# ai-coding Published articles for ai-coding. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Applitools Adds Figma Design Baselines for Visual Testing of AI-Generated Interfaces DevFeed: [Applitools Adds Figma Design Baselines for Visual Testing of AI-Generated Interfaces]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-18T16:39:07Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Artificial Intelligence](), [Figma](), [AI-assisted coding](), [ai-coding](), [applitools](), [Front end](), [web design]() Tags: [agentic-coding](), [ai](), [ai-assisted-development](), [ai-coding](), [ai-coding-agents](), [ai-design-tools](), [ai-tech](), [ai-tech-visual-ui-design](), [ai-web-development](), [applitools](), [claude-code](), [codex](), [cursor](), [design-systems](), [design-to-code](), [figma](), [figma-design-baselines](), [front-end-development](), [github-copilot](), [release](), [ui-design](), [ux-design](), [visual-ai](), [visual-testing](), [visual-ui-design](), [web-design](), [web-development]() ### AI overview Applitools has introduced an integration that uses Figma frames as visual baselines for testing interfaces generated or modified by AI coding tools. It automatically compares the implemented interface with the approved design to detect visual drift before production. ### Source excerpt AI can now build a UI in seconds, but that doesn't mean it built the UI you actually designed. A new Applitools integration turns Figma designs into visual baselines, automatically catching the subtle differences AI coding agents leave behind before they make it into production. ## Headroom vs Caveman: A Practical Guide to Reducing AI Coding Costs DevFeed: [Headroom vs Caveman: A Practical Guide to Reducing AI Coding Costs]() Original publisher: [Read original article]() Author: Yuvaraj Mohan Published: 2026-09-18T13:28:39Z Content type: article Language: en Sources: [Syncfusion Blogs]() Topics: [ai-coding](), [Compression](), [Optimization](), [superProductivity](), [Open Source](), [Pull Request](), [bug](), [audit]() Tags: [ai](), [ai-coding](), [ai-coding-agents](), [ai-coding-agents-code-studio-developer-tools-llm-optimization-syncfusion-token-management](), [ai-coding-assistant-costs](), [ai-token-optimization](), [ai-token-reduction](), [audit](), [caveman](), [code-studio](), [compression](), [cost](), [debugging](), [developer](), [developer-productivity](), [developer-tools](), [guide](), [headroom](), [headroom-vs-caveman](), [llm-cost-reduction](), [llm-optimization](), [mcp-tools](), [open-source](), [optimization](), [practical](), [pull-request](), [syncfusion](), [token-management](), [tokens](), [vs]() ### AI overview This practical comparison explains how the open-source tools Headroom and Caveman reduce AI coding costs in Syncfusion Code Studio. Headroom compresses input context, while Caveman optimizes model output, helping manage token usage during repository analysis, debugging, audits, and other agent workflows. ### Source excerpt AI coding costs rising fast? See how Headroom and Caveman reduce input and output tokens in Syncfusion Code Studio while maintaining developer productivity. ## Study: Developers are addicted to AI, and managers are making it worse DevFeed: [Study: Developers are addicted to AI, and managers are making it worse]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-17T15:12:36Z Content type: article Language: en Sources: [The New Stack]() Topics: [Artificial Intelligence](), [coding](), [Claude Code](), [Claude](), [codex](), [GitHub Copilot]() Tags: [ai](), [ai-coding](), [ai-coding-tools](), [ai-engineering](), [ai-operations](), [ai-strategy](), [claude](), [claude-code](), [codex](), [contributed-octopus-deploy](), [copilot](), [developers](), [gemini]() ### AI overview The article discusses findings from an AI Coding Addiction Report based on more than 300 developer responses. It argues that AI coding tools may encourage dependence and prolonged use, and that managers may reward heavier usage. The supplied text also describes reported differences among users of Claude Code, Google Gemini, OpenAI Codex, and GitHub Copilot. ### Source excerpt AI is addictive, and managers are rewarding those who use it the most (even though they are shipping stuff they The post Study: Developers are addicted to AI, and managers are making it worse appeared first on The New Stack. ## AI Skills with Matt Pocock DevFeed: [AI Skills with Matt Pocock]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-17T11:29:00Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Artificial Intelligence](), [Software Engineering](), [coding](), [ai-coding](), [Test-driven development](), [Claude Code](), [JavaScript](), [cursor](), [Web app](), [codex](), [Linear](), [Vercel]() Tags: [agents](), [ai](), [ai-coding](), [coding](), [developer](), [engineering](), [software]() ### AI overview Matt Pocock discusses using AI coding skills and agents to plan, delegate, and build software while emphasizing software engineering fundamentals. The conversation covers strategic coding, managing agent context, local versus cloud workflows, TDD, and how engineers learn in an AI-assisted environment. ### Source excerpt Matt Pocock explains how he uses AI coding skills and agents to plan and build software, and why engineering fundamentals matter more than ever. ## Shopify Drops React Native for Swift and Kotlin as AI Changes Cross-Platform Development Tradeoffs DevFeed: [Shopify Drops React Native for Swift and Kotlin as AI Changes Cross-Platform Development Tradeoffs]() Original publisher: [Read original article]() Author: Bruno Couriol Published: 2026-09-16T12:45:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [React Native](), [Shopify](), [Mobile](), [cross-platform](), [Kotlin](), [Swift](), [AI Models]() Tags: [ai](), [ai-coding](), [android](), [apple](), [cross-platform](), [declarative](), [development](), [kotlin](), [mobile](), [news](), [react-native](), [shopify](), [shopify-drops-react-native](), [swift](), [web-development]() ### AI overview Shopify is rewriting its flagship mobile apps in Swift and Kotlin after reassessing React Native. The decision followed the expected work to adopt React Native's New Architecture and improvements in AI coding models, which Shopify said made a clean-slate native rewrite more attractive than migration. ### Source excerpt Shopify recently announced it is abandoning React Native to rewrite its flagship apps in Swift and Kotlin. With the significant jump in the quality of AI models, Head of Mobile Mustafa Ali reassessed Shopify's commitment to React Native, estimating that the benefit/cost ratio of maintaining native codebases across mobile platforms was now above that of using an abstraction layer. By Bruno Couriol ## How sandbox boundaries affect AI coding agent evaluations DevFeed: [How sandbox boundaries affect AI coding agent evaluations]() Original publisher: [Read original article]() Author: Waldek Mastykarz Published: 2026-09-16T09:09:51Z Content type: opinion Language: en Sources: [Developer Blogs]() Topics: [AI-assisted coding](), [Testing](), [Artificial Intelligence](), [coding]() Tags: [agent-experience](), [ai](), [ai-coding](), [ax](), [coding](), [developers](), [eval](), [evaluation](), [measurement](), [sandbox]() ### AI overview The article explains that an AI coding agent evaluation can produce misleading scores when the agent retrieves answers from its prompt, environment, or other accessible resources. It argues that evaluators should define the capability being tested and set information boundaries accordingly, including restricting access to evidence when measuring internal model knowledge. ### Source excerpt Your AI coding agent passed the eval. But did the model know the answer, or did it find it somewhere on your machine? A correct answer can still invalidate your measurement. The post Your AI coding agent evaluation is only as good as its sandbox appeared first on Microsoft for Developers. ## Manage Cursor costs with Datadog Cloud Cost Management DevFeed: [Manage Cursor costs with Datadog Cloud Cost Management]() Original publisher: [Read original article]() Author: Dom Nguyen; Doug Gunter Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [cloud cost management](), [cursor](), [finops](), [ai-coding]() Tags: [ai](), [ai-coding](), [analysis](), [cloud-cost-management](), [configuration](), [datadog](), [features](), [filter](), [finops](), [monitor](), [monitors](), [product](), [software]() ### AI overview This tutorial explains how Datadog Cloud Cost Management helps teams analyze Cursor spending by user, model, usage mode, and billing group. It covers identifying cost drivers, detecting unexpected changes, correlating spend with usage, and using monitors, budgets, and dashboards to manage AI coding costs alongside cloud and SaaS spending. ### Source excerpt Analyze Cursor spend by user and model, catch unexpected cost changes, and manage AI coding costs alongside your cloud and SaaS spend. ## I Had Never Built A VR App - 8 Days Later, Mine Was On The Quest Store DevFeed: [I Had Never Built A VR App - 8 Days Later, Mine Was On The Quest Store]() Original publisher: [Read original article]() Author: Craig Storm Published: 2026-09-14T15:12:36Z Content type: article Language: en Sources: [UploadVR]() Topics: [ai-coding](), [codex](), [ChatGPT](), [coding](), [Code](), [App](), [Meta](), [APK](), [SDKs]() Tags: [ai](), [ai-coding](), [apk](), [app](), [app-development](), [chatgpt](), [code](), [codex](), [coding](), [meta](), [openxr](), [sdk]() ### AI overview The article describes an eight-day attempt to build and publish Punchable Face, a simple VR game for Meta Quest, despite having little prior development experience. Using Unity, OpenXR, Meta XR SDK components, ChatGPT, and Codex, the project progressed from an empty Unity project through coding, headset testing, store asset creation, submission, and approval on the Meta Quest Store. ### Source excerpt Until a little over a week ago, Craig Storm had never built a VR app. Eight days later, his AI-assisted Quest game Punchable Face was live on Meta's store. ## 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. ## Quiz: Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Quiz: Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-14T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [AI-assisted coding](), [Code]() Tags: [ai-coding](), [ai-coding-agents](), [code](), [coding](), [coding-agents](), [engineering](), [how-to](), [python]() ### AI overview An interactive eight-question quiz about agentic engineering in Python, covering bounded task delegation to AI coding agents, agent and human review loops, and the RECAP method for evaluating candidate diffs. ### Source excerpt Test your understanding of agentic engineering in Python, from bounded tasks and review loops to the evidence that makes a diff safe to keep. ## 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. ## "Same mission, bigger stage": OpenAI hires Git AI founders to help Codex prove its ROI DevFeed: ["Same mission, bigger stage": OpenAI hires Git AI founders to help Codex prove its ROI]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-12T14:46:53Z Content type: news Language: en Sources: [The New Stack]() Topics: [ai observability](), [cursor]() Tags: [ai](), [ai-agents](), [ai-coding](), [codex](), [cost](), [cursor](), [developer-tools](), [git](), [open-source](), [openai]() ### AI overview OpenAI hired Git AI's founders to help measure the performance, cost, and return on investment of Codex and other AI coding tools. ### Source excerpt OpenAI has hired the founders of Git AI, an open-source tool that tracks how much code is written by AI The post "Same mission, bigger stage": OpenAI hires Git AI founders to help Codex prove its ROI appeared first on The New Stack. ## Better context, smarter testing: How to give your AI coding agent direct access to k6 docs DevFeed: [Better context, smarter testing: How to give your AI coding agent direct access to k6 docs]() Original publisher: [Read original article]() Author: İnanç Gümüş Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [k6](), [Agent Skill](), [AI Agent](), [Command-line interface](), [coding]() Tags: [agent-skill](), [ai](), [ai-agent](), [ai-coding](), [analytics](), [cli](), [grafana](), [k6](), [logs](), [metrics](), [monitoring](), [observability](), [performance-testing](), [testing](), [traces]() ### AI overview The article introduces k6 x docs, an official k6 2.0 command that provides k6 documentation directly in the terminal. It supports API references, guides, best practices, and examples; works offline after the first lookup; matches documentation to the installed k6 version; and includes an agent skill for AI coding assistants. ### Source excerpt As testing workflows become more AI-assisted, fast access to accurate documentation matters more than ever. Whether you're writing a new load test, troubleshooting an issue, or having an AI agent generate a script for you, you need reliable guidance that keeps pace with the way you work. But most documentation still lives in a browser. Every time you or your agent needs to verify an API or look up a best practice, you're forced to leave your terminal or editor and interrupt your workflow. That's why, in k6 2.0, we introduced k6 x docs, an official k6 command that puts the entire k6 documentation library, including API references, guides, best practices, and examples, directly in your terminal. It works offline after first use, matches the docs to your exact k6 version, and includes a built-in agent skill, so AI coding assistants can look up k6 docs faster and most cost-effectively. The problem: documentation lives in the wrong place Most developers know the feeling. You're writing a k6 script, you need to check the signature for http.post or remember how thresholds work, and suddenly you're in a browser tab, searching, scrolling, clicking through navigation, and losing the context you had in your editor. For AI agents, the problem is worse. When they need to reference k6 APIs, best practices, or examples, they either rely on stale training data, hallucinate a function signature, or burn expensive tokens on a web search that may not return the right version of the docs. k6 x docs solves both problems by making documentation a first-class part of the k6 CLI. It gives agents CLI access to k6 docs that automatically detect the k6 version in use and deliver accurate content without leaving the session or performing web searches. How k6 x docs works The use is simple: type k6 x docs, optionally followed by the topic you want to look up. k6 x docs # See all available topics k6 x docs http # Learn about the k6/http module k6 x docs http get # Look up a specific function k6 ## 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. ## The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents DevFeed: [The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents]() Original publisher: [Read original article]() Author: Taylor Mullen; Christian Gunderman Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [AI-assisted coding](), [ai observability]() Tags: [ai-agents](), [ai-coding](), [benchmarks](), [engineering](), [evaluation]() ### AI overview The article recommends behavioral evaluations for AI coding agents: fast checks of discrete actions that complement broad end-to-end benchmarks. These evaluations help teams diagnose changes, iterate on prompts and tools, and prevent regressions during model upgrades. ### Source excerpt While end-to-end benchmarks like SWE-bench provide broad performance scores for AI agents, they are often expensive, slow, and lack the root-cause diagnostics needed to explain exactly where an agent's logic broke down. To solve this, developers should adopt behavioral evaluations--fast, local, unit-style tests that assert on discrete intermediate actions, such as verifying specific tool calls or file modifications rather than final string equality. By building these inexpensive micro-checks alongside macro benchmarks, engineering teams can confidently iterate on system prompts and upgrade models without the risk of regressions. ## GitHub Copilot app for Beginners: Using the diff, terminal, and browser DevFeed: [GitHub Copilot app for Beginners: Using the diff, terminal, and browser]() Original publisher: [Read original article]() Author: Kayla Cinnamon Published: 2026-09-10T21:31:19Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot](), [AI-assisted coding](), [dev-tools](), [web applications]() Tags: [ai-coding](), [ai-ml](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners](), [pull-request](), [review](), [web-browser]() ### AI overview A beginner-oriented guide to using the GitHub Copilot app's diff, terminal, and browser panels to review agent-made code changes, run a project, preview a web feature, iterate, and create a pull request. ### Source excerpt Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app. The post GitHub Copilot app for Beginners: Using the diff, terminal, and browser appeared first on The GitHub Blog. ## Stop AI code sprawl before it destroys your software design DevFeed: [Stop AI code sprawl before it destroys your software design]() Original publisher: [Read original article]() Author: Emmanuel Akita Published: 2026-09-10T12:30:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [AI-assisted coding](), [CI/CD](), [ci]() Tags: [ai](), [ai-coding](), [ai-engineering](), [andela](), [architecture](), [ci-cd](), [post-contributed](), [python](), [sponsor-andela](), [sponsored-post-contributed](), [testing]() ### AI overview The article argues that AI-generated code can create "Comprehension Debt" by silently violating architectural boundaries. It advocates enforcing architecture through automated tests and CI/CD pipelines rather than relying on documentation and manual review. ### Source excerpt While AI code generators help teams ship faster than ever, that speed brings a hidden killer: Comprehension Debt. As soon The post Stop AI code sprawl before it destroys your software design appeared first on The New Stack. ## 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 ## Use a local and open source code assistant DevFeed: [Use a local and open source code assistant]() Original publisher: [Read original article]() Author: Seth Kenlon Published: 2026-09-09T14:01:45Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [ai-coding](), [Local AI](), [Open Source Models & Datasets](), [Large Language Model](), [Model Context Protocol](), [ide](), [Security, Privacy and Abuse Prevention](), [Command-line interface](), [Homebrew](), [Linux](), [macOS]() Tags: [ai-coding](), [ai-inference](), [artificial-intelligence](), [command-line](), [developer-tools](), [ide](), [linux](), [llm](), [local-ai](), [macos](), [model-context-protocol](), [ollama](), [open-source](), [privacy]() ### AI overview This Red Hat Developer article explains how to use OpenCode as a local, open source AI coding assistant. It covers OpenCode's terminal, desktop, and IDE extension interfaces, its use of the Model Context Protocol, installation requirements, and the need to configure an LLM. For privacy-conscious local development, it recommends open source local AI tools such as Ollama or OpenVINO. ### Source excerpt There's a lot of excitement about AI coding assistants, but many of the available options either aren't open source, or don't respect your data privacy by sending what you're working on to the cloud for processing. If you're looking for an alternative to closed AI, then you need an open coding assistant and an open source IDE. The post Use a local and open source code assistant appeared first on Red Hat Developer. ## Testing Astra 6 v Fable 5.1 on a Gradle docs bug DevFeed: [Testing Astra 6 v Fable 5.1 on a Gradle docs bug]() Original publisher: [Read original article]() Author: Laura Kassovic Published: 2026-09-09T04:00:00Z Content type: article Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Testing](), [ai-coding](), [Benchmark](), [Fable](), [Documentation](), [bug]() Tags: [ai-coding](), [benchmark](), [bug](), [docs](), [fable](), [gradle](), [testing]() ### AI overview An engineering blog compares Astra 6 and Fable 5.1 after each agent fixes the same Gradle Kotlin DSL documentation bug. Both fixes passed independent judging, while the comparison examines cost, speed, and maintainability. The author emphasizes that the result is based on one run per model, one judge, and one bug. ### Source excerpt On the afternoon of September 7th, claude-fable-5-1 was handed a git repository, a GitHub issue, and fifty turns to fix it. Just over three hours later, gpt-6-astra finished the same assignment in another worktree. Same issue. Same constraints. Different CLI wrapped around each model: claude-code for Fable, codex for Astra. Both Agents fixed the bug. Both results were graded as successes by an independent judge. But these results were quite different. You would be forgiven for wondering why an engineering blog is running a two-model bakeoff on a single documentation bug, in a year when everybody and their intern has published an AI coding benchmark. Here's the honest answer: we wanted to know which one actually did the better job, as a side quest of the Agentic Gradle project. But "better" was never going to mean pass-or-fail. Both of these agents passed; if that were the whole story, this post would be four sentences long. What actually separates a fix worth merging from a fix worth sending back is cost, speed, and whether the result is something we would want to maintain, and those three do not all point the same direction, as you are about to see. So: one bug, two agents, and a very literal stopwatch. Here is what this looks like when you actually read the agents' transcripts. Real talk up front: this is n=1. One run per model, one judge, one bug. That's a thin base for anything you'd call a leaderboard, and I'll say so again at the end, but it didn't stop me from landing on an opinion by the time I'd finished reading both transcripts. The bug, briefly Issue #34751, filed August 21st by our very own cobexer, is the kind of bug that is very easy to describe and mildly annoying to fix. In the Kotlin DSL API docs, a type like Attribute linked to the Java 21 Javadoc. It should have linked to Java 17, because Gradle targets Java 17. The reason is almost embarrassingly mundane once you see it. Gradle is built with a JDK 21 toolchain but targets JDK 17, which i ## Build full-stack AWS applications in minutes with AI-powered scaffolding DevFeed: [Build full-stack AWS applications in minutes with AI-powered scaffolding]() Original publisher: [Read original article]() Author: Jack Stevenson Published: 2026-09-08T21:24:59Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [App](), [cloud-infrastructure](), [Amazon Bedrock](), [Command-line interface](), [Kiro](), [Terraform](), [Bun]() Tags: [agentic](), [ai](), [ai-coding](), [amazon-bedrock](), [announcements](), [applications](), [aws](), [kiro](), [observability](), [security](), [technical-how-to](), [terraform]() ### AI overview The article introduces version 1.0 of the Nx Plugin for AWS, an open-source set of deterministic Nx generators for scaffolding deployable AWS application components and their infrastructure. It describes using AI assistants or a CLI to generate applications with security, observability, and type-safety practices included, and begins a quick start for an agentic application using Amazon Bedrock. ### Source excerpt AI assistants can stand up an app or website that runs on AWS in minutes. Getting to a production-ready version you would put in front of real customers is still the hard part. Security, observability, type-safety, and resilience are non-negotiable for production. An assistant rarely gets all of that right in one pass, and hardening [...] ## Loop engineering: stop prompting, start looping DevFeed: [Loop engineering: stop prompting, start looping]() Original publisher: [Read original article]() Author: Anthony Viard Published: 2026-09-08T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Loop Engineering](), [AI Agent](), [AI-assisted coding](), [API]() Tags: [2026](), [agent](), [agentic](), [agents](), [ai](), [ai-agents](), [ai-coding](), [api](), [api-testing](), [blog](), [code](), [coding-agents](), [developer](), [engineering](), [general](), [guide](), [idea](), [loops](), [model](), [post](), [postman-cli](), [tool](), [verify](), [work]() ### AI overview This article explains loop engineering as a way to improve AI coding agent reliability. Instead of relying on a single prompt, the system repeatedly generates code, runs it against a real API or other source of truth, verifies the result, and decides whether to stop or continue. The article distinguishes this outer verification loop from the inner ReAct tool-use cycle and presents a reproducible setup for allowing an agent to correct its own mistakes. ### Source excerpt Stop prompting your AI agent, start looping. A guide to loop engineering: wire a real API call in as the oracle so generated code stops guessing. The post Loop engineering: stop prompting, start looping appeared first on Postman Blog. ## Scaling your money safely with AI DevFeed: [Scaling your money safely with AI]() Original publisher: [Read original article]() Published: 2026-09-08T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI-assisted coding](), [sdlc](), [Code]() Tags: [agentic-ai](), [ai](), [ai-coding](), [autonomous](), [loops](), [podcast](), [sdlc](), [se-stackoverflow](), [se-tech](), [security]() ### AI overview A podcast conversation about validating AI-generated code for security, building autonomous SDLC harnesses with feedback loops, and creating a headless checkout experience. ### Source excerpt Ryan chats with Srini Venkatesan, CTO at PayPal, about validating AI-generated deterministic code for security, developing autonomous SDLC harnesses with iterative feedback loops, and creating a seamless headless checkout experience. ## Moving AI Coding Agents from Local Hardware to Cloud Hosting DevFeed: [Moving AI Coding Agents from Local Hardware to Cloud Hosting]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-09-08T07:04:07Z Content type: opinion Language: en Sources: [Refactoring]() Topics: [Cloud](), [Orchestration](), [ai-coding](), [Bot](), [Claude Code](), [MCP]() Tags: [agents](), [ai-coding](), [cloud](), [mcp](), [orchestration]() ### AI overview A personal essay about moving AI coding workflows from a Mac Mini and laptop toward cloud hosting. It describes shifting from a single local orchestrator agent to a five-agent Grok Bot panel hosted in the cloud, alongside updates to the author's Tolaria development workflow. ### Source excerpt How I moved most orchestration to Grok Bot [Next page]()