# AI-assisted coding Published articles for AI-assisted 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. ## 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 ## 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 ## Faster reviews and quality recognition for Microsoft Edge extensions DevFeed: [Faster reviews and quality recognition for Microsoft Edge extensions]() Original publisher: [Read original article]() Author: Microsoft Edge Team Published: 2026-09-08T16:02:17Z Content type: article Language: en Sources: [Microsoft Edge Blog]() Topics: [Edge](), [Extension](), [Microsoft](), [AI-assisted coding]() Tags: [add-ons](), [ai-assisted-coding](), [automation](), [edge](), [extension](), [extensions](), [review](), [security](), [uncategorized](), [validation]() ### AI overview Microsoft Edge describes automating repeatable extension-review validation checks and streamlining its review pipeline to shorten turnaround times while maintaining quality and security standards. ### Source excerpt Building an extension has never been easier. Rapid adoption of AI-assisted coding is enabling developers to build extensions faster than ever. We have seen that momentum on the Edge Add-ons site< The post Faster reviews and quality recognition for Microsoft Edge extensions appeared first on Microsoft Edge Blog. ## AI programming: a layered model DevFeed: [AI programming: a layered model]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-09-05T14:02:12Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [AI-assisted coding](), [Vibe coding](), [Programming](), [Software]() Tags: [ai](), [ai-assisted-coding](), [code](), [programming](), [software](), [vibe-coding]() ### AI overview This opinion proposes a layered model for AI-assisted coding: maintain a small, deliberately managed core whose code is read and tested, while allowing outer layers to change rapidly and absorb bugs. Dependencies should flow inward, with outer layers depending on the core but not vice versa. ### Source excerpt In the late 1960s and 1970s, people like David Parnas faced a problem. A decade earlier there were almost no programmers. Suddenly there were hordes of inexperienced ones. What could have been a golden era was turning into a mess: far more software, much of it falling apart. It sent Edsger Dijkstra into a ... Continue reading AI programming: a layered model ## Why Code Verification Matters More Than Ever in the Age of AI DevFeed: [Why Code Verification Matters More Than Ever in the Age of AI]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-24T15:31:01Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Code](), [AI-assisted coding](), [Artificial Intelligence]() Tags: [ai](), [ai-assisted-coding](), [code](), [cto](), [developers](), [verification]() ### AI overview The article explains why code verification is becoming more important as AI-assisted coding makes software production faster. It discusses the growing review burden, low confidence in AI-generated code, and research indicating that increased AI use can create pressure on delivery stability and developer productivity. ### Source excerpt In this article, we will look at how code verification works, why the rise of AI-generated code puts more pressure on it, along with the extremely useful insights from Andrea on what the future may look like. ## Increasing AI-Assisted Coding Throughput Without Losing Technical Judgment DevFeed: [Increasing AI-Assisted Coding Throughput Without Losing Technical Judgment]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-08-23T04:01:03Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [AI-assisted coding](), [coding](), [AI Agent](), [Software]() Tags: [agent](), [ai](), [ai-assisted-coding](), [architecture](), [coding](), [process](), [software](), [visibility]() ### AI overview This article examines how AI-assisted software engineering can increase coding throughput while reducing an engineer's visibility into planning, implementation, testing, deployment, and review. It argues that engineers must maintain technical judgment by defining goals and architectural constraints while also understanding and optimizing the processes used by AI agents. ### Source excerpt A zoom-in, zoom-out system for increasing AI-assisted coding throughput without losing the technical judgment needed to improve it ## Cursor Origin vs GitHub: What changes for developers DevFeed: [Cursor Origin vs GitHub: What changes for developers]() Original publisher: [Read original article]() Author: Aditya Oberai Published: 2026-08-18T00:00:00Z Content type: comparison Language: en Sources: [Appwrite Blog]() Topics: [cursor](), [GitHub](), [pull-requests](), [AI-assisted coding](), [hosting](), [Development]() Tags: [ai-assisted-coding](), [coding](), [comparisons](), [cursor](), [cursor-origin](), [development](), [github](), [hosting](), [pull-requests](), [workflow]() ### AI overview This comparison explains how Cursor Origin, an early-beta code hosting platform built into Cursor, differs from GitHub and what it changes for developers. Origin combines repositories, pull requests, code browsing, GitHub sync, and agent workflows in one editor, but initially targets paid Cursor accounts and is not positioned as an immediate replacement for GitHub. ### Source excerpt Cursor Origin vs GitHub: how Cursor's new code hosting handles repos, pull requests, and GitHub sync, and what actually changes in your daily workflow. ## Are REA's Architecture Principles still relevant in an AI-assisted world? DevFeed: [Are REA's Architecture Principles still relevant in an AI-assisted world?]() Original publisher: [Read original article]() Author: Jarrod Boyes Published: 2026-08-14T05:19:28Z Content type: article Language: en Sources: [REA Group]() Topics: [AI-assisted coding](), [systems](), [Development](), [Software]() Tags: [ai-assisted-coding](), [architecture](), [guardrails](), [software](), [systems](), [tech]() ### AI overview REA reviews whether its architecture principles remain relevant as AI-assisted coding and broader AI integration become part of software delivery. The review concludes that the principles still provide a strong foundation, while stronger guardrails and contextual adjustments are increasingly important. ### Source excerpt Are REA's Architecture Principles still relevant in an AI-assisted world? Introduction At REA, we use a set of architectural principles to guide how we build and evolve the systems behind our products. My colleague Shane previously shared these principles in the article Scaling technology with architectural principles. Every year we review these principles, not because we expect them to change every time, but because we want to test them against the environment we're working in now. ## Inside Android Skills - Built for deprecation DevFeed: [Inside Android Skills - Built for deprecation]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-08-06T16:00:00Z Content type: article Language: en Sources: [Android Developers Blog](), [Android Developers Blog]() Topics: [Android skills](), [Android](), [Large Language Model](), [Command-line interface]() Tags: [ai-assisted-coding](), [android](), [android-skills](), [cli](), [developer](), [evals](), [gemini](), [llm](), [skills]() ### AI overview This article explains the philosophy behind Android Skills: they target specific, fast-moving Android topics where state-of-the-art models have verifiable knowledge gaps. It discusses token costs, evaluation requirements, model and agent compatibility, and the role of Android documentation and Android CLI. ### Source excerpt Posted by Jose Alcérreca, Developer Relations Engineer, Android Developer Relations We released the official Android Skills in April, and the response surpassed all our expectations. In this blog post, I'll address some of the feedback we received, explaining the philosophy and methodology behind the project. Hopefully, this will also help you understand what happens behind the scenes when you install and use skills, allowing you to make better use of tokens and your own time. Why are there so few official skills? Currently, we only consider new skills when there's a verifiable knowledge gap in state-of-the-art (SOTA) models. Put simply: you don't need to teach the model what it already knows. (Though there are a few exceptions--read on!) We've released around 20 official skills so far, and they intentionally target highly specific, fast-moving areas that standard models aren't fully grounded on yet--things like AGP 9, Navigation 3, advanced Camera APIs, and Perfetto SQL. What about core, more general, skills? Every installed skill injects 100-200 tokens into the baseline context of every task you start. If that skill actually activates, that count can quickly jump into the thousands. In most cases, hoarding basic skills is both counterproductive and expensive. Before installing a skill for writing basic Kotlin or Compose, consider if your LLM of choice really needs it, or if it knows those topics well enough already. Evaluating skills Before their release, each skill is tested against a comprehensive set of evals that prove that the skill delivers clear value. These evals should pass when the skill is active, and fail otherwise. Evals are to skills what integration tests are to code. timeout_s: 1200 repository: url: [redacted - internal git repo] working_dir: wear_compose_m3_empty_app category_ids: - wear prompt: |- Add a horizontal pager to MainActivity.kt. Have three pages in the pager. Each page should contain the text "Page 1", "Page 2", and "Page 3" respectively ## Android Skills: Philosophy, Selection Criteria, and Evaluation Methodology DevFeed: [Android Skills: Philosophy, Selection Criteria, and Evaluation Methodology]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-08-06T16:00:00Z Content type: opinion Language: en Sources: [Android Developers Blog]() Topics: [Android skills](), [Large Language Model](), [Android Studio](), [Command-line interface]() Tags: [agent](), [agents](), [ai-assisted-coding](), [android-skills](), [android-studio](), [antigravity](), [cli](), [docs](), [gemini]() ### AI overview Android Developer Relations explains how official Android Skills are selected, focusing on verifiable knowledge gaps in state-of-the-art models and fast-moving topics. The article also discusses context costs, evaluation requirements, model compatibility, and use of the Android Knowledge Base. ### Source excerpt Posted by Jose Alcérreca, Developer Relations Engineer, Android Developer Relations We released the official Android Skills in April, and the response surpassed all our expectations. In this blog post, I'll address some of the feedback we received, explaining the philosophy and methodology behind the project. Hopefully, this will also help you understand what happens behind the scenes when you install and use skills, allowing you to make better use of tokens and your own time. Why are there so few official skills? Currently, we only consider new skills when there's a verifiable knowledge gap in state-of-the-art (SOTA) models. Put simply: you don't need to teach the model what it already knows. (Though there are a few exceptions--read on!) We've released around 20 official skills so far, and they intentionally target highly specific, fast-moving areas that standard models aren't fully grounded on yet--things like AGP 9, Navigation 3, advanced Camera APIs, and Perfetto SQL. What about core, more general, skills? Every installed skill injects 100-200 tokens into the baseline context of every task you start. If that skill actually activates, that count can quickly jump into the thousands. In most cases, hoarding basic skills is both counterproductive and expensive. Before installing a skill for writing basic Kotlin or Compose, consider if your LLM of choice really needs it, or if it knows those topics well enough already. Evaluating skills Before their release, each skill is tested against a comprehensive set of evals that prove that the skill delivers clear value. These evals should pass when the skill is active, and fail otherwise. Evals are to skills what integration tests are to code. timeout_s: 1200 repository: url: [redacted - internal git repo] working_dir: wear_compose_m3_empty_app category_ids: - wear prompt: |- Add a horizontal pager to MainActivity.kt. Have three pages in the pager. Each page should contain the text "Page 1", "Page 2", and "Page 3" respectively ## MAI-Code-1-Flash: early results from real developer workflows DevFeed: [MAI-Code-1-Flash: early results from real developer workflows]() Original publisher: [Read original article]() Author: Faith Xu Published: 2026-07-29T00:00:00Z Content type: article Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [AI-assisted coding](), [GitHub Copilot](), [vs-code](), [Artificial Intelligence](), [Microsoft](), [Claude]() Tags: [ai](), [ai-assisted-coding](), [blog](), [claude](), [code](), [coding](), [copilot](), [cost](), [developer](), [developers](), [efficiency](), [github](), [github-copilot](), [microsoft](), [models](), [tokens](), [tools](), [vs-code]() ### AI overview The article reports early production results for MAI-Code-1-Flash, a lightweight coding model built for fast, iterative developer workflows in GitHub Copilot. In VS Code's coding harness, it delivered strong coding quality with lower token usage. The evaluation found higher quality than Claude Haiku 4.5 and GPT-5.4 Mini, while larger models achieved some quality advantages at substantially higher token and interaction costs. ### Source excerpt MAI-Code-1-Flash is a lightweight coding model purpose built for the fast, iterative work developers do every day in GitHub. In this post, we share what the data is telling us about how it performs in real developer workflows. Read the full article ## The New Wave of AI-Assisted Coding Interviews DevFeed: [The New Wave of AI-Assisted Coding Interviews]() Original publisher: [Read original article]() Author: Hemant Pandey Published: 2026-07-15T14:00:15Z Content type: article Language: en Sources: [The Hustling Engineer]() Topics: [AI-assisted coding](), [coding]() Tags: [ai](), [ai-assisted-coding](), [coding]() ### AI overview The article discusses a new wave of AI-assisted coding interviews. ### Source excerpt The new wave of AI assisted coding interviews ## Open source maintainership in the age of AI DevFeed: [Open source maintainership in the age of AI]() Original publisher: [Read original article]() Author: CLA enforcement Published: 2026-06-26T18:00:00Z Content type: article Language: en Sources: [Kubernetes Contributors Blog]() Topics: [AI-assisted coding](), [Kubernetes](), [Open Source](), [Pull Request](), [Code quality]() Tags: [ai](), [ai-assisted-coding](), [kubernetes](), [open-source](), [policy](), [pull-request](), [quality]() ### AI overview This article explains how the Kubernetes community is adapting its maintainership practices to AI-assisted coding. It describes an AI policy requiring contributors to disclose AI assistance, remain responsible for changes, avoid attributing commits to AI, satisfy contributor license agreement checks, and personally respond to review comments. ### Source excerpt AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usage. The AI policy helps steer the conversation around the project's stance on AI and provides a clear signal to contributors on how to use these tools responsibly. Kubernetes AI policy The Kubernetes project has established clear guidelines for AI-assisted contributions that balance innovation with accountability. These policies are designed to maintain code quality and ensure human oversight while acknowledging that AI tools can be valuable aids in the development process. Transparency first Contributors must disclose when AI tools have been used to assist with a pull request. A simple statement in the PR description such as "This PR was written in part with the assistance of generative AI" is sufficient. This transparency helps reviewers understand the context and apply appropriate scrutiny. Human accountability While AI tools can assist, the human contributor remains fully responsible for every change. The policy explicitly prohibits: Listing AI as a co-author on commits Using AI co-signing on commits Adding trailers like "assisted-by" or "co-developed" that attribute work to AI This isn't about diminishing AI's role as a tool--it's about maintaining clear accountability. If something breaks, there needs to be a human who understands why and can fix it. CLA enforcement for co-authors The CNCF provides a tool for verifying the ## Open source maintainership in the age of AI DevFeed: [Open source maintainership in the age of AI]() Original publisher: [Read original article]() Author: CLA enforcement Published: 2026-06-26T18:00:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [AI-assisted coding](), [Open Source](), [Code quality](), [Pull Request](), [software-development](), [coding]() Tags: [agents](), [ai](), [ai-assisted-coding](), [code-quality](), [kubernetes](), [open-source](), [policy](), [pull-request](), [software-development]() ### AI overview This article explains how the Kubernetes community is adapting open-source maintainership to AI-assisted coding. It describes an AI policy requiring disclosure of AI use, preserving human accountability, maintaining code quality, and enforcing contributor license agreement checks and human participation in review. ### Source excerpt AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usage. The AI policy helps steer the conversation around the project's stance on AI and provides a clear signal to contributors on how to use these tools responsibly. Kubernetes AI policy The Kubernetes project has established clear guidelines for AI-assisted contributions that balance innovation with accountability. These policies are designed to maintain code quality and ensure human oversight while acknowledging that AI tools can be valuable aids in the development process. Transparency first Contributors must disclose when AI tools have been used to assist with a pull request. A simple statement in the PR description such as "This PR was written in part with the assistance of generative AI" is sufficient. This transparency helps reviewers understand the context and apply appropriate scrutiny. Human accountability While AI tools can assist, the human contributor remains fully responsible for every change. The policy explicitly prohibits: Listing AI as a co-author on commits Using AI co-signing on commits Adding trailers like "assisted-by" or "co-developed" that attribute work to AI This isn't about diminishing AI's role as a tool--it's about maintaining clear accountability. If something breaks, there needs to be a human who understands why and can fix it. CLA enforcement for co-authors The CNCF provides a tool for verifying the ## New Open-Source Context Specs Released DevFeed: [New Open-Source Context Specs Released]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-06-24T16:58:13Z Content type: article Language: en Sources: [Capital One Tech]() Topics: [Spec Driven Development](), [AI-assisted coding](), [context window](), [Open Source](), [Development]() Tags: [ai-assisted-coding](), [context-engineering](), [context-window](), [development](), [open-source](), [software-engineering](), [spec-driven-development]() ### AI overview Capital One has open-sourced Context Specs, a spec-driven development framework that uses context engineering for AI-assisted coding. It captures domain knowledge in reusable experts and uses automated feedback signals to verify an agent's work, addressing context-window constraints such as context decay, pollution, and compaction loss. ### Source excerpt Capital One open-sources Context Specs, an SDD framework using context engineering to bridge business ideas and code. Every developer has had the same experience: The AI writes code confidently, only for you to realize that it has missed the mark. The fix isn't a better model, it's better context. Right context, right time. That's the whole game. To address this challenge, Capital One is open-sourcing Context Specs, a framework for spec-driven development (SDD) that treats context engineering-the practice of building systems that dynamically decide what your agent sees and when it sees it-as the primary lever for AI-assisted coding. The framework achieves this by capturing a team's domain-specific knowledge into reusable "experts" that you create once and compose across the entire workflow, and then using automated feedback "signals" to verify the agent's work. The real problem: Your agent's context window is finite Every AI coding agent operates within a context window, a fixed amount of information it can see at any given moment. This constraint creates three mechanical failure modes that plague every developer using AI tools today: Context decay: Older messages in a conversation get ignored, summarized or dropped entirely. That careful instruction you gave 20 messages ago? Gone. Context pollution: When an agent searches your codebase autonomously, it pulls in irrelevant files. Every irrelevant token displaces a useful one. Compaction loss: When the window fills up, the system summarizes history to make room. You don't control what gets dropped. These aren't edge cases; they're the default experience. Most frameworks for AI-assisted development weren't designed with these constraints in mind. Some frameworks generate thousands of lines of specification markdown before a single line of code is written, burning millions of tokens on ceremony. Other frameworks are so lightweight they leave everything to interpretation, forcing the agent to search blindly and pollute ## Building a design system specced for engineers and agents DevFeed: [Building a design system specced for engineers and agents]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-06-17T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Design system](), [User interface design](), [AI-assisted coding](), [cursor](), [Claude]() Tags: [ai](), [ai-assisted-coding](), [claude](), [components](), [cursor](), [design](), [design-system](), [devtools](), [dx](), [figma](), [llms](), [ui-design]() ### AI overview Evil Martians describes how it built a design system for Currents in seven weeks. The system standardized UI decisions and was structured to be readable by engineers and AI-assisted coding tools, including Cursor and Claude, helping the lean team reuse components and maintain a consistent product interface. ### Source excerpt Coding without design guidelines can leave teams with an inconsistent UI design and a complex feature development process. Here's how we built a design system for Currents that's readable by engineers and AI agents. ## A new pkg.go.dev API for Go DevFeed: [A new pkg.go.dev API for Go]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-06-12T18:30:00Z Content type: release Language: en Sources: [Google Open Source Blog]() Topics: [API](), [Go Language](), [JSON](), [OpenAPI Specification](), [Command-line interface](), [AI-assisted coding]() Tags: [agents](), [ai-assisted-coding](), [api](), [developer-tools](), [go](), [json](), [open-source](), [openapi](), [pkg-go-dev]() ### AI overview The Go Team introduces a structured API for pkg.go.dev, providing programmatic access to Go package metadata. The stateless, GET-only API returns JSON metadata, is currently available under a v1beta path, and is intended to evolve toward a formal v1 release after community feedback and stability validation. ### Source excerpt by Ethan Lee, Jonathan Amsterdam & Hana Kim, Go Team Access to Go metadata has been an everpresent need for the Go community. Since its launch, pkg.go.dev has served as a central hub for Go package documentation and discovery. While we initially prioritized providing this comprehensive access via a web interface, the need for streamlined programmatic access has become increasingly clear. Structured API access has been one of the most highly requested features for pkg.go.dev for a while now. Developers building tools, IDE integrations, automated workflows, and other systems have had to rely on inconsistent and fragile scraping methods. By providing a formal API, we can provide fast and efficient access to required data. This foundation also sets Go up for the future of AI-assisted coding. Large language models and agents can access the context necessary to reason about the Go ecosystem with greater precision and accuracy. Empowering Tool Builders Our goal with this API is to reduce the technical churn for builders and innovators. By offering structured JSON metadata, we address the following use cases: Search and Discovery: The API enables fast and efficient search across the entire Go module ecosystem. Driving AI Innovation: As AI-assisted coding evolves, LLMs and agents need precise context. This API provides the data required for agents and models to reason deterministically about Go packages. The Service Interface Built for stability and efficient caching, the API uses a stateless, GET-only architecture. Primary endpoints are currently hosted under the v1beta path. Following a period of feedback from the Go community and confirmed stability, we intend to transition toward a formal v1 release. For a complete interactive reference of all endpoints, query parameters, and response shapes, see pkg.go.dev/api. The machine-readable API contract is also published directly at pkg.go.dev/v1beta/openapi.yaml. Endpoint Description /v1beta/imported-by/{path} Paths of packages ## How Endava is redesigning software delivery around AI agents DevFeed: [How Endava is redesigning software delivery around AI agents]() Original publisher: [Read original article]() Published: 2026-06-04T12:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Software Engineering](), [AI-assisted coding](), [OpenAI](), [ChatGPT](), [codex](), [AI Platform](), [Deployment]() Tags: [ai](), [ai-agents](), [ai-assisted-coding](), [ai-platform](), [chatgpt](), [codex](), [deployment](), [developers](), [software-engineering]() ### AI overview Endava is making AI central to enterprise work by adopting OpenAI as its enterprise AI platform and providing employees with ChatGPT Enterprise and Codex. Its DavaFlow methodology embeds OpenAI technology across meeting preparation, business planning, product discovery, software engineering, and deployment, while AI-assisted workflows also support legal, project management, and commercial teams. ### Source excerpt Learn how Endava is using AI agents, ChatGPT Enterprise, and Codex to accelerate software delivery, automate workflows, and build an AI-native culture across the enterprise. ## How I Validated Design Decisions Before Writing Production Code DevFeed: [How I Validated Design Decisions Before Writing Production Code]() Original publisher: [Read original article]() Author: andyacevedo Published: 2026-06-04T01:29:47Z Content type: article Language: en Sources: [Slack Design]() Topics: [AI-assisted coding](), [Data visualization](), [Algorithms](), [Figma]() Tags: [ai-assisted-coding](), [algorithms](), [data-visualization](), [figma](), [projects](), [uncategorized]() ### AI overview A Slack designer explains how AI-assisted coding can make design exploration practical before production work begins. Interactive prototypes tested gallery layout algorithms and max-width strategies against real screen-size data, revealing edge cases and helping evaluate which decisions hold up across conditions. ### Source excerpt The biggest shift AI has brought to design isn't that designers can now write production code. It's that we can finally generate evidence before committing to anything. There's a lot of conversation about AI affecting production: designers pushing code and shipping something closer to the final product. That matters. But the bigger opportunity is even [...] The post How I Validated Design Decisions Before Writing Production Code appeared first on Slack Design. ## Introducing the pkg.go.dev API DevFeed: [Introducing the pkg.go.dev API]() Original publisher: [Read original article]() Author: Ethan Lee, Hana Kim, and Jonathan Amsterdam Published: 2026-05-21T00:00:00Z Content type: release Language: en Sources: [The Go Blog]() Topics: [SDKs](), [AI-assisted coding]() Tags: [ai-assisted-coding](), [api](), [go](), [ide](), [openapi]() ### AI overview pkg.go.dev introduces an official API for querying metadata about published Go modules. The GET-only, stateless interface aims to replace scraping-based access and requires unambiguous module selection for package queries. ### Source excerpt Introducing the new programmatic API for pkg.go.dev, allowing developers to fetch package and module data directly. ## Leveraging AI to build a faster iOS CI system DevFeed: [Leveraging AI to build a faster iOS CI system]() Original publisher: [Read original article]() Author: Muhieddine EL Kaissi Published: 2026-04-21T18:16:01Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [CI/CD](), [Testing](), [iOS](), [AI-assisted coding]() Tags: [ai](), [ai-assisted-coding](), [ci-cd](), [continuous-integration](), [developer](), [developer-productivity](), [ios](), [ios-development](), [mobile](), [pipeline](), [testing]() ### AI overview Thumbtack describes Smart Test Selection, an iOS CI system that reduces pipeline time by running only tests relevant to each pull request. The article explains why parallelization stopped scaling, including node contention, large test suites, and slow snapshot and UI tests, while noting that AI-assisted coding could increase CI demand. ### Source excerpt Every iOS pull request at Thumbtack used to wait 30 to 40 minutes for Continuous Integration (CI) -- sometimes over an hour on Fridays. We built Smart Test Selection (STS) to cut that in half. Here's how. I'm a Senior Software Engineer on the iOS Infrastructure team. We own the CI/CD pipelines, build tooling, and shared frameworks that keep Thumbtack's apps shipping reliably. We also build AI-powered developer tooling -- 35+ AI Agent Skills, MCP integrations for Jenkins, Figma, Firebase, and Xcode -- that help the broader iOS team ship faster. Over the past few quarters, we built Smart Test Selection (STS), a system that reduces CI time by running only the tests that matter for each pull request. Why Full-Suite Testing Stopped Scaling Before STS, we had already optimized CI through parallelization -- running consumer, pro, and shared framework tests on separate nodes, and splitting snapshot tests across multiple simulator clones. But parallelization hit a ceiling. The more nodes we spread CI across, the more engineers had to wait for free nodes, cancelling out the speed gains. Snapshot tests dominated the suite but couldn't be split further without diminishing returns, and UI tests were inherently slow per-test. We needed a fundamentally different approach: run fewer tests, not run the same tests faster. Every iOS PR ran the full test suite for each affected app. We have three main targets -- a consumer app, a pro app, and a shared framework -- totaling 1,500+ test classes. The pipeline already skipped unaffected apps (a change to the consumer app would not run pro app tests), but within each affected app, every test ran regardless of what specifically changed. A one-line bug fix in one screen would trigger all 400+ tests for that entire app. This was becoming unsustainable. Human-generated code was already putting CI at the limit due to ever-increasing code size and test counts. We had increased our CI node count multiple times to keep up with demand, but each expansion ## How "Clinejection" Turned an AI Bot into a Supply Chain Attack DevFeed: [How "Clinejection" Turned an AI Bot into a Supply Chain Attack]() Original publisher: [Read original article]() Author: Stephen Thoemmes Published: 2026-02-19T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI-assisted coding](), [AI Bots](), [CI/CD](), [ci](), [incident]() Tags: [agents](), [ai](), [ai-agents](), [ai-assisted-coding](), [anthropic](), [attacks](), [awareness](), [bash](), [blog](), [cache](), [ci](), [ci-cd](), [claude](), [cli](), [developer](), [devrel](), [github](), [github-actions](), [incident](), [interest](), [open-source-security](), [openclaw](), [payload](), [security](), [snyk-open-source](), [supply-chain](), [supply-chain-security](), [vulnerabilities](), [vulnerability](), [vulnerability-insights]() ### AI overview The article examines Clinejection, a vulnerability chain that used indirect prompt injection, GitHub Actions cache poisoning, and credential weaknesses to turn Cline's AI issue-triage bot into a supply-chain attack vector. An unauthorized Cline CLI version was published to npm and installed OpenClaw globally during an approximately eight-hour window. ### Source excerpt The Clinejection vulnerability chain illustrates a dangerous new era of supply chain attacks where AI agents are turned into exploit vectors. By combining indirect prompt injection with GitHub Actions cache poisoning, attackers successfully pushed unauthorized code to thousands of developers. This incident highlights the critical need for hardened CI/CD pipelines and rigorous security for AI-assisted coding tools. ## Reusable Prompts: The Future of Starter Templates DevFeed: [Reusable Prompts: The Future of Starter Templates]() Original publisher: [Read original article]() Author: Andre Landgraf Published: 2026-01-08T16:26:24Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding](), [Boilerplate](), [cursor](), [Drizzle](), [vercel ai sdk]() Tags: [agent-skills](), [ai](), [ai-agents](), [ai-assisted-coding](), [ai-coding](), [anthropic](), [claude-code](), [cursor](), [drizzle](), [mcp](), [product](), [tailwind](), [vercel](), [vibe-coding]() ### AI overview The article argues that reusable prompts, skills, MCP integrations, and plugins can replace traditional starter-template boilerplate for AI-assisted development. ### Source excerpt Looking back at 2025, AI changed coding for good. Early in the year, skepticism around AI-assisted coding was still high and capabilities were limited. Now it's clear that vibe coding has gone mainstream, and AI-assisted coding is the future of software engineering. That said, we... ## The only rule: Don't look at the code DevFeed: [The only rule: Don't look at the code]() Original publisher: [Read original article]() Published: 2025-12-29T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Vibe coding](), [AI Development](), [container-security](), [supply-chain-security]() Tags: [ai](), [ai-assisted-coding](), [ai-engineering](), [chainguard-ai](), [chainguard-vibelympics](), [code-quality](), [container-security](), [software-supply-chain](), [vibe-coding]() ### AI overview Chainguard's Vibelympics was a vibe-coding tournament in which 59 individuals and teams built software without looking at the generated code. The article describes the competition's challenges, including an emoji-only interface and a package ecosystem security auditor, and highlights selected submissions and results. ### Source excerpt Chainguard's Vibelympics competition brought out the best and most creative ideas in vibe coding and AI-assisted software development. [Next page]()