84%
AI use is already the baseline.
Developers use or plan to use AI tools in their development process, while 46% actively distrust the accuracy of what those tools produce.
Stack Overflow Developer Survey 2025 · 49,000+ responsesLoading page
Vibecoding assessments for hiring, hackathons, and training your own engineers. Participants 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.
Assessments are not self-serve yet. The first five participants will be free at launch, and all participant data shown here is synthetic.
Why the signal has to change
Assessments need observable work, realistic tasks, and verification, not another outcome-only score.
84%
Developers use or plan to use AI tools in their development process, while 46% actively distrust the accuracy of what those tools produce.
Stack Overflow Developer Survey 2025 · 49,000+ responses78%
Developers said coding assessments do not align with real-world tasks, and 56% called algorithm questions irrelevant to their jobs.
HackerRank 2025 Developer Skills Report19% slower
Experienced developers took longer on real tasks with AI in a narrow randomized study, after predicting it would speed them up, and still believed it had.
METR 2025 · 16 developers, 246 tasks69% vs. 16%
Recruiters said they use resume review to assess technical talent, but far fewer believed it predicts performance.
CoderPad State of Tech Hiring 2026Three assessment conditions
One assessment can use a different condition for each question. Participants see the policy before they begin. You see it beside the evidence.
Agentic contract
Use it when
You need participants to direct, constrain, verify, and recover work produced with AI systems.
Participant receives
Models and modes you approve (Plan, Ask, Code), terminal, web search, and parallel agents.
You receive
Lineage from prompt to tool to edit, plus approvals, sources, ownership, tests, browser validation, and human judgment.
Work-sample breadth
Launch breadth should include production engineering, product judgment, data work, and game development.
Production reliability
Recover an incident queue from stale requests
Inspect task blueprintAPI and data
Plan and execute a backwards-compatible schema migration
Inspect task blueprintSecurity review
Find and repair a role-escalation path
Inspect task blueprintFrontend product
Implement an accessible checkout from a specification
Inspect task blueprintData and ML
Investigate a drift alert and defend the next action
Inspect task blueprintSystems judgment
Choose a release strategy under partial failure
Inspect task blueprintGame development
Repair input sync and frame-budget regressions
Inspect task blueprintCode review
Audit an AI-generated patch before release
Inspect task blueprintAssessment lifecycle
These views reuse production components with synthetic data. The controls, hierarchy, states, and decision model are the product’s own.
Describe the work
Assessment Copilot accepts a job description or brief, a general command, or both before it proposes tasks or conditions.
Build a reviewable assessment plan from the skills the work actually requires.
Paste a job description or a brief, tell the Copilot what you want, or combine both in one message. It pulls from 8,000+ tested algorithmic problems with hidden test cases, plus ready-made build and systems-design questions, and drafts custom builds only for the gaps.
Try an example
Product truth, before product theater
We do not have customer benchmarks to advertise yet. These are the product commitments a design partner can inspect now.
Competency explanations link to the prompt, tool call, edit, test, browser check, or human decision that supports them.
The published pricing and five-participant allowance describe launch pricing, not general availability today.
Production surfaces
Every product crop below is a live production component.
Workspace
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
Select rows, sort real columns, open action menus, preview participant access, and move into the assessment.
Synthetic data inside current production components. Sort, filter, open menus, or use the visible review controls.
Real-world reliability, accessibility, and release judgment.
Total results
8
Completed this week
4
Completed this month
8
Recent completion trend
3 reviewed · latest 4
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 |
Monitor a cohort
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.
maya.chen@example.com
Senior Product Engineer · Reliability
3 questions · Mixed AI conditions
54 of 75 min
AI-native build · five-competency-v1· Review cue: Technical reviewNo role/level calibration key
| Question | Correctness | Time | Replay |
|---|---|---|---|
Incident Queue Recovery Q1 · debug · Deliverable: Pending grade | 2/2 | ~18 min | |
Release Decision Simulator Q2 · debug · Deliverable: Pending grade | 1/2 | ~18 min | |
Accessible Checkout Q3 · debug · Deliverable: Pending grade | 2/2 | ~18 min |
Read the evidence
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.
Participant review
Your decision stays separate from performance, evidence, and integrity signals.
Generate follow-up questions from ambiguous or conflicting evidence, then keep your notes beside the decision.
Make the call
Open the decision editor, choose a disposition, record the evidence reviewed, save it to the audit trail, or generate follow-up questions.
Synthetic data inside current production components. Sort, filter, open menus, or use the visible review controls.
Integrity with due process
The integrity surface should help a human investigate what happened, disclose uncertainty, and avoid turning telemetry into an automatic verdict.
Nothing here is inferred from a participant's face. Why webcam proctoring is off by default.
AI and tool ledger
Policy-aware
Model, mode, prompt, tool, command, approval, and delegation events are read against the policy you set for that question.
Participant experience
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 you 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 us. ATS work is done on request.
Buyer-criteria comparison
This framework compares what a team must evaluate, not isolated editor features. AlgoArena's launch gaps are stated alongside its strengths.
| Criterion | AlgoArena | HackerRank | CodeSignal | CoderPad |
|---|---|---|---|---|
| Evidence-linked rubric, but 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 | |
| Assessment Copilot blueprint and a rubric approved per assessment | 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 | |
| Lineage from prompt through tool, edit, test, and browser to decision | Detailed replay, AI Fluency, and evidence excerpts | Replay, AI conversations, AI Insights, and grading | Captured AI conversation and enhanced playback | |
| Policy-aware process signals, without an independent certification claim | Proctoring, identity, plagiarism, environment alerts, and replay | Proctoring, identity, plagiarism, and recorded sessions | Integrity controls, playback, and reviewer context | |
| Evidence dossier, but 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 | |
| Project builds, systems design, and legacy-debug tasks, backed by an 8,000+ problem library | 7,000+ questions publicly presented | Certified and custom assessment libraries | Screen, projects, take-home, and interview formats | |
| Disclosure and accommodation workflow are launch requirements, with 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, with 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 | |
| Meet with us now. First 5 participants free at launch | Try-free and sales-assisted entry | Self-service and sales-assisted plans | Trial or demo, plus paid plans |
Process observability
Can you 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.
No. Assessments are not self-serve yet while we finish the workflow. Meet with us and we will walk through current reports, participant workflows, AI-policy controls, and evidence replay.
Run Tests on an algorithmic assessment question now grades the hidden cases too and shows one pass count, and a program that crashes shows as a runtime error with its traceback.
ReadIn Assessment Copilot, an approved assessment now goes live from a Publish button on its card, and a card you have approved or dismissed stays that way.
ReadA question can now hand each participant a working API key, kept out of the question text and set up on a machine that can reach the internet.
ReadAt launch, the first five participants will be free. Published pricing applies after that allowance.