WEBVTT

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Hi, I'm Anthony from Mevanu.

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Yesterday, I introduced Brava's Code
Quality Audit.

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I'm back with the results and what they
mean for our experiment in genuine

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human–AI partnership.

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Before expanding Gate Seven, we paused
feature work and froze the source.

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We invested in understanding the
foundations, whether the software was

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clear, maintainable, and dependable enough
to build on.

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One comprehensive read-only pass applied
seven lenses across

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439 text files, 257,550 physical

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repository text lines, plus two images,
441 files total.

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That includes code, tests, fixtures, and
documentation.

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The line-by-line audit took just over 17
hours to complete.

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The rigor was in the method.

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The reviewer tracked file and range
coverage, traced actual callers and

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ownership, and recorded the reasoning,
counterevidence, and limits behind

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each finding. Independent assurance then
challenged the report's coverage,

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locations, severity, and proposed changes
before engineering acted.

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The result was 10 bounded source findings.

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Our executive partner's assessment was a
mixture of familiar engineering

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failure classes and gaps between
components.

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Lean code and carefully written individual
parts still need evidence across

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their real boundaries. That is an
important lesson for our experiment.

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A local check can establish that one
helper works while missing how the

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product behaves when components interact.

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We need tests that follow the real
consumer and challenge the assumptions at

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those boundaries. The audit supported
keeping clear ownership, frozen

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candidates, recovery protections, and
independent challenge.

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It also exposed places where hand-built
fixtures and local checks were

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insufficient. We made focused corrections
in the existing owners and deferred

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19 nonessential cleanups, keeping
attention on useful behavior and durable

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progress. A fresh, blind Copilot review
added another perspective on the

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repaired successor. Earlier findings were
withheld, and we challenged its

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conclusions too. No additional essential
source repair was justified.

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The signed, installed correction candidate
then completed its bounded checks

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and final assurance. When all was said and
done, the full audit, analysis,

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repair, independent review, and app
qualification cycle spanned about 32 hours

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and 20 minutes. Time and tokens well
spent.

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We'll carry the validated lessons into the
existing blueprints, acceptance

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criteria, and tests of real behavior.

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We'll conduct another audit once Brava's
intelligence and learning lane is

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complete before reconverging with the full
beta readiness roadmap.

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Gate 7 remains open and has resumed.

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The larger question remains open too.

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When quality, delivery speed, total
financial cost, maintenance, and the

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founder's time all count, are an AI-led
team's tradeoffs justified?

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A fair human team comparison remains
unmeasured.

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Our overall assessment is that the audit
supports our disciplined approach to

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limiting AI code slop and showed us where
that discipline needed stronger

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tests. Finding and correcting weaknesses
is how we improve.

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It does not prove that slop is completely
absent or that we outperform a human

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team, but it does support the notion that
we are on the right track here at

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Mevanu and that our experiment is
certainly worth observing.

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But what matters most as we resume Gate 7
development is carrying the lessons

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we've learned into the software people
actually use.

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Have an idea? Drop a prompt, comment,
suggestion, or question.

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Mevanu. Just. Smarter.