# Concurrent Published articles for Concurrent. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PostgreSQL 19 graph queries fail the 'would you ship this?' test DevFeed: [PostgreSQL 19 graph queries fail the 'would you ship this?' test]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-15T09:42:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [PostgreSQL](), [SQL](), [Graphs](), [Databases]() Tags: [bugs](), [concurrent](), [databases](), [graph](), [postgresql](), [sql]() ### AI overview The article reports that PostgreSQL 19's SQL/PGQ graph queries were rejected because of unresolved bugs. It also discusses concurrent REPACK as a way to reduce overnight maintenance calls for database administrators. ### Source excerpt SQL/PGQ gets bounced over unresolved bugs as concurrent REPACK promises fewer midnight calls for DBAs ## Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts DevFeed: [Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:23:21Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Inference](), [Inference Performance](), [Orchestration](), [GPU](), [Multi-tenancy](), [sglang](), [TensorRT](), [vllm](), [Security]() Tags: [ai](), [cache](), [concurrent](), [enterprise](), [gpu](), [inference](), [latency](), [nvme](), [orchestration](), [security]() ### AI overview Lightbits Labs is introducing Inferra, a KV cache orchestration engine for AI inference. It virtualizes GPU memory across DRAM and NVMe storage, preserving attention states for long-context and multi-session workloads. Lightbits claims up to 16 times more concurrent sessions, more than 100 times lower latency than recomputation, and context windows of up to 10 million tokens. Inferra supports vLLM, TensorRT, and SGLang and includes tiering, predictive prefetching, tenant isolation, and encrypted data transfer. ### Source excerpt Lightbits Labs, the company that invented NVMe over TCP, is moving into inference software with Inferra, a KV cache orchestration engine that makes its public debut tomorrow, September 15, at the AI Infra Summit in Santa Clara. The software virtualizes GPU memory across DRAM and NVMe storage tiers and turns the KV cache into a The post Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts appeared first on StorageReview.com. ## Why didn't ReadDirectoryChangesW provide a way to correlate the two sides of a rename operation? DevFeed: [Why didn't ReadDirectoryChangesW provide a way to correlate the two sides of a rename operation?]() Original publisher: [Read original article]() Author: Raymond Chen Published: 2026-09-14T14:00:00Z Content type: article Language: en Sources: [Raymond Chen]() Topics: [Filesystems](), [systems]() Tags: [concurrent](), [events](), [files](), [ids](), [implementation](), [information](), [old-new-thing](), [other](), [post](), [systems]() ### AI overview The article explains why ReadDirectoryChangesW did not explicitly provide a mechanism for correlating the two events generated by a file rename. It suggests that the events were expected to occur consecutively, but that assumption was never documented and could fail when concurrent renames or disk activity interleave events. File IDs provide the information needed to correlate the two sides reliably. ### Source excerpt Perhaps it wasn't necessary, or perhaps it didn't occur to them that this a problem. The post Why didn't ReadDirectoryChangesW provide a way to correlate the two sides of a rename operation? appeared first on The Old New Thing. ## Scaling real-time AI agents with session-aware load balancing DevFeed: [Scaling real-time AI agents with session-aware load balancing]() Original publisher: [Read original article]() Author: Simerus Mahesh Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [real-time](), [Artificial Intelligence](), [Streaming](), [Server](), [gRPC](), [WebSocket](), [cpu](), [Concurrent Programming]() Tags: [ai-agents](), [concurrent](), [cpu](), [real-time](), [routing](), [server](), [streaming]() ### AI overview This article explains why real-time AI agents require session-aware load balancing. Long-lived, stateful bidirectional streams make request rates and CPU utilization insufficient measures of backend capacity. The proposed approach tracks active sessions at the application level and combines session counts with CPU metrics to distribute traffic and avoid bottlenecks. ### Source excerpt Real-time AI agents break traditional request-response load balancing paradigms because they rely on long-lived, stateful bidirectional streams that obscure true server capacity. To solve this, developers must implement application-level session tracking directly within the runtime to accurately measure the committed concurrent workload of active conversations. By feeding these precise session counts alongside standard CPU utilization metrics into a hybrid routing algorithm, infrastructure can effectively distribute stateful AI traffic and prevent individual backend bottlenecks. ## Redesigning Kubernetes Workqueues and Resyncs to Reduce Thundering Herd Effects DevFeed: [Redesigning Kubernetes Workqueues and Resyncs to Reduce Thundering Herd Effects]() Original publisher: [Read original article]() Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Kubernetes](), [systems](), [Concurrent Programming](), [Latency](), [Maintainers]() Tags: [architecture](), [concurrent](), [defense-in-depth](), [failover](), [goroutines](), [kubernetes](), [latency](), [maintainers](), [reconciliation](), [thundering-herd]() ### AI overview This article explains how Kubernetes controllers can experience thundering herd effects when restarts, leader failovers, or periodic resyncs cause many keys to enter an in-memory workqueue at once. It describes the queue and informer behavior behind the problem and introduces Chainguard's redesign of workqueues and resyncs to improve reconciliation under these conditions. ### Source excerpt The thundering herd is an architecture problem. See how Chainguard redesigned workqueues and resyncs to keep reconciliation running smoothly. ## OAuth token refresh has a race condition. Fix it with a conditional write, not a distributed lock. DevFeed: [OAuth token refresh has a race condition. Fix it with a conditional write, not a distributed lock.]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-31T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [OAuth](), [race-condition](), [Authorization](), [Auth0](), [Redis]() Tags: [auth0](), [authorization](), [concurrent](), [oauth](), [race-condition](), [redis](), [the-result]() ### AI overview This article explains how concurrent OAuth token refreshes can cause two distinct failures: stale tokens from lost updates and user disconnection when refresh-token rotation treats concurrent reuse as replay. It recommends layered defenses, including conditional writes, and explains why a Redis lock does not address both problems. ### Source excerpt Concurrent refreshes don't just fail. They can disconnect the user entirely. Here are four layers of defense, cheapest first, and why the Redis lock everyone reaches for isn't the one keeping you safe. ## Concurrent Servers: Part 8 - Go DevFeed: [Concurrent Servers: Part 8 - Go]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-22T14:52:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [Go Language](), [Concurrency](), [Server](), [Concurrent Programming](), [Network]() Tags: [capacity](), [concurrency](), [concurrent](), [go](), [goroutines](), [misc](), [network-programming](), [servers](), [threads]() ### AI overview Part 8 of a series on concurrent network servers explains how Go implements sequential and concurrent servers. It demonstrates serving each client with a lightweight goroutine and discusses why concurrency may still need to be limited, including when tasks compete for finite CPU capacity. ### Source excerpt This is part 8 in a series of posts on writing concurrent network servers. In this part, we'll switch to Go and see how it tackles the challenges described earlier in the series. All posts in the series: Part 1 - Introduction Part 2 - Threads Part 3 - Event-driven Part 4 - libuv ... ## How to store and refresh session tokens correctly with the WorkOS iOS SDK DevFeed: [How to store and refresh session tokens correctly with the WorkOS iOS SDK]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [iOS](), [SDK](), [Authentication](), [JSON Web Tokens]() Tags: [apple](), [auth](), [authentication](), [concurrent](), [guide](), [how-to](), [ios](), [jwt](), [native](), [plist](), [rotation](), [sdk](), [storage](), [tokens](), [tutorial]() ### AI overview This tutorial explains how to store and refresh session tokens with the WorkOS iOS SDK. It recommends storing access and refresh tokens in the Keychain, persisting each rotated refresh token, and distinguishing terminal refresh failures from transient network or concurrency-related failures. ### Source excerpt A practical guide to Keychain storage, refresh token rotation, and terminal vs. transient failures on iOS ## Concurrent Servers: Part 7 - Rust DevFeed: [Concurrent Servers: Part 7 - Rust]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-15T16:41:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [Rust](), [Concurrent Programming](), [Network](), [Server](), [Programming]() Tags: [concurrency](), [concurrent](), [misc](), [network](), [network-programming](), [programming](), [programming-language](), [rust](), [servers](), [threads]() ### AI overview Part 7 of a series on concurrent network servers explains how to implement the series' state-machine protocol in Rust. It covers a sequential server, one thread per client, and a fixed thread pool. ### Source excerpt This is part 7 in a series of posts on writing concurrent network servers. In this part, we discuss how the challenges described in earlier parts are tackled in the Rust programming language. All posts in the series: Part 1 - Introduction Part 2 - Threads Part 3 - Event-driven Part 4 - libuv ... ## wasmCloud 2.7: Bidirectional host component plugins, concurrent pooling, and multi-backend binding by default DevFeed: [wasmCloud 2.7: Bidirectional host component plugins, concurrent pooling, and multi-backend binding by default]() Original publisher: [Read original article]() Author: Eric Gregory Published: 2026-08-12T00:00:00Z Content type: release Language: en Sources: [wasmCloud Blog]() Topics: [WebAssembly](), [configuration](), [networking]() Tags: [component](), [concurrent](), [configuration](), [networking](), [release](), [secrets](), [wasmcloud](), [webassembly]() ### AI overview wasmCloud 2.7 introduces bidirectional host component plugins, concurrent pooled instances, and multi-backend binding by default. It also adds workload-level HTTP connection pooling, raw-socket quotas, opt-in egress policies, configurable CA trust, and updated Helm chart CRDs. ### Source excerpt wasmCloud 2.7 makes host component plugins bidirectional (plugins can call workload exports), multiplies warm-instance capacity with maxConcurrency, enables multi-backend binding by default, and hardens workload networking with quotas and private CA trust. ## Bun runtime for Vercel Functions now accepts Bun.serve as an entrypoint DevFeed: [Bun runtime for Vercel Functions now accepts Bun.serve as an entrypoint]() Original publisher: [Read original article]() Author: Florentin Eckl Published: 2026-08-10T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Bun](), [Vercel](), [WebSocket](), [servers]() Tags: [bun](), [compute](), [concurrent](), [data](), [external](), [pricing](), [server](), [vercel]() ### AI overview Vercel Functions now support Bun.serve() as a Bun runtime entrypoint, including WebSocket handlers. Locally run Bun servers can deploy as-is without being wrapped in a framework, with routes defined in a project-level server.ts file. ### Source excerpt The Bun runtime for Vercel Functions now supports Bun.serve() as a function entrypoint, including WebSocket handlers. The server you run locally with Bun deploys as-is, without being wrapped in a framework. Enable the runtime by setting "bunVersion": "1.x" in vercel.json. Deploy a routes-based server Create a server with a routes map in server.ts at the project root. Accept WebSocket connections Add a websocket handler and call server.upgrade(request) in fetch to upgrade matching requests. The rest of the server stays the same. WebSocket connections run on Fluid compute with Active CPU pricing, so you pay only for time spent processing messages, not idle connection time. A connection is pinned to one function instance for its lifetime, and a single instance can handle multiple concurrent connections. Use an external data store to coordinate messages across instances. Read the documentation to get started. Read more ## Vercel Sandbox now supports 10,000 concurrent sandboxes and 5,000 vCPUs per minute DevFeed: [Vercel Sandbox now supports 10,000 concurrent sandboxes and 5,000 vCPUs per minute]() Original publisher: [Read original article]() Author: Brandon Tuttle Published: 2026-08-05T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel]() Tags: [concurrent](), [enterprise](), [pricing](), [quotas](), [sales](), [sandbox](), [sandboxes](), [vercel]() ### AI overview Vercel Sandbox has increased its default quotas for Pro and Enterprise plans to support up to 10,000 concurrent sandboxes and 5,000 vCPUs per minute. ### Source excerpt We've increased the default quotas for Vercel Sandbox on Pro and Enterprise plans: Quota Before After Concurrent sandboxes 2,000 10,000 vCPUs allocation rate 200/min (Pro), 400/min (Enterprise) Up to 5,000/min The higher vCPU allocation rate is possible thanks to a new dynamic quota: instead of a fixed limit, the allowed rate now ramps with sustained usage: it starts at 150 vCPUs per minute and increases by 500 vCPUs per minute as you continue creating sandboxes, up to 5,000 vCPUs per minute The new quotas apply automatically to all Pro and Enterprise teams. If you need higher quotas, contact sales. Learn more about Sandbox pricing and dynamic quotas. Read more ## v19.2.5 Squid released DevFeed: [v19.2.5 Squid released]() Original publisher: [Read original article]() Author: Yuri Weinstein Published: 2026-07-14T00:00:00Z Content type: news Language: en Sources: [Ceph Blog]() Topics: [changelog](), [Amazon S3](), [bug](), [TLS (Transport Layer Security)](), [Authentication](), [Authorization](), [SSL]() Tags: [authentication](), [authorization](), [blog-post](), [bug](), [changelog](), [concurrent](), [configuration](), [en-article](), [en-blog-post](), [object-storage](), [queuing](), [release](), [s3](), [squid](), [ssl](), [tls](), [vector]() ### AI overview Ceph v19.2.5 Squid is the fifth backport release in the Squid series. It fixes issues across MDS, OSD, and RGW, including request queuing faults, rollback and erasure-coding behavior, TLS 1.3 cipher configuration, S3 ACL handling, multisite synchronization, policy evaluation, presigned URL authentication, object tagging, frontend concurrency, and bucket statistics. ### Source excerpt This is the fifth backport release in the Squid series. We recommend all users update to this release. Notable Changes ¶ MDS (Metadata Server) ¶ Fixed a segmentation fault relating to MDSContext completion and request queuing. OSD (Object Storage Daemon) ¶ Rollback & Vector Fixes: Corrected rollback logic for partial write object information (OI) and optimized Erasure Coding (EC) by ensuring Twiddle creates a full-sized vector. RGW (RADOS Gateway) ¶ TLS 1.3 Ciphersuites: Introduced the ssl_ciphersuites configuration option for the Beast front-end to support TLS 1.3 cipher customizations. S3 PutObject ACLs Fix: Fixed a bug where PutObject requests were erroneously rejected under BlockPublicAcls due to an incorrect comparison return value from canned_acl.compare(). Multisite Sync Robustness: Resolved an infinite loop scenario in RGWBucketFullSyncCR when a source bucket was deleted mid-sync by introducing a mechanism to clear stateful data in reused bucket_list_result objects. SNS Policy Evaluation: Updated ListTopics to use the account root ARN for policy evaluation when invoked by a non-root account user, preventing unexpected implicit denies from identity-based policies. Presigned URLs: Fixed an internal server error occurring during the authentication process of presigned URLs. Prerequest Hook: Restructured the order of execution so that the prerequest hook now runs after the authorization process has completed. Object Tagging: Added handling for plain-text object tags during execution of RGWObjTags::decode(). Beast Frontend Stability: Applied a strand executor to the Beast timeout timer to avoid concurrent socket access and potential race conditions. Bucket Statistics: Added a fix for bucket stats execution when the underlying bucket index does not exist. Changelog ¶ [Stretch Mode] mon: restrict changing mon election strategy post stretch mode (pr#65458, Kamoltat Sirivadhna) Check if HTTP\_X\_AMZ\_COPY\_SOURCE header is empty (pr#66028, Suyash Dongre) Fix multifs ## Turso v0.7.0 DevFeed: [Turso v0.7.0]() Original publisher: [Read original article]() Author: Pekka Enberg Published: 2026-07-13T00:00:00Z Content type: release Language: en Sources: [Turso Blog]() Topics: [Turso](), [SQLite](), [.NET](), [Python](), [Databases](), [SQL]() Tags: [concurrent](), [database](), [garbage-collection](), [memory](), [net](), [process](), [python](), [release](), [sql](), [sqlite](), [turso]() ### AI overview Turso 0.7 improves performance and robustness for production and embedded workloads. The release speeds up MVCC concurrent writes, reduces recovery and memory costs, adds cooperative CPU yielding and safer allocation behavior, and expands SQL features. It also introduces a SQLite-compatible .NET provider and full-text search in the Python SDK. ### Source excerpt Turso v0.7.0 release with faster MVCC concurrent writes, a non-blocking embeddable engine, PostgreSQL-style sequences, locale-backed collations, SQLite-compatible .NET bindings, and full-text search in Python. ## Releasing Execution Contexts DevFeed: [Releasing Execution Contexts]() Original publisher: [Read original article]() Author: Julien Portalier Published: 2026-07-12T00:00:00Z Content type: release Language: en Sources: [Crystal]() Topics: [Crystal](), [Concurrency](), [Concurrent Programming](), [context](), [cpu]() Tags: [concurrent](), [cpu](), [kotlin](), [main-thread](), [multithreading](), [parallelism](), [threads]() ### AI overview Crystal is introducing Execution Contexts, an interface for orchestrating fibers across one or more threads. The article describes concurrent and parallel contexts, configurable parallelism, and execution across CPU cores while preserving a single-threaded default. ### Source excerpt Two and a half years ago, with the invaluable support from 84codes, we re-examined the multithreading model inherited from Crystal 0.28 (preview MT). ## Timers, Timeouts, and the Art of Waiting in Temporal DevFeed: [Timers, Timeouts, and the Art of Waiting in Temporal]() Original publisher: [Read original article]() Author: Joshua Smith Published: 2026-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [SDKs](), [Code](), [Processes]() Tags: [concurrent](), [crashes](), [deployment](), [developers](), [how-to](), [scaling](), [sdks](), [temporal-concepts](), [timeout](), [waiting]() ### AI overview This tutorial explains that Temporal timers provide durable business-logic waiting, while Activity and Workflow timeouts detect failures. It describes how timers survive Worker or process failures and can support large numbers of concurrent waits. ### Source excerpt Timers and timeouts look similar in Temporal but solve different problems. Learn how each one works and how to choose the right tool for durable waiting. ## How We Built an AI Agent to Clean Up Dead Code After A/B Tests DevFeed: [How We Built an AI Agent to Clean Up Dead Code After A/B Tests]() Original publisher: [Read original article]() Author: Aseem Upadhyay Published: 2026-06-23T10:54:43Z Content type: article Language: en Sources: [Housing.com]() Topics: [AI Agent](), [experiments](), [Code](), [Pull Request](), [context](), [Android]() Tags: [agent](), [ai](), [ai-agent](), [ai-agents-in-action](), [automated](), [code](), [concurrent](), [experiment](), [experiments](), [infrastructure](), [jira](), [llm]() ### AI overview Housing.com describes building an AI-agent pipeline to help clean up code after A/B tests. The workflow interprets experiment tickets, reports experiment status, and applies instructions to code, while using scripts for deterministic steps and an LLM where judgment is required. ### Source excerpt Photo by Microsoft Copilot on Unsplash At Housing.com, running product experiments is a continuous cycle. A/B tests go live, collect data, and eventually reach a conclusion. That's the exciting part. Then comes the mundane reality where someone has to clean up the code .i.e. remove a feature flag, promote a winning variant or revert the loser variant and finally raise a change request. Sounds simple? Maybe Is it tedious and quietly expensive? Yes! lifecycle of a taskThe Problem Worth Solving An experiment conclusion ticket typically lands on an engineer's desk looking something like this: Experiment: show_listing_map_widget Platform: Android Result: Negative - revert to control The job of the assigned engineer involves four distinct steps: Find every reference to the flag across the codebase. Delete the losing variant's code path. Trace every side-effect that only existed to support that variant Commit, open a PR, and comment on the Jira ticket. Step 3 is where the trap lies. Be it applying or removing a change, changing all the infrastructure code dependent on it could increase the complexity and risk of creating technical debt. But what if we automated a part of it? The AI Agent Pipelineupdated AI enabled lifecycle The problem statement became simple: Let stakeholders own the trigger. We built two agents to make it happen, 1. to interpret tickets and report experiment status 2. to take the instructions and code. Then came the hard part. Navigating Roadblocks The real complexity lies in building an AI agent that runs autonomously and serves different users across different use cases We found ourselves wrestling with questions we hadn't fully anticipated: How do we optimise on the tokens used per request? How do we handle concurrent requests? How do we ensure that the consistency in the output? So we went looking for answers.. Optimising Tokens per request Not every step needs AI. At each point in the workflow, we asked one question: is this operation deterministic ## How Shopify Uses MySQL for Oversell Protection at Scale DevFeed: [How Shopify Uses MySQL for Oversell Protection at Scale]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-06-22T15:30:09Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [MySQL](), [Database](), [Concurrency](), [Deadlock](), [Redis](), [Transactions]() Tags: [concurrent](), [database](), [deadlock](), [mysql](), [redis](), [transactions]() ### AI overview The article describes Shopify's inventory oversell protection challenge and implementation techniques centered on MySQL, including bounded row pools, composite primary keys, READ COMMITTED isolation, consistent lock ordering, and shadow-mode migration from Redis. It emphasizes concurrency, ACID guarantees, and production-scale reservation handling. ### Source excerpt PLUS: SpaceX buys Cursor for $60B 💰, AWS Blocks framework launch ⚡, Terminal optimization tips 👨💻 ## Adaptive write request scheduling in Redpanda's Cloud Topics DevFeed: [Adaptive write request scheduling in Redpanda's Cloud Topics]() Original publisher: [Read original article]() Author: Evgeny Lazin Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Amazon S3](), [Latency](), [Cloud](), [data]() Tags: [batching](), [cloud](), [concurrent](), [cost](), [efficiency](), [engineering](), [kafka](), [latency](), [object-storage](), [s3]() ### AI overview This article explains Redpanda's adaptive write-request scheduler for Cloud Topics. Because each produce batch is uploaded to Amazon S3 before acknowledgment, the scheduler dynamically adjusts upload parallelism across CPU cores to balance batching efficiency, producer latency, throughput, and storage cost. It also describes how centralized batching avoids the latency and request-cost problems of running one batcher per shard. ### Source excerpt How we turned to the buddy allocator algorithm for Redpanda's Cloud Topics to balance batching efficiency against latency and cost. ## Logic for Programmers extra credits DevFeed: [Logic for Programmers extra credits]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2026-06-02T14:48:48Z Content type: article Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [Math and Logic](), [Concurrent Programming]() Tags: [book](), [concurrent](), [math](), [writing]() ### AI overview The author announces four supplementary pieces for Logic for Programmers covering concurrent-process orderings, first-order logic and sets of functions, Liskov's history rule in subtyping, and orders on sets. The supplements contain roughly 2,000-3,000 words of math content and may be less polished than the book. ### Source excerpt So I said there wasn't a proper newsletter this week, since I'm in Budapest prepping for a conference. But I still got a thing for y'all. There's a lot of interesting topics I wanted to cover for Logic for Programmers, but the book is dense enough as it is and many of these were too tangential or technical to fit in well. So I've been writing some supplements and uploading them here. I've got four so far: How we compute the number of orderings of multiple concurrent processes How first-order logic can quantify over "a set of functions", what a "set of functions" looks like, and how functions can be defined in terms of sets (plus a bit on currying and type theory) Barbara Liskov's "history rule" in subtyping Total and partial orders on sets. Now I'm going to caveat that these were written off the cuff and haven't gone through the obsessive editing of the book itself, so they may be rough and there might be errors in them. Still, it's like 2-3000 words of math content, so hopefully covers not having a proper newsletter this time. Seeya next week! ## A Five-Pattern Playbook for Running Parallel AI Coding Agents with Pulumi DevFeed: [A Five-Pattern Playbook for Running Parallel AI Coding Agents with Pulumi]() Original publisher: [Read original article]() Author: Engin Diri Published: 2026-06-02T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [AI-assisted coding](), [ai-coding](), [Git](), [GitHub](), [Pull Request]() Tags: [ai](), [ai-agents](), [ai-coding-agents](), [automation](), [best-practices](), [coding-agents](), [concurrent](), [git](), [github](), [infrastructure-as-code](), [platform-engineering](), [pull-request](), [pulumi-neo](), [self-healing]() ### AI overview The article presents a five-pattern playbook for running multiple AI coding agents concurrently. It uses issues as specifications, a plan/build/validate workflow, isolated Git worktrees, fresh-session review, and a self-healing layer. The approach is described for application code, with infrastructure work identified as a further question. ### Source excerpt AI coding has two shapes right now. One agent in a loop, sequential work, you babysitting the chat window. Call that 2x. Most teams live here. Five agents in worktrees, parallel work, fresh-context review on every change. Call that 10x. The trick: 2x is mostly prompting, 10x is mostly plumbing. The parallel coding playbook is a five-pattern setup for running multiple AI coding agents at the same time without them stepping on each other: an issue used as the spec, a plan/build/validate loop, parallel git worktrees, fresh-session review, and a self-healing layer. The whole thing targets application code. The interesting question, and the one I keep ending up at, is what changes when the five agents are touching infrastructure. 2x is prompting, 10x is plumbing 2x is one human, one agent, one repo, one branch. The agent writes, you review, you tell it to try again, it tries again. The bottleneck is your attention. Whatever the agent's raw throughput, your reading speed sets the ceiling. 10x moves you out of the per-change loop and into the issue loop. You write five issues with sharp acceptance criteria, send each one to its own agent in its own worktree, and let them plan, build, and validate end-to-end. You read five PRs at lunch instead of pair-programming on one all morning. Concurrent isolation does the work. And isolation is mostly an infrastructure problem. The five pillars The five pillars, in one sentence each. Issue is the spec. The GitHub issue carries the acceptance criteria. The pull request is the artifact that gets validated. Input and output of every implementation are versioned, scoped, and reviewable on their own. Plan, build, validate. Three stages, three artifacts. A markdown plan you can read in thirty seconds. A build that produces a diff. A validate step that checks the diff against the spec. Parallel worktrees. Each agent runs in its own git worktree so concurrent changes never trample each other. One repo, five working trees, five branches. Fres ## Maestro CLI 2.6.0: introducing Maestro Viewer DevFeed: [Maestro CLI 2.6.0: introducing Maestro Viewer]() Original publisher: [Read original article]() Author: Manu Armani Published: 2026-05-25T09:00:00Z Content type: release Language: en Sources: [mobile.dev - Medium]() Topics: [Mobile](), [Artificial Intelligence](), [ai-coding](), [MCP](), [Single-page application (SPA)](), [web applications](), [Android](), [iOS]() Tags: [ai](), [ai-coding](), [android](), [concurrent](), [ios](), [maestro](), [mcp](), [mobile](), [parallel](), [parsing](), [release](), [validation](), [web-app]() ### AI overview Maestro CLI v2.6.0 introduces Maestro Viewer, a web app that embeds an iOS simulator, Android emulator, or physical device inside a coding agent or browser. The release also adds more reliable concurrent local iOS execution, removes the bundled web-based Maestro Studio and Rhino JavaScript engine, and improves parsing errors, output paths, and iOS XCTest log collection. ### Source excerpt Maestro CLI v2.6.0 ships Maestro Viewer: your coding agent now has a mobile device, live inside the agent. Plus faster iOS, cleaner output, and fixes. ## Concurrent, atomic MSI hash tables DevFeed: [Concurrent, atomic MSI hash tables]() Original publisher: [Read original article]() Published: 2026-05-06T02:01:17Z Content type: tutorial Language: en Sources: [Chris Wellons]() Topics: [Concurrency](), [Concurrent Programming](), [Code](), [gcc](), [Processes]() Tags: [article](), [c](), [code](), [concurrency](), [concurrent](), [gcc](), [processes](), [thread]() ### AI overview This article explains how to add atomic operations to Mask-Step-Index (MSI) hash tables so they can be accessed concurrently without data races. It covers single-producer and multiple-consumer designs, relaxed atomics, and acquire-release synchronization when published objects must be safely observed. ### Source excerpt Readers will be familiar with Mask-Step-Index (MSI) hash tables, a technique for building fast, open-addressed hash tables in a dozen lines of code. If multiple threads or processes access an MSI table with at least one still inserting elements, care must be taken to avoid data races. This article will show how to add atomic operations to MSI tables in order to support different concurrency constraints. Let's begin with the simplest case: An integer hash set, no deletions, only one insert thread (single producer), and consumers do not care about insert order. That is, the producer inserts A then B, but consumers may observe B in the table before A. Suppose this is the hash table in the single-threaded case: int32_t *lookup(int32_t key, int32_t *table, int exp) { uint64_t hash = ((uint64_t)key * 1111111111111111111u) >> 32; uint32_t mask = ((uint32_t)1 << exp) - 1; uint32_t step = (hash >> (32 - exp)) | 1; for (uint32_t index = hash;;) { index = (index + step) & mask; if (!table[index] || table[index]==key) { return table + index; } } } Keys must be non-zero, and tables are zero-initialized. Usage example: // Initialization enum { exp = 8 }; int32_t table[1<<8] = {}; // Producer for (int i = 0; i < nkeys; i++) { *lookup(keys[i], table, exp) = keys[i]; } // Consumer int32_t key = 1234; bool present = *lookup(key, table, exp); The only problem is the data race on table slots. Since consumers can tolerate out-of-order insertions, ordering does not matter and relaxed atomics eliminate the data race. Insert and query now have different requirements, so it makes sense to distinguish them. Starting with the latter: bool contains(int32_t key, int32_t *table, int exp) { uint64_t hash = ((uint64_t)key * 1111111111111111111u) >> 32; uint32_t mask = ((uint32_t)1 << exp) - 1; uint32_t step = (hash >> (32 - exp)) | 1; for (uint32_t index = hash;;) { index = (index + step) & mask; int32_t k = __atomic_load_n(table+index, __ATOMIC_RELAXED); if (!k) { return false; } else if (k == ke ## How AI Agents Challenge Enterprise Database Capacity Assumptions DevFeed: [How AI Agents Challenge Enterprise Database Capacity Assumptions]() Original publisher: [Read original article]() Author: flashdba Published: 2026-05-04T09:17:00Z Content type: opinion Language: en Sources: [flashdba]() Topics: [Database](), [Artificial Intelligence](), [Cloud]() Tags: [agentic](), [ai](), [ai-agents](), [autoscaling](), [cloud](), [concurrent](), [cost](), [database](), [databases](), [databases-and-agentic-ai](), [inferencing](), [parallel](), [performance](), [real-time](), [scale]() ### AI overview This commentary explains how AI agents can invalidate traditional enterprise database capacity assumptions. Unlike human-driven workloads, agents can issue recursive, parallel requests at machine speed, potentially changing concurrency, autoscaling behavior, and cloud cost forecasts. ### Source excerpt Every enterprise database capacity model rested on assumptions about human behaviour. Agents remove those assumptions - and in the cloud, that gap renews every month. [Next page]()