
Keep CI fast as AI coding speeds up
Use dependency-aware suite selection to shorten CI feedback and reduce compute while keeping the checks each pull request needs.

Use dependency-aware suite selection to shorten CI feedback and reduce compute while keeping the checks each pull request needs.

AI coding draws on human work and abundant compute; reliable delivery depends on clear requirements, architecture and enforced quality gates.

How deep tests, stacked pull requests, adversarial review and isolated environments let me delegate parallel software delivery safely.

AI slop is the result of weak direction and absent editorial judgement, not an excuse to blame the tool.

A multi-agent quality pipeline that replaces routine line-by-line review with executable evidence and deterministic gates.

Long context windows do not guarantee reliable recall. Design retrieval, reranking and prompt assembly so the model can use the evidence it receives.

How to run a nightly AI security audit without giving the model credentials, production access or authority to merge its own fixes.

Claude's invisible marks could improve provenance, but Anthropic has not yet shown that they leave generated code quality and optimisation untouched.