# Diagnostics Published articles for Diagnostics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## New Bug-Fix Releases Are Available for MPS Versions 2026.1.1, 2025.3.2, 2025.2.4, and 2025.1.4 DevFeed: [New Bug-Fix Releases Are Available for MPS Versions 2026.1.1, 2025.3.2, 2025.2.4, and 2025.1.4]() Original publisher: [Read original article]() Author: Vaclav Pech Published: 2026-09-16T20:06:31Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [JetBrains MPS](), [releases](), [version](), [jetbrains]() Tags: [2](), [2025](), [2026](), [3](), [agent](), [agents](), [bug](), [changes](), [create](), [development](), [diagnostics](), [editor](), [improvements](), [major](), [manipulate](), [mps](), [practical](), [release](), [releases](), [updates](), [version]() ### AI overview JetBrains has released bug-fix updates for MPS versions 2026.1.1, 2025.3.2, 2025.2.4, and 2025.1.4. The MPS 2026.1.1 update includes improvements to the Projectional Agent Toolkit, editor context, diagnostics, agent guidance, and test execution. ### Source excerpt We've released updates for multiple major MPS versions that fix several issues. DOWNLOAD MPS 2026.1.1 Check out all the updates in each particular version below: MPS 2026.1.1 The Projectional Agent Toolkit receives several practical improvements that allow agents to: See and invoke intentions and quick-fixes on individual nodes. Manipulate NodeAttributes, PropertyAttributes, and others. Create copies [...] ## MSVC Build Tools Preview updates - September 2026 DevFeed: [MSVC Build Tools Preview updates - September 2026]() Original publisher: [Read original article]() Author: Eric Brumer Published: 2026-09-09T21:26:30Z Content type: release Language: en Sources: [C++ Team Blog]() Topics: [C++](), [Visual Studio 2026]() Tags: [c](), [c-plus-plus](), [diagnostics](), [msvc](), [race-condition](), [release](), [september-2026](), [updates](), [visual-studio](), [visual-studio-2026]() ### AI overview MSVC Build Tools Preview v14.52 adds C and C++ compiler conformance, diagnostics, and stability fixes, including fixes for compiler errors and C++ modules. ### Source excerpt The MSVC Build Tools Preview is updated regularly with the latest features and fixes from the MSVC development team. This post covers updates from the past month, currently targeting the v14.52 release. This encompasses changes across the compiler frontend, backend, linker, standard library, and related tools. Although you can acquire the MSVC Build Tools Preview [...] The post MSVC Build Tools Preview updates - September 2026 appeared first on C++ Team Blog. ## A Week of Symfony #1027 (August 31 - September 6, 2026) DevFeed: [A Week of Symfony #1027 (August 31 - September 6, 2026)]() Original publisher: [Read original article]() Author: Javier Eguiluz Published: 2026-09-06T07:46:00Z Content type: news Language: en Sources: [Symfony Blog]() Topics: [bug](), [ci](), [Command-line interface](), [pull-requests]() Tags: [bug](), [ci](), [command-line](), [diagnostics](), [pull-requests](), [symfony]() ### AI overview Symfony released versions 6.4.45, 7.4.18, and 8.1.6 with bug fixes and hardening improvements. The update also introduces the symfony lsp:check command for CI diagnostics covering unknown routes, missing templates, and configuration errors. ### Source excerpt This week, Symfony 6.4.45, 7.4.18, and 8.1.6 were released with numerous bug fixes and hardening improvements. Meanwhile, we introduced symfony lsp:check, a new command that brings Symfony-aware diagnostics to your CI pipeline, catching unknown routes, missing... ## The hidden work of modernizing Malwarebytes DevFeed: [The hidden work of modernizing Malwarebytes]() Original publisher: [Read original article]() Author: Anna Tukhtarova Published: 2026-09-04T17:15:42Z Content type: article Language: en Sources: [Malwarebytes]() Topics: [.NET](), [migration](), [Endpoint Security & XDR](), [Operating system]() Tags: [diagnostics](), [drivers](), [endpoint-security](), [engineering](), [inside-malwarebytes](), [migration](), [modernization](), [net](), [security]() ### AI overview The article explains Malwarebytes' migration of its managed Windows components to .NET 10 and why runtime and dependency upgrades require security-feature-level rigor in endpoint software. ### Source excerpt Why disciplined dependency modernization is one of the highest-leverage engineering investments a security product can make. ## New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1 DevFeed: [New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T14:00:05Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [vcf 9.1](), [vcf operations](), [Artificial Intelligence](), [Conversational AI](), [Kubernetes](), [private cloud](), [observability](), [Security]() Tags: [ai](), [cloud-infrastructure](), [conversational-ai](), [costoptimization](), [diagnostics](), [kubernetes](), [operations](), [private-cloud](), [release](), [security](), [vcf-9-1](), [vcf-management-services](), [vcf-operations](), [vcf-operations-for-logs](), [vcf-operations-for-networks](), [visibility](), [vks](), [vmware-cloud-foundation]() ### AI overview VMware announces new operations capabilities in VMware Cloud Foundation 9.1.1, including expanded visibility, an optional locally configured conversational AI assistant, full-stack Kubernetes observability, and enhanced security operations. ### Source excerpt We are excited to announce new operations capabilities with the release of VMware Cloud Foundation (VCF) 9.1.1. By expanding visibility capabilities, giving you the option to configure a conversational AI assistant to assist your daily workflows, and hardening security, VCF 9.1.1 helps IT teams resolve issues faster and scale their infrastructure. As private cloud infrastructure ... Continued The post New AI and Kubernetes Private Cloud Operations Capabilities in VMware Cloud Foundation 9.1.1 appeared first on VMware Blogs. ## LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs' Probabilistic Beliefs DevFeed: [LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs' Probabilistic Beliefs]() Original publisher: [Read original article]() Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [Uncertainty quantification LLMs](), [AI Inference](), [Data Science]() Tags: [ai](), [data-science](), [diagnostics](), [llms](), [research]() ### AI overview This research examines whether large language models update probabilistic beliefs in accordance with Bayes' rule. It introduces the information processing gap to quantify deviations from Bayesian updates, compares evidence-integration approaches, and finds that heuristic, non-Bayesian updates can outperform exact Bayesian updates on downstream tasks. ### Source excerpt Modern AI systems are being deployed in complex domains such as medicine, science, and law, where there is often not a single correct answer given the observed evidence. Such systems must be able to represent and update uncertain beliefs about the world as new evidence arrives to make rational decisions. We introduce the novel technique of studying LLMs as information processing rules and utilize the information processing gap--the deviation from Bayes updates--to study the internal (in)consistencies of how LLMs update their probabilistic beliefs from evidence. Our extensive experiments evaluate... ## The Visual Studio Debugger Agentic Workflow Gets a Test-Driven Upgrade DevFeed: [The Visual Studio Debugger Agentic Workflow Gets a Test-Driven Upgrade]() Original publisher: [Read original article]() Author: Harshada Hole Published: 2026-08-26T16:00:47Z Content type: article Language: en Sources: [Visual Studio Blog]() Topics: [debugging](), [AI-assisted coding]() Tags: [agent](), [agentic](), [bug](), [copilot](), [debugger](), [debugging](), [debugging-and-diagnostics](), [devops](), [diagnostics](), [github](), [visual-studio](), [visual-studio-2026](), [workflow]() ### AI overview Visual Studio Debugger Agent adds a test-driven investigation workflow that creates or identifies a focused failing test, debugs it to find the root cause, and reruns it to validate a fix. It is intended for issues without reliable manual reproduction steps when supported test infrastructure is available. ### Source excerpt A few months ago, we introduced a new guided agentic workflow in the Visual Studio Debugger Agent designed to help you move from a bug report to a verified fix without spending your morning guessing where to begin. If you missed that announcement, check out Stop Hunting Bugs: Meet the New Visual Studio Debugger Agent [...] The post The Visual Studio Debugger Agentic Workflow Gets a Test-Driven Upgrade appeared first on Visual Studio Blog. ## Just landed in the Neon CLI DevFeed: [Just landed in the Neon CLI]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-25T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Command-line interface](), [Databases](), [MCP](), [Agent Skills](), [API keys]() Tags: [agent-skills](), [agents](), [ci](), [cli](), [coding](), [database](), [diagnostics](), [mcp](), [product](), [tool]() ### AI overview An update on recent Neon CLI changes, including the shorter npm package name, agent-focused setup for MCP and skills, project and branch linking, environment-variable management, database diagnostics, dashboard access, and project-scoped keys. ### Source excerpt The Neon CLI moved fast over the last couple of months. It is a key tool for agents, who do all the work without ever opening the Console - let's take a look at some of recent changes. ## Enabling the next-generation trait solver on nightly DevFeed: [Enabling the next-generation trait solver on nightly]() Original publisher: [Read original article]() Author: lcnr Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Rust Blog]() Topics: [toolchain]() Tags: [bugs](), [diagnostics](), [inference](), [performance](), [rust](), [types]() ### AI overview Rust is enabling its next-generation trait solver by default on nightly ahead of planned stabilization. The compiler-internal replacement changes trait and associated-type handling, fixes many known issues, and needs testing for breakage, performance regressions, and diagnostics. ### Source excerpt After nearly 4 years of active development, the next-generation trait solver is close to stabilization. We are enabling it by default on nightly to surface any remaining issues and plan to stabilize it in the next months. This is the largest single change to the Rust compiler since its initial release. It completely replaces how we prove where-clauses, normalize associated types, and much more. Please try out the latest nightly and open an issue if you encounter any bugs or regressions. This is an internal component of the compiler. The main benefits of this rework will come in the future. The removal of the old implementation will unblock features such as Type Alias Impl Trait and Return Type Notation, allow us to add new implicit default trait bounds (e.g., Move and Forget), and enable us to fix the remaining type system unsoundnesses. Even so, this already fixes a huge number of issues. As an underapproximation, we currently know of more than 200 issues on GitHub fixed by this change. This also has a significant impact on compile times; more on that later. When developing on nightly, you may accidentally rely on behavior only supported by the new trait solver. This is an incredibly big change which results in a non-trivial amount of breakage. Most of these changes are intended improvements to type inference or the removal of undesirable behavior. We are tracking the known issues and breakage in a pinned GitHub issue. What can I do? Please update to the latest nightly version by using rustup update nightly and use it to test your existing projects and libraries. Please tell us if you encounter any breakage, compile-time performance regression, or bad diagnostics. We have not yet spent too much time on error messages for the next-generation trait solver, so we would also appreciate you using this nightly for development to find poor diagnostics and other bugs in our error handling. If you encounter any issue, take a quick look at the pinned GitHub issue to see if t ## Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch DevFeed: [Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch]() Original publisher: [Read original article]() Author: James Morle Published: 2026-08-17T23:37:48Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [telemetry]() Tags: [amazon](), [amazon-aurora](), [amazon-cloudwatch](), [cost](), [dash](), [database](), [diagnostics](), [dsql](), [insights](), [intermediate-200](), [metrics](), [monitoring](), [mysql](), [observability](), [performance](), [postgresql](), [sql](), [technical-how-to]() ### AI overview This article explains Amazon Aurora DSQL's time-based observability model and its link to performance and DPU-based cost. It introduces DASH session sampling and using Amazon CloudWatch Database Insights, PromQL, and system diagnostics to identify bottlenecks. ### Source excerpt Amazon Aurora DSQL offers time-based observability through Amazon CloudWatch Database Insights. Learn how the DSQL observability model, DASH, Database Insights, PromQL, and the system diagnostics AI skill help you find performance bottlenecks and connect session time directly to cost. ## MSVC Build Tools Preview updates - August 2026 DevFeed: [MSVC Build Tools Preview updates - August 2026]() Original publisher: [Read original article]() Author: Eric Brumer Published: 2026-08-13T15:01:10Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [MSVC](), [C++](), [Compiler](), [toolchain](), [Code generation](), [Optimization](), [Visual Studio]() Tags: [c-plus-plus](), [code-generation](), [diagnostics](), [features](), [ide](), [msvc](), [optimization](), [release](), [tools](), [updates](), [visual-studio](), [visual-studio-2026]() ### AI overview The article summarizes August 2026 updates to the MSVC Build Tools Preview, targeting the v14.52 release. It covers compiler conformance, diagnostics, C++ modules, code generation, optimization, and ARM64 improvements. ### Source excerpt Try the latest updates to MSVC Build Tools Preview The post MSVC Build Tools Preview updates - August 2026 appeared first on C++ Team Blog. ## \`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 ## Monitor Your Drive Health with Performance Co-Pilot on Fedora DevFeed: [Monitor Your Drive Health with Performance Co-Pilot on Fedora]() Original publisher: [Read original article]() Author: Paul Evans Published: 2026-08-10T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Monitoring](), [Grafana](), [telemetry]() Tags: [data](), [diagnostics](), [drive](), [farm](), [fedora-project-community](), [grafana](), [hdd](), [health](), [home-server](), [metrics](), [monitor](), [monitoring](), [nvme](), [open-source](), [performance](), [performance-metrics](), [self-hosted](), [server](), [smart](), [ssd](), [telemetry](), [wwid]() ### AI overview This article explains how to monitor drive health on Fedora using Performance Co-Pilot (PCP). It covers SMART metric collection for HDDs, SSDs, and NVMe drives, NVMe error-log decoding, WWID-based tracking, Seagate FARM telemetry, continuous metric collection, and Grafana dashboards for viewing drive-health trends. ### Source excerpt You wake up one morning to find your home server unresponsive, after some investigation you discover a failed NVMe drive taking your self-hosted services and data with it. Perhaps you're a system administrator and a workstation's SSD has been silently accumulating errors for months, and now a user is reporting corrupted files. Drive failures are [...] ## Under the hood: how Amazon EKS Auto Mode detects, repairs, and diagnoses node failures DevFeed: [Under the hood: how Amazon EKS Auto Mode detects, repairs, and diagnoses node failures]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-08-05T18:48:18Z Content type: article Language: en Sources: [Containers]() Topics: [Amazon EKS](), [Kubernetes](), [Monitoring](), [systemd](), [Cloud]() Tags: [advanced-300](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [cloud](), [diagnostics](), [kubernetes](), [monitoring](), [systemd](), [technical-how-to]() ### AI overview This article explains how Amazon EKS Auto Mode detects, diagnoses, drains, and replaces failed Kubernetes nodes. It describes the collaboration between the Node Monitoring Agent and Karpenter, including system-level fault detection, repair policies, safety thresholds, and node diagnostics without SSH. ### Source excerpt On Amazon EKS Auto Mode, node failures are detected, drained, and replaced automatically before anyone reaches for a laptop. This post shows how the Node Monitoring Agent and Karpenter form a detect-and-replace cycle that runs by default, why specific faults trigger node replacement, and how to collect node diagnostics without SSH. ## The benefits of medical AI assistance vary based on user expertise DevFeed: [The benefits of medical AI assistance vary based on user expertise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-08-04T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [AI Development](), [AI Chat]() Tags: [ai](), [artificial-intelligence](), [bias](), [computer-science-and-technology](), [decision-making](), [dermatological-diagnosis](), [diagnosing-skin-disease](), [diagnostics](), [electrical-engineering-and-computer-science-eecs](), [explainability](), [explainable-ai](), [health-care](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [jameel-clinic](), [laboratory-for-information-and-decision-systems-lids](), [large-language-model](), [machine-learning](), [marzyeh-ghassemi](), [medicine](), [research](), [technology-and-society](), [users]() ### AI overview A study found that AI assistance improved skin-disease diagnosis for non-experts and clinicians, but explainability affected users differently. Non-experts often deferred to LLM-based explanations even when the AI was wrong, while clinicians performed best with the model's prediction alone. ### Source excerpt Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors. ## AI for Kubernetes: Complete Guide DevFeed: [AI for Kubernetes: Complete Guide]() Original publisher: [Read original article]() Author: Nimesha Jinarajadasa Published: 2026-07-31T03:12:00Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [Artificial Intelligence](), [Kubernetes](), [Tooling](), [Model Context Protocol](), [GitOps]() Tags: [agents](), [ai](), [clusters](), [diagnostics](), [gitops](), [guide](), [kubernetes](), [mcp]() ### AI overview A guide to AI for Kubernetes that distinguishes read-only diagnostic tools, write-capable agents, and command copilots. It explains the role of MCP, outlines the limits and risks of each category, and recommends starting with diagnosis before supervised agents and GitOps-managed workflows. ### Source excerpt The complete map of AI for Kubernetes: diagnostics, agents, and copilots, what each does today, the honest limits, and where to start. ## NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure DevFeed: [NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-30T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [debugging](), [Processes]() Tags: [ai](), [ai-infrastructure](), [benchmarks](), [blackwell](), [cloud](), [cloud-services](), [cpu](), [data-center-cloud](), [debugging](), [developer-tools-techniques](), [dgx-cloud](), [diagnostics](), [distributed-training](), [featured](), [gb200](), [gb300-nvl72](), [grace-cpu](), [hardware](), [hopper](), [infiniband](), [infrastructure](), [kernel](), [linux](), [nvidia](), [nvl72](), [performance](), [training]() ### AI overview A troubleshooting guide for closing AI-training throughput gaps between NVIDIA reference architectures and partner clusters. It covers configuration and installation issues across memory management, CPU power and NUMA placement, NCCL queue-pair concurrency, and hardware setup. ### Source excerpt Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We... ## Debug your Postgres from the terminal: a tour of \`neon inspect db\` DevFeed: [Debug your Postgres from the terminal: a tour of \`neon inspect db\`]() Original publisher: [Read original article]() Author: Savannah Longoria Published: 2026-07-22T18:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Database](), [telemetry](), [SDKs]() Tags: [cli](), [debug](), [diagnostics](), [json](), [observability](), [performance](), [postgres](), [product](), [scripting](), [sql](), [statistics](), [tooling]() ### AI overview Neon introduces `neon inspect db`, a Neon CLI command suite for read-only Postgres diagnostics. Its subcommands run bounded, versioned queries against Postgres statistics and catalog views, returning tables or JSON/YAML without requiring users to assemble connection strings or write catalog queries. ### Source excerpt The Neon CLI now ships `neon inspect db`: read-only, high-signal Postgres diagnostics that answer "what is slow?" without leaving the terminal or writing a single catalog query. Each subcommand runs one known-good query against Postgres' own statistics and catalog views and prints a clean table (or JSON/YAML for scripting). ## The Postmark MCP server, one year later: from 4 tools to 24 DevFeed: [The Postmark MCP server, one year later: from 4 tools to 24]() Original publisher: [Read original article]() Author: Jabal Torres (jtorres@activecampaign.com) Published: 2026-07-21T16:05:00Z Content type: opinion Language: en Sources: [Postmark (en-US)]() Topics: [MCP](), [MCP Server](), [API](), [AI Agent]() Tags: [ai-agent](), [api](), [diagnostics](), [mcp](), [mcp-server](), [product-news](), [tool]() ### AI overview Postmark reflects on a year of developing and operating its MCP server, which grew from four tools to 24 in the official @activecampaign/postmark-mcp package. The article explains why designing tools around user outcomes, such as the diagnoseDelivery workflow, is different from mechanically wrapping API endpoints. ### Source excerpt About a year ago, we introduced something experimental from Postmark Labs: an MCP server that let an AI assistant send email through Postmark. It shipped with exactly one useful tool, sendEmail, plus three supporting ones (four total). You gave it a recipient, a subject, and a body, and it sent. We said at the time we'd started with a single Postmark server "because we had to start somewhere." A lot has happened since. The project graduated from Labs and became the official @activecampaign/postmark-mcp package, and along the way, we learned a lot about what it actually takes to put an AI assistant in front of a production email API responsibly. The latest release, v2.1.1, ships 24 tools across eight categories: sending, templates, message search, delivery diagnostics, bounces, suppressions, stats, and webhooks. But the tool count isn't really the story. The story is what a year of running this thing taught us: designing tools for an AI agent is a different discipline than wrapping an API, and once your server has an official name, everything downstream of that name needs to earn it. From one endpoint to a whole surface The original four tools mirrored individual Postmark endpoints directly. Getting from there to 24 the naive way -- one tool per endpoint, mechanically -- is where we started. But somewhere around tool ten, we realized that users interacting with an AI assistant don't conceptualize their needs as API calls. They approach it with human inquiries. That reframing shaped everything that mattered about the v2.0 release. Designing for outcomes, not endpoints The best example of that shift doesn't map to a single Postmark endpoint at all. It's called diagnoseDelivery, and it exists to answer one very human question: did my email actually reach this person, and if not, why? Before, answering that meant a five-step investigation across the dashboard: search outbound messages, pull the message details, read the event timeline, check the suppression list, check the ## Faster C++ iterative builds with GitHub Copilot DevFeed: [Faster C++ iterative builds with GitHub Copilot]() Original publisher: [Read original article]() Author: David Li Published: 2026-07-17T14:22:57Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [build times](), [C++](), [AI Bots]() Tags: [agent](), [build-insights](), [build-optimization](), [build-performance](), [build-times](), [c-plus-plus](), [copilot](), [diagnostics](), [game-development](), [github](), [github-copilot](), [optimization](), [performance](), [tool](), [visual-studio](), [visual-studio-2026](), [windows](), [workflow]() ### AI overview GitHub Copilot build performance for Windows adds an iterative build tool that evaluates optimization changes against incremental builds as well as clean builds. ### Source excerpt Slow builds are a consistent theme of feedback from C++ developers. We built GitHub Copilot build performance for Windows so you can leverage Copilot to optimize your project's build times. This workflow will find optimizations that bring your build times down. At first, we only measured the impact of build optimizations on full clean builds. [...] The post Faster C++ iterative builds with GitHub Copilot appeared first on C++ Team Blog. ## User Diagnostics Reports: Solving app bugs faster with AI Agents DevFeed: [User Diagnostics Reports: Solving app bugs faster with AI Agents]() Original publisher: [Read original article]() Author: Antoine van der Lee Published: 2026-07-13T07:21:09Z Content type: article Language: en Sources: [SwiftLee]() Topics: [App](), [debugging](), [bug](), [Open Source](), [HTML](), [Xcode](), [Artificial Intelligence]() Tags: [ai-agents](), [bugs](), [debugging](), [diagnostics](), [html](), [open-source](), [optimization](), [xcode]() ### AI overview The article introduces Diagnostics, an open source library for app support and bug investigation. It generates a detailed HTML diagnostics report containing app and OS versions, device and storage details, session information, and system or debug logs. The report can be attached when users submit feedback, reducing back-and-forth and helping developers analyze issues; the article also notes support for agentic development. ### Source excerpt A user diagnostics report that helps you solve bugs and improve user support at the same time. I've been writing apps since 2009, and the Diagnostics repository is an outcome of all that experience. Used by major apps from companies like WeTransfer, it has proven its value time after time. I've seen consistent inefficiency at ... -> The post User Diagnostics Reports: Solving app bugs faster with AI Agents appeared first on SwiftLee. ## Introducing GeneBench-Pro DevFeed: [Introducing GeneBench-Pro]() Original publisher: [Read original article]() Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI research agents](), [AI, ML & Data Engineering]() Tags: [agents](), [ai](), [ai-agents](), [analysis](), [benchmark](), [complexity](), [data](), [datasets](), [diagnostics](), [experimentation](), [explore](), [model](), [models](), [performance](), [research](), [skills](), [support](), [testing](), [workflow]() ### AI overview GeneBench-Pro is a research-level benchmark for evaluating whether AI agents can make judgment-heavy decisions during computational-biology analysis. It uses messy datasets and downstream decision targets to assess ambiguity handling, analytical-path selection, iterative experimentation, and revision of initial plans. ### Source excerpt Introducing GeneBench-Pro, a new benchmark testing AI performance in genomics, biology, and scientific research using complex, real-world datasets. ## Node.js 24.15.0 (LTS) DevFeed: [Node.js 24.15.0 (LTS)]() Original publisher: [Read original article]() Published: 2026-04-15T18:17:11Z Content type: release Language: en Sources: [Node.js Blog]() Topics: [Node.js](), [JavaScript](), [cross-platform](), [Open Source](), [Command-line interface](), [servers](), [Web]() Tags: [benchmark](), [c-plus-plus](), [cli](), [command-line](), [cross-platform](), [diagnostics](), [javascript](), [node-js](), [open-source](), [performance](), [release](), [servers](), [sqlite](), [web]() ### AI overview Node.js 24.15.0 is an LTS release with new command-line options, raw key format support in KeyObject APIs, additional filesystem and HTTP/2 configuration, and several stable or release-candidate module features. It also includes updates to SQLite, diagnostics channels, streams, testing, performance, and build tooling. ### Source excerpt Node.js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts. ## Introducing Dejavu: Recomposition Testing for Jetpack Compose DevFeed: [Introducing Dejavu: Recomposition Testing for Jetpack Compose]() Original publisher: [Read original article]() Author: Matt McKenna Published: 2026-03-09T19:26:54Z Content type: release Language: en Sources: [Matt McKenna]() Topics: [Jetpack Compose](), [Testing](), [AI Agent]() Tags: [ai-agent](), [android](), [compose](), [diagnostics](), [error-messages](), [jetpack](), [jetpack-compose](), [kotlin](), [recomposition](), [testing](), [ui]() ### AI overview The article introduces Dejavu, a testing tool for Jetpack Compose that turns recomposition behavior into test assertions. It describes asserting expected recomposition counts, receiving diagnostic information on failures, and using CompositionTracer without production-code instrumentation, a compiler plugin, bytecode manipulation, or a Gradle plugin. ### Source excerpt Where This Idea Came From At Square, most of the app is built on Workflow. There is an internal testing framework that lets you assert exactly how many render passes are triggered for a given interact [Next page]()