Operations | Monitoring | ITSM | DevOps | Cloud

Cloud computing empowers organizations to access IT resources on demand, over the Internet, and on a pay-per-use basis. Thus, your company does not need to purchase, install, operate, and upgrade hardware for physical data centers. Instead, you can rent resources as needed from cloud service providers such as Amazon Web Services (AWS). For instance, AWS provides compute, storage, database, networking, machine learning, data lake, analytics, security, and IoT resources/services.

Distributed architecture is increasingly being adopted in current software systems because it brings great scalability and flexibility, keeping them resilient under real-world conditions, Unfortunately, this new distribution also introduces new points of failure in the systems. Traditional testing methods are no longer enough; they focus only on whether a system works, not on whether it keeps working under stress or failure. That is where chaos engineering comes in.

CircleCI gives you deep visibility into your builds, workflows, and tests, but jumping between browser tabs, copying project URLs, or re-authenticating across tools can slow things down. What if your IDE could just show you the projects you’re working on and let you act on them directly? This post shows how to use the list_followed_projects tool in the CircleCI MCP server to browse and interact with your CircleCI projects by chatting with an AI assistant inside your IDE.

Heroku and AWS offer distinct benefits for startups and mid-size companies. This guide compares pricing, scalability, security, and developer experience to help you choose the right cloud platform based on your team’s needs and growth goals.

One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information. This is where RAG comes in; RAG addresses these critical gaps in traditional LLMs by incorporating current and new information that serves as a reliable source of truth for these models.

Earlier this year, I spent a few weeks in India, visiting universities, speaking at meetups, and catching up with founders. What stood out wasn’t just the excitement about AI, but the focus on what it can actually do today. The curiosity about GenAI and big-picture questions around AGI is there, but most conversations centered around real needs: learning faster, applying for jobs, and getting healthier.

Data centers are energy-intensive, and with the surge in AI-driven workloads, their global energy consumption is projected to more than double by 2030, potentially surpassing the current electricity consumption of Japan. For most data center operators, energy is one of their largest recurring expenses. As demand for data center capacity continues to grow and energy prices fluctuate, energy efficiency is no longer just a sustainability goal, it's a core business concern.

Security logs are the quiet sentinels of your Linux systems, recording critical information that can mean the difference between detecting an intrusion and discovering a breach months too late. For most DevOps professionals and system administrators, these logs contain valuable insights that often go untapped. While they're essential for compliance, their real value lies in providing visibility into your system's security posture and operational health.