MLNavigator and adapterOS
Company and product overview: market need, product scope, validation status, and business model.
A structured reading list and technical notes from MLNavigator. If you’re new here, start with the short list below.
Recommended reading order for understanding adapterOS, offline deployment, and auditable execution.
Company and product overview: market need, product scope, validation status, and business model.
Target frameworks and current status for CMMC, AS9100, and ITAR compliance.
Most AI stacks assume external services. Building reliable offline systems requires controlled dependencies across artifacts, packages, telemetry, and licensing.
AI citations are failing because they lack verifiable provenance. Execution receipts offer a path toward AI outputs that can be meaningfully cited.
Industry context, compliance positioning, and deployment planning.
Field observations informing this work:
What adapterOS verification covers, what it does not cover, and where human oversight applies.
How generation ends is as important as how it begins. Recording the termination reason in the cryptographic receipt makes stop conditions an auditable decision.
A technical governance study examining how nondeterminism in AI systems creates audit, compliance, and operational control failures across regulated industries.
Token usage variance is a measurable financial loss. Verifiable token accounting closes this gap.
KV-cache reuse is one of the largest inference speedups available, but adapters change the attention weights that produced the cache. A per-layer state hash turns this from a gamble into a verifiable policy.
Running multiple tenants on shared inference hardware requires more than access control. Sealed adapter containers and cryptographic isolation make tenant boundaries provable.
What changes when you remove the network. Risks eliminated and risks amplified by offline-first architecture.
Detailed engineering notes on repeatable runs, offline operation, receipts, and artifact formats.
A practical guide to GPU nondeterminism for regulated deployments: where variance comes from, what controls work, and how to document deterministic scope honestly.
When an inference provider reuses cached computation, the customer should pay less. Verifiable cache credits make this cryptographically provable, moving beyond contractual assurance.
Apple M3 Ultra vs NVIDIA Grace-Blackwell on CPU-GPU data movement and the business tradeoffs of unified memory.
Binding chip version, neural engine revision, and framework version into the cryptographic receipt makes hardware identity part of the audit trail.
A research case for hardware-level unified memory to make deterministic heterogeneous execution viable in adapterOS.
Most systems treat determinism as a binary switch. Configurable kernel allow-lists let you choose which operations must be reproducible and which can trade reproducibility for speed.
Floating-point computation does not need to be deterministic if you quantize before committing. The commit boundary is where reproducibility actually lives.
MoE architectures add a discrete routing layer that amplifies the floating-point nondeterminism already present in GPU execution.
Measuring inference efficiency in Joules per token, and how to do it repeatably on macOS.
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