slaptijack

A practical way for senior engineers to grow organizational scope by creating durable leverage instead of becoming the default owner of every hard problem.

A practical coaching system for engineering managers who want senior ICs to grow their scope and influence without treating people management as the default next step.

The difference between staff engineer impact and senior engineer output is not that staff engineers stop writing code.

The difference is that output describes what you personally produced, while impact describes the useful change that persists because you were involved.

Senior engineers need both. They are expected to ship difficult …

Many excellent senior engineers stall before staff engineer for a simple reason: they keep optimizing for the job that made them successful.

That is not a character flaw. Senior engineers are usually promoted because they can take hard work, make it concrete, and get it across the finish line. They …

An AI coding agent does not need a clever prompt. It needs an assignment that a reviewer could recognize after the fact.

That distinction matters. “Fix the flaky test” may be enough for a human who already knows the service, its constraints, and the team’s habits. To an agent …

Most build tool wrappers start as kindness.

Someone notices that the "real" command is too long, too easy to forget, or too different between local development and CI. They add a Make target, a justfile recipe, a shell script, a package-manager alias, or a small Python helper. Now the team …

The move from senior engineer to staff engineer is not just a promotion with a bigger title and more meetings.

It is a change in the operating model.

Senior engineers are usually rewarded for strong execution inside a meaningful scope. They can take ambiguous work, turn it into a plan …

AI is very good at making CI failures feel less lonely.

That is useful. A large CI log can be hostile terrain: thousands of lines of setup output, dependency chatter, repeated warnings, retry noise, test framework boilerplate, and one real clue hiding near the bottom. Asking an AI tool to …

AI coding agents can produce more code than your review process can absorb.

That is the useful part and the dangerous part.

The same agent that can trace a bug across five files, update tests, adjust documentation, and clean up a few nearby rough edges can also turn a small …

Build failures become expensive when they stop being reproducible.

The first failure is usually just a problem. The third person saying "it only happens in CI" is when the problem starts turning into folklore. Someone reruns the job. Someone else clears a cache. A third person changes an unrelated file …

CI output is part of your developer experience.

That sounds obvious until you look at the average failed build. A pull request goes red, the developer opens the CI job, and the first thing they see is a scrollback landfill: dependency installation noise, folded shell wrappers, progress bars, warnings from …

AI coding agents are very good at refactors until they are not.

That is the uncomfortable part. The same agent that can rename a helper across a repository, split a giant function, update tests, and clean up repetitive call sites can also make one tiny semantic change that hides inside …

Remote cache misses are where build-system optimism goes to get humbled.

The sales pitch for remote caching is simple: someone already built the thing, so you should not have to build it again. In a healthy Bazel setup, that can be beautiful. CI writes reusable outputs. Developers pull the same …

Local CI commands should be boring.

That sounds like faint praise, but boring is exactly what you want from the command that tells a human developer, a coding agent, or a pull request bot whether the repository is healthy enough to trust.

The problem is that many repositories make this …

AI-written tests are dangerous in exactly the way good-looking tests are always dangerous: they can make you feel safer without actually reducing much risk.

That is not an argument against using AI coding agents to write tests. I use them for test scaffolding, edge-case enumeration, fixture cleanup, and regression coverage …