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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Cut your environment setup time in half with Chunk sidecar snapshots

When you’re building with AI, you can get a lot done in 30 seconds. Waiting minutes for CI feedback on your latest change can feel like an eternity. Chunk sidecars are designed to give you feedback fast, running your full test suite against the same Linux environment as CI, directly inside the agentic loop. Traditional CI pipelines can take five or ten minutes to catch a basic lint error or failing unit test.

Why we built relaxAI, and where your AI data actually goes

Sandboxing your AI agent is only half the story. The other half is where your data goes when it hits your LLM provider's API. In this clip from our secure execution agents webinar, Ben Norris, founding engineer at relaxAI, explains why the sovereignty of your AI provider matters just as much as the security of your agent's environment and why relaxAI was built on a sovereignty-first principle, with inference running exclusively in the UK and no foreign data transfer.

Escaping the AI Tokenomics Trap in Enterprise IT

AI adoption has accelerated faster than most organizations expected. What started with chatbots has quickly evolved into AI systems capable of making decisions across enterprise environments, with the promise of faster service and more efficient teams. But many organizations are discovering an unexpected challenge: as AI usage expands, costs become harder to predict. Most AI platforms operate on token-based pricing models.

Introducing Upsun Dispatch

AI has made writing code fast, and you can feel it. Commits are up, pull requests are up, new repos spin up over a weekend, and your engineers swear they are faster. But where are all the new products? If every team really got faster, the software you use every day should be getting visibly better. AI helped your engineers ship more code. It didn't help your team ship more products.

What nobody tells you about platform engineering at scale

Platform engineering has become one of the most discussed topics in cloud native infrastructure. Yet despite the rising focus, most conversations around platform engineering skip over the uncomfortable truths. What actually works at scale? When should you build versus buy? And how do you avoid the traps that trip up even experienced teams?
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From firefighting to forward planning: a practical route to operational innovation

Operational innovation is often treated as a back-office efficiency exercise, but in practice, it is becoming a strategic discipline. As AI moves deeper into day-to-day operations, technical leaders need a clearer way to cut toil, reduce risk and build the capacity to innovate. For many operations teams, it starts with incident management. When responders are trapped in noisy alert streams, manual escalations and fragmented workflows, innovation is pushed aside by the urgent work of keeping services available.
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Five things your logs will never tell you

A customer escalation hit my queue when I was on the customer smoke jumpers team at an observability vendor. My team was the group that parachutes into Fortune 500 accounts one bad week from churning and usually after a big customer outage. The customer had filed a billing dispute three weeks earlier and their on-call engineers were stuck. They had our full stack: logs, metrics, traces, end-to-end instrumentation, every product we sold and some we didn't. They could see the request came in. They could see it returned a 500. They could not see the body. The trace was sampled out. The log line was truncated at 4KB.

Why ITSM Still Isn't Solving Tickets (And What Comes Next)

Most ITSM platforms make it easier to submit tickets. They don't make it easier to resolve them. As we said in our webinar: "A better front door without backbone orchestration is just a faster handoff." The future of IT isn't faster ticket creation. It's autonomous ticket resolution powered by AI, automation, and orchestration.