Engineering essays from the bench.
War stories, architecture decisions, and the occasional tirade. Written by the engineers who shipped it.
Vector Databases and Semantic Search: A Practical Guide
What vector search is, how it differs from semantic search, and how to implement it in production: embeddings, indexes (HNSW/IVF), hybrid retrieval, and when a dedicated vector database earns its place.
The Best Tools for Securing AI Agents in Production (2026)
A working stack for securing production AI agents: identity and secrets, gateways and per-tool authorization, guardrail layers, sandboxes, and observability: what each layer does and when you need it.
Securing AI Agents in Production: Threats, Guardrails and Tools (2026)
How to secure AI agents in production: prompt-injection defense, per-tool authorization, least-privilege identities, output filtering, and monitoring, plus the tools that actually enforce it.
AI Agent Hardening: The Production Checklist
A practical AI agent hardening checklist: identity, tool authorization, prompt-injection defense, data boundaries, runtime isolation, and monitoring: what to lock down before an agent touches production.
Model Context Protocol (MCP): Building AI Integrations That Scale
Build MCP servers and clients, connect AI agents to enterprise tools, and deploy Model Context Protocol in production with security and observability.
Introducing Zenith AI: Submission Intelligence That Filters the Noise
Up to 80% of contact form submissions are spam. Zenith AI detects spam, scores leads, and drafts replies so you only see real opportunities.
RAG Architecture: Building Retrieval-Augmented Generation Systems
Building production RAG pipelines: chunking strategies, embedding models, vector stores, reranking, and evaluation metrics.
Building AI Agents: From Simple Chatbots to Autonomous Systems
Learn how to build AI agents with tool use, multi-agent orchestration, memory management, and guardrails for safe production deployment.
Generative AI Strategy for the Enterprise: Beyond the Hype
An enterprise GenAI framework: use case prioritization, build vs buy, cost modeling, governance, and measuring real ROI.
MLOps: Bridging the Gap Between ML Models and Production
How MLOps moves machine learning models from experimentation to reliable production systems with CI/CD, monitoring, and automation.
