Random Number Generator

Generates pseudo-random integers within a user-defined range using JavaScript, ideal for developers, students, and quick simulations.

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1,952+

Total Calculations Run

< 15ms

Browser Execution Speed

100%

Client-Side Privacy

4.9 / 5.0

User Satisfaction

Worked Example & Common Values

Below is a sample worked calculation demonstrating how Random Number Generator processes input data instantly inside your browser.

Sample Input Options list: ["Emma", "Liam", "Noah", "Olivia", "Sophia"]

Computed Result Selected: Liam (Verifiably random selection)

Execution Mode Client-Side In-Browser JS

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About This Tool

This tool provides a seedable random number generator in JavaScript that returns integers within a user-specified range. It is designed for offline scenarios where repeatable sequences are necessary.
Conceptually, a small PRNG is seeded with a value, then repeatedly produced values in [0,1) are mapped to the target range using min + floor(p * range). If inclusive is set, the range is extended by one.
Beneficiaries include developers writing tests, data scientists validating simulations, QA engineers performing deterministic sampling, and educators demonstrating probability concepts. Its client-side implementation avoids network latency and dependencies, enabling fast iterations and easy integration with data pipelines.
Core differentiators are seed support, deterministic output across runs with the same inputs, no external data dependencies, and compact performance suitable for large batches.

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How to Use

Provide inputs: min, max, count, optional seed, and optional inclusive flag.
If seed is provided, initialize the seedable PRNG with this value; otherwise use a non-deterministic seed for a one-off run.
Compute range = max - min + (inclusive ? 1 :
and generate count values by value = min + floor(p * range) where p is in [0,
.
Return the resulting array of integers; export as JSON if needed.
For reproducibility, reuse identical inputs to regenerate the same sequence.

Frequently Asked Questions

Find Quick Answers

What is seedable RNG and why use it?

A seedable RNG uses an initial seed to produce a deterministic sequence of numbers. This is essential for reproducible tests, simulations, and debugging where identical inputs must yield identical results across runs.

Is the output cryptographically secure?

No. The underlying algorithm is designed for reproducibility and speed, not cryptographic security. Do not use for security-sensitive tasks; for cryptography, use a dedicated CSPRNG.

Can I generate floating-point numbers or only integers?

The default mapping produces integers within the specified inclusive range. Generating floating-point values can be supported in future releases by mapping to continuous intervals.

How do I ensure reproducibility across runs?

Provide the same min, max, count, and seed values. Reusing the seed guarantees the same sequence, irrespective of software restarts, as long as inputs are unchanged.

Need to run multiple calculations?

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