Operations | Monitoring | ITSM | DevOps | Cloud

Supercharge your LLM Using Production Data Context

Are your LLM coding agents (like Cursor or Claude Code) hallucinating fixes because they don't know what's actually happening in production? In this video, Matt from Speedscale shows you how to bridge the gap between your local IDE and live production traffic using the Model Context Protocol (MCP). Most observability tools just give you telemetry. Speedscale’s MCP server gives your agent the "inner workings" of actual API calls and payloads, so it can check its assumptions against reality. No more "vibe-coding" and hoping it works; let your agent find the 500 errors and rate limits for you.

Let Your LLM Debug Using Production Recordings

Modern LLM coding agents are great at reading code, but they still make assumptions. When something breaks in production, those assumptions can slow you down—especially when the real issue lives in live traffic, API responses, or database behavior. In this post, I’ll walk through how to connect an MCP server to your LLM coding assistant so it can pull real production data on demand, validate its assumptions, and help you debug faster.

Speedscale vs. LocalStack for Realistic Mocks

API mocking plays a crucial role in modern software development allowing developers to simulate external API endpoints. It’s an effective way to isolate your application for testing and ensure that code changes don’t inadvertently break critical dependencies. Essentially, API mocking helps you create robust, reliable software by allowing you to test how your application interacts with external services.

How to Do Full-Text Search Across All Application Traffic with Speedscale

Modern DevOps observability tools are excellent for monitoring system health, tracking distributed traces, and aggregating metrics. However, they lack the fidelity needed for full-text search across application traffic. While observability platforms excel at showing what happened and when, they often fall short when you need to find where a specific piece of data (like an email address, user ID, or transaction token) appears as it flows through your entire application stack.

Kubernetes is Hard. Here is the "Easy Mode" for 2026

Is Kubernetes actually hard, or are we just using the wrong tools? In 2026, the Kubernetes ecosystem has become a "dependency jungle." Between GitOps, YAML configuration, kubectl mastery, and complex CI/CD pipelines, developers are spending more time managing infrastructure than writing code. In this video, Ken breaks down the "hard parts" of K8s and introduces a more efficient workflow using Speedscale. Learn how to gain instant visibility into your cluster, pull logs without the headache, and turn real-world traffic into actionable load tests.

Is Kubernetes actually HARD? #speedscale #kubernetes #k8s #devops #cloudnative

Thinking about learning Kubernetes in 2026? You’ll need GitOps, kubectl, and CI/CD pipelines... OR you can just use Speedscale. See how a single operator replaces a million dependencies and gives you the traffic insights you actually need to survive production.

3AM Pager: When You Know the Data but Can't Search It

Ever tried searching your entire production stack for one user? Getting paged at 3 AM is bad enough. It’s worse when you only have a single username and zero visibility into what’s actually happening across your microservices. With Speedscale, you can perform full-text searches across every API call and database interaction in real-time. Stop guessing and start debugging with total context.