Operations | Monitoring | ITSM | DevOps | Cloud

The Journey to Achieving Hyperscale Availability with AI-Driven Prediction

At hyperscale, a regional cloud outage is not merely a technical disruption—for Samsung Account, which serves 2.1 billion users across three global regions, it is an immediate global service crisis. Fragmented, region-siloed monitoring creates blind spots that make early detection nearly impossible, leaving SRE teams perpetually reactive rather than predictive. The path to proactive reliability requires both a philosophical shift and a foundational change in how observability data is collected, unified, and reasoned over.

From a $28,000 AI Bill to $0.60 Per Ticket

Engineering teams are burning through AI budgets with nothing to show for it — $100M across 10,000 engineers and no cost per run, no cost per outcome, just a number that keeps climbing. When it runs dry, your infrastructure upgrade gets cut. Harness ties every AI token to the outcome it created: cost per run, cost per resolved ticket, and anomaly detection before the invoice hits. One customer went from a $28,000 black box bill to $0.60 per ticket.
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From Dashboards to Conversational AI: The Evolution of UI in IT Products

The way IT teams interact with technology has changed dramatically over the years. From early text-based interfaces to today's dashboards and now conversational AI, each stage has reshaped how we monitor, diagnose, and understand complex IT environments. But while dashboards gave us visibility, they often led to more questions than answers. In this post, we briefly explore the evolution of UI in IT products and how conversational AI is bridging the gap between data and understanding.

Which Bugs AI Agents Fix Better With Traffic

In the first experiment, I wanted a baseline: if an AI coding agent gets the same production signal a human would get, can it fix bugs in a codebase it has never seen? Yes, but only when I gave it better context. With only an alert, the agent passed 51% of the runtime tests. When I added captured traffic, the actual request and response for the failing call, it climbed to 77%. This post is the second pass.

What happens to software when agents never stop coding?

Before AI, developers pushed code a few times a day. Now agents are pushing it thirty times, and they’re not stopping. Aditya Jayaprakash (JP), the founder who hit a million in ARR with four people in under two years, joins our CEO, Michael Reid to break down what software looks like when agents never stop coding, why pipelines now run dozens of times a day, and how his platform absorbs customer bursts the hyperscalers can’t.

AI Is Not a Switch: The Real Path to AI-First Operations

Organizations are no longer asking whether to adopt AI; that question is settled. The focus now is on reaching a point where AI is doing meaningful operational work—or as the industry calls it, being “AI-first.” But being “AI-first” isn’t binary. You don’t go from zero AI to meaningful autonomy by flipping a switch. In reality, getting there means moving through distinct stages.

CI Can't Keep Up With AI | Blacksmith CEO & Co-Founder Aditya Jayaprakash

AI coding agents are writing software faster than ever. But what happens when the systems responsible for testing and validating that code can't keep up? In this episode of Uplink, Michael Reid sits down with Aditya "JP" Jayaprakash, Co-founder & CEO of Blacksmith, to explore why continuous integration (CI) has become one of the biggest bottlenecks in modern software development.