# coding Published articles for 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. ## Junie /demo launches apps and records UI checks from the Junie CLI DevFeed: [Junie /demo launches apps and records UI checks from the Junie CLI]() Original publisher: [Read original article]() Author: Aliaksei Kartynnik Published: 2026-09-18T11:51:27Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [junie](), [Command-line interface](), [App](), [ui](), [issue tracker](), [Docker]() Tags: [added](), [agentic-ai](), [ai](), [cli](), [code](), [coding](), [docker](), [feature](), [issue-tracker](), [junie](), [testing](), [tests](), [ui]() ### AI overview Junie /demo is a new Junie CLI mode that builds and launches an app, interacts with its UI, and records the run. It produces an HTML report, screenshots, and a video for review. ### Source excerpt You have the change ready and the tests are green. Now someone has to launch the app, find the right screen, and check the flow. Often, that someone is still you, even when an agent helped write the code. You should be able to delegate that part too. Junie /demo is a new mode in [...] ## GitHub and Anthropic used their own agents for major Rust rewrites -- with very different playbooks DevFeed: [GitHub and Anthropic used their own agents for major Rust rewrites -- with very different playbooks]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-17T18:06:03Z Content type: article Language: en Sources: [The New Stack]() Topics: [GitHub](), [Rust](), [AI-assisted coding](), [GitHub Copilot app](), [GitHub Copilot CLI](), [rustrover]() Tags: [ai](), [ai-agents](), [bun](), [claude](), [coding](), [coding-agents](), [github](), [github-copilot](), [github-copilot-app](), [memory-management](), [programming-languages](), [rust]() ### AI overview The article examines GitHub's rewrite of the GitHub Copilot agent runtime from TypeScript on Node.js and V8 to more than 800,000 lines of Rust, using GitHub's Copilot agents across incrementally merged pull requests. It compares this with Anthropic's Bun rewrite, where a pre-release Claude model helped convert more than half a million lines of Zig to Rust amid memory-management problems. ### Source excerpt Rust is seemingly the language of the moment, with open-source projects and companies forming an orderly queue to move core The post GitHub and Anthropic used their own agents for major Rust rewrites -- with very different playbooks 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. ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## The Future of Data Engineering in the Age of AI | Erfan Hesami DevFeed: [The Future of Data Engineering in the Age of AI | Erfan Hesami]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-16T13:21:19Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [data-engineering](), [AI Engineering](), [agent observability](), [coding](), [Security]() Tags: [agents](), [ai](), [ai-agents](), [ai-engineering](), [coding](), [data-engineering](), [fundamentals](), [governance](), [security]() ### AI overview An interview with Erfan Hesami examines how AI and agents may change data engineering, including the evolving role of data engineers, the overlap with AI engineering, the continuing importance of fundamentals, and the need to manage governance, security, costs, technical debt, and human judgment. ### Source excerpt AI Agents, Coding & Fundamentals ## 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. ## Why developers should avoid relying on frontier-model APIs in trusted systems DevFeed: [Why developers should avoid relying on frontier-model APIs in trusted systems]() Original publisher: [Read original article]() Author: Niels Provos Published: 2026-09-16T05:42:22Z Content type: opinion Language: en Sources: [APNIC Blog]() Topics: [Artificial Intelligence](), [Frontier Model](), [agentic-coding](), [coding](), [Claude]() Tags: [agentic-coding](), [ai](), [anthropic](), [api](), [artificial-intelligence](), [coding](), [frontier-model](), [gateway](), [guest-post](), [security](), [tech-matters]() ### AI overview This opinion article argues that relying on frontier-lab APIs creates risks around pricing, availability, model behavior, and output integrity. It presents open-weight models as a way to retain control over critical dependencies, while distinguishing coding assistance from placing frontier models in live request paths. ### Source excerpt Guest Post: A frontier API can refuse, change, or vanish out from under you. Open weights keep the model you depend on yours. ## Building the new GitHub Copilot Inline Suggestions Model: Part One DevFeed: [Building the new GitHub Copilot Inline Suggestions Model: Part One]() Original publisher: [Read original article]() Author: Julia Gong, Ben Liggett, Ulugbek Abdullaev Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [GitHub Copilot](), [AI-assisted coding](), [coding](), [Development](), [GitHub](), [Software Engineering]() Tags: [agent](), [ai-assisted-coding](), [blog](), [coding](), [github](), [github-copilot](), [quality](), [vs-code](), [workflows]() ### AI overview GitHub Copilot unified completion-style ghost text, nearby next edit suggestions, and long-distance edits into a single "3-in-1" model. The article explains that training, evaluation, and editor design evolved together, and that the unified model can improve suggestion selection and cache additional edits for faster subsequent interactions. ### Source excerpt Explore how GitHub Copilot unified completion, next edit, and long-distance suggestions into one model for a faster, more cohesive coding experience. Read the full article ## Running Agent Harnesses with Local Models DevFeed: [Running Agent Harnesses with Local Models]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-15T21:59:31Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Large Language Model](), [coding](), [Hardware](), [Open Source](), [Claude Code](), [codex]() Tags: [agent](), [coding](), [hardware](), [local](), [models](), [open-source](), [opentelemetry](), [profiling](), [recommendations](), [run-agent](), [video]() ### AI overview A video walkthrough explains how Magnitude profiles computer hardware, benchmarks local models, recommends practical candidates, and connects a selected model to coding agent harnesses such as Claude Code, Codex, OpenCode, and Pi. The article also presents a Dynatrace reference application for tracing LLM pipelines with OpenTelemetry. ### Source excerpt ...explained with a full video walkthrough. ## Source material: Artist and educator Zach Lieberman likes to break things DevFeed: [Source material: Artist and educator Zach Lieberman likes to break things]() Original publisher: [Read original article]() Author: Figma Published: 2026-09-15T20:52:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [coding](), [Artificial Intelligence]() Tags: [animation](), [art](), [coding](), [creative](), [design]() ### AI overview An interview with artist and educator Zach Lieberman about connecting drawing with computation, developing creative coding experiments, and finding inspiration in early computer artists and animation pioneers. ### Source excerpt The creative coder explains the throughline between drawing and computation, and why he's jealous of pioneering artist Lillian Schwartz. ## COBOL dev won .Net hackathon with help from AI - and their CIO loves it DevFeed: [COBOL dev won .Net hackathon with help from AI - and their CIO loves it]() Original publisher: [Read original article]() Author: Simon Sharwood Published: 2026-09-15T01:47:17Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [AdventureX 2025](), [Artificial Intelligence](), [.NET](), [Development](), [coding]() Tags: [ai](), [ai-and-ml](), [australia](), [cobol](), [coding](), [copilot](), [dev](), [hackathon](), [mainframe](), [net](), [public-sector]() ### AI overview The article reports that a COBOL developer won a .NET hackathon with help from AI, a result praised by their CIO. The supplied summary also describes the Australia Taxation Office using Copilot practice to develop skills for future AI work. ### Source excerpt Australia Taxation Office sees footling with Copilot as a way to develop 'muscles' for real AI work ## Introducing Encore x E2B DevFeed: [Introducing Encore x E2B]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Encore Updates]() Topics: [AI-assisted coding](), [Cloud](), [backends](), [SDKs](), [Databases](), [Deployment](), [API](), [Code]() Tags: [agents](), [api](), [backend](), [cloud](), [code](), [coding](), [coding-agents](), [database](), [deployment](), [pull-request](), [sandbox](), [sdk]() ### AI overview Encore and E2B can provide coding agents with isolated sandboxes containing complete local backends. The integration lets agents build and verify end-to-end changes against infrastructure such as databases, queues, and APIs, while supporting deployment in the user's AWS or GCP account. ### Source excerpt Give coding agents an isolated sandbox with a complete backend inside it -- and keep both in your own cloud. ## AI's best coding agent fails 60% of the time -- and the data backs it up DevFeed: [AI's best coding agent fails 60% of the time -- and the data backs it up]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-14T22:22:27Z Content type: news Language: en Sources: [The New Stack]() Topics: [Benchmark](), [AI-assisted coding](), [Fable](), [Claude Code](), [codex](), [Command-line interface](), [anthropic]() Tags: [ai](), [ai-engineering](), [ai-models](), [benchmark](), [benchmarks](), [claude-code](), [cli](), [codex](), [coding](), [fable](), [performance](), [software-testing]() ### AI overview Real-SWE evaluates coding agents on private company codebases and reports substantially lower success rates than public-repository benchmarks. Claude Fable 5.1, running through Claude Code, led the comparison with a 38.8% score, while the tested systems often failed most attempts. ### Source excerpt Claude Fable 5.1 just won a new coding benchmark despite failing more than six out of 10 times. Its 38.8% The post AI's best coding agent fails 60% of the time -- and the data backs it up appeared first on The New Stack. ## Product Engineering for PMs, Part 3: Build a SaaS App Without Coding DevFeed: [Product Engineering for PMs, Part 3: Build a SaaS App Without Coding]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-09-14T19:12:58Z Content type: tutorial Language: en Sources: [The Product Compass]() Topics: [Software as a service](), [web applications](), [stripe](), [Firewall](), [Security](), [agentic-engineering]() Tags: [agentic](), [application](), [coding](), [firewall](), [payments](), [saas](), [saas-app](), [security]() ### AI overview A tutorial for product managers on building a multi-tenant SaaS web application without coding, including real Stripe payments, a web application firewall, and agentic reviews focused on logic, security, and performance. ### Source excerpt Enable real Stripe payments, add a web application firewall (Cloudflare or Netlify), and run agentic reviews of a real multi-tenant SaaS app: logic, security, performance. No coding. ## 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. ## Brownfield Agentic Engineering DevFeed: [Brownfield Agentic Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-09-14T14:32:06Z Content type: article Language: en Sources: [Elevate]() Topics: [coding](), [Testing](), [systems](), [Artificial Intelligence](), [Software]() Tags: [agentic](), [agents](), [code](), [coding](), [engineering](), [software](), [systems](), [technical](), [testing]() ### AI overview The article discusses agentic engineering in older brownfield codebases, where important constraints and system behavior exist outside the repository. It emphasizes making hidden constraints visible, using strong testing and verification, and introducing autonomous changes carefully to avoid brittle tests and technical debt. ### Source excerpt What it takes to run agents in a codebase older than the team ## 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. ## Python Practice Problems: Parsing CSV Files DevFeed: [Python Practice Problems: Parsing CSV Files]() Original publisher: [Read original article]() Author: Jim Anderson Published: 2026-09-13T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [CSV](), [Python](), [Test-driven development](), [Command-line interface](), [pass]() Tags: [code](), [coding](), [command-line](), [data](), [python](), [testing](), [tutorial](), [tutorials](), [writing-code]() ### AI overview An intermediate-level tutorial that helps Python developers practice parsing CSV files through interview-style problems. It covers writing solutions, test-driven development with pytest, solution improvements, and the trade-offs between Python's built-in csv module and pandas. ### Source excerpt In this tutorial, you'll prepare for future interviews by working through a set of Python practice problems that involve CSV files. You'll work through the problems yourself and then compare your results with solutions developed by the Real Python team. ## It passed CI. It passed your evals. The customer still got the wrong answer. DevFeed: [It passed CI. It passed your evals. The customer still got the wrong answer.]() Original publisher: [Read original article]() Author: Sean O'Dell Published: 2026-09-13T14:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI Agent](), [agent observability](), [observability](), [tracing](), [dynatrace](), [ci](), [coding]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-engineering](), [ci](), [coding](), [dynatrace](), [observability](), [post-contributed](), [sponsor-dynatrace](), [sponsored](), [sponsored-post-contributed](), [traces]() ### AI overview The article explains how AI-agent failures can pass CI and evaluations while still producing slow or incorrect customer-facing results. It presents distributed traces and agent trajectories--model calls, tool calls, arguments, and results--as evidence for debugging retrieval behavior, release context, and feature-flag state. ### Source excerpt A diff is not evidence. It's a statement of intent. The tests passed. The review's done. The change is live. The post It passed CI. It passed your evals. The customer still got the wrong answer. appeared first on The New Stack. ## AI Agents in 2026 DevFeed: [AI Agents in 2026]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-13T09:23:56Z Content type: opinion Language: en Sources: [iHateReading]() Topics: [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agents](), [ai-agents-ai-business-agents-ideas](), [ai-agents-in-2026-why-every-founder-s-stack-just-changed-for-good](), [backend](), [blockchain](), [business-agents](), [coding](), [development](), [frontend](), [ideas](), [ihatereading](), [javascript](), [node-js](), [product](), [programming](), [react](), [typescript](), [web-development]() ### AI overview The author revisits an earlier post that called AI agents "the next big thing," arguing that by September 2026 this no longer needs to be argued. ### Source excerpt Hey there!A while back, I wrote a short post calling AI agents "the next big thing." It's September 2026 now, and I don't need to convince anyone of that anymo... ## Company's Building AI Agent Sandbox SDKs & APIs DevFeed: [Company's Building AI Agent Sandbox SDKs & APIs]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-12T13:15:15Z Content type: opinion Language: en Sources: [iHateReading]() Topics: [AI Agent](), [SDKs](), [Artificial Intelligence](), [Development]() Tags: [ai](), [ai-agent](), [ai-sandboxes](), [ai-sandboxes-vercel-aws-google-daytona](), [apis](), [aws](), [backend](), [blockchain](), [coding](), [company-s-building-ai-agent-sandbox-sdks-apis](), [daytona](), [developer](), [development](), [frontend](), [google](), [ihatereading](), [javascript](), [node-js](), [product](), [programming](), [react](), [sdks](), [typescript](), [vercel](), [web-development]() ### AI overview An opinion article discusses companies building AI agent sandboxes, SDKs, and APIs, in the context of an ongoing competition around AI sandboxes. ### Source excerpt Company's Building AI Agent Sandbox SDKs & APIs I wrote about the fight for AI sandboxes back in March -- the short version was that every AI agent that wri... ## Coding Challenge #136 - Hyperfine DevFeed: [Coding Challenge #136 - Hyperfine]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-09-12T08:03:11Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [benchmarking](), [coding](), [Command-line interface](), [Processes]() Tags: [benchmarking](), [coding](), [command-line](), [performance](), [process](), [programming]() ### AI overview A coding challenge asks readers to build a command-line benchmarking tool modeled on hyperfine. The tool should run commands repeatedly, measure execution time and variation, compare commands, export results, and warn about unreliable measurements. ### Source excerpt This challenge is to build your own benchmarking tool. ## 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. [Next page]()