Organized by research focus.
Cross-linked where ideas overlap.
How I deployed Strapi on a Hetzner dedicated server, connected it to a host PostgreSQL 17 instance, fixed three Dockerfile failures, and got a production CMS running for multiple platforms at near-zero marginal cost.
The way we're building AI today is going to break at scale. Not because the models are wrong but because the architecture around them is. Introducing SAAL: AI Systems As A Layer.
Multi-agent systems look elegant on paper. In production they fail at handoffs. Here's the pattern and the fix.
Research explains mechanisms. Production teaches consequences. The lessons that only come from actually shipping.
Over 80% of AI projects never reach meaningful production. Here's the five-layer architecture model that explains why — and what to do about it.
Regulators don't care if models are conscious. They care if they manipulate, mislead, or harm. What that means for production AI teams building compliant systems.
One architectural decision that changes how production RAG systems handle documents vs. chunks — and why validation at clear boundaries beats enforcement everywhere.
Just as software decomposed into SaaS and PaaS, AI systems can decompose into modular, independently scalable layers. This is the SAAL thesis.
A reference map across learning paradigms, model purposes, and data modalities — so you reach for the right family for the problem.
110+ research papers analyzed: what production AI engineers need to know about neuro-symbolic systems, world models, embodied intelligence, and the post-LLM era.