# software-development Published articles for 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. ## Why human oversight is shifting from writing code to defining requirements DevFeed: [Why human oversight is shifting from writing code to defining requirements]() Original publisher: [Read original article]() Author: Naseeb Ahmed Mian Published: 2026-09-17T13:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [Requirements](), [AI Agent](), [Code](), [Availability]() Tags: [agent](), [agents](), [ai-agents](), [ai-engineering](), [andela](), [automated](), [availability](), [code](), [pipeline](), [post-contributed](), [requirements](), [review](), [software-development](), [sponsor-andela](), [sponsored-post-contributed](), [tests]() ### AI overview The article argues that human oversight of AI-assisted software development should focus on defining and reviewing requirements, not only checking whether generated code conforms to them. It illustrates the risk with a flawed availability-related requirement that passed specification review, generated six passing tests, traceability checks, and automated QA while violating the feature's intended outcome. ### Source excerpt This walks through the pipeline our agents operate inside--from a recorded scoping meeting through unit specs, spec review, generated code, The post Why human oversight is shifting from writing code to defining requirements appeared first on The New Stack. ## Zed launches Delta public beta for collaborative code review with developers and coding agents DevFeed: [Zed launches Delta public beta for collaborative code review with developers and coding agents]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-16T19:45:30Z Content type: article Language: en Sources: [The New Stack]() Topics: [GitHub](), [AI-assisted coding](), [pull-requests](), [Developer Platform](), [Tooling]() Tags: [ai](), [ai-agents](), [beta](), [coding-agents](), [collaboration](), [developer](), [developer-tools](), [github](), [pull-requests](), [review](), [software-development]() ### AI overview Zed has announced the public beta of Delta, a collaborative environment where developers and coding agents work, review, and revise code together in shared threads rather than pull requests. The article presents Delta as a response to the volume of agent-generated code and limitations of the pull-request model. ### Source excerpt Something of a consensus has emerged from the developer fraternity in 2026 -- GitHub, a platform built substantively for human The post "Everyone's in a race to replace GitHub": Zed launches Delta because agents made pull requests obsolete appeared first on The New Stack. ## AI Is Changing How We Code. It's Also Changing How We Learn. DevFeed: [AI Is Changing How We Code. It's Also Changing How We Learn.]() Original publisher: [Read original article]() Author: James Rempt Published: 2026-09-16T14:00:09Z Content type: opinion Language: en Sources: [Visual Studio Blog]() Topics: [Artificial Intelligence](), [Development](), [AI Development](), [Software]() Tags: [ai](), [code](), [developers](), [development](), [learning](), [software](), [software-development](), [visual-studio]() ### AI overview The article argues that AI tools are changing software development by helping developers write and understand code, troubleshoot problems, learn frameworks, and get answers quickly. It also emphasizes the continuing value of learning directly from other developers through practical education, hands-on discussion, and professional networking at events such as VSLive!. ### Source excerpt AI is changing software development at a pace no one can ignore. Developers now have powerful AI tools that can help us write code, understand unfamiliar codebases, troubleshoot problems, learn new frameworks, and get answers in seconds. That is an incredible opportunity. But as these tools become more capable, I believe something else is becoming more valuable [...] The post AI Is Changing How We Code. It's Also Changing How We Learn. appeared first on Visual Studio Blog. ## What to Expect at the Zephyr Project Meetup (September 19, 2026) - Buenos Aires, Argentina DevFeed: [What to Expect at the Zephyr Project Meetup (September 19, 2026) - Buenos Aires, Argentina]() Original publisher: [Read original article]() Author: Susan Remmert Published: 2026-09-15T22:12:26Z Content type: article Language: en Sources: [Zephyr Project]() Topics: [Embedded Systems](), [Open Source](), [Programming](), [amazon]() Tags: [2026](), [amazon](), [blog](), [community](), [embedded](), [embedded-systems](), [event](), [events](), [meetup](), [open](), [open-source](), [programming](), [software-development](), [zephyr]() ### AI overview The article announces an in-person Zephyr Project Meetup in Buenos Aires on September 19, 2026, organized by Iomico. It describes the event's audience, schedule, technical presentations, and a talk on integrating Amazon Sidewalk's closed protocol into a Zephyr application. ### Source excerpt The Zephyr Project community is heading to Buenos Aires, Argentina! Join us on Saturday, September 19, 2026, for an in-person Zephyr Project Meetup organized by Iomico. The event is open to developers, engineers, contributors, students, technology professionals, and anyone interested in open source, embedded systems programming, and software development. Whether you are an experienced Zephyr developer or are just beginning to explore embedded technologies, you are welcome to join. ## What to Expect at the Zephyr Project Meetup (September 19, 2026) - Buenos Aires, Argentina DevFeed: [What to Expect at the Zephyr Project Meetup (September 19, 2026) - Buenos Aires, Argentina]() Original publisher: [Read original article]() Author: Susan Remmert Published: 2026-09-15T22:12:26Z Content type: news Language: en Sources: [Zephyr Project]() Topics: [Embedded Systems](), [Open Source](), [Programming](), [Development]() Tags: [2026](), [blog](), [community](), [developers](), [embedded](), [embedded-systems](), [event](), [events](), [meetup](), [open-source](), [programming](), [software-development]() ### AI overview The Zephyr Project community will hold an in-person meetup in Buenos Aires on September 19, 2026, organized by Iomico. The event includes networking and technical presentations on Zephyr-based products, subsystems, development experiences, and integrating Amazon Sidewalk into a Zephyr application. ### Source excerpt The Zephyr Project community is heading to Buenos Aires, Argentina! Join us on Saturday, September 19, 2026, for an in-person Zephyr Project Meetup organized by Iomico. The event is open to developers, engineers, contributors, students, technology professionals, and anyone interested in open source, embedded systems programming, and software development. Whether you are an experienced Zephyr developer or are just beginning to explore embedded technologies, you are welcome to join. ## How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests DevFeed: [How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests]() Original publisher: [Read original article]() Author: Umair Mirza Published: 2026-09-14T22:23:01Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Application Security](), [Security](), [Vulnerabilities](), [Secret Scanning](), [Git](), [pull-requests](), [ci](), [Command-line interface](), [.NET](), [Python](), [coding]() Tags: [application-security](), [ci](), [cli](), [code](), [devsecops](), [git](), [net](), [pull-requests](), [python](), [sast](), [secret-scanning](), [security](), [security-vulnerabilities](), [software-development](), [testing](), [vulnerabilities]() ### AI overview This tutorial explains how to shift security checks earlier in development by running SAST locally with Git pre-commit hooks. It demonstrates using the DevSkim CLI to detect insecure coding patterns, pairing it with Gitleaks for dedicated secret scanning, validating the setup with an intentional failure, and enforcing the checks in CI. ### Source excerpt Security reviews are most effective when developers receive feedback while the code is still fresh in their minds. Waiting until a pull request, CI build, or penetration test to find exposed credentia ## Your AI coding spend bought 25% more output. Duplication rose 81%. DevFeed: [Your AI coding spend bought 25% more output. Duplication rose 81%.]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-14T14:39:14Z Content type: article Language: en Sources: [The New Stack]() Topics: [ai-coding](), [Claude Code](), [cursor](), [datasets]() Tags: [agentic](), [ai](), [ai-coding](), [ai-impact](), [ai-operations](), [ai-strategy](), [ai-tools](), [claude-code](), [coding](), [contributed](), [contributed-octopus-deploy](), [cursor](), [dataset](), [software-development]() ### AI overview The article examines the return on investment from AI coding tools. It reports that heavy users gained 25% over their previous velocity, while code duplication rose 81%, and argues that output measures such as lines of code, pull requests, and feature counts do not reliably represent business value. ### Source excerpt Since they arrived on the scene, a great swathe of the software industry has pinned its hopes on AI tools, The post Your AI coding spend bought 25% more output. Duplication rose 81%. appeared first on The New Stack. ## On-Device AI Series (Part 5): LiteRT-LM DevFeed: [On-Device AI Series (Part 5): LiteRT-LM]() Original publisher: [Read original article]() Author: Oğuzhan Aslan Published: 2026-09-14T05:59:12Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [LiteRT](), [On-device AI](), [Large Language Model](), [Inference](), [Streaming](), [GPU](), [LLM evaluation / benchmarking](), [PyTorch]() Tags: [ai](), [android](), [android-development](), [benchmarking](), [gpu](), [inference](), [litert](), [llama](), [llm](), [multimodal](), [on-device](), [on-device-ai](), [programming](), [pytorch](), [qwen](), [software-development](), [streaming]() ### AI overview This tutorial explains LiteRT-LM for running large language models on-device. It covers the Engine/Session API, streaming output, system prompts, tool calling, multimodal inputs, thinking mode, and CPU-versus-GPU benchmarking. The article also discusses tradeoffs involving privacy, network independence, latency, memory, sampling configuration, and model capability compared with cloud APIs. ### Source excerpt Put your phone in airplane mode. Open the app, type a question, and watch the answer arrive one token at a time -- no spinner waiting on a network round-trip, no API key, no per-token bill, and nothing you typed ever leaving the device. LiteRT-LM removes the genuinely hard parts of running an LLM on-device -- KV-cache management, token streaming, backend selection -- but it doesn't remove your job so much as relocate it. What's left on your plate is a short, specific list: sizing a combined input+output token budget, owning your own sampling defaults, hand-building system prompts and tool calling out of raw text, and one native-library collision that presents as a SIGSEGV rather than a build error. Know those going in and the API itself is a clean three-step pattern. We'll get there in that order: Why you'd choose this runtime and what it costs you versus the cloud. The Engine/Session model you need to read the code at all. Real implementation samples -- streaming, system prompts and tool calling, multimodal inputs, thinking mode, and CPU-vs-GPU benchmarking. The anti-patterns to avoid. A developer-friendliness rating on the same rubric as Parts 1-4. Why Use LiteRT-LM? You reach for LiteRT-LM instead of hand-rolling generation on top of raw LiteRT when: You need multi-turn conversation, not single-shot inference -- session state and KV-cache bookkeeping are handled for you, and resetting a conversation is a session swap, not a model reload. You need streaming output -- token-by-token delivery for a responsive chat UI, instead of a blocking call that returns everything at once. You're choosing between CPU and GPU per device -- the explicit backend parameter turns that into a runtime decision instead of a build-time guess. You want a pre-converted model without doing your own PyTorch-to-LiteRT conversion work -- the Model Zoo covers Gemma, Qwen, Llama, and more out of the box. You're willing to own sampling -- the engine won't pick sane decoding defaults for you; that's on the ## Why Go is an Ideal Language for AI-Assisted Software Engineering DevFeed: [Why Go is an Ideal Language for AI-Assisted Software Engineering]() Original publisher: [Read original article]() Author: Cameron Balahan; Richard Seroter Published: 2026-09-12T11:04:33.891311Z Content type: opinion Language: en Sources: [Google Developers Blog]() Topics: [Go Language](), [Software Engineering](), [AI Development](), [Architecture & Design](), [Tooling]() Tags: [ai](), [ai-coding](), [compatibility](), [developer-tools](), [engineering](), [go](), [software-development](), [software-engineering]() ### AI overview The article argues that AI-assisted software engineering shifts developers' work from writing boilerplate toward reviewing, verifying, maintaining, and architecting systems. It presents Go as well suited to this model because its simplicity, readability, tooling, compatibility guarantees, and team-oriented design provide consistency and guardrails for AI-generated code. ### Source excerpt As AI coding assistants shift the developer's primary role from writing boilerplate to reviewing and maintaining systems, language choice becomes critical for long-term architectural integrity. Go directly addresses this new paradigm by utilizing its strict compiler, integrated toolchain, and uncompromising readability to provide deterministic guardrails that help AI models self-correct and generate highly standardized code. By enforcing ecosystem-wide consistency and strict backward compatibility, the Go platform empowers engineering teams to efficiently verify, optimize, and maintain high-velocity, AI-generated output in production environments. ## Designing Documentation for LLMs and Coding Agents DevFeed: [Designing Documentation for LLMs and Coding Agents]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-09-11T22:55:25Z Content type: opinion Language: en Sources: [The CommonsBlog]() Topics: [Documentation](), [Large Language Model](), [AI-assisted coding](), [SDKs]() Tags: [ai](), [blog](), [coding-agents](), [documentation](), [llms](), [markdown](), [sdk](), [sdks](), [software-development](), [vibe-coding]() ### AI overview This opinion article argues that SDK and library documentation should be designed for both human developers and AI systems. It recommends text-based formats such as Markdown, smaller topic-focused sections, and strong indexing to make documentation more efficient for LLMs and coding agents, while retaining formats such as PDFs and videos for human consumption. ### Source excerpt Let's continue to explore how we can succeed with libraries and SDKs at a time when coding agents will be consuming what we create, as will ordinary developers ## OpenAI's safety system is already cutting off API responses mid-task DevFeed: [OpenAI's safety system is already cutting off API responses mid-task]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-11T17:52:56Z Content type: news Language: en Sources: [The New Stack]() Topics: [releases](), [AI Bots]() Tags: [agents](), [ai](), [ai-models](), [ai-strategy](), [api](), [cybersecurity](), [developers](), [model-development](), [models](), [openai](), [reinforcement-learning](), [release](), [responses](), [safety](), [software-development]() ### AI overview OpenAI is reportedly considering slower development of its most advanced AI systems as safety concerns could delay releases and limit access. The article cites pauses in model work and restrictions following cybersecurity evaluations and an AI-agent containment incident. ### Source excerpt AI companies have spent the last few years competing to build the best models, faster than the other, with each The post OpenAI's safety system is already cutting off API responses mid-task appeared first on The New Stack. ## Shopify spent years on React Native -- then rebuilt everything in 12 weeks DevFeed: [Shopify spent years on React Native -- then rebuilt everything in 12 weeks]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-10T19:46:50Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI-assisted coding]() Tags: [ai](), [ai-agents](), [ai-engineering](), [android](), [coding](), [ios](), [llms](), [mobile](), [react-native](), [shopify](), [software-development]() ### AI overview Shopify is moving its consumer mobile app from React Native to fully native development, arguing that improved coding agents and LLMs reduce the cost of platform-specific implementation. The article also cites an AI-assisted JavaScript-runtime port as an example of rapidly changing rewrite economics. ### Source excerpt In 2020, Shopify made a bet that resonated across the developer world by writing mobile code once in React Native The post Shopify spent years on React Native -- then rebuilt everything in 12 weeks appeared first on The New Stack. ## Every animation was one rename away from breaking a live show DevFeed: [Every animation was one rename away from breaking a live show]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-09-10T16:31:01Z Content type: tutorial Language: en Sources: [Whatnot Engineering]() Topics: [iOS](), [Swift](), [Code](), [SwiftUI]() Tags: [animation](), [code](), [ios-app-development](), [software-development](), [swift](), [swiftui]() ### AI overview This engineering post explains how a designer's property rename could silently break a production iOS animation because Swift code referenced asset properties through string literals. It describes replacing hand-written bindings with generated animation binding code from asset files so the compiler can enforce the contract and reduce reliance on tribal knowledge. ### Source excerpt Seth Sandler There's a bug we never shipped, and it kept me up anyway. Here's how it goes: a designer renames a property from purchaseCount to purchase_count inside an animation file. Every place our Swift code references that property is a string literal. The build stays green. Code review passes; nothing in the diff even mentions animations. And the celebration animation at the heart of our live auctions silently stops firing, in production, in front of a live audience. Nothing stood between us and that outcome except everyone remembering to be careful. And the strings were only the most visible tripwire. Driving our animation runtime correctly also meant knowing things the compiler never checks: call order, load timing, which properties were designer-internal and untouchable, how not to leak memory. Every engineer touching an animation had to know all of it, and every new animation was another chance to get it wrong. If you've done animation work on iOS, some version of this unease is familiar. This post is the story of how we defused it: by generating the entire animation binding layer from the asset files themselves, so the compiler enforces the contract and the generated code encodes the nuance. The methodology isn't specific to our stack, our vendor, or our app. If your code talks to any designer-owned asset through string literals and tribal knowledge, you can build this too. Why hand-rolled animation code collapses Our first instinct, like every iOS team's, was to build the animations ourselves. SwiftUI has withAnimation. Core Animation has CAKeyframeAnimation. We'd shipped complex UI before. How hard could it be? The animation we were building wasn't a loop. It was state-driven: it needed a user's avatar, their name, a running count that ramps up the visuals, and different behavior depending on who's watching. Each input changes the intro, the escalation, the outro. In native Swift, that decomposed into: State explosion. A @State for the image, a @Binding ## Article: When Spec-Driven Development Pays Off DevFeed: [Article: When Spec-Driven Development Pays Off]() Original publisher: [Read original article]() Author: Nitin Garg Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [AI-assisted coding](), [AI Development](), [Software Engineering](), [code productivity]() Tags: [ai](), [ai-assisted-coding](), [ai-coding](), [ai-development](), [ai-ml-data-engineering](), [architecture](), [article](), [artificial-intelligence](), [code-generation](), [generative-ai](), [ml-data-engineering](), [productivity](), [reasoning](), [review](), [security](), [software-development](), [spec-driven-development](), [when-spec-driven-development-pays-off]() ### AI overview The article argues that AI-assisted coding shifts the main constraint from writing code to verifying it. It presents specification-first development as a governance approach for hard, multi-constraint work, while noting its time and cost and warning that apparent gains may instead come from reasoning. ### Source excerpt AI coding assistants have become a core part of software development. AI-generated code has shown productivity gains, but it's also contributing to security weaknesses and familiar bug patterns. In this article, author Nitin Garg highlights the bottleneck has moved from code generation to code verification, and how to detect & mitigate it when the AI-generated behavior diverges from the intent. By Nitin Garg ## OpenAI gave an AI the power to block its own engineers' code DevFeed: [OpenAI gave an AI the power to block its own engineers' code]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-09T19:53:08Z Content type: news Language: en Sources: [The New Stack]() Topics: [Code review](), [Pull Request](), [vulnerability](), [LLM evaluation / benchmarking](), [Deployment]() Tags: [ai](), [ai-engineering](), [benchmark](), [codex](), [model](), [openai](), [pull-request](), [review](), [security](), [software-development](), [vulnerability]() ### AI overview OpenAI uses automated AI security review for every engineer pull request, blocking merges when a vulnerability is found. The article describes specialized code-review models, benchmarking claims, and a shift in human review toward discussing intent earlier in development. ### Source excerpt Every pull request submitted by an OpenAI engineer now goes through an automated security review, and the AI model can The post OpenAI gave an AI the power to block its own engineers' code appeared first on The New Stack. ## Building Codex with Tibo Sottiaux DevFeed: [Building Codex with Tibo Sottiaux]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-09T15:57:24Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [codex](), [OpenAI](), [Development](), [software-development](), [Command-line interface](), [Git]() Tags: [agentic](), [agents](), [cli](), [codex](), [git](), [openai](), [rust](), [software-development]() ### AI overview In a Pragmatic Engineer Podcast episode, OpenAI's Tibo Sottiaux discusses how Codex was built and iterated, including the Rust-based open-source CLI, evolving harness and models, support for multiple model providers, and use across software development tasks such as code reviews, maintenance, and system rearchitecture. ### Source excerpt OpenAI's Tibo Sottiaux shares how Codex was built and how it's reshaping software development. ## Introducing Consort: Test-driven development on a branching database DevFeed: [Introducing Consort: Test-driven development on a branching database]() Original publisher: [Read original article]() Author: Kevin Hartman Published: 2026-09-09T13:41:25Z Content type: article Language: en Sources: [Databricks]() Topics: [Test-driven development](), [Database](), [Integration testing](), [Software Engineering](), [Refactoring](), [Continuous Delivery (CD)](), [Pull Request](), [Reverse Engineering]() Tags: [code](), [continuous-delivery](), [database](), [development](), [integration](), [partners](), [platform](), [postgres](), [pull-request](), [refactoring](), [reverse-engineering](), [software](), [software-development](), [testing]() ### AI overview The article introduces Consort, a workflow for applying test-driven development to a branching database. It argues that copy-on-write branches of a real database can replace mocks, bring integration testing into the inner development loop, and let engineers run destructive tests in isolation. Versioned schema migrations can move with the dependent code, enabling what the article calls Data CD and helping catch production database problems during pull requests and merges. ### Source excerpt For 25 years I built software on the practices I grew up with: Kent Beck's TDD, Martin... ## Android A2UI (AI to UI) Made Simple DevFeed: [Android A2UI (AI to UI) Made Simple]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-09T07:23:57Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Artificial Intelligence](), [Android](), [Jetpack Compose](), [Mobile](), [Protocol (disambiguation)](), [Google]() Tags: [a2ui](), [ai](), [android](), [android-app-development](), [api](), [artificial-intelligence](), [deprecated](), [google](), [jetpack-compose](), [mobile-app-development](), [software-development](), [user-interfaces]() ### AI overview This tutorial introduces A2UI, a protocol for rendering AI responses as user interfaces, and discusses trying it on Android with Jetpack Compose. It notes that an older Jetpack A2UI library is deprecated and that newer development is ongoing. ### Source excerpt Converting AI Prompt Output Into User Interface in Jetpack Compose Continue reading on Mobile App Development Publication " ## How Lorari scaled to 130+ fitness studios solo with Appwrite DevFeed: [How Lorari scaled to 130+ fitness studios solo with Appwrite]() Original publisher: [Read original article]() Author: Aditya Oberai Published: 2026-09-09T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Cloud](), [Development](), [web applications]() Tags: [analytics](), [backend](), [cloud](), [customer-stories](), [fitness](), [instagram](), [payments](), [software-development](), [stripe](), [web-app]() ### AI overview A customer story about solo founder Marti Mestre Faus building Lorari on Appwrite Cloud. The platform helps yoga, pilates, and fitness studios manage classes, memberships, payments, events, waitlists, and student bookings; more than 130 studios serving 13,000 students were onboarded in seven months. ### Source excerpt Learn how solo founder Marti Mestre Faus built Lorari on Appwrite Cloud and onboarded 130+ yoga and pilates studios serving 13,000 students in seven months. ## Android 17 AppFunction Walkthrough First Experience DevFeed: [Android 17 AppFunction Walkthrough First Experience]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-07T12:51:23Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Android](), [Mobile](), [App](), [Development](), [Artificial Intelligence](), [Android Studio](), [Emulator](), [Android Gradle Plugin]() Tags: [ai](), [android](), [android-app-development](), [android-gradle-plugin](), [android-studio](), [app-development](), [artificial-intelligence](), [development](), [emulator](), [gemini](), [mobile](), [mobile-app-development](), [software-development]() ### AI overview A first-hand walkthrough of Android 17 AppFunction, which lets Gemini discover an app's functionality. The article covers the required Android Studio version, Android 17 emulator, Android SDK API 37.0 target, and Android Gradle Plugin requirements. ### Source excerpt Enabling Gemini to Find Your App Functionalities Continue reading on Mobile App Development Publication " ## What is platform engineering? DevFeed: [What is platform engineering?]() Original publisher: [Read original article]() Author: Luca Galante Published: 2026-09-03T16:27:44Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [AI Bots](), [Artificial Intelligence](), [cloud-infrastructure]() Tags: [agents](), [ai](), [ai-agents](), [developer-platform](), [engineering](), [idp](), [infrastructure](), [internal-developer-platform](), [platform](), [platform-engineering](), [software-development](), [workflows]() ### AI overview Platform engineering is presented as the discipline of building internal platforms that provide self-service paths for software teams and increasingly AI agents. The article explains how Internal Developer Platforms standardize and automate engineering work, and how Agentic Development Platforms add infrastructure and path specifications for collaboration between engineers and AI agents. ### Source excerpt Platform engineering is the method and discipline of designing and building platforms that enable self-service capabilities for teams, and increasingly AI agents, to automate the recurring aspects of knowledge work. ## Maybe We Shouldn't Be Reviewing All This Code DevFeed: [Maybe We Shouldn't Be Reviewing All This Code]() Original publisher: [Read original article]() Author: Rachel Laycock (rlaycock@thoughtworks.com) Published: 2026-09-02T13:32:00Z Content type: opinion Language: en Sources: [Martin Fowler]() Topics: [Code review](), [pull-requests](), [Artificial Intelligence](), [Software Engineering](), [engineering-culture](), [Meta]() Tags: [ai](), [development](), [meta](), [pull-request](), [pull-requests](), [rachels-ramblings](), [review](), [software](), [software-development]() ### AI overview This opinion argues that AI-generated code is increasing the volume of code beyond what humans can realistically review, but that the deeper problem is relying on code review to provide knowledge sharing, mentoring, collective ownership, and architectural understanding. It advocates moving valuable feedback and collaboration earlier in the development process instead of treating pull requests as its center. ### Source excerpt TL;DR Or, perhaps the problem isn't that AI has broken code review, maybe it's that we've been using code review to solve the wrong problems I was on a panel recently with Brian Houck from DX at Code Remix, hosted by Moderne. It was one of the more interesting panels I've done, largely because we disagreed. As my colleague Martin Fowler says, panels are much more interesting when people disagree and both sides have a good argument. Brian and I definitely did. Brian has since written a thoughtful piece called What are code reviews even for? He is clearly passionate about his position, and I am passionate enough about mine that I'm writing this response. To be clear, I think we mostly want the same things. I just don't think code review is the best way to get them. Brian is lovely, by the way, and encouraged me to write this. But I'd be lying if I said I didn't want you to think I'm right by the end :) So what were we disagreeing about? AI is producing more code than humans can realistically review. Brian cites some pretty striking numbers: at Meta, significant lines of code per human-landed diff reportedly increased 106% in a year, while DX's own data shows median pull request size increasing 64%. His concern, which I share, is that simply automating code review away risks losing all the other things we use it for. Code review isn't just about finding bugs. It's how teams share knowledge, teach junior engineers, build collective ownership and spread architectural understanding. My question is: why are we waiting until code review to do all of those things? I've never particularly liked pull requests as the centre of the software development process. Not because engineers shouldn't look at each other's code, but because I've always struggled with the idea that we should build something, finish it, package it up, throw it over to somebody else and then have the important conversation about whether we built the right thing in the right way. And don't even get me started ## When to Reject Change in Software Development: Shannon's Demon and Volatility DevFeed: [When to Reject Change in Software Development: Shannon's Demon and Volatility]() Original publisher: [Read original article]() Author: Kent Beck Published: 2026-09-02T13:29:36Z Content type: opinion Language: en Sources: [Software Design: Tidy First?]() Topics: [Development](), [Software Engineering](), [Programming]() Tags: [change](), [product-development](), [software-development]() ### AI overview The article uses Shannon's Demon--a strategy that repeatedly rebalances between a stable asset and a volatile one--to explain how software development can create value by managing volatility. It discusses the exception to always embracing change and applies the metaphor to software product development. ### Source excerpt Volatility, Shannon's Demon, & Free Money ## Nitpicking in code reviews wastes time DevFeed: [Nitpicking in code reviews wastes time]() Original publisher: [Read original article]() Author: Dan Lew Published: 2026-09-01T13:00:13Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Code](), [code productivity](), [Software Engineering](), [Development]() Tags: [bugs](), [code](), [code-reviews](), [development](), [features](), [problems](), [refactor](), [software-development](), [time]() ### AI overview An opinion article argues that nitpicking minor issues in code reviews wastes time. Small problems usually have limited impact, may never cause trouble, and can often be fixed later, so review effort should prioritize major problems, bugs, and features. ### Source excerpt I once wrote about how I've stopped nit-picking in code reviews, which improved the signal-to-noise ratio and helped everyone get along better. I'm back with a third reason to ditch the practice: nit-picking wastes time. By definition, nitpicks are about minor problems: [Next page]()