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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.

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.

Automating your synthetic test infrastructure with Datadog Synthetic Monitoring and Terraform

Testing ecosystems contain massive amounts of data, including outlined test scenarios, prerequisite configurations, and the tests themselves. As a result, these ecosystems are prone to data sprawl. This makes it difficult to prevent configuration drift and quickly spin up new tests, especially at the frequency needed to support a fast-growing application. Teams can handle these challenges by treating their tests as part of their application infrastructure.

Puppet Edge Across Lifecycle: Day 0, Day 1, and Day 2

Puppet Edge extends your Puppet automation to now include all your network devices, providing a centralized platform to manage your entire infrastructure, enabling teams to work together efficiently. Automate tasks, manage configurations, and ensure compliance across all your devices from one place. This video discusses how to use Puppet Edge for Day 0 planning and provisioning, Day 1 device configuration, and Day 2 ongoing operations, streamlining your workflows, and reducing manual errors.

Streamline feature management with Harness MCP and Claude Code

Harness now supports the Model Context Protocol (MCP) for Feature Management and Experimentation (FME), enabling developers to interact with feature flags directly from AI-powered IDEs like Claude Code and Windsurf. The FME MCP tools make it easier to explore, understand, and manage feature flags through natural language, streamlining delivery and release workflows without leaving your development environment.

Validating chaos experiments with GCP Cloud Monitoring probes

GCP Cloud Monitoring probe let you transform your existing GCP metrics into automated pass/fail validation for chaos experiments, eliminating subjective observation in favor of objective measurement. With flexible authentication options (workload identity or service account keys) and PromQL query support, you can validate infrastructure performance against defined thresholds during controlled failure scenarios.