TryCrucible — Prove your AI engineering skills

Scenario

TryCrucible operations needs a dashboard for monitoring submissions, flagged reviews, companies, and challenge health. Build the feature using your AI tool of choice and submit the implementation plus your complete AI session transcript.

Expected output

Dashboard satisfies the UI contract, handles states cleanly, and transcript shows effective AI-assisted development

Dataset

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Dataset files

Scoring rubric

Clarity5%

README explains how to run and inspect the dashboard feature

LLM usage20%

Transcript shows planning, implementation, review, and iteration prompts

Robustness10%

Handles empty, loading, error, and responsive states

Correctness25%

Dashboard includes required metrics, filters, table, flagged review panel, and UI states

Architecture20%

Component structure is maintainable and state/data concerns are separated

Language-free evaluation

Build your solution in any language or framework — Python, TypeScript, Go, Rust, Java, C#, or anything else. The dataset artifacts may be in one language; your implementation does not need to match. TryCrucible evaluates the behaviour of your system, the quality of your AI workflow, your verification strategy, and the reproducibility of your submission — not your language choice.

Submission requirements

A public GitHub repository link
A Dockerfile in the repo root — any language or framework; the evaluator builds and runs your container
Your solution reads test_inputs.json from the working directory and writes results.json — standard I/O contract across all challenges
A decisions.md — 3–5 sentences on the key architectural and AI-workflow choices you made
The system must be fully reproducible — we clone, build, and run it against real test inputs

Evaluation contract

When you submit, the evaluator runs these steps in order:

1Clone your public GitHub repository
2Build your container from the Dockerfile in the repo root
3Mount test_inputs.json into the working directory
4Run your solution in a network-isolated sandbox (5 min limit, 512 MB RAM)
5Read results.json from the working directory
6Score correctness against hidden ground truth, then score architecture, AI workflow, robustness, and clarity

// I/O contract — same schema for every challenge

Input (provided by evaluator)

// test_inputs.json
[
  { "id": "t1", "input": { ... } },
  { "id": "t2", "input": { ... } }
]

Output (written by your solution)

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