# Code quality Code quality is the assessment and improvement of software code's characteristics, including correctness, reliability, efficiency, maintainability, readability, and security. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Code review is burning out your best engineers DevFeed: [Code review is burning out your best engineers]() Original publisher: [Read original article]() Author: Ankit Jain Published: 2026-09-18T12:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [ai code review](), [Code review](), [AI-assisted coding](), [engineering-culture](), [Code quality](), [Reverse Engineering]() Tags: [abstraction](), [ai](), [ai-code-review](), [ai-engineering](), [aviator](), [bugs](), [code-quality](), [code-review](), [developer-tools](), [engineering](), [post-contributed](), [reverse-engineering](), [review](), [software-development](), [sponsor-aviator](), [sponsored-post-contributed]() ### AI overview The article argues that widespread AI-assisted coding is overwhelming experienced engineers with larger review queues and shifting their work from writing code to verifying machine-generated output. It explains that AI-generated code often lacks intent and context, can be plausibly wrong or over-engineered, and may ignore repository conventions. ### Source excerpt Every team I talk to has the same problem. Their best engineers, the ones who care most about code quality, The post Code review is burning out your best engineers appeared first on The New Stack. ## 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. ## How Coinbase used Code Connect to guide agents and shrink token costs DevFeed: [How Coinbase used Code Connect to guide agents and shrink token costs]() Original publisher: [Read original article]() Author: Jenny Xie Published: 2026-09-02T20:28:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Model Context Protocol](), [AI-assisted coding](), [Code quality](), [Accessibility](), [Cryptocurrency]() Tags: [accessibility](), [agents](), [code](), [coding](), [coinbase](), [cost](), [cryptocurrency](), [figma](), [mcp](), [mcp-server](), [tokens](), [web]() ### AI overview The Coinbase Design System team evaluated Code Connect in Figma's MCP server with coding agents. The article reports improved adherence to the design system, better code quality, and an average 22.5% reduction in token costs when converting Figma designs into code. ### Source excerpt The Coinbase Design System team put Code Connect to the test against coding agents--and found that it boosted design system adherence while cutting token costs by an average of 22.5%. ## 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. ## 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. ## 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. ## 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 Contributors Blog]() Topics: [AI-assisted coding](), [Kubernetes](), [Open Source](), [Pull Request](), [Code quality]() Tags: [ai](), [ai-assisted-coding](), [kubernetes](), [open-source](), [policy](), [pull-request](), [quality]() ### AI overview This article explains how the Kubernetes community is adapting its maintainership practices to AI-assisted coding. It describes an AI policy requiring contributors to disclose AI assistance, remain responsible for changes, avoid attributing commits to AI, satisfy contributor license agreement checks, and personally respond to review comments. ### 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 ## 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 ## 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. ## 7 steps in a Legacy Modernization Project DevFeed: [7 steps in a Legacy Modernization Project]() Original publisher: [Read original article]() Author: Federico Tomassetti Published: 2026-02-25T06:55:00Z Content type: tutorial Language: en Sources: [Federico Tomassetti]() Topics: [Agile](), [Code quality](), [Code](), [modules]() Tags: [automated](), [business-value](), [code-quality](), [legacy-modernization](), [modernization](), [modules](), [project](), [rpg](), [technical]() ### AI overview This tutorial outlines seven steps for modernizing an RPG-based ERP/MRP system: assess the legacy landscape, define a roadmap, secure buy-in and budget, build the right team and partnerships, execute iterative value-driven increments, manage change, and measure and adapt the modernization effort. ### Source excerpt Embarking on an RPG-based ERP/MRP system modernization is undeniably complex. But it's also highly rewarding when aligned with your business's long-term success. By addressing both the technical and strategic facets, you turn an aging system from a liability into a competitive asset. Let's outline the 7 steps in a legacy modernization project: Step 1: Assess [...] The post 7 steps in a Legacy Modernization Project appeared first on Federico Tomassetti. ## The 3 curves that make a scalable business DevFeed: [The 3 curves that make a scalable business]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-02-10T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Software](), [Code quality](), [Processes](), [Users]() Tags: [automate](), [bugs](), [code-quality](), [documentation](), [engineering](), [revenue](), [scale](), [software](), [standard](), [support](), [test](), [users]() ### AI overview An opinion article explains three growth curves behind a scalable business: users and revenue should grow exponentially, bugs linearly, and support work logarithmically. It argues that retention, automation, code quality, testing, documentation, and streamlined processes help engineering organizations scale. ### Source excerpt You want users and revenue to grow exponentially. Bugs per day to grow linearly. Support work to grow logarithmically. Your goal is to make this happen. ## In praise of the stacked pull request DevFeed: [In praise of the stacked pull request]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-02-04T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [stacked pull requests](), [Code review](), [Code quality](), [Development](), [Git]() Tags: [code-quality](), [code-review](), [git](), [merge](), [pull-request](), [queue](), [soc2](), [stacked-pull-requests](), [team](), [velocity]() ### AI overview An opinion piece argues that stacked pull requests help teams work on larger or dependent changes without creating oversized, difficult-to-review pull requests. It presents small, independent, testable pull requests as units of work that can be reviewed and merged incrementally. ### Source excerpt How fast you do code review is how fast you do everything. Your PR queue is the leading indicator of team velocity. Stacked PRs can help. ## How Caseware Unified 700+ Projects in an Nx Monorepo DevFeed: [How Caseware Unified 700+ Projects in an Nx Monorepo]() Original publisher: [Read original article]() Author: Philip Fulcher Published: 2026-01-16T00:00:00Z Content type: article Language: en Sources: [Juri Strumpflohner]() Topics: [Platform Engineering](), [Development](), [Angular](), [migration](), [Code quality]() Tags: [angular](), [cache](), [code-quality](), [customer-story](), [migration](), [monorepo](), [platform-engineering]() ### AI overview This case study describes how Caseware consolidated more than 700 projects into a unified monorepo using Nx. Its platform engineering team began with shared Angular libraries in a frontend monorepo, then incrementally moved backend applications into the shared workspace. The supplied summary reports a 93% cache hit rate and 181 days of compute saved weekly. ### Source excerpt Discover how Caseware unified 700+ projects into one monorepo, achieved a 93% cache hit rate, and saves 181 days of compute weekly with the Nx platform. [Next page]()