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The latest News and Information on Continuous Integration and Development, and related technologies.

Parallel Execution in Modern CI: Best Practices & Results | Harness Blog

Definition: Parallel execution in CI is the practice of running independent build, test, or deployment tasks concurrently to reduce feedback time, improve resource utilization, and control infrastructure costs. Developers often spend almost half their time waiting for builds that could be faster. Simply adding more resources is not enough. Real improvements come from planned parallelism, using concurrency together with test intelligence, caching, and strong governance.

Intelligent Caching for CI/CD Build Optimization | Harness Blog

‍ We've all been there. You push a PR, grab coffee, check Slack, maybe start a side conversation — and your build is still running. Multiply that across a team of 50 engineers, and you're looking at hours of lost focus every single day. Slow CI/CD builds don't just waste time. They generate a steady stream of "CI is slow" tickets that eat into your platform team's roadmap. Intelligent caching is one of the fastest ways to break that cycle.

CI Pipeline Optimization Guide for Platform Engineering Leaders | Harness Blog

Definition: CI pipeline optimization is the practice of reducing build and test time and the cost per build by running only what matters, reusing unchanged components, and enforcing standardized governance. Platform teams are wasting thousands of hours every year because their pipelines aren't working right. Developers wait 45 minutes for builds. Jenkins consumes 20% of your team's capacity on maintenance.

What are test hooks in AI-native development?

Summary: A test hook connects a test or lint command to an event in your AI coding agent’s workflow. When the event fires, the agent runs the command automatically. If it fails, the agent’s action is blocked. You can wire your existing test commands into your agent’s lifecycle hooks to get deterministic local validation before code ever reaches CI. AI coding agents write code at a pace where stopping to manually run tests breaks your flow.