How agent intelligence becomes inspectable infrastructure.
Weavatrix is built in public. This journal explains the decisions behind its graphs, retrieval engines, memory, proof systems, trust boundaries, and research—along with the limits of every claim.
APPLE SILICON RESEARCH
Heterogeneous inference, measured rather than assumed
Apple Silicon offers CPU cores, a Metal GPU, and an Apple Neural Engine behind Core AI. The attractive story is that a language-model runtime should simply split work across all of them. The harder question is whether a particular split is faster without changing the answer—and whether that result survives an external control.
Weavatrix Hetero treats that question as an evidence problem. On an Apple M4 with Qwen3-0.6B, the admitted exact path routes short C32 work to Metal, C512 prefill to a Core AI M512 graph, C2048 prefill to Core AI SDPA M2048, and decode back to Metal. The compiled FP16 contract matched 26/26 reference tokens before performance could count.
Across 18 exact-prompt pairs against Ollama F16, with six balanced repetitions per context, exact prefill was 5.31% faster at C512 and 6.22% faster at C2048. C32 prefill was 36.34% slower. That loss is part of the result: the router keeps the short path on Metal instead of turning one successful graph into a universal policy.
The experimental full-Q8 path made C512 complete compute 22.03% faster than Ollama F16 in all 6/6 pairs, but it passed only 2/4 strict portable quality cases and did not clear the 20% target against Ollama Q4_K_M. It remains approximate and off by default. Performance PASS is not quality PASS.
The failed ideas matter just as much. Serial per-layer Metal-to-ANE offload amplified numerical drift; a whole Core AI decode step was exact but 1.6× slower than Metal and unstable under mixed placement; unmanaged overlap regressed foreground decode TPOT by +18%. Simulations remain explicitly non-hardware evidence until a physical run earns promotion.
This is the point of Hetero: not to claim that every available processor must be used, but to build a runtime that can say which route won, under which precision contract, on which machine—and refuse the route when any part of that evidence changes.
The repository should remember more than the prompt
A coding agent usually arrives with a short-lived context window and leaves with most of its investigation discarded. The next agent pays for orientation again, and architectural intent is reconstructed from filenames, search results, and confidence.
Weavatrix treats repository understanding as durable evidence instead: typed nodes, relations, exact source spans, revisions, provenance, and bounded memory. A question can begin with a small projection of that graph and expand only when the evidence requires it.
The goal is not to make the model sound more certain. It is to let the agent show what it observed, what remains unproven, and which revision made the answer true.
Understanding code, changing code, and sending evidence across a network are different powers. Packaging them behind one opaque switch makes an agent convenient, but makes its authority hard to inspect.
Weavatrix keeps them separate. Core is source-read-only and network-free. Refactor adds reviewed local mutation with preview, confirmation, journaling, and rollback. Online owns the explicitly authorized network boundary without silently bundling either local layer.
The separation costs a little more explanation. It also means installation, context size, and runtime configuration cannot quietly grant a capability the user never intended.
A benchmark without its fixture, platform, cache state, output contract, and losing cases is marketing—not evidence. Weavatrix publishes the boundary around its performance claims so agents and people can decide whether the comparison applies.
The resident search index is dramatically faster than starting a fresh ripgrep process on one disclosed Windows fixture; on a disclosed Ubuntu fixture, ripgrep wins. Git and filesystem results are published with exact parity contracts and reproducible sources rather than promoted as universal rankings.
Speed matters because bounded retrieval protects context and latency. Honesty about scope matters because an agent must never turn one favorable measurement into a claim about every repository.