# observability Published articles for observability. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Equinix cut operational overhead with a shared services architecture on Amazon EKS DevFeed: [How Equinix cut operational overhead with a shared services architecture on Amazon EKS]() Original publisher: [Read original article]() Author: Chhavi Kaushik Published: 2026-09-17T18:00:56Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon EKS](), [Kubernetes](), [Architecture & Design](), [Cloud](), [Deployment](), [Provisioning](), [observability](), [Security](), [CI/CD](), [Open Source]() Tags: [amazon-eks](), [amazon-elastic-kubernetes-service](), [architecture](), [customer-solutions](), [deployment](), [infrastructure](), [kubernetes](), [observability](), [pipelines](), [provisioning](), [security]() ### AI overview Equinix moved from independently managed Kubernetes clusters on Amazon EC2 to a shared services architecture on Amazon EKS. The approach centralizes governance and common infrastructure services while addressing operational sprawl, lifecycle complexity, security, networking, observability, and deployment consistency. ### Source excerpt Equinix, the world's digital infrastructure company, built a shared services architecture on Amazon EKS to eliminate the operational sprawl of its self-managed Kubernetes environment. Learn how a multi-account North Star architecture centralized governance and shared services, delivering 4x faster deployments and 40% less operational overhead. ## What is AIOps? DevFeed: [What is AIOps?]() Original publisher: [Read original article]() Author: Databricks Staff Published: 2026-09-17T16:49:43Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [AIOps](), [Artificial Intelligence](), [Monitoring](), [Grafana](), [Microservices](), [telemetry](), [Cloud]() Tags: [ai](), [aiops](), [cloud](), [data-plus-ai-foundations](), [devops](), [logs](), [machine-learning](), [microservices](), [monitoring](), [network](), [observability]() ### AI overview This guide explains AIOps, which applies AI and machine learning to IT operations to detect anomalies, correlate events, identify root causes, and trigger responses. It describes how AIOps analyzes logs, traces, events, and network topology, while complementing observability, DevOps, and human judgment. ### Source excerpt Artificial Intelligence for IT Operations (AIOps) applies AI and machine learning to IT operations to detect anomalies... ## A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore DevFeed: [A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Shridhar Navanageri Published: 2026-09-17T15:41:03Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [AI Platform](), [observability](), [Authentication](), [Orchestration](), [scaling](), [control-plane](), [tracing](), [Self-hosted]() Tags: [agentic](), [agentic-ai](), [amazon-bedrock-agentcore](), [authentication](), [control-plane](), [customer-solutions](), [infrastructure](), [observability](), [orchestration](), [product](), [systems]() ### AI overview Wood Mackenzie built APEX, a shared agentic platform on Amazon Bedrock AgentCore, to help teams move AI agents from experimentation into production. The platform centralizes runtime orchestration, identity, observability, connectivity, safety, and persistent state so teams can focus on product-specific business logic. ### Source excerpt Wood Mackenzie built APEX, a shared agentic AI platform on Amazon Bedrock AgentCore so every team can ship production agents without rebuilding runtime, identity, observability, and guardrails from scratch. Learn why they chose AgentCore, how APEX Studio operates it, and where multi-agent systems go next. ## Accelerating Operational Efficiency in Modern Transportation DevFeed: [Accelerating Operational Efficiency in Modern Transportation]() Original publisher: [Read original article]() Author: Emily Kasman Published: 2026-09-17T13:30:51Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Cisco](), [Network](), [Critical Infrastructure](), [legacy](), [Low Latency](), [Security](), [observability](), [Resilience]() Tags: [cisco](), [cisco-connected-rail](), [cisco-industrial-ethernet-switches](), [cisco-industrial-routers](), [cisco-industrial-security](), [connected-roadways](), [critical-infrastructure](), [industrial-ai](), [industrial-iot](), [legacy](), [low-latency](), [modernization](), [network](), [observability](), [resilience](), [security](), [transportation]() ### AI overview This Cisco article discusses how transit and roadway agencies can modernize legacy network infrastructure. It presents secure, low-latency connectivity, end-to-end operational visibility, and security-focused architecture as ways to support real-time processing, connected systems, and more resilient transportation operations. ### Source excerpt Learn how Cisco helps transit agencies overcome legacy network bottlenecks with secure, AI-ready connectivity, full visibility, and robust cyber resilience. ## OpenTelemetry everywhere: Migrating a metrics platform at scale DevFeed: [OpenTelemetry everywhere: Migrating a metrics platform at scale]() Original publisher: [Read original article]() Author: Iris Grace Endozo, Farzad Vazirnia and Albert Kerr, Atlassian Published: 2026-09-17T11:00:00Z Content type: article Language: en Sources: [Cloud Native Computing Foundation]() Topics: [OpenTelemetry](), [migration](), [observability](), [telemetry](), [atlassian](), [Open Source]() Tags: [atlassian](), [blog](), [collector](), [metrics](), [migration](), [observability](), [opentelemetry](), [pipeline](), [scale](), [sidecar]() ### AI overview Atlassian describes replacing a large-scale gostatsd metrics pipeline with OpenTelemetry while preserving the existing StatsD interface for service teams. The migration uses purpose-built OpenTelemetry Collector distributions across collection, ingest, aggregation, and forwarding stages, with support for both StatsD and OTLP during the transition. ### Source excerpt Why we did this at all For most of the last decade our metrics pipeline ran on gostatsd, the open-source StatsD implementation we maintain. It primarily did two jobs: as sidecar on every host and the... ## Build Your Own AI Agent Harness in C#, the MafClaw Live Series DevFeed: [Build Your Own AI Agent Harness in C#, the MafClaw Live Series]() Original publisher: [Read original article]() Author: Bruno Capuano Published: 2026-09-16T21:00:00Z Content type: tutorial Language: en Sources: [.NET Blog]() Topics: [Agent Harness](), [Microsoft Agent Framework](), [AI Agent](), [C#](), [.NET]() Tags: [agent](), [agent-framework](), [agent-harness](), [ai](), [building](), [c-sharp](), [cloud](), [csharp](), [microsoft-agent-framework](), [microsoft-reactor](), [net](), [observability]() ### AI overview This article introduces a four-part live series that builds a complete C# AI agent using the Microsoft Agent Framework harness. It describes adding tools, planning, file access, approvals, memory, skills, shell commands, code execution, background agents, observability, governance, evaluations, and hosted deployment. ### Source excerpt I am building a complete C# agent live, from a single call around an IChatClient to a production-ready, observable, governed agent, using the Microsoft Agent Framework harness in a 4-part Microsoft Reactor series. The post Build Your Own AI Agent Harness in C#, the MafClaw Live Series appeared first on .NET Blog. ## 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 ## Native Splunk brings real-time insights to Cisco Nexus One DevFeed: [Native Splunk brings real-time insights to Cisco Nexus One]() Original publisher: [Read original article]() Author: David Keith Published: 2026-09-16T15:00:59Z Content type: release Language: en Sources: [Cisco Blogs]() Topics: [Nexus Dashboard](), [observability](), [telemetry](), [datacenter](), [Security](), [sensitive data](), [audit](), [configuration]() Tags: [analytics](), [audit](), [cisco](), [cisco-nexus-dashboard](), [configuration](), [data-center](), [data-center-networking](), [network](), [nexus-one](), [observability](), [real-time](), [sensitive-data](), [splunk](), [troubleshooting]() ### AI overview Cisco describes native Splunk embedded in Cisco Nexus Dashboard as an on-premises analytics and observability capability for data center and AI workloads. It processes telemetry locally, correlates network, security, configuration, and audit data, and provides dashboards, searches, and alerts for troubleshooting, data sovereignty, compliance, and cost efficiency. ### Source excerpt Discover how native Splunk embedded in Cisco Nexus Dashboard delivers real-time analytics, faster troubleshooting, and on-premises data sovereignty for modern data center and AI workloads. ## Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time DevFeed: [Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time]() Original publisher: [Read original article]() Author: Egor Klimov Published: 2026-09-16T12:34:47Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [OpenTelemetry](), [Microservices](), [observability](), [telemetry](), [Traces](), [ide]() Tags: [all-things-web](), [backstage](), [environment-variables](), [goland](), [ide](), [idea](), [intellij-idea](), [intellij-platform](), [jetbrains](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [plugin-development](), [plugin-highlights](), [plugins](), [pycharm](), [research](), [rider](), [telemetry](), [traces](), [webstorm]() ### AI overview This article explains how the JetBrains OpenTelemetry Plugin generates a service map from runtime telemetry. It describes using logs, metrics, and especially standardized trace spans to visualize how microservices communicate, along with the plugin's lightweight local OpenTelemetry backend. ### Source excerpt We've all been there: you join a new project, and the first thing you ask for is the architecture diagram. You're handed a diagram that looks great, but after a week of debugging, you realize it's six months out of date. Service A hasn't talked to Service B since the spring, and there's a new [...] ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## What's new in ClickStack - Aug '26 DevFeed: [What's new in ClickStack - Aug '26]() Original publisher: [Read original article]() Author: The ClickStack Team Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [observability](), [dashboards](), [clickhouse](), [Prometheus](), [LLM observability](), [MCP Server](), [tracing]() Tags: [dashboard](), [improvements](), [llm-observability](), [metrics](), [observability](), [prometheus](), [tracing]() ### AI overview The August 2026 ClickStack release update covers dashboard variables, chart formulas, PromQL support, a metrics explorer, release markers, LLM observability, alerting improvements, OIDC authentication for the collector, and MCP server improvements. ### Source excerpt The August ClickStack update brings dashboard variables, chart formulas, PromQL support, a metrics browser, LLM observability, and improvements to alerting and tracing. ## Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte DevFeed: [Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte]() Original publisher: [Read original article]() Author: Jordi Mon Companys,Lucy Wang Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Serverless](), [Elastic Cloud](), [Cloud](), [observability](), [Security](), [SIEM, Security, Observability]() Tags: [architecture-cloud-migration-migrating-cloud-native-scaling-search-analytics-search-applicatio](), [cloud](), [cloud-elasticsearch-platform](), [customers](), [elastic](), [elastic-cloud](), [observability](), [observability-security-search](), [security](), [serverless]() ### AI overview Elastic announced the general availability of cross-project search for Elastic Cloud Serverless. It lets teams query multiple linked projects in a single session across regions, project types, and cloud providers, while keeping data in place and avoiding certificates, remote-cluster configuration, and per-connection authentication. ### Source excerpt Cross-project search is now GA on Elastic Cloud Serverless. Link multiple projects and query all of them from a single Discover or ES|QL session: no certificates, no remote cluster config, and three clicks to set up. Available from September 1, 2026. ## 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. ## QEMU Google Summer of Code 2026 project report DevFeed: [QEMU Google Summer of Code 2026 project report]() Original publisher: [Read original article]() Published: 2026-09-15T07:00:00Z Content type: article Language: en Sources: [QEMU]() Topics: [qemu](), [AdventureX 2025](), [Open Source](), [virtual machines](), [x86](), [observability](), [Rust]() Tags: [cpu](), [development](), [google-summer-of-code](), [gsoc](), [internships](), [observability](), [open-source](), [qemu](), [rust]() ### AI overview QEMU's Google Summer of Code 2026 report covers four contributors' open-source projects from May through August, including lazy snapshot loading, x86 Process Context Identifier support in COCONUT-SVSM, and observability support for confidential virtual machines. ### Source excerpt QEMU participated in Google Summer of Code 2026 with 4 contributors working on open source internships from May through August. The contributors gained experience in open source software development working on 12-week projects. ## How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes DevFeed: [How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-15T06:06:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [systems](), [Programming](), [Kernel](), [observability](), [browsers](), [web browsers]() Tags: [architecture](), [architecture-design](), [architectures](), [blocking](), [concurrency](), [development](), [efficiency](), [kernel](), [locking](), [memory](), [news](), [observability](), [operating-systems](), [solaris](), [storage](), [systems](), [turnstile-system-design]() ### AI overview The article examines how Solaris innovations influenced modern systems engineering. It discusses the Slab Allocator, OpenZFS, DTrace, Zones, and turnstiles, focusing on how turnstiles reduce per-lock memory overhead while supporting priority inheritance for contended locks. ### Source excerpt Sun's Solaris influenced modern systems engineering, with key innovations like the Slab Allocator for efficient memory management, OpenZFS for advanced storage, and DTrace for observability. Its turnstile mechanism addressed issues with mutexes, promoting lean locking designs that are reflected in contemporary programming languages and web browsers, enhancing performance and memory efficiency. By Olimpiu Pop ## 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 ## Why your Redis is slow: it is single threaded and you sent it KEYS DevFeed: [Why your Redis is slow: it is single threaded and you sent it KEYS]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Redis](), [Latency](), [Concurrency]() Tags: [blocking](), [caching](), [command](), [latency](), [observability](), [performance](), [redis](), [single-threaded]() ### AI overview The article explains that Redis executes commands one at a time on a single thread, so expensive commands can block every client and cause latency spikes across all operations. It identifies KEYS as a key offender and also discusses large DEL operations, unbounded LRANGE, SMEMBERS on large sets, and FLUSHALL. Redis 6 threaded I/O helps with socket handling but does not change single-threaded command execution. ### Source excerpt Redis executes commands one at a time on a single thread, so any command that takes 200ms makes every other client wait 200ms regardless of how trivial their request was. KEYS scans the entire keyspace and is the obvious offender, but DEL on a large collection, unbounded LRANGE, SMEMBERS on a big set and FLUSHALL are all linear in the size of the data and block identically. The tell is a latency spike across all operations at once while CPU and network look normal. ## Inside the LLM Call: GenAI Observability with OpenTelemetry DevFeed: [Inside the LLM Call: GenAI Observability with OpenTelemetry]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-14T16:56:42Z Content type: tutorial Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [agent observability](), [telemetry](), [AI Agent](), [Large Language Model]() Tags: [agent](), [ai-agent](), [claude-code](), [codex](), [observability](), [opentelemetry](), [token](), [tool](), [visibility]() ### AI overview This tutorial explains how OpenTelemetry Semantic Conventions for Generative AI record LLM calls, tool invocations, token counts, and related events. It demonstrates exporting telemetry from an LLM-powered application, viewing it with Aspire Dashboard, and considering sensitive-data implications of optional content capture. ### Source excerpt Your AI agent just took 45 seconds to answer a simple question. Was it the model? A slow tool call? A retry loop? Every time an application calls an LLM, a chain of model calls, tool invocations, and token exchanges happens behind the scenes -- and without observability, you are guessing. The OpenTelemetry Semantic Conventions for Generative AI give you that visibility. They standardize how GenAI operations are recorded -- the model being called, input and output token counts, and when opted in, the full content of prompts, completions, tool calls, and tool results. ## How to Use Differential Testing During a Legacy Migration DevFeed: [How to Use Differential Testing During a Legacy Migration]() Original publisher: [Read original article]() Author: Hugo Teijiz Published: 2026-09-14T16:25:50Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [migration](), [Testing](), [TypeScript](), [Integration testing](), [API](), [observability](), [Artificial Intelligence]() Tags: [ai](), [api](), [architecture](), [artificial-intelligence](), [code](), [how-to](), [integration](), [legacy-code](), [legacy-modernization](), [migration](), [modernization](), [observability](), [software-architecture](), [software-testing](), [testing](), [tests](), [tutorial](), [typescript]() ### AI overview This tutorial explains how to use differential testing during a legacy migration by running old and new implementations with identical inputs and comparing their behavior. It covers equivalence rules, output normalization, nondeterministic values, errors, side effects, automated testing, mismatch analysis, production shadow traffic, and using AI to classify divergences without allowing it to determine correctness. ### Source excerpt The most dangerous moment in a legacy migration isn't necessarily when you start writing the new implementation. It's when the new implementation looks finished. The code compiles, the tests pass, the ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## A Daily Digest of What Actually Got Merged, Built from MQTT and a Local LLM DevFeed: [A Daily Digest of What Actually Got Merged, Built from MQTT and a Local LLM]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-09-14T07:00:00Z Content type: article Language: en Sources: [Philipp Lehmann]() Topics: [pull-requests](), [MQTT](), [Large Language Model](), [SQLite]() Tags: [architecture](), [ci-cd](), [llm](), [local-llm](), [mqtt](), [observability](), [pull-requests](), [python](), [sqlite]() ### AI overview This article describes a daily digest that uses MQTT merge events, a SQLite subscriber, and a locally run LLM to summarize merged pull requests by repository without sending diffs to a cloud API. ### Source excerpt paho-mqtt subscriber to SQLite plus a local LLM: a daily digest that summarises merged pull requests per repository from diff stats, no cloud API. ## nginx: The Map Variable That Fixes the URI in proxy\_pass DevFeed: [nginx: The Map Variable That Fixes the URI in proxy\_pass]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-09-14T07:00:00Z Content type: tutorial Language: en Sources: [Philipp Lehmann]() Topics: [nginx](), [proxy](), [Caching](), [configuration]() Tags: [cache-control](), [ci-cd](), [configuration](), [docker](), [networking](), [nginx](), [observability](), [proxy](), [reverse-proxy]() ### AI overview This article explains an nginx build failure caused by using a URI-bearing proxy_pass inside a regex location. A newer nginx version treated the previously accepted configuration as an error. The proposed fix uses a bare upstream in the regex location and a map on $uri to apply a path-specific Cache-Control policy. ### Source excerpt nginx: [emerg] invalid URI prefix in proxy_pass: a regex location needs a bare upstream, and a map on $uri keeps a path-specific Cache-Control header. ## Data Engineering Weekly #287 DevFeed: [Data Engineering Weekly #287]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-14T02:52:23Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Kafka](), [data observability](), [Embeddings](), [Multi-tenancy](), [Event-Streaming](), [OpenAI](), [Library](), [Code]() Tags: [architecture](), [architectures](), [data-engineering](), [data-quality](), [embedding](), [engineering](), [kafka](), [multi-tenancy](), [object-storage](), [observability](), [openai](), [streaming](), [vector]() ### AI overview Data Engineering Weekly #287 covers building data platforms from scratch, including composable architectures, data quality, and observability. It also previews talks on governed machine-executable ontologies for marketing activation and fair, order-preserving Kafka consumption for many tenants. The issue links to OpenAI's storage platform scaling for ChatGPT and Pinterest's embedding retrieval platform. ### Source excerpt The Weekly Data Engineering Newsletter [Next page]()