Operations | Monitoring | ITSM | DevOps | Cloud

Starting on Aug. 1, 2025, AWS will bill for the initialization (INIT) phase of Lambda functions, bringing a key change to how you are charged for serverless workloads. This billing update will impact functions using managed runtimes with ZIP archive packaging, which previously excluded the INIT phase from the billed duration. For teams that rely heavily on AWS Lambda, this is a small but significant change. The INIT phase, while short, could introduce costs that were previously invisible.

Rancher Continuous Delivery (known as Fleet) can be used in a workflow to deploy applications to many clusters. With its GitOps support, it enables downstream clusters to pull updates from a Git repository. We know of users that monitor several hundred Git repositories and deploy to a thousand clusters. To make this scale possible, several intermediate steps are necessary. First, the application is converted into separate bundles, which are then targeted at clusters.

Mobile apps are a little different from services run on servers. You build your mobile app, you ship it off to the world, and then it gets run by the end user on their own machine. If your app is running poorly on some percentage of users’ devices, you may never know. That’s where observability comes in. There are certain important metrics that every mobile app has in common.

Modern engineering teams spend a lot of time and resources in setting up monitoring of their production systems - tracking uptime, catching errors, and responding to incidents before customers ever notice. But what about the journey before code reaches production? For most teams, observing the CI/CD pipeline is either an afterthought or completely overlooked. While we recognize its importance, do we truly understand how well our CI/CD process is functioning?

So you're building an MCP server for your project or service, to allow AI chatbots and agents to interact with it? Great! You've decided to build it using Cloudflare Workers, have written the code, shipped it, and the first users are getting onboard: you're officially running it in production. That's when problems start. I'm not here to dissuade you from shooting your shot, but let's make sure you've got your bases covered in production when something inevitably goes wrong.

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.

KIP‑1150 - Diskless Topics is live. KIP-1150 Diskless Topics ship in Aiven Kafka BYOC on AWS, GCP, and Azure. Data goes straight to object storage—no local replicas, up to 80% lower TCO.

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.

Let's face it - from an experienced developer's perspective, most software trends are, put lightly, incredibly annoying. The last thing a grizzled, old, technical wizard wants to hear is some half-brained junior developer telling them to switch their SQL server to MongoDB, replace the PHP EC2 with serverless Python, or rewrite their entire front-end with HTMX. The hype-train is so intense that even watching TV feels risky, as you might see something as absurd as an ad for AI toothpaste.

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.