Deep dives into enterprise AI, MLOps, DevOps, and modern infrastructure.
Anthropic's Model Context Protocol, OpenAI's Responses API, and Google's A2A have quietly converged on how LLMs talk to tools. A practitioner's guide to the stack that is emerging – and what to commit to.
In 2026, the difference between an AI product that survives contact with reality and one that quietly erodes user trust is almost always the evaluation suite behind it. A practitioner's guide.
The Act's extraterritorial reach rewrites vendor risk for teams in Singapore, Sydney, Bangkok, and Hanoi. A plain-English map of the obligations that actually bite – and the ones that won't.
Multi-agent patterns are fashionable. They are also expensive and brittle when misapplied. A straight read on when the architecture earns its complexity.
Long context, contextual retrieval, and agentic search have changed what "good RAG" means. A decision framework for teams pruning their retrieval stack in 2026.
Vision-language models have moved from demo to production faster than any model class before them. A field report on what works, what breaks, and where they are replacing purpose-built vision pipelines.
Clever prompts don't scale. System prompting – versioned, tested, owned by a named person – is how mature teams turn prompts from tricks into maintainable assets.
Schema-on-read was a decade-long compromise. Data contracts fix the silent-incident problem that makes dashboards lie and ML pipelines drift. A pragmatic adoption guide.
Feature stores were built for a world of tabular ML. LLM and RAG workloads partly replaced them and partly exposed their weak spots. A pragmatic take for 2026 data platform leads.
Seven APAC data regimes, in plain English, with the specific clauses that trip AI deployments and the architectural moves that keep compliance and velocity compatible.