M-151, ChromeOS version 16733.60.0 (Browser version 151.0.7922.222) has rolled out to ChromeOS devices on the Stable channel. If you find new issues, please let us know one of the following ways: File a bug Visit our ChromeOS communities General: Chromebook Help Community Beta Specific: ChromeOS Beta Help Community Report an issue or send feedback on Chrome Interested in switching channels? Find out how. Andy Wu Google ChromeOS
Congress pauses the OMB's attempt to rewrite how research is funded.
The FBI is reportedly investigating a massive data breach that is unfolding in real time.
A 438-billion-parameter reasoning model isn’t an obvious choice when speed is a priority. Multiverse Computing is betting that compression can The post Multiverse says its 438B model is fast enough for AI agents. The benchmarks tell a more complicated story. appeared first on The New Stack .
OpenAI said Tuesday that its upcoming Astra model is the company’s first to reach the Critical cybersecurity threshold in its The post Your next OpenAI API timeout might not be a timeout at all appeared first on The New Stack .
Google launched Gemini 3.8 Flash, arriving just a few weeks after its predecessor. The company claims the new model "works harder" than Gemini 3.7 Flash by performing more reasoning steps on complex tasks and "calling tools iteratively." It has the same introductory pricing as 3.7 Flash, $0.75 per million input tokens and $3.75 per million […]
The IRS slashed its auditing staff last year — but the move may have backfired as tax collections from enforcement efforts plummeted, an Inspector General report shows.
fal's post-trained MiniMax H3 for quality video production Discussion | Link
Article URL: https://signalundefied.bearblog.dev/i-wanna-live-an-npc-life/ Comments URL: https://news.ycombinator.com/item?id=49541519 Points: 264 # Comments: 228
Article URL: https://github.com/PhiloLabs/fable51-worlds Comments URL: https://news.ycombinator.com/item?id=49541458 Points: 277 # Comments: 80
Quick Look code previews with 700+ editor themes Discussion | Link
https://research.meta.ai/blog/introducing-muse-spark-1-3 Comments URL: https://news.ycombinator.com/item?id=49541256 Points: 432 # Comments: 287
The security tool intercepts potentially dangerous agent actions, blocking clear threats and requesting human approval when intent is uncertain. The post OpenLeash Adds a Human Check to Risky AI Agent Actions appeared first on SecurityWeek .
Easy git-based visual diffing Discussion | Link
IntelliJ IDEA 2026.2.2 is now available. You can update to this version from inside the IDE, using the Toolbox App, or by using snaps if you are a Ubuntu user. You can also download it from our website. IntelliJ IDEA 2026.2.2 brings the following refinements: To see the full list of issues addressed in this version, please […]
Recast motion with your characters, locations & products Discussion | Link
App Demo Studio for mobile apps Discussion | Link
You can now set your preferred GitHub Copilot model as the default for new conversations through enterprise-managed settings. This lets you choose the default model that best fits your workflows.… The post Enterprise-managed settings support any default model appeared first on The GitHub Blog .
Kubernetes v1.37 includes API support for horizontal autoscaling of workloads down to zero replicas. This feature is now Beta and enabled by default. A HorizontalPodAutoscaler (HPA) that uses a suitable object metric or external metric can now scale a workload to zero replicas, then bring it back when the metric changes. Before v1.37, you needed an add-on or external component, or you had to enable the Alpha feature gate, to scale from zero. It is now part of core Kubernetes. Scaling to zero removes the last idle Pod from workloads such as queue consumers and batch processors. The savings are largest when each Pod reserves expensive resources, including dedicated CPUs or GPUs. The trade-off is cold-start time: the HPA must observe the metric, schedule a Pod, and start the application. This works well when work can wait in a durable queue. Kubernetes Services do not buffer requests while no Pods are ready, so HTTP and other request-driven workloads need a separate buffering layer. Why scaling from zero needs a different metric The HPA commonly scales on CPU or memory usage. Both metrics come from running Pods. Once the replica count reaches zero, there are no Pods left to measure and no signal that can tell the HPA to scale back up. Object and external metrics do not have that limitation. A queue length, for example, exists independently of the workers that consume it. The HPA can continue reading the queue length while no workers are running. The following example scales a queue consumer to and from zero using an external metric. Configure an external metric The following example uses a Prometheus metric named queue_consumer_lag . It assumes that Prometheus already collects a series similar to this one: queue_consumer_lag { namespace = " default ", name = " worker_tasks "} Kubernetes needs a metrics adapter to make that value available through the External Metrics API. One implementation is the Prometheus Adapter , which can expose the series using an externalRules entry: externalRules : - seriesQuery : '{__name__="queue_consumer_lag",name!=""}' metricsQuery : sum(<<.Series>>{<<.LabelMatchers>>}) by (name) resources : overrides : namespace : resource : namespace The exact adapter installation and discovery rules depend on your monitoring setup. See the Prometheus Adapter guide to external metrics for the full configuration options. Before creating the HPA, you can verify that Kubernetes can read the metric: kubectl get --raw \ '/apis/external.metrics.k8s.io/v1beta1/namespaces/default/queue_consumer_lag?labelSelector=name%3Dworker_tasks' The request should return the current value for worker_tasks . If it does not, fix the metrics pipeline before configuring the HPA. An HPA cannot scale from zero when its metric is unavailable. Configure the HPA The following HPA targets a Deployment named queue-worker . It allows between zero and ten replicas, with one replica requested for each 30 queued tasks: apiVersion : autoscaling/v2 kind : HorizontalPodAutoscaler metadata : name : queue-worker annotations : kubernetes.io/description : "Scales queue-worker based on the number of queued tasks" spec : scaleTargetRef : apiVersion : apps/v1 kind : Deployment name : queue-worker minReplicas : 0 maxReplicas : 10 metrics : - type : External external : metric : name : queue_consumer_lag selector : matchLabels : name : worker_tasks target : type : Value value : "30" When the queue is empty, the HPA can reduce the Deployment to zero replicas. When tasks arrive, the external metric remains available and the HPA calculates a new replica count, capped at ten by maxReplicas . Start the Deployment with at least one replica. Manually setting a Deployment to zero has always paused autoscaling. The HPA preserves that behavior and will not wake a workload that it did not scale down itself. Normal HPA behavior still applies. In particular, the default downscale stabilization window is five minutes. The window prevents a short drop in queue length from immediately removing all workers. You can configure the window through spec.behavior.scaleDown if your workload needs different behavior. How the HPA distinguishes zero from paused Scaling from zero creates an ambiguity. A replica count of zero can mean that the HPA scaled the workload down, or that an operator manually paused it. The controller resolves this with a ScaledToZero status condition. When the HPA scales a workload from one or more replicas to zero, it records ScaledToZero=True . The condition tells later reconciliation loops that the controller owns the zero state and should continue evaluating object or external metrics. After scaling the workload back up, the controller changes the condition to ScaledToZero=False with the reason NotScaledToZero . A workload at zero without the ScaledToZero=True condition remains paused. You can inspect the conditions with: kubectl describe hpa queue-worker If the adapter cannot return the configured metric, the HPA reports ScalingActive=False with a reason such as FailedGetExternalMetric . Restore the metric or manually scale the workload to recover capacity. Before upgrading or rolling back In Kubernetes v1.37, the HPAScaleToZero feature gate is enabled by default on both the kube-apiserver and kube-controller-manager . The API server accepts minReplicas: 0 ; the controller manager performs the condition-based scaling. During a version-skewed control plane upgrade, wait until both components support the feature and have it enabled before creating HPAs with minReplicas: 0 . A controller manager with the feature disabled treats replicas: 0 as a manual pause and may leave a workload at zero. Before disabling the feature gate or downgrading to a version without the condition-based implementation: Change affected HPAs to minReplicas: 1 or higher. Scale any workload currently at zero to at least one replica. minReplicas: 0 also requires at least one object or external metric. The API server rejects an HPA that only contains resource metrics such as CPU or memory. From Alpha to Beta The first Alpha implementation shipped in Kubernetes v1.16. Kubernetes v1.36 added the ScaledToZero condition and the controller behavior needed to distinguish an automatic scale-down from a manual pause. Kubernetes v1.37 enables the feature by default after adding integration and end-to-end coverage for scaling down to zero and back up from an external metric. The next step is to gather operational feedback before considering graduation to GA. How can I learn more? Read the documentation for scaling to and from zero . Read KEP-2021: HPA supports scaling to and from zero pods for object and external metrics . Learn how to configure the Prometheus Adapter for external metrics . How to get involved This feature is owned by SIG Autoscaling . Join Kubernetes Slack and the #sig-autoscaling channel to share feedback from Beta usage. Acknowledgements Thanks to the SIG Autoscaling contributors who took this feature from the original v1.16 implementation to the condition-based redesign and Beta graduation. Thanks also to Guy Templeton and Adrian Moisey for reviewing the KEP, and to the release, documentation, and production-readiness reviewers who helped prepare it for Kubernetes v1.37.
Google on Wednesday announced Gemini 3.8 Flash Cyber, which it described as its most capable cybersecurity model, and has made it available to a set of trusted defenders via a new initiative called the Fairwind Program. "The Fairwind Program gives high-priority defenders (like governments, healthcare providers, and telecommunications services) early access to advanced models that help them
In the class-action lawsuit involving The New York Times, the US Department of Justice argues that training AI models on copyrighted text qualifies as fair use. The filing directly contradicts a report from the US Copyright Office. Its director was fired by the Trump administration shortly after the report was published. The article US Department of Justice backs fair use for AI training in landmark copyright case appeared first on The Decoder .
If the high court accepts the petition, it would tee up a case with sweeping implications for prediction markets, an industry that has unleashed billions of dollars of betting across nearly every aspect of modern life.
The GitHub Copilot app and Copilot CLI now respect content exclusion policies configured by enterprise, organization, and repository administrators. Copilot won’t use excluded files as context, helping you protect sensitive… The post Content exclusions generally available in Copilot app and CLI appeared first on The GitHub Blog .
The kingdom condemns the targeting of Sidr, which was reportedly hit by unknown projectiles in the Strait of Hormuz on Monday.