# developer-experience Published articles for developer-experience. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an Internal Developer Platform with Artificial Intelligence DevFeed: [Building an Internal Developer Platform with Artificial Intelligence]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-17T11:11:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [internal developer platform](), [AI Development](), [AI search](), [agent observability](), [OpenTelemetry](), [telemetry]() Tags: [agent](), [agents](), [artificial-intelligence](), [culture-methods](), [developer-experience](), [developer-platform](), [distributed-tracing](), [guardrails](), [internal-developer-platform](), [logging](), [logs](), [metrics](), [news](), [opentelemetry](), [platform-artificial-intelligence](), [platform-engineering](), [prometheus](), [traces]() ### AI overview This news article covers a KubeCon presentation about using AI agents as an internal developer platform. It discusses semantic search across sources such as Git, Slack, Jira, repositories, pull requests, and wiki pages; guardrails for controlling actions; and logs, metrics, and traces for understanding agent behavior. The speakers also describe OpenTelemetry conventions for GenAI and related observability tools. ### Source excerpt Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior. By Ben Linders ## Migrating the GitHub Copilot runtime to Rust, using Copilot DevFeed: [Migrating the GitHub Copilot runtime to Rust, using Copilot]() Original publisher: [Read original article]() Author: Stephen Toub Published: 2026-09-17T00:26:43Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot](), [Rust](), [Agent Harness](), [GitHub Copilot SDK](), [GitHub]() Tags: [agent-harness](), [ai-agents](), [ai-ml](), [developer-experience](), [generative-ai](), [github](), [github-copilot](), [github-copilot-sdk](), [rust]() ### AI overview The GitHub Copilot agent runtime was rewritten from TypeScript on Node.js and V8 into more than 800,000 lines of production Rust. AI agents wrote most of the code across 128 pull requests, and the runtime was shipped incrementally while regressions were fixed and performance improved by orders of magnitude. ### Source excerpt A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took. The post Migrating the GitHub Copilot runtime to Rust, using Copilot appeared first on The GitHub Blog. ## Platform Engineering in the Age of AI DevFeed: [Platform Engineering in the Age of AI]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [Artificial Intelligence](), [AI Agent](), [internal developer portal](), [API](), [Deployment](), [sdlc]() Tags: [agent](), [ai](), [api](), [deployment](), [developer-experience](), [engineering](), [metrics](), [platform](), [sdlc]() ### AI overview This commentary examines how platform engineering is adapting as AI coding tools and agents become part of software delivery. It discusses the build-versus-provide decisions facing platform teams, how agents may consume internal developer platforms through APIs, the need for guardrails, and the challenge of measuring the results of AI investment. ### Source excerpt 94% of engineering leaders say their AI metrics are missing. Here's how platform engineering is changing to close that gap. | Blog ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## Kotlin on the server at Khan Academy DevFeed: [Kotlin on the server at Khan Academy]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-06-28T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Kotlin](), [backend-development](), [Python](), [Google]() Tags: [api](), [bigquery](), [blog-post](), [developer-experience](), [engineering](), [google](), [http](), [infrastructure](), [java](), [kotlin](), [news](), [python](), [server-side-development]() ### AI overview Khan Academy describes testing Kotlin for user-facing server requests during a 2018 hackathon and deciding to adopt Kotlin as a second server-side language after reporting positive efficiency results and a positive developer experience. ### Source excerpt By Colin Fuller At Khan Academy, we run our web application using Python 2.7 on Google's App Engine ... Read more ## Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI DevFeed: [Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-14T23:05:28Z Content type: article Language: en Sources: [Product Growth]() Topics: [Artificial Intelligence](), [context](), [repo](), [context window](), [Developer experience](), [GitHub](), [Linear]() Tags: [ai](), [context](), [context-window](), [developer-experience](), [github](), [linear](), [product](), [repo](), [team](), [tools]() ### AI overview The article examines Together AI's AI-oriented product team and describes four parts of its working stack: a shared context repository, reusable skills for research and product documentation, an orchestrator for product leaders, and agent evaluations. It explains how shared context files and workflows support collaboration across product areas, including automated weekly status updates using Linear, GitHub, and strategy documents. ### Source excerpt I got a truly AI-pilled product team to demo the 4 key tools in their stack ## Driving Developer Excellence: Inside the Program Sprints DevFeed: [Driving Developer Excellence: Inside the Program Sprints]() Original publisher: [Read original article]() Author: Anant Nawalgaria; Eric Schmidt; Sokratis Kartakis; Aman Khan Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-governance](), [developer-experience](), [devex](), [documentation](), [google-cloud](), [policy]() ### AI overview Google Cloud's Gemini Enterprise DevEx program tests developer workflows without internal shortcuts to find and resolve friction. This sprint focused on governed AI-agent deployment, covering identity provisioning, registry enrollment, gateway routing, policy enforcement, content safety, and auditable request verification. ### Source excerpt The Gemini Enterprise Developer Experience (DevEx) program conducts ongoing sprint testing of end-to-end developer workflows to identify and rapidly resolve friction points without relying on internal shortcuts. This recent sprint focused on optimizing enterprise AI governance, including refining setup prerequisites, securing extension configurations, and clarifying policy enforcement mechanics to ensure a smoother, more reliable deployment. Developers can now leverage updated documentation and standardized code samples to improve their experience with Agent Gateway and Semantic Governance configurations. ## 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 ## Build your own company brain: the enterprise AI playbook from Stripe's engineering team | Sharadh Krishnamurthy DevFeed: [Build your own company brain: the enterprise AI playbook from Stripe's engineering team | Sharadh Krishnamurthy]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-07T12:04:19Z Content type: article Language: en Sources: [Lenny's Newsletter]() Topics: [stripe](), [Artificial Intelligence](), [AI Agent](), [context](), [Developer experience](), [data](), [systems]() Tags: [agent](), [ai](), [data](), [developer](), [developer-experience](), [governance](), [platform](), [stripe](), [systems]() ### AI overview A podcast episode about Stripe's internal AI agent, Kai, and how the company scaled it across more than 10,000 employees. The discussion covers building the agent in-house, governance through projects, safe data access, infrastructure, skills, evaluation, telemetry, and lessons from incidents involving production systems. ### Source excerpt Watch now | 🎙 Learn how Stripe scaled AI to everyone at a complex global company by building context, governance, and a shared skills platform directly into their internal agent ## 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 ## From Chrome DevTools to AI Engineering, with Addy Osmani DevFeed: [From Chrome DevTools to AI Engineering, with Addy Osmani]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-19T16:53:57Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [AI Engineering](), [AI Agent](), [Developer experience](), [Google](), [developer tooling](), [engineering-culture](), [Loop Engineering](), [Chrome]() Tags: [agents](), [ai-agents](), [ai-engineering](), [developer](), [developer-experience](), [devtools](), [engineering](), [google](), [software-engineering](), [workflows]() ### AI overview Addy Osmani discusses his 14 years at Google, including work on Chrome, DevTools, Core Web Vitals, and AI developer experience. The conversation covers AI agents, loop engineering, cognitive surrender, engineering culture, and the broader skills engineers need. ### Source excerpt Addy Osmani shares lessons from 14 years at Google and how AI agents are reshaping software engineering, developer workflows, and the skills engineers need to succeed. ## Super-comma-nix: A Rust helper for Nix shell and run commands with sandboxing DevFeed: [Super-comma-nix: A Rust helper for Nix shell and run commands with sandboxing]() Original publisher: [Read original article]() Author: ksvivek Published: 2026-08-14T08:01:27Z Content type: article Language: en Sources: [Announcements - NixOS Discourse]() Topics: [Nix](), [Rust](), [Command-line interface](), [Shell](), [ci]() Tags: [announcements](), [ci](), [code](), [command](), [commands](), [config](), [dependencies](), [developer-experience](), [environments](), [integrations](), [runner](), [rust](), [shell]() ### AI overview Super-comma-nix is a Rust command-runner helper for Nix that shortens common nix shell, nix run, and version-checking commands. It integrates with nix-output-monitor and landrun, supports configurable environment variables, and can produce commands without running them. ### Source excerpt Hi All super-comma is an ultra-fast, zero-dependency Nix command runner helper written in Rust and powered directly by nixpkgs-multiverse. It is inspired by the project comma nix. But my project is loaded with batteries and much more DX(Developer eXperience). You'll love it. This project makes the consumption easier for nix shell, nix run commands and versions list all with simple handy commands like ,(nix run) ,s(nix shell) ,v(versions checking) . The project have some cool flags and also integrations like nix-output-monitor and landrun for sandboxing all with just few flags. Dont want to type these nom and sandboxing flags verbose? U can add it to env variable for session or globally as like config. Go read the README, you'll love it. Though plenty of features, I sticked to this principle: Simple by default and powerful when needed So learn as u go. Want more integrations? Please feel free to raise an issue Sandboxing is pretty cool, u want to try a software but dont want it to connect to inernet? This project enables some real adhoc shell environments and with the sandboxing etc figure out the possibilities for everyday usage, CI etc The project has zero dependencies for rust. Coz this project just makes u type short commands and the program just does some splitting and joining of strings. So please feel free to unslop some logic written by the LLM there if u think it can be simplified. Skeptic coz LLM(though it'll change in future)? just use the program with -o flag and u can just use the program to give you the final command without running. The Github README is carefully crafted to explain all the features, so take your time there. I'm a nix and nixos user since 2024, so I know some nix and the project design and integration of nom and landrun etc purely my design and idea and instructed the llm with my knowledge. But i'm not having good rust knowledge, so that part is covered by LLM. I wanted to write it with PF languages like haskell etc, but I wanted to qui ## Introducing Gemini 3.7 Flash DevFeed: [Introducing Gemini 3.7 Flash]() Original publisher: [Read original article]() Author: Tulsee Doshi Published: 2026-08-13T17:04:18Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [coding](), [Software Engineering](), [debugging](), [Benchmark](), [Web Development](), [Developer experience](), [web applications]() Tags: [agents](), [ai](), [benchmark](), [coding](), [cost](), [debugging](), [developer-experience](), [gemini](), [model](), [none](), [scale](), [software-engineering](), [testing]() ### AI overview Gemini 3.7 Flash is presented as a more capable workhorse model for coding, agents, software engineering, web development, knowledge work, and multi-step workflows. The article highlights gains in debugging, code generation, UI and app generation, reasoning, automation, and developer experience compared with Gemini 3.6 Flash, alongside introductory token pricing. ### Source excerpt Gemini 3.7 Flash is our most intelligent workhorse model yet for coding and agents. ## Why Amp Became Sebastian's Default Coding Harness DevFeed: [Why Amp Became Sebastian's Default Coding Harness]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-08-11T10:30:28Z Content type: opinion Language: en Sources: [freek.dev - all blogposts]() Topics: [coding](), [User Experience]() Tags: [agents](), [ai](), [coding](), [developer-experience](), [experience](), [harness](), [mcp](), [tools](), [user-experience](), [verification]() ### AI overview Sebastian explains why Amp became his default coding harness, emphasizing user experience over model churn and describing AGENTS.md changes that reduced overzealous verification during everyday coding. ### Source excerpt Sebastian explains why Amp became his default coding harness, and why user experience now matters more to him than model churn. He also shares the AGENTS.md tweaks that reduced overzealous verification during day-to-day coding. Read more ## Q2 2026 Product Update: Harness Pipeline DevFeed: [Q2 2026 Product Update: Harness Pipeline]() Original publisher: [Read original article]() Author: Vishal Vishwaroop Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [ci](), [Git](), [opa](), [configuration](), [Developer experience](), [Monitoring](), [API]() Tags: [2026](), [api](), [ci](), [developer-experience](), [git](), [opa](), [pipeline](), [pipelines](), [update]() ### AI overview Harness's Q2 2026 Pipeline update introduces beta DAG pipelines, allowing stages to declare explicit dependencies. It also adds looping strategies for chained pipelines, earlier matrix exclusions, template and governance improvements, Git-backed entity enforcement, execution visibility, and a pipeline dry-run API. ### Source excerpt DAG pipelines, template overrides, OPA enforcement on Git-backed entities, Git Experience monitoring, and 20 pipeline improvements -- Q2 2026 in review. | Blog ## Prevent cognitive debt by manually retyping LLM-generated code DevFeed: [Prevent cognitive debt by manually retyping LLM-generated code]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-08-04T12:56:26Z Content type: opinion Language: en Sources: [freek.dev - all blogposts]() Topics: [Large Language Model](), [Code]() Tags: [ai](), [best-practices](), [code](), [code-quality](), [cognitive-load](), [developer-experience](), [llm](), [model](), [practical](), [prevent](), [understand]() ### AI overview Ankur Sethi argues that manually retyping code generated by large language models can help developers understand the code, identify poor suggestions, and maintain a strong mental model of their codebase, despite slowing development. ### Source excerpt Ankur Sethi argues that manually retyping LLM-generated code is a practical way to avoid cognitive debt. It slows you down, but helps you understand the code, catch bad suggestions, and keep a strong mental model of your codebase. Read more ## We Became Editors-in-Chief, and Nobody Trained Us DevFeed: [We Became Editors-in-Chief, and Nobody Trained Us]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-08-03T14:56:25Z Content type: opinion Language: en Sources: [freek.dev - all blogposts]() Topics: [Artificial Intelligence](), [Development](), [Code]() Tags: [ai](), [career](), [code](), [code-review](), [developer-experience](), [development](), [essay](), [soft-skills](), [writing-code]() ### AI overview An essay examines how AI-assisted development shifts engineers from writing code toward reviewing AI-generated diffs. It argues that this change creates cognitive fatigue and may widen the gap between senior and junior engineers. ### Source excerpt An essay on how AI-assisted development turns engineers into full-time reviewers. We spend our days evaluating AI-generated diffs instead of writing code, and that shift carries a new cognitive fatigue and may widen the senior/junior gap. Read more ## AI-generated code is overwhelming reviewers and changing pull request approval practices DevFeed: [AI-generated code is overwhelming reviewers and changing pull request approval practices]() Original publisher: [Read original article]() Author: Ishan Khanna Published: 2026-08-01T19:45:03Z Content type: opinion Language: en Sources: [Ishan Khanna]() Topics: [Artificial Intelligence](), [Pull Request](), [Code review](), [GitHub]() Tags: [ai](), [code](), [code-review](), [code-reviews](), [developer-experience](), [gen-ai](), [generative-ai](), [github](), [large-language-models](), [llms]() ### AI overview The article argues that AI-generated code is increasing the volume of pull requests beyond what engineers can thoroughly review. It describes slow approvals, ignored code-review-agent comments, and a growing practice of approving changes without close examination. ### Source excerpt "Can you review this PR quickly?" has become every engineer's most-sent message. On AI-generated code, drowning reviewers, and how Meta's RADAR auto-approves low-risk diffs. ## 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. ## Backstage Is Dead. The Platform Engineering World Moved On DevFeed: [Backstage Is Dead. The Platform Engineering World Moved On]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-07-30T11:26:23Z Content type: opinion Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Backstage](), [Platform Engineering](), [internal developer portal](), [Developer experience](), [React](), [Terraform]() Tags: [backstage](), [developer-experience](), [developer-portal](), [internal-developer-portal](), [kubernetes](), [platform-engineering](), [react](), [terraform]() ### AI overview The article argues that Backstage, while foundational to internal developer portals and platform engineering, no longer fits how many teams work. It highlights production challenges including the need for React expertise, limited data-model flexibility, and the maintenance burden of customization. ### Source excerpt Why Backstage is dead: explore limitations, challenges, and why teams are moving to modern internal developer portal alternatives. ## Agentic Engineering: Evolution of Developer Portals DevFeed: [Agentic Engineering: Evolution of Developer Portals]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-07-30T11:25:32Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [AI Development](), [Software Engineering](), [Developer experience](), [DevOps](), [Cloud Native Ecosystem](), [Microservice](), [Kubernetes](), [GitOps](), [CI/CD](), [incident](), [Vulnerabilities]() Tags: [agentic](), [ai](), [ai-agents](), [ci-cd](), [cloud-native](), [developer](), [developer-experience](), [devops](), [engineering](), [kubernetes](), [microservices](), [tech-debt]() ### AI overview The article presents agentic engineering as the next evolution of software engineering, with AI agents autonomously handling code, tests, pull requests, incidents, vulnerabilities, technical debt, and other software development lifecycle activities. It argues that developer portals can help provide the guidance and guardrails needed to manage the complexity created by DevOps, cloud-native systems, microservices, Kubernetes, GitOps, and extensive CI/CD automation. ### Source excerpt Internal developer portals are changing. Read all about Port's next evolution, the agentic engineering platform. ## 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. ## Internal Developer Portal: How Do I Get Started? DevFeed: [Internal Developer Portal: How Do I Get Started?]() Original publisher: [Read original article]() Author: Sooraj Shah Published: 2026-07-30T10:23:42Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Developer experience](), [Platform Engineering](), [dev-tools](), [Microservice](), [Cloud Native Ecosystem](), [.NET MAUI]() Tags: [catalog](), [cloud-native](), [dev-tools](), [developer-experience](), [developer-portal](), [internal-developer-portal](), [jobs](), [microservices](), [onboarding](), [platform](), [platform-engineering](), [user-stories]() ### AI overview This article explains how platform engineering teams can start an internal developer portal by building a focused minimum viable product. It recommends reducing developer complexity, grounding the portal in real use cases and user stories, gathering stakeholder feedback, and improving the portal iteratively. ### Source excerpt You're using more dev tools that solve different problems, and now your breaking the developer experience. Learn the first steps to get started. [Next page]()