WEBVTT

mega-cue-01
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Answers are a starting point.

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Ask what supports the claim.

mega-cue-03
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Follow the source.

mega-cue-04
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Keep component boundaries visible.

mega-cue-05
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WDBX has CPU primitives: dot products, norms, cosine and top-k.

mega-cue-06
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Accelerator capability states separate availability, initialization, execution and runtime verification.

mega-cue-07
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The durable store integrates snapshots, write-ahead recovery and HNSW search.

mega-cue-08
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WAL frames checksum JSON bytes. Bad checksums report corruption.

mega-cue-09
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Recovery treats an incomplete final frame differently from a bad checksum.

mega-cue-10
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Store-facing completion persistence belongs to MCP, outside the pure AI core.

mega-cue-11
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Three persona profiles.

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Abbey.

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Aviva.

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Abi.

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The local router adjusts weights using keyword prefixes, then selects a profile.

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An explicit leading persona address bypasses that scoring path.

mega-cue-17
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Abbey combines conversation and technical care. Aviva leads directly. ABI handles orchestration.

mega-cue-18
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The training path records metadata and profile vectors; model weights remain unchanged.

mega-cue-19
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Inspect the integrity boundary.

mega-cue-20
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A block hash includes its predecessor, timestamp, sequence, profile and metadata.

mega-cue-21
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That SHA-256 construction is a hash chain, not a digital signature.

mega-cue-22
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Strict checks recompute hashes; link-only checks do not.

mega-cue-23
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ABI’s audit matches thirteen case-insensitive patterns and scores six principles.

mega-cue-24
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Responses avoiding those phrases can escape its checks.

mega-cue-25
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Source inspection establishes what is implemented, with runtime qualification still separate.

mega-cue-26
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Ask for the test, the environment, and the precise scope behind a result.

mega-cue-27
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Broader reasoning is a research direction.

mega-cue-28
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Keep the roadmap visible.

mega-cue-29
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Our vision is a distributed cognitive fabric.

mega-cue-30
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That vision does not establish support across every device or hardware tier.

mega-cue-31
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Compatibility needs execution evidence on each intended path.

mega-cue-32
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A proposed direction becomes an engineering task when its acceptance evidence is defined.

mega-cue-33
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For a project, define the workload, data boundary, failure behavior and review process before promising delivery.

mega-cue-34
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Start with a bounded question. Name the source, inspect the implementation, and agree how the result will be checked.

mega-cue-35
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These equations illustrate concepts. They do not establish a benchmark, semantic truth or a deployment guarantee.

mega-cue-36
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Use the illustration to frame questions: which inputs matter, which assumptions hold, and what evidence would change the decision?

mega-cue-37
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Bring a scoped project.

mega-cue-38
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Let’s review the evidence together.