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By Micha Duman
The race to better AI-assisted observability has been a race for bigger and better models. But intelligence was never the real bottleneck. Structure and context were.
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By Annie Freeman
Recently, someone talked Chipotle’s customer support agent into reversing a linked list – a task completely unrelated to burritos in any way. Screenshots circulated, people laughed, but underneath the joke sat a sharper question. If a production support agent will do that on a public channel, what else will it do that nobody is screenshotting? The bug is funny. The trust gap behind it is not.
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By Jonny Steiner
Kubernetes dashboards often mask a systemic infrastructure failure. When a critical Java service fluctuates and restarts, the post-mortem often confirms an Out-of-Memory (OOM) event. While CPU metrics appear healthy, memory has silently hit a ceiling, forcing the kernel to terminate the process.
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By Micha Duman
TCO policies have always been one of the most impactful cost levers in Coralogix. Route business-critical data to High, push monitoring data to Medium, archive compliance logs to Low. With the addition of DataPrime expressions (DPXL) – a subset of the DataPrime query language designed for inline filtering at ingest – that routing became even more precise, matching on any field in the event payload, not just application, subsystem, and severity.
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By Jonny Steiner
It’s 20 minutes into a P0 incident, and you have already switched between four different tools, re-authenticated twice, and translated queries across three incompatible syntax languages. The root cause you are searching for. Well, that is still out there somewhere. The reality of investigative latency is that most engineering teams face navigation problems, not data problems. During high-pressure incidents, teams lose cognitive momentum due to context switching between disconnected telemetry silos.
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By Ofri Grushka
There is a moment every engineer knows too well. Something is wrong in production. You have an alert, a vague symptom, and pressure to find the one signal that explains what changed. You open your logs and traces, and you immediately hit the same two problems: the dataset is huge, and the path from “I see something odd” to “I understand why” is full of tiny, exhausting steps. Meet new Explore, our redesigned investigation experience for logs, traces, and spans.
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By Coralogix Team
A well-instrumented service tells your on-call engineer which deploy broke checkout, which span ate the latency budget, and which line to revert before the support queue fills up. Getting there depends on how cleanly your application performance monitoring layer turns telemetry into answers. The sections ahead walk through how APM works, the metrics and components worth tracking, the cloud-native challenges at scale, and how to evaluate APM tooling against your real workload.
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By Coralogix Team
The fastest incident response teams treat coordination as a craft. Someone owns the call, drives the decisions, and keeps everyone moving in the same direction while the team puts the system back together. That person is the incident commander (IC), and getting the role right is what separates your 15-minute fix from a four-hour war room where nobody’s sure who’s making the call.
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By Coralogix Team
Catching a cascading failure in the first 90 seconds is one of the better feelings in production engineering, and it almost always comes back to your log monitoring pipeline doing its job upstream of the alert. The teams that land there consistently treat log monitoring as a real-time detection layer in its own right, and the choices you make in that pipeline shape how every incident plays out for years.
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By Jonny Steiner
OpenTelemetry made telemetry possible everywhere – turning observability pipelines into distributed production infrastructure. Distributed infrastructure requires a control plane for inventory, governance, and safe change. At 500 collectors across hybrid environments, operational overhead becomes a production risk. The moment telemetry pipelines become a distributed infrastructure, they inherit the operational problems of one.
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By Coralogix
Agent mode in the Coralogix CLI cuts token consumption by nearly 50%, without sacrificing the context your agents need to actually do their job.
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By Coralogix
Dataspaces and Datasets | The Structured Data Layer for Teams and AI | Coralogix Dataspaces and Datasets from Coralogix: the structured data layer teams and AI were waiting for. Turn a single query into a reusable dataset, share it across teams, and keep dashboards fast as your data scales. In this video: Timestamps: Dataspaces and Datasets are available now in Coralogix. Whether you're building dashboards, running background queries, or powering AI agents with telemetry data, Dataspaces give your organization a governed, high-performance data architecture that scales with your teams.
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By Coralogix
Learn how to create a user-defined dataset in Coralogix and route telemetry data into it using TCO policies with granular DataPrime expressions. In this walkthrough, you'll learn how to:• Create a new dataset with its own schema, permissions, retention, and cost visibility• Configure PBAC settings for governed access control• Route data using DataPrime expressions in TCO policies• Fan out events to multiple datasets from a single source.
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By Coralogix
I tested the new Anthropic model Fable 5 truly is using data, not vibes. Why does it feel faster? Does it actually cost double? Is it better at coding?
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By Coralogix
Stop editing raw YAML by hand. Discover how to build, validate, and scale complex OTel pipelines visually with the Visual Builder for Coralogix Fleet Management.
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By Coralogix
Kubernetes dashboards often mask a system infrastructure failure. When a critical application crashes, it often points to an Out-of-Memory event. Even while standard CPU metrics appear completely healthy. This quick walkthrough shows you how Coralogix integrates continuous memory profiling directly into your production environment. We pair OpenTelemetry trace data with continuous background sampling via the Async Profiler. It helps teams isolate resource heavy code paths before they trigger system degradation.
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By Coralogix
The new MCP (Model Context Protocol) release makes it stateless, here's what you need to know if you are building AI agents and remote MCP servers.
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By Coralogix
WATCH THE FULL EPISODE: click the link above Welcome to Episode 2 of Live Laugh Logs, the podcast from Annie, Lewis, and Andre from the Coralogix Developer Relations team, where we get together and recap everything going on in our worlds!
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By Coralogix
Are companies blowing through their entire 2026 AI budget in a matter of months? Welcome to Episode 2 of Live Laugh Logs, the podcast from Annie, Lewis, and Andre from the Coralogix Developer Relations team, where we get together and recap everything going on in our worlds!
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By Coralogix
There are numerous types of logs in AWS, and the more applications and services you run in AWS, the more complex your logging needs are bound to be. Learn how to manage AWS log data that originates from various sources across every layer of the application stack, is varied in format, frequency, and importance.
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Coralogix helps software companies avoid getting lost in their log data by automatically figuring out their production problems:
- Know when your flows break: Coralogix maps your software flows, automatically detects production problems and delivers pinpoint insights.
- Make your Big Data small: Coralogix’s Loggregation automatically clusters your log data back into its original patterns so you can view hours of data in seconds.
- All your information at a glance: Use Coralogix or our hosted Kibana to query your data, view your live log stream, and define your dashboard widgets for maximum control over your data.
Our machine learning powered platform turns your cluttered log data into a meaningful set of templates and flows. View patterns and trends, and gain valuable insights to stay one step ahead at all times!