Programming After AI: Why System Boundary Taste Matters

Since coding agents came on the scene, a subset of programmers panic about AI replacing their jobs, but I think they're panicking about the wrong thing. The fear seems focused on the mechanical aspects—will AI write better functions than me? Will it debug faster? Will it remember

Getting vibe coding to work for me

I bounced off of Cursor a couple of months ago. It was good having code in context in the sidebar, a leg up from cutting and pasting, but I found its agent UI confusing and it made odd edits. In addition, when I left it open for a week, it’

Elm as target language for vibe coding

Having been a fan of functional programming and following the breadcrumbs of reactive systems, it's not surprising that I'm a fan of Elm, and had experience building different systems in it. It's too bad that enthusiasm for Elm didn't last until vibe

Lab note #073 Existing in a digital space

I ended up deciding on SolidJs. For one, I spent way too much time deliberating on a choice that wouldn't matter as much at the end of the day. But I think I wanted to give functional programming a good shake, since it's aligned with my

Lab note #072 A language with affordances for vibe coding

This week was a little stalled. Had some daily life administrivia to take care of, but I did manage to focus on one question: what, if any difference was there to the choice of language for vibe coding? After looking into some of the other reactive systems, React stopped being

Lab note #071 Agentic loop for buying decisions

Switched gears this week to start a new, but related project. I started a prototype on an app that helps users make buying decisions with an AI agent. Lately, I've been using ChatGPT to help me make buying decisions, but I found some aspect of the experience wanting.

Lab note #070 Vibe-coding is half a skill issue

I've been doing an implementation of DBSP, which is a way of doing incremental computation built up from some basic concepts from digital signal processing. While I use LLMs in my daily work to ask it questions, spitball with it, and do some basic stuff, I hardly ever

Lab note #069 Partial implementation and prompt prompting

It's been two or three weeks since the last lab note. I've been on vacation with my family for two weeks, and then caught a cold this last week when I got back. I've been doing two things: * Continuing the implementation of DBSP * Trying

Lab note #068 Dealing with collections incrementally

Had bunch of distractions like doing taxes this week, so I fell behind with the weekly update. However there's been progress these past two weeks. * Generated Deep Research report on the strengths and weaknesses of different notebooks * Learned about E-graphs and Interaction Nets * Read papers on Incremental Lambda

TIL: Covariant vs Contravariant Types

When working with type systems in programming languages like TypeScript, Scala, Java, or even Python (via type hints), you'll encounter the concepts of covariance and contravariance. These terms describe how the subtype relationships of generic types behave when you substitute their type parameters. Understanding them can help you

Lab note #067 Pipelines operate on collections of data

Wrapped type computing Despite resisting the draw for writing an interpreter/compiler, I did think about it some more, and find it almost inevitable, given the properties and constraints that I have. I went back to the three things I want the notebook to do: reactivity, collaboration, and observability. Under

Lab note #066 But I don't want to write a compiler

I bit the bullet and went towards re-doing the effects system implementation with the native async/await, to see if I can get a baseline implementation. I was successful, but not without roadblocks along the way. This version is much simpler and handicapped than what I wanted originally, but I