HDNA Workbench: open-box AI research platform where every neuron is inspectable with one-line PyTorch inspection wrapper [P]

GitHub: https://github.com/staffman76/HDNA-Workbench

I built this because I was tired of studying AI through black boxes. Existing interpretability tools explain models after the fact. I wanted an architecture where transparency is the design, not an afterthought.

What it does

Open-box AI engine (numpy only):

- Neurons with persistent memory — query any neuron's activation history, routing, and health at any time

- Pluggable reasoning daemons with quality-gated phase progression

- Audit log that traces every decision with full causal chain

- Compiles to dense matrices for ~100x speedup, decompiles back for inspection

One-line PyTorch inspection wrapper:

model = workbench.inspect(model)

Swaps every layer for an inspectable subclass. Same math, same state_dict, same isinstance(). Gives you per-layer traces, attention head entropy/redundancy, embedding usage tracking, anomaly detection. Supports 14 layer types. Revert with workbench.revert(model).

Universal adapter protocol:

Connect HDNA, PyTorch, HuggingFace, ONNX, or API models to the same research tools (inspector, decision replay, experiment forge, daemon studio, exporter).

Built-in curricula:

Math (14 phases, counting to probability), Language (sentiment/topic/emotion/intent), Spatial (grid pattern recognition). All procedurally generated.

Status:

v0.1.0. The HDNA architecture is proven from prior projects (101/101 math levels at 100%, 97-100% on 4 language tasks) but this unified version is a fresh release. The PyTorch inspection wrapper is solid and useful today.

Would especially appreciate feedback from anyone working on mechanistic interpretability or AI compliance.