# AI Development Published articles for AI Development. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Our framework for reporting model misalignment DevFeed: [Our framework for reporting model misalignment]() Original publisher: [Read original article]() Published: 2026-09-16T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [AI Development](), [AI Strategy](), [Framework](), [Development]() Tags: [ai](), [ai-development](), [alignment](), [behavior](), [developers](), [model](), [monitoring](), [openai](), [reporting](), [research](), [standards]() ### AI overview OpenAI introduces a framework for tracking, investigating, and disclosing model misalignment, accompanied by six reports on unexpected or concerning model behavior observed over the previous six months. The framework favors disclosure even when the significance of an instance is uncertain and is intended to evolve through experience and public feedback. ### Source excerpt OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior. ## Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions DevFeed: [Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T18:47:23Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI-assisted coding](), [Training AI Models](), [Open Source Models & Datasets](), [Agent Harness]() Tags: [agent](), [agent-harness](), [ai](), [ai-development](), [ai-engineering](), [ai-models](), [coding-agents](), [data](), [developer-tools](), [developers](), [training]() ### AI overview Bolt.new is testing Forge, a research preview for individual Pro subscribers that offers up to 50 times more usage of open-weight coding models in exchange for opting in to share anonymized coding sessions. The sessions may include prompts, source code, fix traces, and conversations with the coding agent, and will support an Arcee AI project to train a trillion-parameter-class open-weight model. ### Source excerpt Bolt.new, StackBlitz's browser-based AI development platform, is testing a new trade with developers: more coding-model usage in exchange for training The post Bolt is giving developers 50x more compute. But there's a catch. appeared first on The New Stack. ## 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. ## Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here DevFeed: [Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-11T15:32:16Z Content type: release Language: en Sources: [ByteByteGo]() Topics: [Artificial Intelligence](), [Claude Code](), [AI Development](), [AI Engineering]() Tags: [ai](), [ai-development](), [ai-engineering](), [ai-evals](), [claude-code](), [development]() ### AI overview ByteByteGo announces ByteByteGo Live, a membership offering live courses on Claude Code, production AI systems, AI engineering, AI evaluations, cost optimization, and related topics. The announcement cites higher completion rates for live cohorts and says the membership covers courses offered over the next 12 months. ### Source excerpt Most online courses never get finished (~4% completion). Live cohorts get ~40%, roughly 10x higher. Live courses are the only courses people actually finish. So we're launching ByteByteGo Live. ## 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 ## The iOS Simulator Inside Cursor, Claude Code, and Codex DevFeed: [The iOS Simulator Inside Cursor, Claude Code, and Codex]() Original publisher: [Read original article]() Author: Antoine van der Lee Published: 2026-09-07T13:07:15Z Content type: opinion Language: en Sources: [SwiftLee]() Topics: [iOS](), [cursor](), [Claude Code](), [codex](), [App](), [Tooling]() Tags: [agent](), [agent-skills](), [ai-development](), [app-development](), [claude-code](), [codex](), [cursor](), [developer-tools](), [ios]() ### AI overview An opinion article examines how the iOS Simulator is becoming integrated into Cursor, Claude Code, and Codex as app development shifts toward orchestrating AI agents. It argues that visual testing, performance review, and critical human validation remain essential, and describes AI Agents for the iOS Simulator as a way to automate navigation and validation within the developer workflow. ### Source excerpt App development is changing, and the iOS Simulator is too, as it starts to appear in Cursor, Claude Code, and Codex in different ways. I've been developing apps since 2009 and the RocketSim developer tool since 2019, and I can tell we're reinventing ourselves like never before. I also started to wonder: what is the ... -> The post The iOS Simulator Inside Cursor, Claude Code, and Codex appeared first on SwiftLee. ## ISO 42001 Evidence: What Auditors Ask For DevFeed: [ISO 42001 Evidence: What Auditors Ask For]() Original publisher: [Read original article]() Author: info@goteleport.com (Preet Dhatt) Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [audit](), [AI Development](), [Deployment](), [CI/CD](), [data-governance](), [Monitoring](), [Requirements]() Tags: [ai](), [ai-development](), [article](), [audit](), [ci-cd](), [data-governance](), [deployment](), [eu](), [iso](), [monitoring](), [requirements]() ### AI overview This article explains what auditors look for in ISO 42001 evidence and how organizations can connect access, approvals, infrastructure, CI/CD activity, releases, and production changes into an evidence trail. It also discusses monitoring, human oversight, third-party access, and data governance within an AI Management System. ### Source excerpt Prepare ISO 42001 audit evidence that proves your controls work in practice. ## CircleCI is now available on Cursor Origin DevFeed: [CircleCI is now available on Cursor Origin]() Original publisher: [Read original article]() Author: Nathan Fish Published: 2026-09-01T18:45:00Z Content type: release Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [CI/CD](), [Testing](), [AI-assisted coding](), [Pull Request]() Tags: [agentic-ci-cd](), [ai-coding-agents](), [ai-development](), [ci](), [ci-cd](), [circleci](), [circleci-cursor-origin](), [circleci-news](), [cursor-origin](), [cursor-origin-ci-cd](), [integrations](), [mcp](), [origin-ci-cd-integration](), [pull-request](), [testing]() ### AI overview CircleCI has released a beta integration with Cursor Origin, allowing teams to connect Origin-hosted repositories and run CI/CD pipelines on pushes and pull requests. The integration supports agent-oriented validation through parallel execution, targeted testing, and feedback via the CircleCI CLI and MCP. ### Source excerpt CircleCI now integrates with Cursor Origin in beta. Connect an Origin repo, keep your existing .circleci/config.yml, and add agent-friendly CI/CD to your Origin projects. ## MCP vs API: How they work together and when to use each DevFeed: [MCP vs API: How they work together and when to use each]() Original publisher: [Read original article]() Author: Jacob Schmitt Published: 2026-08-20T21:30:00Z Content type: tutorial Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Model Context Protocol (MCP)](), [API](), [Artificial Intelligence]() Tags: [agents](), [ai-development](), [api](), [mcp](), [mcp-vs-api](), [mcp-vs-rest](), [when-to-use-mcp-vs-api](), [will-mcp-replace-apis]() ### AI overview This tutorial explains how APIs and the Model Context Protocol differ and work together. APIs expose operations for software to call, while MCP provides a standardized way for AI applications to discover and invoke selected capabilities at runtime, often through existing REST, GraphQL, or RPC interfaces. ### Source excerpt An API exposes operations software can call. MCP lets AI agents discover and invoke them at runtime. Learn how the two work together, and when to use each. ## Klibs.io Grows to 4,200+ KMP Projects With Smarter Discovery and New AI Integrations DevFeed: [Klibs.io Grows to 4,200+ KMP Projects With Smarter Discovery and New AI Integrations]() Original publisher: [Read original article]() Author: Daria Voronina Published: 2026-08-17T13:12:57Z Content type: article Language: en Sources: [Kotlin : A concise multiplatform language developed by JetBrains | The JetBrains Blog]() Topics: [AI-assisted coding]() Tags: [agents](), [ai-development](), [evaluation](), [kotlin](), [kotlin-multiplatform-libraries](), [llms](), [mcp](), [mcp-server](), [multiplatform](), [news]() ### AI overview Klibs.io has expanded its Kotlin Multiplatform library catalog and adds AI integrations through an MCP server. Coding agents can search projects by platform and retrieve current package-version data, while an expert skill supports library discovery and comparison. ### Source excerpt Explore a growing Kotlin Multiplatform catalog in your browser, or bring up-to-date library data directly into your AI development workflow through the klibs.io MCP server. When we introduced klibs.io in December 2024, the goal was simple: make it easier to find a Kotlin Multiplatform library that fits both your use case and target platforms. Since [...] ## Starting your engineering career in the AI era: 6 takeaways for junior developers DevFeed: [Starting your engineering career in the AI era: 6 takeaways for junior developers]() Original publisher: [Read original article]() Author: Jacob Schmitt Published: 2026-08-11T16:05:00Z Content type: article Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Software Engineering](), [Artificial Intelligence](), [Development](), [Generative AI](), [coding]() Tags: [ai](), [ai-and-software-engineering](), [ai-development](), [career](), [developers](), [early-career-software-engineer](), [generative-ai](), [junior-developers](), [junior-engineer-ai](), [podcast](), [software-engineering](), [software-engineering-mentorship](), [starting-an-engineering-career]() ### AI overview This article presents six takeaways from the Confident Commit podcast about starting a software engineering career in the AI era. It argues that AI is changing the skills expected of junior developers rather than eliminating the need for them, emphasizing judgment, product sense, testing, debugging, and problem framing. ### Source excerpt AI isn't closing the door on early-career engineers. Six takeaways from the Confident Commit podcast on starting a software career in the AI era. ## AGENTS.md vs. skills: How to steer a coding agent DevFeed: [AGENTS.md vs. skills: How to steer a coding agent]() Original publisher: [Read original article]() Author: Jacob Schmitt Published: 2026-08-11T16:00:00Z Content type: article Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [AI-assisted coding](), [coding](), [OpenAI](), [anthropic]() Tags: [ai-development](), [anthropic](), [coding](), [coding-agents](), [config](), [documentation](), [evals](), [openai]() ### AI overview This article compares AGENTS.md and skills as ways to steer coding agents. AGENTS.md provides always-on repository context, while skills are modular instruction bundles loaded on demand for specialized or procedural tasks. The article recommends using a concise AGENTS.md for stable, broadly applicable guidance and skills for situational procedures, with reproducible evaluations used to determine whether the guidance changes agent behavior. ### Source excerpt AGENTS.md or skills? The format matters less than whether your steering actually changes agent behavior. Learn how to test agent config with reproducible evals in your pipeline. ## Building AI Agents in Dart with the Genkit Dart SDK DevFeed: [Building AI Agents in Dart with the Genkit Dart SDK]() Original publisher: [Read original article]() Author: Abhishek Doshi Published: 2026-08-10T23:16:05Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [Dart](), [Genkit](), [AI Development](), [Flutter](), [Cloud Functions](), [Firebase](), [SDKs]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-development](), [cloud-functions](), [dart](), [firebase](), [flutter](), [generative-ai-tools](), [genkit](), [sdk]() ### AI overview This tutorial introduces the Genkit Dart SDK for building AI agents natively in Dart and deploying them to Cloud Functions for Firebase. It explains agent orchestration, conversational state, history, streams, models, tools, flows, and multi-agent delegation. ### Source excerpt Because your Flutter app deserves a brain, and you deserve to never look at a Python traceback again. Let's be honest. We all love Dart. It's clean, it's safe, and it powers our beloved Flutter. But until recently, if you wanted to build serious, multi-step AI agents, the industry basically told you to pack your bags, leave your cozy typed ecosystem, and go write Python. Gross 🤮 Thankfully, Google finally heard our collective sighs and dropped the Genkit Dart SDK. Now, you can build full-stack, hyper-intelligent AI agents natively in Dart and deploy them straight to Cloud Functions for Firebase. No context-switching. No spinning up a random Node.js microservice just to talk to an LLM. Grab your coffee (or your energy drink of choice). Let's look at how Genkit turns your Dart code into an absolute powerhouse. https://medium.com/media/91c2efa4198c043f3f400f216214650e/hrefThe Shift: From "Dumb Chatbots" to "Autonomous Overlords" Building traditional chatbots or strict multi-step UI flows often feels like playing one of those terrible, rigid video games that block you at a level until you do exactly what the developers scripted. Agentic AI, by contrast, is the ultimate open-ended sandbox. An agent doesn't just blindly answer a prompt. It analyzes what the user wants, figures out which tools it needs to achieve the goal, and dynamically orchestrates the steps to get there. With Genkit's new Agents API, all the messy plumbing: maintaining conversational state, keeping track of history, and parsing streams, is handled behind a single API. You just focus on giving it a brain. The Holy Trinity: Models, Tools, and Flows To stop your AI from just hallucinating fan-fiction, Genkit uses a few core primitives. Think of this as the toddler-proofing stage of AI development. Models (ai.generate()): The actual brain. Whether you are using Gemini, Claude, or OpenAI, the API stays exactly the same. Tools (defineTool): This is how you give your AI hands. By defining strict input schemas ## Agentic AI Development Tools: How to Build Your Stack DevFeed: [Agentic AI Development Tools: How to Build Your Stack]() Original publisher: [Read original article]() Author: Aaron Taylor Published: 2026-08-10T11:34:27Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Development](), [developer tooling](), [AI-assisted coding](), [sdlc](), [CI/CD](), [observability]() Tags: [agentic-ai](), [agents](), [ai-agents](), [ai-development](), [ci-cd](), [code-generation](), [development-tools](), [observability](), [sdlc](), [software-delivery]() ### AI overview This guide explains how to choose and connect agentic AI development tools across the software development lifecycle. It distinguishes the agent layer, the agentic infrastructure layer, and the existing foundation of source control, CI/CD, and observability tools, then introduces criteria for evaluating tools and begins with code generation agents. ### Source excerpt Explore agentic AI development tools and learn how to build a practical stack for faster, more reliable AI software delivery. ## Build your own coding agent DevFeed: [Build your own coding agent]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-08-05T20:06:19Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [Agent Harness](), [coding](), [AI-assisted coding](), [AI Development](), [Code](), [Go Language](), [Shell](), [Language models](), [Open Source](), [codex](), [Claude Code]() Tags: [agent](), [agent-harness](), [agents](), [ai](), [ai-development](), [claude](), [claude-code](), [code](), [codex](), [coding](), [commands](), [development](), [go](), [open-source]() ### AI overview This tutorial explains the basic architecture of coding agents and implements a minimal Go-based agent called Micro Neo. The agent connects to an existing AI model and supports reading files, editing files, and running shell commands. It also describes the harness loop that sends tasks and tool results between the user, model, and local machine. ### Source excerpt Why I built my own coding agent and lessons learned ## Running Ollama Locally with Podman on Fedora Linux DevFeed: [Running Ollama Locally with Podman on Fedora Linux]() Original publisher: [Read original article]() Author: Yazan Monshed Published: 2026-08-05T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Large Language Model](), [Linux](), [Open Source Models & Datasets](), [API](), [cURL](), [GPU]() Tags: [ai-development](), [api](), [curl](), [fedora-project-community](), [gpu](), [large-language-models-llms](), [linux](), [llama](), [nvidia](), [offline](), [ollama](), [open-source](), [podman](), [privacy](), [python](), [terminal](), [using-software]() ### AI overview This article explains how to run Ollama locally in a Podman container on Fedora Linux. It covers installing or verifying Podman, creating persistent storage for model weights, running the Ollama container, downloading Llama 3, optionally enabling Nvidia GPU acceleration, and using Ollama's local REST API with curl. ### Source excerpt Running Large Language Models (LLMs) locally has become increasingly popular for development, privacy, and offline testing. Ollama makes this incredibly straightforward, allowing you to run models like Llama 3 or Mistral directly on your machine. By leveraging Podman on Fedora Linux, you can isolate Ollama inside a container. This approach keeps your host system clean [...] ## What is an AI sandbox? A developer's guide DevFeed: [What is an AI sandbox? A developer's guide]() Original publisher: [Read original article]() Author: Roger Winter Published: 2026-07-28T16:00:00Z Content type: tutorial Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [AI Sandbox](), [AI-assisted coding](), [AI Agent](), [Development]() Tags: [ai-code-sandbox](), [ai-coding-agents](), [ai-development](), [ai-sandbox](), [ai-sandboxing](), [coding-agents](), [database](), [devops-101](), [github]() ### AI overview This developer guide explains AI sandboxes as isolated execution environments for code generated or run by AI coding agents. It describes how sandboxing limits access to the host machine, other projects, production systems, and credentials, reducing the impact of destructive or mistaken commands while allowing agents to execute with fewer approval interruptions. ### Source excerpt An AI sandbox isolates code from AI coding agents so destructive commands can't reach your machine or production systems. Learn how AI sandboxing works and where it fits in the development workflow. ## How to free up Xcode disk space safely with an AI Agent DevFeed: [How to free up Xcode disk space safely with an AI Agent]() Original publisher: [Read original article]() Author: Antoine van der Lee Published: 2026-07-27T08:34:16Z Content type: tutorial Language: en Sources: [SwiftLee]() Topics: [Xcode](), [AI Agent](), [Agent Skill](), [Open Source]() Tags: [agent-skill](), [agent-skills](), [ai-agent](), [ai-development](), [derived-data](), [developer-tools](), [disk-space](), [how-to](), [open-source](), [workflow](), [xcode]() ### AI overview This tutorial explains why Xcode can consume substantial disk space, especially when multiple AI agents use separate Git worktrees. It introduces an open-source Xcode Disk Cleanup Agent Skill that searches common storage locations and requires explicit approval before deleting anything. ### Source excerpt Xcode disk space can grow rapidly without you noticing. Especially these days, with multiple agents turning multiple worktrees into silent disk space occupiers. Where I used to only focus on deleting old Simulators every now and then, I'm now also actively managing worktrees and derived data folders. There are great Mac apps and open-source projects ... -> The post How to free up Xcode disk space safely with an AI Agent appeared first on SwiftLee. ## Build a Multi-Camera 3D Tracking Application with NVIDIA DeepStream 9.1 Skills DevFeed: [Build a Multi-Camera 3D Tracking Application with NVIDIA DeepStream 9.1 Skills]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-15T23:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [multi-camera tracking](), [Nvidia](), [AI Development](), [Jetson](), [GitHub](), [Open Source](), [real-time]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [ai](), [ai-development](), [analytics](), [applications](), [automation](), [computer-vision-video-analytics](), [deepstream](), [developers](), [development](), [edge](), [featured](), [github](), [jetpack](), [jetson](), [metropolis](), [multi-camera-tracking](), [nvidia](), [real-time](), [research](), [spaces](), [video](), [video-analytics]() ### AI overview This article introduces NVIDIA DeepStream 9.1 capabilities for building multi-camera 3D tracking applications. It explains how AutoMagicCalib and Multi-View 3D Tracking combine detections from auto-calibrated cameras in a shared 3D coordinate system, maintaining consistent object IDs across views. It also highlights agentic skills, JetPack support for Jetson edge platforms, and open-source reference implementations. ### Source excerpt Developers building video analytics applications across large spaces must track the same object as it moves between camera views. Single-camera 2D tracking... ## Our Droidcon US 2026 Picks - Sam Hill DevFeed: [Our Droidcon US 2026 Picks - Sam Hill]() Original publisher: [Read original article]() Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Mobile](), [multiplatform](), [Kotlin](), [kotlin-native](), [AI Development](), [Artificial Intelligence](), [Rust](), [Testing](), [Gradle](), [scaling]() Tags: [2026](), [ai](), [ai-development](), [android](), [droidcon](), [gradle](), [ios](), [js](), [kmp](), [kotlin](), [kotlin-multiplatform](), [kotlin-native](), [multiplatform](), [rust]() ### AI overview Touchlab previews selected Droidcon USA 2026 talks covering AI and cross-platform mobile development, Kotlin Native project structures, KMP integration with Rust, migration decisions, and mobile testing. ### Source excerpt With Droidcon right around the corner, here are the talks we are most excited about ## Rebuilding the CircleCI CLI from scratch DevFeed: [Rebuilding the CircleCI CLI from scratch]() Original publisher: [Read original article]() Author: Pete Steyert-Woods Published: 2026-07-07T16:00:00Z Content type: article Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Command-line interface](), [Go Language](), [MCP Server](), [OAuth](), [Text-based user interface](), [debugging](), [telemetry](), [configuration](), [YAML](), [AI-assisted coding]() Tags: [ai-development](), [ci](), [circleci-cli](), [cli](), [coding-agents](), [configuration](), [debugging](), [developer-tools](), [go](), [mcp](), [mcp-server](), [oauth](), [terminal](), [yaml]() ### AI overview CircleCI rebuilt its CLI from scratch in Go, focusing on human-first terminal output, predictable JSON, stable exit codes, browser OAuth login, debugging features, and an MCP server for coding agents. ### Source excerpt We rebuilt the CircleCI CLI from the ground up in Go: human-first output, -json everywhere, browser OAuth login, a built-in debugging TUI, and an MCP server for your coding agent. ## Introducing Skills for Dart and Flutter DevFeed: [Introducing Skills for Dart and Flutter]() Original publisher: [Read original article]() Author: Mariam Hasnany Published: 2026-05-06T18:19:09Z Content type: release Language: en Sources: [Flutter - Medium]() Topics: [Agent Skills](), [Dart](), [Flutter](), [Agent Skill](), [MCP](), [MCP Server](), [AI-assisted coding]() Tags: [agent-skill](), [agent-skills](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-development](), [coding](), [coding-agents](), [dart](), [efficiency](), [flutter](), [flutter-app-development](), [libraries](), [mcp](), [mcp-server](), [skills](), [tests](), [tools]() ### AI overview Flutter is introducing Agent Skills for Dart and Flutter to give AI tools domain-specific expertise for professional development tasks. The task-oriented skills provide tailored instructions for workflows such as adaptive layouts and enhance the Dart MCP server, aiming to improve accuracy and reduce token usage. ### Source excerpt Introducing prepackaged Dart and Flutter Skills!Improving AI with domain expertise AI agents are generalists, but when it comes to professional Flutter development, "general" isn't enough. To build production-grade apps, you need an assistant that understands the nuance of localization, the latest Dart language features, and how to add integration tests. Today, we're introducing Agent Skills for Flutter and Dart -- a new way to give your AI tools domain-specific expertise. Beyond the knowledge gap One of the primary challenges in AI development is the "knowledge gap." Flutter and Dart can launch new features more quickly than LLMs can update their fixed training data. As a part of how we are thinking about AI, we are looking for ways to not only address the knowledge gap but also ensure the agent applies that knowledge to achieve the task accurately and efficiently following the most optimal workflows. A little over a year ago, Model Context Protocols (MCP) were the way to provide more AI domain-specific expertise. While MCP gives an agent access to specialized tools, an Agent Skill teaches the agent how to use those tools for a specific task. Think of it this way: MCP provides the hammer and nails (the tools), while a Skill provides the blueprint and the professional know-how to build the house. Skills improve context efficiency through "progressive disclosure". This is similar to how deferred loading works in Flutter, where apps can load libraries when needed, coding agents load Skills when they are relevant to what you're trying to do . For Flutter and Dart, these Skills provide tailored instructions for common workflows, and enhance the tools provided in the Dart MCP server to reduce the knowledge gap, which improves accuracy and lowers token usage. A task-oriented approach Our early experimentation revealed that Skills that only provide documentation don't add as much value as we initially assumed. Since Flutter's comprehensive and well written documentation is ## JPMorgan Just Published a Cyber To-Do List and Snyk Covers 8 of the 10 Items. How do you stack up? DevFeed: [JPMorgan Just Published a Cyber To-Do List and Snyk Covers 8 of the 10 Items. How do you stack up?]() Original publisher: [Read original article]() Author: John Carione Published: 2026-04-23T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Resilience](), [Security](), [snyk-iac](), [supply-chain-security](), [vulnerability](), [Infrastructure as code](), [Open Source](), [AI Development](), [releases](), [pull-requests](), [Code]() Tags: [ai](), [ai-development](), [awareness](), [blog](), [cloud-security](), [code](), [contentlab](), [developer](), [devops](), [executive](), [iac-security](), [interest](), [open-source](), [open-source-security](), [pull-requests](), [releases](), [resilience](), [sbom](), [secrets](), [security](), [snyk-cloud](), [snyk-code](), [snyk-container](), [snyk-iac](), [snyk-open-source](), [snyk-platform](), [snyk-security-intel](), [supply-chain-security](), [vulnerability]() ### AI overview JPMorganChase's 10-point cyber resilience checklist addresses enterprise security priorities spanning software versions, open-source dependencies, SBOMs, build pipelines, secrets, infrastructure as code, and AI development. The article explains how Snyk covers eight of the ten actions through developer workflows and its security platform. ### Source excerpt JPMorganChase published a 10-point cyber resilience checklist. See how Snyk covers 8 of the 10 actions and where it fits in your security stack. ## The Glue Problem in Modern AI Development DevFeed: [The Glue Problem in Modern AI Development]() Original publisher: [Read original article]() Author: James Skelton Published: 2026-04-02T21:30:07Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Artificial Intelligence](), [AI Platform](), [Development](), [Cloud](), [Deployment](), [Orchestration](), [Inference]() Tags: [ai](), [ai-development](), [ai-ml](), [cloud](), [deployment](), [developers](), [efficiency](), [engineering](), [fragmentation](), [infrastructure](), [orchestration](), [overhead]() ### AI overview The article examines how fragmented AI infrastructure creates a glue-code problem for developers. It argues that integrated cloud platforms can reduce integration work, operational overhead, and total cost of ownership when deploying and scaling AI applications. ### Source excerpt AI is now central to modern software development. Teams across industries are turning to AI to solve product and workflow problems in software. But building production systems is still complex. The hardest part of deploying AI isn't the model, it's everything around it. That complexity becomes a glue-code problem when storage, compute, orchestration, networking, authentication, and inference live in separate systems with different operating models. The more seams a workflow crosses, the more developer effort shifts from building product logic to wiring services together. A more integrated platform model reduces that burden. This article examines what it takes to deploy and operate AI applications in today's cloud landscape. Using two examples, we will compare the process in two landscapes: a neocloud combined with a hyperscaler versus a vertically integrated cloud stack. While surface-level costs may look similar, the integrated model offers clear advantages in efficiency by reducing the time developers spend writing glue code and managing the problems that emerge as AI products scale. Key Takeaways The biggest cost in AI systems isn't infrastructure: it's integration. Fragmented, multi-provider stacks force developers to spend time writing and maintaining glue code instead of building product features, turning engineering effort into the real cost center. Raw infrastructure pricing is no longer the differentiator; total cost of ownership is. Even when platform costs are nearly identical, the added complexity of cross-cloud orchestration increases operational overhead, failure points, and staffing requirements at scale. The future of AI platforms is vertical integration, not more tools. Platforms that unify compute, storage, and inference reduce friction, accelerate development, and allow smaller teams to build and scale AI applications more efficiently. The Real Problem Is Fragmentation Consider the modern landscape for AI deployment. AI applications rely on far mo [Next page]()