Context Protocol](), [large-language-models](), [User Interfaces]() Tags: [agentic](), [agents](), [automation-testing](), [browser](), [code](), [cost](), [developer-productivity](), [e2e-testing](), [innovation](), [language-models](), [large-language-models](), [mcp](), [speed](), [testing](), [uncategorized](), [workflow]() ### AI overview The article examines agent-driven end-to-end testing as an exploratory complement to deterministic tests. Based on more than 200 runs using Playwright MCP, Playwright CLI, and agent-generated Playwright tests, it compares how agents pursue goals through variable UI paths and considers reliability, execution time, and cost. ### Source excerpt Abstract Agent-driven end-to-end (E2E) tests add a new exploratory layer to testing, but should they replace traditional deterministic tests? We ran more than 200 agentic E2E workflows using the Playwright MCP, Playwright CLI, and agent-generated Playwright tests in test workspaces using non-production data to find out how agentic testing could fit into both our and... ## Modern Mainframe DevOps: Automate CI/CD for z/OS Application DevFeed: [Modern Mainframe DevOps: Automate CI/CD for z/OS Application]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-06-10T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Mainframe](), [CI/CD](), [DevOps](), [Platform Engineering](), [Deployment](), [Developer experience](), [dora metrics]() Tags: [automated](), [ci](), [ci-cd](), [declarative](), [deployment](), [developer-experience](), [developer-platform](), [developer-productivity](), [devops](), [dora-metrics](), [os](), [pipelines](), [platform](), [platform-engineering](), [platforms](), [software](), [toolchain](), [tooling](), [workflows]() ### AI overview The article explains how platform engineering teams can integrate mainframe applications into unified CI/CD workflows alongside cloud-native services. It argues that replacing isolated, script-heavy mainframe processes with automated, declarative pipelines can improve visibility, coordinate cross-tier releases, and reduce operational overhead. ### Source excerpt Drive developer productivity by replacing brittle, legacy mainframe scripts with declarative, secure, and fully automated multi-tier release pipelines. | Blog ## Breakpoint 2026: Highlights from Day 1 DevFeed: [Breakpoint 2026: Highlights from Day 1]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-06-01T09:25:38Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Software Testing](), [Testing](), [Artificial Intelligence](), [Test automation](), [Automation](), [API](), [Security](), [prompt injection](), [code productivity](), [Accessibility]() Tags: [2026](), [ai](), [automation](), [breakpoint](), [developer-productivity](), [prompt-injection](), [qa](), [security](), [software-testing](), [testing]() ### AI overview Breakpoint 2026 Day 1 examines how AI-native quality engineering can reduce the traditional trade-off between testing speed and quality. It covers AI-assisted test execution, Copilot-driven automation, intent-based workflows, agentic QA systems, and frameworks for testing AI agents across reasoning, actions, and end-to-end outcomes. ### Source excerpt Explore the biggest announcements, expert insights, and conversations from Breakpoint 2026 Day 1, covering AI in software testing, test automation, developer productivity, accessibility testing, and the future of intelligent testing workflows. ## Beyond code generation: rethinking engineering productivity in the age of AI agents DevFeed: [Beyond code generation: rethinking engineering productivity in the age of AI agents]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura Published: 2026-05-28T18:00:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [code productivity](), [AI Development](), [Software Engineering](), [ci](), [pull-requests]() Tags: [agents](), [ai](), [ci](), [code-generation](), [code-quality](), [developer-productivity](), [developer-tools](), [developer-velocity](), [productivity](), [pull-requests](), [software-development](), [workflows]() ### AI overview Dropbox describes a shift from AI coding tools that accelerate implementation to agentic systems that execute scoped tasks. The article explains how increased code throughput creates pressure on review, CI, validation, release coordination, and production operations, requiring broader changes to engineering systems and workflows. ### Source excerpt How Dropbox is moving from AI tools that assist engineers to agentic systems that can execute scoped tasks, and how we're building platforms to support those workflows. ## Cisco and OpenAI redefine enterprise engineering with Codex DevFeed: [Cisco and OpenAI redefine enterprise engineering with Codex]() Original publisher: [Read original article]() Published: 2026-05-27T11:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [C++]() Tags: [ai-engineering](), [ai-security](), [c-plus-plus](), [cli](), [codex](), [compliance](), [developer-productivity](), [enterprise](), [open-source](), [openai](), [production-engineering]() ### AI overview Cisco describes using Codex across enterprise engineering workflows to build AI Defense, accelerate feature delivery, and increase defect-resolution throughput. ### Source excerpt Cisco and OpenAI are redefining enterprise engineering with Codex, helping Cisco scale AI-native development, accelerate AI Defense work, and automate defect remediation. ## Vespa Newsletter, May 2026 DevFeed: [Vespa Newsletter, May 2026]() Original publisher: [Read original article]() Author: Bonnie Chase Published: 2026-05-27T00:00:00Z Content type: news Language: en Sources: [Vespa Blog]() Topics: [AI search](), [Artificial Intelligence](), [Cloud](), [code productivity](), [configuration]() Tags: [ai](), [cloud](), [configuration](), [developer-productivity](), [embedding](), [newsletter](), [ranking](), [search](), [vector-search]() ### AI overview The May 2026 Vespa newsletter announces updates for retrieval and ranking systems, including improved ranking flexibility, embedding integrations with Voyage AI, OpenAI, and Mistral AI, Vespa Cloud dashboards and backups, maintenance controls, custom resource tags, agent skills, and new query and array-field capabilities. ### Source excerpt Advances in Vespa include finer control over deployments, smarter ranking, richer embedding integrations, and more scalable vector search. ## Introducing Nova, our internal platform for coding agents DevFeed: [Introducing Nova, our internal platform for coding agents]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler Published: 2026-05-21T16:00:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [AI-assisted coding](), [Nova](), [AI Development](), [software-development](), [Software Engineering](), [cloud-infrastructure](), [debugging](), [test-coverage]() Tags: [agents](), [ai](), [ai-agents](), [cloud](), [coding](), [debugging](), [developer](), [developer-productivity](), [developer-tools](), [developer-velocity](), [machine-learning](), [nova](), [platform](), [software-development](), [test-coverage](), [workflows]() ### AI overview Dropbox introduces Nova, an internal cloud platform for running coding agents. The platform supports parallel coding sessions and automated workflows across the software development lifecycle, including debugging, dependency updates, test-coverage improvements, and flaky-test fixes. ### Source excerpt Nova lets engineers run multiple coding sessions in parallel and lets internal systems use AI agents as part of automated workflows. [Next page]()