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Binary visualization for human pattern recognition - Ghidra plugin by Chris Domas (xoreaxeaxeax)
Basé sur la classification professionnelle SOC
Query and explore the 2600: The Hacker Quarterly magazine archive (1984-present) via DuckDB. Provides structured access to 168+ issues covering hacker culture, security, privacy, telephony, and digital rights without loading full content into context.
ACSets (Attributed C-Sets): Algebraic databases with Specter-style bidirectional navigation. Category-theoretic formalism for relational databases.
Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.
ACSets (Attributed C-Sets): Algebraic databases with Specter-style bidirectional
Bridge active inference theory with robot control using K-Scale's JAX/MuJoCo stack. Use when connecting predictive coding to locomotion policies, mapping KL divergence minimization to RL training, applying mean field approximation to robotics state estimation, or implementing sim2real as inference about future observations.
Implement affective valence as directional derivative of interoceptive energy landscapes for AI alignment. Use when building alignment-aware RL agents, validating GF(3) conservation in reward signals, training Langevin-based policies, or analyzing fold-change detection signals in POMDP environments.
| name | cantordust-viz |
| description | Binary visualization for human pattern recognition - Ghidra plugin by Chris Domas (xoreaxeaxeax) |
| metadata | {"trit":-1,"color":"#E54951","gf3_role":"MINUS","version":"1.0.0","repo":"Battelle/cantordust","author":"Chris Domas (xoreaxeaxeax)","interface_ports":["References"]} |
Use when embeddings fail: humans see patterns algorithms miss.
Visual binary analysis tool for Ghidra. Converts binary data to bitmaps/visualizations where structural patterns become visible to human pattern recognition.
cantordust-viz (-1) ⊗ skill-embedding-vss (0) ⊗ radare2-hatchery (+1) = 0 ✓
| Tool | Approach | Strength |
|---|---|---|
| Cantordust | Visual/human | Sees patterns ML misses |
| Zignatures | Soft signatures | Fuzzy matching + keyspace reduction |
| skill-embedding-vss | MLX embeddings | O(1) similarity at scale |
git clone https://github.com/Battelle/cantordust.git
# Add to Ghidra Script Manager
From xoreaxeaxeax's work:
| Repo | Stars | Category |
|---|---|---|
| movfuscator | 10,075 | obfuscation |
| sandsifter | 4,998 | hardware security |
| rosenbridge | 2,380 | hardware backdoors |
| REpsych | 1,031 | anti-RE |
# When embeddings show high similarity but you want visual confirmation
from cantordust import visualize_binary
from skill_embedding_vss import SkillEmbeddingVSS
vss = SkillEmbeddingVSS('/path/to/skills')
similar = vss.find_nearest('target', k=5)
# Visual confirm top matches
for name, dist in similar[:3]:
visualize_binary(f'/path/to/{name}') # Human reviews
# cantordust_gay_bridge.jl connects:
# 1. Cantordust 2-tuple byte pair visualization
# 2. CJ Carr spectral features (diffusion transformers)
# 3. Gay.jl deterministic coloring (SPI)
result = analyze_binary_with_gay("target.bin")
# Returns: matrix, diagonal_score, ascii_score, trit_sum, sample_colors
| Domain | Representation | Gay.jl Mapping |
|---|---|---|
| Binary (Cantordust) | 2-tuple → 256×256 | entropy → trit → color |
| Audio (CJ Carr) | Mel spectrogram | centroid/flatness → HSL |
| Color (Gay.jl) | SplitMix64 + golden angle | SPI deterministic |