# parallel Published articles for parallel. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## ESPHome 2026.9.0: Faster builds and encrypted updates DevFeed: [ESPHome 2026.9.0: Faster builds and encrypted updates]() Original publisher: [Read original article]() Author: Jesse Hills Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [ESPHome - Smart Home Made Simple - Blog]() Topics: [esphome](), [PlatformIO](), [ESP8266](), [Encryption](), [build performance](), [ChaCha](), [toolchain](), [Home Assistant]() Tags: [2026](), [benchmarks](), [builds](), [ci](), [encryption](), [esp32](), [esp8266](), [esphome](), [ota](), [parallel](), [releases](), [toolchain](), [updates]() ### AI overview ESPHome 2026.9.0 improves build performance by parallelizing PlatformIO setup, lays groundwork for a native ESP8266 toolchain, adds Noise-encrypted OTA updates, and reduces ESP8266 RAM usage. The release also includes component additions, platform updates, and fixes. ### Source excerpt ESPHome 2026.9.0 parallelizes PlatformIO installs, lays the groundwork for a native ESP8266 toolchain, adds Noise-encrypted OTA updates, and frees ESP8266 RAM. ## Build with Claude Code: Enrollment for a Two-Day Cohort Course DevFeed: [Build with Claude Code: Enrollment for a Two-Day Cohort Course]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-15T19:31:01Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Claude Code](), [Development](), [Code](), [Git]() Tags: [build](), [claude-code](), [code](), [development](), [git](), [hooks](), [how-to](), [learn](), [mcps](), [parallel]() ### AI overview The article announces the relaunch of Build with Claude Code, a two-day cohort-based course taught by John Kim. It covers Claude Code fundamentals and production workflows, including context engineering, memory, Skills, MCPs, hooks, Git worktrees, subagents, and agent teams. ### Source excerpt We're relaunching Build with Claude Code, a 2-day intensive cohort-based course taught by John Kim, who has trained hundreds of engineers at Meta to use Claude Code in real production workflows. ## Deploying Parallel Remote Coding Agents with Decode DevFeed: [Deploying Parallel Remote Coding Agents with Decode]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-09-10T05:02:03Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI-assisted coding](), [Command-line interface](), [Serverless](), [Python]() Tags: [cli](), [coding-agents](), [modal](), [parallel]() ### AI overview This tutorial explains how to deploy the Decode coding-agent harness as remote background jobs. It covers moving from a TUI to a headless CLI, running jobs on Modal, and supporting parallel sessions across multiple projects. ### Source excerpt Run the harness remotely on Modal, triggered by your CLI, a webhook, or a nightly cron. ## Hot Chips 2026: XCENA and Samsung's Near-Memory Compute CXL Device DevFeed: [Hot Chips 2026: XCENA and Samsung's Near-Memory Compute CXL Device]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-30T07:25:37Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [samsung](), [ddr5](), [RISC-V](), [data](), [Cache](), [Arm](), [intel]() Tags: [2026](), [arm](), [cache](), [capacity](), [clusters](), [compute](), [core](), [ddr5](), [dram](), [intel](), [memory](), [parallel](), [pcie](), [performance](), [risc-v](), [samsung]() ### AI overview The article examines XCENA and Samsung's MX1, a CXL memory expansion device that can host up to 2 TB of DDR5 memory, connect SSDs, and provide onboard compute through 3,072 RISC-V cores. It describes the device's memory bandwidth, cache hierarchy, power use, and focus on data-parallel workloads. ### Source excerpt CXL memory expansion, with a side of compute ## Hot Chips 2026: Samsung's Processing-in-Memory (PIM) DevFeed: [Hot Chips 2026: Samsung's Processing-in-Memory (PIM)]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-29T05:36:33Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [samsung](), [Latency]() Tags: [2026](), [compute](), [dram](), [latency](), [memory](), [model](), [operations](), [parallel](), [precision](), [samsung]() ### AI overview The article examines Samsung's LPDDR5X Processing-in-Memory implementation presented at Hot Chips 2026. It describes PIM blocks placed in each DRAM bank, enabling access to internal memory bandwidth and supporting MAC computations near the data. ### Source excerpt In-memory compute with LPDDR5X ## CircleCI Smarter Testing: Stop running tests that don't matter DevFeed: [CircleCI Smarter Testing: Stop running tests that don't matter]() Original publisher: [Read original article]() Author: Nathan Fish Published: 2026-08-26T19:00:00Z Content type: article Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Testing](), [CI/CD]() Tags: [accelerate](), [auto-rerun-failed-tests](), [ci-cd](), [circleci](), [circleci-news](), [developer-productivity](), [dynamic-test-splitting](), [engineering-productivity](), [flaky](), [intelligent-test-selection](), [parallel](), [pipeline](), [quality](), [reduce](), [smarter-testing](), [test-impact-analysis](), [testing](), [tests](), [time]() ### AI overview CircleCI describes Smarter Testing, a set of features designed to reduce CI/CD test execution time by skipping tests unaffected by changes, balancing parallel nodes, and retrying flaky tests. The article says early users have seen test runs up to four times faster. ### Source excerpt Testing eats up to half your pipeline time. See how CircleCI Smarter Testing skips unaffected tests, rebalances parallel nodes, and retries flaky tests. ## Postgres 19: How Our Advice Has Changed Since We Wrote It DevFeed: [Postgres 19: How Our Advice Has Changed Since We Wrote It]() Original publisher: [Read original article]() Author: Christopher Winslett Published: 2026-08-18T19:00:00Z Content type: article Language: en Sources: [CrunchyData Blog]() Topics: [data](), [JIT](), [Latency](), [Temporal data](), [Linux]() Tags: [advice](), [async](), [benchmarks](), [index](), [jit](), [latency](), [linux](), [maintenance](), [parallel](), [partitioning](), [postgres](), [postgres-19](), [production-postgres](), [release](), [storage](), [upgrade]() ### AI overview This article revisits earlier Crunchy Data guidance on loading, storage, indexes, and partitioning for the upcoming Postgres 19 release. It explains which changes in Postgres 18 and 19 affect that advice, including asynchronous I/O, parallel maintenance, BRIN and skip-scan behavior, partition operations, and JIT being disabled by default. The details are based on current betas and may change before general availability. ### Source excerpt Revisiting Crunchy posts on COPY, TOAST, BRIN, covering indexes, and partitioning: what we said then, which Postgres versions changed the story, and what we recommend on Postgres 19. ## Lessons Learned from Fixing Flaky Tests with Claude DevFeed: [Lessons Learned from Fixing Flaky Tests with Claude]() Original publisher: [Read original article]() Author: Henrik Warne Published: 2026-08-15T15:50:33Z Content type: article Language: en Sources: [Henrik Warne]() Topics: [Claude](), [Testing]() Tags: [claude](), [flaky-tests](), [integration](), [learning](), [llm](), [net-9](), [parallel](), [programming](), [running](), [testing](), [tests]() ### AI overview The article describes lessons learned from using Claude to diagnose and fix flaky integration tests after parallelizing a test suite. It explains that blindly accepting plausible fixes was ineffective, while understanding and reviewing each change was important. ### Source excerpt We recently sped up our integration tests by a factor of ten by running many of them in parallel. There were several flaky tests in the new test suite. Claude was really useful in getting rid of almost all flakyness, ... Continue reading -> ## Multi-Core Fiber and How It Is Used in Data Centers DevFeed: [Multi-Core Fiber and How It Is Used in Data Centers]() Original publisher: [Read original article]() Author: j2sw Published: 2026-08-12T14:27:20Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [data centers](), [datacenter]() Tags: [400g](), [cabling](), [connectors](), [data-center](), [data-centers](), [fiber](), [fiber-optics](), [it-operations](), [mpo](), [mtp](), [multi-core-fiber](), [network-engineering-resources](), [optics](), [panel](), [parallel](), [single-mode](), [structured-cabling]() ### AI overview This article explains how "multi-core fiber" is used in data center discussions, distinguishing multi-fiber or high-fiber-count cables from true multicore fiber. It covers cable construction, fiber counts, single-mode and multimode options, and MPO and MTP connectors. ### Source excerpt If you work around data centers and high-density builds, you have probably heard the term "Multi-Core Fiber" thrown around. People use it in a few different ways. Sometimes, it means a new fiber trunk that comes into a cabinet as a single cable and breaks out into dozens or even hundreds of fibers. That setup ... Read more The post Multi-Core Fiber and How It Is Used in Data Centers appeared first on Justin Wilson (j2sw). ## \`exit()\` may silently break your parallel tests DevFeed: [\`exit()\` may silently break your parallel tests]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-08-12T10:30:31Z Content type: tutorial Language: en Sources: [freek.dev - all blogposts]() Topics: [PHPUnit](), [Exception](), [Processes]() Tags: [best-practices](), [crash](), [diagnostics](), [exception](), [parallel](), [php](), [phpunit](), [process](), [testing](), [tests]() ### AI overview The article explains that calling exit() can cause parallel PHPUnit test workers to crash without useful diagnostics because the process ends outside PHPUnit's control. It recommends throwing an exception instead so the failure is reported with a stack trace. ### Source excerpt Michael explains why exit() can make parallel test workers crash without useful diagnostics, and why verbose flags do not help when the process dies outside PHPUnit's control. The fix is simple: throw an exception instead, so the failure is reported normally with a stack trace. Read more ## Running out of runway: Migrating Temporal Cloud's billing store to ClickHouse DevFeed: [Running out of runway: Migrating Temporal Cloud's billing store to ClickHouse]() Original publisher: [Read original article]() Author: Paul Oh Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [clickhouse](), [Cloud](), [data](), [Databases]() Tags: [clickhouse](), [cloud](), [control-plane](), [cost](), [data](), [database](), [databases](), [parallel](), [results](), [table](), [temporal-voices]() ### AI overview Temporal Cloud migrated its billing store to ClickHouse after its previous warehouse had limited capacity. The migration provided three to five years of projected headroom, reduced steady-state costs by about 70%, and delivered usage data to customers roughly 30 minutes sooner. Temporal validated billing accuracy by running both databases in parallel for six weeks. ### Source excerpt How Temporal Cloud migrated its billing store to ClickHouse, gaining years of headroom, cutting steady-state costs by about 70%, and improving data freshness. ## Parallel agent orchestration is compatible with deep work DevFeed: [Parallel agent orchestration is compatible with deep work]() Original publisher: [Read original article]() Author: Jeff Morhous Published: 2026-08-03T10:21:16Z Content type: opinion Language: en Sources: [The AI-Augmented Engineer]() Topics: [agent orchestration](), [Orchestration](), [Large Language Model](), [Cosmos]() Tags: [agent](), [agent-orchestration](), [ai](), [claude](), [claude-code](), [codex](), [cosmos](), [parallel]() ### AI overview The author argues that AI agents can be managed in parallel as background tasks while preserving focused attention for complex design and decision-making. They recommend delegating routine implementation, while keeping nuanced architectural thinking with humans. ### Source excerpt Agent orchestration is making me rethink how I work ## πŸ”πŸ§  Software Factories: Harnessing Loops At Scale DevFeed: [πŸ”πŸ§  Software Factories: Harnessing Loops At Scale]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-07-27T15:30:10Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Loop Engineering](), [Orchestration](), [Software Engineering](), [Artificial Intelligence]() Tags: [agent](), [ai](), [architecture](), [automated](), [code](), [loops](), [parallel](), [review](), [sandbox](), [sandboxes](), [scale](), [software](), [software-engineering](), [test](), [verification]() ### AI overview The article explains how software factories use repeated agent loops, safety harnesses, parallel orchestration, work queues, and review gates to automate code production. It argues that verification, rather than code generation, is the main bottleneck, and warns that fully automated "dark factories" can accumulate comprehension debt when humans stop reading the code. ### Source excerpt PLUS: In-process load balancing πŸ’¨, On-disk ANN indexes πŸ’Ύ, Rust integ tests πŸ§ͺ ## How Lawmatics Cut CI Compute Cost by 39.3% and Shortened Pipeline Time by 15.8% DevFeed: [How Lawmatics Cut CI Compute Cost by 39.3% and Shortened Pipeline Time by 15.8%]() Original publisher: [Read original article]() Author: Christian GΓ³mez Alonso Published: 2026-07-27T15:23:37Z Content type: article Language: en Sources: [Semaphore Engineering]() Topics: [ci](), [Optimization](), [cpu](), [browser]() Tags: [browser](), [ci](), [ci-cd](), [compute](), [cost](), [cpu](), [optimization](), [parallel](), [parallelism](), [pipeline](), [time]() ### AI overview This case study describes how Lawmatics optimized its CI pipeline by changing the machine class used for parallel browser end-to-end tests while initially preserving parallelism. The reported result was a 39.3% reduction in average application compute cost per pipeline and a 15.8% reduction in active pipeline duration. ### Source excerpt CI optimization is easiest to reason about when the problem is concrete: one pipeline, one critical path, and one cost model. Lawmatics reached out to us with that kind of problem. Their application pipeline was already parallelized and already using a sensible CI structure. The remaining question was whether the most expensive part of the [...] The post How Lawmatics Cut CI Compute Cost by 39.3% and Shortened Pipeline Time by 15.8% appeared first on Semaphore. ## A Recap of the 2026 Experimentation Conference at Booking.com DevFeed: [A Recap of the 2026 Experimentation Conference at Booking.com]() Original publisher: [Read original article]() Author: Mel JI Mueller Published: 2026-07-16T08:18:03Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [experiments](), [decision-making](), [Artificial Intelligence](), [Tooling]() Tags: [2025](), [ai](), [analysis](), [data-science](), [events](), [experimentation](), [organizational](), [parallel](), [recap](), [themes]() ### AI overview A recap of Booking.com's 2026 Experimentation Conference, which brought together more than 150 experimentation practitioners from 49 companies. The article summarizes survey findings, conference themes, and sessions on AI-assisted experimentation, experimentation quality and velocity, and organizational culture. ### Source excerpt By Kevin Anderson, Angelica Goetzen, Jorden Lentze, and Melanie Mueller On May 18, 2026, we hosted the third annual Experimentation Conference at Booking.com on our Amsterdam campus. What started in 2024 as an experiment itself -- would large-scale experimentation practitioners come together to learn from each other? -- has grown into an event which brings together over 150 practitioners from 49 companies which run experiments at scale. About one third of attendees came back a second or third time. The room collectively ran 56,000 experiments per year. It's a unique crowd, and that's exactly the point. The day opened with sharing the results of the survey data we collected from the participating companies on the state of experimentation across the room, revealing some interesting findings: most teams operate a centre of excellence model, roughly a third release over 90% of features through controlled experiments, and the top challenges are scaling, coordination, platform tooling, and culture. These shared experiences helped shape the programme. We had three sessions, grouped by the three conference themes: AI and experimentation: AI-assisted analysis, no-code experimentation Quality / velocity tradeoff: High-quality vs high-speed experimentation Experimentation culture: Build organizational buy-in and data-driven decision-making Each session followed the same format: two talks, then a panel discussion on the same topic. We closed with nine parallel breakout groups for deeper conversation. Below is a recap of the key sessions. Read the recap of 2025 | Read the recap of 2024 Session 1: AI and experimentation The conference started off with the hot topic of AI in experimentation. AI is changing how we experiment and how we support experimenters. How Experimentation Protects Decisions in an AI-Written World -- Marcel Toben Marcel Toben, Head of Engineering at Zalando, opened with a provocation he'd recently heard from software engineers in Berlin: nobody on his team had wr ## Agentic Autonomy Levels DevFeed: [Agentic Autonomy Levels]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-03T14:30:33Z Content type: article Language: en Sources: [Elevate]() Topics: [Artificial Intelligence](), [Orchestration](), [Agent Skill](), [Claude](), [Claude Code](), [codex]() Tags: [agent](), [agentic](), [agentic-engineering](), [agents](), [ai](), [claude](), [claude-code](), [codex](), [delegation](), [multi-agent](), [orchestration](), [parallel](), [scopes](), [trust](), [verification]() ### AI overview This article proposes a two-axis model for evaluating agentic engineering: agency, describing how independently a single agent operates, and orchestration, describing the skill of coordinating multiple agents. It argues that a single autonomy ladder is insufficient for multi-agent work and emphasizes matching autonomy levels with appropriate verification. ### Source excerpt A working model of autonomy for agentic engineering ## NVIDIA GPU Ecosystem, Simply Explained DevFeed: [NVIDIA GPU Ecosystem, Simply Explained]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-06-30T11:37:56Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [GPU](), [Nvidia](), [Tensor Cores](), [data centers](), [Large Language Model]() Tags: [architecture](), [data-centers](), [gpu](), [guide](), [llms](), [nvidia](), [parallel](), [tensor-cores]() ### AI overview A plain-English guide to NVIDIA GPU architecture, including parallel processing, Streaming Multiprocessors, CUDA and Tensor Cores, GPU memory, interconnects, and scaling for AI workloads in data centers. ### Source excerpt A guide to NVIDIA GPU architecture, interconnects, and scaling in plain English. ## Strategies for Migrating from a Monolith to Microservices DevFeed: [Strategies for Migrating from a Monolith to Microservices]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-06-23T07:55:12Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [Architecture & Design](), [microservices architecture](), [migration](), [legacy systems](), [gateway]() Tags: [api-gateway](), [architecture](), [automated](), [big-bang](), [feature-flags](), [incremental](), [legacy-systems](), [microservices](), [migration](), [monolith](), [parallel](), [rewrite](), [strategies](), [testing]() ### AI overview This tutorial outlines patterns for gradually moving from a monolithic architecture to microservices. It describes the Strangler Fig Pattern, Parallel Run Pattern, and Collaborator Pattern, covering techniques such as API gateways, incremental migration, traffic splitting, feature flags, and automated comparison testing. ### Source excerpt Top Strategies ## The Thundering Herd Problem in Agentic AI: Why Traditional Fixes Fall Short DevFeed: [The Thundering Herd Problem in Agentic AI: Why Traditional Fixes Fall Short]() Original publisher: [Read original article]() Author: Quentin Packard Published: 2026-06-19T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Agentic AI Architecture](), [agent orchestration](), [AI Agent](), [Artificial Intelligence](), [Concurrency]() Tags: [agent-orchestration](), [agentic-ai](), [ai-architecture](), [load-testing](), [parallel](), [thundering-herd]() ### AI overview This article examines how agentic AI can create a thundering herd through intentional fan-out and parallel execution. It argues that traditional mitigations only partly transfer because agent-generated synchronization can produce a sharp saturation point that staging load tests may not reveal. ### Source excerpt The thundering herd of the past was externally triggered. ## Working Effectively with Claude Code DevFeed: [Working Effectively with Claude Code]() Original publisher: [Read original article]() Author: Amy Laws Published: 2026-06-18T09:09:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Claude Code](), [parallel](), [Terminal](), [Code](), [Orchestration](), [Git]() Tags: [agent](), [agents](), [artificial-intelligence](), [claude-code](), [code](), [copilot](), [github-copilot](), [orchestration](), [parallel](), [round-robin](), [terminal](), [vs-code]() ### AI overview This article shares practical lessons from switching from GitHub Copilot in VS Code to Claude Code. It focuses on parallel development with multiple agents, using terminal sessions and Git worktrees, and managing concurrent work through Claude Code's agent view. ### Source excerpt After months working with GitHub Copilot in VS Code and recently switching to Claude Code, the transition turned out to be more involved than expected. Claude Code operates differently and in ways that take time to adjust to. In this post, I share my experiences and tips drawn from that experience. ## Palantir's Elasticsearch reindexing system supports online index rebuilds DevFeed: [Palantir's Elasticsearch reindexing system supports online index rebuilds]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-06-15T15:30:53Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [elasticsearch](), [Software Engineering](), [Database](), [observability]() Tags: [architecture](), [dashboards](), [database](), [elasticsearch](), [implementation](), [observability](), [parallel](), [rate-limiting]() ### AI overview The article describes Palantir's Elasticsearch reindexing system, which rebuilds large search indices while live traffic continues. It uses shadow indices, parallel database-to-index pipelines, multidimensional rate limiting, crash-safe state tracking, and support for multiple clusters. ### Source excerpt PLUS: Claude skills guide πŸ‘¨πŸ’», Call queue architecture ⚑, when NOT to use Kafka πŸ‘¨πŸ’» ## Building a Production AI Deep Research Agent with Temporal, Neo4j, Auth0, and Redpanda DevFeed: [Building a Production AI Deep Research Agent with Temporal, Neo4j, Auth0, and Redpanda]() Original publisher: [Read original article]() Author: Chandler Mayo Published: 2026-06-10T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Orchestration](), [Persistence](), [observability](), [Security](), [Auth0](), [Neo4j]() Tags: [agents](), [ai](), [ai-agents](), [auth0](), [community](), [infrastructure](), [observability](), [orchestration](), [parallel](), [pdf](), [persistence](), [production](), [security](), [streaming](), [teams]() ### AI overview The article describes a production-oriented deep research agent built by four teams using Temporal, Neo4j, Auth0, and Redpanda. It explains how orchestration, persistence, security, and event streaming support a long-running, multi-agent workflow. ### Source excerpt Four teams built a production AI deep research agent using Temporal, Neo4j, Auth0, and Redpanda, revealing the infrastructure beneath every great AI agent. ## How to build deep research agents using Temporal and Braintrust DevFeed: [How to build deep research agents using Temporal and Braintrust]() Original publisher: [Read original article]() Author: Martin Bergman Published: 2026-06-03T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Multi Agent Systems](), [observability](), [Pydantic](), [parallel](), [retry](), [Large Language Model]() Tags: [agents](), [build](), [how-to](), [multi-agent](), [observability](), [parallel](), [research](), [retry](), [tracing]() ### AI overview This tutorial explains a multi-agent deep research pipeline built with Temporal and Braintrust. It covers planning, query generation, parallel web search, report synthesis, and the use of Durable Execution, evals, and observability to handle timeouts, partial failures, and difficult debugging. ### Source excerpt Deep research agents are fragile in production. Here's how Temporal and Braintrust make them resilient with Durable Execution, evals, and tracing. ## 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. [Next page]()