open-standards/jouleclaw
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jouleclaw-loader-gguf: 0 warnings, and -D warnings now gates it
12 -> 0, in both the default and `rayon fabric-meter` configurations, and
the crate joins the `-D warnings` clippy step in standards-build.yml.

The last dozen needed judgement rather than a script:

- two `from_str` methods that are not FromStr-shaped (one returns Option,
  the other is infallible) renamed to `parse`. Both are file-local, so
  there is no caller churn -- except that my check for callers missed
  mlx.rs, which reaches SafetensorsDtype through a type ALIAS, so
  `SafetensorsDtype::from_str(` never matched. Renaming wants a grep for
  the method name across the workspace, not for the type names you happen
  to know.
- `assert!(FLASH_KV_CHUNK.is_power_of_two())` and `>= 64` moved out of a
  `#[test]` into `const _: () = assert!(..)` beside the constant. These
  are properties of a constant; a test only holds them if someone runs it.
- the `kv_quant_tests` module moved to the end of kv_cache_inplace.rs,
  because 300 lines of real items sat after it.
- an example discarded the `Result` from `matmul_bf16_threaded`, so a
  failure would have been timed as a success.
- `needless_range_loop` allowed per-file in three benches and one test:
  those are separate crates, so src/lib.rs's decision does not reach them.
- checked_div for two guarded divisions, and one unused `mut`.

Two near-misses worth recording:

- `fabric_dispatch.rs` calls `ledger_record` twice with identical
  arguments, which reads exactly like a copy-paste bug. The comment three
  lines above says attention is "recorded as two GEMM-equivalents" so the
  measured µJ/GFLOP table prices it. Deliberate. Left alone.
- clippy's `chunks_exact_mut` suggestion is `as_chunks_mut::<N>().0.iter()`
  -- a non-mutable iterator over a mutable chunk. It does not compile, and
  only the fabric-meter configuration contained one, so the default build
  stayed green while that example was broken. Checking both configurations
  is what caught it.

The gate covers default features only. Under `rayon fabric-meter` this
crate is also clean, but clippy reports jouleclaw-backend-apple through
the path dependency, and that crate has 43 warnings -- 27 `unused_unsafe`
and 15 `unused_imports` that are unused in THAT configuration and needed
in others. Those want cfg work, not deletion, which is the trap #373
already paid for once.

Verified: four lint specs pass, 343 loader-gguf tests, 77 omni tests.

Co-Authored-By: Claude Opus 5 <[email protected]>
2026-10-21 20:40:32 -04:00
..
conformance/v1 jouleclaw SPEC.md: normative §§8-12 for waves 12-21 2026-06-02 10:42:25 -04:00
jouleclaw-rs jouleclaw-loader-gguf: 0 warnings, and -D warnings now gates it 2026-10-21 20:40:32 -04:00
scales compliance: publish the EU draft PUE bands as a sourced data pack, not as code 2026-09-06 06:24:25 -04:00
vocab jouleclaw: wave-24 — contract + handoff + mission + value report 2026-06-03 02:35:34 -04:00
LICENSE jouleclaw: standard skeleton + Phase 1 foundation crates 2026-05-26 09:55:11 -04:00
LICENSE-SPEC licences: ship the Apache-2.0 text the code already declares 2026-10-09 17:22:32 -04:00
README.md licences: ship the Apache-2.0 text the code already declares 2026-10-09 17:22:32 -04:00
ROADMAP.md docs: strip vanity metrics from README + ROADMAP 2026-06-02 07:30:50 -04:00
SPEC.md energy meters: a GPU counter reading low is refuted in sandbox-meter, eoc-meter and jcp-energy too 2026-10-20 04:15:36 -04:00

JouleClaw — Energy-Optimized AI Runtime

The substrate for AI that costs Joules, not tokens.

Capability per Joule, not capability per parameter.

What JouleClaw is

JouleClaw is a pure-Rust, omni-modal AI runtime + harness + agentic layer governed by Transaction Science as an open standard (5th in the open-standards quartet, alongside OpenPay, Smart Byte, EOC, and WAI).

Every operation JouleClaw performs walks an explicit energy-tiered cascade, top to bottom, stopping at the first stage that closes the query:

Tier Name Cost class What runs here
L0 Cache picojoules Content-addressed cache hits
L1 Lawful nanojoules Deterministic, pre-compiled primitive (text/code only)
L2 Embed sub-millijoules Matryoshka embeddings + nearest-neighbor + hybrid search
L3 Model joules Local SSM / ternary / multimodal / diffusion / etc.
L4 Wire tens of joules Remote frontier RPC (escape hatch only)

L1 (Lawful) is only meaningful for text and code modalities. For image, audio, video and 3D generation, the cascade collapses to L0 / L2 / L3 / L4 — the energy savings come from caching plus dispatch to the cheapest model that meets the quality bar.

Why JouleClaw exists

The mainstream "agentic AI" ecosystem treats inference as the entry point. Every state shift, every tool call, every routing decision runs through a 70-billion-parameter stochastic engine, burning kilojoules to do work that deterministic compute or a hot cache could resolve for microjoules.

JouleClaw inverts the default. Inference is the last resort, not the first. Frozen model weights are less trustworthy than current world state; fresh, provenance-stamped retrieval beats synthesis from training cuts. Every operation is metered in microjoules where the hardware permits it, and in millijoules where it doesn't — the spec is honest about the floor on every platform.

The thermodynamic circuit breaker is the safety mechanism. Set an energy budget; when measured consumption exceeds the budget, the breaker trips and the operation halts. Hallucination loops cannot survive an honest joule ledger.

What's here

Path Contents
SPEC.md JouleClaw v1 spec — cascade tiers, energy contract, receipt
CHARTER.md Stewardship pattern (shared across open-standards)
jouleclaw-rs/ Apache-2.0 Rust reference implementation
spec/ Tier specifications, energy provenance, receipt format
conformance/v1/ Signed wire vectors for cross-implementation verification

Reference implementation

jouleclaw-rs/ is the Apache-2.0 reference implementation in Rust. It runs on Apple Silicon (M3/M4/M5 with UMA + Metal 4 Tensor APIs), AMD Strix Halo / Ryzen AI Max, Intel Lunar Lake, NVIDIA Jetson, and discrete NVIDIA / AMD GPUs.

It is omni-modal: text, code, vision, audio, video, image diffusion, 3D Gaussian splatting, and cross-modal fusion all dispatch through the same energy-tiered cascade.

The MCP tool surface is a JCP consumer: jouleclaw-jcp wraps any jouleclaw-mcp metered tool so every dispatch is decided by the JCP runtime against a signed, metered Grant before a byte reaches the tool — the tool's identity is the content-addressed resource, the call's energy estimate is debited from the grant's joule budget, and every decision (allow and deny alike) seals as a JCR-1 receipt. An injected instruction can name an ungranted tool; it cannot make the gate dispatch it.

Status

v0.1.0 reference implementation in active development. The spec is draft status. Cross-implementation conformance vectors will land with v1.0.0.

License