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Summaries are not the original full articles. ## Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers DevFeed: [Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers]() Original publisher: [Read original article]() Author: Josh Parker Published: 2026-09-16T13:00:33Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [data centers](), [AI Infrastructure](), [Google](), [Nvidia](), [Artificial Intelligence](), [datacenter](), [benchmarking](), [Requirements]() Tags: [ai](), [ai-data-centers](), [ai-infrastructure](), [capacity](), [corporate](), [data-centers](), [energy](), [google](), [infrastructure](), [launch](), [metrics](), [nvidia](), [performance-metrics](), [requirements](), [resource](), [systems]() ### AI overview Emerald AI, Google and NVIDIA announced the AI Energy Management Alliance, a coalition focused on flexible AI data centers that can dynamically adjust electricity use in response to grid conditions. The article describes technology-neutral, performance-based requirements covering response speed, duration, predictability and emergency behavior. ### Source excerpt AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center. Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its-kind coalition advancing data centers that can dynamically manage their electricity use [...] ## From error mitigation to fault-tolerant quantum computing DevFeed: [From error mitigation to fault-tolerant quantum computing]() Original publisher: [Read original article]() Published: 2026-09-15T14:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Hardware](), [ibm]() Tags: [compilation](), [deep-dive](), [errors](), [hardware](), [quantum](), [quantum-computing](), [quantum-error-correction-mitigation](), [quantum-software](), [real-time](), [reduce](), [resource]() ### AI overview The article describes a continuum from quantum error mitigation to quantum error correction as a path toward useful quantum computing. It reports emerging techniques with lower effective error rates, reduced sampling overhead, and lower resource requirements than conventional approaches. ### Source excerpt A spectrum of error-correcting techniques is enabling useful quantum computation, measured not by logical qubits but by the circuits you can run with them. ## Kubernetes v1.37: Scheduler Preemption for In-Place Pod Resize (Alpha) DevFeed: [Kubernetes v1.37: Scheduler Preemption for In-Place Pod Resize (Alpha)]() Original publisher: [Read original article]() Author: Natasha Sarkar Published: 2026-09-10T18:30:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [Containers]() Tags: [containers](), [cpu](), [feature](), [kubernetes](), [memory](), [node](), [resource](), [scale]() ### AI overview Kubernetes v1.37 adds an alpha scheduler-preemption feature for in-place Pod resizes. It can preempt lower-priority workloads to make capacity available for valid, higher-priority resize requests that are deferred on full nodes. ### Source excerpt In Kubernetes, resource allocation has historically been a static decision made during a Pod's initial scheduling and placement. With the graduation of the core in-Place Pod resize feature to General Availability in v1.35, application developers and cluster operators gained the powerful ability to dynamically adjust CPU and memory allocations of running containers without incurring disruptive restarts or application downtime. However, in-place resizing introduced a unique resource scheduling gap: if a running Pod requested a resource scale-up that exceeded the host node's allocatable headroom, the Kubelet was forced to mark the request as Deferred. The Pod would remain parked in this state indefinitely, waiting for resources on the node to naturally free up. To bridge this scheduling gap, Kubernetes v1.37 introduces scheduler preemption for in-place Pod resize (Alpha), behind the InPlacePodVerticalScalingSchedulerPreemption feature gate. This feature allows the Kubernetes scheduler to actively free up capacity on a fully-utilized node by preempting lower-priority workloads, enabling the pending in-place resizes of critical, higher-priority applications to succeed. The "deferred" resize challenge To understand why this preemption mechanism is needed, it is helpful to look at how Kubernetes handles running Pod resizing. When a user or controller (such as the Vertical Pod Autoscaler) updates the resource requests of an active container, the Kubelet evaluates whether the underlying node has enough spare allocatable capacity to fulfill the increase. If the node's resources are fully utilized and cannot satisfy the new limits, the Kubelet sets the container's resizeStatus (reported in the Pod's status.containerStatuses[]) to Deferred. Unlike an Infeasible resize request (which is immediately rejected because it exceeds physical machine boundaries, namespace limit ranges, or admission quotas) a Deferred status indicates that the request is valid but is temporarily unable t ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## Somebody else's API, safely on your menu DevFeed: [Somebody else's API, safely on your menu]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-08T14:28:00Z Content type: tutorial Language: en Sources: [Tyk API Management]() Topics: [API](), [shadow AI](), [audit trail](), [Template](), [Tool](), [data]() Tags: [ai](), [ai-gateway](), [ai-studio](), [api](), [api-management](), [api-platform-teams](), [article](), [audit-trail](), [community](), [governance](), [llm](), [llm-governance](), [markdown](), [password](), [policy](), [resource](), [shadow-ai](), [tools](), [tutorial]() ### AI overview This tutorial explains how to submit an internal API for governed use by colleagues and organizational language models. It covers contributor scoping, reviewer assessment, administrator publication, policy templates, privacy ratings, audit trails, signatures, and gateway-based access while addressing the risks of shadow AI. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today's assignment is one I have been looking forward to rather a lot. Somebody on your engineering team has written an API. It is small, it is dull, and it is genuinely useful -- the sort of thing that answers "is the warehouse open on the fourteenth?" without [...] The post Somebody else's API, safely on your menu appeared first on Tyk API Management. ## Kubernetes v1.37: DRA Updates DevFeed: [Kubernetes v1.37: DRA Updates]() Original publisher: [Read original article]() Author: Kashish Verma Published: 2026-09-03T18:30:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [releases]() Tags: [api](), [devices](), [drivers](), [kubernetes](), [release](), [resource](), [updates]() ### AI overview Kubernetes 1.37 updates Dynamic Resource Allocation, including GA support for extended resources, device status reporting, stable device taints and tolerations, and a standardized NUMA-node device attribute. ### Source excerpt Kubernetes 1.37 is here and Dynamic Resource Allocation (DRA) keeps pushing past where it started! This release brings DRA Extended Resource support to GA, a milestone the team has been building toward for three straight releases. Several more features graduate to Beta or GA. A fresh batch of alpha features rounds out the release. I'll dive into what's new for DRA in Kubernetes 1.37! What's stable in 1.37 DRA Extended Resource support has graduated to GA. This is the mechanism that lets DRA drivers satisfy requests made through the traditional extended resource API, think example.com/gpu in a Pod spec, without requiring a separate device plugin alongside the DRA driver. An extended resource name can be set directly on a DeviceClass, and Pods requesting it get matched to a device through DRA with no ResourceClaim needed on the workload's part. It's been on a steady path since KEP acceptance in 1.34. Alpha landed in 1.35, Beta in 1.36, and now it's Stable. For cluster operators, this is what makes DRA adoption gradual. Existing workloads written against extended resources keep working unmodified while the backend allocation logic moves over to DRA. ResourceClaims status with possible standardized network interface data adds a devices field to ResourceClaim .status, letting DRA drivers report per-device status, including, for network devices, the interface name, MAC address, and IP addresses. This gives users and controllers visibility into device state that was previously invisible once a device was configured in a Pod, and makes it possible to build things like network services that rely on a device's reported IPs. DRA: device taints and tolerations is now Stable; DRA drivers can mark devices as tainted so they're skipped for new Pod scheduling, and cluster admins can apply the same taints cluster-wide via a DeviceTaintRule, without reconfiguring drivers. Pods already using a tainted device can be evicted automatically, unless their ResourceClaim explicitly tolerates ## Let's Use the Emergent CSS random() Function in all the Browsers DevFeed: [Let's Use the Emergent CSS random() Function in all the Browsers]() Original publisher: [Read original article]() Author: Lee Meyer Published: 2026-08-31T15:12:19Z Content type: article Language: en Sources: [CSS-Tricks]() Topics: [CSS](), [Web Development](), [browser](), [JavaScript]() Tags: [articles](), [browser](), [browsers](), [css](), [css-functions](), [javascript](), [polyfill](), [random](), [resource](), [ui](), [ux]() ### AI overview This article explores the emerging CSS random() function and the effort to create a polyfill that works across browsers. It discusses controlled randomness in web layouts and UX, including a custom JavaScript confetti implementation, and considers how CSS could reduce the need for JavaScript or third-party frameworks. ### Source excerpt The journey to create a polyfill for the upcoming CSS random() function that works in all browsers. Let's Use the Emergent CSS random() Function in all the Browsers originally handwritten and published with love on CSS-Tricks. You should really get the newsletter as well. ## Kubernetes v1.37: Metrics API graduates to stable DevFeed: [Kubernetes v1.37: Metrics API graduates to stable]() Original publisher: [Read original article]() Author: ChengHao Yang Published: 2026-08-27T18:30:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [API](), [autoscaling](), [cpu](), [Monitoring]() Tags: [api](), [autoscaling](), [cpu](), [developers](), [kubernetes](), [memory](), [metrics](), [monitoring](), [production](), [release](), [resource](), [v1]() ### AI overview Kubernetes v1.37 promotes the Metrics API to the stable v1 version. The API provides CPU and memory usage for nodes and Pods, supporting commands such as kubectl top and resource-metrics-based autoscaling, without changing the collected metrics, resource types, or fields. ### Source excerpt Kubernetes v1.37 promotes the metrics.k8s.io API to stable (v1). This API provides CPU and memory usage for nodes and Pods, and is the API behind commands such as kubectl top and resource-metrics-based autoscaling. For cluster operators and application developers, this graduation means that the API now has the stability guarantees associated with a Kubernetes stable API. The v1 API has the same resource types and fields as v1beta1; this is an API-version graduation, not a change to the metrics that are collected or returned. A long-lived API reaches stable The resource Metrics API was introduced as alpha in Kubernetes v1.6 and became beta in v1.8. It has remained unchanged and has been used in production for years by clients including the HorizontalPodAutoscaler (HPA) and kubectl top. Kubernetes v1.37 formally graduates that proven API to metrics.k8s.io/v1. The API exposes two resource types: NodeMetrics, for CPU and memory usage for a node. PodMetrics, for CPU and memory usage for a Pod, with a per-container breakdown in its containers field. The API remains intentionally small. It provides the resource metrics needed for autoscaling and basic inspection; it is not a replacement for a full monitoring pipeline or the custom metrics (custom.metrics.k8s.io) API. What changed with the v1.37 release? The v1 API surface is identical to v1beta1, except for the API version. There are no renamed fields, new fields, or changes to the meaning of the returned CPU and memory values. For example, a client can retrieve node metrics from the stable endpoint: kubectl get --raw /apis/metrics.k8s.io/v1/nodes Likewise, it can retrieve metrics for the pods in a namespace: kubectl get --raw /apis/metrics.k8s.io/v1/namespaces/default/pods kubectl top supports both API versions. It prefers v1 when available and automatically falls back to v1beta1 on clusters that do not yet serve v1. The HPA controller currently supports only v1beta1. Support for discovery-based selection between v1 and v ## MicroLighter: Syntax Highlighter DevFeed: [MicroLighter: Syntax Highlighter]() Original publisher: [Read original article]() Author: Geoff Graham Published: 2026-08-25T14:10:34Z Content type: article Language: en Sources: [CSS-Tricks]() Topics: [Syntax Highlighting](), [CSS](), [JavaScript](), [WordPress](), [Code]() Tags: [code](), [css](), [custom-highlight-api](), [javascript](), [links](), [resource](), [web](), [wordpress]() ### AI overview MicroLighter is a CSS-focused syntax highlighter for code blocks that uses Custom Highlights and aims to provide themes, line numbers, multiple-language support, semantic markup, and modular features with less JavaScript and fewer dependencies. The article also reports a substantially smaller bundle than Prism after integrating it into a WordPress site. ### Source excerpt Syntax highlighting for code blocks without the complicated markup, spans, classes, and bloated JavaScript, courtesy of Uncle Dave. MicroLighter: Syntax Highlighter originally handwritten and published with love on CSS-Tricks. You should really get the newsletter as well. ## What's new in ECK 3.5 DevFeed: [What's new in ECK 3.5]() Original publisher: [Read original article]() Author: Kostas Stamatakis Published: 2026-08-20T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Elastic Cloud]() Tags: [cloud](), [cloud-native-kubernetes](), [elastic-cloud](), [elasticsearch](), [feature](), [infrastructure](), [kubernetes](), [memory](), [onboarding](), [orchestration](), [platform](), [release](), [resource](), [scale](), [security](), [selectors](), [tls](), [workflows]() ### AI overview ECK 3.5 adds label-selector-based namespace management, pausable orchestration for maintenance, and automatic mutual TLS client certificates across Elasticsearch-connected components. ### Source excerpt ECK 3.5 brings dynamic namespace management via label selectors, pause orchestration for safe maintenance windows, mutual TLS across all stack components, simplified resource specs, and a reduced operator memory footprint. ## How to Explain Complex Design Ideas So People Get Excited and Promote You DevFeed: [How to Explain Complex Design Ideas So People Get Excited and Promote You]() Original publisher: [Read original article]() Author: Rikke Friis Dam Published: 2026-08-17T14:00:00Z Content type: tutorial Language: en Sources: [UX Daily - User Experience Daily]() Topics: [User Experience](), [Usability](), [Template](), [App]() Tags: [communication](), [how-to](), [resource](), [skills](), [usability](), [user-experience](), [video]() ### AI overview Don Norman explains how to communicate complex design ideas clearly and build audience interest. He recommends beginning with practical examples, then introducing the abstractions that connect them, while conveying genuine enthusiasm for the subject. ### Source excerpt You are an expert! If you want to move up in a company or convince people to support your ideas, you need to communicate your expertise and excitement clearly. Sounds easy, right? The problem is that the more expertise you have, the harder it can be to communicate. In this video, Don Norman shares his ideas for avoiding this problem. One is to start with practical examples that your audience can easily understand, then talk about abstractions that tie those examples together. So, before you talk about the history of art, show some paintings, or start your next talk on usability with a live demo of an app with great usability, or a usability fail! You should also convey your excitement. If yo... ## Securing the Infrastructure of Intelligence DevFeed: [Securing the Infrastructure of Intelligence]() Original publisher: [Read original article]() Author: 黄仁勋 Published: 2026-08-17T12:34:51Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [cloud-infrastructure](), [Nvidia](), [OpenAI](), [Inference](), [model-deployment](), [Cloud]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [cloud](), [compute](), [corporate](), [frontier-ai](), [inference](), [infrastructure](), [model](), [nvidia](), [openai](), [platform](), [products](), [resource](), [resources](), [revenue](), [scale](), [supply-chain](), [systems]() ### AI overview NVIDIA describes a partnership with SB Energy to secure land, power and services capacity for an AI factory at the PORTS-Pike Technology Campus in Ohio, where OpenAI will be the tenant. OpenAI is expected to build and operate the facility using NVIDIA's DSX AI factory platform, with an initial capacity of 4.25 gigawatts. ### Source excerpt AI factories are the defining infrastructure of the AI era -- where compute transforms energy and data into intelligence that powers every business, industry and country. In the AI economy, compute is revenue. AI factories require a full stack of critical resources: advanced chips, packaging, memory and networking -- as well as land, power and [...] ## Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost DevFeed: [Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost]() Original publisher: [Read original article]() Author: Anubhav Sharma Published: 2026-08-12T20:52:35Z Content type: article Language: en Sources: [Nirmata]() Topics: [Kubernetes](), [Cloud](), [Automation](), [data]() Tags: [agents](), [ai](), [automation](), [cloud](), [cloud-native](), [clusters](), [cncf](), [compute](), [cost-savings](), [deployment](), [devsecops](), [kubernetes](), [kyverno](), [policy-management](), [resource](), [verification]() ### AI overview Nirmata describes applying its Cost Analyzer and Resource Hygiene Cloud Agents across an enterprise customer's 40-cluster production Kubernetes fleet. The scans identified a roughly $107,000 monthly compute baseline and about 50% recoverable through right-sizing before stale-resource cleanup, while revealing recurring sources of waste and governance gaps. ### Source excerpt Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost Most Kubernetes Cost overruns don't come from one singularly bad decision. They come from dozens of reasonable ones -- made independently, by different teams, at different times -- that... The post Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost first appeared on Nirmata. ## Fixed cadence to seconds: making ClickHouse Cloud autoscaling more reactive DevFeed: [Fixed cadence to seconds: making ClickHouse Cloud autoscaling more reactive]() Original publisher: [Read original article]() Author: Marvin Beckers Published: 2026-08-04T13:19:15Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [Orchestration](), [Kubernetes]() Tags: [autoscaling](), [clickhouse](), [cloud](), [concurrency](), [cpu](), [event](), [kubernetes](), [latency](), [memory](), [orchestration](), [reactive](), [real-time](), [resource](), [scale]() ### AI overview ClickHouse describes rebuilding Cloud autoscaling orchestration to combine periodic recommendation passes with a reactive fast path for urgent scale-up signals. ### Source excerpt How we rebuilt ClickHouse Cloud's autoscaling orchestration on Kubernetes' controller-runtime and a ClickHouse-powered signals table, adding a reactive fast path that scales services up in seconds instead of waiting for the next scheduled pass. ## Kubernetes v1.37 Sneak Peek DevFeed: [Kubernetes v1.37 Sneak Peek]() Original publisher: [Read original article]() Author: Arsh Sharma; Christopher Tineo; Kirti Goyal; Sophia Ugochukwu; Swathi Rao; Troy Connor Published: 2026-07-31T16:00:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [Command-line interface](), [bug](), [Linux](), [configuration]() Tags: [blog](), [bug](), [cli](), [kernel](), [kubernetes](), [logging](), [memory](), [release](), [resource](), [secrets](), [startup]() ### AI overview This Kubernetes blog previews planned changes for the v1.37 release, including deprecations to kubectl run's filename flag and kube-proxy's ipvs mode. It also describes a fix preventing Static Pods from referencing Secrets or ConfigMaps and the phaseout of cgroup v1 support. ### Source excerpt As we get closer to the release date for Kubernetes v1.37, the project develops and matures, features may be deprecated, removed, or replaced with better ones for the project's overall health. This blog outlines some of the planned changes for the Kubernetes v1.37 release that the release team feels you should be aware of for the continued maintenance of your Kubernetes environment and keeping up to date with the latest changes. The information below reflects the current status of the v1.37 release and may change before the actual release date. Deprecations and removals for Kubernetes v1.37Kubectl: kubectl run --filename/-f to be deprecated The --filename (or -f) flag for kubectl run is being deprecated as the generated pod is always built purely from CLI arguments like NAME and --image. See kubernetes/kubernetes#138671 for the original issue and discussion. Kubelet: Static Pods can no longer reference Secrets or ConfigMaps Static Pods were never meant to read API resources directly, since they aren't created through the API server -- but a bug let them reference Secrets or ConfigMaps via fields like configMapRef or secretRef. That bug is now fixed: as of v1.37 these references are strictly prohibited, and the PreventStaticPodAPIReferences feature gate that previously let you opt out of the restriction has been removed. See kubernetes/kubernetes#140226 for the original issue and discussion. Deprecating kube-proxy's support for ipvs mode kube-proxy support for ipvs mode was introduced in v1.8 to resolve iptables performance bottlenecks. However, since the kernel ipvs API alone cannot fully implement Kubernetes Services, ipvs mode continues to use iptables underneath (KEP-3866, "The ipvs mode of kube-proxy will not save us"). Clusters running kube-proxy in ipvs mode (or mode: ipvs in KubeProxyConfiguration) would now be logging a deprecation warning on startup. The deprecation timeline looks like this: By v1.40, ipvs mode for kube-proxy is expected to be disabled by de ## Kubernetes 1.35: 10 new Alpha features DevFeed: [Kubernetes 1.35: 10 new Alpha features]() Original publisher: [Read original article]() Author: Nigel Douglas Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Kubernetes](), [autoscaling](), [Hardware](), [Release notes](), [Security]() Tags: [ai](), [autoscaling](), [hardware](), [kubernetes](), [release-notes](), [resource](), [upgrades]() ### AI overview This article examines ten new Alpha features in Kubernetes 1.35. It highlights Dynamic Resource Allocation improvements for AI-oriented workloads and hardware such as GPUs and FPGAs, along with mixed-version upgrade proxying, resource utilization improvements, and other emerging platform capabilities. ### Source excerpt Discover Kubernetes 1.35's top 10 new Alpha features, including major DRA upgrades for AI hardware, mixed-version upgrade proxying, CSI security improvements, node feature declarations, smarter autoscaling, gang scheduling, in-place vertical scaling, and new pod restart actions. ## Using an Internal Developer Portal For Superior FinOps Visibility DevFeed: [Using an Internal Developer Portal For Superior FinOps Visibility]() Original publisher: [Read original article]() Author: Aidan O'Connor Published: 2026-07-22T12:04:29Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [finops](), [internal developer portal](), [Cloud](), [Developer experience](), [Optimization](), [data]() Tags: [cloud](), [developer-experience](), [developer-portal](), [finops](), [internal-developer-portal](), [optimization](), [platform](), [resource](), [resources](), [tools]() ### AI overview The article explains how an internal developer portal can improve FinOps visibility by combining cloud cost data with development context. It describes how this helps developers, technical leaders, engineering managers, and FinOps teams understand resource ownership, service costs, budgeting, and optimization opportunities. ### Source excerpt Port transforms FinOps by integrating and contextualizing data from multiple sources to optimize cloud costs efficiently. ## Announcing the AWS Exporter | Port DevFeed: [Announcing the AWS Exporter | Port]() Original publisher: [Read original article]() Author: Tal Sabag Published: 2026-07-22T12:04:28Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Amazon Web Services](), [Amazon S3](), [Amazon Simple Queue Service (SQS)](), [AWS IAM]() Tags: [aws](), [catalog](), [cloud](), [configuration](), [developer-portal](), [encryption](), [iam](), [internal-developer-portal](), [queue](), [resource](), [s3](), [serverless](), [software-catalog]() ### AI overview Port's AWS exporter imports live AWS resource data into a software catalog and internal developer portal. It supports more than 500 resource types, maps them to blueprints and relations, and exposes metadata, tags, console links, and resource-specific details such as encryption and access configuration. ### Source excerpt The AWS exporter brings us even closer to realizing our vision of getting everything into the software catalog and the internal developer portal. ## When the Postgres query planner goes rogue DevFeed: [When the Postgres query planner goes rogue]() Original publisher: [Read original article]() Author: Nick Van Wiggeren Published: 2026-07-13T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [cpu]() Tags: [cpu](), [database](), [engineering](), [insights](), [latency](), [postgres](), [postgresql](), [resource](), [sql](), [statistics]() ### AI overview PlanetScale describes containing a runaway Postgres query whose planner choice caused severe latency and CPU saturation. A strict Database Traffic Control budget stopped further executions while the customer investigated the root cause. ### Source excerpt How PlanetScale Database Traffic Control contained a runaway Postgres query after the planner abandoned an index. ## More granular observability for Vercel Sandbox DevFeed: [More granular observability for Vercel Sandbox]() Original publisher: [Read original article]() Author: Tom Lienard Published: 2026-07-07T00:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [observability](), [Vercel](), [dashboards](), [Command-line interface](), [data](), [Network]() Tags: [cli](), [enterprise](), [metrics](), [monitor](), [network](), [observability](), [resource](), [scale](), [vercel]() ### AI overview Vercel Sandbox has added more granular observability with metrics for CPU usage, provisioned memory, data transfer, and running sandboxes and sessions. Metrics can be grouped by sandbox and session, queried through the Vercel CLI, and viewed at team and project levels to connect resource usage with billing. ### Source excerpt Vercel Sandbox observability now includes detailed resource metrics, giving you deeper visibility into how your sandboxes consume compute and networking. From the Observability tab in your dashboard, you can now monitor: Active CPU and CPU usage: Measures when your code actively uses the CPU. Active CPU is measured in core-hours and time spent waiting on I/O, such as network requests or model calls, is not billed Provisioned Memory: Memory allocated to your sandboxes multiplied by runtime, measured in GB-hours Data Transfer: Total data transferred in and out of your sandboxes, including package downloads and API calls Running sandboxes and sessions: How many sandboxes are running at any give time, and when sessions are stopped or started Each metric can be grouped by Sandbox Name and Sandbox Session ID, so you can drill down from aggregate usage to the individual sandbox responsible. You can also query and visualize metrics via the Vercel CLI: Metrics are available at both the team and project level and align directly with how Sandbox usage is billed, so you can attribute costs to specific workloads and catch unexpected usage early. This is useful for tracking agent workloads that create sandboxes at scale, right-sizing sandbox configurations based on actual utilization, and identifying sandboxes with unexpectedly high data transfer. Observability for Sandbox is included on all plans, and manual queries are available on Pro and Enterprise plans. Learn more in the Sandbox documentation. Read more ## Why we rewrote WAL-G for Postgres backups in Rust: Meet WAL-RUS DevFeed: [Why we rewrote WAL-G for Postgres backups in Rust: Meet WAL-RUS]() Original publisher: [Read original article]() Author: Sai Srirampur; Philip Dubé Published: 2026-06-25T16:00:33Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [Tooling](), [Go Language]() Tags: [clickhouse](), [cloud](), [go](), [infrastructure](), [memory](), [open-source](), [postgres](), [resource](), [rust](), [tool](), [tooling]() ### AI overview ClickHouse describes WAL-RUS, an open-source Rust implementation for PostgreSQL backups and WAL archival. It aims to provide a more predictable, memory-efficient alternative compatible with WAL-G. ### Source excerpt How we built WAL-RUS, an open-source Rust-based Postgres backup tool that reduces virtual memory usage by over 70% compared to WAL-G while maintaining full compatibility. ## How we saved over $3 million in idle compute costs with Datadog Kubernetes Autoscaling DevFeed: [How we saved over $3 million in idle compute costs with Datadog Kubernetes Autoscaling]() Original publisher: [Read original article]() Author: Jacob Simonov; Danny Driscoll; Jesse Feinman; Sophie Wigmore Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [kubernetes autoscaling](), [autoscaling](), [Kubernetes](), [Cloud](), [migration](), [configuration](), [cpu](), [data centers]() Tags: [autoscaling](), [cloud](), [compute](), [cost](), [cpu](), [data-centers](), [guide](), [kubernetes](), [kubernetes-autoscaling](), [memory](), [migration](), [platform](), [post](), [production](), [reduce](), [resource](), [scale](), [tooling]() ### AI overview Datadog describes how its Kubernetes Autoscaling system helped a platform team automate horizontal scaling and vertical resource rightsizing across a large production fleet. The migration addressed manual pod sizing, difficult-to-maintain scaling configurations, idle compute costs, and reliability risks, contributing to more than $3 million in annualized idle compute savings across Datadog. ### Source excerpt See how how multidimensional autoscaling reduced overprovisioning and mitigated reliability risks at scale. ## How Netflix Simplified Batch Compute with Kueue DevFeed: [How Netflix Simplified Batch Compute with Kueue]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-06-22T21:35:01Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [kueue](), [Netflix](), [Kubernetes](), [migration](), [Cloud](), [App]() Tags: [applications](), [aws](), [batch](), [cloud](), [compute](), [ecosystem](), [infrastructure](), [jobs](), [kubernetes](), [kueue](), [migration](), [netflix](), [platform](), [resource](), [work]() ### AI overview Netflix describes its transition toward a more Kubernetes-native compute infrastructure and its adoption of Kueue, a cloud-native job queueing system for batch workloads. The article explains how Kueue replaced custom queuing and scheduling logic in Compute Managed Batch, supported the migration of millions of batch jobs, and enabled tenant-based capacity management and workload execution through Titus. ### Source excerpt By Alvin Bao, Alex Petrov, Jennifer Lai, Aidan Sherr, and Samartha Chandrashekar As a part of the journey to transition Netflix's compute infrastructure to be more Kubernetes-native, we have leaned into incorporating components from the Kubernetes ecosystem into our container platform Titus. One example of this is our use of Kueue, a cloud-native job queueing system for batch workloads, which has largely replaced the custom queuing and scheduling logic in our homegrown managed batch solution Compute Managed Batch (CMB). In this post, we'll give an overview of what motivated the migration, how we migrated millions of batch jobs to use Kueue, and what Kueue allows us to offer as a Compute platform. Brief Overview of CMB and Titus CMB is a managed batch solution that allows users and applications to execute and manage workloads that run to completion. Using a tenant hierarchy, workloads are managed and queued with ordered execution through priorities, and capacity is managed on a per-tenant basis. Workloads that are submitted to CMB are then run on Titus. The features of Titus relevant to CMB are workload federation across multiple cells (Kubernetes clusters) and federated capacity reservations. This means CMB can talk to a single Titus endpoint to get/submit workloads and update capacity reservations without having to worry about the underlying cell/cluster topology. CMB Tenant Hierarchy Tenants provide a grouping mechanism for jobs submitted on behalf of certain organizations, platforms, or applications. Users can create and organize tenants however best suits their organization or use case. For example, an organization may use a single tenant across several applications or a complex hierarchical structure that matches its team and application ownership structure. Tenants are associated with a capacity configuration. The capacity configuration defines the amount of compute capacity available to the tenant and provides certain guarantees around isolation from other te ## Palana (Part 2): Architecting isolation, identity, and auditability for AI agents DevFeed: [Palana (Part 2): Architecting isolation, identity, and auditability for AI agents]() Original publisher: [Read original article]() Author: Kevin Littlejohn Published: 2026-06-21T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [Kubernetes](), [Artificial Intelligence](), [systems](), [observability](), [Network](), [Authentication](), [Large Language Model](), [Command-line interface](), [Filesystems]() Tags: [agents](), [ai](), [ai-agents](), [architecture](), [artificial-intelligence](), [authentication](), [autonomous](), [command-line](), [devsecops](), [engineering](), [http](), [kubernetes](), [llm](), [network](), [observability](), [platform](), [policy](), [production](), [resource](), [secrets](), [security](), [systems](), [workflow]() ### AI overview Palana Part 2 explains the architecture of Grab's Kubernetes-native secure execution platform for autonomous AI agents. It describes isolated user-and-agent namespaces, default-deny networking, proxy-mediated traffic, Vault-based secret separation, operator-managed resources, admission controls, persistent storage, observability signals, and identity handling. ### Source excerpt Introduction In Part 1, we introduced Palana, Grab's Kubernetes-native secure execution platform for autonomous AI agents. We discussed the underlying need for isolated environments and covered its core design principles: treating isolation as the unit of trust, keeping credentials out of agent hands, and mediating all network access. In this second part, we'll dive under the hood into Palana's architecture, look at the agent lifecycle, and share the key lessons we learned from putting this system into production. Architecture overview The core request path looks like this: Figure 1. Palana architecture overview. The agent pod runs in a namespace owned by one user and one agent. It gets default-deny style network policy, domain name system (DNS), access to required platform services, and a persistent /data volume. Browser traffic enters through Traefik. LLM traffic goes to the LiteLLM wrapper in the gateway namespace. General Hypertext Transfer Protocol (HTTP) and Hypertext Transfer Protocol Secure (HTTPS) egress goes through the proxy namespace. Secrets are read from Vault only by the component authorized to use them. The operator is responsible for turning a user request into the concrete Kubernetes shape: The user creates an agent through pcli (Palana command-line interface) or the portal. Palana writes a UserAgent or Agent custom resource with the raw user identity. The operator creates the user and agent namespaces, service accounts, role bindings, storage, network policies, and ingress. The user runs a template or container image. Admission webhooks inject proxy environment variables and enforce pod-level restrictions. Logs, policy decisions, and activity signals are emitted to observability systems. Agent lifecycle From a user's perspective, the basic workflow is intentionally small: ./pcli login ./pcli create demo ./pcli secrets add demo GRABGPT_API_KEY token= ./pcli run demo --template claudecodeui Behind those commands, Palana provisions an isolated [Next page]()