open-standards/jouleclaw
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mesh-meter: a span shorter than its counter's minimum window is an estimate
A counter declares the smallest window over which its readings are not noise
(min_window_ns: about 10 ms for RAPL and IOReport, 100 ms for NVML; 2 s for a
Mac's SMC power integral). Across a shorter span its delta is whatever its
updates and quantization gave, often zero or a whole update period, yet mesh
graded it at the counter's grade: a few-millisecond request could be sealed
HwShunt at 0 µJ, or ModelBased at a power reading's extrapolation.

- SPEC 3.3: a span shorter than the minimum window of the counter that would
  grade it MUST NOT be graded HwShunt or ModelBased from that counter; it is
  an estimate of the request, and SHOULD be billed by an estimator that
  resolves the span's own length, so a short request is never billed zero.
- HwMeter opens its own window (Meter::open_span), which Session uses: it
  times the span, and one shorter than the backend's floor
  (HwMeter::min_window) is billed this crate's EstimatorMeter over the span's
  own length, graded Estimator with no measured_uj, as a span across a failed
  read is. Past the floor the counter's figure stands, graded as before.
  min_window is zero where the bridge already bills by its estimator.
- ProcessMeter: where ProcessSpanMeter falls back to the host meter, a span
  shorter than the host meter's floor is billed the same way. Spans its own
  CPU and GPU counters metered are not the host meter's and are unaffected.
- jouleclaw-energy: ProcessSpanMeter::fallback_min_window, the active fallback
  meter's min_window_ns, so a caller can apply the rule. Additive.

Each span is timed from after its opening read to before its closing one, a
little shorter than the counter's own window, so a span at the floor is judged
short rather than long.

Tests: under a 50 ms floor a 20 ms span is Estimator at the estimator's figure
for 20 ms, and a 60 ms span keeps the counter's figure and grade (HwShunt and
ModelBased); the bridge's minimum window is its counter's until a failed read,
then zero; a host-meter fallback span under the host floor is an estimate,
past it the host figure stands, and a span this process's own counters
metered keeps its grade. Linux x86_64: mesh-meter (default, hw-meter-rapl),
mesh-core (hw-meter-rapl) and mesh-conformance pass; clippy clean. macOS
(M3 Ultra): mesh-meter and mesh-core with hw-meter-apple pass, including a
real in-process serve sealed HwShunt; clippy clean for mesh-meter and
jouleclaw-energy.

Co-Authored-By: Claude Opus 5.5 <[email protected]>
2026-11-04 21:29:44 -05:00
..
conformance/v1 jouleclaw SPEC.md: normative §§8-12 for waves 12-21 2026-06-02 10:42:25 -04:00
jouleclaw-rs mesh-meter: a span shorter than its counter's minimum window is an estimate 2026-11-04 21:29:44 -05:00
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