# debugging The practice of diagnosing and correcting errors in software. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Libreboot 20140729 release DevFeed: [Libreboot 20140729 release]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: release Language: en Sources: [News about Libreboot releases and development]() Topics: [libreboot](), [Documentation](), [debugging]() Tags: [apple](), [article](), [bios](), [canoeboot](), [compatibility](), [coreboot](), [debugging](), [documentation](), [free-software](), [intel](), [libre](), [libreboot](), [opensource](), [release](), [uefi]() ### AI overview Libreboot 20140729 is a fourth beta release with improved security documentation, confirmed MacBook2,1 testing, a script and instructions for flashing while Apple EFI firmware is running, a smaller source archive, Italian QWERTY GRUB ROMs, and an Intel register utility for debugging LCD panel compatibility. ### Source excerpt Article: Libreboot 20140729 release Web link: https://libreboot.org/news/libreboot20140729.html ## Logpoints Walkthrough DevFeed: [Logpoints Walkthrough]() Original publisher: [Read original article]() Author: Igor Kulakov Published: 2026-09-16T14:14:04Z Content type: tutorial Language: en Sources: [The JetBrains Blog]() Topics: [IntelliJ IDEA](), [debug](), [debugging](), [gRPC](), [Java](), [2026.2]() Tags: [2026-2](), [bug](), [client](), [command-line](), [debug](), [debugger](), [debugging](), [debugging-tools](), [idea](), [intellij](), [intellij-idea](), [java](), [kotlin](), [logpoints]() ### AI overview This walkthrough explains how to use logpoints in IntelliJ IDEA to investigate a bug in a gRPC client-server example that returns incorrect discount values. It covers reproducing the issue, attaching the debugger to the server, and observing program behavior without suspending execution, including improvements in IntelliJ IDEA 2026.2. ### Source excerpt Modern development tools, especially IntelliJ IDEA, have such comprehensive debugging support that for virtually any niche use case, there is a specialized tool for the job. This can make it hard to know where to begin. If you are new to debugging tools and want the biggest return on your learning investment, the best feature [...] ## C++ in the Age of AI: Visual Studio at CppCon 2026 DevFeed: [C++ in the Age of AI: Visual Studio at CppCon 2026]() Original publisher: [Read original article]() Author: Augustin Popa Published: 2026-09-14T19:34:43Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [C++](), [Visual Studio](), [GitHub Copilot](), [Visual Studio 2026](), [build performance](), [MSVC](), [debugging](), [Optimization](), [Refactoring](), [Artificial Intelligence]() Tags: [ai](), [build-optimization](), [build-performance](), [c-plus-plus](), [copilot](), [cppcon](), [debugging](), [github-copilot](), [msvc](), [visual-studio](), [visual-studio-2026]() ### AI overview This companion reading guide covers how Visual Studio 2026 is evolving C++ development with improved code navigation, build and runtime performance, language feature support, debugging, modernization, refactoring, and AI-assisted workflows using GitHub Copilot. ### Source excerpt At CppCon 2026, we shared how Visual Studio is evolving to help you work more effectively with large C++ codebases, improve build and runtime performance, adopt the latest language features, and incorporate AI into your existing workflows. This post is a companion reading guide for our session, C++ in the Age of AI: How Visual [...] The post C++ in the Age of AI: Visual Studio at CppCon 2026 appeared first on C++ Team Blog. ## Open-Source Project Brings Full iOS 27 Virtualization to Apple Silicon DevFeed: [Open-Source Project Brings Full iOS 27 Virtualization to Apple Silicon]() Original publisher: [Read original article]() Author: Sergio De Simone Published: 2026-09-12T16:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [iOS](), [Tooling](), [Command-line interface](), [debugging](), [App]() Tags: [apple](), [cli](), [debugging](), [development](), [ios](), [ios-27-virtualization](), [iphone](), [kernel](), [macos](), [mobile](), [mobile-testing](), [news](), [open-source](), [research](), [reverse-engineering](), [security](), [ssh](), [testing](), [virtualization](), [xcode]() ### AI overview vphone-cli runs a full iOS 27 system as a virtual machine on Apple Silicon using Apple's Virtualization.framework. The article contrasts it with the Xcode iPhone Simulator and highlights uses in security research, reverse engineering, debugging, and automated testing. ### Source excerpt The open-Source project vphone-cli enables a full iOS 27 system to run as a virtual machine on Apple Silicon. Built on Apple's own Virtualization.framework rather than traditional emulation, the project opens up new possibilities for security research, reverse engineering, and automated iOS testing. By Sergio De Simone ## Quiz: Python Debugging With Pdb DevFeed: [Quiz: Python Debugging With Pdb]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-12T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [debugging](), [debug]() Tags: [bugs](), [course](), [debugger](), [debugging](), [how-to](), [learn](), [python](), [resources](), [tool](), [tutorial]() ### AI overview An interactive 10-question quiz that tests practical knowledge of Python's built-in pdb debugger, including inspecting variables, stepping through code, using conditional breakpoints, watching expressions, and interpreting stack traces. ### Source excerpt Practice using pdb, Python's built-in debugger. Check variables, step through code, set conditional breakpoints, and read a stack trace. ## Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance DevFeed: [Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance]() Original publisher: [Read original article]() Author: Sarah Constant Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Large Language Model](), [Grafana Cloud](), [debugging](), [incident](), [telemetry](), [observability ai agents]() Tags: [ai](), [ai-agents](), [analytics](), [debugging](), [grafana](), [grafana-cloud](), [incident](), [knowledge-graph](), [llms](), [logs](), [metrics](), [monitoring](), [observability](), [telemetry](), [traces]() ### AI overview The article reports Grafana Labs experiments testing whether providing AI agents with Grafana Cloud's Knowledge Graph improves incident debugging compared with raw telemetry alone. In one incident replayed 16 times per approach, Knowledge Graph context led to the correct root cause 15 times, versus once with raw telemetry. The article also describes challenges including misleading signals, unsupported confident answers, and inconsistent investigations, arguing that well-structured context matters more than context-window size alone. ### Source excerpt On the product team here at Grafana Labs, we consider AI agents our users, too. That's why we set out to test how well agents can debug incidents across the full stack, and how much better they perform with Grafana Cloud's Knowledge Graph vs. using raw telemetry alone. Our early results are promising. In one real incident we replayed 16 times each way, an agent with Knowledge Graph context found the correct root cause 15 times, compared with just once using raw telemetry alone. Along the way, we also uncovered some of the challenges that still stand in the way of reliable AI-assisted debugging, from chasing the wrong signals to confidently making things up and producing inconsistent answers. We're still early, but our findings point to an important idea. The industry's shorthand right now is that a bigger context window will lead to better outputs. Our findings suggest it's not just about more context; it's about structuring your data well enough to serve the right context. Here's a look at what we've learned so far, as we continue to experiment out in the open and bring you along, the Grafana Labs way. Giving an agent access to telemetry is just the beginning Give a current-generation model like Opus 4.8 access to your raw telemetry during a live incident, and it genuinely starts to figure things out: querying metrics and logs, forming a hypothesis, and checking it. We have watched it work on our own incidents, and it does it affordably. But if you run software at scale, where uptime is business-critical and large teams share the responsibility, a better model alone doesn't get you all the way there for debugging. From analyzing how LLMs do root-cause analysis on our own infrastructure, and from speaking to our customers, we've uncovered three problems that get in the way: The further the cause is from the alert, the more likely the model is to get it wrong. An agent may confidently make things up when it doesn't have the evidence it needs. The same investigation c ## How to add Sentry monitoring to a Webflow Cloud app DevFeed: [How to add Sentry monitoring to a Webflow Cloud app]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [error tracking](), [Next.js](), [Workers](), [session replay](), [debugging](), [Traces](), [configuration](), [Instrumentation](), [Command-line interface]() Tags: [cloudflare-workers](), [debugging](), [error-tracking](), [guides](), [how-to](), [instrumentation](), [monitoring](), [next-js](), [session-replay](), [traces]() ### AI overview This tutorial explains how to manually add Sentry monitoring to a Webflow Cloud app running Next.js. It covers SDK installation, initialization files, environment variables, a Cloudflare-specific tunnel fix, verification, alerts, and common silent failure modes. The setup enables error grouping, stack traces, performance traces, and session replay. ### Source excerpt Learn how to add Sentry monitoring to a Webflow Cloud Next.js app. Get error grouping, stack traces, and session replay working. ## Module Tracking in Swift Debug Info DevFeed: [Module Tracking in Swift Debug Info]() Original publisher: [Read original article]() Author: Adrian Prantl Published: 2026-09-11T15:30:00Z Content type: tutorial Language: en Sources: [Swift.org]() Topics: [debugging](), [debug](), [toolchain](), [C++]() Tags: [cmake](), [debug](), [debugger](), [debugging](), [jit](), [swift]() ### AI overview Swift 6.3 and the upcoming Swift 6.4 improve how explicitly built Swift modules are referenced in debug information. The article explains how this helps LLDB locate modules for expression evaluation, improving debugging reliability and reducing build products, while noting that custom build systems may need adjustments. ### Source excerpt When your Swift program hits a breakpoint and stops so you can inspect it, the debugger's expression evaluator has to find the exact Swift module your code was built from. Until now, that lookup wasn't always precise. The upcoming Swift 6.4 release will include changes, begun in Swift 6.3, that address this by updating how the Swift compiler references explicitly-built Swift modules in debug info. The majority of developers will automatically benefit from faster, more reliable debugging and smaller build products, without any modifications to their SwiftPM or Xcode projects. For developers who maintain their own build systems using, for example, Bazel, Buck, or CMake, some adjustments may be necessary to take advantage of these changes. This article explains how the debugger uses Swift modules. Next, it describes how Swift 6.3 changes the way modules are tracked in debug info to solve several problems with the previous representation. Finally, it shows how to adjust build systems to take advantage of the new representation and eliminate some build steps that are no longer necessary. Swift modules and expression evaluation LLDB's standout feature is its powerful expression evaluator. Because LLDB embeds the Clang and Swift compilers, it can JIT-compile any valid source code and run it in the context of your application while stopped at a breakpoint. This includes not just calling code in your application, but also defining new data types, functions, and closures. Debugging features that are usually reserved for interpreted or JIT-compiled languages like JavaScript become available to ahead-of-time-compiled languages like C++ and, of course, Swift! In order to JIT-compile user expressions that make use of data types defined in the debugged program, LLDB's embedded Swift compiler needs to import the Swift modules defining those types. In a world before explicitly-built modules, LLDB would find the base name of the main module at the current breakpoint in the debug info ## Debugging our AI search assistant with agent tracing DevFeed: [Debugging our AI search assistant with agent tracing]() Original publisher: [Read original article]() Author: Dominik Buszowiecki; Shaun Kaasten Published: 2026-09-11T09:00:00Z Content type: article Language: en Sources: [Sentry Blog]() Topics: [AI search](), [agent observability](), [debugging](), [tracing](), [Large Language Model]() Tags: [agent](), [ai](), [api](), [debugging](), [errors](), [eval](), [llm](), [search](), [sentry](), [tracing]() ### AI overview Sentry engineers describe how they debugged the Search Query Assistant, which converts natural-language prompts into Sentry Syntax queries. They used evals for performance measurement and AI Conversation tracing to investigate failures, including a bug involving custom numerical attributes that caused queries to return no results. ### Source excerpt See how Sentry engineers used AI Conversations to debug a natural language search assistant and fix a tricky query generation bug. ## Session Traces and Cost Controls Help Diagnose AI Agent Failures DevFeed: [Session Traces and Cost Controls Help Diagnose AI Agent Failures]() Original publisher: [Read original article]() Author: Mark Silvester Published: 2026-09-11T08:14:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [ai observability](), [AI Bots](), [debugging]() Tags: [agents](), [ai](), [ai-agent](), [ai-ml-data-engineering](), [artificial-intelligence](), [cost](), [debugging](), [devops](), [llm](), [loops](), [ml-data-engineering](), [monitoring](), [news](), [observability](), [observability-ai-agents](), [opentelemetry](), [traces]() ### AI overview The article describes using nested session traces, execution metrics, and cost limits to investigate and contain AI agent failures such as repeated tool calls and runaway spending. ### Source excerpt Session traces and cost controls are emerging as key observability techniques for diagnosing AI agent failures, helping teams spot tool-call loops and runaway spend while preserving enough execution context for post-incident debugging. By Mark Silvester ## Introducing Fast and Reliable Wireless Debugging with Android Debug Bridge (ADB) Wi-Fi 2.0 DevFeed: [Introducing Fast and Reliable Wireless Debugging with Android Debug Bridge (ADB) Wi-Fi 2.0]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-09-09T16:00:00Z Content type: release Language: en Sources: [Android Developers Blog](), [Android Developers Blog]() Topics: [Wireless Debugging](), [debugging]() Tags: [adb-wi-fi-2-0](), [android](), [android-studio](), [debugging](), [wireless-debugging]() ### AI overview ADB Wi-Fi 2.0 updates Android wireless debugging with a redesigned server stack, smarter network handling, and improved Android Studio discovery. The article reports higher automatic connection success rates and faster connections, and lists the required versions and pairing steps. ### Source excerpt Posted by Steven Jenkins, Product Manager, Sherif Eid, Senior Software Engineer, and Fabien Sanglard, Staff Software Engineer, Android Studio Wireless debugging on Android is now faster, more reliable, and easier to set up than ever. With ADB Wi-Fi 2.0, we've introduced a new server stack and smarter network handling to directly address developer feedback around usability gaps. How ADB Wi-Fi 2.0 Improves Wireless Debugging To ensure ADB Wi-Fi 2.0 is even more reliable, we reworked all three core components of the stack: the adb server, the adbd daemon, and Android Studio. Here are the new features: A new server stack (adb): Previously, wireless device connections would sever when network configurations changed or devices were turned off. This meant that connections would drop for common occurrences. With our new mDNS stack, we've replaced both Bonjour and legacy mDNS so that your wireless devices more reliably stay connected as you go about your day. Smarter network handling (adbd): Previously, the workstation's mDNS client would sporadically drop services. Now, the daemon automatically turns off ADB Wi-Fi when it detects an untrusted network and re-enables itself once running on a user-allowed network. Improved discoverability in Android Studio: Previously, Wi-Fi pairing was difficult to find. Now, you simply enable wireless debugging on your phone and it will show in Android Studio's Device Manager. With ADB Wi-Fi 2.0, auto-connection success rates improved by 32% and connection speeds increased by 66% for 90% of connections. Getting Started You can use ADB Wi-Fi 2.0 on your phone, tablet, Wear OS, and TV. Here's how to get started: Update to Android 17, Android SDK Platform-Tools 37.0.0, and Android Studio Quail 3 or later. Ensure your workstation and your Android device are connected to the same Wi-Fi network. On your device, navigate to Developer Options and enable Wireless debugging. Open the Android Studio Device Manager and click the pair over Wi-Fi icon. Sc ## The Rider 2026.3 Early Access Program Is Open DevFeed: [The Rider 2026.3 Early Access Program Is Open]() Original publisher: [Read original article]() Author: Alexander Kurakin Published: 2026-09-07T09:35:21Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [debugging](), [C++]() Tags: [c-plus-plus](), [code-completion](), [debugger](), [debugging](), [eap](), [game-development](), [gamedev](), [net-tools](), [rider](), [test-coverage](), [unreal-engine]() ### AI overview Rider 2026.3's first Early Access build adds rainbow brackets, streamlined data-breakpoint setup, a consolidated game-development plugin category, and code-completion filters. ### Source excerpt The first Early Access build for Rider 2026.3 is now available! It includes rainbow brackets, an easier way to set data breakpoints, a new Game Development plugin category, and filters in code completion. You can get the build from our website or through the Toolbox App. Here's a look at what's new. Rainbow brackets: You [...] ## Rust debugging survey 2026 results DevFeed: [Rust debugging survey 2026 results]() Original publisher: [Read original article]() Author: Sam Kellam Published: 2026-09-07T00:00:00Z Content type: article Language: en Sources: [Rust Blog]() Topics: [debugging](), [Rust](), [ide](), [Command-line interface]() Tags: [2026](), [command-line](), [debugger](), [debugging](), [developers](), [ide](), [rust](), [survey]() ### AI overview The Rust project reports results from its first Rust Debugging Survey, which received more than 2,300 responses. The article examines who uses debuggers, how Rust developers debug programs, and the debugging challenges they encounter. More than 46% of respondents currently use debuggers, while print debugging and the macro are widely used, followed by debugging inside an IDE and on the command line. ### Source excerpt One of the biggest challenges Rust developers report in our annual surveys is a subpar debugging experience. So, back in February, we ran our first Rust Debugging Survey, in the hopes of identifying how Rust developers are using debuggers and what problems they are facing when doing so. We received over 2,300 responses, and we'd like to thank everyone who took the time to participate in the survey! In this report, we'll go over some of the results of the survey. If you'd like, you can also check out the complete results of the survey. If you'd like to skip ahead to any particular section, you can do so with this index: Who Uses Debuggers? How Are Debuggers Used? Challenges Debugger Visualizers Closing Remarks Who Uses Debuggers? The first step to making sense of the survey results is understanding who took the survey. We asked respondents to rate their Rust expertise, from "Never used it" to "Advanced". Over 80% reported themselves as "Advanced" or "Intermediate", split roughly evenly between the two: [PNG] [SVG] We also asked respondents if they currently use or have used debuggers in Rust. Over 46% said they currently do, with the remaining responses split between "have in the past" and "never have". That means that over half of respondents do not currently use a debugger for Rust! [PNG] [SVG] Categorized by expertise, the responses reveal that roughly half of "beginners" have never used debuggers in Rust! On the other hand, nearly half of "advanced users" currently do use debuggers in Rust: [PNG] [SVG] For respondents who indicated they had previously used Rust but no longer did, we asked if challenges with debugging support were why they stopped. For nearly 3%, the answer was "yes", with an additional 24% reporting debugging issues as being partially responsible (though mind the small response count; most respondents were active users of Rust): [PNG] [SVG] How Are Debuggers Used? Knowing what debuggers developers are using and how is another important part of un ## The case of the progress callback that never got called when progress happened DevFeed: [The case of the progress callback that never got called when progress happened]() Original publisher: [Read original article]() Author: Raymond Chen Published: 2026-09-03T14:00:00Z Content type: article Language: en Sources: [Raymond Chen]() Topics: [debugging](), [C#](), [debug](), [Code]() Tags: [breakpoint](), [c-plus-plus](), [c-sharp](), [code](), [debugging](), [handler](), [old-new-thing](), [providers]() ### AI overview A progress callback is never invoked because an intermediary method returns its own operation without forwarding progress reports from the provider's underlying operation. The suggested fix is to return the provider's operation directly so callers can attach to its progress. ### Source excerpt Are you talking to the right guy? The post The case of the progress callback that never got called when progress happened appeared first on The Old New Thing. ## When Traceroutes Lie: How TTL Jumps Can Give Us a False Impression of the Internet's Topology DevFeed: [When Traceroutes Lie: How TTL Jumps Can Give Us a False Impression of the Internet's Topology]() Original publisher: [Read original article]() Author: Sebastian Kappes Published: 2026-09-03T12:03:26Z Content type: article Language: en Sources: [RIPE Labs]() Topics: [Internet](), [Networks](), [debugging]() Tags: [atlas](), [competition](), [debugging](), [internet](), [measurements](), [networks](), [research](), [router]() ### AI overview This article explains how routers that rewrite or fail to decrement TTL values can cause traceroute to omit entire networks and create a false impression of Internet topology. It describes the normal traceroute mechanism and measurements using RIPE Atlas probes to investigate the behavior. ### Source excerpt Traceroute is a powerful tool for uncovering how packets travel across the Internet. However, as we recently discovered, some routers tamper with the TTL values in traceroute's probing packets, giving you a false view of how networks are actually interconnected. ## Goroutine Leak Profiles DevFeed: [Goroutine Leak Profiles]() Original publisher: [Read original article]() Author: Vlad Saioc Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [The Go Blog]() Topics: [Go Language](), [Concurrency](), [debugging](), [Tooling]() Tags: [bugs](), [concurrency](), [debugging](), [go](), [performance](), [production](), [testing](), [tools]() ### AI overview Go 1.27 introduces a goroutine leak profiler for detecting goroutines blocked permanently in running programs, including production systems. ### Source excerpt Go 1.27 includes new goroutine leak profiles. ## How one connection kills a database DevFeed: [How one connection kills a database]() Original publisher: [Read original article]() Author: Simeon Griggs Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [debugging](), [migration](), [dashboards](), [vitess]() Tags: [app](), [cli](), [database](), [debugging](), [errors](), [exception](), [github](), [management](), [migration](), [mysql](), [postgres](), [product](), [transactions](), [vitess]() ### AI overview The article explains how an unclosed database transaction can block a migration and queue later queries, causing downtime. It discusses diagnosing live MySQL and Postgres connections and preventing stuck Postgres transactions with a timeout. ### Source excerpt Unblock stuck connections with live connection management in the PlanetScale CLI and dashboard ## Debugging my new network, when 10 Gigabit Ethernet Runs at 300 Megabits DevFeed: [Debugging my new network, when 10 Gigabit Ethernet Runs at 300 Megabits]() Original publisher: [Read original article]() Published: 2026-08-27T15:25:48Z Content type: article Language: en Sources: [Scott Hanselman]() Topics: [Homelab](), [debugging](), [UniFi UNAS Pro](), [intel](), [Windows](), [NVMe]() Tags: [debugging](), [homelab](), [intel](), [musings](), [nvme](), [smb](), [ssd](), [storage](), [unifi-unas-pro](), [windows]() ### AI overview A Windows desktop connected to a 10GbE homelab network was limited to roughly 100-200 megabits per second despite 10-gigabit link indications. Direct testing traced the problem to the Windows/NIC path: increasing the Intel E610 driver's receive buffers eliminated discarded packets and improved receive throughput, while disabling IPv4 Large Send Offload V2 raised transmit throughput to 7.03 Gbit/sec. ### Source excerpt I've been moving my home storage over to a UniFi UNAS Pro 8 as part of a larger homelab moderization. My main IRONHEART (the ultimate PC from a few years back) desktop now has an Intel E610-XT2 10GbE card, the NAS is on 10GbE, and there's a Minisforum MS-01 miniPC on the same network with a 10GbE SFP+ connection running Immich and Portainer and a few other things. Everything says 10 gigabit. Windows says 10 gigabit. UniFi says 10 gigabit. SMB copies are using the correct NIC but my file copies are running at around 100 to 200 megabits per second which is sad making. Naturally, I blamed the NAS, and the spinning rust within. The UNAS has six 16 TB spinning disks in RAID 6 and a pair of NVMe SSDs being used as cache. I'm also running Immich on the MS-01, with its photo library living on the UNAS, so there are lots of thumbnails, metadata reads, and little background writes happening. All seem like reasonable suspects. I switched the UNAS SSD cache from read-write to read-only. No meaningful difference. I stopped Immich completely. No difference. I looked at iostat; the disks weren't saturated. We looked at SMB signing and Windows Defender network scanning. Still slow. Then I stopped testing the NAS and ran iperf3 directly between the Windows desktop and the MS-01: iperf3 -c 192.168.1.222 -P 4 133 Mbit/sec Oops. The reverse test was better, but still wrong: iperf3 -c 192.168.1.222 -P 4 -R 1.33 Gbit/sec That's weird. Now the disks, SMB, Immich, RAID, and the NAS itself were completely out of the equation. This was a Windows/NIC problem and it's weirdly asymmetrical. Looking at the Intel adapter statistics shows me... Get-NetAdapterStatistics -Name "Ethernet - 10 Gig Intel" There were nearly a million ReceivedDiscardedPackets. During one ten-second iperf3 test, the counter increased by another 268. Why? The E610 driver had its receive buffers at the default 512, although it supported up to 4096. I increased them. I love an increased buffer. Set-NetAdapterAdvancedProperty ## VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions DevFeed: [VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions]() Original publisher: [Read original article]() Author: James Rempt Published: 2026-08-27T14:00:09Z Content type: article Language: en Sources: [Visual Studio Blog]() Topics: [AI-assisted coding](), [.NET](), [Developer Tools](), [debugging](), [WinForms]() Tags: [ai](), [events](), [github-copilot](), [modernization](), [net](), [visual-studio]() ### AI overview A VSLive! @ Microsoft HQ recap highlights developer discussions about AI-assisted development, Visual Studio fundamentals, and .NET modernization. ### Source excerpt VSLive! @ Microsoft HQ 2026 was one of the most energizing VSLive! events I've attended. What stood out was the level of engagement over five days of learning and conversation from July 27 through July 31. Developers came ready to dig into the future of Visual Studio, .NET, and AI-assisted software development, and many Microsoft engineers and product managers were there to have those conversations directly. If you weren't able to [...] The post VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions appeared first on Visual Studio Blog. ## Debug live production code without redeploying with Datadog Live Debugger DevFeed: [Debug live production code without redeploying with Datadog Live Debugger]() Original publisher: [Read original article]() Author: Eric Metaj; Sarah Stonehill Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [debugging](), [Instrumentation](), [AI-assisted coding](), [Code]() Tags: [agents](), [ai](), [apm](), [bits-ai](), [code](), [debug](), [debugger](), [debugging](), [developer](), [ide](), [instrumentation](), [production](), [root-cause-analysis](), [tracing]() ### AI overview Datadog Live Debugger helps developers investigate production bugs without changing, restarting, or redeploying application code. It captures runtime details through logpoints, including variable values, method arguments, execution context, and request paths. Bits AI can analyze linked source code, place non-breaking logpoints, interpret collected data, and suggest fixes grounded in production behavior. ### Source excerpt Learn how Live Debugger helps you investigate production code and debug faster using Bits AI. ## 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. ## How to evaluate session replay software: a developer's guide DevFeed: [How to evaluate session replay software: a developer's guide]() Original publisher: [Read original article]() Author: Lindsay Piper Published: 2026-08-26T09:00:00Z Content type: tutorial Language: en Sources: [Sentry Blog]() Topics: [session replay](), [Playback](), [sensitive data](), [debugging](), [Mobile]() Tags: [debug](), [debugging](), [developer](), [engineering](), [guide](), [libraries](), [monitoring](), [mutation](), [playback](), [privacy](), [sdk](), [sensitive-data](), [session-replay](), [tool](), [ux]() ### AI overview A developer guide to evaluating session replay software by comparing recording methods, privacy architecture, error-monitoring integration, overhead, AI debugging compatibility, and mobile support. It distinguishes UX analytics tools from developer debugging tools and explains the tradeoffs between DOM snapshot recording and pixel capture. ### Source excerpt Compare session replay tools on recording methodology, privacy architecture, integration depth, overhead, AI-readability, and mobile support ## Banner or no banner, that's the question DevFeed: [Banner or no banner, that's the question]() Original publisher: [Read original article]() Author: Claudio Baccigalupo Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Ruby on Rails: Compress the complexity of modern web apps]() Topics: [Rails](), [SQLite](), [debugging]() Tags: [data](), [debugging](), [migration](), [news](), [sqlite]() ### AI overview This week's Rails changes add console banner control, HTTP QUERY support, scoped Active Record updates, improved MIME handling, broader Ractor safety, batched schema reads including SQLite support, and several cleanup, deprecation, debugging, and test behavior updates. ### Source excerpt Hi, it's Claudio Baccigalupo. Let's explore this week's changes in the Rails codebase. ## Observability best practices for Lambda durable functions DevFeed: [Observability best practices for Lambda durable functions]() Original publisher: [Read original article]() Author: D Surya Sai Published: 2026-08-14T12:49:49Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [AWS Lambda](), [observability](), [Monitoring](), [log management](), [tracing](), [Instrumentation](), [debugging](), [Amazon API Gateway](), [stripe](), [Serverless]() Tags: [advanced-300](), [amazon-api-gateway](), [aws](), [aws-lambda](), [debugging](), [instrumentation](), [logging](), [metrics](), [monitoring](), [observability](), [payment](), [payment-processing](), [payments](), [serverless](), [technical-how-to](), [tracing]() ### AI overview This tutorial presents an observability pattern for AWS Lambda durable functions using a Stripe payment-processing workflow. It covers durable-function metrics, custom business metrics, alarms, structured logging, AWS X-Ray tracing, instrumentation across suspension boundaries, and end-to-end debugging of callback timeouts. ### Source excerpt Learn observability best practices for AWS Lambda durable functions, including CloudWatch metrics, custom alarms, structured logging, and X-Ray tracing for debugging callback timeouts end-to-end. [Next page]()