Notes on building AI systems that hold up beyond the demo.

A practical framework for writing verifiable completion contracts for Codex, Claude Code, and long-running autonomous agent workflows.

AI doesn't fail loudly. It fails gradually, convincingly, and at scale. The failure modes that quietly wreck production systems before anyone notices.

Most AI agents fail not because the model is wrong, but because the infrastructure around it is missing. This is a beginner-to-expert guide on what an agent harness is, how it works, and why it is the most overlooked layer in AI systems.

A solid harness makes your agent reliable at launch. A self-improving system keeps it reliable over time. This is the engineering discipline that separates production AI from drifting AI — failure mining, eval generation, regression gating, and the feedback loop architecture that ties it all together.

The March 2026 axios npm compromise and LiteLLM PyPI attack show how package trust breaks. Practical dependency habits that reduce your exposure.

Your prompt travels through 7 infrastructure layers before a single token comes back. A plain-language walkthrough of API gateways, tokenization, prefill, decode, post-processing, billing, and the network physics underneath.

A practical OpenClaw guide for beginner to advanced builders. Learn the gateway architecture, message-to-action data flow, and the security controls that matter before real deployment.

Sometimes the dumbest approach turns out to be the smartest solution. The Ralph Wiggum technique for autonomous AI coding.

RLMs solve the context window problem by letting AI write code to explore information. The result? Tasks going from 0% to 91% success. Here's how it works and when to use it.

AI outputs fail when signals lack owners and judgment.

Why 80% of AI projects fail and how to avoid being one of them. A practitioner's framework for evaluating AI use cases before you write a single line of code.