# Code quality Published articles for Code quality. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Using static analysis in Python, JavaScript and more to make your system safer DevFeed: [Using static analysis in Python, JavaScript and more to make your system safer]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-07-26T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [JavaScript](), [Code quality](), [Python](), [Code review](), [Web Development](), [React]() Tags: [analysis](), [best-practices](), [code](), [code-quality](), [engineering](), [eslint](), [formatting](), [frontend](), [infrastructure](), [javascript](), [lint](), [news](), [python]() ### AI overview This article describes how Khan Academy uses custom linters and static analysis across JavaScript, Python, and Kotlin to prevent known bad patterns, keep duplicated logic or shared interfaces synchronized, and enforce frontend migration practices. ### Source excerpt By Kevin Dangoor "Linting" source code to look for errors is nothing new (the original "lint" tool turned ... Read more ## SwiftUI Agent Skill: Install and use with AI coding tools DevFeed: [SwiftUI Agent Skill: Install and use with AI coding tools]() Original publisher: [Read original article]() Author: Antoine van der Lee Published: 2026-09-14T11:49:33Z Content type: article Language: en Sources: [SwiftLee]() Topics: [SwiftUI](), [Agent Skill](), [ai-coding](), [Code quality](), [context window](), [Command-line interface]() Tags: [agent-skill](), [ai-coding](), [ai-development](), [cli](), [code-quality](), [context-window](), [installation](), [open-source](), [swiftui]() ### AI overview This article introduces an open-source SwiftUI Agent Skill for AI coding tools. The skill helps agents build or refactor SwiftUI views, improve generated code quality, and load focused references only when they are relevant to a task. It also explains installation, updates, and compatibility considerations for the skills command-line tool. ### Source excerpt A SwiftUI Agent Skill that helps you build better views or refactor existing ones. It's the reality we're in today, and I honestly can't live without it anymore myself. Several skills helped me improve the code quality produced by agents, and I'm happy to introduce you to my open-source skill for SwiftUI. Before reading this ... -> The post SwiftUI Agent Skill: Install and use with AI coding tools appeared first on SwiftLee. ## Putting models to the secure coding test: Plan vs default mode DevFeed: [Putting models to the secure coding test: Plan vs default mode]() Original publisher: [Read original article]() Author: Kennedy Toomey Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Datadog Security Labs]() Topics: [AI-assisted coding](), [Application Security](), [code security](), [Code quality](), [Vulnerabilities](), [Claude](), [cursor](), [codex]() Tags: [application-security](), [claude](), [code-quality](), [code-security](), [codex](), [command-line](), [cursor](), [supply-chain](), [vulnerabilities]() ### AI overview This article describes an experiment comparing plan mode with default mode for secure code generation. The author tested Sonnet 5, Composer 2.5, and GPT 5.5 through Claude, Cursor, and Codex, using security and code-quality analysis to assess the results. ### Source excerpt We tested Sonnet 5, Composer 2.5, and GPT 5.5 in plan mode and default mode to see whether plan mode produces measurably more secure code. ## Extreme Programming remains relevant for building software teams in 2026 DevFeed: [Extreme Programming remains relevant for building software teams in 2026]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-08-18T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [Programming](), [Development](), [Refactoring](), [Test-driven development](), [pair\_programming](), [Code review]() Tags: [code](), [code-quality](), [development](), [pair-programming](), [programming](), [redesign](), [tdd]() ### AI overview The author revisits Kent Beck's Extreme Programming after experiencing frustration with Scrum and the pressures facing modern engineering teams. The article argues that XP remains relevant because it combines five values, 14 principles, and numerous practices aimed at improving both software and team life, rather than reducing the methodology to TDD and pair programming alone. ### Source excerpt A method created 30 years ago is still your best guide for building software ## Agentic Code Quality DevFeed: [Agentic Code Quality]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-08T14:31:15Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Code quality](), [Testing](), [mutation-testing](), [Code review]() Tags: [agentic](), [code-quality](), [code-review](), [mutation-testing](), [testing]() ### AI overview The article argues that code quality for agent-generated software depends on constraints enforced by the surrounding harness, environment, and operating system. It describes quality gates including unit, property, and acceptance tests, mutation testing, code-quality metrics, and checks on proposed changes, while noting risks from autonomy, ambiguity, brittle environments, nondeterministic builds, missing permissions, and weak tests. ### Source excerpt Quality now depends on the constraints you set around your agents. ## Agentic Code Quality DevFeed: [Agentic Code Quality]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-08T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Code quality](), [Testing](), [mutation-testing](), [engineering-culture]() Tags: [code-quality](), [mutation-testing](), [testing](), [tests](), [time]() ### AI overview The article argues that code quality for agent-generated software depends on quality gates and deterministic constraints enforced through the surrounding harness, environment, and operating system. It discusses unit, property, acceptance, and mutation testing, along with code-quality metrics and checks on the scope and safety of proposed changes. ### Source excerpt Quality now depends on the constraints you set around your agents. ## Better code, fewer tokens: The benefits of Code Connect in MCP DevFeed: [Better code, fewer tokens: The benefits of Code Connect in MCP]() Original publisher: [Read original article]() Author: Tom Weightman Published: 2026-08-05T18:16:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [MCP Server](), [Model Context Protocol](), [AI-assisted coding](), [agentic-coding](), [Front end](), [Code quality]() Tags: [agentic-coding](), [agents](), [case-study](), [code](), [code-quality](), [coding](), [design](), [figma](), [mcp]() ### AI overview Figma's Code Connect gives coding agents production-component context through the Figma MCP server. Evaluations found shorter task durations, higher code quality, and lower token usage when Code Connect templates were available. ### Source excerpt When going from design to code, agents lack the context of your production components. With Code Connect in Figma's MCP, they get that context. We measured its impact on token usage, task duration, and code quality. ## 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 ## Qodana 2026.2: More Security, Better Coverage, Less Configuration DevFeed: [Qodana 2026.2: More Security, Better Coverage, Less Configuration]() Original publisher: [Read original article]() Author: Kerry Beetge Published: 2026-07-29T13:47:03Z Content type: release Language: en Sources: [Kotlin : A concise multiplatform language developed by JetBrains | The JetBrains Blog]() Topics: [Security, Privacy and Abuse Prevention](), [Pull Request](), [configuration](), [Cryptography](), [PHP](), [.NET](), [Go Language]() Tags: [2026-2](), [code-quality](), [compliance](), [cryptography](), [ide](), [laravel](), [pull-request](), [release](), [security]() ### AI overview Qodana 2026.2 adds pull-request coverage insights, automatic coverage-report detection, and expanded security and compliance checks. ### Source excerpt Qodana 2026.2 makes it easier for development teams to act on code quality, security, and compliance findings throughout the development workflow. This release introduces clearer code coverage insights for pull requests, highlights uncovered new lines directly in the IDE, and automatically detects coverage reports in common project locations - reducing the configuration required to get [...] ## How to clean up AI-generated code with Fallow DevFeed: [How to clean up AI-generated code with Fallow]() Original publisher: [Read original article]() Author: David Omotayo Published: 2026-07-21T13:00:28Z Content type: tutorial Language: en Sources: [LogRocket Blog]() Topics: [Code quality](), [AI-assisted coding](), [TypeScript](), [JavaScript](), [Refactoring]() Tags: [ai](), [ai-coding](), [analysis](), [article](), [blog](), [code](), [code-quality](), [coding](), [complexity](), [dev](), [developer](), [development](), [how-to](), [javascript](), [learn](), [refactoring](), [software](), [tool](), [typescript](), [uncategorized](), [workflow]() ### AI overview This tutorial explains how to use Fallow, a TypeScript and JavaScript code analysis tool, to identify unused code, duplicate logic, and complexity in AI-generated code. It also covers setting up Fallow, interpreting its reports, and integrating automated code-quality checks into an AI-assisted development workflow. ### Source excerpt Learn how to use Fallow to analyze AI-generated code, detect dead code, duplicate logic, and complexity issues, and integrate automated code quality checks into your AI-assisted development workflow. The post How to clean up AI-generated code with Fallow appeared first on LogRocket Blog. ## Context engineering with Dex Horthy DevFeed: [Context engineering with Dex Horthy]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-07-15T16:08:59Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [AI-assisted coding](), [Large Language Model](), [AI Agent](), [Code quality]() Tags: [ai](), [ai-agent](), [code-quality](), [context-engineering](), [langchain](), [llm]() ### AI overview Dex Horthy discusses context engineering for working around LLM context limitations in AI-assisted software development. The conversation covers context engineering, harness engineering, loop engineering, software factories, and automating more of the software development lifecycle while maintaining code quality. ### Source excerpt Dex Horthy explains why context engineering is key to building more effective AI-assisted software without sacrificing code quality. ## RPG Metrics: A Free-Beta Code Quality Scanner for IBM i RPG DevFeed: [RPG Metrics: A Free-Beta Code Quality Scanner for IBM i RPG]() Original publisher: [Read original article]() Author: Federico Tomassetti Published: 2026-07-15T09:22:39Z Content type: opinion Language: en Sources: [Federico Tomassetti]() Topics: [Code quality](), [Software](), [ibm]() Tags: [code-processing](), [code-quality](), [free](), [maintainability](), [platform](), [rpg]() ### AI overview This commentary reviews RPG Metrics, a code quality scanner from KDP Software for IBM i RPG source. The tool measures metrics including cyclomatic complexity and maintainability, identifies problematic files and refactoring points, and is free while in beta. The article compares it with enterprise-oriented static-analysis products. ### Source excerpt KDP Software has worked the IBM midrange platform since 1985. Their new RPG Metrics tool measures cyclomatic complexity, maintainability, and RPG Free convertibility across your RPG source. It is free during beta, it took me minutes to get a number out of it, and when I had questions the founder answered them himself. The post RPG Metrics -- The IBM i Tool the Big Vendors Wouldn't Sell You appeared first on Federico Tomassetti. ## Experiences with local models for coding DevFeed: [Experiences with local models for coding]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-07-08T11:57:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [AI-assisted coding]() Tags: [agentic](), [agentic-coding](), [code-quality](), [coding](), [hardware](), [llms](), [local](), [models](), [review](), [speed](), [tool](), [user-experience]() ### AI overview An account of evaluating local small language models for agentic coding on developer machines. It describes a viability funnel covering RAM, speed, tool calling, correctness, context handling, task complexity, and code quality. ### Source excerpt Birgitta Böckeler now reports on her recent experiences trying local LLMs for coding. She compares them using two standard tasks, and tries out the most promising model for day-to-day use. more... ## The software engineering war DevFeed: [The software engineering war]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-07-07T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [Software Engineering](), [Large Language Model](), [Code quality](), [Development]() Tags: [code](), [code-quality](), [engineering](), [llms](), [software-engineering]() ### AI overview The article discusses a recurring conflict in software teams between builders who prioritize rapid product delivery and keepers who prioritize well-built systems and code quality. It argues that the rise of LLMs has intensified this disagreement. ### Source excerpt Most of us picked a side and won't budge. Which one are you on? ## Open source maintainership in the age of AI DevFeed: [Open source maintainership in the age of AI]() Original publisher: [Read original article]() Author: CLA enforcement Published: 2026-06-26T18:00:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [AI-assisted coding](), [Open Source](), [Code quality](), [Pull Request](), [software-development](), [coding]() Tags: [agents](), [ai](), [ai-assisted-coding](), [code-quality](), [kubernetes](), [open-source](), [policy](), [pull-request](), [software-development]() ### AI overview This article explains how the Kubernetes community is adapting open-source maintainership to AI-assisted coding. It describes an AI policy requiring disclosure of AI use, preserving human accountability, maintaining code quality, and enforcing contributor license agreement checks and human participation in review. ### Source excerpt AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usage. The AI policy helps steer the conversation around the project's stance on AI and provides a clear signal to contributors on how to use these tools responsibly. Kubernetes AI policy The Kubernetes project has established clear guidelines for AI-assisted contributions that balance innovation with accountability. These policies are designed to maintain code quality and ensure human oversight while acknowledging that AI tools can be valuable aids in the development process. Transparency first Contributors must disclose when AI tools have been used to assist with a pull request. A simple statement in the PR description such as "This PR was written in part with the assistance of generative AI" is sufficient. This transparency helps reviewers understand the context and apply appropriate scrutiny. Human accountability While AI tools can assist, the human contributor remains fully responsible for every change. The policy explicitly prohibits: Listing AI as a co-author on commits Using AI co-signing on commits Adding trailers like "assisted-by" or "co-developed" that attribute work to AI This isn't about diminishing AI's role as a tool--it's about maintaining clear accountability. If something breaks, there needs to be a human who understands why and can fix it. CLA enforcement for co-authors The CNCF provides a tool for verifying the ## How Dropbox uses MCP and Dash to close the design-to-code security gap DevFeed: [How Dropbox uses MCP and Dash to close the design-to-code security gap]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra Published: 2026-06-12T18:00:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [Model Context Protocol](), [code security](), [Security](), [Code review](), [Artificial Intelligence](), [Language models]() Tags: [agentic-ai](), [agents](), [ai](), [code-quality](), [code-security](), [developer](), [developer-tools](), [engineering](), [mcp](), [review](), [security](), [workflow]() ### AI overview Dropbox built an agentic AI system combining Model Context Protocol, foundational large language models, and Dash to retrieve relevant threat models during code review. The system evaluates whether code changes align with documented security requirements and helps expose gaps between design decisions and implementation. ### Source excerpt Using an agentic AI system to surface threat models during code review and spot gaps between security requirements and implementation. ## Angular in 2026: Mid-Year Reality Check, Signals, and AI Code Quality! DevFeed: [Angular in 2026: Mid-Year Reality Check, Signals, and AI Code Quality!]() Original publisher: [Read original article]() Author: Angular Published: 2026-06-05T04:23:01Z Content type: article Language: en Sources: [Angular]() Topics: [Angular](), [Artificial Intelligence](), [Model Context Protocol (MCP)](), [Tooling](), [Code quality]() Tags: [ai](), [angular](), [angular-newsletter](), [angular-weekly](), [code-quality](), [mcp](), [tooling](), [videos]() ### AI overview This mid-year Angular community roundup reviews 2026 framework predictions and highlights discussions about zoneless applications, Signals, AI-assisted tooling using the Model Context Protocol, AI-generated Angular code quality, Angular v21, and modern learning resources. ### Source excerpt We are already halfway through 2026 📅 That means it's the perfect time to look back at the big framework predictions made at the start of the year and see which trends are actually dominating our codebases. From checking our apps for a zoneless future to auditing AI-generated code, this week's community round-up has you covered. Dive into these expert insights and level up your stack: Predictions for Angular in 2026: Were They Right? (Spanish) Now that we are midway through the year, it's time to check in on our expectations! This video features an insightful conversation mixed with a massive panel of experts -- including Alejandro Cuba Ruiz @zorphdark, Eduardo Roth @eduardoRoth, Alfredo Pérez, Nicolás Molina @nicobytes, Vanessa Marely, and Ricardo Chavarría. Together, they look back at their original visions for 2026 to see which predictions have already become reality. Watch the round-table discussion: https://youtu.be/xzaXVSrzuTs From Commands to Conversations: AI-Assisted Tooling Sonu Kapoor @SonuKapoor1978 explores how the Model Context Protocol (MCP) is fundamentally transforming Angular development this year. Discover how tooling has evolved to reason about your workspace, moving far beyond basic CLI scaffolding. Read the full analysis: https://www.codemag.com/Article/2601071/From-Commands-to-Conversations-How-AI-Assisted-Tooling-Is-Transforming-Angular-Development Your AI is Coding Angular Wrong & Quick Angular v21 Recap Muhammad Ahsan Ayaz @codewith_ahsan drops two incredibly valuable videos. First, a rapid-fire refresher on everything that landed in Angular v21. Second, a crucial look at why standard AI models frequently still write outdated Angular code, and how you can fix it. Watch the v21 Recap: https://youtu.be/pVTiAZgEslw Watch "Your AI is Coding Wrong": https://youtu.be/tCROVJMcAF8 Modern Angular Free Course Looking for a comprehensive, up-to-date guide to the framework's modern features as of 2026? Loiane Groner @loiane has released an entirely free ## Beyond code generation: rethinking engineering productivity in the age of AI agents DevFeed: [Beyond code generation: rethinking engineering productivity in the age of AI agents]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura Published: 2026-05-28T18:00:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [code productivity](), [AI Development](), [Software Engineering](), [ci](), [pull-requests]() Tags: [agents](), [ai](), [ci](), [code-generation](), [code-quality](), [developer-productivity](), [developer-tools](), [developer-velocity](), [productivity](), [pull-requests](), [software-development](), [workflows]() ### AI overview Dropbox describes a shift from AI coding tools that accelerate implementation to agentic systems that execute scoped tasks. The article explains how increased code throughput creates pressure on review, CI, validation, release coordination, and production operations, requiring broader changes to engineering systems and workflows. ### Source excerpt How Dropbox is moving from AI tools that assist engineers to agentic systems that can execute scoped tasks, and how we're building platforms to support those workflows. ## The test suite as a regression sensor DevFeed: [The test suite as a regression sensor]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-05-27T15:01:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [AI-assisted coding](), [coding](), [Code quality](), [React](), [TypeScript](), [Front end](), [Back end](), [mutation-testing](), [Web](), [dashboards](), [data]() Tags: [agents](), [ai](), [apis](), [backend](), [code](), [code-quality](), [coding-agents](), [data](), [frontend](), [react](), [sensor](), [skip-home-page](), [testing](), [typescript](), [web]() ### AI overview This article examines how maintainability sensors can help coding agents and developers detect declining internal code quality and support self-correction. It discusses functional correctness, architectural fitness, maintainability, codebase structure, and the use of test suites as regression sensors, including mutation testing. The examples use an internal analytics dashboard built with TypeScript, NextJS, and React, with a backend that combines data from external APIs. ### Source excerpt Birgitta Böckeler finishes her post on sensors for coding agents by examining the role of a test suite as a regression sensor, focusing on the role mutation testing can play. more... ## Outcomes we want to see from AI at Medium Engineering DevFeed: [Outcomes we want to see from AI at Medium Engineering]() Original publisher: [Read original article]() Author: Jacob Bennett Published: 2026-05-26T15:33:52Z Content type: article Language: en Sources: [Medium]() Topics: [Artificial Intelligence](), [Code quality](), [Testing](), [Code review](), [migration]() Tags: [ai](), [ai-agent](), [code-quality](), [migration](), [review](), [software-development](), [software-engineering](), [testing]() ### AI overview This Medium Engineering article describes the outcomes Medium wants from AI adoption: reducing repetitive work, improving test coverage and maintainability, accelerating learning across unfamiliar parts of the stack, enabling broader technical contributions, and supporting more rigorous planning and risk reduction. ### Source excerpt This was originally written and published by Christophe Spy (Director of Engineering, Medium) as an internal Medium Eng post. Desired outcomes (what "good" looks like) 🙏🏻 These are the kinds of changes we want to see if AI is going well at Medium. Less drudgery, more leverage Engineers use AI to remove repetitive, low-leverage work (e.g. tedious refactors, boilerplate, large test suites), not just to ship more tickets. We see examples like: "We took a part of the codebase that was basically untestable and, with AI, made it testable and added meaningful coverage in days instead of months." "We cleaned up a legacy area we had been afraid to touch for years, using AI for the mechanical work." Faster learning and broader technical range Engineers use AI to ramp on new languages, frameworks, and parts of the stack much more quickly. Engineers can explore unfamiliar areas safely by asking AI to explain tradeoffs, pitfalls, risks, and how the system uses this areas of the code. We see fewer cases where work stalls or we avoid implementing things because "nobody knows this code anymore". Frontend/mobile engineers are able to make safe, reviewable backend changes with AI's help (plus a review from a backend engineer). Teams can ship more end-to-end features without long handoffs. Higher technical ambition, not just more speed Teams take on work that used to be "too expensive": Meaningful test coverage in critical areas. Pieces of M2 migration or other large refactors. Structural cost savings. Incident/action items evolve from "add an alert" to "Let's improve tests here". Engineers use AI pre-work to explore edge cases and tradeoffs (design rigor). Better planning, reasoning, and risk reduction: Engineers use AI to think through complex scenarios before building, exploring edge cases, stress-testing assumptions, and comparing multiple approaches, and better understand the pros and cons of each approach Design docs become more rigorous as AI is used to challenge thinking and l ## Announcing etcd 3.7.0-beta.0 DevFeed: [Announcing etcd 3.7.0-beta.0]() Original publisher: [Read original article]() Author: SIG-etcd Leads Published: 2026-05-20T00:00:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [Databases](), [Remote Procedure Call (RPC)](), [Kubernetes](), [Refactoring](), [Code quality]() Tags: [availability](), [code-quality](), [database](), [kubernetes](), [latency](), [refactoring](), [release](), [rpc]() ### AI overview The article announces the first beta release of etcd v3.7.0, introducing the RangeStream RPC for processing large result sets in chunks. It also describes legacy-component cleanup, the removal of v2store, and expected upgrade compatibility issues. ### Source excerpt SIG-Etcd announces the availability of the first beta release of etcd v3.7.0. This new version of the popular distributed database and key Kubernetes component includes the long-requested RangeStream feature, as well as a refactoring and cleanup of multiple legacy components and interfaces. v3.7 will deliver improved security, better operational reliability, and an improved experience for working with large resultsets. First, however, the project needs users to test the beta. You can find v3.7.0-beta.0 here: Source code Binaries Official container images Please try it out and report issues in the etcd repo. This beta also determines the EOL of version 3.4. RangeStream In etcd v3.6 and earlier, it is challenging to work with requests that return large resultsets. The client or requesting application is forced to wait for the full result set, leading to unpredictable latency and memory usage. The RangeStream RPC lets calling applications accept result sets in chunks, reducing latency and making buffering memory usage more predictable. Much of the work on RangeStream was done by a relatively new contributor to etcd, Jeffrey Ying, a software engineer at Google. New contributors can have a substantial impact on etcd development. "I've always been fascinated by database internals, and building RangeStream was a great opportunity to solve a bottleneck we were hitting in production with Kubernetes. It was the perfect opportunity to collaborate across projects and improve the ecosystem as a whole. Jumping into etcd as a new contributor had a bit of a learning curve, but the community is incredibly welcoming. The leads were very receptive to my ideas and helped me iterate quickly, while maintaining the project's high bar for reliability and code quality," said Jeffrey. Instructions on how to use RangeStream in gRPC calls and in etcdctl can be found in the etcd documentation. Users should try it out for their own applications. Removal of v2store The last vestiges of etcd v2sto ## How Ramp engineers accelerate code review with Codex DevFeed: [How Ramp engineers accelerate code review with Codex]() Original publisher: [Read original article]() Published: 2026-05-20T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Code review](), [codex](), [Artificial Intelligence](), [AI Development](), [Code quality](), [Command-line interface](), [Tooling](), [incident](), [Concurrency]() Tags: [agentic](), [cli](), [code-quality](), [codex](), [concurrency](), [gpt](), [incident](), [pull-request](), [review](), [software-development](), [tooling]() ### AI overview Ramp engineers use Codex with GPT-5.5 to accelerate code review, improve software development velocity and code quality, and develop internal agentic tooling. Codex provides substantive pull request feedback in minutes and supports both CLI and app-based workflows. ### Source excerpt How Ramp engineers use Codex with GPT-5.5 to review code and ship improvements, allowing them to get substantive feedback in minutes instead of hours. ## AutoScout24 scales engineering with AI-powered workflows DevFeed: [AutoScout24 scales engineering with AI-powered workflows]() Original publisher: [Read original article]() Published: 2026-05-12T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [codex](), [ChatGPT](), [AI Strategy](), [Code](), [Code quality](), [Software]() Tags: [ai](), [ai-adoption](), [ai-tools](), [canada](), [chatgpt](), [code-quality](), [codex](), [coding](), [development](), [europe](), [learn](), [productivity](), [software](), [speed](), [strategy](), [usability]() ### AI overview AutoScout24 Group reports using ChatGPT across the organization and Codex in engineering, data, and product workflows. The company says this approach enabled approximately 2,000 employees, supported around 1,000 builder roles, accelerated development cycles from weeks to days, and improved productivity and code quality. ### Source excerpt Learn how AutoScout24 Group uses Codex and ChatGPT to speed development cycles, improve code quality, and expand AI adoption. ## Agentic code workflows with Nick Tune DevFeed: [Agentic code workflows with Nick Tune]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-03-26T16:01:37Z Content type: article Language: en Sources: [Tech World With Milan Newsletter]() Topics: [agentic-coding](), [ai-coding](), [Claude Code](), [Code quality](), [code reviews](), [Test-driven development](), [Testing](), [Command-line interface](), [Domain-driven design (DDD)](), [migration](), [Requirements](), [Continuous Delivery (CD)]() Tags: [agentic](), [agentic-coding](), [agents](), [ai-coding](), [ai-tools](), [best-practices](), [claude-code](), [cli](), [code-quality](), [code-reviews](), [continuous-delivery](), [domain-driven-design](), [migration](), [requirements]() ### AI overview An interview with Nick Tune about advanced agentic coding workflows. It covers planning requirements, modeling development as a tested state machine, implementing features with Claude Code and guardrails, enforcing architecture rules deterministically, reviewing pull requests, applying TDD, and using custom tooling. ### Source excerpt Most engineers who talk about AI productivity talk in abstractions. [Next page]()