Numbers
Random Noise Seed Generator
Used by developers, writers, and creators worldwide.
A random noise seed generator gives you the starting values that drive procedural generation in games, simulations, and generative art. Every seed feeds a pseudo-random number generator (PRNG); because PRNGs are deterministic, the same seed always reproduces the same terrain, dungeon, or noise pattern. That reproducibility lets developers share worlds, debug generation bugs, and store a single integer instead of an entire map. This generator produces seeds in five formats: 32-bit integer, 64-bit integer, hex string, and 8- or 16-character alphanumeric strings. Pick the format your engine or library expects, set the count, and grab a batch in one click.
Loading usage…
Free forever — no account required
How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Set the count field to the number of seeds you want to generate in one batch.
- Select your target format — integer (32-bit) for most game engines, 64-bit for wider-range PRNGs, hex for shaders, or alphanumeric for player-shareable strings.
- Click Generate to produce the seed list instantly.
- Copy any seed you want to keep and paste it directly into your engine's PRNG initialisation call, config file, or world-creation screen.
- Re-click Generate as many times as needed to get fresh candidates without changing your settings.
Use Cases
- •Seeding Unity's Random.InitState with a 32-bit integer before generating a terrain mesh
- •Generating 64-bit seeds to feed into FastNoiseLite for a large-scale universe simulator
- •Creating alphanumeric seeds for a Minecraft-style world so players can share them as readable strings
- •Locking a Monte Carlo simulation seed in a Python script for reproducible peer-reviewed results
- •Batch-generating 20 candidate seeds to A/B test dungeon layouts in a Roguelike before shipping
Tips
- →Test at least five to ten seeds visually before committing; small integer seeds like 1, 2, or 3 often produce degenerate noise patterns in poorly seeded PRNGs.
- →Store seeds in a named constants file alongside the algorithm version — if you update your generation code, old seeds may no longer reproduce the same output.
- →For Monte Carlo work, generate seeds in a batch and assign one per simulation run so results across runs remain independent and reproducible.
- →Hex seeds paste cleanly into GLSL and HLSL shader uniforms without type conversion — prefer that format when working directly in shader code.
- →If your engine hashes string seeds internally, two alphanumeric seeds that hash to the same 64-bit value will produce identical worlds — test for collisions in large seed libraries.
- →When sharing a seed publicly (e.g., on a game forum), note the game version alongside it — world generation algorithms change between patches and the same seed may produce a different map.
FAQ
what seed format should I use for Unity or Unreal Engine
Unity's Random.InitState and Unreal's FMath::RandInit both accept 32-bit integers, so the integer (32-bit) format is the safest default. If you're using a custom 64-bit PRNG or a library like FastNoiseLite, switch to 64-bit for a wider unique-seed space.
can the same seed produce different worlds in different games
Yes — a seed only guarantees identical output when paired with the same PRNG algorithm and the same generation logic. Two engines using the same seed will produce completely different results. Seeds are not portable across engines unless the underlying algorithms are explicitly identical.
are the seeds generated here actually random or just pseudo-random
They're generated from the browser's cryptographically secure source (crypto.getRandomValues where available), so they're as random as your OS can provide. They're not a PRNG themselves — they're the starting input your engine or noise library consumes to produce its deterministic sequence.