The developer evaluation
benchmark for the AI era.

Live screen-share sessions. 14-dimension rubric scoring. Elo updated weekly against what top teams are actually shipping.

Reeval Evaluation — Live Session
Live47:23
TSqueue-worker.ts×
Sharing
1import Redis from "ioredis";
2 
3const QUEUE = "jobs:pending";
4const PROCESSING = "jobs:processing";
5const TTL = 30;
6 
7interface Job {
8 id: string;
9 payload: unknown;
10 attempts: number;
11}
12 
13export async function processNext(redis: Redis) {
14 const raw = await redis.rpoplpush(QUEUE, PROCESSING);
15 if (!raw) return;
16 
17 const job: Job = JSON.parse(raw);
18 try {
19 await execute(job);
20 await redis.lrem(PROCESSING, 1, raw);
21 } catch (err) {
Alex K.
AI
Reeval Agent
Transcript
A

Why rpoplpush instead of a simple LPOP? Walk me through that design choice.

Mic on
Screen sharing
47:23

2,000+/Developers evaluated·150+/Companies hiring·50,000+/Sessions scored

The evaluation engine that reflects real work. Not a sandboxed textarea. Not a contrived algorithm puzzle. Rubrics recalibrated weekly against what top teams are shipping.

How it works

Four pillars of a trustworthy evaluation.

Each pillar contributes to a unified rubric score — no single dimension can inflate the result.

01

Live execution context

Candidates share their actual screen — real IDE, real terminal. No sandboxed playgrounds.

02

14 scoring dimensions

Architecture, communication, execution, testing, system design — all graded independently.

03

Weekly recalibration

Rubric weights updated from production-grade engineering signals every 7 days.

04

AI-aware evaluation

We evaluate how you use AI tools — because that's how the best engineers work.

Not another coding platform

What actually predicts job performance.

DimensionHackerRank / CodeSignalReeval
Evaluation environmentIsolated browser sandboxNative screen share + live execution context
Assessment methodPaste code, match expected outputMulti-dimensional transcript analysis across 14 rubric categories
Rubric freshnessStatic problem sets, updated quarterlyRecalibrated weekly against production codebases and frontier tooling
Scoring signalPass / fail on test casesElo derived from rubric scoring across communication, architecture, and execution
AI tooling awarenessPenalizes or ignores AI-assisted workflowsEvaluates how candidates use AI tools — because that's how work gets done

Updated weekly

The evaluation engine that never goes stale.

Software development practices shift faster than any static test bank can track. Reeval ingests weekly signals from production codebases, frontier AI tooling, and what top teams are actually shipping — then recalibrates rubric weights automatically.

When a new LLM workflow becomes industry standard, or a framework pattern becomes the norm, your rubrics reflect it within the week. You're never evaluating against last year's bar.

Weekly recalibration

Rubric weights updated from production-grade engineering signals every 7 days.

Frontier AI awareness

Evaluates AI-assisted workflows, not just raw code output.

14 scoring dimensions

Architecture, communication, execution, testing, system design — all graded independently.

Expert-anchored baselines

Automated scoring calibrated against human expert judgment at the 95th percentile.

Rating & Matching

A verified signal — and the intelligence to act on it.

Elo measures capability. Match Score connects that capability to the specific context of each role and company.

Elo Rating

0Expert
Beginner
0 – 999
Intermediate
1000 – 1499
Advanced
1500 – 1899
Expert
1900 – 2299
Grandmaster
2300+

Ratings recomputed after every session. See the leaderboard →

Match Score

Candidate
Systems DesignGo / RustAI ToolingDistributed
94%match
Company
Backend InfraScale: 10M+AI-firstFast-paced
Skill alignment96%

Rubric strengths vs. role requirements

Team fit91%

Communication style, collaboration patterns

Growth trajectory88%

Learning velocity across evaluation history

Match Score considers 40+ signals from both sides — skills, culture preferences, team dynamics, and growth potential.

Expert validation

Rubric quality backed by experts.

UC Berkeley

Professors & Researchers

Evaluation rubrics grounded in decades of systems and software engineering research — not just textbook correctness, but professional execution quality.

OpenAI

Research Scientists

AI-assisted scoring calibrated against expert human judgment at scale. The signal-to-noise ratio is significantly better than traditional coding assessments.

xAI

Engineering Leaders

Benchmarks that reflect what high-performance teams actually ship — including how engineers reason about tradeoffs, not just whether their code compiles.

The bar is updated weekly. Are your candidates?

Hire with verified signal. Evaluate against a live execution context. Stop guessing from resumes.