As reflected in the rising cost of graphics processing units (GPUs), today’s builders of artificial intelligence (AI) infrastructure have made a clear, but faulty assumption: more and faster GPUs will
Artificial intelligence creates code at staggering speeds for development teams. This rapid generation introduces unexpected problems with accuracy and security. Programmers face mounting pressure to verify these automated outputs before
“AI will take our jobs!” has become a classic joke nowadays. While many don’t take it seriously, some industries are already approaching this technology with caution. In sales, where trust,
It’s hard for us to imagine life without mobile communications or the internet. And when a new solution appears on our desktops, it quickly becomes a part of our everyday
Your New 24/7 Listening Ear: Why AI Therapist Chatbot is a Go-To for Daily Support – The time is 11 PM. A hard work deadline, a fight with a friend,
With 18 years of experience spanning backend engineering, microservices architecture, and AI infrastructure, Rajesh Kesavalalji has witnessed the evolution from traditional server monitoring to the complex observability challenges of modern
AI is expanding its capabilities. It’s no longer just about generating text or code — today’s systems can serve as 24/7 assistants, launch full applications, and oversee entire multinational operations,
From customer service chatbots to AI-assisted coding tools and document summarization systems, AI employees are already embedded in workflows across support, sales, product, and engineering. While the front-end footprint of
Traditional observability focuses on metrics like uptime, CPU usage, and HTTP errors. But AI systems introduce new failure modes – hallucinations, low-quality outputs, performance drift, and untraceable bugs. You can’t just