InterviewOS

Replace coding puzzles with real-work simulations.
Open infrastructure for production-grade technical assessments.

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What is InterviewOS?

InterviewOS helps teams evaluate candidates through job-relevant tasks instead of algorithm puzzles.

Candidates complete realistic assignments using their normal tools. InterviewOS records the full workflow and generates structured evaluation reports so reviewers can see:

• what the candidate shipped
• how they approached the work
• how they debugged, used tools, and made tradeoffs
• how they explained decisions along the way

Not just output. The process.

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How it works

1) Create an assessment

Define the task, add follow-up questions, and configure reporting requirements.

2) Invite a candidate

Send a secure link. No account setup required.

3) Candidate completes the work end-to-end

Candidates work normally using their preferred tools. InterviewOS captures the workflow and responses.

4) Review a structured report

Reviewers get a high-signal summary plus the underlying evidence needed for calibration.

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Why InterviewOS exists

Hiring is still optimized for puzzle performance. Modern engineering is not.

Real work involves:

• debugging unfamiliar code
• reading and applying documentation
• using AI tools responsibly
• working through ambiguity
• communicating tradeoffs and decisions

Short algorithm challenges rarely measure these skills. InterviewOS is opinionated: real work beats rehearsed tricks.

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InterviewOS vs traditional coding interviews

Traditional puzzle interviews	InterviewOS
tests memorization and prep	tests job-relevant execution
output-focused	workflow and decision-focused
easy to game	harder to fake
no realistic context	realistic tasks and constraints
weak reviewer calibration	evidence-rich review (recording + report)

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Run locally in one command

Prerequisite: Docker Desktop running.

make dev

This starts:

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• Local email inbox (Mailpit): http://localhost:8025

A bundled sample assessment is included so you can test the full flow immediately.

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What works today

• end-to-end invite flow with frontend + backend
• POST /assessments/start plus legacy POST /start-assessment
• invite lifecycle APIs: bulk send, resend, verify token, and mark-taken
• local out-of-box mode (no AWS account required)
• bundled sample assessment archive for immediate testing
• local SMTP inbox via Mailpit in Docker Compose
• admin dashboard foundation at /dashboard backed by:
  • GET /api/assessments
  • GET /api/candidates?assessmentId=<id>
• assessment creation flow foundation:
  • /new-assessment (title + context)
  • /selection-questions (question pick + create)
  • GET /api/questions, GET /api/assessments/check-title, POST /api/new-assessments
• assessment result + invite-management foundation:
  • /assessment_result/:id
  • GET /api/assessments/{id}
  • POST /api/invite/bulk and POST /api/invite/resend now support assessmentId / candidateId
  • resend UX polish: success toast + status label rendered as resent at <timestamp>
• candidate flow baseline at /take-assessment?token=... using copied Assessment.js recording pipeline with compatibility endpoints:
  • GET /api/public/assessment/{id}
  • GET /api/reflection/sections
  • POST /get-presigned-upload-url, PUT /local-upload/{key}, POST /notify-recording-upload
  • POST /api/recording/start-multipart-upload, POST /api/recording/upload-part, POST /api/recording/complete-multipart-upload
  • POST /upload-zip, POST /download-assessment
• report experience baseline:
  • report route at /report/:id backed by persisted candidate report payloads from local artifacts
  • report API compatibility endpoint: GET /report/{id}
  • assessment-result action now includes View Report
  • POST /upload-zip now triggers assessment-linked background scoring dispatch and persists report records in local SQLite
  • local screen-time analyzer hook is enabled for uploaded workflow recordings (duration-based baseline in OSS mode)
  • candidate completion now lands on a loading screen that polls report readiness and auto-redirects to /report/:id
  • assessment4 dual-artifact upload path is supported via POST /upload-assessment4 (submission zip + notebook)

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Architecture (high level)

InterviewOS is split into:

• frontend/: candidate and admin UI
• backend/: API, invite lifecycle, local SQLite state, assessment packaging, and report scoring pipeline
• docker-compose.yml: local end-to-end dev environment (including Mailpit)

For development details, see CONTRIBUTING.md.

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Roadmap

Planned next steps:

• recording reliability hardening for multipart and resume/error handling
• deepen report parity (newreport) with richer diff/code-review panels and media playback
• tighten candidate-to-report linkage so completion always resolves to the correct report id
• expand evaluator parity from baseline checks to old-repo full autograder/test-case flows by assessment type
• automated evaluation and rubric scoring
• ATS and webhook integrations

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Status

InterviewOS v1.0.0 is production-ready for the open-source local workflow. Core migration includes invite, dashboard, assessment creation/result, candidate assessment flow, recording upload, and generated report viewing with readiness polling.

If you try it and hit sharp edges, please open an issue. Feature requests and PRs are welcome.

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Contributing

See CONTRIBUTING.md.

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Security

See SECURITY.md. Please report sensitive issues responsibly.

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Hosted version

InterviewOS is the open infrastructure.

If you prefer a fully managed platform with ready-to-use simulations, automated scoring, and enterprise features:

https://foretokenai.com

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License

MIT. See LICENSE.