# instrumentation Published articles for instrumentation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## Faster Starts, Less JavaScript Overhead DevFeed: [Faster Starts, Less JavaScript Overhead]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [JavaScript](), [Compiler](), [Instrumentation]() Tags: [benchmarks](), [compiler](), [instrumentation](), [javascript](), [profiling]() ### AI overview The article describes startup-performance work in Codename One. It identifies launch delays caused by repeated screen-scale queries, theme scans, unnecessary synchronous dispatches, premature GC-park handshakes, and JavaScript suspension preparation. The fixes publish screen state atomically, avoid waiting when operations can proceed immediately, index theme keys, and improve instrumentation so profiling reveals hidden stalls. ### Source excerpt Codename One removes native startup waits, repeated style scans, and unnecessary JavaScript suspension. Profiles and compiler benchmarks expose costs that bundle size and frame rates miss. ## 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 ## 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 ## How to add Sentry monitoring to a Webflow Cloud app DevFeed: [How to add Sentry monitoring to a Webflow Cloud app]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [error tracking](), [Next.js](), [Workers](), [session replay](), [debugging](), [Traces](), [configuration](), [Instrumentation](), [Command-line interface]() Tags: [cloudflare-workers](), [debugging](), [error-tracking](), [guides](), [how-to](), [instrumentation](), [monitoring](), [next-js](), [session-replay](), [traces]() ### AI overview This tutorial explains how to manually add Sentry monitoring to a Webflow Cloud app running Next.js. It covers SDK installation, initialization files, environment variables, a Cloudflare-specific tunnel fix, verification, alerts, and common silent failure modes. The setup enables error grouping, stack traces, performance traces, and session replay. ### Source excerpt Learn how to add Sentry monitoring to a Webflow Cloud Next.js app. Get error grouping, stack traces, and session replay working. ## Wrapture: a Python package for monkey patching, testing, and observability DevFeed: [Wrapture: a Python package for monkey patching, testing, and observability]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T13:51:32Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Python](), [Testing](), [observability](), [Instrumentation](), [tracing](), [OpenTelemetry](), [Flask](), [jupyterlab]() Tags: [code](), [graham-dumpleton](), [graham-dumpleton-6](), [how-to](), [instrumentation](), [monkey-patching](), [monkey-patching-10](), [observability](), [observability-10](), [open-source](), [open-source-320](), [opentelemetry](), [python](), [python-1-283](), [testing](), [testing-95](), [tracing]() ### AI overview This article reviews wrapture, an alpha Python package by Graham Dumpleton for monkey patching, unit testing, call recording, live and zero-code tracing, timing analysis, and OpenTelemetry export. It highlights tutorials and JupyterLab workshops covering the package and related instrumentation. ### Source excerpt Graham Dumpleton's new monkey patching package wrapture is shaping up to be an indispensable tool for Python developers. I'm not sure why I've seen so little buzz about it! Graham has been posting new tutorials for it almost daily since the initial release on August 31st. Here's everything he's published so far: Introducing wrapture - a new monkey patching library that serves both testing and observability (think New Relic style tracing) at the same time. Unit testing with wrapture - how to use it for the same kinds of thing as unittest.mock. Recording calls with wrapture - recording method calls as timelines and processing and displaying them as trees. Phased behaviour in wrapture - arranging patched methods to change behavior across multiple calls. Beyond callables in wrapture - monkey patching attributes, dictionaries, generators. Live tracing with wrapture - tracing a live application to see exactly how it works. Zero-code tracing with wrapture - configuring tracing in a separate TOML file without modifying Python code at all. Tracing Flask with wrapture - using the separate wrapture-instrumenation package to instrument a Flask application. That package also provides instrumentation for aiohttp.client, aiohttp.web, django, fastapi, flask, grpc, http.client, httpx, jinja2, requests, sqlalchemy, sqlite3, starlette, urllib.request, urllib3, uvicorn, werkzeug.serving, wsgiref.simple_server, xmlrpc.client, xmlrpc.server. Finding slow code with wrapture - wrapture's tools for recording timing information, both individually and aggregated across multiple calls. OpenTelemetry export in wrapture - exporting traces to OpenTelemetry. Graham also has a set of interactive workshops for wrapture, implemented as JupyterLab notebooks. Wrapture is still alpha software but it's already very usable - especially given you can configure and try it out with a TOML file without modifying any Python code at all. This feels like one of those Swiss Army Knife packages that, once mastered ## What does DX do? Key features, use cases, and alternatives DevFeed: [What does DX do? Key features, use cases, and alternatives]() Original publisher: [Read original article]() Author: John Crowley Published: 2026-09-09T15:53:52Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [code productivity](), [developer-productivity](), [Developer experience](), [atlassian](), [Git](), [Instrumentation](), [sdlc](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [atlassian](), [developer-experience](), [developer-productivity](), [engineering](), [git](), [instrumentation](), [metrics](), [sdlc](), [survey]() ### AI overview This article explains what DX does as an engineering intelligence platform. DX measures developer productivity, developer experience, delivery data, and AI's impact on codebases using developer surveys and data from Git and Jira. It describes DX's DevEx Cloud, DX Data Cloud, DX AI, and Fabric offerings, along with their research-backed metrics, use cases, limitations, and alternatives. ### Source excerpt What DX does: its engineering intelligence features, survey-based metrics, common use cases, where it stops, and the alternatives ## Distributed tracing for CI pipelines without touching a single workflow file DevFeed: [Distributed tracing for CI pipelines without touching a single workflow file]() Original publisher: [Read original article]() Author: George Sims, downtherabbithole.dev Published: 2026-09-08T11:00:00Z Content type: article Language: en Sources: [Cloud Native Computing Foundation]() Topics: [ci]() Tags: [blog](), [ci](), [github](), [github-actions](), [instrumentation](), [jobs](), [opentelemetry](), [tracing](), [workflow](), [workflows]() ### AI overview The article describes collecting GitHub Actions events at the organization level and converting them into OpenTelemetry spans to trace CI workflows without editing individual workflow files. ### Source excerpt You've probably felt this one: GitHub Actions usage creeps up across your org, and your actual visibility into it doesn't keep pace. Which workflows are slow? Which are flaky? How long are jobs sitting queued for... ## Coordinate product launches with Datadog DevFeed: [Coordinate product launches with Datadog]() Original publisher: [Read original article]() Author: Milene Darnis; Adam Virani Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [Instrumentation](), [experiments]() Tags: [ai](), [bits-ai](), [digital-experience-monitoring](), [experimentation](), [experiments](), [feature-flags](), [instrumentation](), [launch](), [product-analytics](), [session-replay]() ### AI overview A tutorial on using Datadog Product Analytics Launches to plan product releases, define measurement questions, create tracking plans, and identify missing events and properties before rollout. ### Source excerpt Learn how to turn a product brief and feature flag into a connected launch workflow for instrumentation, experimentation, QA, and reporting. ## Open by design: How Elastic supports MOSA, the DoD's Modular Open Systems Approach DevFeed: [Open by design: How Elastic supports MOSA, the DoD's Modular Open Systems Approach]() Original publisher: [Read original article]() Author: Woody Walton Published: 2026-09-04T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Architecture & Design](), [data](), [Instrumentation](), [telemetry]() Tags: [acquisition](), [analytics](), [data](), [defense-government-open-source-standards](), [design](), [elasticsearch-platform](), [government-public-sector](), [instrumentation](), [integration](), [open](), [opentelemetry](), [scalability](), [systems]() ### AI overview Elastic explains how its data and analytics platform can support the US Department of Defense Modular Open Systems Approach through modularity, open standards, and component removability. ### Source excerpt Major US defense acquisition programs are required to be designed around a Modular Open Systems Approach (MOSA). Here's what program offices need to know about how Elastic can support MOSA at the data and analytics layer. ## The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough DevFeed: [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-02T17:15:57Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Optimization](), [GPU](), [toolchain](), [debug](), [Instrumentation](), [Algorithms]() Tags: [algorithms](), [benchmark](), [cuda](), [debug](), [developer-tools-techniques](), [gpu](), [how-to](), [image-processing](), [instrumentation](), [memory](), [optimization]() ### AI overview A step-by-step CUDA optimization walkthrough that improves an image-processing pipeline with debugging, benchmarking, optimized algorithms, GPU memory management, pinned transfers, streams, and asynchronous transfers. ### Source excerpt NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing... ## Making Rust observability reliable at scale with OpenTelemetry DevFeed: [Making Rust observability reliable at scale with OpenTelemetry]() Original publisher: [Read original article]() Author: Björn Antonsson; Paul Le Grand des Cloizeaux; Scott Gerring Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [tracing](), [telemetry](), [Traces](), [Instrumentation]() Tags: [apm](), [instrumentation](), [latency](), [observability](), [opentelemetry](), [production](), [rust](), [scale](), [traces](), [tracing]() ### AI overview Datadog describes building an opinionated Rust tracer on OpenTelemetry to improve trace propagation, sampling consistency, and trace quality in production services. ### Source excerpt Learn how Datadog improved Rust tracing by building an opinionated OpenTelemetry-based library to help ensure consistent sampling, propagation, and trace quality at scale. ## Reduce sensitive data exposure with build-time allowlists DevFeed: [Reduce sensitive data exposure with build-time allowlists]() Original publisher: [Read original article]() Author: Congyao Zheng; Rick Klein; Seth Fowler Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [real user monitoring](), [SDKs](), [Web Development]() Tags: [browser](), [digital-experience-monitoring](), [frontend](), [instrumentation](), [javascript](), [privacy](), [real-user-monitoring](), [rust](), [session-replay]() ### AI overview Datadog explains build-time allowlists for RUM action names. A plugin extracts static text from compiled artifacts so the Browser SDK can preserve readable static labels while masking runtime-generated text that may be sensitive. ### Source excerpt Learn how build-time allowlists preserve useful RUM action names while reducing the risk of exposing runtime-generated sensitive data. ## Debug live production code without redeploying with Datadog Live Debugger DevFeed: [Debug live production code without redeploying with Datadog Live Debugger]() Original publisher: [Read original article]() Author: Eric Metaj; Sarah Stonehill Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [debugging](), [Instrumentation](), [AI-assisted coding](), [Code]() Tags: [agents](), [ai](), [apm](), [bits-ai](), [code](), [debug](), [debugger](), [debugging](), [developer](), [ide](), [instrumentation](), [production](), [root-cause-analysis](), [tracing]() ### AI overview Datadog Live Debugger helps developers investigate production bugs without changing, restarting, or redeploying application code. It captures runtime details through logpoints, including variable values, method arguments, execution context, and request paths. Bits AI can analyze linked source code, place non-breaking logpoints, interpret collected data, and suggest fixes grounded in production behavior. ### Source excerpt Learn how Live Debugger helps you investigate production code and debug faster using Bits AI. ## How to reduce Rails boot time with require-profiler and Bootsnap DevFeed: [How to reduce Rails boot time with require-profiler and Bootsnap]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-19T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Rails](), [Optimization](), [Ruby](), [Instrumentation](), [Docker](), [Development]() Tags: [development](), [docker](), [dx](), [instrumentation](), [optimization](), [performance](), [rails](), [ruby]() ### AI overview This guide explains how to profile and reduce Rails application boot time using require-profiler, sampling profilers, and Bootsnap. It describes profiling results from real projects, including a 40% reduction for a 200-component monolith, and covers cache precompilation and verification in Docker-based production builds. ### Source excerpt Rails boot time is a DX metric in the AI age: meet require-profiler, learn to actually read sampling profilers, and see the pit stop that cut a 200-component monolith's boot by 40%. ## 7 lessons for IT leaders on using observability to monitor AI applications DevFeed: [7 lessons for IT leaders on using observability to monitor AI applications]() Original publisher: [Read original article]() Author: Brad Quarry Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [ai observability](), [telemetry](), [Language models](), [AI, ML & Data Engineering]() Tags: [ai](), [applications](), [business](), [events](), [generative-ai](), [instrumentation](), [large-language-models](), [llm](), [monitor](), [observability](), [telemetry]() ### AI overview An Elastic IT case study argues that LLM observability should be included in AI application MVPs so teams can measure usage, time savings, and return on investment from the start. ### Source excerpt Discover the seven lessons we learned as we evolve our LLM observability practice to monitor and improve our AI applications. ## Trace AWS Lambda durable functions with Datadog DevFeed: [Trace AWS Lambda durable functions with Datadog]() Original publisher: [Read original article]() Author: Joey Zhao; Yiming Luo; Pablo Martinez Bernardo; Ethan Gracer Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry](), [Node.js](), [SDKs]() Tags: [apm](), [aws](), [aws-lambda](), [instrumentation](), [node-js](), [observability](), [python](), [serverless-monitoring](), [telemetry](), [traces](), [tracing](), [workflow]() ### AI overview Datadog instruments AWS Lambda durable executions to connect telemetry from multiple Lambda invocations into one trace, helping developers investigate workflow operations, retries, waits, and failures. ### Source excerpt Trace AWS Lambda durable executions across invocations to investigate operations, retries, failures, waits, and function status in Datadog. ## Observability best practices for Lambda durable functions DevFeed: [Observability best practices for Lambda durable functions]() Original publisher: [Read original article]() Author: D Surya Sai Published: 2026-08-14T12:49:49Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [AWS Lambda](), [observability](), [Monitoring](), [log management](), [tracing](), [Instrumentation](), [debugging](), [Amazon API Gateway](), [stripe](), [Serverless]() Tags: [advanced-300](), [amazon-api-gateway](), [aws](), [aws-lambda](), [debugging](), [instrumentation](), [logging](), [metrics](), [monitoring](), [observability](), [payment](), [payment-processing](), [payments](), [serverless](), [technical-how-to](), [tracing]() ### AI overview This tutorial presents an observability pattern for AWS Lambda durable functions using a Stripe payment-processing workflow. It covers durable-function metrics, custom business metrics, alarms, structured logging, AWS X-Ray tracing, instrumentation across suspension boundaries, and end-to-end debugging of callback timeouts. ### Source excerpt Learn observability best practices for AWS Lambda durable functions, including CloudWatch metrics, custom alarms, structured logging, and X-Ray tracing for debugging callback timeouts end-to-end. ## ClickStack and Hud bring runtime intelligence to AI-powered development DevFeed: [ClickStack and Hud bring runtime intelligence to AI-powered development]() Original publisher: [Read original article]() Author: May Walter, Hud.io Published: 2026-08-13T12:53:04Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI-assisted coding](), [observability](), [Model Context Protocol](), [OpenTelemetry](), [telemetry](), [Artificial Intelligence](), [Instrumentation](), [Deployment](), [Incident response](), [distributed-systems](), [clickhouse](), [log management]() Tags: [agents](), [ai](), [clickhouse](), [code](), [coding](), [deployment](), [developers](), [development](), [distributed-systems](), [engineering](), [incident](), [incident-response](), [infrastructure](), [instrumentation](), [integration](), [mcp](), [metrics](), [monitoring](), [observability](), [open]() ### AI overview ClickHouse and Hud announce an integration that combines ClickStack service-level observability with Hud's function-level runtime context for AI-assisted software development. Shared trace IDs and MCP servers help coding agents assess risky changes before deployment, monitor releases, and investigate incidents using production context. ### Source excerpt ClickStack and Hud now share trace IDs, pairing service-level observability with function-level runtime forensics so coding agents can assess risky changes before they ship, catch regressions right after deploy, and fix them with real production context. ## How we improved APM Java startup by encoding a prefix trie as a JVM constant DevFeed: [How we improved APM Java startup by encoding a prefix trie as a JVM constant]() Original publisher: [Read original article]() Author: Stuart McCulloch Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry]() Tags: [apm](), [instrumentation](), [java](), [jit](), [observability](), [open-source](), [optimization](), [performance](), [startup]() ### AI overview Datadog explains how it reduced Java APM startup overhead by encoding multiple class-name prefix matches as a JVM string constant. The approach improves class matching while preserving targeted instrumentation for observability. ### Source excerpt Learn how the Datadog APM team improved Java startup performance by encoding a prefix trie as a JVM string constant. ## Connecting OpenTelemetry Traces with Sentry Errors in One Trace Waterfall DevFeed: [Connecting OpenTelemetry Traces with Sentry Errors in One Trace Waterfall]() Original publisher: [Read original article]() Author: Johannes Daxböck; Neel Shah Published: 2026-08-05T09:00:00Z Content type: tutorial Language: en Sources: [Sentry Blog]() Topics: [OpenTelemetry](), [tracing](), [telemetry](), [SDKs]() Tags: [errors](), [instrumentation](), [logs](), [opentelemetry](), [sdk](), [sdks](), [sentry](), [tracing]() ### AI overview Sentry's OtlpIntegration connects OpenTelemetry traces with Sentry errors and other events, allowing them to appear together in a trace waterfall. It reads the active OTel trace context and associates it with Sentry events while configuring an OTLP exporter for Sentry. ### Source excerpt The OtlpIntegration bridges OTel traces and Sentry errors. Keep your OTel setup, add Sentry for errors, and see both in one trace waterfall. ## ClickHouse joins the Open Secure AI Alliance DevFeed: [ClickHouse joins the Open Secure AI Alliance]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-30T19:44:40Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [ai security](), [AI Agent](), [Securing AI](), [agent observability](), [clickhouse](), [Open Source](), [tracing](), [observability](), [Instrumentation](), [hugging face](), [Nvidia]() Tags: [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-security](), [clickhouse](), [hugging-face](), [instrumentation](), [nvidia](), [observability](), [open-source](), [security](), [tracing]() ### AI overview ClickHouse joins the Open Secure AI Alliance with NVIDIA and other industry leaders to develop open tools for securing AI agents. The article highlights Langfuse for agent tracing, evaluations, guardrail monitoring, and open audit trails, including air-gapped deployments that store traces in ClickHouse. ### Source excerpt ClickHouse is joining the Open Secure AI Alliance alongside NVIDIA and other industry leaders to help build open tools that keep AI agents secure. ## Discover and install eve integrations from the CLI DevFeed: [Discover and install eve integrations from the CLI]() Original publisher: [Read original article]() Author: Owen Kephart Published: 2026-07-29T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Bots]() Tags: [agent](), [cli](), [extension](), [instrumentation](), [integration](), [mcp](), [review](), [vercel]() ### AI overview Eve CLI can now discover, inspect, and install agent integrations from its official catalog or compatible third-party registries. Because integrations can add dependencies and write project files, the article advises reviewing them and the resulting diff before running an agent. ### Source excerpt You can now discover and install integrations for eve agents directly from the eve CLI. Integrations come from the official eve catalog and third-party sources. Run eve add from your eve project to install an integration: Integrations write their files directly into your project and can add anything an eve agent uses, from a single tool to a channel to a full extension. Review the generated files and add any required configuration before running your agent. Find integrations with the new eve registry commands: eve registry list: List available integrations. eve registry search : Search the catalog for a capability, like browser. eve registry view : Inspect an integration before you install it. You can also browse the integrations directory to see the official catalog. Add third-party sources with a namespace and URL template: Then install from that source with eve add @acme/analytics. Registries use the shadcn registry format, so any compatible registry works. You can also pass an integration URL directly without configuring a source: Integrations can add dependencies and write files, so treat them like project code. Add sources you trust, inspect integrations with eve registry view, and review the project diff before you run the agent. Get started by reading the documentation and browsing the directory. Read more ## The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust DevFeed: [The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust]() Original publisher: [Read original article]() Author: Ryan Detwiller Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Resilience](), [dynatrace](), [observability](), [Instrumentation](), [distributed-systems]() Tags: [announcements](), [distributed-systems](), [dynatrace](), [engineering](), [gremlin](), [incident](), [instrumentation](), [metrics](), [observability](), [real-time](), [resilience](), [safety](), [systems](), [testing](), [uptime]() ### AI overview The Gremlin app for Dynatrace adds resilience testing and reliability scoring to Dynatrace workflows. Teams can run reliability tests, observe their impact in real time, and track service-level reliability scores using existing Dynatrace metrics, alerts, instrumentation, and health checks. ### Source excerpt With the Gremlin app for Dyantrace, you get resilience testing and reliability scoring built on the observability you already trust. [Next page]()