Operations | Monitoring | ITSM | DevOps | Cloud

Synthetic Monitoring Is Broken. Your Production Traffic Can Fix It.

Synthetic monitoring has been a critical part of application reliability for years. It gives engineering and operations teams a way to proactively test applications, APIs, and critical customer journeys before users encounter problems. But there is a fundamental limitation with the traditional approach: Someone has to create the tests. As applications become more distributed and customer journeys become more complex, organizations can end up maintaining hundreds or even thousands of synthetic scripts.

Building an End-to-End Drone Ecosystem: The Technologies That Need to Work Together

Commercial drone technology is rarely a single application running alongside an aircraft. A complete solution may include flight software, onboard sensors, telemetry, cloud infrastructure, web and mobile interfaces, data processing pipelines, analytics tools, and integrations with existing business systems.

Observe Opaque Services With OpenTelemetry eBPF + proxymock

Every SRE team operates services it cannot see into: a vendor binary, an inherited legacy deployment, a container whose owning team dissolved two reorgs ago. The routes are undocumented, the dependencies are unknown, and when a request takes 130 milliseconds nobody can say whether that time is application work or a wait across a network boundary.

Best news APIs in 2026: 6 platforms compared for coverage, enrichment, and cost

Content teams and AI builders no longer treat "news API" as one category. Some products need a raw headline feed for a dashboard; others need clustered, entity-tagged articles that slot straight into a retrieval pipeline without building a separate NLP layer on top. This list compares six providers worth testing in 2026: Newscatcher API, NewsAPI.org, GNews, Mediastack, NewsData.io, and Webz.io, judged on source coverage, enrichment, real production pricing, and how far the free tier actually goes.
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Flamegraphs Find It. Replay Proves It.

I made an API endpoint 13 times faster. Then I realized my first verification only checked the status, headers, and response schema. I had not checked the totals. I had made the bug faster. That is the problem with giving an AI coding agent one kind of evidence. A CPU profile can show where the application is slow, but not whether an optimization preserves behavior. A traffic replay can prove that behavior stayed stable, but not explain why the code burns CPU. This walkthrough gives the agent two independent witnesses: Together, they turn AI code verification into an experiment with two independent checks.

The Pod Was Cheaper. The Service Wasn't.

A smaller Kubernetes pod can lower allocation cost while completing less work. Green status codes and matching schemas can hide it. This walkthrough combines OpenCost allocation data with proxymock behavior and performance evidence. A candidate passes only when behavior and throughput hold while unit cost falls.

Diagnose Serial N+1 API Calls With Tempo + proxymock

One API request took 302 milliseconds. Nothing failed. CPU was mostly idle. The response was correct. The trace made the problem obvious: eight inventory calls, each waiting for the previous one. But the trace could not tell me why the application made eight calls, or whether changing their execution would preserve the response. It showed the shape of the wait, not the input that created it.

Cut AI coding defects by 33% #mcpserver #aicoding #aiagents #grafana #aitools

We spend thousands of dollars "token maxing" and running endless debugging cycles just to walk our LLMs through a problem. But is the AI actually failing, or are we just withholding the right environment? Giving your AI assistant its own sandbox to test hypotheses might just be the missing link in your development workflow.