Waaseyaa governance series
<p>Ahnii!</p> <p>This series covers how <a href="https://github.com/waaseyaa/framework" rel="noopener noreferrer">Waaseyaa</a> — a PHP framework monorepo of 52 packages — went from accumulated architectural drift to a governed, verifiable implementation platform.</p> <h3> 1. <a href="https://jonesrussell.github.io/blog/waaseyaa-governance-audit/" rel="noopener noreferrer">The audit that started everything</a> </h3> <p>What architectural drift looks like in a 52-package PHP monorepo, how the invariant-driven M1 audit was designed with frozen vocabularies before the first finding was written, what it found across five concern passes, and how M2 turned 36 findings into a dependency-ordered eight-milestone program.</p> <h3> 2. Eight milestones, one chain </h3> <p>How the remediation program ra
Ahnii!
This series covers how Waaseyaa — a PHP framework monorepo of 52 packages — went from accumulated architectural drift to a governed, verifiable implementation platform.
1. The audit that started everything
What architectural drift looks like in a 52-package PHP monorepo, how the invariant-driven M1 audit was designed with frozen vocabularies before the first finding was written, what it found across five concern passes, and how M2 turned 36 findings into a dependency-ordered eight-milestone program.
2. Eight milestones, one chain
How the remediation program ran from M3 through M8, how the exit-gate verified nothing drifted during execution, and how the program completion artifact locked the outputs as fixed inputs to everything downstream.
3. The conformance engine
How M9 froze a canonical model, classified repo-wide drift, built a dependency-ordered execution DAG, and activated M10 batch execution — including the live code changes landing on m10-batch-d1 right now.
Each post stands alone if you need a specific part. Start at Part 1 for the full story.
Baamaapii
Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelplatformfindings![[R] Differentiable Clustering & Search !](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-graph-nodes-a2pnJLpyKmDnxKWLd5BEAb.webp)
[R] Differentiable Clustering & Search !
Hey guys, I occasionally write articles on my blog, and I am happy to share the new one with you : https://bornlex.github.io/posts/differentiable-clustering/ . It came from something I was working for at work, and we ended up implementing something else because of the constraints that we have. The method mixes different loss terms to achieve a differentiable clustering method that takes into account mutual info, semantic proximity and even constraints such as the developer enforcing two tags (could be documents) to be part of the same cluster. Then it is possible to search the catalog using the clusters. All of it comes from my mind, I used an AI to double check the sentences, spelling, so it might have rewritten a few sentences, but most of it is human made. I've added the research flair
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.







Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!