Frankly speaking ...

For some time, I have been following research on LUCA—the last universal common ancestor of all life on Earth. This short article draws mainly on recent work, especially the 2024 paper by Moody and colleagues, as an introduction to current thinking. What makes LUCA so compelling is the scale of the question it represents. This is not just another organism in the history of life, but the most recent point from which all modern biology descends. What seems increasingly clear is that this starting point was not simple in any everyday sense of the word. Many of us picture the earliest life as fragile and barely formed—a transitional step between chemistry and biology. In that picture, complexity emerges slowly over immense stretches of time. Current evidence points in a different direction. LUCA appears to have been a recognisable, free-living microbe rather than a primitive intermediate. It likely possessed core systems we associate with modern cells, including: A genetic code and...

As AI models become more powerful, they are also becoming much more accessible. I have been interested in what happens when you trade a little model accuracy for the ability to run everything on your own machine, without relying on paid cloud infrastructure. For developers, researchers, and hobbyists, that shift is useful for more than just cost. It also changes how private, flexible, and portable these tools can be. This week I spent some time trying two popular ways of running local AI models: Ollama and LM Studio . Both are open source and free to use, and both make it much easier to get started than I expected. What I wanted to understand was not just whether they worked, but how they felt to use in practice and where each one made more sense. What I like about running models locally is that the benefits are immediate. There is the obvious cost saving, especially if you are experimenting often or working through lots of prompts, but privacy matters just as much. When the mode...

Introduction This week, I experimented with Google AI Studio. The objective was to see how difficult it would be to create a web front-end for a word puzzle solver targeting games like the Nine Letter Word , NYT Spelling Bee , and Scientific American Spellements . Developing the Requirements I had several command line programs that solved the problem. Now I want a web front-end. I had used Flutter before and knew it could deliver not only a web front-end but also native desktop and mobile apps. I drafted a requirements document in Markdown and asked Copilot and Gemini to review and suggest improvements. The document covered puzzle-solver inputs, examples, expected behaviour, and UI style. For this solution, I expected a client-server architecture. You can see the initial application requirements document on GitHub. Once satisfied, I submitted these requirements to AI Studio. First Iteration The first iteration produced a functional website. AI Studio provided a mock solver ...