# Error Handling Published articles for Error Handling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Libreboot Build System Audit 2 DevFeed: [Libreboot Build System Audit 2]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: article Language: en Sources: [News about Libreboot releases and development]() Topics: [audit](), [Development](), [Shell](), [Code](), [Documentation]() Tags: [audit](), [bios](), [bugs](), [build](), [canoeboot](), [code](), [coreboot](), [development](), [documentation](), [error-handling](), [free-software](), [general](), [libre](), [libreboot](), [opensource](), [shell](), [uefi]() ### AI overview This article reports on a completed audit of Libreboot's build system, lbmk, as of 11 September 2023. It describes bug fixes, improved documentation and error handling, fewer scripts, unified logic, and a reduction in shell source lines from 3,388 to 2,644, or 22%. ### Source excerpt Article: Libreboot Build System Audit 2 Web link: https://libreboot.org/news/audit2.html ## Libreboot 20230413 released! DevFeed: [Libreboot 20230413 released!]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: release Language: en Sources: [News about Libreboot releases and development]() Topics: [libreboot](), [coreboot](), [boot](), [Embedded Software Dev](), [x86]() Tags: [bios](), [boot](), [canoeboot](), [coreboot](), [debian](), [error-handling](), [firmware](), [free-software](), [hardware](), [libre](), [libreboot](), [opensource](), [release](), [time](), [uefi]() ### AI overview Libreboot 20230413 is a testing release and bugfix release relative to Libreboot 20230319. The article highlights build-system fixes, code cleanup, improved error handling, and build testing on Debian Sid. It also describes Libreboot's firmware support for selected x86 and ARM machines. ### Source excerpt Article: Libreboot 20230413 released! Web link: https://libreboot.org/news/libreboot20230413.html ## Libreboot Build System Audit 3 DevFeed: [Libreboot Build System Audit 3]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: article Language: en Sources: [News about Libreboot releases and development]() Topics: [audit](), [maintenance](), [Error Handling](), [Shell]() Tags: [audit](), [audits](), [bios](), [bug](), [build-system](), [canoeboot](), [complexity](), [coreboot](), [efficiency](), [error-handling](), [free-software](), [integrity](), [libre](), [libreboot](), [opensource](), [safety](), [threading](), [uefi]() ### AI overview This article reports on Libreboot Build System Audit 3, focusing on the lbmk build system. It describes improved error handling, bug fixes, efficiency improvements, reduced complexity, stronger vendor-file integrity checks, and broader use of multithreading. The audit reduced the build system from 2,644 to 1,744 shell-script source lines compared with the previous audit, without reducing functionality. ### Source excerpt Article: Libreboot Build System Audit 3 Web link: https://libreboot.org/news/audit3.html ## Is Agentic now tailors its audit by site type DevFeed: [Is Agentic now tailors its audit by site type]() Original publisher: [Read original article]() Author: Rich Haines Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [audit](), [App](), [API](), [Authentication](), [Error Handling](), [SDKs]() Tags: [api](), [app](), [audit](), [authentication](), [error-handling](), [sdk]() ### AI overview Is Agentic reports now support four site-type views: Docs & content, Business, App, and Commerce. The selected view changes which checks are highlighted, while scores remain comparable and unchanged. ### Source excerpt Is Agentic reports now let you view your checks through one of four site types: Docs & content, Business, App, or Commerce. For example, the Commerce view highlights payment and checkout standards like x402, UCP, and ACP, while the App view highlights API discovery, authentication, error handling, and SDK support. Your score stays the same in every view, and you can change views on an existing report without rescanning. Only the highlighted checks change, so you can focus on what matters for your kind of site while keeping scores comparable across sites. To set the default view, add a single meta tag to your page's : Set the value to match your site type: content for Docs & content business for Business app for App store for Commerce The tag only sets which view readers see first. It doesn't add points or change your score. If no type is declared, Is Agentic infers one and shows which type it chose. Run a check at is-agentic.com or read the documentation. Read more ## OpenVX 1.3.2 Released: More Precise Errors, Better Consistency, and Groundwork for 2.0 DevFeed: [OpenVX 1.3.2 Released: More Precise Errors, Better Consistency, and Groundwork for 2.0]() Original publisher: [Read original article]() Author: jphilips (jeff@khronosgroup.org) Published: 2026-08-31T13:00:00Z Content type: release Language: en Sources: [Blogs Khronos Blog]() Topics: [API](), [systems]() Tags: [api](), [blog](), [consistency](), [debugging](), [developers](), [error-handling](), [errors](), [openvx](), [release]() ### AI overview Khronos released OpenVX 1.3.2, adding more precise error codes, clearer image-handling rules, API enhancements, and terminology changes intended to improve conformance, portability, implementation, and debugging. The release is described as groundwork for OpenVX 2.0. ### Source excerpt Khronos released OpenVX 1.3.2, an update to the open, royalty-free standard that uses a high-level graph-based framework to deliver portable, efficient vision processing. This focused update strengthens error handling, improves conformance, and makes OpenVX easier for vendors to implement and for developers to debug -- laying the groundwork for the next major release, OpenVX 2.0. ## Clean Architecture: Use Cases Should NOT Catch Stripe Exceptions DevFeed: [Clean Architecture: Use Cases Should NOT Catch Stripe Exceptions]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-08-27T06:01:02Z Content type: tutorial Language: en Sources: [Optivem Journal]() Topics: [Error Handling](), [stripe]() Tags: [architecture](), [error-handling](), [exceptions](), [stripe]() ### AI overview The article addresses error handling in the application layer and argues that Clean Architecture use cases should not catch Stripe exceptions. ### Source excerpt Error Handling - Application layer ## How we teach LLMs to write BadgerQL DevFeed: [How we teach LLMs to write BadgerQL]() Original publisher: [Read original article]() Author: Kevin Webster Published: 2026-08-18T07:00:00Z Content type: article Language: en Sources: [Honeybadger]() Topics: [Large Language Model](), [Testing](), [clickhouse](), [SQL]() Tags: [ai](), [automated](), [clickhouse](), [devops-articles](), [error-handling](), [llms](), [observability](), [tests]() ### AI overview The article explains how Honeybadger taught LLMs to generate BadgerQL queries for Insights searches. It describes using a structured, example-driven system prompt and automated integration tests to measure whether the generated queries produced the expected results. ### Source excerpt LLMs don't understand BadgerQL out of the box. Here's how we used our existing integration tests to teach them BQL--and measure whether the resulting queries actually worked. ## Building a Testable Network Layer and Three-Tier State Management in Flutter DevFeed: [Building a Testable Network Layer and Three-Tier State Management in Flutter]() Original publisher: [Read original article]() Author: Veli Bacık Published: 2026-07-08T15:56:52Z Content type: tutorial Language: en Sources: [Flutter Community - Medium]() Topics: [Flutter](), [Network](), [API](), [HTTP](), [Refactoring](), [Error Handling](), [JSON](), [ui](), [App]() Tags: [api](), [backend](), [building](), [code](), [dart](), [error-handling](), [flutter](), [how-to](), [http](), [implementation](), [integration](), [json](), [masterclass](), [network](), [refactoring](), [server](), [state-management]() ### AI overview This tutorial explains how to refactor a Flutter application's network and state-management architecture. It introduces a generic, testable network manager and service/model layers, then applies a three-tier state model using Cubit for business state, BlocSelector for targeted rebuilds, and ValueNotifier for widget-local UI state. ### Source excerpt How to wrap HTTP behind a generic, testable network manager with sealed results -- then drive your screens with a three-tier state model: global Cubit, page Cubit, and widget-local ValueNotifier. Flutter Refactoring Masterclass -- Part 3 GitHub - VB10/flight_booking: Flight App for refactoring real project PRs: #11, #12 📺 Video Series: Network & Service Layer https://medium.com/media/2d40d555e4d8968f1d15fb35e2e91553/hrefhttps://medium.com/media/3d8a8a32a77c98177ba10492a892aed6/href 🤖 Want to apply these changes to your project? See the AI prompt at the end. The Problem In Part 2 we split the login page into View, ViewModel, and Mixin -- but the ViewModel still new'd up a Dio() instance and hard-coded http://localhost:8080 inline. Every screen that talked to the backend did the same thing: create a fresh Dio, paste the base URL, decode JSON by hand, and copy-paste the same if (statusCode == 200) ... else ... catch (e) ladder. That pattern rots fast. A URL change means a project-wide search. A new header (auth token, API version) has to be added everywhere. Error handling drifts -- one screen returns 'Server hatası', another swallows the exception. And nothing is testable, because the network call is welded to the widget. As the video puts it: the person writing the code shouldn't be able to say "this only works with internet" -- the network is a dependency, and dependencies must be managed. There's a second rot: state. setState rebuilds the whole widget on every field change. A page with a loading flag, an error string, and a list re-renders all three even when only one changed. And there's no consistent home for "where does the loading state live" -- sometimes a ValueNotifier, sometimes a bool, sometimes buried in _PageState. This article fixes both on the same module. PR #11 builds a generic network + service + model layer. PR #12 layers a three-tier state model on top -- Cubit for business, BlocSelector for surgical rebuilds, ValueNotifier for widget-local UI. What We Change ## Next.js error handling: a practical guide DevFeed: [Next.js error handling: a practical guide]() Original publisher: [Read original article]() Author: Farhan Hasin Chowdhury Published: 2026-06-18T07:00:00Z Content type: tutorial Language: en Sources: [Honeybadger]() Topics: [Next.js](), [API](), [bug](), [React]() Tags: [api](), [authentication](), [error-handling](), [error-reporting](), [guide](), [javascript](), [javascript-articles](), [next-js](), [nextjs]() ### AI overview A practical guide to handling expected and unexpected errors in Next.js applications using the App Router. It covers return-value-based handling for expected errors, Server Actions, Server Components, 404 responses, error boundaries, and Honeybadger-based production error reporting. ### Source excerpt Next.js gives developers a structured way to handle errors at every level of an application -- from form validation to root-level crashes. Learn how to manage expected errors with return values, catch uncaught exceptions with error boundaries, and set up automatic error reporting in production. ## How to build a resilient platform control plane with Temporal DevFeed: [How to build a resilient platform control plane with Temporal]() Original publisher: [Read original article]() Author: Joshua Smith Published: 2026-06-01T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [control-plane](), [Provisioning](), [Python](), [SDKs](), [API]() Tags: [build](), [error-handling](), [how-to](), [infrastructure](), [lifecycle](), [provisioning](), [python](), [retries](), [sdk](), [workflow]() ### AI overview This tutorial explains how to build a resilient platform control plane with Temporal. It compares basic infrastructure Workflows with long-running Entity Workflows that manage a resource's lifecycle, including message handling, retries, compensation, and durable state. ### Source excerpt Learn how to build a resilient platform control plane with Temporal using the Entity Workflow pattern: message handling, Continue-as-New, and compensation. ## New updates to A2UI and Flutter's GenUI package DevFeed: [New updates to A2UI and Flutter's GenUI package]() Original publisher: [Read original article]() Author: Andrew Brogdon Published: 2026-05-14T14:18:22Z Content type: tutorial Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [ui](), [Protocol (disambiguation)](), [Framework](), [Large Language Model](), [Error Handling]() Tags: [android](), [architecture](), [error-handling](), [flutter](), [framework](), [generative-ai-tools](), [generative-ui](), [ios](), [language-models](), [protocol](), [retry](), [updates]() ### AI overview This migration guide explains updates to Flutter's genui package and the A2UI protocol. The package adopts A2UI v0.9, shifts from a Structured Output First to a Prompt First approach using JSON blocks in agent responses, and separates UI state and rendering, message transport, and conversation management into distinct layers. ### Source excerpt Generative UI, or GenUI for short, is a user experience pattern in which an agent not only generates content, but also makes decisions about how that content should be displayed and made interactive for the user. For Flutter developers, implementing GenUI means using A2UI, an open protocol that defines a way for agents and clients (or "renderers") to collaborate on the composition and state of a user interface. To capitalize on this, the Flutter team built genui, a package that uses A2UI to connect with an agent and provide it with a catalog of widgets to use, and then presents those widgets to the user. Both the genui package and the A2UI protocol recently got an update! The latest release of genui introduces several architectural changes to the framework. Driven by adoption of v0.9 of the A2UI protocol, this update shifts genui from a "Structured Output First" philosophy in which A2UI messages were streamed through structured output APIs to a "Prompt First" approach in which agents include blocks of JSON as text in their responses. It also decouples the architecture, providing more direct control over how your application interacts with Large Language Models (LLMs). If you are migrating an app from v0.7.0 to v0.9.0 of the genui package, this guide covers the necessary steps, from dependency cleanup to wiring up your new chat loops. Architecture decoupling In previous versions, GenUI relied on a series of classes based on ContentGenerator. These classes hid the details of prompt construction, LLM network calls, and response parsing. The latest version of package:genui removes ContentGenerator. Instead, the framework is now split into distinct layers: Engine (SurfaceController): Manages the state and rendering of your UI. Transport (A2uiTransportAdapter): Streams messages between the agent and renderer. Facade (Conversation): Provides a high-level API for managing chat states. This decoupling means you have control over chat history, retry logic, and error handling. ## Four Habits for Shipping Software with AI Agents DevFeed: [Four Habits for Shipping Software with AI Agents]() Original publisher: [Read original article]() Author: Eric Roby Published: 2026-04-28T12:25:25Z Content type: opinion Language: en Sources: [Eric Roby]() Topics: [Artificial Intelligence](), [Code](), [Large Language Model](), [context](), [Error Handling](), [test]() Tags: [ai](), [ai-agents](), [code](), [context](), [error-handling](), [llm](), [test](), [verification]() ### AI overview A backend engineer shares lessons from using AI agents daily: provide strong context, verify generated work by running code and testing edge cases, and plan for context loss when conversations reset or reach limits. The article argues that effective AI-assisted development requires active engineering judgment rather than blind trust. ### Source excerpt The four habits that separate engineers who ship from engineers who just generate code ## Structured Concurrency DevFeed: [Structured Concurrency]() Original publisher: [Read original article]() Published: 2026-04-13T00:00:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [Structured concurrency](), [Coroutines](), [kotlin-coroutines](), [Concurrency](), [Error Handling]() Tags: [coroutines](), [error-handling](), [guide](), [kotlin](), [memory-leaks](), [processes](), [structured-concurrency](), [workshop-learning-programming]() ### AI overview A Kotlin-focused guide to structured concurrency explains how coroutine hierarchies establish ownership and completion guarantees. It covers cancellation of child coroutines, avoiding unnecessary work and resource leaks, awaiting completion, and handling errors. ### Source excerpt The only guide to structured concurrency you'll ever need. ## Workflow-as-code: orchestration in pure code DevFeed: [Workflow-as-code: orchestration in pure code]() Original publisher: [Read original article]() Author: Ruben Fiszel Published: 2026-04-03T00:00:00Z Content type: release Language: en Sources: [Windmill Blog]() Topics: [Orchestration](), [Code](), [Python](), [TypeScript](), [Concurrency](), [Error Handling]() Tags: [architecture](), [code](), [concurrency](), [editor](), [error-handling](), [execution](), [launch-week](), [orchestration](), [parallelism](), [python](), [releases](), [typescript](), [version-control](), [workflow](), [workflows]() ### AI overview Windmill introduces workflow-as-code, allowing complex workflows to be defined in TypeScript or Python. The system uses checkpointing and replay, runs tasks as separate jobs, supports parallelism and fault tolerance, and releases workers while workflows wait. ### Source excerpt Introducing workflow-as-code: define complex workflows in TypeScript or Python with checkpointing, parallelism, and fault tolerance built in. ## Revamping Myntra App Analytics persistence with KMP and SQLite DevFeed: [Revamping Myntra App Analytics persistence with KMP and SQLite]() Original publisher: [Read original article]() Author: Kartik Sharma Published: 2026-03-31T13:01:09Z Content type: article Language: en Sources: [Myntra]() Topics: [App](), [SQLite](), [Persistence](), [SDK](), [Databases](), [legacy](), [Error Handling]() Tags: [android](), [app](), [databases](), [error-handling](), [ios](), [kotlin-multiplatform](), [legacy](), [mobile-app-development](), [persistence](), [sdk](), [sqlite]() ### AI overview This engineering post describes Myntra's effort to re-architect the persistence layer of its app analytics SDK. It explains how a legacy Android pipeline using in-memory queues and SharedPreferences created performance and data-reliability bottlenecks as event volume grew, and introduces a SQLite-backed persistence engine intended to improve app stability and data integrity. ### Source excerpt Introduction In the dynamic landscape of e-commerce, data is the bedrock of decision-making, and app stability is the foundation of user experience. At Myntra, where millions of users engage with our platform daily, ensuring the reliability of both is paramount. This necessity drove us to re-architect a critical piece of our infrastructure: the persistence layer of the Myntra app's analytics SDK. This post details our journey of augmenting our legacy analytics SDK with a robust new persistence engine backed by SQLite[1], achieving significant gains in app stability and data integrity. Background: The Limits of the Legacy Pipeline For years, our legacy in-house analytics SDK was the workhorse for collecting user interaction data. However, as our app's event volume grew, the SDK's aging persistence and processing logic created significant bottlenecks that led to degraded app performance and compromised the reliability of analytics data our product teams depended on. On Android: The Legacy Pipeline and its Limits On Android, the legacy analytics pipeline followed a straightforward event processing model: Capture, Persist & Batch: Events generated by used actions were held in an in-memory queue and then serialized into a persistent queue backed by SharedPreferences[2]. Once a batch threshold was met (typically 10 events), the batch was dispatched to the server. Background Flushing: Periodic background jobs flushed unsent events and retried failed batches. Error Handling: Failed batches were pushed to a separate retry queue, with older events silently purged if the queue hit its cap. High level overview of legacy android pipeline At the time of the initial implementation, SharedPreferences was a pragmatic choice to prioritize velocity. The ecosystem for local databases on Android was still maturing, and raw SQLite often required significant boilerplate compared to the simplicity of a key-value store. However, SharedPreferences is architecturally limited for high-throughp ## From error-handling to structured concurrency DevFeed: [From error-handling to structured concurrency]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2026-03-23T15:30:00Z Content type: opinion Language: en Sources: [Nelson Elhage]() Topics: [Concurrency](), [Concurrent Programming](), [Error Handling](), [Exception]() Tags: [concurrency](), [error-handling](), [examples](), [exception](), [exception-handling](), [structured-concurrency]() ### AI overview This article examines how error-handling patterns based on stack unwinding and cleanup should be adapted for concurrent programs with multiple tasks. It introduces the problem of unhandled errors in concurrent execution and compares possible behaviors when one task fails. ### Source excerpt How should we think about error-handling in concurrent programs? In single-threaded programs, we've mostly converged on a standard pattern, with a diverse zoo of implementations and concrete patterns. When an error occurs, it is propagated up the stack until we find a stack frame which is prepared to handle it. As we do so, we unwind the stack frames in-order, giving each frame the opportunity to clean up or destroy resources as appropriate. ## Introducing the Temporal Developer Skill DevFeed: [Introducing the Temporal Developer Skill]() Original publisher: [Read original article]() Author: Ethan Ruhe Published: 2026-03-20T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [AI-assisted coding](), [cursor](), [SDKs](), [Error Handling](), [retry](), [Testing](), [configuration]() Tags: [agent](), [ai-coding-agents](), [cli](), [configuration](), [coverage](), [developer](), [error-handling](), [product-news](), [retry](), [sdk]() ### AI overview Temporal announces the Developer Skill in public preview, providing coding agents with on-demand guidance on Temporal's programming model, SDK best practices, workflows, retries, error handling, testing, and worker configuration. ### Source excerpt Supercharge your AI coding agent with the Temporal Developer Skill. Get expert-level guidance on Workflow determinism, retry policies, and SDK best practices in real-time. ## Error Handling in C, Go, Rust, and Google's Absl DevFeed: [Error Handling in C, Go, Rust, and Google's Absl]() Original publisher: [Read original article]() Author: Nevin Zheng Published: 2026-02-28T00:00:00Z Content type: opinion Language: en Sources: [nevzheng]() Topics: [Error Handling](), [Programming](), [Embedded Systems](), [Microcontroller](), [C](), [Go Language](), [Rust](), [Hardware]() Tags: [c](), [embedded](), [embedded-systems](), [error-handling](), [errors](), [go](), [hardware](), [microcontrollers](), [opinions](), [rust](), [stm32]() ### AI overview An opinionated survey of error handling across C, Go, Rust, and Google's Absl. The article traces error recovery from hardware status registers, interrupts, watchdog timers, and resets to modern language features and libraries, with particular attention to firmware, embedded systems, and microcontrollers. ### Source excerpt Errors, opinions, and the rakes I've stepped on. ## Scalable API Response Handling Across Multi-Layered Architectures with Sandwich DevFeed: [Scalable API Response Handling Across Multi-Layered Architectures with Sandwich]() Original publisher: [Read original article]() Author: Jaewoong Eum Published: 2026-02-08T06:25:14Z Content type: tutorial Language: en Sources: [Stories by Jaewoong Eum on Medium]() Topics: [Android](), [Kotlin](), [API](), [kotlin-coroutines](), [Error Handling](), [Network]() Tags: [android](), [api](), [code](), [communication](), [coroutines](), [error-handling](), [exceptions](), [flow](), [kotlin](), [kotlin-multiplatform](), [mvi](), [mvvm](), [network](), [networking](), [presentation](), [responses](), [retrofit](), [viewmodel]() ### AI overview This tutorial explains how to handle Retrofit API responses, errors, and exceptions across multi-layered Android architectures such as MVVM and MVI. It presents Sandwich as a type-safe, composable approach and covers response handling, composition, merging, global error mapping, and Flow integration. ### Source excerpt Unsplash@picoftasty Modern Android applications commonly adopt multi-layered architectures such as MVVM or MVI, where data flows through distinct layers: a data source, a repository, and a ViewModel (or presentation layer). Each layer has a specific responsibility, and network responses must propagate through all of them before reaching the UI. While this separation produces clean, testable code, it introduces a real challenge: how do you handle API responses, including errors and exceptions, as they cross each layer boundary? Most developers solve this by wrapping API calls in try-catch blocks and returning fallback values. This works for small projects, but as the number of API calls grows, the approach creates ambiguous results, scattered boilerplate, and lost context that downstream layers need. You end up with ViewModels that cannot tell whether an empty list means "no data" or "network failure," repositories that swallow important error details, and data sources that repeat the same error handling pattern dozens of times. In this article, you'll explore the problems that emerge when handling Retrofit API calls across layered architectures, why conventional approaches break down at scale, and how Sandwich provides a type safe, composable solution that simplifies response handling from the network layer all the way to the UI. You'll also walk through the full set of Sandwich APIs, from basic response handling to advanced patterns like sequential composition, response merging, global error mapping, and Flow integration, each with real world use cases that show when and why you would reach for them. Retrofit API calls with coroutines Most Android projects use Retrofit with Kotlin coroutines for network communication. A typical service interface looks like this: https://medium.com/media/ebcba33386d664d1cf69a89234d2e22a/href The service returns a List directly. Retrofit deserializes the JSON response body and gives you the data. This works perfectly when the ## Making AI assistants better at email: Postmark's new documentation tooling DevFeed: [Making AI assistants better at email: Postmark's new documentation tooling]() Original publisher: [Read original article]() Author: Postmark team (fdossetto+postmark@activecampaign.com) Published: 2025-11-24T18:13:00Z Content type: article Language: en Sources: [Postmark (en-US)]() Topics: [Documentation](), [Artificial Intelligence](), [Model Context Protocol](), [MCP Server](), [API](), [ChatGPT](), [Tooling](), [Claude]() Tags: [ai](), [ai-assistants](), [ai-tools](), [api](), [chatgpt](), [claude](), [code](), [documentation](), [error-handling](), [mcp](), [mcp-server](), [product-news](), [security](), [tooling]() ### AI overview Postmark describes three updates intended to help AI assistants provide more accurate email-integration guidance: an llms.txt file with structured API and documentation context, pre-built prompts for common integration tasks, and an experimental MCP server that connects assistants to Postmark capabilities. ### Source excerpt Getting an AI assistant to help with your email integration shouldn't feel like explaining SMTP to your cat. But until recently, that's kind of what it was like - AI tools could tell you about email APIs, but they often got the details wrong or gave you outdated information. We've been thinking about this problem, and we've shipped three updates to make AI assistants genuinely useful when you're working with Postmark. What we built1. llms.txt: Context for AI assistants We published an llms.txt file that gives AI tools like ChatGPT and Claude accurate, structured information about Postmark's API, features, and best practices. Instead of AI assistants guessing or making assumptions about our documentation, they can now reference this file to give you correct details about authentication, endpoints, message streams, deliverability features, and integration patterns. How to use it: When you need help with Postmark integration, troubleshooting, or understanding specific features, paste https://postmarkapp.com/llms.txt into your AI tool. Your assistant will have the context it needs to provide better, more accurate guidance. 2. Pre-built AI prompts We added an AI prompts section to our documentation with ready-to-use prompts for common integration tasks. Each prompt is designed to generate production-ready code that includes error handling, best practices, and proper Postmark API usage. Copy a prompt, paste it into your AI tool, and get working code. Available prompts: Node.js email integration Rails with ActionMailer setup Laravel Mail configuration Password reset flows Event-driven notification systems Inbound email processing Better Auth integration You'll save time - no more documentation deep-dives just to send a welcome email. You'll also avoid common mistakes since the prompts include our recommendations for deliverability, security, and reliability. 3. Postmark MCP server: AI assistants that actually do things Earlier this year, we launched our Model Context Proto ## Building an AI REPL with QuickJS DevFeed: [Building an AI REPL with QuickJS]() Original publisher: [Read original article]() Author: James Long Published: 2025-11-18T12:00:00Z Content type: opinion Language: en Sources: [James Long]() Topics: [Artificial Intelligence](), [wasm](), [WebAssembly](), [anthropic](), [Claude Code](), [User experience (UX)](), [C](), [debug](), [Error Handling]() Tags: [ai](), [anthropic](), [bytecode](), [c](), [claude-code](), [debugger](), [error-handling](), [github](), [ux](), [webassembly]() ### AI overview The article describes a three-hour experiment building an AI-integrated REPL with a custom WebAssembly build of QuickJS. The system evaluated JavaScript locally, exposed function bytecode and execution controls, and sent failed evaluations to Anthropic's Sonnet 4.5. The author concludes that the approach was not very useful for simple scripts but sees potential in sharing an executing environment with AI and debugger hooks. ### Source excerpt Yesterday I did a livestream where I built a usable REPL with AI integration. Mostly from scratch, all in 3 hours. It uses a custom WASM build of QuickJS for evaluation. If the evaluation fails, it sends the text to Anthropic to speak to Sonnet 4.5. The idea was to meld a live evaluation environment together with AI. By using QuickJS as an evaluator, we get more than just a sandbox: we are able to inspect bytecode of functions, we could pause execution as needed, etc. I thought we might discover use cases that would be difficult to do by sending JS to eval off to a remote sandbox (or a separate node instance). It's also a UX experiment: you can either write code or talk to AI. In the end, it didn't prove to be super useful. The cases where I want to provide deep introspection at runtime to AI are where I'm already working with a very complex codebase, not writing simple scripts. For the UX, I generally want to write code in a dedicated editor and send it off to be evaluated. I still think there's merit in the idea of sharing an executing environment with AI with the debugger hooks enabled; I'm researching companies working on this. Let me know if you know of any. Still, it was fun to give AI the ability to get the bytecode for a function and describe what the function does at a very low-level. I asked to write a function that sums the numbers in an array, and then asked it to get the bytecode and describe it: Some notable things that happened during this: QuickJS doesn't come with a WASM build by default. I told Claude Code to compile it to WebAssembly, and it got Emscripten all setup and successfully built it to WASM Sonnet 4.5 was able to very easily write a bunch of C code to provide new APIs in QuickJS Changing how error handling works, another example of how nice it was to get AI to work with the C code I intended to eventually dig into QuickJS and change some fundamental features but decided to pause this experiment Adding tools is really fun. Here I added a t ## Promise based Web Worker Messaging DevFeed: [Promise based Web Worker Messaging]() Original publisher: [Read original article]() Author: Stanko Published: 2025-11-03T00:00:00Z Content type: tutorial Language: en Sources: [Stanko Tadić]() Topics: [Promise](), [Messaging](), [Web](), [async/await](), [Code](), [Error Handling]() Tags: [async](), [await](), [code](), [error-handling](), [event](), [implementation](), [messaging](), [thread](), [web](), [worker]() ### AI overview This tutorial presents a Promise-based wrapper for Web Worker messaging. It explains how unique message identifiers map worker responses to stored promises, allowing the main thread to resolve or reject the corresponding promise and use async/await with simpler error handling. The same pattern can also be applied to Service Workers. ### Source excerpt If you've ever used Web Workers (or any other event-based communication), you probably noticed that this kind of code can be hard to read and reason about. You also have to implement some kind of identifier for each message to recognize which worker response corresponds to which request. To simplify that, we can write a small wrapper that lets us use Promises to communicate with Workers. I'll show you an example for Web Workers, but the same pattern can be applied to Service Workers as well. The resulting API looks like this: const workerResponse = await sendToWorker(data); I first used this approach in Pulsar, because I wanted to parse and execute the user's code in a Web Worker, but also wait for the worker to finish before providing data for the next frame. Implementation # The idea is fairly simple - before sending a message to the worker, we create a unique id and a promise. We store the promise in a map using the id as the key. Then we send an event to the worker, including both the data and the id, and return the promise to the caller. When the worker finishes its calculation, it sends back a message that includes the result and the same id. In the main thread, we listen for these messages. When one arrives, we use the id to find the corresponding promise in our map. Finally, based on the worker's result, we resolve or reject that promise. It might sound like a lot, but the code is actually quite straightforward: send-to-worker.jsCopy // Worker initialization const worker = new Worker("./path-to-your-worker.js"); // Map of promises const promises = {}; worker.addEventListener("message", (e) => { // Listen to worker messages and find the correct promise matching the id // Then resolve or reject it depending on the response if (e.data.error) { promises[e.data.id].reject(e.data.error); } else { promises[e.data.id].resolve(e.data.data); } // Remove the resolver reference delete promises[e.data.id]; }); // For identifiers it is safe to use a simple integer // whic ## How to keep your Postmark account secure: Best practices guide DevFeed: [How to keep your Postmark account secure: Best practices guide]() Original publisher: [Read original article]() Author: Postmark team (fdossetto+postmark@activecampaign.com) Published: 2025-10-21T14:42:00Z Content type: tutorial Language: en Sources: [Postmark (en-US)]() Topics: [Secrets Management](), [Security](), [Application Security](), [API](), [Node.js]() Tags: [api-security](), [application-security](), [authentication](), [deployment](), [engineering](), [error-handling](), [secrets-management](), [security]() ### AI overview This guide explains how to secure Postmark accounts and API tokens using OWASP-aligned practices. It emphasizes validating tokens, securing configuration and deployment processes, validating data, preventing credential leaks in error messages, and storing credentials in dedicated secrets management tools rather than code repositories. ### Source excerpt Your Postmark API tokens are the keys to your email kingdom. One exposed token can mean unauthorized access to your account, potential data breaches, and a whole lot of stress you don't need. The good news? Keeping your account secure doesn't have to be complicated. This guide covers the essential practices that'll help you sleep better at night, knowing your Postmark credentials are locked down tight. Following Industry Standards: OWASP Guidelines When it comes to web application security, you don't have to reinvent the wheel. The Open Web Application Security Project (OWASP) provides comprehensive, industry-standard guidance that's trusted by security professionals worldwide. The OWASP Web Security Testing Guide The OWASP Web Security Testing Guide is an essential resource for developers who want to build secure applications. While it covers much more than just secret management, several sections are particularly relevant to protecting your Postmark credentials: Key areas that apply to API security: Authentication Testing - Ensuring your API tokens are properly validated Configuration and Deployment Management Testing - Securing your deployment pipeline Data Validation Testing - Validating inputs that might expose credentials Error Handling - Ensuring error messages don't leak sensitive information Applying OWASP Principles to Your Email Infrastructure Here's how OWASP best practices translate to protecting your Postmark setup: Secure Configuration Management: Follow OWASP's guidance on configuration security by keeping all sensitive Postmark credentials in dedicated secrets management tools, never in code repositories. Why OWASP Matters for Email Security Email infrastructure is often overlooked in security assessments, but it's a critical attack vector. A compromised email service can lead to: Account takeover attacks through password reset emails Data exfiltration via email forwarding Social engineering attacks using legitimate email channels Compliance violati ## Medium Android App -- Migrating from Apollo Kotlin 3 to 4: Lessons Learned DevFeed: [Medium Android App -- Migrating from Apollo Kotlin 3 to 4: Lessons Learned]() Original publisher: [Read original article]() Author: Pierrick CAEN Published: 2025-10-06T08:18:42Z Content type: tutorial Language: en Sources: [Medium]() Topics: [GraphQL](), [migration](), [Android](), [Kotlin](), [Cache](), [IntelliJ IDEA]() Tags: [android](), [apollo-client](), [cache](), [error-handling](), [gradle](), [gradle-plugin](), [graphql](), [intellij](), [kotlin](), [migration]() ### AI overview This article describes Medium's migration of its Android app from Apollo Kotlin 3 to 4. It explains the app's use of Apollo's normalized cache, CacheFirst fetching, and watch() for UI updates, then covers identifier changes, revised exception handling, and cache-miss errors encountered during the migration. ### Source excerpt Photo by Mario Verduzco on UnsplashMedium Android App -- Migrating from Apollo Kotlin 3 to 4: Lessons Learned In this post, I'll share my experience migrating the Medium Android app from Apollo Kotlin version 3 to version 4, including the challenges I encountered and how I solved them to improve our GraphQL implementation. Understanding Our Apollo Cache Implementation Before diving into the migration, it's important to understand how we use Apollo's cache in the Medium Android app. Our app relies heavily on Apollo's normalized cache for several critical purposes: Performance Optimization: We use FetchPolicy.CacheFirst as our default strategy, which means we always try to serve data from the cache first before making network requests. This significantly reduces loading times and provides a smooth user experience, especially when users navigate between screens that display similar content. Real-time Updates: We use Apollo's watch() functionality extensively to observe cache changes and automatically update our UI when data changes. This is particularly useful for features like: Live clap counts on posts Real-time follower updates Post viewed updates and more... Starting the Migration The initial plan was straightforward: update Apollo Kotlin from version 3 to 4. The IntelliJ plugin made this process seem simple at first glance. Key Changes in Apollo Kotlin 4 Group id / plugin id / package name: Apollo Kotlin 4 uses a new identifier (com.apollographql.apollo) for its maven group id, Gradle plugin id, and package name. This change from com.apollographql.apollo3 allows running version 4 alongside version 3 if needed. Source: Apollo Kotlin Migration Guide - Group id / plugin id / package name Exception handling: Apollo 4 has a new way of handling exceptions. Instead of throwing exceptions directly, they're now passed through ApolloResponse. Source: Apollo Kotlin Migration Guide -- Fetch errors do not throw ApolloCompositeException: In Apollo Kotlin 3, when both cache and netw [Next page]()