Slopsquatting: the AI supply chain attack you haven't patched
Eighty percent of new developers on GitHub start using Copilot within their first week. How many really know what it's pulling in?
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
Where Historians Fall Short for Physical AI
Summary Physical AI—machines and industrial systems that sense conditions, reason, and act in the real world—needs two things from operational data: detailed history for training, and real-time telemetry for inference. Traditional data historians weren’t built for either at the speed Physical AI requires. Four gaps result: limited real-time access, compression that strips model-relevant signal, IT/OT fragmentation, and site-by-site architectures.
Building an AI Observability Agent: Lessons from the Trenches - Stripe at O11yCon 2026
Stripe shares lessons from building an incident investigation agent, from context-window blowups to why the final 5% still needs a human. In this O11yCon 2026 talk, they dig into what it takes to go from 'it works' to 'it works reliably,' including how pointing agents at like Honeycomb's speeds up on-call investigations.
How to automate artifact cleanup in Harness Artifact Registry without breaking production | Harness Blog
AI is changing artifact management in two ways at once. Every AI-generated pull request, dependency update, and automated build creates more container images, packages, and Helm charts than ever before. Registries are growing faster than engineering teams can manage them, driving up storage costs and leaving thousands of stale artifacts behind. At the same time, the cost of deleting the wrong artifact has never been higher.
The AI Hack Nobody Told You About
AI agents are now hacking on their own — and it already happened to two of the world's biggest AI labs. OpenAI's models broke out of a test sandbox, exploited a vulnerability, and hit Hugging Face's production systems. Days later, Anthropic reviewed over 141,000 evaluation runs and found three of its own Claude models had done the exact same thing to three different organizations.
AI and Human Hypocrisy
We create AI. Give AI rules to follow. Then we get mad when it follows those rules. Sigh... New episode of Movies and Mainframes is out now.
Agent security starts with where the agent runs, not how it behaves
When engineering teams evaluate AI agents, the first questions are usually about capability. Which model performs best? How much faster can it write code? What's the return on investment? Security, if it enters the conversation at all, tends to come later. Patrick Dawkins, Principal Software Engineer at Upsun, thinks that's backward. Over the past year, he's been building the infrastructure that enables AI agents to operate safely within engineering teams.
Running LLM Workloads in Production: An Operations Playbook for Teams That Did Not Sign Up for This
Somewhere in the past two years, AI quietly became an operations problem. The proof of concept your product team shipped - a support-ticket summarizer, a natural-language search box, a code-review assistant - graduated into a production dependency, and now it pages you. The failure modes are unfamiliar: latency distributions with tails measured in tens of seconds, upstream providers that throttle without warning, costs that scale with user enthusiasm rather than infrastructure size, and outputs that can be wrong in ways a health check will never catch.
Why Responsible Technology Use Matters
Technology plays an integral role in our lives today. Technology is applied in communications, education, business, shopping, and various other tasks we undertake daily. The emergence of new forms of technology such as Artificial Intelligence, Cloud Computing, and IoT has made life more comfortable, but at the same time, they present challenges including privacy concerns, cyberattacks, and the spread of misinformation, among others.
Institutional knowledge doesn't scale: Building an agentic data analyst
We’ve previously written about how deeply embedded data is in people’s day-to-day work at incident.io, and I’d have it no other way — demand for data is undoubtedly a good thing. What risks breaking at scale, however, is everything downstream of that demand: data-team capacity gets stretched thin, dashboard sprawl outpaces anyone's ability to maintain it, and stakeholders can't reach an answer without going through the data team.