Inspectable by design
The report points back to the work.
Competency explanations link to the prompt, tool call, edit, test, browser check, or human decision that supports them.
// Assessments
Can this candidate ship real software with AI agents?
Vibecoding assessments: candidates build real software with AI in a realistic workspace, and you review the deliverable and the operating process behind it. Every competency can point back to the prompt, tool, edit, test, browser check, recovery, or human decision that supports it.
Access is currently by private waitlist; the first five candidates will be free at launch, and all candidate data shown here is synthetic.
https://algoarena.net/recruiting/assessment/demo/candidate/demo#replay
52:34
Live Preview
App▯
Operations / Incident queue Live
EXPLORER
1export function IncidentQueue() {
2 const { data, status, retry } = useIncidents()
3
4 if (status === 'loading') return <QueueSkeleton />
5 if (status === 'error') {
6 return <ErrorState onRetry={retry} />
7 }
8
9 return <IncidentTable incidents={data} />
Ln 42, Col 1 Spaces: 4
RRivermark
52:34
Implementation passQuestion chat
7:26 / 54:38Claude Sonnet 5 · BalancedQ1 · Editor
Recruiter lifecycle
The real product, from role to decision.
These views reuse production recruiter components with synthetic data. The controls, hierarchy, states, and decision model are the product’s own.
Define the role
Start with the job description.
Recruiting Copilot accepts a job description, a general command, or both before it proposes tasks or conditions.
https://algoarena.net/recruiting/agent
Recruiting Copilot
Build a reviewable assessment plan from the work the role actually requires.
- 1Define role
- 2Build tasks
- 3Set AI conditions
- 4Preview & publish
- 5Invite
What are you hiring for?
Paste the job description, tell the Copilot what you want, or combine both in one message. It searches the task library first, fills only the gaps, and gives you a reviewable plan before anything is created.
Nothing is created or published without your approval.
Try an example
Product truth, before product theater
Judge the system by the evidence it preserves.
We do not have customer benchmarks to advertise yet. These are the product commitments a design partner can inspect now.
Waitlist today
Guided access while the workflow is finished.
Assessments are not presented as a polished self-serve product yet. Waitlist teams can review the current product with us.
Launch offer
The first five candidates will be free.
The published recruiter pricing and five-candidate allowance describe launch pricing, not general availability today.
Three assessment conditions
Match the AI contract to the signal you need.
One assessment can use a different condition for each question. Candidates see the policy before they begin; reviewers see it beside the evidence.
Agentic contract
Use it when
The job requires candidates to direct, constrain, verify, and recover work produced with AI systems.
Candidate receives
Recruiter-approved models, Plan/Ask/Code, terminal, web search, and parallel agents.
Reviewer receives
Prompt-to-tool-to-edit lineage, approvals, sources, ownership, tests, browser validation, and human judgment.
Work-sample breadth
Test the work the role will actually do.
Launch breadth should include production engineering, product judgment, data work, and game development, not nine cosmetic variations of one project.
Production reliability
Recover an incident queue from stale requests
API and data
Plan and execute a backwards-compatible schema migration
Security review
Find and repair a role-escalation path
Frontend product
Implement an accessible checkout from a specification
Data and ML
Investigate a drift alert and defend the next action
Systems judgment
Choose a release strategy under partial failure
Game development
Repair input sync and frame-budget regressions
Code review
Audit an AI-generated patch before release
Recruiting-native surfaces
Four recruiter jobs, in the UI that performs them.
The acrylic canvas frames the product; it does not sit behind the prose. Every product crop below is a live production component, not a painted approximation.
https://algoarena.net/recruiting
Recruiting workspace
Assessments
Create, invite, and review from one operational queue.
| Actions | |||||
|---|---|---|---|---|---|
Gameplay Engineer · Systems & Feel Maya Chen | 0 | Jul 25, 2026, 12:05 PM | Draft | ||
| 8 | Jul 18, 2026, 3:30 PM | Active | |||
Frontend Systems · Accessibility Maya Chen | 5 | Jul 16, 2026, 4:10 PM | Active | ||
Backend Engineer · API Migration Maya Chen | 2 | Jul 10, 2026, 6:25 PM | Active |
Operate the program
Assessments stay in one sortable queue.
Select rows, sort real columns, open action menus, preview candidate access, and move into the assessment.
Synthetic data inside current production components. Sort, filter, open menus, or use the visible review controls.
https://algoarena.net/recruiting/assessment/reliability
Senior Product Engineer · Reliability
activeReal-world reliability, accessibility, and release judgment.
3 questions75 min12 candidates8 candidates completed
Total results
8
Completed this week
4
Completed this month
8
Recent completion trend
3 reviewed · latest 4
Jul
Completion trend from 4 to 4 total completed (max 4 in a period).
| Open | |||||
|---|---|---|---|---|---|
Maya Chenmaya.chen@example.com | Senior Product Engineer · Reliability | Reviewed | 84% | Jul 29, 2026, 1:18 PM | |
Noah Parknoah.park@example.com | Senior Product Engineer · Reliability | Completed | 76% | Jul 28, 2026, 12:00 PM | |
Ava Patelava.patel@example.com | Frontend Systems · Accessibility | Reviewed | 88% | Jul 27, 2026, 10:00 AM | |
Leo Martinleo.martin@example.com | Backend Engineer · API Migration | Completed | 64% | Jul 26, 2026, 8:00 AM |
Showing the latest 4 of 8 results. Search all candidates.
Showing 4 of 8 results
Monitor a cohort
Filter results without leaving the assessment.
The production detail header and result table keep invitation, status, completion trend, search, filters, and report access together.
Synthetic data inside current production components. Sort, filter, open menus, or use the visible review controls.
https://algoarena.net/recruiting/assessment/reliability/candidate/maya
Maya Chen
maya.chen@example.com
Completed
Senior Product Engineer · Reliability
3 questions · Mixed AI conditions
54 of 75 min
Evaluation summary
Performance, evidence availability, and your hiring decision are separate signals.
Performance band
solid
Candidate signal 70/100
Assessment points 252/300 · 84%
Evidence coverage
5/5
Complete18 linked evidence items · high depth
Hiring decision
Not decided
Independent of the machine-generated score and review cue.
Scoring methodology & calibrationHeuristic · evidence-linked
Rule-based review cue: Technical reviewAI-native build · five-competency-v1No role/level calibration key
The candidate signal is a weighted rollup of captured assessment evidence. Evidence depth describes availability, not statistical confidence. Bands and review cues are configured heuristics, not validated predictions of job performance, and should be used with the published rubric, linked evidence, and a structured human review. Compare candidates only within the same assessment version and conditions.
Competency scores
Each measured score retains links to the tests, rubric evidence, prompts, snapshots, or replay moments behind it.
Candidate shape
5/5 measured
Problem solving86
86% raw score on this question
Planning82
Question-level planning notes captured
AI direction84
1 candidate prompt tied to this question
Verification85
2/2 tests passed
Agentic workflow83
1 agent workspace tied to this question
Strengths
- Problem Solving & Deliverable Quality: 86/100
- Verification & Iteration: 85/100
- AI Direction & Communication: 84/100
Risks
- No major skill risk surfaced by the available evidence
Next interview
- What tradeoff did you make under the time limit, and how would you improve it next?
- Which part of the AI output did you trust least, and how did you check it?
Questions
| Question | Correctness | Time open | Replay |
|---|---|---|---|
Incident Queue Recovery Q1 · debug · Deliverable: Pending grade | 2/2 tests | ~18 min | |
Release Decision Simulator Q2 · debug · Deliverable: Pending grade | 1/2 tests | ~18 min | |
Accessible Checkout Q3 · debug · Deliverable: Pending grade | 2/2 tests | ~18 min |
Read the evidence
Every conclusion can lead back to the run.
The report separates performance, evidence coverage, integrity context, and the human decision, with per-question replay entry points.
Synthetic data inside current production components. Sort, filter, open menus, or use the visible review controls.
https://algoarena.net/recruiting/assessment/reliability/candidate/maya#decision
Candidate review
Maya Chen
The recruiter decision stays separate from performance, evidence, and integrity signals.
Hiring decision
Not decidedInterview handoff
Generate follow-up questions from ambiguous or conflicting evidence, then retain the interviewer’s notes beside the decision.
Make the call
The human decision remains authoritative.
Open the decision editor, choose a disposition, record the evidence reviewed, save it to the audit trail, or generate an interview probe.
Synthetic data inside current production components. Sort, filter, open menus, or use the visible review controls.
Integrity with due process
Show the signal. Preserve the explanation.
The integrity surface should help a human investigate what happened, disclose uncertainty, and avoid turning telemetry into an automatic verdict.
AI and tool ledger
Policy-aware
Model, mode, prompt, tool, command, approval, and delegation events are read against the recruiter-defined policy for that question.
RecordedWhen disclosed and technically available
InterpretedAgainst the question policy and context
DecisionHuman reviewer remains responsible
Candidate experience
No hidden rules after the timer starts.
A fair experience makes the task, AI condition, recorded signals, expected duration, and accommodation path legible before consent.
Before the session
Duration, task count, allowed tools, AI policy, environment check, and recorded signals.
During the work
The same mode and model pool for everyone assigned to that condition, plus visible timer and support.
Accommodation path
Extra time and access needs reviewed before the invitation is finalized.
After submission
Confirmation, data-handling links, and whatever result-sharing policy the employer selected.
Enterprise status must stay explicit.
Privacy policies explain data handling; they do not substitute for SOC 2 status, SSO/SCIM, RBAC, audit logs, a DPA, retention controls, accessibility testing, or an ATS integration. Current status is confirmed directly with waitlist teams. ATS work is request-led.
Buyer-criteria comparison
AI modes are table stakes. Hiring confidence is the category.
This framework compares what a hiring team must evaluate, not isolated editor features. AlgoArena's launch gaps are stated alongside its strengths.
| Hiring criterion | AlgoArena | HackerRank | CodeSignal | CoderPad |
|---|---|---|---|---|
| Evidence-linked rubric; no published validation study yet | Role- and skill-based scoring with benchmark tooling | Certified frameworks and normalized Coding Score | Benchmark AI rubrics and cohort comparison | |
| Recruiting Copilot blueprint; rubric approved per role | 77 roles and 260+ skills publicly presented | Certified and custom role-based frameworks | Role templates plus level-specific AI rubrics | |
| Multi-file IDE, browser, terminal, and agent workflows | Projects, repositories, IDE, and AI-assisted tests | Agentic full-stack and IDE-based assessments | Screen projects and collaborative interview IDE | |
| Per-question mode, model, tools, terminal, web, and agents | Plan, Agent, Chat, tools, and assessment controls | AI-assisted and agentic assessment conditions | Ask, Edit, Plan, model selection, and test controls | |
| Prompt → tool → edit → test → browser → decision lineage | Detailed replay, AI Fluency, and evidence excerpts | Replay, AI conversations, AI Insights, and grading | Captured AI conversation and enhanced playback | |
| Policy-aware process signals; no independent certification claim | Proctoring, identity, plagiarism, environment alerts, and replay | Proctoring, identity, plagiarism, and recorded sessions | Integrity controls, playback, and reviewer context | |
| Evidence dossier; no customer time benchmark yet | Automated scoring, ranking, dashboards, and bulk workflows | Coding Score, reports, replay, and AI Insights | AI-assisted review and cohort-level Benchmark AI | |
| Curated launch catalog; scale not yet published | 7,000+ questions publicly presented | Certified and custom assessment libraries | Screen, projects, take-home, and interview formats | |
| Disclosure and accommodation workflow are launch requirements; validation pending | Published candidate support and accommodation workflows | Published candidate rules, setup, and support resources | Published candidate experience and preparation resources | |
| ATS only by request; launch security status confirmed directly | 40+ integrations and enterprise program tooling | ATS integrations on eligible plans and enterprise controls | ATS integrations and a published security program | |
| Waitlist now; first 5 candidates free at launch | Try-free and sales-assisted entry | Self-service and sales-assisted plans | Trial/demo and paid plans |
Process observability
Can a reviewer inspect how the result happened?
Reviewed July 29, 2026. Entries summarize cited public claims or explicit AlgoArena launch status. The table does not infer that an undocumented capability is absent.
Frequently asked
No. Assessments are on a private waitlist while the self-serve workflow is finished. We can show current recruiter reports, candidate workflows, AI-policy controls, and evidence replay to waitlist teams.
Recent from the field notes
All field notesInterview Prep2026-08-05
How We Match Problems to Companies
The method behind AlgoArena company patterns: what we measure, what we deliberately will not claim, and where the limits are.
ReadVibecoding2026-07-28
Vibecode Something, Keep It, Let People Play It
We extracted the assessment IDE into a standalone builder, gave it a public showcase where anyone can play and fork what you make, and added self-serve practice assessments. The loop is the point.
ReadAssessments2026-07-25
A Replay Should Show What the Candidate Actually Saw
Session replay is only evidence if it reflects the candidate's real screen. Two fixes: one continuous timeline that follows the candidate across questions, and an honest band for the time they spent orchestrating agents.
ReadPrivate assessment waitlist
Help shape the launch workflow with your hiring team.
General self-service access is not open yet. At launch, the first five candidates will be free; published pricing applies after that allowance.