Technical perspectives on AI systems, implementation strategy, and what actually works in production.
Andrej Karpathy identified four failure patterns in AI coding agents. I apply the same four rules to business AI deployment — and they explain why most business AI fails by Month 3 while mine works on Day 300.
I watched a builder compare Vapi, Bland, Dogora, and Retell — not marketing, actual developer experience. Here's what I learned, why I chose Vapi for my stack, where it's weak, and what I'd switch to if those weaknesses matter for your deployment.
After auditing 40+ AI deployments across retail, finance, and logistics, a pattern emerges. The organisations that succeed share five non-negotiable practices. Those that skip them — regardless of budget — build systems that collapse under their own weight within six months. Here is what the data shows.
There is a story in AI circles that autonomous agents will soon remove the need for humans in the loop entirely. I build these systems — and that story is wrong. Not because the technology is not good enough. It is. But because the loop is not a bug you eventually fix. The loop is the value proposition. Here is what my own HITL dashboard actually looks like every morning, and why I am not planning to close the loop any time soon.
The promise is seductive: deploy a chatbot, reduce support tickets by 60%, watch the savings compound. The reality is messier. Most chatbots degrade rapidly after launch not because the model is bad but because the integration layer was never designed for continuous adaptation. Here is what breaks and how to fix it before it does.
Many consultancies offer "AI readiness assessments" that amount to a deck of generic recommendations and a bill. A proper audit is different. It traces data lineage end to end, stress-tests integration points, and quantifies what the system actually costs to run per decision. This is a walkthrough of the methodology we use and why it surfaces problems that never appear in quarterly reviews.