AI Engineering & Projects

AI coding is not about delegating software engineering to a prompt. It is about shifting focus from typing syntax to heavy specification, code reading, compounding skill layers, and rapid feedback loops.

At their core, AI agents are just LLM loops wrapped in context and tools. Discover why the context layer is the true bottleneck of agent performance, and why context quality matters more than unconstrained planning.

Before writing custom data pipelines or context abstractions, you must answer one core question: How do you measure what is effective? Discover the 4 tiers of agent evaluation and benchmark-driven context engineering.

Learn how to structure a Context Layer into four functional layers around a real-world CRM scenario: Raw Data, Analytical Data, Preprocessed Signals, and Semantic High-Level Memory.

Build Layer 1 with reliable ingestion, domain modeling, and dedicated indexes that unify operational data for AI Agents.

Everyone is racing to build autonomous AI agents. But letting an LLM decide every micro-action is expensive, slow, and unreliable. Here is why the most effective AI systems are workflows, not agents.

The story of building a Hebrew language AI assistant on Cloudflare Workers: how to get real time data, make it talk like a real person, and keep latency under control.