Thoughts on strategy, modernization, AI enablement, and building systems that actually work for people.
The era of prompt engineering is fading. Context engineering, building AI systems with memory, knowledge bases, and real-time data connections, is what separates companies that extract real value from AI.
Moltbook and Moltbot aren’t signs of AI “awakening.” They’re event-driven systems: humans design the environment, permissions, and connectors—and the agents react. The real risk is security and misuse, not autonomy.
A guide for when to leverage the strength of agents and when to apply the rigidity of code.
Your demo worked. Production won’t be so polite. Here are the 10 mistakes that blow up LLM apps at scale—cost blind spots, missing prompt versioning, latency creep, weak guardrails, no evals/observability, and brittle vendor dependencies—plus practical fixes.
How we leverage AI and automation to enhance human capabilities, not replace them. Real examples from our work with enterprise clients who wanted to scale without losing the human touch.
Traditional monitoring won’t catch hallucinations, prompt injection, or context collapse. Here’s the practical LLM observability stack—traces, prompt/versioning, evals, retrieval quality, and alerts—to debug faster and ship safer.