An architectural analysis of the enterprise AI market, mapping how major tech companies compete for control points across workflows, models, infrastructure, and silicon.
Explores when to keep AI on-premises based on data gravity, latency, sovereignty, and cost for enterprise workloads.
A physicist turned AI developer explains creating Matrix Designer to enforce architecture and design before AI writes code, solving the problem of fragmented AI-generated software.
AI cost management shifts from infrastructure to architectural decisions, making context a primary design concern.
Explains the multi-layered architecture of production generative AI systems, covering hardware, models, orchestration, and tooling.
Explores the concept of memory in AI agents, detailing short-term and long-term memory architectures to overcome LLM statelessness.
Explains the architecture of the Model Context Protocol (MCP), detailing its client-server model, core components, and message flow for connecting AI models to tools and data.
Explores AI engineering architecture patterns and user feedback methods, from simple APIs to complex agent-based systems.
Explores the common architectural components and implementation steps for building a scalable generative AI platform, from basic models to complex systems.
Explains AI transformers, tokens, and embeddings using a simple LEGO analogy to demystify how language models process and understand text.
