Generate random numbers with a seed, useful for reproducible tests
Native JS murmur hash implementation
A Node.js module for the optimized JavaScript implementation of the MurmurHash algorithms.
GRC's UHE PRNG in node (Ultra-High Entropy Pseudo-Random Number Generator by Gibson Research Corporation)
A package to seed your database using Drizzle ORM
A small library for generating random numbers
Cryptographic key pairs for the XRP Ledger
Linting for Angular applications, following angular.io/styleguide.
Avatar style for DiceBear
Unique avatars from dozens of styles — deterministic, customizable, vector-based.
Avatar style for DiceBear
Avatar style for DiceBear
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Avatar style for DiceBear
Avatar style for DiceBear
Avatar style for DiceBear
Avatar style for DiceBear
Avatar style for DiceBear
Avatar style for DiceBear
Avatar style for DiceBear
Avatar style for DiceBear
Avatar style for DiceBear
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Avatar style for DiceBear
Seed Do is an attempt to once and for all solve the problem of inserting and maintaining seed data in a database. It uses a variety of techniques gathered from various places around the web and combines them to create what is hopefully the most robust seed data system around.
Hunting down random test failures can be very very difficult, sometimes impossible, but minitest-bisect makes it easy. minitest-bisect helps you isolate and debug random test failures. If your tests only fail randomly, you can reproduce the error consistently by using `--seed <num>`, but what then? How do you figure out which combination of tests out of hundreds are responsible for the failure? You know which test is failing, but what others are causing it to fail or were helping it succeed in a different order? That's what minitest-bisect does best.
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