SuperRand is a real-time true random number generation (TRNG) service that provides access to randomness derived from a live physical entropy source, primarily natural electromagnetic noise.
Unlike pseudo-random systems, its outputs are effectively unpredictable and non-repeatable in practice, with no caching or reuse of entropy between requests.
This repository contains interactive demonstrations and example applications that showcase what can be built using SuperRand.
SuperRand provides a flexible API for generating a wide range of truly random data types, including:
- Booleans
- Integers (within custom ranges)
- Floating-point values
- Strings (custom character sets)
- Colours (RGB / RGBA)
- UUIDs
- Cryptographic keys (AES)
- Binary seeds
- Byte arrays
- Timestamps
- Lottery tickets (reducing & non-reducing)
- True randomness sourced from physical entropy (not algorithms)
- Real-time generation — no pre-generated pools or caching
- Statistically validated output across multiple test suites
- No storage of generated values (privacy-first design)
- Verification signatures — independently validate that randomness has not been altered in transit
curl -X POST "https://api.super-rand.io/v1/?key=YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"request":"integer","min":1,"max":100}'Example response:
{
"res": 89,
"jobType": "integer",
"attempts": 0,
"length": 1,
"generatorUUID": "30eb5630e57430e5",
"timestamp": "2026-04-13T06:16:50.452Z",
"priority": 0
}This repository hosts a collection of interactive demos and experiments that highlight the capabilities of true randomness.
Each demo is designed to be:
- Visually interesting
- Technically insightful
- Easily extendable
A real-time generative visualisation driven entirely by true randomness.
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Continuously renders particles with properties derived from live random input:
- Position
- Direction and velocity
- Size
- Colour
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Uses concurrent entropy streams to generate particles in parallel
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Produces an evolving, non-repeating visual output with no predefined patterns
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Includes simple controls:
- Pause/resume simulation
- Clear all particles
Purpose: Demonstrate how true randomness can drive generative visuals, producing continuously evolving, unpredictable artwork.
A full-scale lottery simulation powered by true randomness, modelling both player outcomes and operator economics.
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Generates large volumes of lottery tickets using true random input
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Supports:
- Standard entries
- System tickets (multi-number combinations)
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Performs an independent official draw after all tickets are created
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Evaluates every game against the results using division-based prize rules
Simulation Outputs:
-
Player Perspective
- Total spend vs winnings
- Net outcome and return rate
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Operator Perspective
- Revenue, payout, and gross margin
- Payout ratio and win rates
- Division-level prize distribution
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Statistical Diagnostics
- Marginal distribution spread (standard deviation)
- Serial overlap between consecutive games
- Pairwise and triple frequency anomalies (Z-scores)
- Gap analysis between number occurrences
Purpose: Explore lottery dynamics, expected returns, and statistical behaviour using true randomness at scale.
A research-oriented tool for generating lottery games using weighted probability distributions driven by true randomness.
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Accepts user-defined ball data with frequency indicators (e.g. weeks since last draw)
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Transforms frequencies into weights using configurable curves:
- Linear
- Exponential
- Logistic
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Supports multiple sampling methods:
- Efraimidis–Spirakis (weighted sampling without replacement)
- Sequential weighted selection
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Generates games based on weighted probabilities using true random input
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Provides statistical analysis, including:
- Actual vs expected appearances
- Per-ball probability deltas
- Shannon entropy of the distribution
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Supports both formatted and JSON output
Purpose: Explore how weighted probability models influence outcomes, and analyse their statistical behaviour using true randomness.
Generates controlled variations of a base lottery combination using true random input.
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Starts from a user-defined base combination
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Applies a mix of:
- Full random replacements
- Controlled positional shifts
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Variation behaviour is influenced by a configurable scale parameter
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Ensures all generated games:
- Stay within the defined number range
- Contain no duplicate values
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Supports both formatted and JSON output
Purpose: Explore nearby combinations and controlled deviations using true randomness.
/examples
/particle-simulator
/lottery-simulator
/weighted-generator
/variation-generator
README.md
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Clone the repository:
git clone https://github.com/spence-tech/SuperRand.git
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Add your API key to the demo files — (look for
YOUR_API_KEYin the scripts and replace it with your key) -
Open any demo in your browser:
/examples/particle-simulator/index.html
All API requests require an API key.
Create an account and generate your API key here:
👉 https://portal.super-rand.io
Once you have your key, include it in your requests:
https://api.super-rand.io/v1/?key=YOUR_API_KEY
wss://api.super-rand.io/v1/?key=YOUR_API_KEY
Pseudo-random generators (PRNGs) are deterministic — given the same seed, they produce the same output.
SuperRand uses physical entropy, meaning:
- Outputs cannot be predicted
- Outputs cannot be reproduced
- Each request is genuinely unique
In practice, this matters for systems where predictability is a risk:
- Cryptographic key generation
- Fair gaming systems
- Scientific simulations
- High-integrity random sampling
Some demos (e.g. lottery tools) are provided for educational and research purposes only and do not improve chances of winning.
MIT License — feel free to use, modify, and build upon these examples.
Contributions are welcome!
If you've built something interesting with SuperRand:
- Submit a PR
- Share your demo
- Help expand the showcase
For support, documentation, and API access: