Observability in Go: Where to start and what matters most
Sometimes the hardest part of debugging a system isn’t fixing the problem—it’s figuring out what’s actually happening in the first place.
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The latest News and Information on Observabilty for complex systems and related technologies.
We Know Before it Breaks: Observability-Driven Development
When stakeholders push for faster growth (new markets, new features, newly modernized stack) your engineering model has to change too. At FitnessPassport, the shift from offshore waterfall delivery to an in-house team meant rebuilding not just services, but confidence: legacy systems with weak logging and little visibility made it hard to know whether changes were working and impossible to spot issues before users did. In this talk, Director of Engineering Rob Mitchell will share how FitnessPassport adopted Datadog and used structured logs, metrics, and traces to tighten feedback loops.
Paris | Observability Unleashed - Boostez vos opérations IT, DevOps & SRE
La complexité des environnements IT ne cesse de croître. La visibilité en temps réel n'est plus une option. Le 14 avril 2026, Stéphane Estevez , EMEA Observability Market Advisor chez Splunk, vous invite chez Cisco à Paris pour un événement dédié à l'observabilité, avec les équipes Splunk & Cisco. Au programme : Observabilité assistée par l'IA Stratégies de données intégrées OpenTelemetry simplifié De la donnée à l'action, avec des cas concrets et démos live Observabilité pour l'IA et par l'IA.
KubeCon + CloudNativeCon EU 2026: What We Learned About AI, Observability, and Fast Feedback Loops
Honeycomb was excited to attend KubeCon + CloudNativeCon Europe, where one theme stood out across sessions: as AI reshapes how software is built and run, teams are being pushed to rethink how they understand their systems. Without strong observability and feedback loops, AI can accelerate confusion, misalignment, and operational risk.
The Business Case for AI-Driven Observability in Network Operations
Modern network operations generate an extraordinary amount of telemetry. Metrics, logs, events, topology data, cloud signals, and service context all contribute to a richer picture of system behavior. As environments expand across cloud, data center, edge, and SaaS, the opportunity for operations teams is clear: when that telemetry is unified and understood in context, it becomes a powerful source of resilience, efficiency, and business insight.
When we say "Observability AI Reckoning," what are we actually talking about?
We’ve spent the last decade collecting more telemetry. Now AI is analyzing it. Here’s the catch: AI needs the full dependency chain to reason correctly. If it sees spans but not storage contention… Services but not Kubernetes scheduling… Frontend metrics but not downstream providers… It will confidently optimize the wrong thing. AI doesn’t lower the need for observability. It raises the standard.
Profiling Java apps: breaking things to prove it works
Coroot already does eBPF-based CPU profiling for Java. It catches CPU hotspots well, but that's all it can do. Every time we looked at a GC pressure issue or a latency spike caused by lock contention, we could see something was wrong but not what. We wanted memory allocation and lock contention profiling. So we decided to add async-profiler support to coroot-node-agent. The goal: memory allocation and lock contention profiles for any HotSpot JVM, with zero code changes. Here's how we got there.
Accelerate Your OpenTelemetry Migrations With Honeycomb's Agent Skills
Since releasing our hosted MCP server last year, we've been thrilled to see customers not just adopt it but build Honeycomb deeply into their agentic development and observability workflows. Users have embraced it, leveraging Honeycomb to stay in conversation with their code and understand how it runs in production.
AI Needs Better Inputs: Why Observability Is Becoming the Foundation of Enterprise AI Maturity
Organizations across industries are accelerating their investments in AI for operations, yet the path to meaningful impact is proving far more complex than early expectations suggested. Analysts at Gartner, Forrester, Deloitte, and McKinsey continue to highlight the same structural barrier. AI cannot produce accurate predictions or safe automation when the operational data feeding it is fragmented, incomplete, or inconsistent.
Grafana Cloud Demo in Under 5 minutes | Full Stack Observability and more
Overview & demo of how Cloud provides an end to end Observability Platform that empowers users who have adopted open standards like or to improve their systems reliability using & a shift left approach with performance testing while optimizing their observability costs.