Thoughts on strategy, modernization, AI enablement, and building systems that actually work for people.
You bought the tools and hired the right talent. Yet campaigns go out without the right data behind them, and reporting still takes a day. You're not missing another tool. You're missing a foundation.
Many eCommerce brands introduce AI at the operator level, but maximum ROI is realized at the systems layer. Five pillars for high-impact AI adoption, where value accelerates first, and a Shopify checklist for AI readiness.
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.
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.
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.
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.
Agentic workflows are moving from demos to production. Here’s a practical governance playbook for 2026: evals, permissions, observability, and human-in-the-loop patterns that keep velocity high and risk low.
The privacy story is changing. Between on-device models and confidential compute, 2025–2026 is the shift from “trust the provider” to “prove the data boundary.” Here’s how to design for it.