November 17, 2025
The LLM structural crisis: solving context decay with the AI Memory Prosthesis
Introducing Simulated Recall via Shallow Indexing (SR-SI), an architectural pattern for reducing context drift in long-running AI workflows.
Context-decay
December 10, 2025
The 200-Prompt Wall
Why long-running AI projects collapse around 200 prompts — and the architectural solution that breaks the limit.
November 10, 2025
AI-assisted development breaks around prompt 200, but it doesn't have to
The 200-prompt wall isn’t a model limitation — it’s a memory architecture problem. SR-SI adds an external memory layer to prevent context collapse.
January 29, 2026
The end of documentation debt: how an AI “memory prosthesis” automates the PRD
PRDs decay on contact with reality. This post outlines an AI-native operating model that turns documentation from writing into evidence-based extraction.
February 16, 2026
I shipped a beta, almost production-grade PWA fast, less than a day
Speed with AI isn’t the hard part. Staying coherent is. Here’s the SR-SI workflow that prevents drift and makes fast builds stay structurally clean.
February 18, 2026
SR-SI context savings scale progressively
SR-SI doesn’t just reduce AI re-orientation — it scales with repo complexity. Small repos save 10–20%. Large repos can reach 40–60% when governance stays tight.
February 19, 2026
The way Einstein's brain worked is how AI should retrieve information
Genius isn’t storing more. It’s retrieving better. SR-SI turns AI retrieval into short, indexed pathways instead of full-context scavenging.
February 20, 2026
The biggest lie in AI-assisted development
Better specs don’t fix AI projects. Context decay does. SR-SI compresses architectural memory into a shallow index the AI consults and maintains to prevent drift.
February 20, 2026
The moat hiding in plain sight is memory
General AI is stateless by design. The durable moat is structural memory — a system that reconstructs context on demand and compounds coherence over time.
February 23, 2026
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.
February 26, 2026
SR-SI accidentally solved the documentation problem too
SR-SI forces compact architectural clarity for AI orientation — and that same structure produces always-current human documentation as a byproduct.
February 27, 2026
Computation is killing collaboration
Most teams treat LLM memory as a compute problem. It’s an architecture problem. SR-SI replaces bloated scaffolds with a simple retrieval prosthesis built on indices, markdown, and Git.
February 28, 2026
Why I went the opposite direction from spec-first
Spec-first tools aren’t wrong — they just don’t match how everyone thinks. SR-SI supports an architect’s workflow: sketch, test, refine, repeat, without context loss.
March 26, 2026
What SR-SI actually is - and why I stopped explaining it as an AI trick
SR-SI isn’t prompt engineering or RAG. It’s an index architecture that lets models re-orient on demand, preventing coherence drift and turning documentation into a zero-cost byproduct.
March 30, 2026
Why AI context degrades — and the architectural fix that actually works
AI context drift isn’t a model limitation—it’s an architectural failure. SR-SI replaces brute-force context with indexing, enabling persistent coherence across long-running projects.
May 12, 2026
The memory problem
The first SR-SI lesson was not that AI needed more intelligence. It needed a better way to orient itself before it worked.
June 2, 2026
Context decay in AI isn't a model problem. Here's what it actually is.
Context decay usually comes from the way relevant information gets diluted inside long sessions, not from the model suddenly becoming less capable.