lab notes
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 notes
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 notes
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 notes
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
Today I Learned
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 notes
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 notes
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
lab notes
These weeks are passing by faster and faster. I too would like to work faster, but I didn't think vibe coding was going to be it. I work with AI everyday, and I mostly have it do basic debugging or explaining Python minutiae to me. I know what
ai-chat
âť“This is a conversation with GPT o3-miniđź’¬What is a Kleisli Category? Explain it to me as someone that knows functional programming well. A Kleisli category provides a way to encapsulate and compose effectful computations, which is especially appealing if you're familiar with functional programming and monads. Core
lab notes
The work on the scheduler is to support async effect handlers. It won't do at all, if a network request always blocks the evaluation of the computational graph. So I embarked on it about two weeks ago. Right now, the API looks like this (subject to change). There&
lab notes
This was a tough past week. I was working on supporting async handlers, even if the reactive part was synchronous. I didn't want the graph to have to wait on network requests. This turned out to be tough. Also I got food poisoning this past week. The design
Essays
There are some programmer that love types and there are others who eschew them. When reasonable people fall on both sides, it's good reason to look for a latent variable that better explains the underlying motivation or cause. Latent variables, by definition, are hard to measure or observe