please do not escape

please do not escape

A curated dataset of sandbox environments for AI coding agents, published with a raw YAML data file and hosted on GitHub; intended as a discoverable, contributor-driven collection of examples and primary-source references.

please do not escape is coding software teams evaluate for software & gaming. Use this page to review pricing, integration signals, and the best alternatives before you commit.

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#36 in Coding (36 tools)
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Quick Overview

Best for: Software & Gaming

What it does

Coding software for decision-makers comparing workflow fit and alternatives.

Best fit

Software & Gaming

Pricing snapshot

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please do not escape

please do not escape is a curated dataset of sandboxes intended for AI coding agents. The site provides access to the raw YAML dataset and points to a GitHub repository (github.com/pjlsergeant/ai-sandboxes) where every claim traces to a primary source. The project is contributor-friendly — PRs are welcome — and is presented by the same author who built byre, a constrained container box for coding agents.

A structured, evidence-carrying dataset of sandboxes and adjacent approaches for running AI coding agents with isolation, network control, or credential mediation.

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Key Features

Curated dataset of sandboxes

A collection of sandbox entries specifically for AI coding agents, organized as a dataset.

Raw YAML available

The site links to a raw YAML file for the dataset for direct access and processing.

GitHub-hosted with primary sources

Data lives in github.com/pjlsergeant/ai-sandboxes and each claim traces to a primary source; the repository accepts PRs to correct or add entries.

Contributor workflow

Visitors are invited to contribute via pull requests or contact the maintainer by email.

Alternate view options

UI affordances include showing non-product alternative approaches and clearing filters to customize the view.

Pricing

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Use Cases

Evaluating AI coding agents

Provide sandbox environments and example scenarios that researchers or developers can use to test agent behavior.

Curating reproducible examples

Serve as a traceable dataset where each entry links to primary sources, useful for reproducibility and documentation.

Community contributions and discovery

Allow contributors to add or correct sandbox entries via GitHub PRs, improving coverage of sandboxes over time.

Integrations

GitHub

Primary hosting and contribution mechanism for the dataset (github.com/pjlsergeant/ai-sandboxes).

byre (related project)

The dataset is built by the same person behind byre, a constrained container box for coding agents; referenced as related tooling.

Benefits

Provides a centralized, traceable dataset of sandboxes for AI coding agents.
Raw YAML and GitHub hosting make the data machine-readable and easy to incorporate into workflows.
Community-driven contributions (PRs) enable rapid correction and expansion of entries.

Limitations

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Frequently Asked Questions

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Getting Started

  1. 1 Step 1: Visit the project page at https://pleasedonotescape.com/ to read the overview.
  2. 2 Step 2: View or download the raw YAML dataset linked from the page or visit the GitHub repository at https://github.com/pjlsergeant/ai-sandboxes.
  3. 3 Step 3: Contribute by opening a pull request on the GitHub repo or contact the maintainer via [email protected] to suggest changes.

Support

email

Contact the maintainer at [email protected] for questions or suggestions.

docs

Project data and contribution instructions are available on the GitHub repository (https://github.com/pjlsergeant/ai-sandboxes).

API

Available: No

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