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

Lab note #065 Scheduler Retrospective

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

Explain a Kleisli Category

❓This is a conversation with GPT o3-mini💬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 note #064 Current API and developer affordance with more scheduler work

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 note #063 Vibe coding a scheduler

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

Types as a design tool

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

Lab note #062 Automagic juice is not quite worth the squeeze

I made good progress on the reactive engine. * Simplified the requirements for define a handler. * Started work on the scheduler * No longer need to handler to ability type checking method, by converting from protocols into abstract base classes. This was one example of where AI lead me astray. I also

When declarative systems break

A couple of months ago, I had to reload everything I knew about reactivity back into my head. I discovered that I missed a large corner of the development around unstructured reactivity, by way of Signals. From arguments about signals vs. functional components on Twitter and in post comments, it