Classical Compression Hit a Wall. We Built Around It.
Most compressors fight entropy file by file. ByteLite treats structured data as a domain: shared dictionaries, deterministic cascades, and a model that travels with the data. The result? We're chasing ratios that would rewrite the leaderboards.
Patent Filed
US 63/807,027 - Protecting groundbreaking compression mathematics
Lossless Compression
Perfect data reconstruction with SHA-256 verification
Not Incremental. Architectural.
We're not chasing 10% gains. We're chasing a different order of magnitude.
What If "Compressed" Meant Something Completely Different?
Classical compression asks: "How small can I make this file?" ByteLite asks: "If we share the model, how little do I actually need to send?" For structured data, the answer might be shockingly small.
Shared-Model Compression Architecture
Engine + pre-trained dictionaries = shared context
Only the difference needs to travel
GB-scale corpora like enwik9
Represented in bytes, not megabytes
1GB corpora → handful of bytes. That's the engineering goal.
Verification will tell us how close we get.
The Architecture
ByteLite doesn't just compress data — it exploits shared context. When compressor and decompressor share the engine and dictionaries, only a small key needs to travel. The ratio depends on how well the data fits the model.
- Real-world structured data — text, documents, code, logs
- Shared engine + pre-trained dictionaries = massive effective compression
- Not magic. Architecture. The ratio depends on domain similarity.
- We're building toward numbers that would reset the Hutter Prize leaderboard.
Deep Kore: Compression as a Blueprint for Intelligence
What if the math that compresses data is also the math that organizes thought? Deep Kore is our physics-inspired architecture for testing that hypothesis. Only real-world experiments will show how far it actually goes.
Research Notice: Deep Kore is blueprint-stage. Dual-use considerations apply. Technical details gated. We're building carefully.
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