open-standards/joule-code/examples/l3-jouleclaw

L3 binding — JouleClaw model tier → Joule Code cascade

A consumer binding example: how to wire JouleClaw's L3 (Model) tier into the Joule Code cascade. It is a standalone workspace, so the standard's reference library keeps no JouleClaw dependency.

A coding request that misses the cheaper tiers (L0 cache, L1 lawful, L2 embed) is generated by a local model at L3, producing a content-addressed CodingOp and a joules-class Receipt.

Three backends, same slot

All implement JouleClaw's jouleclaw_llm_cheap::LlmBackend, so they drop into the same cascade stage:

• EchoBackend (from JouleClaw) — deterministic, model-free; used by the unit tests so the wiring is verifiable with no model present.
• JouleclawRuntimeBackend (here) — runs the model in-process on JouleClaw's own from-scratch GGUF tensor runtime (jouleclaw-runtime): no external binary, no FFI. Tokenize → build the decode-step graph → compile against the registered kernels (jouleclaw-backend-reference / -apple) → autoregressive decode → detokenize, all in Rust the family wrote. This is the default backend for the run binary.
• LlamaCompletionBackend (here) — drives a real on-device GGUF via llama-completion (llama.cpp). Set JOULE_CODE_L3_ENGINE=llama to use it.

Either local path: no datacenter, no network. In production the cheapest capable backend is chosen by jouleclaw_dispatch::ModelBank's cost table.

Run it against a local model

# Default: in-process on jouleclaw-runtime (the from-scratch tensor engine).
cargo run --release --bin run -- /path/to/model.gguf "Write a Rust function that reverses a string."
# or: JOULE_CODE_L3_MODEL=/path/to/model.gguf cargo run --release --bin run

# Use the llama.cpp shell-out backend instead:
JOULE_CODE_L3_ENGINE=llama cargo run --release --bin run -- /path/to/model.gguf "..."

The run binary generates with the local model, content-addresses the result as a CodingOp (tier L3), and puts the host energy spent during the inference on the receipt, tagged with the provenance of whatever metered it:

• Apple Silicon whose IOReport CPU energy rail is live — JouleClaw's IOReport counter, a live vendor energy counter: a real local measurement, tagged HwShunt (with measured_uj set).
• Apple Silicon whose CPU rail is not live — macOS 27.0.1 for an unprivileged process, observed on an M5 Max and an M3 Ultra — JouleClaw's load-average estimator, tagged Estimator. Not a measurement.
• Anywhere else, Linux and Windows included — the token-count estimate, tagged Estimator. This binding builds jouleclaw-energy with the ioreport feature only, so it reads no RAPL or NVML counter even where one exists.

The printout names the meter that ran and the IOReport status, and says whether the figure was measured or estimated.

Verified live: the llama.cpp path against a local 350M GGUF, and the in-process from-scratch runtime against ternary-bonsai-1.7b-q2_0 (qwen3, Q2_0) — real text generated, a real op_id, the cascade closing at L3.

Tests

cargo test   # EchoBackend path — no model required

# In-process from-scratch runtime against a real GGUF (opt-in):
JOULE_BONSAI=/path/to/ternary-bonsai-1.7b-q2_0.gguf \
  cargo test --release --test runtime_l3 -- --ignored --nocapture