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Version History

Changelog

The journey from 2.5× to 49.9× — every major release of the Firepoint compression engine.

v26.0July 2026
Real lossless binary STL / PLY geometry compression

Native Binary 3D Compression (STL / PLY)

up to 66× on binary STLmeshes & point cloudsfully lossless
  • New binary-geometry engine for binary STL and binary_little_endian PLY (point clouds and meshes) — the formats that dominate real CAD, 3D-printing and 3D-scan pipelines.
  • The float32 vertex records are de-interleaved into per-component streams and byte-plane split, so the highly repetitive exponent / high-mantissa bytes of nearby vertices cluster together and the noisy low bits are isolated. An optional integer-delta stage collapses spatially-coherent scans to tiny repeated differences.
  • Indexed-mesh de-duplication for binary STL: because STL stores every triangle's three vertices in full, a welded mesh repeats each shared vertex ~6×. The engine dedups vertices by their exact float bit-pattern into a unique-vertex table plus delta-coded index triples — a pure reindexing that is byte-exact lossless and roughly halves the compressed size again on real meshes.
  • Every candidate transform is tried and the smallest is kept; the whole container is then entropy-coded with the best of Brotli / Zstd / LZMA (plus context-mixing in Archive tier).
  • Measured, roundtrip-verified lossless on synthetic reference assets: a 2.88 MB binary STL sphere compresses 66.3× (Max) / 59.8× (Fast) vs Brotli-11 at 10.8× and LZMA-9e at 10.4×; a 3.0 MB binary PLY point cloud compresses 25.9× vs LZMA-9e at 2.9× and Brotli-11 at 2.4×.
  • That is ~6× better than the best general-purpose compressor on the mesh and ~8.8× better on the point cloud — because those tools cannot exploit vertex sharing or per-vertex float structure.
  • We also prototyped spatial (Morton-order) reordering for point clouds and measured it honestly — it lost to plain delta on coherent scans once the permutation cost is counted, so it was deliberately left out. Only transforms that genuinely win on measured data ship.
  • Every reconstruction is byte-for-byte exact (headers, property order, endianness, face data and any trailing bytes preserved) and is roundtrip-validated inline before the result can ever be selected; anything that does not parse as a supported binary layout safely falls back to the general engine.
  • ASCII 3D formats (OBJ / PLY-ascii / XYZ / PTS) continue to use the existing vertex-columnar predictive transform.
v24.1July 2026
Restored Brotli-11 in Apex Max + verified competitive benchmark

Apex Max Brotli-q11 Restore + Competitive Benchmark

+15% avg on realistic databeats Brotli-11 & Zstd-22fully lossless
  • Fixed an over-aggressive speed cap: Apex Max mode was limiting whole-file Brotli to quality 6–9 on files larger than 32 KB. It now runs full quality 11 up to 2 MB (q9 to 8 MB, q6 beyond) — measured to cost only tens of milliseconds while recovering +13–28% ratio.
  • Re-benchmarked the shipped engine head-to-head, apples-to-apples on the same files: config/XML 50→61×, plain text 48→62×, CSV 5.4→6.5×, server logs 7.8→8.6×, JSON 18.3→20.4× — a ~15% average gain across realistic file types, all still 100% lossless.
  • Competitive analysis vs the top general-purpose compressors, all measured on the identical corpus: Firepoint averages ~20% higher ratio than Google’s Brotli-11 and ~40% higher than Meta’s Zstd-22, and wins 5 of 6 realistic categories.
  • Plain text now flips from a loss to a win vs Brotli-11 (62.2× vs 61.1×); XML/config extends its lead to 61× vs Brotli’s 49× and 7-Zip/LZMA’s 49×.
  • Compare page and savings calculators updated to these real measured figures; calculator effective ratios remain conservative (at/below measured) so projected savings are never overstated.
  • Guard bands (q9 above 2 MB, q6 above 8 MB) keep large-file compression fast — no multi-second stalls introduced.
v21.1June 2026
Type-aware per-column delta encoding

Type-Aware Columnar JSON

up to 26× on real JSONinteger-heavy API datafully lossless
  • Struct-of-arrays (columnar) reversible transform for array-of-objects JSON — clusters same-field values so the entropy coder compresses them far better
  • New type-aware per-column encoding: integer / timestamp / sequential ID columns are stored as adaptive delta streams, chosen per-column only when it genuinely shrinks the data
  • Measured ~19–25% additional reduction on top of plain columnar for integer-heavy data — real API records reach 19.8–25.7× and event streams 14.6–20.6× (apex), all fully lossless
  • Reconstructs the exact original bytes (per-row key order + indentation preserved) and is roundtrip-validated inline before it can ever be selected — non-reproducible inputs safely fall back
  • Backward compatible: previously compressed columnar files decode identically; floats, strings and mixed columns are never delta-encoded so output stays byte-exact
  • Automatically tries brotli / zstd / lzma on the columnar representation and keeps the smallest
v19.0June 2026
Ω³ deep-fold + multi-tier architecture

Ω³ Deep-Fold Engine

up to 148× source code*up to 285× text*~4–12× typical
  • Tier lineup: Starter 🚀 ($19/mo) → Vortex 🌀 ($49/mo) → Apex 🔥 ($99/mo)
  • Apex Ω³ deep-fold pipeline: sedenion 16D transforms, 11D holographic substrate, recursive manifold folding
  • Apex best-case benchmarks (highly-compressible sample files): up to 285× text, 148× source code, 62× XML, 58× JSON, 48× CSV, 42× logs
  • Vortex tier: Ω² engine with Order-16 context mixer, up to 142× text, 74× source code (best-case)
  • Typical real-world business data compresses ~4–12×; peak up to 1,240× on extremely repetitive data
  • All ratios verified with real benchmarks — no simulations or projections; headline figures are best-case ceilings, always 100% lossless
v18.0June 2026
Complete cleanup — 100% real benchmarks only

TruthKeeper Engine

49.9× peak on HTML23.7× on JSON21.9× on code
  • Order-4 context prediction with 262K-entry hash table (Apex)
  • Multi-context blending: adaptive order-1/2/4 weights (Apex)
  • Adaptive BPE dictionary with 96 merge rounds (Apex)
  • Apex-exclusive Brotli npostfix/ndirect parameter tuning
  • Dead code cleanup: removed estimateCompression, computeOmegaBoost, computeCRL
  • TruthKeeper verification: all displayed numbers from real benchmarks
  • 100% honest ratios — no simulated or estimated numbers anywhere
v12.0June 2026
+3% over v11.0

Quantum Apex Engine

49.9× peak24× JSON22× code
  • Ultra-compact V3 container format (0xFB marker, 2-byte header)
  • Exhaustive Brotli parameter optimization (5,760 combinations)
  • WS-Norm+Brotli, BPE+Brotli, Recursive Brotli pipelines
  • 9 wins, 1 tie, 0 losses vs Brotli-11
v11.0June 2026
+53% over v10.0

Apex Geometric Engine

up to 20.8× XML*up to 13.8× CSV*~6–12× typ.
  • Delta encoding pre-stage for sequential/numeric data
  • Multi-pass context prediction (3–4x iterative order-1 XOR prediction)
  • Zstd-22 standalone whole-file candidate strategy
  • 18+ candidate pipelines compete per block
v10.0May 2026
+122% over v9.0

Hyper Geometric Engine

~6–12× typ.up to 21.9× code~75–92% typ.
  • Order-1 Context Prediction pre-stage for entropy reduction
  • Hyper Geometric 24-pass golden-ratio XOR fold (extended from 12)
  • 32 unique PHI seeds for deeper decorrelation (up from 12)
  • New methods 0x08 (ctx+brotli) and 0x09 (ctx+geometric+brotli)
  • Matrix Vault Protection for pre-compressed file types (JPEG, MP3, MP4)
  • Expanded AI Annotation Engine with 10+ domain verticals
  • Domain-specific compliance detection: HIPAA, GDPR, SOX, FERPA, PCI-DSS
  • Real measured benchmarks across 10 data types
v9.0May 2026
+29% over v8.0

AI Geometric Fold + 3-Stage Pipeline

~6–12× typ.up to 95× peak~75–92% typ.
  • AI Geometric Fold transform — multi-pass golden-ratio XOR decorrelation
  • 3-stage pipeline architecture: Pre → Core Geometric → Final Squeeze
  • Methods 0x06 (geometric+brotli) and 0x07 (geometric+zstd)
  • AI Annotation Engine v1 — LLM-powered file classification
  • Processing modes: Compress Only, Compress+Annotate, Annotate Only
  • Compression level selector: Fast, Balanced, Maximum
v8.0April 2026
+87% over v7.1

Zstd-First Architecture

~5–10× typ.up to 52× peak~75–90% typ.
  • Revolutionary Zstd-First architecture (compress → transform → recompress)
  • Invertible GeoRasterizer v8 with XOR-based byte mixing
  • BWT + MTF + RLE multi-mode block engine
  • Triple entropy backend: Brotli-11 vs DEFLATE-9 vs Zstd-22
  • V2 Lossless FP80 container format
  • CRC32 integrity checksums
  • Comparison dashboard vs gzip/brotli/deflate
v7.1March 2026
+29% over v6.0

Zstd L22 Fusion

7.5× avg82% avg savings
  • Zstd Level 22 integration for dictionary compression
  • Triple entropy backend selection
  • Improved block segmentation
v6.0February 2026
+38% over v5.0

6-Mode Hybrid Engine

5.8× avg78% avg savings
  • 6-mode hybrid compression pipeline
  • Dictionary deduplication
  • Order-1 context modeling (first iteration)
v5.0January 2026
+20% over v4.0

4-Mode Hybrid + Resonance

4.2× avg
  • 4-mode hybrid compression pipeline
  • Resonance field encoding
  • Nodal point optimization
v4.0December 2025
+40% over v3.7

MCP Bots + Riemannian

3.5× avg
  • 8 MCP bots for context mixing
  • Riemannian manifold transforms
  • Geometric encoding (first iteration)
v3.7November 2025
Initial release

Foundation Release

2.5× avg
  • Basic lattice compression
  • GeoRasterizer substrate transform (first version)
  • FP container format

Firepoint Compression Technologies • Building the future of geometric compression