core-stack

This reference accompanies the hanzo-ml-research skill.

Hanzo ML Research: Core Stack Reference

This reference accompanies the hanzo-ml-research skill.

Canonical repositories

hanzoai/ml

Rust-native ML compute stack.

Important crates include:

• hanzo-ml
• hanzo-nn
• hanzo-train
• hanzo-transformers
• hanzo-kernel
• hanzo-research

The hanzo-research crate is the canonical Rust client for the unified cloud research plane and automatically records source/runtime provenance.

hanzoai/engine

Native Rust inference/serving engine.

Use it when research touches:

• serving
• batching
• quantized inference
• model loading
• attention kernels
• KV cache
• speculative decoding
• backend-specific runtime behavior
• end-to-end latency

hanzoai/decision

Kai typed-decision research and production stack.

Use it when research touches:

• typed decisions
• decision programs
• calibration
• safety heads
• option retrieval/reranking
• joint decoding
• benchmarking
• training objectives
• StateHandle / cached decision execution

Hanzo DSL / kernel stack

Use the local Hanzo DSL/kernel implementation exposed by the active workspace. Prefer extending existing Rust DSL/kernel primitives to introducing a second implementation path.

Research API

The native Rust SDK in hanzo-research uses:

• POST /v1/research/experiments
• GET /v1/research/experiments
• GET /v1/research/totals
• POST /v1/research/artifacts

Configuration:

• HANZO_API_KEY
• RESEARCH_BASE
• RESEARCH_PROJECT

The server keys canonical experiment identity by the research record's project/stable experiment identity, while provenance captures git/repository/runtime details.

Typical research object

hypothesis
prediction
control
treatment
primary metric
non-inferiority metrics
train/dev/final lineage
artifact hashes
raw observations
verdict
because

Keep the experiment falsifiable and durable.

Rust-first rule

Python/notebooks are acceptable for disposable visualization or inspection.

They are not canonical for:

• final model training
• benchmark generation
• feature extraction
• serving latency
• kernel timing
• final threshold selection
• production model behavior

unless the experiment explicitly studies a Python/PyTorch implementation.

Canonical results should be reproduced in Hanzo's Rust stack.