News For Dev-ops

Today, AWS announces the general availability of node lifecycle actions in AWS Parallel Computing Service (PCS). With node lifecycle actions, you can run custom scripts automatically at defined points in a compute node's lifecycle. You can use them to prepare your nodes for work. For example, you can mount shared storage, join a directory service, install software, or set up monitoring. You define node lifecycle actions in your PCS compute node group configuration when you create or update the group, and you can reuse the same script across multiple compute node groups and clusters. For each script, you set its location as an Amazon S3 or HTTPS URI, the arguments to pass, which lifecycle stage it runs in, whether it re-runs on reboot, and its error-handling behavior. AWS PCS writes the output to a dedicated log file, giving you visibility into what ran. AWS PCS is a managed service that simplifies running and scaling high performance computing (HPC) workloads on AWS using Slurm...

Amazon Connect Customer now automatically surfaces relevant examples of an agent's evaluations when managers are preparing coaching feedback, so they can deliver actionable, evidence-backed coaching to agents. When a manager prepares agent coaching feedback in Amazon Connect Customer, they automatically receive examples of recent evaluations where the agent scored high or low on the chosen coaching topic, along with human or AI evaluator notes explaining the agent behaviors that drove the result. For example, while preparing feedback for an agent on "de-escalation," a manager receives example calls where the agent successfully calmed a frustrated customer, alongside calls that were escalated, with insights into the agent behaviors that led to each outcome. This enables managers to deliver tailored coaching that accelerates agent performance improvement, while saving the time spent manually searching for examples. This feature is available in all regions where Amazon Con...

Not every question deserves the same amount of thought. Renaming a variable isn't the same as debugging a memory leak, and they don't need the same level of thinking. So why should your model treat them the same way every time? Starting in Visual Studio 18.9 Insiders 2, it doesn't have to. Supported models now come with a thinking effort control, so you can dial up the reasoning when a problem is genuinely hard and dial it back down when it isn't. It's a small knob with a big payoff: better-matched answers, and more control over how many credits you spend getting them. A dial, not a switch Thinking effort is exactly what it sounds like: how much reasoning a model does before it answers. It shows up as a set of named levels, and which ones you get depends on the model. Here's what each level is for: Low  - Quick responses with minimal reasoning. Great for straightforward questions and everyday code suggestions, and it consumes fewer AI credits. Mediu...

Learn what's new in Visual Studio Code 1.132 (Insiders) Read the full article Post Updated on July 29, 2026 at 06:00PM Thanks for reading from devamazonaws.blogspot.com

Last time, we fixed the problem of creating a unique_ptr whose deleter's constructor was might throw an exception . But we're not out of the woods yet. Let's take another look at what we have: if (d.try_as<::INoMarshal>()) { void* p; if constexpr (std::is_reference_v<Delegate>) { p = winrt::detach_abi(d); } else { winrt::copy_to_abi(d, p); } return [p = std::unique_ptr<void, in_context_deleter>(p, {}), token = get_context_token()](auto&&...args) { if (token == get_context_token()) { std::remove_reference_t<Delegate> d; winrt::copy_from_abi(d, p.get()); d(std::forward<decltype(args)>(args)...); } else { throw winrt::hresult_error(CO_E_NOT_SUPPORTED); } }; } We had originally broken the rule that the u...

AWS Glue Data Quality now supports a new Distribution Analyzer that generates frequency distribution profiles for your data. Using this new Distribution Analyzer in the Data Quality Definition Language (DQDL), you can generate histograms for numeric columns and value distributions for categorical, date, and boolean columns. With support for custom bin counts, you can explore the shape and patterns of your data at the granularity that matters most to your use case. Understanding how data is distributed is foundational to building reliable data pipelines. Distribution statistics help you quickly identify skewness, outliers, and unexpected patterns across your datasets, without writing custom code. The capability integrates directly with your existing DQDL rulesets, so you can add distribution profiling alongside your current data quality checks in a single evaluation run. Distribution statistics are stored in Amazon S3 for future querying through services like Amazon Athena, and are al...