# Queue Published articles for Queue. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Worker Backpressure (Part 1) DevFeed: [Worker Backpressure (Part 1)]() Original publisher: [Read original article]() Author: Mikalai Barysau Published: 2026-09-17T00:00:00Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [backpressure](), [Back end](), [reliability]() Tags: [backend](), [backpressure](), [dependencies](), [outage](), [queue](), [reliability](), [worker]() ### AI overview Canva describes Worker Backpressure, a reliability mechanism for queue workers that slows processing when dependencies fail and speeds it back up after recovery. During a cloud-provider outage, the mechanism helped prevent failed messages from accumulating on the Dead Letter Queue and avoided an on-call page. ### Source excerpt How we taught Canva's queue workers to slow down when dependencies fail, then speed back up on their own. ## Khan Academy Uses Local Storage and Request Queues to Handle Offline Actions DevFeed: [Khan Academy Uses Local Storage and Request Queues to Handle Offline Actions]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-08-17T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [LocalStorage](), [client](), [Code](), [Internet](), [servers]() Tags: [architecture](), [change](), [code](), [engineering](), [function](), [hints](), [linear](), [local](), [news](), [offline](), [queue](), [request](), [server](), [web-frontend]() ### AI overview This article explains how Khan Academy addressed offline hint cheating by changing the client's request architecture. Actions are stored in local storage and placed in a queue for retry when connectivity returns, with linear backoff to avoid repeated requests during outages. ### Source excerpt By Phillip Lemons The problem Recently, a number of students on Khan Academy found a way to cheat ... Read more ## Mercure Broadcasting in Laravel 13.32 DevFeed: [Mercure Broadcasting in Laravel 13.32]() Original publisher: [Read original article]() Author: Paul Redmond Published: 2026-09-16T02:44:52Z Content type: release Language: en Sources: [Laravel]() Topics: [Laravel](), [releases](), [Release notes](), [real-time](), [enum](), [WebSocket]() Tags: [enums](), [features](), [github](), [laravel](), [laravel-releases](), [news](), [queue](), [real-time](), [release](), [release-notes](), [storage](), [update](), [websockets]() ### AI overview Laravel 13.32 adds a Mercure broadcast driver, filesystem methods for copying and moving files between disks, and enum support for queue pause and resume methods. The release also includes additional fixes and updates. ### Source excerpt Laravel 13.32 adds a Mercure broadcast driver, Storage::copyToDisk() and moveToDisk(), enum support in queue pause/resume, and more. The post Mercure Broadcasting in Laravel 13.32 appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## TypeSafe AI's Jev now available on AI Gateway DevFeed: [TypeSafe AI's Jev now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [gateway](), [SDKs](), [API](), [Software]() Tags: [ai-gateway](), [api](), [evaluation](), [gateway](), [model](), [queue](), [retry](), [sdk](), [software](), [support](), [typesafe-ai]() ### AI overview Vercel's AI Gateway now supports TypeSafe AI's Jev, a probabilistic decision model that returns typed Choice, Score, and Boolean answers with probabilities. The changelog describes parallel evaluation, AI SDK 7 integration through an experimental evaluate API, example routing and review workflows, and provider options including Zero Data Retention and No Training. ### Source excerpt Jev from TypeSafe AI is now available on AI Gateway. Jev is a probabilistic decision model for software: state goes in, typed Choice, Score, and Boolean answers come out. Regular language models generate text one token at a time, which the application then parses and validates. Jev evaluates all declared questions in parallel and returns typed answers plus probabilities directly. That removes unnecessary text generation and makes it straightforward to automate clear cases while routing uncertain ones to review. TypeSafe reports Jev was up to 193.6x faster and 444.6x cheaper than LLMs on its workflow evaluations. Example use cases include: Choosing the next tool or subagent in an agent loop Deciding whether to continue, retry, ask the user, or stop Scoring urgency or risk before an action Verifying model outputs and enforcing guardrails. AI SDK 7 exposes Jev through the experimental evaluate API. Choice selects an option, Score grades an ordered rubric, and Boolean estimates the probability of true. Install the current AI SDK (AI SDK 7.0.105 onwards supports the evaluate API): Each evaluation specifies: model: the evaluation model to call, state: the shared string, object, or array to evaluate, and questions: a map of named decisions to make about that state. Call the model with typesafe-ai/jev. This example turns one support case into a queue, priority, and refund-review decision, with uncertain routing sent for manual review: The result preserves question IDs and Choice keys. TypeSafe reports separate Choice and Score confidence in result.providerMetadata.typesafe.confidence. Calibrate probabilities and confidence against labeled examples from your workflow. Jev supports Zero Data Retention and No Training, enabled per request in the example. Evaluation calls also appear in logs and custom reporting, count toward budgets, and accept other Gateway provider options in the same providerOptions.gateway object. Read the documentation on evaluation models on AI Gateway f ## How to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## Building EasyReply: How We Used Laravel to Unify Customer Support DevFeed: [Building EasyReply: How We Used Laravel to Unify Customer Support]() Original publisher: [Read original article]() Author: Emma Blake Published: 2026-09-11T16:06:51Z Content type: article Language: en Sources: [Laravel]() Topics: [Laravel](), [Support](), [Artificial Intelligence](), [React](), [WebSocket](), [PHP](), [Single-page application (SPA)](), [email](), [Front end](), [Instagram]() Tags: [ai](), [api](), [development](), [email](), [frontend](), [instagram](), [laravel](), [queue](), [react](), [sponsor](), [support](), [ui](), [websocket]() ### AI overview This article explains how EasyReply uses Laravel to combine email, Slack, Instagram, and WhatsApp conversations in an AI-powered shared inbox. It describes Laravel Reverb for real-time updates, Inertia and React for the frontend, and queues for background processing. ### Source excerpt EasyReply pulls email, Slack, Instagram, and WhatsApp into one AI shared inbox, built on Laravel with Reverb, Inertia, React, and queues. The post Building EasyReply: How We Used Laravel to Unify Customer Support appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## Message Queue vs Event Bus vs Broker DevFeed: [Message Queue vs Event Bus vs Broker]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-10T12:22:39Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [systems](), [Latency]() Tags: [architecture](), [consumer](), [fraud-detection](), [latency](), [messages](), [notifications](), [payment](), [queue]() ### AI overview This article distinguishes message queues, event buses, and brokers by the meaning of the message. It explains that queues assign work to consumers, support retries and dead-letter queues, smooth traffic spikes, and can develop growing backlogs and higher latency under sustained overload. ### Source excerpt They often show up in the same architecture diagram, which makes them look interchangeable. They're not. ## Curating queues beats approving actions DevFeed: [Curating queues beats approving actions]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T14:28:13Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [Access Control](), [Security]() Tags: [access-control](), [agent](), [permission](), [queue](), [security]() ### AI overview This opinion article argues that continuous agents make one-action-at-a-time permission prompts impractical. For reversible work, it proposes generating a surplus of candidates, placing them in a queue, and having a human select which work to pursue. ### Source excerpt The permission prompt breaks down once an agent works continuously. What replaces it is curation: a surplus of candidates, a queue, and a human who picks. ## Measure time between steps in Vercel Workflows DevFeed: [Measure time between steps in Vercel Workflows]() Original publisher: [Read original article]() Author: Mitul Shah Published: 2026-08-05T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [observability](), [SDKs]() Tags: [linux](), [macos](), [observability](), [queue](), [sdk](), [vercel](), [windows](), [workflows]() ### AI overview Vercel Workflows and the Workflow SDK now let users measure the time between steps in the trace viewer. Measurements show sequential gaps, start-time differences for overlapping or nested steps, and timeline gaps that can reveal delayed starts and queue waits. ### Source excerpt You can now measure the time between any two steps in the trace viewer for Vercel Workflows and the Workflow SDK. Select a step, hold Option on macOS or Alt on Windows and Linux, then hover another step to see a measurement line between them: Between sequential steps, the line measures from the end of the earlier step to the start of the later step. Between overlapping or nested steps, it measures the difference between their start times. With no step selected, hold the modifier to see the gaps across the timeline, now drawn with the same measurement line. This can be useful for spotting delayed starts and queue waits when a run takes longer than its steps account for. To get started, open a workflow run from the Workflows tab in Vercel Observability, or locally with npx workflow@beta web. Read more ## Your SQS consumer can hang forever by default DevFeed: [Your SQS consumer can hang forever by default]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [Amazon Simple Queue Service (SQS)](), [SDK](), [Rust](), [Amazon Web Services]() Tags: [aws](), [health-check](), [queue](), [retry](), [rust](), [sdk](), [sqs](), [tcp](), [timeout]() ### AI overview The article explains that the AWS Rust SDK has no request timeout by default. A failed or silently dropped connection can leave an SQS receive waiting indefinitely, stopping a sequential consumer loop while the process and health check still appear healthy. It describes fixing the issue with a per-attempt timeout. ### Source excerpt The AWS Rust SDK ships no request timeout by default, so one SQS receive on a dead connection can hang a whole consumer with nothing in the logs. ## Announcing the AWS Exporter | Port DevFeed: [Announcing the AWS Exporter | Port]() Original publisher: [Read original article]() Author: Tal Sabag Published: 2026-07-22T12:04:28Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Amazon Web Services](), [Amazon S3](), [Amazon Simple Queue Service (SQS)](), [AWS IAM]() Tags: [aws](), [catalog](), [cloud](), [configuration](), [developer-portal](), [encryption](), [iam](), [internal-developer-portal](), [queue](), [resource](), [s3](), [serverless](), [software-catalog]() ### AI overview Port's AWS exporter imports live AWS resource data into a software catalog and internal developer portal. It supports more than 500 resource types, maps them to blueprints and relations, and exposes metadata, tags, console links, and resource-specific details such as encryption and access configuration. ### Source excerpt The AWS exporter brings us even closer to realizing our vision of getting everything into the software catalog and the internal developer portal. ## Why teams run Temporal workers with Encore DevFeed: [Why teams run Temporal workers with Encore]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-07-09T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Back end](), [distributed-systems](), [Provisioning](), [Code]() Tags: [backend](), [community](), [distributed](), [incident](), [provisioning](), [queue](), [retries](), [worker](), [workflows]() ### AI overview The article explains how teams combine Temporal workers with Encore services to build backends with durable, long-running workflows and infrastructure provisioned across environments. Temporal preserves workflow state, resumes after worker crashes, avoids repeating completed side effects, and supports waiting for external signals; Encore handles infrastructure provisioning and orchestration. ### Source excerpt How teams run Temporal workers as an Encore service to get durable workflows, typed APIs, and infrastructure provisioned from the same code. ## Taming my intimidating RSS reader queue DevFeed: [Taming my intimidating RSS reader queue]() Original publisher: [Read original article]() Author: Harsh Shandilya Published: 2026-06-18T07:22:00Z Content type: opinion Language: en Sources: [Posts on Harsh Shandilya]() Topics: [Miniflux](), [RSS]() Tags: [analysis](), [habits](), [miniflux](), [queue](), [reading](), [rss]() ### AI overview The author explains how changes to Miniflux improved their RSS reading experience. They found that perception of an overwhelming unread queue, rather than individual feeds alone, was the main problem, and reduced the pressure by hiding high-volume or low-signal feeds from the global unread list. ### Source excerpt How I regained control of my reading habits ## Workflow SDK now supports TanStack Start DevFeed: [Workflow SDK now supports TanStack Start]() Original publisher: [Read original article]() Author: Peter Wielander Published: 2026-06-16T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [SDKs](), [Vercel](), [Vite](), [configuration](), [TypeScript]() Tags: [config](), [guide](), [knowledge-base](), [persistence](), [queue](), [sdk](), [typescript](), [vercel](), [vite](), [workflow]() ### AI overview Vercel's Workflow SDK now supports TanStack Start applications. Because TanStack Start uses Vite and Nitro, the existing workflow/vite plugin can be added to the Vite configuration. Developers can then define durable, resumable workflow and step functions in TypeScript that survive restarts, sleep for days, and retry on failure, while the plugin handles compilation, queue configuration, and persistence. ### Source excerpt Workflow SDK now supports TanStack Start applications on Vercel. TanStack Start is built on Vite and Nitro, so the existing workflow/vite plugin works directly. Add it to vite.config.ts alongside tanstackStart(). From there, write workflow and step functions in standard TypeScript with "use workflow" and "use step". They run as durable, resumable operations that survive restarts, sleep for days, and retry on failure, with compilation, queue configuration, and persistence handled by the plugin. Read the TanStack Start guide to learn more and create your first durable workflow, or browse TanStack resources in the Vercel Knowledge Base. Read more ## Agentic Code Review DevFeed: [Agentic Code Review]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-06-15T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [agentic code review](), [Code review](), [AI-assisted coding](), [Claude Code](), [codex]() Tags: [agentic](), [agentic-code-review](), [ai](), [claude-code](), [code-review](), [codex](), [queue]() ### AI overview This article argues that coding agents have shifted the main engineering constraint from writing code to deciding whether generated changes are trustworthy. It presents code review as increasingly important and describes using Claude Code and Codex to triage batches of incoming pull requests. ### Source excerpt Coding agents are extraordinarily good now, and getting better fast. The interesting consequence is that the hard part of engineering moved from writing code to deciding whether to trust it, which makes review the most leveraged skill in software right now. How you approach it depends enormously on who you are: a solo developer with no users and a team maintaining a ten-year-old application are not solving the same problem. ## Metering LLM Token Usage: An Architecture Guide for AI SaaS DevFeed: [Metering LLM Token Usage: An Architecture Guide for AI SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-17T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Large Language Model](), [Software as a service](), [Instrumentation](), [telemetry]() Tags: [ai](), [architecture](), [guide](), [instrumentation](), [llm](), [pitfalls](), [queue](), [reconciliation](), [saas](), [token](), [usage-based-billing]() ### AI overview This architecture guide explains how to build a production LLM token-metering pipeline for AI SaaS. It covers instrumentation at the provider-call boundary, queued billing events, hosted aggregation, customer attribution, retry deduplication, partial-failure handling, and reconciliation. ### Source excerpt Build a metering pipeline for LLM token usage that survives retries, supports multiple providers, and feeds clean billing events. Architecture and pitfalls. ## Task Queue Priority and Fairness: Your Task Queue, your way DevFeed: [Task Queue Priority and Fairness: Your Task Queue, your way]() Original publisher: [Read original article]() Author: Conna Lanzafane Published: 2026-05-12T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [SDKs]() Tags: [fairness](), [priority](), [product-news](), [queue](), [queues](), [scheduling](), [sdks]() ### AI overview Temporal Task Queue Priority and Fairness is generally available for all SDKs. Priority controls task execution order, while Fairness prevents a single Workflow, user, or tenant from monopolizing Workers. ### Source excerpt Task Queue Priority and Fairness are GA for all Temporal SDKs, helping teams prioritize critical work and prevent tenant starvation without custom infra. ## PGKeeper: Building the bouncer we needed for Postgres DevFeed: [PGKeeper: Building the bouncer we needed for Postgres]() Original publisher: [Read original article]() Author: Lihao He; Tim Goh; Mehant Baid Published: 2026-05-04T14:52:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [PostgreSQL](), [Scalability](), [Architecture & Design](), [benchmarking](), [Databases](), [Network]() Tags: [architecture](), [database-scalability](), [performance](), [postgres](), [postgresql](), [queue](), [routing](), [scalability](), [sharding]() ### AI overview Figma describes why it built PGKeeper, a scalable PostgreSQL connection and load management service that replaced PgBouncer in front of its Postgres fleet. The article covers the database workload pressures, request routing, connection pooling, and rollout of the new service. ### Source excerpt This is the story of why and how we built PGKeeper, a scalable and reliable service to support Figma's rapidly growing products and database workload. ## How Asynchronous Systems Trade Execution Order for Scale and Availability DevFeed: [How Asynchronous Systems Trade Execution Order for Scale and Availability]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-03-28T13:04:17Z Content type: article Language: en Sources: [System Design Classroom]() Topics: [systems](), [event driven](), [Availability]() Tags: [async](), [availability](), [consistency](), [event-driven](), [ordering](), [parallel](), [pipelines](), [queue](), [scale](), [state](), [systems]() ### AI overview The article explains how asynchronous systems improve scale, throughput, and availability by processing work in parallel, while making execution order and consistency harder to control. Queues, retries, and parallel consumers can reorder related events, producing incorrect final state even when every component succeeds. ### Source excerpt Many developers only realize it after consistency breaks. ## A Single Subscriber Doesn't Turn Pub/Sub Into a Queue DevFeed: [A Single Subscriber Doesn't Turn Pub/Sub Into a Queue]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-02-28T12:52:05Z Content type: tutorial Language: en Sources: [System Design Classroom]() Topics: [systems](), [API]() Tags: [api](), [async](), [patterns](), [pdf](), [queue](), [retry](), [systems]() ### AI overview The article explains that asynchronous system patterns should be classified by execution semantics rather than by the messaging technology used. A report-generation flow with one worker processing each job, retries after failure, and additional workers sharing increased demand is a work queue, not pub/sub. ### Source excerpt Why execution semantics -not tooling- define async patterns. ## In praise of the stacked pull request DevFeed: [In praise of the stacked pull request]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-02-04T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [stacked pull requests](), [Code review](), [Code quality](), [Development](), [Git]() Tags: [code-quality](), [code-review](), [git](), [merge](), [pull-request](), [queue](), [soc2](), [stacked-pull-requests](), [team](), [velocity]() ### AI overview An opinion piece argues that stacked pull requests help teams work on larger or dependent changes without creating oversized, difficult-to-review pull requests. It presents small, independent, testable pull requests as units of work that can be reviewed and merged incrementally. ### Source excerpt How fast you do code review is how fast you do everything. Your PR queue is the leading indicator of team velocity. Stacked PRs can help. ## Unordered mode for S3 ClickPipes is now available DevFeed: [Unordered mode for S3 ClickPipes is now available]() Original publisher: [Read original article]() Author: Marta Paes Published: 2025-12-09T15:06:25Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [Amazon S3](), [clickhouse](), [Amazon Simple Queue Service (SQS)](), [event driven](), [data]() Tags: [amazon-s3](), [amazon-sqs](), [clickhouse](), [data](), [event-driven](), [object-storage](), [queue](), [s3]() ### AI overview ClickPipes now supports unordered ingestion from Amazon S3 into ClickHouse Cloud using S3 Event Notifications through Amazon SQS. This handles backfills, retries, late-arriving data, and other out-of-order files without relying on lexicographical file order. ### Source excerpt Ingest data from Amazon S3 into ClickHouse Cloud in any order for event-driven, blazing-fast analytics. ## Best Practices for Scalable Cloud Infrastructure with Laravel Cloud DevFeed: [Best Practices for Scalable Cloud Infrastructure with Laravel Cloud]() Original publisher: [Read original article]() Author: Laravel Team Published: 2025-11-27T15:47:33Z Content type: article Language: en Sources: [Laravel Blog]() Topics: [cloud-infrastructure](), [autoscaling](), [Laravel](), [hosting](), [Caching](), [Redis](), [k6]() Tags: [autoscaling](), [best-practices](), [caching](), [cloud](), [cloud-infrastructure](), [hosting](), [infrastructure](), [k6](), [laravel](), [performance](), [queue](), [redis](), [scale](), [server](), [speed]() ### AI overview This article presents best practices for using Laravel Cloud to build scalable cloud infrastructure. It explains autoscaling strategies, load balancing, caching with Redis and Laravel Valkey, and managed queue workers for handling traffic spikes while maintaining application performance and stability. ### Source excerpt Tips for scaling cloud infrastructure in Laravel Cloud: set smart baselines, tune autoscaling triggers, and optimize replicas for stable performance. [Next page]()