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
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
❓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 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
lab notes
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
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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
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
❓This is a Deep Research report with GPT o3-mini💭<goal> If I wanted to build and design an algebraic effect system in a language that didn't support delimited continuations (like python), but I wanted multi-resumption, I wouldn't be able to use generators. So how
Underlying great creations that you love—be it music, art, or technology—its form (what it looks like) is driven by an underpinning internal logic (how it works). I noticed this pattern while watching a talk on cellular automaton and realized it's "form follows function" paraphrased
I spent a good part of this week writing and rewriting a blog post, as part of my commitment to publish more this year. I wrote about 10k words, only to keep about 4k of them. Must be the sunk cost fallacy, because it feels bad to delete entire pages
With the release of OpenAI's Deep Research and its testimonies on Twitter, I finally slapped down $200/month for Pro subscription. Deep research is what I do all the time by hand anyway, I figured I'd get a lot of mileage out of it. The second
❓This was a report from GPT3o-mini with Deep Research💭What are the different ways to do handler look up in programming languages that support algebraic effects? I know sometimes, they're also called "abilities" in some languages. A starting point of languages that I know support algebraic
useEffects have a lot of footguns, especially with the dependency list. This is one of them. If I have a web worker that I need to communicate with, I need to have a message handler. The message handler needs to change the state of my frontend application. It's
I'd finished up looking into reactivity (with the exception of data type enforced reactivity, like differential dataflow), and I pulled out code with pedagogical sample code implementing signals and autotracking. * iamwilhelm/pedago_signals: A pedagogical implementation of hybrid push-pull signals in Python * iamwilhelm/pedago_autotracking: A pedagogical implementation
❓This is post is the result of a Socratic conversation I had with GPT-4o about what "syntax represented by the free monad for a functor that provides a signature." meant. After the question and answering, I asked it to write an outline of all that I learned and
I almost never remember what I did in a year. Having written reviews for 2021 and 2023, I've found it helpful to both have a record to look back on as well as a point to review and reflect. I think this should be a habit every quarter,
❓Oleg Kiselyov has a post and corresponding talk, "Having an Effect", about how to represent side effects in a language. While I watched the talk, I conversed with GPT4o about any questions I had. At the end, I had it write a blog post summarizing our conversation, with
