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How Financial Institutions Are Rethinking Risk Management in a Digital-First World

Financial services have undergone a rapid digital transformation over the past decade. Nowadays, institutions are able to scale up faster and service customers more efficiently through cloud infrastructure, real-time payments, and API-driven platforms. But this shift also introduced a more complex risk landscape. Risk management is no longer confined to compliance teams and periodic audits. It's now embedded in day-to-day operations. As financial institutions modernize, they need to rethink how they identify, monitor, and mitigate risks across their entire tech stack.

Advanced Tracking Systems For Modern Hunters

Hunters find new ways to navigate the woods with high-tech gear. Staying on the right path is much easier when you have mapping tools in your pocket. These systems help you track movement in any weather condition. Modern hardware keeps you safe and helps you find the best spots for your next trip. These devices offer better precision than the old paper maps many used in the past. You can see your exact location with the push of a button.

Isolate a User Session in Datadog Synthetics with proxymock

A customer pings support: “I tried to check out twice this morning and got a 500 each time, but it works fine for everyone else.” The session ID is in the email. You have full request/response capture in your environment, you have Datadog Synthetics already running browser checks against the same flow, and you still spend the next two hours grepping logs because none of those tools let you say “show me just this user’s requests, in order, and re-run them.”

DORA Metrics in the AI Era: Why Deployment Isn't Faster

DORA metrics in the AI era reveal a paradox: PR volume is climbing, but deployment frequency is staying flat. In this talk, GitKraken's Director of Product Jeff Schinella breaks down why AI-accelerated code generation is creating a review bottleneck that your DORA metrics can't fully explain on their own. Jeff walks through how PR metrics (cycle time, first response time, code churn, and PR size) serve as the leading indicators behind your DORA data. If your deployment frequency is flat while PR counts go up, the bottleneck isn't your devs. It's your review capacity.

Ticket Taker to Team Leader: Managing an Agentic IT Workforce

The promise of AI in IT service management has been circulating for years. Chatbots that deflect tickets. Virtual agents that answer FAQs. Automation that routes requests. These are useful, but probably not the dream-state you were originally sold. What's different today is the arrival of agentic AI: systems that don't just respond to instructions but reason, act, and adapt across multi-step workflows with real consequences. The question for IT leaders is no longer whether to adopt agentic ITSM.

Context Engineering: How to Manage AI Context at Scale

Context engineering is the practice of managing the information an AI model sees (documents, tool outputs, memory, and structured metadata about the systems it reasons over) so it can make accurate decisions inside a real engineering organization. Most engineering teams have access to the same AI coding agents: Claude, GPT, Gemini, the major variants everyone is shipping. The model is no longer the differentiator.

Why dashboards still matter in the age of AI

I recently gave a talk at Experts Live India 2026 about SquaredUp, and even before getting into the demo, there was one question I knew I had to address: Is the dashboard era over? It's something we're all hearing more. "Just ask AI." "Agentic AI will build your dashboards automatically." "Why bother with static views when a chatbot can answer anything?" It's a fair question. Answering it requires a clear understanding of what a dashboard represents.

Faster fixes, less context sharing: how Grafana Assistant learns your infrastructure before you even ask

When an unexpected alert fires these days, most engineers' first move is to ask their AI assistant for help.You ask why your checkout service is slow and the assistant gets to work, but it can't get any meaningful insights—at least not quickly—without the proper guidance. So, the next thing you know you're sharing deals about your existing data sources, the services you have running, how they connect, which labels and metrics matter, and on and on.