Outship

Outship's core bet is that the interview should look like the job. The job now involves an AI coding agent, a terminal, and a repo full of other people's code, so the assessment environment is a real VM with VS Code, dependencies installed, and Claude Code and Codex available in the same session. The homepage walkthrough shows what that looks like in practice: the candidate reads the Dockerfile, notices the api pods are OOM-killed at 600 MB against a 1.21 GB image, swaps python:3.12 for a multi-stage distroless build, tightens the Kubernetes memory and CPU block, and dry-runs the deployment — all of it captured line by line. What makes that useful rather than voyeuristic is the structure layered on top. Every prompt, edit, command, and decision is recorded into a time-stamped event log, then analyzed against a rubric and tagged into categories. The vendor's own examples are unusually specific and worth reading as a description of what the product actually measures: using plan mode before touching the auth flow (despite the session already running with accept-edits on), pushing back on a hallucinated Pydantic validator, catching an N+1 query the agent left unbatched, writing a three-option tradeoff before choosing SQS over polling, folding three near-identical handlers the agent copy-pasted into one helper, memoizing a list filter to kill avoidable re-renders, adding a timeout on outbound HTTP. That list is the product. It is the difference between 'the tests pass' and 'this person knows what the agent did wrong.' Strengths. The audit trail is the moat — a full replay of file reads and writes, bash commands, and AI interactions means you can review a session asynchronously instead of sitting behind the candidate, and multiple reviewers can assess the same session against a shared rubric. Task setup is flexible: import an interview project from GitHub, write a custom task like Dockerfile optimization or Kubernetes tuning, or have the candidate ship a real PR to your product. GPUs are available for specialized workloads, so ML and inference tasks are in scope rather than theoretical. Running multiple agents simultaneously lets you see whether someone adapts their prompting when they switch tools. Weaknesses and unknowns. The observable agent surface is Claude Code and Codex; if your team standardizes on something else, the session won't reflect how they actually work. The vendor explicitly does not design this for entry-level roles or for organizations committed to whiteboard and resume-based hiring — the value depends on AI fluency being a real job requirement, which it isn't everywhere. Scalability for high-volume pipelines and the commercial model are things the pages we reached don't answer, so budget and rollout questions need to be settled directly with the vendor, not assumed from this page. Where it fits. Platform and infrastructure teams hiring senior or full-stack engineers who will live inside an agent all day; startups that want one