# Developer experience Developer experience (DevEx) is the way an organization's systems, workflows, developer tools, culture, and work environment affect developer productivity and software development. 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. ## 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 ## 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 ## The Real Python Podcast - Episode #310: Performance Engineering: Profiling and Making Apps Fast by Default DevFeed: [The Real Python Podcast - Episode #310: Performance Engineering: Profiling and Making Apps Fast by Default]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-04T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Developer experience](), [Frameworks](), [Microservice](), [Large Language Model]() Tags: [engineering](), [frameworks](), [llms](), [microservices](), [performance](), [podcast](), [profiling](), [python]() ### AI overview This Real Python Podcast episode discusses performance engineering for Python applications with Den Odell, author of "Fast by Default: Practical Performance Engineering." It covers performance budgets, profiling, data-driven decisions, planning for performance, and maintaining speed as systems and user bases grow. ### Source excerpt How do you plan for the performance of your Python applications? What does a performance budget entail, and where should you spend your resources? This week on the show, we speak with Den Odell about his new book "Fast by Default: Practical Performance Engineering." ## A Model Portfolio for cost-efficient AI across the software development lifecycle DevFeed: [A Model Portfolio for cost-efficient AI across the software development lifecycle]() Original publisher: [Read original article]() Author: Praveen Sidda Published: 2026-09-01T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [AI Development](), [AI Models](), [Model Routing](), [sdlc](), [Agile](), [Developer experience](), [Compression](), [Requirements](), [Testing](), [Deployment](), [Code review]() Tags: [agentic-sdlc](), [ai](), [ai-cost-optimization](), [ai-models](), [code-review](), [compression](), [deployment](), [github-copilot](), [llm](), [overhead](), [requirements](), [software-development]() ### AI overview The article examines whether routing software development tasks across a portfolio of AI models can reduce costs compared with using one premium model. It reports that lower-cost models handled well-defined tasks, while premium models were reserved for architecture, implementation, and code review; context compression reduced token usage but risked losing important technical details. ### Source excerpt Image generated by AIWhat one controlled experiment taught me about matching model capability to developer work Topic: Can intelligently routing developer tasks across different AI models outperform relying on a single premium model? In this article, I put that question to the test by mapping software development lifecycle (SDLC) stages to a portfolio of AI models and comparing the outcomes. Motivation As AI becomes embedded throughout the AI-Native Development Lifecycle (AIDLC), an evolution of the traditional Software Development Lifecycle (SDLC), its cost is no longer tied to a single prompt. A single developer task can involve multiple model calls, each carrying source files, conversation history, tool definitions, and generated output. Applying the most capable model to every interaction is straightforward, but it also consumes premium model capacity on tasks that less expensive models can often complete just as effectively. This raises an important question for engineering organizations: How can teams reduce the cost of AI-assisted development without compromising quality, reliability, or the developer experience? My first instinct was to reduce token consumption. Context compression appeared to be the most direct path to lowering inference costs by shortening prompts. Although it reduced token usage, it also introduced risk. Important constraints and technical details could be lost, affecting downstream tasks. Source code, stack traces, and active instructions proved to be especially poor candidates for lossy compression. That experience shifted my focus. The objective was not to process fewer tokens, but to complete developer tasks successfully at a lower overall cost. I then experimented with model allocation. Lower-cost models handled well-defined tasks such as requirements synthesis, planning, routine test generation, deployment artifacts, and final summaries, while premium models were reserved for architecture, implementation, and code review. This appro ## 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. ## 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. ## 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 ## 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. ## 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. ## The best AI artificial intelligence tools for developers DevFeed: [The best AI artificial intelligence tools for developers]() Original publisher: [Read original article]() Author: Liza Sokol Published: 2026-07-30T11:22:11Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Development](), [AI-assisted coding](), [workflow automation](), [Orchestration](), [Tooling](), [Developer experience](), [n8n](), [Claude](), [ChatGPT]() Tags: [agent](), [agentic](), [agentic-ai](), [ai](), [ai-agents](), [ai-tools](), [automation](), [developers](), [orchestration](), [platforms](), [software-development](), [workflows]() ### AI overview This article surveys AI tools for developers across the agentic software development lifecycle, including chat assistants, IDE copilots, coding agents, orchestration platforms, and full-application builders. It explains how development is evolving from assistive AI toward augmentative and agentic workflows, with examples including Claude, ChatGPT, Copilot, Port, n8n, and Zapier. ### Source excerpt Discover the best AI tools for developers to boost productivity, automate workflows, and enhance software development processes. ## 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. ## Port Product Updates: December 2023 DevFeed: [Port Product Updates: December 2023]() Original publisher: [Read original article]() Author: Dudi Elhadad Published: 2026-07-30T10:15:06Z Content type: release Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [dashboards](), [Kubernetes](), [Developer experience](), [configuration](), [API](), [Terraform](), [incident management]() Tags: [api](), [community](), [config](), [dashboards](), [developer-experience](), [incident-management](), [kubernetes](), [product](), [release](), [release-notes](), [terraform](), [updates]() ### AI overview Port's December 2023 product updates introduce engineering scorecard and initiative dashboards, improvements to Kubernetes integration management and visualization, an action-run history widget, new ServiceNow and Terraform Cloud integrations, and a dynamic "Me" table filter. ### Source excerpt 2023 was a breakout year for Port, and these release notes mark the end of a year that was spent making Port better, together with our community. ## When is the right time to adopt an IDP? DevFeed: [When is the right time to adopt an IDP?]() Original publisher: [Read original article]() Author: Trevor Garcia Published: 2026-07-30T10:13:18Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Platform Engineering](), [Developer experience](), [DevOps](), [sdlc](), [Product Management](), [Tooling]() Tags: [article](), [best-practices](), [developer](), [developer-experience](), [developer-portal](), [devops](), [engineering](), [idp](), [internal-developer-portal](), [management](), [organizational](), [platform](), [platform-engineering](), [sdlc](), [software-development](), [tech-stack](), [tooling](), [tools]() ### AI overview This article explains when organizations should consider adopting an internal developer portal (IDP). It identifies scaling challenges such as operational overhead, cognitive overload, duplicated tools, unclear ownership, and inconsistent tooling. It also describes how DevOps, platform engineering, and product management practices can support an IDP that improves developer experience, standardizes tooling, and consolidates organizational knowledge. ### Source excerpt Organizations have to consider a lot of details before adopting an IDP, and it can be hard to know when is the right time. ## Partnerships can keep open source sustainable DevFeed: [Partnerships can keep open source sustainable]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-24T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Open Source](), [Cloudflare](), [JavaScript](), [Developer experience](), [distributed-systems]() Tags: [acquisition](), [cloudflare](), [dev-tools](), [developer-experience](), [developer-tools](), [distributed-systems](), [javascript](), [leaders-of-code](), [open-source](), [partnerships](), [podcast](), [se-stackoverflow](), [se-tech](), [typescript]() ### AI overview Ryan welcomes VoidZero's Evan You and Cloudflare's Dane Knecht to discuss Cloudflare's acquisition of VoidZero, the implications for JavaScript development, and how partnerships can help open-source projects remain maintained and sustainably monetized. ### Source excerpt Ryan welcomes VoidZero's Evan You and Cloudflare's Dane Knecht back to the show to discuss Cloudflare's recent acquisition of VoidZero and what it means for JavaScript development, how partnerships like theirs can help open-source projects stay maintained and sustainably monetized, and how Cloudflare's distributed systems are helping to improve developer experience in Vite and beyond. ## 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 ## What does a platform product manager do? DevFeed: [What does a platform product manager do?]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T08:14:05Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [Product Management](), [Developer experience]() Tags: [architecture](), [developer-experience](), [developer-platform](), [developers](), [engineering](), [internal-developer-platform](), [management](), [organizational](), [platform](), [platform-engineering](), [platforms](), [product](), [product-management](), [product-strategy](), [strategy](), [technical](), [user-research]() ### AI overview This guide explains the role of a Platform Product Manager (PPM) in driving strategy for an Internal Developer Platform. PPMs connect technical platform capabilities with developer needs through user research, feedback loops, roadmaps, stakeholder management, and outcome metrics. The article distinguishes product thinking from project management and describes how continuous prioritization and clear platform boundaries support adoption. ### Source excerpt Platform Product Managers drive internal developer platform strategy, bridging technical capabilities and developer needs through research, roadmaps, and stakeholder management. ## Why platform engineering will eat the world DevFeed: [Why platform engineering will eat the world]() Original publisher: [Read original article]() Author: Kaspar von Grünberg Published: 2026-07-23T08:08:12Z Content type: opinion Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [Developer Platform](), [software-development](), [Software Engineering](), [Developer experience](), [site-reliability-engineering](), [SRE](), [observability](), [Security]() Tags: [developer-experience](), [developer-platform](), [development](), [engineering](), [internal-developer-platform](), [operations](), [platform](), [platform-engineering](), [security](), [software](), [software-development](), [sre]() ### AI overview The article argues that platform engineering is becoming the dominant model for large-scale software development. It describes platforms as structured digital production lines that reduce time to market, consolidate functions such as operations, databases, security, observability, and SRE, and support greater speed, scale, and security. ### Source excerpt Platform engineering is transforming software development, streamlining operations, and redefining roles. Embrace the shift or risk becoming obsolete. ## A framework for integrating AI into platform engineering DevFeed: [A framework for integrating AI into platform engineering]() Original publisher: [Read original article]() Author: Stéphane Teyssier Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Artificial Intelligence](), [Security](), [Model Context Protocol (MCP)](), [shadow AI](), [cursor](), [GitHub Copilot](), [Claude](), [Developer experience](), [Template]() Tags: [ai](), [claude](), [configuration](), [cursor](), [developer-experience](), [github-copilot](), [model-context-protocol](), [platform](), [platform-engineering](), [shadow-ai](), [supply-chain]() ### AI overview This article presents a framework for integrating AI into platform engineering instead of allowing isolated AI silos. It focuses on standardizing AI coding assistants such as GitHub Copilot, Cursor, and Claude through platform templates, organizational prompts, development standards, security requirements, and approved MCP servers. The approach aims to reduce shadow AI, uncontrolled costs, security vulnerabilities, and compliance risks while improving developer experience and operational excellence. ### Source excerpt Avoid AI silos & shadow IT. This 5-stage framework shows how to seamlessly integrate AI into platform engineering for enhanced operations and security. [Next page]()