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A grant's capability names one resource by content address. When that resource is an inference endpoint, the model it runs was chosen inside the payload, and the decision point sees only the payload's hash. So a grant could authorize "this endpoint" but never "this endpoint, running these models". Only PayloadIs came close, and it makes the grant single-use. JCP enforced a model's integrity (refuse wrong bytes) but not authorization over which model runs. - `Frame.model: Option<ContentId>` is the content address of the model the action runs. The dispatching runtime fills it, since it knows what it will load. It is omitted from the encoding when absent, so frames and every receipt and vector over them keep their bytes. - `Caveat::ModelIn(Vec<ContentId>)` requires the frame to name one of these models. It fails closed: a frame naming no model is denied `caveat-unsatisfied`, so a dispatcher that does not say what it will run cannot exercise the grant. A delegate narrows the set by adding a smaller ModelIn beside the inherited one; both must hold, so the intersection governs. This is authorization by content address, not a registry (SPEC §12). - SPEC §4.9 and §6 describe both, and C24's conformance check now covers ModelIn. - Every Frame literal in the repository gains `model: None`. That is 28 sites across jcp-rs, joule-code-rs, joule-ui-rs, jouleclaw-rs and sandbox-rs. Checked on the build host: - jcp-rs: 496 tests pass, and the conformance suite passes all checks, including C24 with ModelIn. - Every vector gen-vectors writes is byte-identical to the published one. - All eight workspaces that depend on jcp-schema pass `cargo check --all-targets`. The consumers jouleclaw-jcp, joule-code-jcp, sandbox-jcp and joule-ui pass their tests. - Mutations: letting a frame with no model pass, and encoding an absent model, each fail tests. Co-Authored-By: Claude Opus 5.5 <[email protected]> |
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| .. | ||
| conformance/v1 | ||
| jouleclaw-rs | ||
| scales | ||
| vocab | ||
| LICENSE | ||
| LICENSE-SPEC | ||
| README.md | ||
| ROADMAP.md | ||
| SPEC.md | ||
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
- Code: Apache-2.0 (see
LICENSE) - Spec text: CC-BY-4.0 (see
LICENSE-SPEC)