How I maintain coherence across 66,000 lines of code without losing the thread
Most AI-augmented development workflows break when they crash into the context wall.
AI Workflow Architecture further reading, page 8
AI workflow architecture matters because AI adoption fails when tools know the task but not the operating context around it.
The problem with AI isn't intelligence. It's orientation.
Every team I talk to has the same complaint: the outputs are generic. The AI sounds confident but misses what matters. Heavy editing required. Back to square one.
RAG gives AI a library. SR-SI gives it something closer to a memory
RAG is human-curated retrieval. SR-SI is self-curated reconstruction. That shift is subtle in mechanics, but huge in implications for long-running AI work.
The index vs the encyclopedia: why giving AI everything it might need is the wrong move
A short SR-SI methodology essay on why AI context works better as a maintained index than as a bloated encyclopedia of every possible detail.
I accidentally fixed AI forgetting without embeddings, fine-tuning, or a database
SR-SI fixes long-running AI context decay using a shallow index and a protocol—no embeddings, databases, or fine-tuning required.