neuron-js

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Benchmarks & proof ​

Honest, reproducible evidence that Neuron-JS is fast enough, small enough, and — above all — inspectable. Every number on these pages comes from a real benchmark run you can reproduce with yarn benchmark; nothing is hand-entered.

What's here ​

  • Benchmark results — measured throughput, cold-start, bundle-size, validation, and explanation-overhead, compared against json-rules-engine, json-logic-js, a hand-coded TypeScript baseline, and rule-engine-js.
  • Methodology — what we compare, how each metric is measured, and our no-fabricated-numbers policy.
  • AI-rule safety — why AI-drafted JSON rules still need schema validation, a developer-owned registry, and explanation traces before production.

How a rule becomes an explainable decision ​

Neuron-JS turns serializable JSON rules into deterministic, auditable decisions: input and rule JSON are schema-validated, executed through a developer-owned registry and Synapse, and returned with an explanation trace showing why a rule matched or failed.

Neuron-JS diagram showing rule JSON and business input flowing through schema validation, developer registry, deterministic Synapse evaluation, result output, explanation trace, and audit-ready decision.

This makes four things visible:

  1. Rules are serializable JSON data.
  2. Schema validation happens before runtime.
  3. Execution is deterministic through a developer-owned registry and Synapse.
  4. Explanation traces show why a rule matched or failed.