Systems | Development | Analytics | API | Testing

The Impact of Network Latency on Cloud Load Testing Accuracy: Why It Matters in 2026

Despite years of progress in cloud testing platforms, network latency remains the most stubborn – and often ignored – variable in load testing reliability. A recent study highlights that network latency can skew load test results by as much as 30%. That’s not a rounding error; it’s the difference between a site that passes in the lab and one that buckles under real-world traffic.

Spotter Enhancements

Spotter just got smarter and more in your control. You can now customize your agent's name, persona, output formatting, and guardrails to dictate exactly how it should (and shouldn't) handle data. Set it once, in plain language, and every user across the organization gets a configured, governed Spotter. Other new features include: Ad-hoc file analysis: Upload any flat file directly into Spotter and start asking questions instantly, solo or blended with your governed data.

OctoPerf MCP Server, Fully On-Premise: AI Load Testing With a Local LLM

But a recurring question came from banks, hospitals, defense and public-sector teams: what if nothing is allowed to leave our network, not even the prompt? This article answers that question with a full walkthrough.. We will stand up a 100% on-premise, air-gapped stack, and it only takes two things to install: OctoPerf Enterprise in Docker, and a local Qwen3 large language model running in LM Studio, which doubles as the Model Context Protocol client.

Agent development and AgentOps with BigQuery, ADK, and MCP

Join this session to learn about Agent Development Kit (ADK) and Model Context Protocol (MCP) integration methods that standardize how agents connect to your data while removing the need to build custom database connectors from scratch. Discover how to build agents with the ADK that accesses BigQuery for analysis, Google Maps for geospatial insights, and AlloyDB for transactions – all in a single workflow. Learn how to implement agent operations (AgentOps) for deep observability into both agent performance and cost with a single line of code.

Demo: Real-Time Context Engine for Fleet Management

Use Real-Time Context Engine and Claude, or any MCP-compatible client, to explore operational data using natural language in real time. That includes everything from simple lookups to multi-step investigative questions like: Confluent’s Real-Time Context Engine gives AI agents live access to operational context as events happen across the business. Instead of relying on stale snapshots, agents can query and reason over continuously updated tables in real time.

Debug a Node.js Memory Leak in Minutes with AI-Powered Heap Snapshot Analysis

Memory leaks are among the most frustrating production issues to investigate. At first, everything looks normal. Requests are processed successfully, users aren't reporting problems, and the application appears healthy. Then memory usage starts climbing. Garbage collection runs more frequently. Performance degrades. Eventually, the process becomes unstable or crashes altogether. Detecting a memory leak isn’t even half the battle.

Get Faster Test Plan Insights in Jira with Xray's Rovo Test Plan Summarizer

Understanding the health of a Test Plan isn't always as straightforward as it sounds. A Test Plan may contain hundreds of Tests, multiple Test Executions, linked requirements, and defects spread across different areas of a project. The information teams need is usually there, but finding answers often means navigating through several screens, reviewing reports, and manually connecting the dots. Questions such as How much testing has been completed?, Are there any coverage gaps?

Your Vercel AI SDK app is missing a session layer

If you have built an AI chat feature with the Vercel AI SDK, you have used its useChat hook. You give it your messages, and it streams the reply into your UI. You may have seen our post on the custom transport we built for the Vercel AI SDK. It swaps useChat's default transport for Ably AI Transport, adding resumable streams, cross-device and multi-user sync, conversation branching, history compaction, and stop-and-approve controls.

Stop vs disconnect - why canceling AI streaming is harder than it looks

You add a stop button to your AI chat app: a customer support agent, a coding assistant, a research tool the user can steer mid-task. A user clicks it mid-response. The frontend stops rendering. Then you check your backend logs and realize the underlying generation is still running, and you’re still paying for every token. This is not a bug.