Skip to main content

autocurricula CI License: MIT Python 3.11+ PyPI Website

Adaptive technical interview practice, powered by Claude. Problems are generated on-the-fly based on your role and skill level, with real-time code execution, structured test results, and AI-driven feedback.

Covers coding, algorithms, math, probability, statistics, and brainteasers.

Installation

pip install autocurricula

Prerequisites: Claude CLI must be installed and authenticated.

Quick start

autocurricula

This opens the app in your browser at http://localhost:8420.

On first launch, create a workspace by choosing a role (e.g. "ML Engineer", "Quant Researcher", "Backend Developer"). Claude generates problems tailored to that role and adapts difficulty as you progress.

A sandboxed virtual environment is automatically created at ~/.autocurricula/.sandbox_venv with numpy, pandas, scipy, torch, and pytest for executing solutions.

How it works

autocurricula uses a self-adjusting curriculum loop:

  1. Claude generates a problem matched to your role, category, and current difficulty level.
  2. You solve it in the built-in editor with full autocompletion and live test feedback.
  3. Claude reviews your submission, gives a verdict (solved / retry / move on), and explains the reasoning.
  4. The system tracks your solve rate and self-rated difficulty to calibrate what comes next.

Categories rotate automatically to ensure broad coverage.

Features

Problem types

Type Description
Code Write a function, run open tests, then submit for hidden tests and Claude review.
Derivation Written answers for math proofs, probability puzzles, system design reasoning, and conceptual questions.

Code editor

Monaco editor with Python syntax highlighting, autocompletion, hover documentation, and function signatures powered by Jedi. Supports numpy, pandas, scipy, and torch out of the box.

Test runner

Each code problem includes an open test suite (visible while solving) and a hidden test suite (run on submit). Tests execute in an isolated sandbox with a 30-second timeout. Results show structured pass/fail/error status with expandable failure details.

Scaffolding

Stuck? Request a scaffold and Claude generates an easier prerequisite targeting the specific concept you're missing. Solve it, then return to the original problem.

Theory

Each problem comes with background material covering relevant formulas, derivations, algorithmic intuitions, and worked examples. Rendered with LaTeX support.

Chat

Ask Claude for hints without getting the answer. Claude sees your current code and problem statement for context. Chat history is preserved per problem.

Progress tracking

Track solve counts, attempt counts, and success rates across all categories. Rate each solved problem's difficulty (1–5) to help the system calibrate. Problems solved but rated as hard are flagged for revisiting.

Workspaces

Maintain separate workspaces for different roles, each with its own problem history, progress state, and difficulty curve.

Configuration

Option Default Description
--port, -p 8420 Port for the web server

License

MIT — free to use, modify, and distribute.

Release files for autocurricula 0.5.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for autocurricula 0.5.0
File Size Uploaded
autocurricula-0.5.0.tar.gz 56.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for autocurricula 0.5.0
File Interpreter ABI Platform
autocurricula-0.5.0-py3-none-any.whl Python 3 none any Details

Total release size:120.2 kB

Release files / autocurricula-0.5.0.tar.gz

Download URL autocurricula-0.5.0.tar.gz
Size 56.7 kB
Tags Source
SHA-256 checksum
How to use checksums
98280c868287fb4ed47627c54748b4788399521ea60efe089cacc3270a3a99b3
BLAKE2b-256 checksum
How to use checksums
ad4eef05fc56e6c59a79f9b7df556a485e93b63731ac730a79d3e80686396351
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Apr 10, 2026.

Transparency log

Release files / autocurricula-0.5.0-py3-none-any.whl

Download URL autocurricula-0.5.0-py3-none-any.whl
Size 63.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
8b92238bf81e06bd6784dedf00ba2fef7fa8e3e526d672f246811ab6c7dd00d5
BLAKE2b-256 checksum
How to use checksums
451add1af2c963be842b0c7fe93552fb4c0aee6390756532eef09a628833dedd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Apr 10, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.5.0 This release

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page