# coding The process of composing and organizing instructions that tell a computer or software program what to do. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Review AI-Generated Python Code Efficiently DevFeed: [How to Review AI-Generated Python Code Efficiently]() Original publisher: [Read original article]() Author: Leodanis Pozo Ramos Published: 2026-09-16T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [AI-assisted coding](), [Code review](), [Python](), [Pytest](), [coding]() Tags: [agents](), [ai](), [ai-coding-agents](), [code](), [coding-agents](), [how-to](), [python](), [review]() ### AI overview This tutorial presents a five-step workflow for reviewing AI-generated Python code. It recommends clarifying the code's intent, running automated checks such as ruff, mypy, bandit, and pytest, inspecting higher-risk areas, and confirming problems by running the code before fixing them. ### Source excerpt Learn an efficient workflow to review AI-generated code in Python: run ruff, mypy, bandit, and pytest, then catch the bugs agents get wrong. ## 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 ## Let's Reduce! A Gentle Introduction to Javascript's Reduce Method DevFeed: [Let's Reduce! A Gentle Introduction to Javascript's Reduce Method]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-07-10T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [JavaScript](), [coding](), [Redux](), [Web Development]() Tags: [array](), [arrow](), [engineering](), [filter](), [introduction](), [javascript](), [map](), [news](), [react](), [reduce](), [web-frontend]() ### AI overview A tutorial explaining JavaScript's reduce method as a foundation for understanding Redux. It describes how reduce processes array items, how each iteration depends on the previous return value, and why the method can be difficult to learn. ### Source excerpt By Josh Comeau Every summer, Khan Academy recruits a few software engineer interns. As part of their onboarding, ... Read more ## Prototyping with Framer DevFeed: [Prototyping with Framer]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-10-03T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Testing](), [coding](), [backends]() Tags: [backends](), [engineering](), [framer](), [news](), [prototype](), [prototyping](), [user-testing](), [web-frontend]() ### AI overview Khan Academy describes how it uses prototypes in user testing to gather feedback on new designs. The article explains the role of low- and high-fidelity prototypes, their limited lifespan, and the choice of Framer for creating and revising prototypes quickly. ### Source excerpt By Nick Breen We strive to make all of Khan Academy's new features & experiences easy to understand ... Read more ## What do software architects at Khan Academy do? DevFeed: [What do software architects at Khan Academy do?]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-05-14T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Software](), [Development](), [coding]() Tags: [architecture](), [article](), [collaboration](), [decision-making](), [engineering](), [infrastructure](), [news](), [platforms](), [process](), [software](), [standards](), [tools]() ### AI overview Kevin Dangoor explains how software architects at Khan Academy view their role: as product managers for the system in which software is built. The article emphasizes improving coding standards, tools, platforms, and processes by collaborating with engineers and engineering management, with DACI used to structure architecture-change decisions. ### Source excerpt By Kevin Dangoor "Architect" is a new role in Khan Academy's engineering team this year, and my colleague, ... Read more ## 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. ## Delta launches public beta for collaborative coding with agents and code review DevFeed: [Delta launches public beta for collaborative coding with agents and code review]() Original publisher: [Read original article]() Author: Nathan Sobo Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [coding](), [pull-requests](), [GitHub](), [browser]() Tags: [beta](), [github](), [mobile](), [pull-requests](), [tooling]() ### AI overview Delta is now available in public beta as a collaborative environment for coding with agents and reviewing their work. It supports shared agent conversations, isolated review worktrees, and access from macOS, Linux, Windows, the web, and mobile browsers. ### Source excerpt Delta is now in public beta. Replacing pull requests is our first step toward replacing GitHub. ## 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. ## Inside OpenAI's agentic software factory DevFeed: [Inside OpenAI's agentic software factory]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-15T15:41:25Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [OpenAI](), [codex](), [Software](), [coding]() Tags: [agentic](), [codex](), [engineering](), [openai](), [software]() ### AI overview This article examines Codex's role at OpenAI, the lab's agentic software factory, and the engineering challenges associated with serving one billion users. ### Source excerpt A deepdive into how Codex has "taken over" OpenAI, how the frontier lab builds its agentic software factory, and the engineering challenges of one billion users. Details from inside OpenAI ## How Software Engineers Can Move Into AI Without Becoming Machine Learning Engineers DevFeed: [How Software Engineers Can Move Into AI Without Becoming Machine Learning Engineers]() Original publisher: [Read original article]() Author: Hemant Pandey Published: 2026-09-15T13:55:50Z Content type: opinion Language: en Sources: [The Hustling Engineer]() Topics: [Artificial Intelligence](), [Software](), [coding]() Tags: [ai](), [ai-engineer](), [software]() ### AI overview The article discusses how software engineers can move into AI without starting their careers over or becoming machine learning engineers. ### Source excerpt How software engineers can move into AI without starting their careers over ## How automated testing improves development of complex features DevFeed: [How automated testing improves development of complex features]() Original publisher: [Read original article]() Author: Dan Lew Published: 2026-09-15T13:16:49Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Testing](), [coding](), [Concurrency](), [Database]() Tags: [automated](), [concurrency](), [database](), [regression](), [testing]() ### AI overview The author contrasts manual testing with automated testing while developing job cancellation in a work queue. Automated tests help expose concurrency and regression risks, cover corner cases, and simulate mid-execution cancellation more reliably. ### Source excerpt It's hard for me to overstate how much my coding has improved since embracing automated testing. And yet, I used to think of it as a waste of time! This post is for my former self, demonstrating why I embrace the practice now. Allow me to demonstrate with ## 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 ## 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 ## 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. ## 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 ## How Grok Bot designers use AI agents to build personal sites and product prototypes | John Bai & Peng Zheng DevFeed: [How Grok Bot designers use AI agents to build personal sites and product prototypes | John Bai & Peng Zheng]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-14T12:04:39Z Content type: article Language: en Sources: [Lenny's Newsletter]() Topics: [Artificial Intelligence](), [Figma](), [coding](), [Website]() Tags: [agents](), [ai](), [ai-agents](), [design](), [figma](), [mcp](), [prototype]() ### AI overview A 42-minute podcast episode featuring Grok Bot designers John Bai and Peng Zheng, who discuss using AI agents to update personal websites, automate design work in Figma, and turn ideas into working prototypes. ### Source excerpt Watch now (42 mins) | 🎙 Two designers at SpaceXAI show me how they use Grok Bot to run Figma from the gym, update a portfolio with one photo, and turn shower thoughts into working prototypes ## Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman DevFeed: [Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman]() Original publisher: [Read original article]() Author: Scott Hanselman Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [AI Development](), [Architecture & Design](), [coding]() Tags: [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [developers](), [infoq](), [junior-developers](), [ml-data-engineering](), [podcast](), [software-engineering](), [the-infoq-podcast](), [train-developers-ai-routine-work]() ### AI overview The podcast discusses how to train software engineers when AI agents perform much of the routine work traditionally assigned to junior developers. Scott Hanselman advocates a preceptorship model, experienced engineers overseeing AI-generated work, and long-term investment in mentorship and human connection. ### Source excerpt In this podcast, Michael Stiefel spoke to Scott Hanselman about developing new software engineers when artificial intelligence agents are doing most of the work on which junior developers were trained. Hanselman suggests the software industry should adopt a preceptorship model similar to the nursing profession. By Scott Hanselman ## commit-rewriter 0.1 DevFeed: [commit-rewriter 0.1]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T00:28:10Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [releases](), [repo](), [coding](), [Git](), [Python]() Tags: [ai-assisted-programming](), [ai-assisted-programming-407](), [git](), [git-55](), [projects](), [projects-553](), [python](), [python-1-283]() ### AI overview Release announcement for commit-rewriter 0.1, a small web app that edits repository commit messages and rewrites commits from the first edited commit through the latest one. It creates a timestamped branch of the current repository state so changes can be reverted. ### Source excerpt Release: commit-rewriter 0.1 I built this little web app the other day to help edit the commit messages for the Datasette security releases. The initial commits were full of coding agent cruft and references to issue IDs from our private repository, so they weren't fit for publication. If you want to edit the commit messages for a repository you can run it like this: uvx commit-rewriter path/to/repo Omit the path if you are already in the directory for that repo. When you submit your edits the tool creates a timestamped branch of your current repo state - to allow you to revert if you need to - and then rewrites every commit from the first one you edited to the most recent. Tags: git, projects, python, ai-assisted-programming ## Java Weekly, Issue 663 DevFeed: [Java Weekly, Issue 663]() Original publisher: [Read original article]() Author: baeldung Published: 2026-09-13T11:00:00Z Content type: article Language: en Sources: [Baeldung]() Topics: [Architecture & Design](), [coding](), [releases]() Tags: [agent](), [agentic](), [ai](), [architecture](), [java](), [kotlin](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [releases](), [spec-driven-development](), [spring](), [spring-boot](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview A Java-focused weekly roundup featuring spec-driven development, agent memory and AI-agent articles, technical presentations, and software release announcements. ### Source excerpt A better spec-driven development and the brain as example architecture. The post Java Weekly, Issue 663 first appeared on Baeldung. ## Use AI to Accelerate Delivery Without Lowering Software Quality DevFeed: [Use AI to Accelerate Delivery Without Lowering Software Quality]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-09-13T04:01:35Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [Software](), [Artificial Intelligence](), [coding](), [context]() Tags: [accelerate](), [ai](), [context](), [quality](), [software]() ### AI overview An opinion piece arguing that AI should speed delivery without reducing software-quality standards, using engineering judgment as scalable context, constraints, and checks. ### Source excerpt AI should accelerate delivery, not lower your standards. Turn engineering judgment into context, constraints, and checks that scale ## 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 [Next page]()