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Our AI guiding principles

Strong design principles in our efforts to encourage thoughtful AI adoption where it provides demonstrable benefits, while avoiding common pitfalls.

Our principles in practice

Graph of LLM pareto frontier, Code Arena Web Dev open weight models

Comparing open weight AI models and providers

Why and how we select open weight LLMs: a practical guide to comparing AI models and providers, with clear criteria and highlighted options

Meagen Voss smiling for a selfie in front of a black and blue sign that says Toronto machine learning summit.

What I learned from two days of hanging out with AI experts

What AI experts are talking about IRL is different from the noisy online discourse. Here are 5 trends we observed at a machine learning summit in Toronto.

Bar chart of Wagtail AI tasks carbon footprint vs. loading one Wagtail website page.  Range of 0 to 0.8 in grams of CO2E, vs median Wagtail page load, at 0.2. One AI task is higher than the page load

The carbon footprint of Wagtail AI

Ever wonder what is the energy use or carbon footprint of your AI adoption? Measuring the energy and emissions of everyday AI tasks in Wagtail

A wagtail bird and a gray, humanoid robot building a nest together.

What AI tools get right and wrong with Wagtail

A general look at how useful AI tools are for building Wagtail projects. Observations based on my own experience with one recent project rather than a deep, empirical study.

Dashboard UI showcasing energy use of AI inference in Neuralwatt, with a graph of daily energy consumption

Open source AI we use to work on Wagtail

A quick recap of AI tools, models, providers found to work well with Wagtail projects. Things you can try right now.