Explores context engineering for AI agents, comparing personal vs. shared context to prevent failures in human and organizational AI use.
Explores the future of personal AI, arguing we're moving toward a single digital assistant with full context about our lives.
Explains good vs bad harness engineering for AI, emphasizing context over prescriptive instructions.
Argues spec-driven development with AI tools is waterfall methodology in disguise, advocating for leaner, discovery-based approaches.
A research paper analyzes LLM performance on SQL generation tasks using different structured data formats and large schemas, comparing frontier and open-source models.
Research paper analyzes LLM performance on large SQL schemas, comparing 11 models across 4 data formats for structured context engineering in agentic systems.
Explores context engineering for AI coding agents, covering configuration features, reusable prompts, and tools like Claude Code to improve developer experience.
Introduces context engineering as a superior alternative to prompt engineering for AI coding assistants, enabling them to understand your codebase for consistent, high-quality results.
Explains the concept of an Agent Harness, a system for managing reliable, long-running AI agents, and its growing importance in AI development.
Explores advanced Context Engineering techniques for AI agents, focusing on combating Context Rot and improving multi-agent coordination.
Explores 'context plumbing' for AI agents, the engineering needed to move relevant context from various sources to where AI systems run.
Explores the concept of memory in AI agents, detailing short-term and long-term memory architectures to overcome LLM statelessness.
Argues that clear thinking and purpose, not prompt or context engineering, are the key skills for effective AI interaction, writing, and coding.
Explains why Context Engineering, not just prompt crafting, is the key skill for building effective AI agents and systems.
