• in AI
  • 8 min read

Effective AI Usage: Understanding Brains

I've talked about the (current) moving parts of AI and AI-fu. But a fundamental aspect of AI-fu is being aware of how we think and how that's different to how LLMs "think". It's probably true that most people are not consciously aware of how they think or aware of how colleagues thinking works differently. So it's well worth raising that topic, because it's crucial to the quality of results.

  • in AI
  • 7 min read

Effective AI Usage Part One - What is AI?

In my last blog post I talked about some lessons I've learned from using AI. I talked about a follow-up article talking about AI use at a higher level. Recent experience has reinforced my thinking on this. In this blog post we're going to focus on what AI is, the initial interaction, and training.

  • in AI
  • 7 min read

More AI Lessons

A little while ago I blogged on developing at speed. The obvious omission from all aspects was AI. But AI – like an IDE – is just a tool. Unless you understand what it can and can't provide, unless you use it intelligently, you will not reap the benefits. But unlike an IDE, AI doesn't come with a set of menus that hint at what it can and can't do. AI doesn't come with a marketplace of extensions that provide functionality shared by the community. And it's so new that we're all working it out. So what are my thoughts?

Supercharging Input to Domino REST API Agents

One of the things I learned when building HCL Volt MX LotusScript Toolkit was that calling a web agent with ?OpenAgent URL populates the NotesSession.DocumentContext with various fields containing useful information from the request. So when I was building agent processing functionality into the POC that became Domino REST API, I utilised the same approach to provide opportunities to pass contextual information across to an agent.

  • in Coding
  • 13 min read

Developing at Speed

One of the main outputs of research development is the proof of concept. An early lesson I picked up when I joined HCL Labs was to deliver working code, not slides. And the key when building a proof of concept is speed. In some cases, it may end up proving why an approach won't work. In many scenarios, it may end up being put on a shelf indefinitely. Even if the concept proves appealing, the implementation choices may not be the preferred option for the final solution. So speed is of the essence: spending a couple of weeks building something that goes nowhere is acceptable; spending a couple of months is not. So the ability to get maximum results in the minimum time is key.

But how do you do that?

  • in Rust
  • 6 min read

Adventures in Rust

One of my core principles for IT research development can be summed up by a saying more than 2500 years old, attributed to Solon and appearing twice in Plutarch’s Life of Solon: “I grow old always learning many things”. In many ways, to stop learning is to stop living.