Early Blog

Early fixes bugs and adds tests. Your engineers review the changes and control the merge.

Give QA a path from finding to repair. Give engineering a concrete fix to review.

Over 50% of estimated implementation effort went to repairs. What does that mean for release confidence?

More changes arrive. The release date stays. QA needs more than a bug count to decide what deserves attention and whether the release is ready.

Coding agents can finish the implementation before the team has answered the questions that determine whether it is ready to release.

A clean pull request is evidence about the change. It is not evidence about every behavior the release could affect.

A 2026 control stack for moving agent-generated code from a completed task to an accountable production decision.

The better question is whether we could have caught the regression before production.

A daily view of release risk, analysis freshness, finding outcomes, and verification activity.

I removed one open source system, doubled down on open source, and rebuilt our website as a repository I could control.

Claude Code and Codex made agents capable of doing the work. They did not transfer responsibility for what ships.

The bottleneck in software delivery has shifted from writing code to verifying what changed.

The hard part is no longer spotting ugly code. It is proving a clean change did not alter behavior the model never understood.

Compare AI code review tools by what they find, how they help fix it, and the evidence engineers can review.

An impact set is useful only when the team knows how it was produced, what it observed, and what remains uncertain.

A prompt-driven workflow for onboarding a repository, setting up Jest, testing backend and frontend code, and reviewing the results.

A clean diff and passing tests are useful evidence. They do not show every existing behavior a release candidate may have changed.

Choose tools by the bottleneck they remove, the evidence they produce, and the work they add elsewhere.

The category has moved beyond AI-assisted test scripts. The practical question is which kind of evidence your team needs before it ships.

Measure how engineering effort becomes valuable, reliable product change. Code volume, pull request counts, and AI usage do not prove productivity.

The best extension stack is not the longest. Choose one tool for each job and remove anything that does not earn its place.

AI code review earns trust through a clear role, maintained context, bounded permissions, evidence, and accountable approval.

The best coding agent depends on where you work, how much autonomy you want, and what your team needs to control.

Choose a language-native collector first, then add the reporting and governance layer your team actually needs.

Read the Tech Crunch article about Early's funding and investors.

It’s been 15 years since I wrote my last line of production code, and then, in the past 7 months, I generated over 30,000 unit tests and 300,000 lines of working test code...