Blog

Notes from the lab

What we're learning while shipping AI to production — engineering practice, field notes and the occasional strong opinion.

Latest

MCP explained: how AI assistants connect to your business tools

The Model Context Protocol is quickly becoming the standard way to plug AI into databases, files and apps. Here's what it is, why it matters and how to adopt it safely.

19 October 2026 4 min read

AI BasicsGenerative AI

ANI, AGI, ASI: the three types of AI explained

Narrow, general and super AI are terms you hear all the time. Here's what each one means, which actually exists today, and where tools like ChatGPT fit.

6 October 2026 4 min read
AI AgentsAutomation

Designing AI agents with a human in the loop

Autonomous doesn't have to mean unsupervised. A practical look at where people should stay in control of agent workflows.

6 October 2026 2 min read
MLOpsEngineering

From AI prototype to production — five things that change

A working notebook is the start, not the finish. These are the gaps we see most often between an AI proof of concept and a dependable product.

6 October 2026 1 min read
AI AgentsEngineering

Loop engineering: design the loop, not the prompt

The most useful AI agents aren't driven one prompt at a time. They run inside a loop that acts, checks its own work and knows when to stop. Here's how to build one.

6 October 2026 4 min read
Generative AIRAG

What it takes to run RAG in production

Retrieval-augmented generation is easy to demo and hard to ship. Here's the checklist we work through before a RAG system goes live.

6 October 2026 2 min read

Notes from the lab

MCP explained: how AI assistants connect to your business tools

ANI, AGI, ASI: the three types of AI explained

Designing AI agents with a human in the loop

From AI prototype to production — five things that change

Loop engineering: design the loop, not the prompt

What it takes to run RAG in production