Next-gen Hiring

The interview should reflect
how developers actually work.

The scarce resource is no longer the ability to write code. It's the judgment to know what's safe to ship. We built the interview to test exactly that.

React(TypeScript) App.tsx index.tsx 123 456 78 interface User { id: number; name: string; email: string; role: 'admin' | 'user'; } class UserManager { Terminal [09:43:36] Starting 'watch-extension:vscode-api-tests'... [09:43:40] Finished 'clean-extension:html-language-features-server' after 4.66s [09:43:43] Starting 'watch-client'... AI Assistant Ask the AI Assistant Explain the problem statement Clarify errors Review the agent's output ▶ Run

From writing code
to orchestrating agents.

The role of the developer is evolving. The next generation will pair strong software fundamentals with the ability to work across this spectrum.

01
Producer

Developers who use agents for their own work.

AI assists with code generation, debugging, refactoring. The developer is still the producer.

02
Supervisor

Developers who move to asynchronous workflows.

The developer breaks work into tasks, runs multiple agents in parallel, and synthesizes the output.

03
Decision-maker

Developers who build and manage agents that run autonomously.

Agents pick up tickets, plan, implement, test, and open PRs. The developer reviews, challenges, and makes the call to ship.

A conversation that reveals
judgment, not just correctness.

The candidate is dropped into a fully functional environment: a real codebase, an agentic IDE with access to the latest AI models, terminal access, file navigation.

Plan
Implement
Review

Plan

The candidate uses the AI assistant to understand the codebase, explore the problem, and produce a plan. The interviewer probes their reasoning, challenges their approach, asks about alternatives and tradeoffs.

It doesn't matter if the plan is perfect. What matters is whether the candidate is intentional about their decisions and can articulate their reasoning as it happens.

More than half the interview time is typically spent here. This is by design. The planning phase is where the richest, most differentiated signal lives.
Signal surfaced in this phase
Problem understanding
Reasoning clarity
Tradeoff awareness
Understanding of the problem

Implement

The candidate builds using the AI assistant. Implementation tends to be fairly quick. Whether they one-shot it or break it down incrementally, there's good conversation here about how they structure the change.

Test cases are hidden, so the candidate can't just run tests to verify. They have to write their own, reason about edge cases, and review the agent's output critically.
Signal surfaced in this phase
Agent collaboration
Edge case reasoning
Code structure
Control over the process

Review

Review is continuous and transcends both Plan and Implement. The candidate reviews the plan before building, reviews generated code during implementation, and sometimes a broader review of the overall approach happens toward the end.

The goal is a conversation that reveals judgment, not a binary pass/fail on correctness. Correctness is table stakes when AI can produce working code reliably.
Signal surfaced in this phase
Critical evaluation
Ownership of outcome
Assumption challenging
Ownership of the outcome

They don't have to
prepare in the traditional sense.

The interview is a reflection of how they actually work — and many are genuinely surprised by that.

Closest to where
this shift is happening.

We work closely with some of the leading organizations going through this shift right now. That proximity gives us a front-row seat to what's actually working.

We move fast.

When something needs fixing, we fix it. When the market shifts, we evolve the experience with it.

Tailored to your stack.

We help create content tailored to the company's stack and the roles they're hiring for.

We train your interviewers.

We work closely with engineering teams to help them train interviewers and conduct the new style of round effectively.

Evolving across
three dimensions.

Developer Experience

AI-enabled by default.

AI-enabled environments by default, agentic capabilities, keeping pace with how engineers actually work.

Agentic IDE Real codebases Terminal access
Content

Problems that require judgment.

Redesigning problem libraries so they require judgment that AI alone cannot provide.

Hidden test cases Real-world tasks Custom content
Evaluation

Signals beyond correctness.

New signals for the AI era — measuring what actually predicts performance on the job.

AI Fluency Code Review Scorecard Assist

The interview process is a reflection
of the engineering culture you're building.

If transforming your company to be AI-first is on your charter, hiring is one of the most consequential places that transformation shows up.

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