slaptijack

Treat slow local tests as a developer workflow investment by measuring iteration cost, choosing the right intervention, and assigning an owner to the result.

Give platform requests a small evidence record and a clear routing decision so support, incidents, exceptions, discovery, and funded work do not collapse into one invisible queue.

Make developer platform office hours a fast path to useful guidance and clear decisions without silently converting support conversations into unplanned implementation work.

Keep repository ownership useful by pairing reviewer routing with reachable fallbacks, decision boundaries, and an escalation path that still works when teams change.

Turn repeated developer-workflow exceptions into evidence-backed platform investments without treating every unusual request as a product requirement.

Retire obsolete developer tooling with evidence about remaining dependencies, funded migration support, and shutdown criteria that end the dual-support burden.

Decide when to expand an AI coding agent pilot using evidence about task fit, review capacity, ownership, support costs, and reversible rollout boundaries.

Make reliable engineering work the convenient default by investing in fast checks, paved paths, visible evidence, and clear escalation boundaries for people and AI coding agents.

Use a small set of platform health signals to locate costly engineering friction, choose an intervention, and verify whether the system improved.

Run an AI coding agent pilot as a bounded engineering decision by defining the evidence, guardrails, and stop conditions that would justify its next level of use.

Give AI coding agents a bounded change budget so teams can expand useful autonomy with evidence instead of granting broad repository access on faith.

Build fast local checks, explicit CI contracts, and clear escalation paths so AI coding agents can make small changes with evidence instead of optimistic guesses.

Measure AI-assisted engineering through flow, quality, learning, and customer outcomes instead of surveillance metrics that encourage activity without improving delivery.

Give AI-assisted changes a compact handoff contract that preserves evidence, makes the review boundary clear, and leaves consequential decisions with an accountable human.

Investigate a production issue or unfamiliar repository from the terminal without losing the commands, observations, and uncertainty a teammate needs to reproduce your reasoning.