# state Published articles for state. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Apple's $2,000 foldable iPhone gives developers more screen space and UI state to manage DevFeed: [Apple's $2,000 foldable iPhone gives developers more screen space and UI state to manage]() Original publisher: [Read original article]() Author: Joab Jackson Published: 2026-09-15T05:55:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [iphone](), [ui](), [Development]() Tags: [apple](), [developers](), [devops](), [iphone](), [mobile](), [screen](), [state](), [ui]() ### AI overview The article examines how Apple's $2,000 foldable iPhone affects development, giving developers more screen space while requiring them to manage additional UI state. ### Source excerpt Cupertino's $2,000 foldable gives devs more screen, and more UI state to manage ## Native Drag and Drop Meets Cross-Device Continuity DevFeed: [Native Drag and Drop Meets Cross-Device Continuity]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [Java](), [Persistence](), [Framework](), [payload](), [LineageOS](), [Processes](), [browser]() Tags: [android](), [apis](), [browser](), [java](), [native](), [payload](), [persistence](), [process](), [screen](), [state]() ### AI overview Codename One adds cross-device continuity and native drag-and-drop through shared Java APIs. The article explains how to save reconstructible application state, restore it after account verification, and transfer it between devices and applications. ### Source excerpt Drag files into another application and pick up a task on another device. Codename One adds native drag and drop and cross-device continuity through shared Java APIs. ## RBP Tracker: Counting the CVE IDs the CVE List Cannot See DevFeed: [RBP Tracker: Counting the CVE IDs the CVE List Cannot See]() Original publisher: [Read original article]() Author: jgamblin Published: 2026-09-14T14:16:42Z Content type: article Language: en Sources: [Jerry Gamblin]() Topics: [data](), [NVD](), [API](), [GitHub]() Tags: [api](), [cve](), [cve-list](), [data](), [ids](), [list](), [nvd](), [report](), [scanner](), [source](), [state](), [uncategorized]() ### AI overview The article examines a corrected RogoLabs tracker for Reserved but Public CVE IDs. It explains that the CVE Services API returns 404 for reserved IDs, while a separate unauthenticated endpoint reveals their RESERVED state. The tracker lists IDs from public advisory feeds and describes how reserved records are absent from the bulk CVE List until publication. ### Source excerpt A new RogoLabs site lists Reserved but Public CVE IDs: 2,315 of them on September 14, drawn from 17 public advisory feeds, refreshed every six hours, and held a week before they appear. The first version of this tracker reported that none of the CVE IDs it found existed in the CVE List, in any ... Read more ## The thread is the Workflow: Durable AI agents without changing Agent code DevFeed: [The thread is the Workflow: Durable AI agents without changing Agent code]() Original publisher: [Read original article]() Author: Greg Haskins Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Langgraph](), [Code](), [Framework]() Tags: [agent](), [ai-agents](), [community](), [crashes](), [durability](), [langgraph](), [state](), [workflow]() ### AI overview The article explains how Temporal Workflows add durable execution to LangGraph-based AI agents without requiring changes to the agent code. It describes mapping Agent Protocol Threads to Temporal Workflows, Runs to Activities, and checkpoints to Workflow state so executions can recover after worker crashes. ### Source excerpt Learn how Temporal Workflows can make LangGraph-based AI agents durable, preserving threads and checkpoints across crashes without changing agent code. ## Mongorewind: Rewind Your MongoDB Test Data Without Restoring a Backup DevFeed: [Mongorewind: Rewind Your MongoDB Test Data Without Restoring a Backup]() Original publisher: [Read original article]() Author: Zelmar Michelini Published: 2026-09-02T11:38:40Z Content type: tutorial Language: en Sources: [Blog - Percona]() Topics: [MongoDB](), [Terminal](), [Command-line interface](), [ci](), [dashboards](), [Streams]() Tags: [ci](), [code](), [commands](), [database](), [mongodb](), [state](), [terminal](), [tests](), [ui]() ### AI overview This article introduces mongorewind, a terminal UI tool that records MongoDB data changes through a cluster-wide change stream and reverses them in reverse chronological order. It is designed to restore test data to its pre-test state without restoring a backup and supports interactive and CI use. ### Source excerpt Mongorewind: Rewind Your MongoDB Test Data Without Restoring a Backup One day, my friend Martín told me about a problem he and his team were dealing with. Every time they needed to run a pre-production test, they had to restore a copy of the production database into their test cluster. That process alone takes about ... Continued The post Mongorewind: Rewind Your MongoDB Test Data Without Restoring a Backup appeared first on Percona. ## The Essential Guide to Multi-Agent Systems DevFeed: [The Essential Guide to Multi-Agent Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-31T11:38:47Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [AI Agent](), [distributed-systems](), [systems]() Tags: [ai-agent](), [architecture](), [consistency](), [distributed-systems](), [multi-agent-systems](), [observability](), [state]() ### AI overview This guide explains what multi-agent systems are, how they differ from workflow engines and generic distributed systems, and why coordination, communication, shared state, consistency, and observability shape their architecture. ### Source excerpt Building one AI agent is hard. But what changes when you start adding more? ## Explicit Task State Enables Incremental Updates in Long Analysis Pipelines DevFeed: [Explicit Task State Enables Incremental Updates in Long Analysis Pipelines]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-08-28T21:11:08Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Artificial Intelligence](), [Code](), [Open Source](), [Command-line interface](), [Text-based user interface](), [systems]() Tags: [ai](), [code](), [command-line](), [open-source](), [state](), [state-management](), [systems]() ### AI overview The article explains how explicit task state can make long analysis pipelines responsive to changing requirements. By tracking plans, completed steps, artifacts, dependencies, and exceptions outside the conversation, a system can preserve valid work and replan only affected downstream steps. It describes Apodex 1.1, Deep Discover, and the open-source FrontierAgent runtime as examples of this approach. ### Source excerpt Full guide with code. ## One App, More Than One Native Window DevFeed: [One App, More Than One Native Window]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [App](), [Windows](), [Linux](), [Java]() Tags: [app](), [capture](), [desktop](), [linux](), [monitor](), [native](), [screen](), [state](), [windows]() ### AI overview Codename One now supports multiple native desktop windows, each with its own component tree, focus owner, animation list, repaint region, native peers, and text editing. The main Form remains the application's primary surface, preserving existing mobile and single-window behavior. ### Source excerpt Codename One now supports multiple native desktop windows with independent component trees, paint surfaces, focus, modality, monitor geometry, and input routing while Form remains the application's main surface. ## Watch Apps: One Codebase, Two Real Applications DevFeed: [Watch Apps: One Codebase, Two Real Applications]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-08-22T00:00:00Z Content type: tutorial Language: en Sources: [CodeName One]() Topics: [SwiftUI](), [Android](), [media3](), [SQLite](), [API]() Tags: [android](), [api](), [apk](), [apple](), [apple-watch](), [asynchronous](), [files](), [release](), [sqlite](), [state](), [swiftui](), [watchos]() ### AI overview Codename One can build Apple Watch companion applications and Wear OS applications from a shared watch entry point. The article explains separate application runtimes and storage, platform-specific rendering, and asynchronous communication for live messages, replicated state, and file transfers. ### Source excerpt Codename One now builds Apple Watch companions and standalone Wear OS products from one watch entry point, with a portable asynchronous API for messages, state, and files. ## Experiment in reducing target directory size on nightly DevFeed: [Experiment in reducing target directory size on nightly]() Original publisher: [Read original article]() Author: Jakub Beránek Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Inside Rust Blog]() Topics: [Rust](), [Compiler](), [data]() Tags: [compilation](), [compiler](), [disk-space](), [information](), [rust](), [space](), [state]() ### AI overview The Cargo Team is testing -Zembed-metadata=no by enabling it by default on the nightly channel. The experiment aims to reduce Rust target directory size by avoiding duplicated crate metadata in build artifacts, while gathering feedback about viability and user impact. ### Source excerpt TL;DR: The Cargo Team will be rolling out an experiment to identify user impact for a proposed change. Cargo will enable the -Zembed-metadata=no feature on the nightly channel by default, which can help reduce the size of the target directory somewhat. This is an experiment designed to gather feedback about viability of this feature. Users are not expected to migrate to support this feature, but to report any issues and opt-out if needed in the meantime. What is this about? High disk usage of Rust compilation artifacts is frequently cited as one of the biggest annoyances of Rust users. In our 2025 State of Rust survey, it was actually the second most commonly reported problem, right after compilation speed. There are various reasons why the target directory can become quite large, such as: Cargo compiles the whole crate graph from scratch by default, which produces a lot of build artifacts. Debug information takes a lot of disk space. Incremental compilation artifacts take a lot of disk space. While you can disable debug information or incremental compilation to reduce the target directory size, that of course comes with severe trade-offs in compilation speed and debuggability of your program. However, there is one source of data in the target directory that currently takes too much size even though it doesn't really have to. It is the "crate metadata", which can be duplicated across multiple files. We will focus on that in this blog post. What causes duplicated (meta)data For years, Cargo has been using pipelined compilation to speed up building of crate graphs. When compiling a library crate, it tells the compiler to produce an .rmeta file (which contains all the crate metadata required to use this library) as soon as possible, even before having the final executable code available. This enables dependent crates to start compiling sooner. However, once the library does finish compiling, the final produced .rlib file will contain both the executable code and the Ru ## Angular Forms, Web AI, and Architecture Community Features DevFeed: [Angular Forms, Web AI, and Architecture Community Features]() Original publisher: [Read original article]() Author: Angular Published: 2026-08-14T13:01:03Z Content type: article Language: en Sources: [Angular]() Topics: [Angular](), [Forms](), [Artificial Intelligence](), [Web](), [reactive](), [Development](), [Tooling]() Tags: [ai](), [angular](), [angular-newsletter](), [angular-weekly](), [coding](), [forms](), [state](), [state-management](), [tooling](), [tutorials](), [web]() ### AI overview This Angular community roundup covers the evolution of form state management from template-driven approaches to the reactive Signal Forms API, client-side machine learning in browsers, Angular v21 architecture, pragmatic AI use, developer tooling, and coding challenges involving Signal Forms and form arrays. ### Source excerpt The Angular ecosystem continues to push forward with deeper AI integration and modern form architectures. This week, our community experts track the complete evolution of form handling, explore client-side AI, and dive into tactical discussions about developer craftsmanship in the age of AI. Catch up with these incredible community features! From Template-Driven to Signal-Driven Angular Forms Sonu Kapoor @SonuKapoor1978 charts the complete architectural evolution of forms in Angular. Learn how form state management has progressed from legacy template-driven approaches all the way to the highly reactive, modern Signal Forms API. Read the full article: https://www.codemag.com/Article/264041/From-Template-Driven-to-Signal-Driven-The-Complete-Evolution-of-Angular-Forms What's New in Web AI? Christian Liebel (@christianliebel) l breaks down the latest advancements in client-side machine learning. Discover how you can leverage powerful Web AI capabilities directly inside browser environments to build faster, privacy-focused web experiences. Watch the conference talk: https://www.youtube.com/watch?v=jFd8FomARsw The Dev Life Podcast: Angular v21 Architecture, Pragmatic AI, and Tooling Lessons Brooke Avery @JediBravery and Matthew Christiansen drop three stellar episodes of The Dev Life podcast covering architecture and real-world AI strategies. First, they host Sander Elias to map out v21 vision and velocity from an architect's lens. Next, legendary author Dave Thomas drops by to discuss developer craftsmanship as a "Pragmatic Agent." Finally, Alfredo Perez joins the show to share favorite tools and hard-earned lessons from using AI in the trenches. Listen to Ep. 11 (Modern Angular Architecture): https://www.youtube.com/watch?v=r0bKFy96UMM Listen to Ep. 13 (The Pragmatic Agent): https://www.youtube.com/watch?v=-rGMJdbH-yA Listen to Ep. 16 (AI in the Trenches): https://www.youtube.com/watch?v=bg38uSYpM8c Hands-On Code Challenges: Signal Forms and Form Arrays Ready to practic ## SCIM Deprovisioning Is a Promise Your App Probably Breaks DevFeed: [SCIM Deprovisioning Is a Promise Your App Probably Breaks]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-06T01:29:36Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Security](), [API](), [HTTP](), [JSON](), [Internet Engineering Task Force (IETF)](), [Protocol (disambiguation)]() Tags: [api](), [http](), [identity](), [idp](), [ietf](), [json](), [net-conf](), [protocol](), [schema](), [security](), [standards](), [state](), [token]() ### AI overview This article explains that SCIM deprovisioning updates identity state but does not automatically invalidate an application's sessions, refresh tokens, or API keys. It describes how identity providers commonly use soft deactivation and why applications must explicitly handle the resulting offboarding state. ### Source excerpt SCIM tells you a user is gone, but sessions, refresh tokens, and API keys often outlive deprovisioning. Here's why offboarding needs more than a user row. ## How React Rendering Works and Why Memoization Is Often Not the First Fix DevFeed: [How React Rendering Works and Why Memoization Is Often Not the First Fix]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-07-28T06:10:09Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [React](), [ui](), [Code](), [JavaScript](), [Development]() Tags: [code](), [component](), [context](), [javascript](), [react](), [render](), [state](), [ui]() ### AI overview This tutorial explains that a React render is a component function call, while DOM updates happen only during the commit phase. It identifies state changes, context changes, and new child elements from a parent as render triggers, and argues that moving state or restructuring children often helps before memoization. It also notes that StrictMode intentionally renders twice in development and that the compiler cannot move component state. ### Source excerpt One rule explains every React re-render. Get it right and memoization becomes the last fix, not the first. ## From Structured Outputs to A2UI Surfaces: Migrating to Flutter GenUI SDK DevFeed: [From Structured Outputs to A2UI Surfaces: Migrating to Flutter GenUI SDK]() Original publisher: [Read original article]() Author: Cagatay Ulusoy Published: 2026-07-08T15:52:59Z Content type: tutorial Language: en Sources: [Flutter Community - Medium]() Topics: [Flutter](), [SDKs](), [Finite-state machine](), [JSON](), [Firebase](), [Database]() Tags: [a2ui](), [ai](), [firebase](), [flutter](), [gemini](), [generative-ui](), [how-to](), [json](), [sdk](), [state]() ### AI overview A tutorial on migrating a hand-built generative UI feature based on Gemini structured outputs to the official Flutter GenUI SDK. It describes a Finnish language-learning app that generates images on demand and uses a five-step wizard to improve image variety. ### Source excerpt Part 3 of a series "Building Image Assisted Language Learning Practice with Flutter, Firebase and Gemini." Part 1 built the image generation and annotation pipeline. Part 2 built a generative UI framework by hand, using Gemini structured outputs. This part is the migration: same feature, official Flutter GenUI SDK. A year ago, Finnish It was just a side project. This spring, I watched it at the Google Cloud Next developer keynote, and again in the Flutter keynote at Google I/O. It still feels surreal to type that. I am grateful to the Flutter and Firebase teams for the collaboration. This series is my way of giving back. I hope it makes Flutter GenUI SDK click for you the way building this feature made it click for me. Finnish it app featured in Cloud Next and Google IOQuick Recap One of the app's practice exercises is simple: show the learner an image, and they describe it in 🇫🇮 Finnish. The images are generated on demand, not fetched from a fixed image database. In Part 2 the problem was variety. A broad topic like "a native Finnish animal in its habitat" kept producing the same handful of scenes. Turning up temperature did not fix it, because it was never really a sampling problem. An under-specified prompt has a few big probability peaks, and the sampler just jitters around them. The fix was to stop asking the model to "be random" and move the entropy into the UI: a five-step wizard where the user picks the season, the setting, the story, the framing, and the visible details. Gemini designs the questions; the user provides the variety. That worked. The repetition problem was solved. The machine I built by hand To make it work, I built a small finite state machine (FSM) which walked the user through five steps. Each step asked Gemini for question set, constrained by a response schema, and parsed the JSON into a typed object that a widget could render: final response = await model.generateContent( [Content.text(_buildRefinementStateRequest(state, topic, selections ## Kubernetes Tutorial: Deploy, Expose, Scale, and Self-Heal Your First App DevFeed: [Kubernetes Tutorial: Deploy, Expose, Scale, and Self-Heal Your First App]() Original publisher: [Read original article]() Author: Nimesha Jinarajadasa Published: 2026-07-06T08:25:48Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [Kubernetes](), [Tutorial](), [kubectl](), [YAML](), [Containers](), [Deployment]() Tags: [beginners](), [declarative](), [guide](), [kubectl](), [kubernetes](), [scale](), [self-healing](), [state](), [tutorial](), [yaml]() ### AI overview A beginner-friendly, hands-on tutorial that walks through deploying an application to a Kubernetes cluster, exposing it with a Service, scaling it, observing self-healing, and repeating the deployment declaratively with YAML and kubectl. ### Source excerpt A hands-on Kubernetes tutorial for beginners: deploy your first app, expose and scale it, see self-healing, and use YAML with kubectl. ## Apache Airflow 3.3.0: Stateful Tasks and Multi-Language Support DevFeed: [Apache Airflow 3.3.0: Stateful Tasks and Multi-Language Support]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-07-06T00:00:00Z Content type: release Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [SDKs](), [Go Language](), [Java]() Tags: [airflow](), [apache-airflow](), [go](), [java](), [release](), [release-notes](), [sdk](), [state]() ### AI overview Apache Airflow 3.3.0 introduces durable state stores for tasks and assets, a Language Task SDK for Java and Go, expanded asset partitioning, and pluggable retry policies. ### Source excerpt We're proud to announce the release of Apache Airflow 3.3.0! Where 3.2 brought precision to data with asset partitioning, 3.3 gives your tasks memory and multi-language support: a first-class state store for tasks and assets, a Language Task SDK for writing task logic in Java and Go, a major expansion of asset partitioning, and pluggable retry policies. 🎯 Release Highlights 📦 PyPI: https://pypi.org/project/apache-airflow/3.3.0/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.3.0/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.3.0/release_notes.html 🐳 Docker Image: docker pull apache/airflow:3.3.0 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.3.0 🗃 Task & Asset State Store (AIP-103): Tasks That Remember Until now, if a task needed to remember something across retries or runs -- a cursor, a checkpoint, a high-water mark -- you reached for XComs, an external store, or a clever Variable hack. Airflow 3.3 makes durable task state a first-class concept. Tasks can persist arbitrary key-value state that survives across retries and runs via a new task_state_store accessor, and assets can carry their own state via asset_state_store -- both available directly from the Task SDK. State lives in the metadata database by default, or in a custom worker-side backend ([workers] state_store_backend), supports per-key retention with periodic garbage collection and an optional clear_on_success, and is fully manageable through the Core API and Execution API. Task State Store Task state is scoped to a specific task instance and persists across retries. Use it to track coordination state like remote job IDs, cursors, or progress checkpoints: @task def extract_data(**context): task_state = context["task_state_store"] # Resume from where we left off on retry cursor = task_state.get("last_cursor", default=0) records = fetch_records(since=cursor) new_cursor = records[-1]["id"] task_state.set("last_cursor", new_cursor) return records Persisted st ## Custom Scopes in Koin: A Practical Example DevFeed: [Custom Scopes in Koin: A Practical Example]() Original publisher: [Read original article]() Author: Gabriel Bronzatti Moro Published: 2026-06-19T12:01:01Z Content type: tutorial Language: en Sources: [Koin developers - Medium]() Topics: [App](), [Open Source](), [Mobile]() Tags: [app](), [components](), [compose-multiplatform](), [data](), [dependency](), [implementation](), [koin](), [kotlin](), [lifecycle](), [open-source](), [project](), [scopes](), [state](), [stateless](), [user-experience]() ### AI overview A practical tutorial on using custom scopes in Koin to keep a selected user profile image available across an application. It demonstrates a session scope, session data, and a stateless session manager using StreamPlayerApp as the example. ### Source excerpt Hey friends 👋 Today, we are going to explore how we can create a custom scope, one of the powerful features provided by Koin. To make things practical 🔨, we'll walk through a real-world use case from the open-source project StreamPlayerApp (created by CodandoTV). Wireframes of a mobile app -- Unsplash by @alvarordesignWhat is the problem? StreamPlayerApp is an open-source project that aims to recreate the core experience of Netflix. One of its features allows users to select a profile, similar to the profile selection screen found in streaming platforms. The selected profile image needs to be accessible across the entire application, ensuring a consistent user experience on every screen. Whenever a user changes their profile, the updated image should be immediately reflected throughout the app, allowing all screens to stay synchronized with the current profile selection. Screenshots demonstrating the user profile selection feature.How we solve this problem using Custom Scopes? First, we create a class that represents the scope of a user session: class SessionScope Next, we create a lightweight data class containing only the information that needs to be kept in memory throughout the user session: data class UserSessionInfo( // represents the time user changed the profile val userTimestamp: Instant, // represents the selected picture url val profileImageUrl: String ) We then created a dedicated class responsible for managing the user's session lifecycle and state. class SessionManager { private val scopeId: String = "user_session" /** * Create custom scope and hold the user's session data */ fun openSession(imageUrl: String) { closeSession() val koin = KoinPlatform.getKoin() val scope = koin.createScope( scopeId = scopeId, ) scope.declare( UserSessionInfo( userTimestamp = Clock.System.now(), profileImageUrl = imageUrl ) ) } /** * Retrieve the current user session */ fun userSessionInfo(): UserSessionInfo? { return KoinPlatform.getKoin() .getScopeOrNull(sc ## Using local LLMs for agentic coding DevFeed: [Using local LLMs for agentic coding]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-06-04T09:01:34Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [coding](), [agentic-coding](), [Large Language Model](), [GitHub Copilot CLI](), [Linux](), [llama.cpp](), [Ollama](), [NVIDIA RTX]() Tags: [agentic-coding](), [agents](), [ai](), [claude](), [coding](), [github](), [linux](), [llama](), [llama-cpp](), [llms](), [local](), [local-llms](), [microsoft](), [nvidia](), [nvidia-rtx](), [ollama](), [quality](), [rocm](), [state]() ### AI overview A practical guide to using local language models for agentic coding. It covers running local models, configuring coding agents such as Copilot and Pi, and evaluating local models against cloud models in terms of cost, privacy, tooling, and performance. ### Source excerpt AI honeymoon pricing is over, but your work is not ## 7 tips for using Figma Make credits more efficiently DevFeed: [7 tips for using Figma Make credits more efficiently]() Original publisher: [Read original article]() Author: Alexia Danton Published: 2026-05-22T12:00:00Z Content type: tutorial Language: en Sources: [Figma Blog]() Topics: [Figma](), [Code](), [ui]() Tags: [best-practices](), [code](), [component](), [context](), [cost](), [figma](), [prompting](), [request](), [screen](), [state](), [structure](), [tips](), [ui]() ### AI overview This tutorial presents seven practices for using Figma Make credits more efficiently. It recommends writing a complete initial prompt, keeping follow-up prompts focused and specific, building in stages, grouping related changes, and switching to direct editing for small visual adjustments. ### Source excerpt While everyone's talking about "tokenmaxxing," we've compiled seven best practices to help you build smarter--without prompting more--in Figma Make. ## Resumable Remote Compaction DevFeed: [Resumable Remote Compaction]() Original publisher: [Read original article]() Author: Hui Xiao Published: 2026-05-19T00:00:00Z Content type: article Language: en Sources: [RocksDB]() Topics: [rocksdb](), [API](), [IO]() Tags: [api](), [architecture](), [blog](), [cpu](), [encoding](), [files](), [implementation](), [rocksdb](), [scale](), [state](), [stateless]() ### AI overview RocksDB's resumable remote compaction adds checkpointing so interrupted compaction jobs can continue from their latest completed output SST instead of restarting from scratch. The article explains checkpoint contents, delta encoding, safety constraints, resume behavior, and configuration requirements. ### Source excerpt Background RocksDB can offload compaction work to remote workers through the CompactionService API. In this model, the primary RocksDB instance selects the input files and sends a serialized CompactionServiceInput to a worker; the remote worker runs DB::OpenAndCompact(), writes output SSTs to output_directory, and returns a serialized CompactionServiceResult that the primary RocksDB instance installs into its LSM tree. See the Remote Compaction wiki for the full architecture. This lets operators scale compaction throughput with stateless workers while keeping the primary RocksDB instance's CPU and I/O available for serving reads and writes. However, remote compaction jobs can be long-running--sometimes processing hundreds of gigabytes of input. When a worker crashes, gets preempted, or times out, the entire compaction must restart from scratch, wasting all output produced before the interruption and increasing compaction debt on the primary RocksDB instance. How Resumable Remote Compaction Works Resumable remote compaction introduces a checkpoint-and-resume mechanism. During a compaction, the worker periodically saves its progress to the output_directory. If the compaction is interrupted, a subsequent call to OpenAndCompact() with the same output directory can pick up from the last checkpoint rather than starting over. Checkpointing After each output SST file is completed, the worker persists a progress checkpoint to a compaction progress file in the output directory output_directory. The checkpoint records which internal key to resume from and the metadata of all completed output files. Progress records use delta encoding--each record only contains files completed since the last checkpoint--to keep serialization cost linear. The worker skips checkpointing at boundaries where resuming could be unsafe or requires complicated handling: when range deletions span the file boundary or when adjacent output files share the same user key. These constraints ensure that resuming ## Keep business processes moving by recovering failed steps without restarting DevFeed: [Keep business processes moving by recovering failed steps without restarting]() Original publisher: [Read original article]() Author: Tao Guo Published: 2026-05-11T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [Processes](), [retry]() Tags: [data](), [error](), [execution](), [state](), [temporal-concepts](), [workflows]() ### AI overview The article explains how to recover failed steps in durable business processes without restarting completed work. It presents a Temporal pattern in which workflows pause on permanent failures, accept corrections from humans or automated agents, and resume from the point of failure. ### Source excerpt Stop restarting broken business processes. See how Temporal Workflows pause on bad data, accept fixes via Signals, and resume without losing progress. ## Illegal vs Unwanted States DevFeed: [Illegal vs Unwanted States]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2026-04-28T15:14:09Z Content type: article Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [systems](), [Software](), [data](), [Network](), [Server]() Tags: [airline](), [data](), [data-type](), [event](), [network](), [optional](), [servers](), [software](), [state](), [systems]() ### AI overview The article distinguishes illegal states, which a system must never enter, from unwanted states, which may be temporarily acceptable but must not persist or develop into illegal states. It argues that systems should represent unwanted states because external inputs and unreliable infrastructure make them unavoidable, while detection and resolution mechanisms limit their consequences. ### Source excerpt An illegal state is a state we never want our system to be in. An unwanted state is a state we don't want to stay in. Many states that we wish were illegal are actually unwanted. Considering a calendaring software which stores calendar events as {user: {events: [event]}}, where each event has a start and end time. This allows one person to attend two events at the same time. We might consider this illegal and replace the data type with {user: {time: optional event}} which makes this impossible. However, a scheduling conflict isn't illegal, only unwanted! It is possible for a person to sign up for two overlapping events. Maybe they're supposed to choose one event, maybe they'll decide which event to go to later, maybe one of the events doesn't actually represent an in-person meeting. In that case it's acceptable, if not ideal, to remain in the unwanted state. Other unwanted states lead to invalid states if not exited quickly. An airline flight is in an unwanted state if there are more passengers booked to fly than seats available. This must be resolved before passengers actually board, as "more passengers physically on the plane than seats available" is an illegal state. In some cases, an unwanted state does not lead to illegal states, but permanently remaining in the unwanted state is still a problem. We might guarantee that a network partition does not ever lead to inconsistent data. Even though the unwanted state of a network partition cannot cause the illegal state of corrupt data, we still have a big problem if we don't ever fix the partition. Why systems must represent unwanted states Generally, unwanted states can happen if we don't have complete control over our system's behavior. We can't guarantee our network is perfectly reliable, our servers are always up, our users all put in correct data. If our system gets input from the external world then the world can push us into an unwanted state. We need to be able to detect these states so we can resolve them. E ## Anonymous credentials: an illustrated primer (Part 2) DevFeed: [Anonymous credentials: an illustrated primer (Part 2)]() Original publisher: [Read original article]() Author: Matthew Green Published: 2026-04-17T17:19:15Z Content type: tutorial Language: en Sources: [Matthew Green]() Topics: [Cryptography](), [Security, Privacy and Abuse Prevention]() Tags: [ai](), [anonymous-credentials](), [artificial-intelligence](), [attacks](), [credentials](), [cybersecurity](), [privacy](), [resources](), [security](), [state](), [technology]() ### AI overview Part two of an illustrated primer on anonymous credentials moves from the theoretical overview in part one toward practical discussion of real-world credential systems. It explains privacy-preserving authentication, credential showing without linkability, limits on credential reuse, cloning attacks, and expressive claims. ### Source excerpt This is the second in a series of posts about anonymous credentials. You can find the first part here. In the previous post, we introduced the notion of anonymous credentials as a technique that allows users to authenticate to a website without sacrificing their privacy. As a quick reminder, an anonymous credential system consists of ... Continue reading Anonymous credentials: an illustrated primer (Part 2) -> ## ETH Rangers Program Recap DevFeed: [ETH Rangers Program Recap]() Original publisher: [Read original article]() Author: Protocol Security Team; Grants Management Team Published: 2026-04-16T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [Security](), [Vulnerabilities](), [Incident response](), [Threat Research](), [Open Source](), [DeFi]() Tags: [community](), [defi](), [ethereum](), [exploits](), [incident-response](), [open-source](), [root-cause-analysis](), [security](), [smart-contract](), [state](), [talks](), [threat-intelligence](), [vulnerabilities](), [web3](), [workshops]() ### AI overview The Ethereum Foundation and partner organizations recap the six-month ETH Rangers Program, which funded 17 stipend recipients conducting public-goods security work across the Ethereum ecosystem. Reported outcomes include recovered or frozen funds, vulnerability and bug reports, threat-awareness work, security challenges, educational events, incident responses, and open-source tooling. ### Source excerpt In late 2024, the Ethereum Foundation, together with Secureum, The Red Guild, and Security Alliance (SEAL), launched the ETH Rangers Program, an initiative to provide stipends for individuals doing public goods security work in the Ethereum ecosystem. The goal of the program was straightforward: to fund independent efforts that... [Next page]()