MindsEye Binary Pattern Engine

A binary-level cognition layer for MindsEye.
Decode, label, map, and traverse binary as time-patterns.

Most systems treat binary as a dumb substrate.

This repo treats binary as a living pattern space.

Instead of just:

• 0 and 1,
• bytes and files,

we look at:

• pattern shape,
• math behavior,
• time of appearance,
• and the identity of the systems that touched it.

This is the foundation for time-labeled binary pattern spaces – a new way for agents and developers to understand computation at the substrate level.

---

Core Ideas

Every binary sequence carries:

• Pattern – the shape of bits.
• Math – transformation rules encoded in its structure.
• Time – when and how it was produced.
• Identity – which app, layer, or system influenced it.

The Binary Pattern Engine exposes all four.

It lets you:

• ingest binary from any source,
• assign time-aware labels,
• generate pattern signatures,
• traverse patterns over time (Meters),
• and track provenance across systems.

---

Components

• Binary Time-Labeler
  Attaches timestamps, context, transformation counts, and pattern classes to raw binary.

• Binary Pattern Recognizer
  Computes pattern signatures (hash, entropy, compression behavior, motifs) and compares / clusters them.

• Binary Meter Navigator
  Defines navigable ranges over time-labeled patterns and lets you traverse them by time, entropy, motif, or transformation count.

• Binary Provenance Engine
  Tracks where a pattern has appeared, which systems touched it, and how it evolved.

• Binary OS Interface
  High-level API that Chrome extensions, SQL services, and MindsEye orchestrators can call.

---

Example Usage

from mindseye_binary.interface import BinaryOSInterface
from my_stores import pattern_store, provenance_store

binary_os = BinaryOSInterface(pattern_store, provenance_store)

with open("example.bin", "rb") as f:
  data = f.read()

pattern_sig, time_label = binary_os.ingest_binary(
  data=data,
  source_system="local_file",
  source_path="example.bin",
  context={"workspace": "ml_research"}
)

print(pattern_sig.hash, time_label.origin_time)