# code productivity The degree to which software developers can perform their work with minimal friction. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Port vs. Jellyfish: Engineering Intelligence Compared DevFeed: [Port vs. Jellyfish: Engineering Intelligence Compared]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-09-15T12:02:54Z Content type: comparison Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Software Engineering](), [Developer experience](), [dora metrics](), [AI Strategy](), [code productivity]() Tags: [ai-adoption](), [compare](), [devex](), [dora-metrics](), [engineering](), [software-engineering](), [workflows]() ### AI overview A comparison of Port and Jellyfish as Engineering Intelligence platforms. Jellyfish focuses on analytics and decision support for engineering performance, investment, developer experience, and AI impact, while Port connects findings to software context, owners, standards, and governed workflows so teams can act and measure outcomes. ### Source excerpt Compare Port and Jellyfish across Engineering Intelligence, AI impact, DevEx, software context, governed workflows, and measurable outcomes. ## Article: When Spec-Driven Development Pays Off DevFeed: [Article: When Spec-Driven Development Pays Off]() Original publisher: [Read original article]() Author: Nitin Garg Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [AI-assisted coding](), [AI Development](), [Software Engineering](), [code productivity]() Tags: [ai](), [ai-assisted-coding](), [ai-coding](), [ai-development](), [ai-ml-data-engineering](), [architecture](), [article](), [artificial-intelligence](), [code-generation](), [generative-ai](), [ml-data-engineering](), [productivity](), [reasoning](), [review](), [security](), [software-development](), [spec-driven-development](), [when-spec-driven-development-pays-off]() ### AI overview The article argues that AI-assisted coding shifts the main constraint from writing code to verifying it. It presents specification-first development as a governance approach for hard, multi-constraint work, while noting its time and cost and warning that apparent gains may instead come from reasoning. ### Source excerpt AI coding assistants have become a core part of software development. AI-generated code has shown productivity gains, but it's also contributing to security weaknesses and familiar bug patterns. In this article, author Nitin Garg highlights the bottleneck has moved from code generation to code verification, and how to detect & mitigate it when the AI-generated behavior diverges from the intent. By Nitin Garg ## 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 ## 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 ## Nitpicking in code reviews wastes time DevFeed: [Nitpicking in code reviews wastes time]() Original publisher: [Read original article]() Author: Dan Lew Published: 2026-09-01T13:00:13Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Code](), [code productivity](), [Software Engineering](), [Development]() Tags: [bugs](), [code](), [code-reviews](), [development](), [features](), [problems](), [refactor](), [software-development](), [time]() ### AI overview An opinion article argues that nitpicking minor issues in code reviews wastes time. Small problems usually have limited impact, may never cause trouble, and can often be fixed later, so review effort should prioritize major problems, bugs, and features. ### Source excerpt I once wrote about how I've stopped nit-picking in code reviews, which improved the signal-to-noise ratio and helped everyone get along better. I'm back with a third reason to ditch the practice: nit-picking wastes time. By definition, nitpicks are about minor problems: ## Six Heroku CLI Commands for Improving Terminal Workflows DevFeed: [Six Heroku CLI Commands for Improving Terminal Workflows]() Original publisher: [Read original article]() Author: Andy Smith Published: 2026-08-25T16:28:53Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Command-line interface](), [Heroku](), [Developer experience](), [code productivity]() Tags: [cli](), [command-line](), [commands](), [config](), [developer-experience](), [developer-tools](), [ecosystem](), [heroku](), [heroku-postgres](), [productivity](), [terminal](), [tools](), [variables]() ### AI overview This tutorial presents six Heroku CLI commands intended to reduce context switching and improve terminal-based workflows. The supplied content discusses exporting app configuration for local environments and checking active Heroku Postgres database queries, while warning about production configuration handling and credential leaks. ### Source excerpt Improving developer experience in the command line relies on minimizing context switches. While most engineers use the Heroku CLI strictly for basic tasks like pushing code, it contains a powerful suite of command line tools built to optimize your workflow. If you want to keep your focus locked on writing code, these six Heroku CLI commands will instantly upgrade your daily productivity. The post 6 Lesser-Known Heroku CLI Commands You Probably Aren't Using appeared first on Heroku. ## 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 ## Conceptual integrity and counting lines of code DevFeed: [Conceptual integrity and counting lines of code]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-19T22:46:07Z Content type: opinion Language: en Sources: [Simon Willison]() Topics: [AI Development](), [AI-assisted coding](), [code productivity](), [Software Engineering](), [Programming]() Tags: [agents](), [ai](), [ai-2-235](), [code](), [coding-agents](), [coding-agents-248](), [generative-ai](), [generative-ai-1-981](), [llms](), [llms-1-947](), [podcast-appearances](), [podcast-appearances-41](), [productivity](), [software-development](), [software-engineer]() ### AI overview This article presents transcript highlights from a Talking Postgres discussion about AI-assisted software development. It argues that lines of code can sometimes indicate productivity when coding agents produce maintainable, tested, production-quality code, while emphasizing that skill and experience remain necessary. It also argues that cognitive capacity and conceptual integrity limit how much agent-generated code engineers can effectively manage. ### Source excerpt Last week I recorded an episode of the Talking Postgres podcast with Claire Giordano on the subject of "How AI is changing software development". We had a really great conversation. Here are a couple of my highlights from a lightly edited transcript (prompt to Claude: "very minor edits to remove disfluencies"). This is the latest version of an argument I've been trying to build about why sometimes it does make sense to talk about lines of code as an indicator of productivity with coding agents, at 35:01: A lot of people will tell you it makes no sense to measure productivity in lines of code. I'd actually disagree, because there's a hard limit. In the before-times, a software engineer could produce a few hundred lines of production-ready code per day -- and 200 lines of working, debugged, production-level code is an incredibly good day. Most days you'd produce 50 or 60. If agents let you produce a thousand lines of debugged code, that really is a very meaningful improvement -- as long as the code is the same quality: maintainable, tested, all of that. You can get to that point with agents, but it takes a huge amount of skill and knowledge and experience. That's what senior engineers are made of. I can do way more work as a single engineer than I could without agents. So you could argue, why should a company have more than one engineer? Beyond the obvious bus factor thing -- a team of one is a very badly designed team -- the answer is that the new limiting factor is cognitive capacity. I can churn out code a hundred times faster. I don't have the cognitive capacity to stay on top of 100 times the amount of code. So you still need a team of engineers, so you can load balance that cognitive capacity across the team. And this section on conceptual integrity at 46:03, which Claire equated to the Winchester Mystery House! Simon: There's a concept in The Mythical Man-Month -- conceptual integrity -- where well-designed software has an integrity to it: there are no surprises in i ## 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. ## AI Adoption Metrics: Connect Usage to Real Engineering Impact DevFeed: [AI Adoption Metrics: Connect Usage to Real Engineering Impact]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-08-10T11:32:12Z Content type: opinion Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Strategy](), [code productivity](), [AI Development](), [data](), [Claude](), [cursor](), [GitHub Copilot]() Tags: [2025](), [ai](), [ai-adoption](), [ai-tools](), [claude](), [copilot](), [cursor](), [development](), [metrics](), [productivity](), [token](), [tokens]() ### AI overview The article argues that AI adoption metrics should connect tool usage with engineering context and outcomes. License activity, user engagement, token consumption, and prompt volume do not by themselves show whether teams are working more effectively. Meaningful evaluation should consider teams, services, processes, ownership, workflows, delivery stability, test coverage, incidents, and concrete results such as reduced latency. ### Source excerpt See why AI adoption metrics need engineering context: how connecting usage to teams, services, and outcomes reveals real impact. ## How to measure ROI for an enterprise CMS migration DevFeed: [How to measure ROI for an enterprise CMS migration]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [Content Management System](), [migration](), [workflow automation](), [Web](), [code productivity]() Tags: [analysis](), [article](), [automation](), [business](), [content](), [core-web-vitals](), [developer](), [development](), [efficiency](), [enterprise](), [how-to](), [metrics](), [migration](), [platform](), [productivity](), [strategy](), [tools](), [web](), [workflow](), [workflow-automation]() ### AI overview This article explains how enterprises can measure the return on investment of migrating to a modern content management system. It covers migration costs, expected gains, baseline metrics, operational efficiency, development costs, website performance, and developer productivity. ### Source excerpt Find out how to measure the ROI for your enterprise's CMS migration. Learn about the timeline you can expect and the improvements your team might see. ## 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. ## Why Platform Engineers Need Developer Feedback DevFeed: [Why Platform Engineers Need Developer Feedback]() Original publisher: [Read original article]() Author: John Crowley Published: 2026-07-30T10:39:13Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Platform Engineering](), [devex](), [code productivity](), [dora metrics](), [sdlc](), [dashboards](), [telemetry](), [Automation]() Tags: [automation](), [dashboards](), [developer](), [developer-experience](), [devex](), [dora-metrics](), [platform](), [platform-engineering](), [productivity](), [sdlc](), [telemetry](), [workflows]() ### AI overview The article explains why platform engineering teams need systematic, direct developer feedback to improve developer experience, reduce friction, and increase engineering performance. It recommends combining qualitative feedback about engineers' experiences with quantitative signals such as DORA metrics and other SDLC measures. ### Source excerpt Understand why platform engineers need direct feedback to improve developer experience, optimize workflows, and align platforms with real needs. ## Fast-tracking service deployment with Port DevFeed: [Fast-tracking service deployment with Port]() Original publisher: [Read original article]() Author: Yarden Holtzer Weiss Published: 2026-07-30T10:39:13Z Content type: tutorial Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Deployment](), [sdlc](), [User Stories](), [code productivity](), [DevOps]() Tags: [cognitive-load](), [deployment](), [devops](), [internal-developer-portal](), [productivity](), [reduce](), [sdlc](), [user-stories]() ### AI overview A practical guide to creating a basic internal developer portal in 4-5 hours. It focuses on streamlining the SDLC by helping developers independently manage deployments, scaffold services, view runtime data, and review deployment history. ### Source excerpt How to create an efficient internal developer portal in just 4-5 hours to streamline SDLC, boost productivity, and enhance developer autonomy. ## 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 ## The 4 levels of agentic software development DevFeed: [The 4 levels of agentic software development]() Original publisher: [Read original article]() Author: Kaspar von Grünberg Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [AI Development](), [code productivity](), [software-development](), [cloud-infrastructure]() Tags: [agentic](), [ai](), [autonomous](), [development](), [platform](), [productivity](), [software-development]() ### AI overview The article presents four levels of agentic software development in the enterprise, defined by how much work humans must initiate and approve. It argues that productivity gains depend less on procuring advanced models than on restructuring the software production platform so agents can reliably access code, execute work in suitable environments, and participate in validation workflows. As organizations advance through the levels, humans shift from execution to review, orchestration, and system design. ### Source excerpt Discover the four levels of agentic software development. Learn how evolving your platform from human-in-the-loop to autonomous systems drives up to 30% throughput gains ## 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. ## The platform scorecard: A practical way to prove value (and what to measure first) DevFeed: [The platform scorecard: A practical way to prove value (and what to measure first)]() Original publisher: [Read original article]() Author: Jordan Chernev Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Kubernetes](), [code productivity](), [CRUD]() Tags: [blog](), [engineering](), [guide](), [kubernetes](), [metrics](), [operations](), [platform](), [platform-engineering](), [unit-economics]() ### AI overview A practical guide to building a Platform Scorecard for measuring and communicating the value of platform engineering. It focuses on four areas: North Star metrics, adoption and engagement drivers, platform health, and financials. ### Source excerpt A practical guide to building a Platform Scorecard to prove the value of your platform engineering team. Learn what to measure first by focusing on four key areas: North star, Adoption, Platform health (including DORA/SLOs), and Financials ## Being a platform engineer in 2026: Career reality check DevFeed: [Being a platform engineer in 2026: Career reality check]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Developer experience](), [software-development](), [code productivity]() Tags: [2026](), [ai](), [article](), [career](), [developer](), [development](), [engineering](), [platform](), [platform-engineering](), [software-development]() ### AI overview This article presents platform engineering as an established industrial practice and examines the career reality of the role. It discusses platform-to-developer ratios, productivity and standardization gains, developer adoption as the leading challenge, specialization across platform roles, salary averages in North America and Europe, and the growing importance of AI literacy. ### Source excerpt Get the unvarnished reality of being a platform engineer in 2026. Data on average salary, the 6 specialized roles, AI competency, and the #1 challenge: developer adoption ## The platform engineering reality check: Is your initiative on track? DevFeed: [The platform engineering reality check: Is your initiative on track?]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [code productivity](), [developer-productivity]() Tags: [developer](), [developers](), [engineering](), [executive](), [measurement](), [metrics](), [platform](), [platform-engineering](), [productivity](), [report]() ### AI overview This article presents a reality check for platform engineering initiatives, emphasizing measurement, return on investment, adoption, culture, and maturity. It reports that many platform teams either do not measure success or cannot demonstrate improvement, and recommends using metrics such as DORA, time to market, or SPACE. It also warns that funded teams may remain reactive without sufficient authority or strategic empowerment. ### Source excerpt Assess if your platform engineering initiative is on track. Learn about measurement, investment, adoption, culture, and maturity benchmarks for success. ## 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 ## How We Use Our Own Developer Portal DevFeed: [How We Use Our Own Developer Portal]() Original publisher: [Read original article]() Author: Mor Paz Published: 2026-07-22T11:37:29Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Developer experience](), [dogfooding](), [internal developer platform](), [Deployment](), [code productivity](), [observability](), [Microservices](), [dashboards]() Tags: [dashboards](), [deployment](), [developer-experience](), [developer-portal](), [dogfooding](), [internal-developer-platform](), [internal-developer-portal](), [microservices](), [observability](), [productivity]() ### AI overview This article explains how Port uses its own internal developer portal as a form of dogfooding to improve developer experience and productivity. It describes the portal's software catalog and several use cases, with particular focus on deployment tracking and environment locks. ### Source excerpt How we use our internal developer portal for a better developer experience and productivity - specifically for deployment tracking and environment locks ## 5 pitfalls to avoid when measuring DevEx in the AI era DevFeed: [5 pitfalls to avoid when measuring DevEx in the AI era]() Original publisher: [Read original article]() Author: Candace Shamieh; Teddy Gesbert Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [devex](), [code productivity](), [engineering-leadership](), [Artificial Intelligence](), [Code review]() Tags: [ai](), [ai-adoption](), [ai-impact](), [ci-cd](), [ci-visibility](), [collaboration](), [developer-experience](), [devex](), [devops](), [dora-metrics](), [engineering-management](), [internal-developer-portal](), [metrics](), [performance](), [productivity](), [review](), [software-delivery](), [test-optimization]() ### AI overview This article examines common pitfalls in measuring developer experience during the AI era. It argues that individual output metrics, including token consumption, lines of code, pull request counts, commits, and story points, can undermine trust and collaboration when treated as measures of personal productivity. ### Source excerpt Don't mistake AI adoption for productivity. Learn how to avoid 5 common pitfalls when measuring DevEx, with practices from Datadog engineering [Next page]()