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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Why AI Coding Assistants Fail (And How to Fix Them)

Why do developers stop using AI coding assistants? According to Carnegie Mellon research, the top reason is unhelpful suggestions. Tabnine's Principal Architect John Feeney explains how context transforms AI coding tools from generic to genuinely useful. Learn the 4 Cs framework for maximizing AI assistant value: Context (workspace indexing), Connection (repo integration), Coaching (rules-based guidance), and Customization (fine-tuning). Discover how Retrieval Augmented Generation (RAG) helps AI understand your codebase, not just open source patterns.

Building dbRosetta Using AI: Part 1 of Many

Like many of you, over the last couple of years, I’ve been using AI, or, well, let’s just name it appropriately, Large Language Models (LLM), as a part of my job. I’ve also used it in my hobby. With it, I’ve generated snippets of code, tested data conversions, even built a small database for a presentation. However, to date, I haven’t tried doing everything through the LLM. Now, I’m going to.

Intent-Driven Assertions are Redefining How We Test Software

Traditional UI testing struggles to keep up with rapid design and workflow changes, often focusing on brittle selectors rather than user outcomes. Harness AI Test Automation introduces intent-driven, natural language assertions that understand what teams want to verify, not just how tests are written.

OTel Updates: Declarative Config - A Steadier Way to Configure OpenTelemetry SDKs

Application configs change over time, often in small ways that are easy to miss. They may start simple — a few environment variables, one exporter, nothing unexpected. As your instrumentation grows, you add rules for filtering health check spans, adjust sampling based on attributes, or introduce environment-specific resource settings. Each change makes sense on its own. But months later, the picture can look different across dev, staging, and production.

Managing Alerts: Car Alarms and Smoke Alarms

Building and shipping an application is exciting, you watch your idea come alive and reach users. But once it’s out there, your real job begins: keeping it alive. An app in production isn’t just code running, it’s a living system. It needs monitoring to stay healthy and alerting to warn when something’s off. But there’s a catch: too few alerts, and you’ll miss real issues; too many, and you’ll drown in noise.

The Outage Anxiety Test: Can You Answer These 3 Questions In Under 10 Minutes?

On Oct. 20, the Internet woke up and seemingly chose violence. For more than 12 hours, Amazon Web Services (AWS) went down. From banking platforms to hospital communications to mobile ordering apps, digital services came to a screeching halt. The cause? Two programs are trying to write a DNS entry simultaneously, failing, and leaving the entry blank. Thus began the incredibly costly failure cascade.

AI And Sustainability: Measuring The Impact Of The Generative AI Boom

Before 2022, Alex Hanna worked on Google’s Ethical AI team. Today, she’s the director of research at the Distributed AI Research Institute, a transition sparked by Google’s handling of a paper exposing AI’s growing environmental footprint. So, how bad is it, really? That depends on who you ask. Take Jesse Dodge, a senior research analyst at the Allen Institute for AI. Jesse told NPR that a single ChatGPT query can use as much electricity as keeping a light bulb on for 20 minutes.

Enterprise data centre security solutions: scaling securely for growth and resilience

Securing a data centre requires multiple layers of protection. Physical access controls, surveillance, and network safeguards reinforce one another to prevent disruption. As estates expand and workloads increase, those measures have to scale. If they don’t, gaps appear in both resilience and compliance. A data centre security solution must therefore protect infrastructure day to day while adapting to future requirements. Pulsant delivers this through an integrated framework.
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The Product Manager's Nightmare: Seeing Features Too Late

Sarah stared at her laptop screen in disbelief. The feature her team had been building for three weeks was finally deployed to staging, and it looked nothing like what she had envisioned. The user interface was cramped, the workflow felt clunky, and the color scheme clashed with their brand guidelines. "Can we change the button placement?" she asked during the demo. "That'll require refactoring the entire component structure," replied the lead developer. "It's probably a two-day task now." What should have been a simple adjustment had become a major undertaking.

Part 2: Building a Production-Grade Traffic Capture, Transform and Replay System

When developers try to build realistic mocks and automated tests from production network traffic, the real challenge isn’t just in the capturing—it’s in the data manipulation. Raw traffic is a chaotic sea of patterns, dynamic tokens, environment-specific secrets, and tangled dependencies that seem impossible to untangle by hand. Over my two decades of building these sytems, I learned that solving this problem requires more than brute-force parsing or ad hoc scripts.