AI writes code quickly. Why does delivery still wait on CI?
A collected excerpt from Tw93 describes CI as a bottleneck after faster AI coding, and mentions work on Mole, Kaku and Pake. Separate queueing, setup and test time before choosing an optimization.
What to consider
Locate the wait in an actual commit before changing the pipeline. Tw93’s collected excerpt says AI sped up coding while validation, especially GitHub Actions, remained slow, and mentions optimizing CI for Mole, Kaku and Pake. It supplies no configuration, before-and-after timings or failure rates.
Separate queueing, setup and execution
Start with a recent run you maintain. Record queue time, dependency setup, build, tests and artifact generation separately. Total duration alone can make a queue look like a slow test suite, or send you toward optimizing an insignificant step.
Keep conditions and outcomes when rerunning a commit. Cache reuse and external-service failures can affect comparisons. One clean run versus one abnormal run does not establish a general speedup. We did not inspect these repositories’ workflows or calculate their results.
Return useful failures earlier
Identify checks that quickly reveal clear errors and those requiring long builds or environment startup. In your own pipeline, consider returning the quick failures earlier so authors can act before the full run finishes. Keep the complete required-check list independent of feedback order.
Before reducing duplicate runs, establish whether a newer commit supersedes the older one and whether old artifacts are still needed. Validate caching or parallelism against dependency changes, stale results and hidden failures. These are evaluation ideas, not implementations disclosed in the excerpt.
Include failure recovery in the delivery comparison
Compare time to actionable feedback, completion of required checks and reruns after failure on similar work. A shorter displayed duration is insufficient if authors struggle to locate errors or must manually restore omitted checks before release.
This approach is for pipelines you maintain. Respect an external repository’s published expectations before proposing changes. The excerpt supports the author’s view that validation remains a bottleneck, not a measured speedup, cost saving or universal outcome.
Source links
The links below were supplied with the collected records. This article offers independent analysis of their summaries; we have not individually checked the original sources or tested the products.