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Latest thinking

Plain-language writing on AI transformation: what works, what it costs, and how to own it.

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CONCEPTS·6 MIN READ·JUL 2026

Context engineering, explained for operators

Why the quality of what your AI sees matters more than which model you pick, and how to audit the context your systems are actually working with.

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When local models beat the API bill

A framework for deciding what belongs on your hardware and what stays in the cloud.

Jun 2026READ MORE >

ENGINEERING·8 MIN

Anatomy of an agentic harness

Skills, tools, memory, guardrails: how the pieces fit around a model to produce dependable work.

Jun 2026READ MORE >

CONCEPTS·4 MIN

Vector database or knowledge graph?

Meaning vs facts: what each structure is for, and why serious retrieval uses both.

May 2026READ MORE >

CASE NOTES·6 MIN

Deploying a virtual employee into a shared inbox

What we learned shipping an AI teammate into email triage at a professional services firm.

May 2026READ MORE >

LOCAL AI·7 MIN

Sizing hardware for on-prem inference

GPUs, quantisation and throughput: a practical sizing guide for your first local deployment.

Apr 2026READ MORE >

CONCEPTS·5 MIN

What “skills” actually are

Reusable, auditable procedures your agents share, and why they beat prompt libraries.

Apr 2026READ MORE >

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Concept explainers, cost benchmarks and case studies. No hype.