This is the last post in the IZE series. In the previous installment, I looked at two ways to generalize the IZE algorithm itself: preferring consistent facets and searching for trees with better goodness scores. Here I want to ask a different question: what does the AI revolution of the last few years actually change …

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In previous posts, I showed off an interactive demo of the IZE algorithm, and discussed how the algorithm worked. Now, it's worth considering some ways we could generalIZE the algorithm. 🤦‍♂️ Perhaps variations on the algorithm might yield hierarchies that are even better at showcasing the contents of the texts? As …

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In the previous installment of this series, I looked at what came after IZE -- faceted search, clustering algorithms, and the various ways web search, personal information management, and e-commerce tried to solve similar problems to what IZE was attacking. None of them ended up doing what IZE did. The question I want …

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In the previous post in this series, I discussed the technical details of IZE and its reception. Here I want to look at what came after — and where IZE-like ideas might still have potential. The short version: IZE was forgotten, but the ideas it embodied — hierarchical clustering, single-word splits, dynamic navigation …

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In the first post in this series, I introduced IZE -- a DOS-era personal information manager with a novel approach to search and navigation. Here I want to go deeper into how it actually worked, what its limits were, and how it was received at the time. The algorithm The core of IZE was patented by Paul Kleinberger …

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Sometimes revisiting old technology is the best way to understand how we got where we are -- and to see what alternative paths might have looked like. This is the first in a series of posts about IZE, a DOS-era personal information manager that I think has some interesting lessons for modern search and discovery. I'll …

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I've supported A/B testing of Algolia search systems at three companies now, and have dived deep into A/B testing generally as well as specifically for search. The Algolia documentation on search A/B testing is technically adequate for getting started, and the dashboard has improved, but there are still many ways that …

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Engineering leadership knows the standard playbook for product teams: the Product Trio, the Spotify model, outcome-based roadmaps, and so on. I've seen teams adopt these practices and still struggle when they add responsibility for advanced algorithms -- search, recommendations, predictive modeling, or generative AI -- …

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If AI helps me get ideas from my head to the readers' heads faster, that's good. Society thrives when ideas are shared, critiqued, and built on. But authors shouldn't take credit for other peoples' ideas -- they should synthesize others' ideas, including the "blurry JPG of the web" that is LLMs. The question …

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What are Post-Query Refinement Suggestions? One of my favorite search UX patterns is post-query refinement suggestions — buttons that appear between the search box and results, adjusting the query in various ways. See, for instance, these suggestions on Etsy, which recommend that I filter by shipping speed, seller …

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