Best of Observability — July 2025

  1. 1
    Article
    Avatar of freecodecampfreeCodeCamp·41w

    Top Application Monitoring Tools for Developers

    Application Performance Monitoring (APM) tools help developers detect issues before users report them. Five key tools are compared: New Relic offers comprehensive full-stack observability with real-time metrics and traces; Datadog excels in cloud-native environments with seamless integrations and powerful alerting; Prometheus + Grafana provides open-source flexibility with custom dashboards and PromQL querying; Sentry specializes in error tracking with detailed stack traces and breadcrumbs; PostHog combines product analytics with session recording and feature flags. For small teams, start with Sentry for errors and Prometheus for metrics, then consider unified solutions like Datadog or New Relic as you scale.

  2. 2
    Article
    Avatar of devtronDevtron·38w

    Setting up Prometheus Stack on Kubernetes

    Kubernetes monitoring is essential for maintaining application health in dynamic containerized environments. Prometheus collects and stores time-series metrics while Grafana provides visualization through dashboards. The kube-prometheus-stack offers a complete monitoring solution with service discovery, alerting, and predefined dashboards. Devtron simplifies the setup process by providing integrated monitoring capabilities - users can install Grafana via Stack Manager, deploy Prometheus using Helm charts, and configure endpoints to get real-time application metrics including CPU usage, throughput, and latency directly in the Devtron dashboard.

  3. 3
    Article
    Avatar of last9Last9·41w

    How to Write Logs to a File in Go

    A comprehensive guide to implementing file logging in Go applications, covering everything from basic file output using the standard library to advanced structured logging with packages like Logrus and Zerolog. The guide addresses production considerations including log rotation, permission management, error handling, and performance optimization. It demonstrates practical patterns for environment-specific configuration, concurrent logging safety, and integration with observability platforms.

  4. 4
    Article
    Avatar of phProduct Hunt·41w

    VoltOps: Trace, debug, and monitor AI agents apps in n8n-style

    VoltOps is a developer-first observability platform specifically designed for AI agents and LLM applications. It provides tracing, debugging, and monitoring capabilities for agent workflows with features like structured traces, rich logs, and an n8n-style visual interface. The platform is framework-agnostic and supports multi-step chains, tool calls, and memory operations. It offers JavaScript/TypeScript and Python SDKs, with integrations for VoltAgent and Vercel AI SDK.

  5. 5
    Video
    Avatar of programmersarealsohumanProgrammers are also human·38w

    Cloud Architects in 2025

    A humorous take on the realities of cloud architecture in 2025, highlighting common pain points including Kubernetes complexity, escalating cloud costs, observability challenges, and the irony of supposed cost savings. The piece satirizes how modern cloud infrastructure often creates more problems than it solves, with bills in scientific notation and teams needed just to manage the cost-saving tools.

  6. 6
    Article
    Avatar of clickhouseClickHouse·40w

    What's new in ClickStack?

    ClickStack introduces native JSON support in beta, delivering up to 9x faster queries compared to the previous Map type implementation. The JSON type preserves data types, reduces I/O operations, and handles deeply nested observability data more efficiently. Updates include OpenTelemetry collector integration, HyperDX UI improvements, Helm chart enhancements, CSV export functionality, and Docker image size optimizations. The new JSON schema stores each unique path as a sub-column, eliminating the need for query-time casting and enabling more intuitive querying of complex nested structures.

  7. 7
    Article
    Avatar of last9Last9·38w

    Monitor Nginx with OpenTelemetry Tracing

    Learn how to instrument NGINX with OpenTelemetry to capture distributed traces across your entire request path. The guide covers a 5-minute Docker setup using the official nginx:1.25-otel image, production configuration patterns with conditional sampling, and integration with the OpenTelemetry Collector. It includes performance benchmarks showing minimal overhead (0.8% CPU, 3MB memory), Kubernetes deployment patterns, and advanced techniques for correlating logs with traces. The setup enables end-to-end visibility from NGINX through backend services to databases, helping identify bottlenecks in seconds rather than hours.

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