Operations | Monitoring | ITSM | DevOps | Cloud

From Datadog to CI Tests: Catch Regressions Before Deploy

I worked in observability for years, and the same pattern showed up across teams. An alert fired, the on-call rotation scrambled, and everyone did what they had to do to stabilize production. Then came the retrospective. Once the immediate pressure was gone, the conversation shifted to one question: how do we make sure this never happens again? My friend Jade Rubick coined a name for that principle: DRI, “don’t repeat the incident”.

Why You Should Stop Buying SaaS and Start Building It

The "Buy vs. Build" rule is dead. Generic CRMs are too slow for lean startups, so we built our own. In this video, Ken breaks down "Radar," the custom AI dashboard we use at Speedscale to automate prospecting and outreach. Stop fighting bloated SaaS and start building the exact tools you need to solve your distribution problem. Learn more: speedscale.com.

Why SaaS is Dying (and what's next) #speedscale #saas #data #datasecurity #devops #technews

Traditional SaaS is a data trap. It’s time to stop sending your most valuable asset to third parties. Enter BYOC (Bring Your Own Cloud): the future of data sovereignty, where the software comes to you. Visit: speedscale.com.

Is OpenTelemetry overkill? There's a lazier (and better) way. #speedscale #sre #ebpf #kubernetes

If you "aspire to be lazy" like we do, you know that building staging environments and mocking complex back-ends (like MySQL, AI models, and 3rd party APIs) is a massive time sink. In this demo, we show you how to use Internet Magic (aka eBPF) to: Stay tuned for Part 2, where we take these recordings and spin up a staging environment automatically.

AI Coding Agents Break What Works

Your AI coding agent just made every test pass. Ship it, right? Not so fast. A growing class of AI-generated bugs doesn’t come from writing bad code. It comes from the AI changing working code to accommodate its own mistakes. This isn’t a theoretical risk. It’s happening now, in production codebases, and it’s harder to catch than any bug the AI might introduce from scratch.

The 4 Golden Signals of Monitoring Explained

As a team, we have spent many years troubleshooting performance problems in production systems. Applications have become so complex that you need a standard methodology to understand performance. Our approach to this problem is called the Golden Signals. By measuring these signals and paying very close attention to these four key metrics, providers can simplify even the most complex systems into an understandable corpus of services and systems.