# Monitoring Published articles for Monitoring. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI Agent Governance: Why It Belongs in Your Platform DevFeed: [AI Agent Governance: Why It Belongs in Your Platform]() Original publisher: [Read original article]() Author: Prateek Mittal Published: 2026-09-16T20:28:57.610955Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [observability](), [audit](), [test](), [Deployment](), [Monitoring](), [pull-requests]() Tags: [ai](), [ai-agent](), [audit](), [deployment](), [governance](), [monitoring](), [observability](), [pull-requests](), [test]() ### AI overview Engineering leaders from Yelp, Platformable, and Harness argue that AI agent governance must be automated, continuously enforced, and built into the platform rather than left to policy documents. The article discusses audit trails, agent-to-agent access controls, experiment tracking, testing, latency monitoring, rollback paths, and observability for agent-driven changes. ### Source excerpt Engineering leaders from Yelp, Platformable, and Harness explain why AI agent governance has to be built into the platform, not a policy doc. | Blog ## AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity DevFeed: [AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-09-16T19:13:53Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [Monitoring](), [reliability](), [Microservices](), [Data pipelines](), [Artificial Intelligence](), [Job]() Tags: [ai](), [bug](), [data-pipelines](), [jobs](), [microservices](), [monitoring](), [reliability]() ### AI overview Spotify describes how rapid change, content-processing weaknesses, capacity limits, and a scheduling bug contributed to delays in publishing episodes. It reports adding end-to-end monitoring, fixing the scheduler, lowering batch-job priority, and increasing capacity. ### Source excerpt Quality and reliability have always been a point of pride for Spotify. We run an extraordinarily complex... The post AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity appeared first on Spotify Engineering. ## Our framework for reporting model misalignment DevFeed: [Our framework for reporting model misalignment]() Original publisher: [Read original article]() Published: 2026-09-16T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [AI Development](), [AI Strategy](), [Framework](), [Development]() Tags: [ai](), [ai-development](), [alignment](), [behavior](), [developers](), [model](), [monitoring](), [openai](), [reporting](), [research](), [standards]() ### AI overview OpenAI introduces a framework for tracking, investigating, and disclosing model misalignment, accompanied by six reports on unexpected or concerning model behavior observed over the previous six months. The framework favors disclosure even when the significance of an instance is uncertain and is intended to evolve through experience and public feedback. ### Source excerpt OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior. ## Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta) DevFeed: [Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta)]() Original publisher: [Read original article]() Author: Harsh Vardhan Published: 2026-09-16T16:00:00Z Content type: tutorial Language: en Sources: [Postman Blog]() Topics: [gRPC](), [Postman](), [Monitoring](), [Authentication]() Tags: [beta](), [general](), [graphql](), [graphql-monitoring](), [how-to](), [monitor-grpc-streaming](), [monitoring](), [monitors](), [postman](), [postman-monitors-grpc-graphql](), [scheduling](), [test]() ### AI overview This tutorial explains how to schedule gRPC and GraphQL requests with Postman Monitors in beta. It covers supported request types, streaming caveats, response testing, authentication, and current feature limitations. ### Source excerpt Postman Monitors now run gRPC and GraphQL requests in beta. Learn how to schedule them, test responses, and send feedback. Start monitoring today. The post Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta) appeared first on Postman Blog. ## Linux's Yogafan Driver Prepares Support For Newer Lenovo Laptops DevFeed: [Linux's Yogafan Driver Prepares Support For Newer Lenovo Laptops]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:29:22Z Content type: news Language: en Sources: [Phoronix]() Topics: [Linux](), [Kernel](), [Monitoring](), [patches](), [Open Source]() Tags: [desktop-linux](), [driver](), [kernel](), [laptops](), [lenovo](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [monitoring](), [open-source](), [open-source-graphics](), [patches](), [phoronix](), [phoronix-test-suite](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview The Yogafan HWMON driver is gaining support for several newer Lenovo laptops in the upcoming Linux 7.4 kernel cycle. Patches queued in the hwmon-next branch add fan speed monitoring support for the Lenovo Yoga Pro 9 16IMH9, Yoga 740-15IML, IdeaPad 3 15ALC6 Ub, and Yoga 14cACN 2021. ### Source excerpt Introduced back in Linux 7.1 was the Yogafan HWMON driver for supporting fan speed monitoring across various Lenovo Yoga, Legion, Flex, Slim, and IdeaPad laptops. An initial set of Lenovo laptops was initially supported by this driver while succeeding kernel versions have continued building out the support... ## pgAssistant 3.8.0 : continuous improvement loop for Postgres DevFeed: [pgAssistant 3.8.0 : continuous improvement loop for Postgres]() Original publisher: [Read original article]() Author: Pgassistant Dev Team Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [PostgreSQL](), [Monitoring](), [recommendations](), [configuration](), [real-time]() Tags: [configuration](), [measurements](), [monitoring](), [postgres](), [postgresql](), [real-time](), [recommendations](), [release]() ### AI overview pgAssistant 3.8.0 expands the PostgreSQL analysis and tuning tool into a continuous improvement platform. It adds historical workload and environment measurements, compares consecutive collections, tracks recommendations and configuration changes, and helps teams measure changes while distinguishing correlation from causation. ### Source excerpt With this release, pgAssistant is evolving beyond PostgreSQL analysis and tuning to become a continuous PostgreSQL improvement platform. The new positioning is built around a continuous improvement loop: Observe -> Diagnose -> Prioritize -> Plan -> Implement -> Collect again -> Measure pgAssistant already helped identify what should be improved and turn recommendations into a prioritized Executive Plan with clear DEV and OPS ownership. Combined with pgAssistant Collector, version 3.8.0 goes further by adding historical workload and environment measurements. The objective is to answer four essential questions: What should we improve? What did we decide to do? What did we actually change? What was the result? Workload Insights compares consecutive collections and highlights: new and no-longer-detected recommendations; changes to the PostgreSQL version and configuration; workload evolution by statement type; changes in execution time and call volume; the queries with the greatest impact on the overall workload. The ambition is to correlate the application of pgAssistant recommendations and the Executive Plan with observed performance changes. Correlation is not causation, and a recommendation that is no longer detected does not necessarily prove that it was implemented. pgAssistant keeps these distinctions explicit while bringing the relevant evidence together in one place. pgAssistant is not intended to replace real-time monitoring. Monitoring shows what is happening now; pgAssistant helps teams decide what to improve next, organize the remediation work, and measure what changed afterwards. From recommendations to action--and from action to measurable evidence. pgAssistant 3.8.0: https://github.com/beh74/pgassistant-community pgAssistant Collector: https://github.com/beh74/pgassistant-collector pgAssistant Grafana : https://github.com/beh74/pgassistant-grafana ## Kubernetes Cost Management Tools: The Best Options 2026 DevFeed: [Kubernetes Cost Management Tools: The Best Options 2026]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-09-16T00:00:00Z Content type: comparison Language: en Sources: [Harness Blog]() Topics: [Kubernetes](), [finops](), [Monitoring](), [Cloud]() Tags: [2026](), [cloud](), [compare](), [finops](), [kubernetes](), [monitoring]() ### AI overview A comparison of Kubernetes cost management tools for 2026, including Kubecost, OpenCost, and CloudZero. It explains how these tools allocate shared cloud node costs across namespaces, workloads, and pods, while accounting for idle and unallocated capacity and supporting optimization actions. ### Source excerpt Compare the best Kubernetes cost management tools for 2026, from Kubecost and OpenCost to CloudZero, on allocation depth, scale, and pricing model. | Blog ## AWS STS simplifies session token size limits and adds session token size monitoring DevFeed: [AWS STS simplifies session token size limits and adds session token size monitoring]() Original publisher: [Read original article]() Author: Rishi Tripathy Published: 2026-09-15T22:21:59Z Content type: release Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services](), [Security](), [Monitoring](), [Amazon CloudWatch](), [AWS CloudTrail](), [API]() Tags: [advanced-300](), [amazon-cloudwatch](), [apis](), [aws](), [aws-cloudtrail](), [aws-security-token-service](), [aws-sts](), [infrastructure](), [metrics](), [monitoring](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to](), [token]() ### AI overview AWS Security Token Service has replaced its separate packed policy and overall session token limits with a single 4,096-byte limit. It now reports session token size and limit utilization in API responses, Amazon CloudWatch metrics, and AWS CloudTrail events. ### Source excerpt AWS Security Token Service (AWS STS) has simplified session token size limits, giving you more room for your session policies and session tags. STS has replaced the packed policy size and the overall session token size limits with a single token size limit of 4,096 bytes. STS now reports session token size in API responses, [...] ## Kubernetes v1.37: Pod-Level Resource Managers graduated to Beta DevFeed: [Kubernetes v1.37: Pod-Level Resource Managers graduated to Beta]() Original publisher: [Read original article]() Author: Kevin Torres Martinez Published: 2026-09-15T18:30:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [cpu](), [Hardware](), [Latency](), [API](), [gRPC](), [Monitoring]() Tags: [api](), [cpu](), [hardware](), [kubernetes](), [latency](), [monitoring](), [numa](), [release](), [v1]() ### AI overview Kubernetes v1.37 graduates Pod-Level Resource Managers to Beta, disabled by default. The feature lets Kubelet resource managers use pod-level declarations for hardware placement, enabling NUMA-aligned exclusive resources for primary containers while placing sidecars in a shared pod-isolated pool. The release also adds pod-level reporting to the PodResources gRPC API. ### Source excerpt With the release of Kubernetes v1.37, the Pod-Level Resource Managers feature has graduated to Beta status (disabled by default)! First introduced as an Alpha feature in Kubernetes v1.36, this enhancement builds on Pod-Level Resources by equipping Kubelet's Topology Manager, CPU Manager, and Memory Manager to use Pod-level resource declarations (.spec.resources) directly when making hardware placement decisions. Bringing pod-level resources to node managers Before this feature, obtaining exclusive NUMA-aligned CPU cores or memory for latency-critical applications forced cluster operators into an all-or-nothing choice: assign integer resource requests to every container in the Pod, or forfeit exclusive NUMA alignment entirely. For modern workloads running lightweight sidecars (such as logging agents or telemetry exporters), allocating dedicated physical cores to auxiliary containers was wasteful. Pod-Level Resource Managers solves this challenge by enabling hybrid allocation models. The Kubelet can reserve exclusive NUMA-aligned resources for primary application containers while placing non-Guaranteed sidecars into a pod-isolated shared pool. This ensures primary workloads get unthrottled, NUMA-local performance while sidecars benefit from running in a pod-isolated shared pool, enjoying local NUMA alignment and protection from external node interference without consuming dedicated physical cores. What's new in Beta Graduating to Beta brings key operational and API enhancements: Graduation to Beta: Controlled by the PodLevelResourceManagers feature gate, available to opt in (disabled by default) in Kubernetes v1.37. PodResources API Reporting: The v1 PodResources gRPC service (PodResourcesLister) introduces top-level cpu_ids and memory fields on PodResources responses. Monitoring tools and device plugins can query pod-level exclusive assignments directly without double-counting container allocations. Getting started and providing feedback For a deep dive into the tech ## How end-to-end SLO monitoring detected a livestream failure that component dashboards missed DevFeed: [How end-to-end SLO monitoring detected a livestream failure that component dashboards missed]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-09-15T16:31:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [dashboards](), [observability](), [incident](), [Monitoring](), [Web](), [client](), [Network]() Tags: [dashboards](), [distributed-systems](), [incident](), [monitoring](), [observability](), [security](), [server](), [site-reliability-engineer](), [software-engineering](), [web]() ### AI overview The article examines a June 8, 2026 incident in which a third-party client-side security script fetched from a provider CDN added URL validation that the video provider did not pass, causing black screens for newly loaded web clients. Most component-level dashboards remained green, while end-to-end service-level objective monitoring detected the broken livestream experience and paged the owning teams within five minutes. ### Source excerpt Karol Gil | Reliability Platform (Poland) On June 8, 2026, newly loaded web clients began showing black screens instead of livestream video. For our platform, that's a serious problem: it's pretty hard to sell Pokémon cards that no one can see. It turned out that a third-party script we use for client-side security monitoring wasn't bundled with our release, but was rather fetched live from the provider's CDN. When the provider updated the script all new web clients fetched it, and it included an additional URL validation which our video provider didn't pass. The result? Black screens for users of the affected web clients, with most internal dashboards staying green. 3,000 users were impacted in the first 30 minutes of the incident. One system did catch it. Our end-to-end service-level objective (E2E SLO) monitoring was already in production and paged the owning teams within five minutes. Here's what it saw. The real problem Most of our dashboards stayed green because they monitor component-level health: a server, an endpoint, a specific function. These are all useful, but can all be healthy while the actual user experience is completely broken. This problem gets worse the more external dependencies there are, or the more sophisticated an experience you want to deliver. In complex, integrated product experiences like ours, a "small" problem can have an outsize impact on the user experience. Measuring this requires a different approach to observability, namely, to model the user journey across multiple surfaces that must be true for a customer to have a good experience. So how do we measure this in a complex distributed application? Joining a livestream is not one thing Joining a livestream sounds like one action, but the user expects at least three things: Video to be playing Auction details to be shown Chat to be visible and up to da Each of those can succeed or fail completely independently of the other two. Our video depends on third-party providers and CDN netwo ## Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent DevFeed: [Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent]() Original publisher: [Read original article]() Author: TNS Staff Published: 2026-09-15T16:21:45Z Content type: article Language: en Sources: [The New Stack]() Topics: [SRE](), [incident](), [Azure](), [observability](), [DevOps](), [Redis]() Tags: [agent](), [ai-agents](), [ai-operations](), [azure](), [code-review](), [deployment](), [devops](), [incident](), [microsoft-azure](), [monitoring](), [observability](), [post](), [redis](), [sponsor-microsoft-azure](), [sponsored](), [sponsored-post](), [sre](), [telemetry]() ### AI overview The article describes Azure SRE Agent as a system that analyzes telemetry, correlates deployment and monitoring data, investigates incidents, identifies root causes, recommends or prepares fixes, and supports mitigation and other operational tasks under human approval. It cites examples involving Microsoft service teams and InEight, including a recommendation to scale Redis. ### Source excerpt What if engineers could spend their time building and optimizing systems rather than maintaining them? It's 3 a.m., and the The post Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent appeared first on The New Stack. ## Digital Experience Monitoring with Grafana Cloud: Session Replay, synthetic checks, and faster investigations DevFeed: [Digital Experience Monitoring with Grafana Cloud: Session Replay, synthetic checks, and faster investigations]() Original publisher: [Read original article]() Author: Bukola Ayodele Published: 2026-09-15T01:35:30.954353Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [digital experience monitoring](), [Grafana Cloud](), [Frontend observability](), [synthetic monitoring](), [real user monitoring](), [session replay](), [Instrumentation](), [web applications](), [JavaScript](), [Core Web Vitals](), [Traces]() Tags: [analytics](), [core-web-vitals](), [digital-experience-monitoring](), [frontend-observability](), [grafana](), [grafana-cloud](), [grafana-cloud-frontend-observability](), [instrumentation](), [javascript](), [logs](), [metrics](), [monitoring](), [observability](), [real-user-monitoring](), [session-replay](), [synthetic](), [synthetic-monitoring](), [traces](), [web-applications]() ### AI overview This article explains how Digital Experience Monitoring in Grafana Cloud combines Frontend Observability, Synthetic Monitoring, real user monitoring, and session replay. It shows how these capabilities help engineering teams detect issues proactively, understand their scope and root causes, correlate frontend signals with backend traces, and resolve production problems faster. ### Source excerpt When something breaks in production, the questions that matter most are also the toughest to answer from metrics alone: who was affected, what did they actually see, and is this worth waking someone up for? Answering those questions requires a fuller picture of the issue and its impact on your users. That's where Digital Experience Monitoring (DEM) in Grafana Cloud comes in. By combining Frontend Observability and Synthetic Monitoring, DEM connects real user experiences with proactive testing, helping engineering teams understand the scope of an issue, investigate its cause, and resolve it faster, all within Grafana Cloud. In this blog post, we'll walk through some of the latest DEM updates in Grafana Cloud, and how to get started. You can also learn more by watching the video below. First, what is Digital Experience Monitoring? Digital Experience Monitoring in Grafana Cloud gives you a complete picture of how users experience your web applications, from real user data to proactive synthetic checks. DEM helps your team achieve: Real user visibility: know how users truly experience your web application, not just what your backend metrics suggest. Proactive detection: catch problems before your users do, using automated checks against your critical user journeys. End-to-end correlation: connect a frontend signal to the backend trace behind it. Faster resolution: cut your mean time to recovery from hours to minutes. Session Replay: see exactly what your users saw Session Replay in Grafana Cloud Frontend Observability lets you visually replay what a user saw and did inside your web application. Your team can watch exactly what users experienced and correlate it with real user monitoring signals like Core Web Vitals, user actions, and traces, which makes it a powerful tool for investigating bugs and running root cause analysis. Session Replay is powered by Faro, Grafana's open source JavaScript instrumentation library for collecting real user monitoring data. Let's walk ## Monitor TAS and gang scheduling for AI training in Kubernetes DevFeed: [Monitor TAS and gang scheduling for AI training in Kubernetes]() Original publisher: [Read original article]() Author: David Lentz; Kathy Lin Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Kubernetes](), [kueue](), [distributed-training](), [GPU](), [Monitoring](), [datadog]() Tags: [ai](), [batch](), [containers](), [datadog](), [distributed-training](), [gpu](), [gpu-monitoring](), [kubernetes](), [kueue](), [monitoring](), [scheduler]() ### AI overview This article explains why Kubernetes scheduling is insufficient for distributed AI training workloads and how topology-aware scheduling and gang scheduling address hardware placement and simultaneous startup requirements. It discusses implementing these capabilities with Kueue and the Coscheduling plugin, and monitoring and troubleshooting them with Datadog GPU Monitoring. ### Source excerpt Learn how Datadog helps you correlate Kueue, Coscheduling, GPU, and training framework signals to validate gang scheduling and topology-aware scheduling. ## Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks DevFeed: [Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks]() Original publisher: [Read original article]() Author: Thangam Vaiyapuri; Jay Palaniappan; B. Micheal Okutubo; Zifei Feng Published: 2026-09-14T21:04:30Z Content type: release Language: en Sources: [Databricks]() Topics: [Streaming](), [databricks](), [real-time](), [Amazon S3](), [API]() Tags: [amazon-s3](), [api](), [data-engineering](), [databricks](), [engineering](), [fraud-detection](), [infrastructure](), [monitoring](), [net-11-preview-7](), [production](), [real-time](), [streaming]() ### AI overview Databricks announces on-demand state repartitioning for Apache Spark Structured Streaming in Public Preview, available in Databricks Runtime 18 and later. The capability lets production stateful streaming queries resize their partition count while preserving checkpoint state, supporting workloads such as aggregations, stream-stream joins, deduplication, sessionization, and transformWithState. Coveo reports reducing related Amazon S3 API costs by 40%. ### Source excerpt Anyone running stateful Apache Spark™ Structured Streaming queries in production... ## Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2 DevFeed: [Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:16Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [Amazon CloudWatch](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Database](), [Amazon EC2](), [Architecture & Design](), [Amazon Web Services](), [AWS CloudFormation]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-ec2](), [aws](), [aws-cloudformation](), [concurrency](), [monitoring](), [performance](), [postgresql](), [rds-for-postgresql](), [simulation](), [technical-how-to]() ### AI overview This tutorial explains how to diagnose and resolve lock contention in Amazon Aurora PostgreSQL using Amazon CloudWatch Database Insights. It demonstrates Lock Analysis and the Lock Tree visualization for identifying blocking sessions, then covers immediate fixes, configuration changes, optimistic concurrency control, asynchronous processing, SKIP LOCKED, and row splitting. ### Source excerpt Part 1 showed how row lock contention degrades Amazon Aurora PostgreSQL throughput. In Part 2, use Amazon CloudWatch Database Insights and its Lock Tree to pinpoint blocking sessions, then resolve contention with query termination, timeout parameters, and architectural patterns such as SKIP LOCKED and row splitting that restore throughput. ## Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL DevFeed: [Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:08Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Transactions](), [Amazon CloudWatch](), [Amazon RDS](), [Architecture & Design](), [Extension]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [architecture](), [availability](), [concurrency](), [database](), [database-performance](), [extension](), [locks](), [monitoring](), [postgresql](), [production](), [rds-for-postgresql](), [technical-how-to](), [transactions](), [troubleshooting]() ### AI overview This first part of a two-part series explains row lock contention in PostgreSQL and Amazon Aurora PostgreSQL. It covers how concurrent transactions competing for the same rows can reduce throughput and cause timeouts despite healthy CPU and I/O, then introduces PostgreSQL locking internals and monitoring techniques using system views, functions, the pgrowlocks extension, and log_lock_waits. The article notes that the same behavior and investigation approach apply to Amazon RDS for PostgreSQL. ### Source excerpt Row lock contention can collapse database throughput during a flash sale even when CPU and I/O look healthy. In Part 1 of this series, learn how PostgreSQL row locking works and how to monitor lock contention in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL using system views, the pgrowlocks extension, and the log_lock_waits parameter. ## Uptime Kuma 2.5.4 Patches Critical JSONata Code Execution Flaw DevFeed: [Uptime Kuma 2.5.4 Patches Critical JSONata Code Execution Flaw]() Original publisher: [Read original article]() Author: Christian Rakoot Published: 2026-09-14T06:55:25Z Content type: article Language: en Sources: [Self Host Lab]() Topics: [Security](), [vulnerability](), [Monitoring](), [configuration]() Tags: [2026](), [cve](), [dependency](), [monitoring](), [monitoring-news](), [news](), [security](), [update](), [uptime-kuma-2-5-4](), [vulnerability]() ### AI overview Uptime Kuma 2.5.4 fixes a critical JSONata vulnerability that could enable arbitrary code execution on the monitor host, along with a second denial-of-service issue. The release updates jsonata to 2.2.2 and also adds an SFTP monitor type and three notification providers. ### Source excerpt Uptime Kuma 2.5.4 patches a critical-rated code execution flaw in the JSONata library (CVE-2026-77415, CVSS 9.3) plus a second denial-of-service fix, and adds an SFTP monitor type and three new notification providers. Here's what the flaw actually requires to exploit, and how to update. ## PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum DevFeed: [PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum]() Original publisher: [Read original article]() Author: Paul Redmond Published: 2026-09-14T04:24:35Z Content type: article Language: en Sources: [Laravel]() Topics: [Laravel](), [PostgreSQL](), [Monitoring](), [ci](), [Databases](), [Structured-data]() Tags: [ci](), [github](), [laravel](), [laravel-packages](), [monitoring](), [postgresql](), [schema](), [sql]() ### AI overview Vacuum is a PostgreSQL monitoring and schema-linting package for Laravel. It analyzes PostgreSQL statistics, reports issues such as bloat, wraparound, dead tuples, unused indexes, slow statements, and unindexed foreign keys, and provides SQL remediation guidance, health scores, dashboards, CI commands, history, and explainers. ### Source excerpt Vacuum checks PostgreSQL in Laravel apps for bloat, wraparound, and unused indexes, and flags unindexed foreign keys in migrations during CI. The post PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## How to monitor Cypress tests with Grafana Cloud DevFeed: [How to monitor Cypress tests with Grafana Cloud]() Original publisher: [Read original article]() Author: Rajesh Mahalingaswamy Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana](), [Node.js](), [ci]() Tags: [analytics](), [cypress](), [grafana](), [grafana-alloy](), [grafana-cloud](), [grafana-cloud-metrics](), [how-to](), [logs](), [metrics](), [monitoring](), [node-js](), [observability](), [prometheus](), [traces]() ### AI overview A tutorial for monitoring Cypress test results by converting them to Prometheus metrics, pushing them through a Pushgateway, scraping them with Alloy, and forwarding them to Grafana Cloud Metrics. ### Source excerpt If your Cypress suite has tests that fail more often or run slower, you know it can be hard to figure out the pattern from a single job. It could be one spec that slowed down, or a single test that fails, or maybe the entire suite is trending slower. The root cause could be a bug in the app, or a flaky test, or something else. Your terminal output and CI log will tell you what happened on a single run, but that doesn't help you spot any larger trends--especially since you lose that data as soon as the job finishes. Thankfully, Cypress, a front-end automated test framework built for web applications, already exposes everything you need through its plugin hooks. After each spec finishes, Cypress hands you a results object with pass and fail counts, per-test durations, and states. You just need to turn that into metrics and ship it somewhere durable. In this post, you'll learn how to monitor your Cypress tests by converting those results into Prometheus metrics inside a Cypress hook, pushing them to a Prometheus Pushgateway, and letting Alloy scrape the gateway and forward everything to Grafana Cloud Metrics--using nothing but the free tiers. By the end you'll have a pipeline running with the following architecture: What you'll need This walkthrough runs everything alongside your existing Cypress project. Before you start, make sure you have: A Cypress project (this example uses Cypress 14.x) with a cypress.config.js you can edit A Prometheus Pushgateway. Cypress runs are short-lived batch jobs, so they can't be scraped directly--the Pushgateway holds the metrics between runs so a scraper can pick them up. Set up a Prometheus Pushgateway in your infrastructure Alloy, our open source collector we use to scrape the Pushgateway and remote-write to Grafana Cloud A Grafana Cloud account. The free tier includes Grafana Cloud Metrics and a Prometheus remote-write endpoint. If you don't have an account, you can sign up here Your Grafana Cloud remote-write URL, numeric user ID, an ## From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud DevFeed: [From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud]() Original publisher: [Read original article]() Author: Mark Meier Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [synthetic monitoring](), [Frontend observability](), [Grafana Cloud](), [observability]() Tags: [analytics](), [frontend-observability](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [synthetic-monitoring](), [traces](), [uptime]() ### AI overview This article explains how to combine Grafana Cloud Synthetic Monitoring with Frontend Observability to connect proactive checks with real-user impact. It describes the blind spots of synthetic-only monitoring and presents a combined workflow for faster triage, impact-aware alerts, and tests that evolve with real traffic. ### Source excerpt Say you get a support escalation about a page in the app that won't load. But when you pull up your synthetic checks, they're all green: 100% uptime, probes are passing. Something's not adding up, but which one do you trust? If you've run Grafana Cloud Synthetic Monitoring, you've been on both sides of this. Sometimes it's the ticket: real users hit a wall on the path but your checks pass cleanly. Other times, it's the inverse: a check is failing, you're in a panic, and you start trying to reproduce things for 30 minutes--only to find it was a blip from a single region, with minimal impact to real users. Neither the green dashboard nor the red alert were lying, they just weren't answering the correct question. This ends up being the root problem. Synthetic Monitoring is exceptionally good at telling you if something broke. It can not, however, tell you who it happened to, how bad it was, or why it matters. This is not a flaw in Synthetic Monitoring; it's the boundary of what a controlled, scheduled test can know. Grafana Cloud Frontend Observability helps to close this gap. Synthetic Monitoring gives you a proactive, outside-in signal; Frontend Observability gives you the real-user, inside-out signal. Together they form a closed loop: synthetic alerts end up getting some real user context, and real user data can make your synthetic tests smart. In this post, we'll look at why a synthetic-only strategy can leave blind spots, what Frontend Observability adds, and walk through practical workflows for running them together in Grafana Cloud. Along the way, you'll learn that the payoff is concrete: faster triage, alerts that carry blast-radius context, and a check suite that evolves with real traffic instead of aging against it. Green checks don't mean happy users Synthetic Monitoring is an active signal. You script a journey or declare a target, run it on a schedule from known probe locations, and in return get clean consistent results. This precise control of variables i ## How to measure and improve instrumentation quality for better full-stack observability DevFeed: [How to measure and improve instrumentation quality for better full-stack observability]() Original publisher: [Read original article]() Author: Arpit kumar Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Instrumentation](), [observability](), [Grafana Cloud](), [telemetry]() Tags: [analytics](), [grafana](), [grafana-cloud](), [how-to](), [instrumentation](), [knowledge-graph](), [logs](), [metrics](), [monitoring](), [observability](), [traces]() ### AI overview This article explains how to measure and improve instrumentation quality for full-stack observability. It introduces Grafana Cloud's continuously updated instrumentation quality report, which evaluates each service using automated checks for telemetry completeness, correctness, signal coverage, naming, Kubernetes correlation, and connections across the service graph. ### Source excerpt Modern engineering teams instrument everything, with metrics, logs, traces, and profiles flowing from hundreds of services at once. But full-stack observability isn't really about collecting more telemetry; it's about having a single, unified picture of how your services connect to every layer beneath them, including their dependencies, the pods and nodes they run on, and the logs, traces, and profiles that explain their behavior. But there's often a quiet problem hiding underneath all that data: not all instrumentation is created equal, and every gap silently breaks one of those connections. One service, for example, might emit metrics but no logs, so when you pivot from "this is erroring" to "show me why," you hit a dead end. Another might have logs but an invalid service.name or a missing k8s.pod.name that breaks correlation, dropping it out of the graph and away from its pods and nodes. A third service might look perfectly healthy right up until an incident, when you discover its traces were never wired up and the trail goes cold exactly when you need it most. To fix this, Grafana Cloud's Knowledge Graph now includes an instrumentation quality report: an automated, continuously updated assessment of how well each of your services is instrumented--and, in effect, how they plug into the full-stack picture. In this post, we'll walk through how to read the instrumentation quality report, how the scoring works, and how to use it to systematically raise the observability bar, keeping every layer of your stack joined up across every service you run. What is instrumentation quality? Instrumentation quality is a measure of how complete and correct the telemetry for a given service is, judged against a set of automated checks. Each service is evaluated by a server-computed set of quality checks: small, focused rules that validate one specific thing about a service's telemetry. A few examples: Does the service emit logs? Are service graph metrics present? Is the service nam ## Better context, smarter testing: How to give your AI coding agent direct access to k6 docs DevFeed: [Better context, smarter testing: How to give your AI coding agent direct access to k6 docs]() Original publisher: [Read original article]() Author: İnanç Gümüş Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [k6](), [Agent Skill](), [AI Agent](), [Command-line interface](), [coding]() Tags: [agent-skill](), [ai](), [ai-agent](), [ai-coding](), [analytics](), [cli](), [grafana](), [k6](), [logs](), [metrics](), [monitoring](), [observability](), [performance-testing](), [testing](), [traces]() ### AI overview The article introduces k6 x docs, an official k6 2.0 command that provides k6 documentation directly in the terminal. It supports API references, guides, best practices, and examples; works offline after the first lookup; matches documentation to the installed k6 version; and includes an agent skill for AI coding assistants. ### Source excerpt As testing workflows become more AI-assisted, fast access to accurate documentation matters more than ever. Whether you're writing a new load test, troubleshooting an issue, or having an AI agent generate a script for you, you need reliable guidance that keeps pace with the way you work. But most documentation still lives in a browser. Every time you or your agent needs to verify an API or look up a best practice, you're forced to leave your terminal or editor and interrupt your workflow. That's why, in k6 2.0, we introduced k6 x docs, an official k6 command that puts the entire k6 documentation library, including API references, guides, best practices, and examples, directly in your terminal. It works offline after first use, matches the docs to your exact k6 version, and includes a built-in agent skill, so AI coding assistants can look up k6 docs faster and most cost-effectively. The problem: documentation lives in the wrong place Most developers know the feeling. You're writing a k6 script, you need to check the signature for http.post or remember how thresholds work, and suddenly you're in a browser tab, searching, scrolling, clicking through navigation, and losing the context you had in your editor. For AI agents, the problem is worse. When they need to reference k6 APIs, best practices, or examples, they either rely on stale training data, hallucinate a function signature, or burn expensive tokens on a web search that may not return the right version of the docs. k6 x docs solves both problems by making documentation a first-class part of the k6 CLI. It gives agents CLI access to k6 docs that automatically detect the k6 version in use and deliver accurate content without leaving the session or performing web searches. How k6 x docs works The use is simple: type k6 x docs, optionally followed by the topic you want to look up. k6 x docs # See all available topics k6 x docs http # Learn about the k6/http module k6 x docs http get # Look up a specific function k6 ## Visual playback of the user journey: Introducing Session Replay in Grafana Cloud Frontend Observability DevFeed: [Visual playback of the user journey: Introducing Session Replay in Grafana Cloud Frontend Observability]() Original publisher: [Read original article]() Author: Lukasz Gut Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [session replay](), [Grafana Cloud Frontend Observability](), [Frontend observability](), [web applications](), [telemetry](), [SDKs]() Tags: [analytics](), [frontend-observability](), [grafana](), [grafana-cloud-frontend-observability](), [instrumentation](), [logs](), [metrics](), [monitoring](), [observability](), [privacy](), [session-replay](), [telemetry](), [traces](), [web-applications]() ### AI overview The article introduces Session Replay, a public-preview add-on for Grafana Cloud Frontend Observability. It visually reconstructs how a web application appeared and changed during a user session, linking that playback to session timelines and technical telemetry so engineering teams can investigate frontend problems more directly. ### Source excerpt Grafana Cloud Frontend Observability helps engineering teams quantify the end user experience by bringing metrics, logs, traces, and user session context to client-side web applications. Teams can monitor application health and performance over time, triage errors, and correlate frontend signals with backend telemetry to investigate issues across the stack. Yet some of the hardest frontend problems remain difficult to diagnose. A support ticket might report that a checkout button did nothing, a form unexpectedly reset, or a workflow broke only in one browser or on one device. Metrics can reveal a performance regression, logs can capture an error, and traces can expose a slow request, but no single signal shows what the interface actually looked like to the user. This is exactly why we built Session Replay, an add-on feature in Frontend Observability that provides a visual reconstruction of a user's journey, connected to the telemetry Grafana Cloud already collects. It helps engineering teams move from a reported problem to seeing what happened and knowing exactly where to investigate next. What is Session Replay in Grafana Cloud Frontend Observability? Session Replay, now in public preview, adds visual playback capabilities to Frontend Observability. It reconstructs how a web application appeared and changed as a user navigated and interacted with it, so you can observe the journey as it unfolded instead of inferring it from individual telemetry events. Frontend Observability already brings together a chronological timeline of the events within a user session. That timeline tells you what happened and when. Session Replay adds the missing visual context: what was happening in the interface around those events and how one interaction led to the next. Because both views belong to the same session, you can move between the user experience and the relevant technical signals without losing the thread of your investigation. Session Replay builds on the Grafana Faro Web SD ## Grafana 13.2 release: easier ways to query and explore your data DevFeed: [Grafana 13.2 release: easier ways to query and explore your data]() Original publisher: [Read original article]() Author: Grafana Labs Team Published: 2026-09-12T11:22:06.456390Z Content type: release Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana](), [dashboards]() Tags: [analytics](), [dashboards](), [enterprise](), [explore](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [release](), [sql](), [traces]() ### AI overview Grafana 13.2 introduces generally available saved queries for Grafana Cloud and Grafana Enterprise, letting organizations store, discover, and reuse vetted queries across dashboards, Explore, and annotation queries. The release also highlights a new View panel sidebar for exploring busy panels. ### Source excerpt Grafana 13.2 is here, bringing more improvements to help you and your team explore your data and get to insights faster. In this post, we'll highlight the latest updates to saved queries, a feature that lets teams share, discover, and reuse queries to get to trusted answers faster and help new teammates get up to speed. We'll also explore how the new View panel sidebar makes exploring busy panels a breeze. If you want to read about all the latest updates in Grafana 13.2, please refer to the changelog or our What's New documentation. Saved queries: reuse trusted queries across dashboards and teams Good queries are hard-won. Writing one means knowing both the query language and your own data, like which of four similarly named metrics is the one you can trust. That knowledge usually sits with a few experienced people, or is gradually learned through exploration (increasingly AI-assisted), validation, and revision. Often teams end up rebuilding the same Grafana queries over and over, and the best ones live in pinned Slack messages or get copy-pasted from old dashboards. New team members feel it most, since their first weeks are often spent reverse-engineering existing dashboards just to work out how to ask a question of their own. The query history in Grafana Explore helps, keeping a couple of weeks of your own queries and letting you "star" the keepers. It's private to you, though. Until recently, there hasn't been a built-in way to take a query you trust and put it somewhere your whole organization can find it. How teams use saved queries We built saved queries, which is now generally available in Grafana Cloud and Grafana Enterprise, to address this challenge by providing a shared query library for your organization. When you write a query worth keeping, you can save it with a title, description, and tags. Saving works from dashboard panels, Explore, and annotation queries. This means teammates who don't know PromQL or SQL can still build dashboards from queries tha [Next page]()