Hire Engineers Who Build with AI. Not Ones Who Memorise Algorithms.
LeetCode tells you if a candidate can reverse a linked list under stress. That skill has never mattered less. Candidline tests what actually matters in 2025 — how engineers think, design systems, and collaborate with AI to ship real products.
4 hrs
Real-world time limit
100%
AI usage permitted & scored
Live
Running app reviewed
The interview process hasn't caught up with how engineering actually works
In 2025, GitHub Copilot writes boilerplate. Claude debugs stack traces. GPT explains unfamiliar APIs. The best engineers are the ones who know what to build, how to break it down, and how to direct AI to build it faster.
Yet most technical interviews still test candidates on problems they'd never solve on the job — without documentation, without Google, and definitely without AI.
You end up filtering out excellent engineers and hiring people who are good at a skill that's becoming increasingly irrelevant.
Candidline vs. Traditional Technical Tests
| What's tested | Candidline | LeetCode / HackerRank |
|---|---|---|
| Tests real-world engineering | ✓ | — |
| AI usage is expected and scored | ✓ | — |
| Working hosted app required | ✓ | — |
| Captures how they think, not just what they write | ✓ | — |
| Problem reflects your actual stack | ✓ | — |
| Reviewer can test the running app | ✓ | — |
| Algo memorisation required | — | ✓ |
A real environment. A real problem. Four hours.
Candidates get a fully configured cloud coding machine — no setup required. AI is not just allowed, it's expected.
Candidate receives a link
No installs, no setup. They open a URL and get a full cloud IDE with a Monaco editor, terminal, and Claude AI assistant — ready to go in seconds.
Problem is pre-loaded in their environment
PROBLEM.md is already there when they open the terminal. Mock API servers are running locally inside their container. They read, they plan, they build.
They build with AI — we watch how
Claude is available throughout. Every message is logged. The best engineers use AI as a thinking partner. The ones to avoid use it as a search engine replacement.
They host and submit
Their running app is accessible via a preview URL. Reviewers can use it live. The submission captures code, Claude conversation log, and the running state.
What candidates get
A full engineering workstation in the browser, backed by an isolated Ubuntu container.
Browser-based IDE
Monaco editor (the engine behind VS Code), file explorer, and syntax highlighting for every language. No installs, no configuration.
Full terminal access
Real bash shell in an isolated Ubuntu container. Install packages, run servers, debug logs — exactly how they'd work on the job.
Claude AI assistant
Built-in Claude chat with a configurable token budget. Every message is logged — revealing how the candidate uses AI as a thinking partner.
Pre-seeded mock services
Your problem statement's mock APIs run automatically inside the container. For the travel booking challenge: 5 live hotel PMS instances on localhost.
Live preview URLs
When the candidate starts their server, it's immediately accessible via a shareable preview URL — so reviewers can test the live app.
Time-bounded sessions
Configurable time limit (typically 4 hours). Auto-submits on expiry. Reviewer gets code, Claude log, and a live running app to evaluate.
What the AI evaluation looks for
Not just "does the code work" — but how they got there.
AI collaboration quality
Does the candidate direct AI effectively, or just paste prompts hoping for magic? The Claude log shows every question they asked, every choice they made.
System design under pressure
How do they break down a complex problem? Do they reason about scale, failure modes, and trade-offs — or just start coding?
Working, deployed code
They don't just write code — they build and host a running application. Reviewers can access it live after submission.
Debugging and problem-solving
When things break (and they will), can they diagnose root causes? The terminal history shows their entire debugging journey.
Technical communication
How well do they explain architectural decisions? Do they document trade-offs? Clear thinking shows up in the code and the chat log.
Scope management
A senior engineer ships something working in 4 hours, not a perfect half-finished system. We assess judgment, not just execution.
The Claude log is the most revealing thing in the submission
Two candidates can produce the same working code. The difference between them shows up in how they used AI to get there.
❌ Junior signal
"Write me a travel booking site with React and Node."
✓ Senior signal
"What are the trade-offs between optimistic and pessimistic locking for a hotel booking system where multiple OTAs compete for the same room?"
You (14:03)
I need to handle the race condition where two users book the same room simultaneously. The PMS returns 409 on conflict. Should I use optimistic locking at my layer or trust the PMS to be the authority?
Claude (14:03)
Trust the PMS as the authority — it holds the lock. Your platform layer should treat POST /reservations as idempotent with a client-generated reservation key...
You (14:11)
The availability check at search time is now stale by the time the user hits confirm. What's the UX pattern for this — show stale price or re-fetch?
Ready to hire engineers for the AI world?
Set up a coding challenge in minutes. Give candidates a real environment, a real problem, and see exactly what they can build.