Ai on Harlan D. Harris

Nine Things I Learned Building a Scrabble App for My Friends

harlan.harris@gmail.com (Harlan Harris) — Sun, 29 Mar 2026 00:00:00 +0000

I grew up playing Scrabble -- my grandma was cuthroat. I've played occasionally through the years, and more recently I've been playing Scrabble Go, the official, licensed Scrabble app, with friends. It's fine, but it's full of ads (unless you pay them), it has annoying side games (gems? why am I earning gems?), and I wished it had features like word definitions and better game history. So, starting last year, I built my own web app. I've had half a dozen friends and family members playing with me, each other, and a robot for the last few months, and it's been a lot of fun. I thought I'd share a few things I've learned along the way.

IZE meets AI -- semantic search, smarter labels, and agentic orientation

harlan.harris@gmail.com (Harlan Harris) — Wed, 18 Mar 2026 00:00:00 +0000

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 about what could be built here?

I see three distinct opportunities, at different layers of the system.

How Search and AI Product Teams are Different

harlan.harris@gmail.com (Harlan Harris) — Tue, 03 Feb 2026 00:00:00 +0000

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 -- without changing how the team is led. The processes and role expectations that work for a typical product team break down when the domain is heavily algorithmic. This post pulls together what I've learned and what others have written about building teams that own these systems.

Defensible Use of AI in Writing (Like This)

harlan.harris@gmail.com (Harlan Harris) — Thu, 29 Jan 2026 00:00:00 +0000

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 is: what makes AI-assisted writing defensible? When is it helpful to society, and when is it plagiarism? When can you still be considered the author of AI-assisted writing?

Use LLMs for Translation and Fallible Reasoning

harlan.harris@gmail.com (Harlan Harris) — Wed, 21 Jan 2026 00:00:00 +0000

I keep seeing AI skeptic takes like "AI is useless" or "LLMs are only good at Natural Language Processing", usually because of hallucinations and AI slop. Both are real problems. But that conclusion still misses the point: it misidentifies what LLMs are actually good at.

My take is: LLMs are great at translation (in the broad sense), and they have emergent but fallible reasoning. Knowledge, in the "just tell me what's true" sense, is not their strong suit, despite how convincingly they can sound.

AI-Assisted Coding, Side Projects, and Donationware

harlan.harris@gmail.com (Harlan Harris) — Fri, 09 Jan 2026 00:00:00 +0000

The rise of AI-assisted coding tools has made it dramatically easier and faster to build small side projects than ever before. What used to take weeks of evenings and weekends can now be done in a few focused sessions, with an AI pair programmer handling much of the boilerplate, debugging, and implementation details. This has opened up new possibilities for building tools that solve personal problems or scratch specific itches, without the overhead of building a full commercial product.